KGGSeq Home

Introduction & References

Install and run
System requirement
Installing KGGSeq

Short Tutorials
Mendelian Disease
Double Hit Gene
Complex Disease

Quick examples
Mendelian disease

Options
General settings

Inputs
  Variants&genotypes
  Phenotype&pedigree

Outputs
   Ordinary outputs
   Binary outputs

Quality control
   Genotype QC
   Variant QC
   Sample QC

Filtering
   Genetic inheritance
   Gene feature
   Allele frequency
   Type&region&gene
   Supper duplicate
   Gene's variant number
   Phenotype

Annotation
   dbSNP RSID
   Alternative splicing
   Structure variation
   Protein interaction
   GeneSet
   Mouse phenotype
   Zebrafish Phenotype
   DDD Study
   PubMed literature
   OMIM
   COSMIC

Prediction at variant
   Mendelian disease
   Cancer somatic driver
   Complex disease
     Composite:PRVCS
     Context:cepip
     GF-Specific

Prediction at gene
   Pathogenicity
   Phenotype mining
   Expression

Statistical tests
   Association at variant
   Association at gene
     SKAT
     Rvtests
   Association at geneset
     SKAT
     Rvtests
   Enrichment at geneset

Plotting
   Allele frequency
   Q-Q

Miscellaneous
   Linkage Disequilibrium
   LD Pruning

FAQ

KGGSeq: A biological Knowledge-based mining platform for Genomic and Genetic studies using Sequence data

(Version 1.0)
Miaoxin Li, Mulin Jun Li, Jiang Li, Jacob Shujui Hsu, Hongsheng Gui, Zhicheng Pan, Suying Bao and JS Kwan


Introduction


KGGSeq is designed to make use of biological knowledge and statistical models to identify genetic loci responsible for human diseases/traits by high-throughput sequencing data. Compared with other tools like PLINK/Seq (“A library for the analysis of genetic variation data”, http://atgu.mgh.harvard.edu/plinkseq/), KGGSeq focuses more on integrative downstream analysis. Since version 1.0, it has been equipped with a series of integrative methods for characterizing genetic mutations/polymorphsim of Mendelian disorders, complex disorders and cancers by using whole genome sequencing data and versatile public resources .
WorkFlowFig

References

1.   Li MX, Gui HS, Kwan JS, Bao SY, Sham PC. A comprehensive framework for prioritizing variants in exome sequencing studies of Mendelian diseases.Nucleic Acids Res. 2012 Apr;40(7):e53. PubMed   NAR ‹For the entire filtration, annotation and prioritization framework›
2.   Li et al. Predicting Mendelian disease-causing non-synonymous single nucleotide variants in exome sequencing studies. PLoS Genet. 2013 Jan;9(1):e1003143. PubMed    PLoS Genet ‹For Mendelian pathogenic prediction of non-synonymous variants by logistic regression›
3.   Li et al.Robust and rapid algorithms facilitate large-scale whole genome sequencing downstream analysis in an integrative framework. Nucleic Acids Res. 2017 Jan 23. pii: gkx019. doi: 10.1093/nar/gkx019 PubMed ‹For the new version of KGGSeq since version 1.0›

Install and run

System requirement

Java Runtime Environment (JRE) version 1.6 (or up) is required for KGGSeq. It can be downloaded from the Java web site. Installing the JRE is very easy in Windows OS and Mac OS X.

In Linux, you have more work to do. Details of the installation can be found http://www.java.com/en/download/help/linux_install.xml.

In Ubuntu, if you have an error message like: "Exception in thread "AWT-EventQueue-0" java.awt.HeadlessException ... " , please install the Sun Java Running Environment (JRE) first.

To install the Sun JRE on Ubuntu(10.04), please use the following commands:
sudo add-apt-repository "deb http://archive.canonical.com/ lucid partner"
sudo apt-get update
sudo apt-get install sun-java6-jre sun-java6-plugin sun-java6-fonts
Detailed explanation of above commands can be found at http://www.ubuntugeek.com/how-install-sun-java-runtime-environment-jre-in-ubuntu-10-04-lucid-lynx.html.


For Mac OS, the JRE 1.6 has been available at http://developer.apple.com/java/download/ since April 2008. Mac OS users may need update the Java application to run KGGSeq. A potential problem is that this update does not replace the existing installation of J2SE 5.0 or change the default version of Java. Similar to the Linux OS, the Java_Home environmental variable has to be configured to initiate KGGSeq.

 

Installing KGGSeq

Simply decompress the archive and run the following command

  java -Xmx10g -jar ./kggseq.jar <arguments>

The arguments -Xmx10g set the maximum Java heap sizes for KGGSeq as 10 gigabytes. Specifying a larger maximum heap size can speed up the analysis when processing large datasets. A higher setting like -Xmx20g is required when there is over 50 million. The number, however, should be less than the size of physical memory of a machine.

Note  <arguments >can be saved in a flat text file.

Note  For some advanced statistical analysis in which some third-party tools are called, you have to run R and C++ make command to install them. Please watch our demo video for details.


Note  To use KGGSeq under Proxy Network, you need specifically use the following java command to configure the proxy settings (Thank Daniele Yumi for this suggestion):
java -Dhttp.proxyHost=xxx.xxx.xxx -Dhttp.proxyPort=xxxx -Dhttp.proxyUser=xxx -Dhttp.proxyPassword=xxx -jar "./kggseq.jar"

Quick example

Example : Prioritize variants for a rare Mendelian dominant disease, Arthrogryposis

Files needed:
1)a VCF file (rare.disease.hg19.vcf); and
2)a linkage pedigree file (rare.disease.ped.txt).

Note  All files were included in the examples folder of KGGSeq.

Run the command below:

java -Xmx5g -jar kggseq.jar examples/param.rare.disease.hg19.txt

We now walk through the parameter file “param.rare.disease.hg19.txt” before going into the results. Lines starting with hash sign # are comments. Detailed interpretation for each argument in the parameter file is included in 'Options' part.

#one argument per line
#I.Environmental setting
--buildver hg19 \ \ #line 1
--nt 4 \ \ #line 2
#--lib-update \ #line 3
#--resource-update \ \ #line 4

#II. Specify the input files
--vcf-file examples/rare.disease.hg19.vcf \ #line 5
--ped-file examples/rare.disease.ped.txt \ #line 6, or specify --indiv-pheno X:1,Y:1,Z:2

#III. Output setting
--out ./test1 \ #line 7
--excel \ #line 8
--o-vcf \ #line 10
#--o-flanking-seq 50 \ #line 11, need large RAM memory

#IV. QC
--gty-qual 20 \ #line 12
--gty-dp 4 \ #line 13
--gty-af-ref 0.05 \ #line 14
--gty-af-het 0.25 \ #line 15
--vcf-filter-in PASS,VQSRTrancheSNP90.00to93.00,VQSRTrancheSNP93.00to95.00,VQSRTrancheSNP95.00to97.00,VQSRTrancheSNP97.00to99.00 \ #line 16
--seq-qual 30 \ #line 17
--seq-mq 20 \ #line 18
--seq-fs 60 \ #line 19
--min-obsa 1 \ #line 20

#V. Filtering
--genotype-filter 3,4,5,6 \ #line 21 for dominant mode
#--ibs-case-filter 1000 \ #line 22, or specify 'ibdregions.txt' file
--regions-out chrX,chrY \ #line 23
--db-filter 1kg201204,dbsnp141,ESP6500AA,ESP6500EA \ #line 24
--rare-allele-freq 0.006 \ #line 25
--db-filter-hard dbsnp138nf \ #line 26
--db-gene refgene,gencode,knowngene \ #line 27
--gene-feature-in 0,1,2,3,4,5,6 \ #line 28
--superdup-filter \ #line 29
--gene-var-filter 4 \ #line 30

#VI. Annotation
--scsnv-annot \#line 31
--dgv-cnv-annot \#line 32
--omim-annot \#line 32
--candi-list ECEL1,MYBPC1,TNNI2,TNNT3,TPM2 \#line 33
--geneset-annot cura \ #line 34
--ppi-annot string \ #line 35
--ppi-depth 1 \ #line 36
--phenotype-term Arthrogryposis,Arthrogryposis+multiplex+congenita \ #line 37
--pubmed-mining \ #line 38
--mouse-pheno \ #line 39
--zebrafish-pheno \ #line 40
--ddd-annot \ #line 41

#VII. Prediction at variants
--db-score dbnsfp \ #line 42
--mendel-causing-predict best \ #line 43
--filter-nondisease-variant \ #line 44

#VIII. Prediction at genes
--patho-gene-predict \ #line 45
--phenolyzer-prediction \# line 46 the '--phenotype-term' option has to be specified prior to this option.

Part (I): Specify general environmental setting

Argument ‘line 1~4’ is used to set general KGGSeq running enviroment, including human genome build, resource path and program update

Part (II): Specify the input files

Arguments ‘line 5~6’ are used to specify the input files which support various data format, and they are compulsory for running KGGSeq.

Part (III): Output setting

Argument ‘line 7~11’ is used to set the output file name and type, which can be produced simutaneously by KGGSeq.

Part (IV): Specify Quality control conditions

Arguments ‘line 12~20’ are used to apply QC conditions (Genotype or Variant level) on input dataset, and then exclude low quality genotypes or variants in the following analysis.

Part (V): Filtering

Arguments ‘line 21~30’ are used to apply a combination of filters (genetic inheritance, gene feature, allele frequency, functional importance, etc) to enable better prioritization of the key variants/genes.

Part (VI): Annotation

Arguments ‘line 31~41’ are used to annotate remained variants/genes by searching various genomic annotation database and literature evidence.

Part (VII) & Part (VIII): Pathogenic prediction at variants and genes

Arguments ‘line 42~46’ are used to predict pathogenicity at remained variants/genes.

Notes Most of the above arguments are optional (except line 5 and 6), so user can mask some lines by “#” or delete the lines. Under this circumstance, user can have a systematic view of the impact for each level or even steps. And it will be easier to produce this parameter file by KGGSeq command generator we provide.


Options

Performance &environmental settings

Set the number of parallel tasks for an analysis job. java -jar kggseq.jar --nt #

The # denotes the number of parallel threads to perform the analysis.

Set the version of reference human genome buildjava -jar kggseq.jar --buildver hg##

All inputs will be analyzed and outputted according to the set genome version.

NOTE  Currently, KGGSeq only supports hg18, hg19, and hg38. It is --buildver hg19 by default.

Specify the local path to store the resource datasets java -jar kggseq.jar --resource path/to/resource/files

Once initiated, KGGSeq will automatically download the resource data from its web server to the local folder. By default, it is --resource "./"

Switch off the auto-checking function for itself. java -jar kggseq.jar --no-lib-check

By default, KGGSeq will automatically check the latest version of the kggseq.jar.

Switch on the auto-checking and-updating function for itself. java -jar kggseq.jar --lib-update

With this option, KGGSeq will automatically check the latest version of the kggseq.jar and update it if necessary.

Switch on auto-checking and-updating for NEEDED resource datasets. java -jar kggseq.jar --resource-update

With this option, KGGSeq will automatically check and download the available latest version of resource datasets, once initiated.

Switch off the auto-log function to write log in a text file. java -jar kggseq.jar --no-log

By default, KGGSeq will automatically write log in a text file.

Input formats

Variants and genotypes


VCF format with genotypes
java -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 The format that KGGSeq supporting best is the VCF format, which stores sequence variants and called genotype information. The VCF file can be flatted as text or compressed as .gz. KGGSeq will read all genotypes either phased or unphased but will ignore the phase status.

VCF format with phased genotypes
java -jar kggseq.jar --vcf-file path/to/file1 --vcf-phased --ped-file path/to/file2 Currently, KGGSeq only allows all genotypes phased in a VCF file. When there a mixture of phased and unphased genotypes in a VCF file, KGGSeq will report an error with this option.

NOTE  To achieve the best of parallel computing advantages of KGGSeq, it is strongly recommended to compress the input data with blocked GNU Zip format by bgzip <path/to/your/file>. See more here. Data compressed with conventional GNU Zip format cannot be handled in parallel.

NOTE  The parallel computing also supports VCF data compressed by Burrows–Wheeler algorithm ( bzip2), which may save over 20% space than the standard GNU Zip. However,KGGSeq may take 3 to 4 folds longer time to decompress the data.

NOTE  To set KGGseq handling input file in parallel, the parameter --nt N should be added. Not only the .gz and .bz2 formats are supported, but the original VCF files can also be read parallel.

NOTE  If the data were stored in different files, chromosome by chromosome, you can use _CHROM_ to denote the chromosome names [1...Y] or directly specify [1,2,X]in the file name so that KGGSeq can read data in multiple files in a run.In this scenario, the separated VCF files must contain the SAME subjects in the SAME order.

NOTE  In the VCF file, each subject should have a unique SubjectID, which is used to link the phenotypes and/or pedigree structure.

VCF format WITHOUT genotypes
KGGSeq can also accept VCF data without genotype information. But there are less usable functions for this type data.
java -jar kggseq.jar --no-gty-vcf-file path/to/file1

ANNOVAR format java -jar kggseq.jar --annovar-file path/to/file NOTE Since v0.4, KGGSeq recognize an extended ANNOVAR format in which a head row and multiple columns for comments are allowed.

Example:
chr startpos endpos ref alt comment1 comment2 comment3
1 69428 69428 T G T 92 129
1 69476 69476 T C T 1 0

KGGSeq binary genotype file set --ked-file

java -jar kggseq.jar --ked-file path/to/fileset The KGGSeq binary genotype produced by KGGSeq, which is more flexible to store the different types of genotypes than the PLINK BED format.

Phenotypes and pedigree


When there are genotypes inputted in VCF format, KGGSeq should know to whom they belong and the phenotypes of the samples. So you need provide your sample information in this case.

Concise subject ID and disease status: --indiv-pheno java -jar kggseq.jar --vcf-file path/to/file --indiv-pheno SubjectID1:0,SubjectID2:2,... Specify the individual IDs and affection status, which is coded as: 0 missing or unknow,1 unaffected,2 affected.

Note   The individual IDs must be unique and identical to the ones defined in the VCF file(s).

Conventional linkage pedigree file: --ped-file java -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 (or --composite-subject-id) Specify the subject IDs, relationship between subjects, and disease status of them; it is a conventional Linkage Format, detailed here.

  • Pedigree Name
    A unique alphanumeric identifier for this individual's family. Unrelated individuals should not share a pedigree name.
  • Individual ID
    An alphanumeric identifier for this individual. Should be unique within his family (see above).
  • Father's ID
    Identifier corresponding to father's individual ID or "0" if unknown father. Note that if a father ID is specified, the father must also appear in the file.
  • Mother's ID
    Identifier corresponding to mother's individual ID or "0" if unknown mother Note that if a mother ID is specified, the mother must also appear in the file.
  • Sex
    Individual's gender (1=MALE, 2=FEMALE).
  • Affection status
    Affection status to be used for association tests (0=UNKNOWN, 1=UNAFFECTED,2=AFFECTED).
Note By default, the subject IDs in the VCF file must be identical to Individual IDs in the pedigree file, which are assumed unique in the whole pedigree file. However, one can also ask KGGSeq to use a composite subject ID in the CVF file as "PedigreeName$IndividualID" by the option --composite-subject-id.



Besides, KGGSeq also accepts a pedigree format with header information (See the following example),in which the phenotype names are specified. Some of the phenotype names may be designated as covariates in statistical association analysis.

                                        fid iid fatid matid sex DISEASE QT  AGE  
                                        1  NA12344 NA12347 NA12348 1   1   94.17   NA   
                                        1  NA12347 0   0   1   NA   19.54  40.0  
                                        1  NA12348 0   0   2   2   NA  36.6 
                                        
Note The head line must start with 'fid iid'. Otherwise, KGGSeq will regard the first row as a subject-line.
Note The missing phenotype values are denoted by "NA".

Outputs

KGGSeq can flexibly output different formats of the prioritization and annotation results for validation or further analysis by third-party tools.

Output file path and file name prefix: --outjava -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 --out path/to/prefixname Specify path and prefix name of outputs. It is "./kggseq" by default.

Note Note all outputs (except for the Excel format) of KGGSeq are to compressed by GNU Zip format automatically by default. Please use --no-gz to disable the automatic compression function.

Ordinary outputs

Text format: This is by defaultjava -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 By default, KGGSeq output results in TEXT format in a file named kggseq.flt.txt, in which the fields (or columns) are delimited by the tabs.

Excel format: --excel java -jar kggseq.jar --vcf-file path//to/file1 --ped-file path/to/file2 --excel The results will be stored in a Excel file with the name kggseq.flt.xls.

For ANNOVAR input: --o-annovar java -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 --o-annovar Generate an extra copy of output for the prioritized variants in ANNOVAR input format, which can be further annotated by ANNOVAR.

For VCF input: --o-vcf java -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 --o-vcf Generate an extra copy of output for the prioritized variants in VCF format, which can be further analyzed by other tools.

Note Please use --missing-gty X to denote missing genotypes in the output VCF file. It is --missing-gty ./. by default.

For VCF input, but in a simplified format: --o-svcf java -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 --o-svcf Generate an extra copy of output for the prioritized variants in simplified VCF format, in which only genotypes are included in the FORMAT column.

The group-file format for EPACTS input or other tools : --o-gene-grp java -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 --o-gene-grp Output coordinates, reference and alternative alleles of left variants of each gene in a group-file format, which can be further analyzed by other tools for more sophisticated gene-based association analysis.

Note According to coordinates, a variant may be mapped onto multiple genes.

For PLINK input: --o-plink-ped java -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 --o-plink-ped Generate an extra copy of output for the prioritized variants in PLINK PED and MAP file, which can be further annotated by PLINK.

Extract the flanking sequence of each sequence variant: --o-flanking-seq java -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 --o-flanking-seq 50 The source sequence information is the UCSC Chromosome dataset hg18 or hg19, which are human genome reference sequences on the forward strand. The unit of the specified flanking sequence is base-pair(bp). The retrieved sequences will be in the *.flt.txt or *.flt.xls with the column label "FlankingSeqXXbp".

Note Users might need set large memory (by -Xmx20g) to carry out this function as KGGSeq will load the whole sequences of a chromosome.

Binary outputs

PLINK binary genotypes files set: --o-plink-bed java -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 --composite-subject-id --o-plink-bed Generate an extra copy of output for the prioritized variants in PLink binary genotype format (SNP-major model), which can store the genotype information more efficiently.
Note This format does not support variants with over 2 alleles and phased genotypes, which will be ignored by KGGSeq.To record this information, you are suggested to use the KGGSeq binary genotype format (below).

KGGSeq binary genotypes files set: --o-ked java -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 --composite-subject-id --o-ked Generate an extra copy of output for the prioritized variants in KGGSeq binary genotype format, which use much less space to store the genotype information.

Here is an example VCF file:
##fileformat=VCFv4.0
#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT 1$A 1$B 1$C
chr1 4793 rs6682385 A G 596.57 PASS AC=2 GT 1/1 0/0 ./.
chr1 53560 . T C 573.51 PASS AC=1; GT ./. 0/0 0/1
chr1 12887054 rs3738105 T C,G 5572.99 PASS AC=2,1 GT 1/2 0/0 0/1
And the corresponding pedigree file:
1 A 0 0 1 1
1 B 0 0 2 1
1 C A B 1 2
After running the command above, KGGSeq will generat three file:
kggseq.ked (the KGGSeq binary genotype file, genotype information)
kggseq.kam (the KGGSeq family data file, first six columns of the conventional Linkage Pedigree file)
kggseq.bim (the KGGSeq variants map data file, two extra columns for reference and alternative allele names, like: A G,C). The kggseq.kim is similar to the *.bim file of PLINK BED format. But it has one additional column (the last column) which denotes the compression status in the kggseq.ked file.The '-1' means that the genotypes are not compressed while another value suggested that the genotypes are compressed for a variant. The value divided by 3 denotes the number of "1" bits in the encoded genotypes of a variant for all subjects. The actual positions of "1" bits in the encoded genotypes are stored in the kggseq.ked file.
An example:
1 rs558604819 0 10642 G A 63
1 rs575272151 0 11008 C G -1
1 rs544419019 0 11012 C G -1
1 rs561109771 0 11063 T G 45
Explanation: 1) The binary genotypes of variant rs558604819 are compressed into 63 bytes. As every 3 bytes denote a position of '1' bits, this number means that there are 21 bits with a value of '1' in the binary genotypes. One can obtain all positions by reading the consecutive 3 bytes. Given the positions of '1', the original binary genotypes can be restored. 2) The binary genotypes of variant rs575272151 are not compressed.

Compared to the plink *.bed format, the kggseq.ked has a different way to present genotypes, which is more flexible. Here is the binary view of kggseq.ked file:
|-magic     number--|   |phase mode|  |------------------genotype data-------------------|
10011110 10000010   00000000       10100100 10010100 10000000 10000000

The followings are the interpretation of the KGGSeq binary genotype file format
  • First two bytes 10011110 10000010 for the validation checking of a KGGSeq binary genotype file.
  • Third byte is 00000000 (Unphased genotypes) or 00000001 (Phased genotypes)
  • Genotype data, either for unphased or phased ones in Variant-major order ((i.e. list all individuals for the first Variant, all individuals for the second Variant, etc) ). The 4th byte (10100100) denotes genotypes of chr1:4793 by two bit-blocks (denoted by green and red respectively). At this site, the genotypes of A, B and C are first encoded as 10, 00 and 11 respectively (See details in the encoding table). The binary codes at first and second places are put into two blocks 101 and 001. The block size is equal to the number of total subjects. It is 3 here. The remaining spaces in a byte, which can contain 8 bits, are set as zero bits.
    The 5th byte (10010100 ) denotes genotypes of chr1:53560. The 6th and 7th bytes (10000000 and 10000000 ) present genotypes of chr1:12887054, which has three alleles.
  • New "row" in the VCF file always starts a new byte (8 bits)
For each sequence variant, the number of bytes required to present a genotype is determined by the number of alleles and the genotype phase status.
I. Unphased -genotypes
I.I Bi-allelic sequence variant (2 bits)
  Reference homozygous Heterozygous Alternative homozygous missing
VCF genotype 0/0 0/1 1/1 ./.
Bits 00 01 10 11
Decimal 0 1 2 3
I.II Tri-allelic sequence variant (3 bits)
  Reference homozygous Heterozygous Heterozygous Alternative homozygous Heterozygous Alternative homozygous missing
VCF genotype 0/0 0/1 0/2 1/1 1/2 2/2 ./.
Bits 000 001 010 011 100 101 110
Decimal 0 1 2 3 4 5 6
I.III Quad-allelic sequence variant (4 bits)
  Reference homozygous Heterozygous Heterozygous Heterozygous Alternative homozygous Heterozygous Heterozygous
VCF genotype 0/0 0/1 0/2 0/3 1/1 1/2 1/3
Bits 0000 0001 0010 0011 0100 0101 0110
Decimal 0 1 2 3 4 5 6
  Alternative homozygous Heterozygous Alternative homozygous missing
VCF genotype 2/2 2/3 3/3 ./.
Bits 0111 1000 1001 1010
Decimal 7 8 9 10

II. Phased -genotypes
II.I Bi-allelic sequence variants (3 bits)
  Reference homozygous Heterozygous Heterozygous Alternative homozygous missing
VCF genotype 0|0 0|1 1|0 1|1 .|.
Bits 000 001 010 011 100
Decimal 0 1 2 3 4
II.II Tri-allelic sequence variant (4 bits)
  Reference homozygous Heterozygous Heterozygous Heterozygous Heterozygous Alternative homozygous Heterozygous
VCF genotype 0|0 0|1 0|2 1|0 1|1 1|2 2|0
Bits 0000 0001 0010 0011 0100 0101 0110
Decimal 0 1 2 3 4 5 6
  Heterozygous Alternative homozygous missing
VCF genotype 2|1 2|2 .|.
Bits 0111 1000 1001
Decimal 7 8 9

II.III Quad-allelic sequence variants (5 bits)
  Reference homozygous Heterozygous Heterozygous Heterozygous Heterozygous Heterozygous Heterozygous
VCF genotype 0|0 0|1 0|2 0|3 1|0 1|1 1|2
Bits 00000 00001 00010 00011 00100 00101 00110
Decimal 0 1 2 3 4 5 6
  Alternative homozygous Heterozygous Heterozygous Heterozygous Heterozygous Alternative homozygous
VCF genotype 1|3 2|0 2|1 2|2 2|3 3|0
Bits 00111 01000 01001 01010 01011 01100
Decimal 7 8 9 10 11 12
  Heterozygous Heterozygous Alternative homozygous missing
VCF genotype 3|1 3|2 3|3 .|.
Bits 01101 01110 01111 10000
Decimal 13 14 15 16

Quality Control

After genetic variants were inputed from a variety of data formats, KGGSeq provides a few optional quality control filters on individual genotype, genomic variant and sample phenotype. By adopting one or a combination of these QCs before performing downstream prioritization/annotation, it could help the user to reduce false positives.

Genotype QC


This is for genotype level quality control. Typical information in each individual genotype field covers read depth(--gty-dp), genotype quality (--gty-qual) and allelic balance ratio (--gty-af-ref, --gty-af-het and --gty-af-alt):

java -jar kggseq.jar --vcf-file path/to/file1 --indiv-pheno Indiv:1,Indiv:2 --gty-qual 20 --gty-dp 4 --gty-sec-pl 20 --gty-af-ref 0.05 --gty-af-het 0.25 --gty-af-alt 0.75 --o-vcf path/to/file2

They are in relationship of logical AND, and not exclusive of each other,user can specify one or any combination of these parameters at one time.

--gty-qual 20 Exclude genotypes with the minimal genotyping quality (Phred Quality Score) per genotype < 10. The default setting is --gty-qual 20.
--gty-dp 4 Exclude genotypes with the minimal read depth per genotype < 4. The default setting is --gty-dp 4.
--gty-sec-pl 20 Exclude genotypes with the second smallest normalized, Phred-scaled likelihoods for genotypes < 20. (otherwise, there would be confusing genotypes).The default setting is --gty-sec-pl 20.
--gty-af-ref 0.05 Exclude genotypes with the fraction of the reads carrying alternative allele >= 5% at a reference-allele homozygous genotype, usually indicating by AF in a VCF file. The default setting is --gty-af-ref 1 , which disables this tag.
--gty-af-het 0.25 Exclude genotypes with the fraction of the reads carrying alternative allele <= 25% at a heterozygous genotype. The default setting is --gty-af-het 0, which disables this tag.
--gty-af-alt 0.75 Exclude variants with the fraction of the reads carrying alternative allele <= 75% at a alternative-allele homozygous genotype. The default setting is --gty-af-alt 0, which disables this tag.
--gty-somat-p 0.05 Exclude genotypes at which the distribution of reads carrying reference and alternative alleles are not significantly (p>0.05) different between tumor tissues and non-tumor tissues. By default, the p value threshold is 0.05.

Note The AF is prioritized in the quality control process if the both AF and Ad are provided in the VCF file as the AF is usually estimated using high quality reads

After genotype QC, two output files will be produced. 1)The default KGGSeq output in txt format (or Excel by specifying "--excel") which includes the basic annotations for each clean variant; 2)The vcf file: the genotype field which does not meet the QC conditions will be replaced by missing value ./., hence not considered in following analysis.

##Key information provided in default basic annotation file:
"AffectedRefHomGtyNum", Number of affected individuals with reference homozygote at this variant;
"AffectedHetGtyNum", Number of affected individuals with heterozygote at this variant;
"AffectedAltHomGtyNum", Number of affected individuals with non-ref homozygote;
"UnaffectedRefHomGtyNum", Number of unaffected individuals with reference homozygote at this variant;
Gene feature filtering "UnaffectedHetGtyNum", Number of unaffected individuals with heterozygote at this variant;
"UnaffectedAltHomGtyNum", Number of unaffected individuals with non-ref homozygote;

Note All above QC functions are designed for VCF input format,which may not be suitable for other input formats.

Note All above QC functions can be turned off by --no-qc.

Note In the LOG file, the null genotype refers to either 1) a missing genotype in the input VCF file or 2) a genotype failing QC. Similarly, the non-null genotype refers to a genotype pass QC.

Variant QC


This is for variant level quality control. In general VCF format, each line corresponds to the variants on the same position. Typical information for one variant includes overall sequencing quality (--seq-qual), RMS Mapping Quality (--seq-mq), and strand bias (--seq-sb or Phred-scaled p-value using Fisher's exact test --seq-fs). To make use of this information:

java -jar kggseq.jar --vcf-file path/to/file1 --indiv-pheno Indiv:1,Indiv:2 --vcf-filter-in PASS,VQSRTrancheSNP95.00to97.00,VQSRTrancheSNP97.00to99.00 --seq-qual 30 --seq-mq 20 --seq-fs 60 --o-vcf path/to/file2

Interpretation of key parameters and their values:
Arguments/values Annotation
--vcf-filter-in exlude the variants with "FILTER" not matching the specified QC labels in the VCF input file. e.g., --vcf-filter-in PASS,VQSRTrancheSNP90.00to93.00,VQSRTrancheSNP93.00to95.00,VQSRTrancheSNP95.00to97.00,VQSRTrancheSNP97.00to99.00 . By default, this tag is ignored.
--seq-qual 30 set the minimum overall sequencing quality score (Phred Quality Score) for the variant at 30;(default: --seq-qual 30)
--seq-mq 20 set the minimum overall mapping quality score (Mapping Quality Score) for the variant at 20;(default: --seq-mq 20)
--seq-fs 60 set the maximal overall strand bias Phred-scaled p-value (using Fisher's exact test) for the variant at 60;(default: this tag is ignored.)
--max-allele 4 ignore variant with alleles over 4; (default: --max-allele 4)

Note All above quality control functions can be turned off by --no-qc.

In addition, KGGSeq can use phenotype information (specified by "--indiv-pheno" or "--ped-file") to filter variants, especially when multiple samples are included in one input file such as trio or case/control study design.

java -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 --min-heta 1(or --min-homa 1) --min-hetu 1(or --min-homu1) --min-obsa 2(or --min-obsu 2) --min-obs 2 --o-vcf path/to/file3

Moreover, It's flexible to match the sample size and missingness of genotypes. Here are the options for different study design. (cases = affected subjects ; controls = unaffected subjects)

Arguments/values Annotation
--min-heta 1 Set minimal observed number of heterozygotes genotypes in cases as 1.
--min-homa 1 Set minimal observed number of alternate homozygsote(s) genotypes in cases as 1; --min-heta and --min-homa are logical OR relation.
--min-hetu 1 Set minimal observed number of heterozygotes genotypes in controls as 1.
--min-homu 1 Set minimal observed number of alternate homozygsote(s) genotypes in controls as 1; --min-hetu and --min-homu are logical OR relation.
--min-obsa 2 Set minimal observed number of non-missing genotypes in cases as 2.
--min-obsu 2 Set minimal observed number of non-missing genotypes in controls as 2; --min-obsa and --min-obsu are logical AND relation.
--min-obs 2 Set minimal observed number of non-missing genotypes in all samples as 2.
--hwe-control 0.01 Exclude variants in controls with the Hardy-Weinberg test p value <= 0.01. By default, this tag is disabled.
--hwe-case 0.01 Exclude variants in cases with the Hardy-Weinberg test p value <= 0.01. By default, this tag is disabled.
--hwe-all 0.01 Exclude variants in all subjects with the Hardy-Weinberg test p value <= 0.01. By default, this tag is disabled.

After running individual genotype QC, there will be two output files produced: one default KGGSeq output stored in txt (or Excel by specifying "--excel"), in which the basic annotations for each clean variant are provided (see above section); the other one is clean vcf file, in which the variant row which does not meet the conditions specified will be removed completely, hence not considered in following analysis.

Note The genomic variant QC is also available for non-vcf type of input, by using type of variants, or genomic position only.

Sample QC


KGGSeq provides two set of functions to facilitate quality assessment of sequencing data of each subject.
Firstly, you can use KGGSeq to convert genotypes from VCF format to PLINK binary genotype format (see Outputs part) so that routine sample QC (pairwise relatedness, mendel error rate and sex check) in genome-wide association study (GWAS) analysis can be performed quickly. The samples with unexpected relationship or error rate are suggested to be removed.

java -jar kggseq.jar --vcf-file path-to-file1 --ped-file path/to/file2 <Variants and Genotype QC Settings> --db-merge <Reference genotype set> --o-plink-bed plink --bfile kggseq.merged --genome The following is the reference genotypes integrated by KGGSeq: (KGGSeq will automatically download it for you according to the set name specified by --db-merge )

Reference genotype set Description
hapmap2.r22.ceu.hg19 Haplotypes of Hapmap 2 release 22. Convert the coordinates to be hg19 from hg18 by UCSC lift over function. Complied from here.
hapmap2.r22.chbjpt.hg19
hapmap2.r22.yri.hg19
hapmap3.r2.ceu.hg19 Haplotpyes of Hapmap 3 release 2. Convert the coordinates to be hg19 from hg18 by UCSC lift over function. Compiled from here.
hapmap3.r2.chbjpt.hg19
hapmap3.r2.mex.hg19
hapmap3.r2.tsi.hg19
hapmap3.r2.yri.hg19
1kg.phase1.v3.shapeit2.asn.hg19 Haplotpyes of 1000 Genomes Project version 3. Donwload from here.
1kg.phase1.shapeit2.v3.eur.hg19
1kg.phase1.shapeit2.v3.afr.hg19
1kg.phase1.v3.shapeit2.amr.hg19
1kg.phase3.v5.shapeit2.eur.hg19 Haplotpyes of 1000 Genomes Project Phase3 of version 5. Donwload from here.
1kg.phase3.v5.shapeit2.afr.hg19
1kg.phase3.v5.shapeit2.amr.hg19
1kg.phase3.v5.shapeit2.sas.hg19
1kg.phase3.v5.shapeit2.eas.hg19
We acknowledge the complied VCF data of 1000 Genomes projects by the author of MACH, Dr. LI Yun. To see detailed the description about the data, please visit
http://www.sph.umich.edu/csg/abecasis/MACH/download/1000G.2012-03-14.html

An unexpectedly high pairwise relatedness (denoted by the PI_HAT of plink) suggests cross-individual contamination

Note To estimate the relatedness (the PI_HAT), you normally do NOT need the genotypes datasets from 1000 Genome Projects (which will require rather large memory). The merging with ancestry-matched HapMap genotype data is often sufficient for an accurate estimation of relatedness. However, the estimation of PI_HAT without reference genotypes in a small local sample will result in an over-estimated value because of the inaccurate allele frequencies derived from the tiny sample. plink --bfile kggseq --mendel plink --bfile kggseq --check-sex

Secondly, we provide plotting function (see Plotting part) to visualization the distribution of minor alleles per individual. The outlier from the distribution of total rare variants (MAF < 0.01, or novel in the common database) across all individuals can be easily detected and then ignored in the following analysis.

java -jar kggseq.jar --vcf-file path-to-file1 --out path/to/prefixname --mafplot java -jar kggseq.jar --vcf-file path-to-file1 --ped-file path/to/file2 --db-filter hg18_dbsnp138,hg18_dbsnp141 --allele-freq 0,0.01 --o-vcf path/to/file3

Note The meanings for -db-filter and --allele-freq in this section are provied in "Allele Frequency Trimming" section.

 

Filtering


KGGSeq offers simple but potentially powerful strategies to variants filtering and prioritization, which help users isolate most promising disease causal candiate variants from sequence variants to be considered.Users could prioritize variants based on some or all of the following evidences and/or functions: Genetic inheritance sharing, gene feature, allele frequency, biological function prediction, sequence variant type, and phenotype-relevant filtering.

Genetic inheritance sharing

Inheritance pattern filter: --genotype-filter

java -jar ./kggseq.jar --buildver hg19 --vcf-file path/to/file1 --ped-file path/to/file2 --genotype-filter 1,2

Filter out variants for which their genotypes are not consistent with the assumption of disease inheritance pattern.

Functions and applicable models for each code of '--genotype-filter'options are listed below:
Code Function Applicable model
1 Exclude variants at which affected subjects have heterozygous genotypes. Recessive
2 Exclude variants at which both affected and unaffected subjects have the same homozygous genotypes. Recessive and full penetrance causal mutation(s) in the sample; compound-heterozygosity
3 Exclude variants at which affected subjects have reference homozygous genotypes. Dominant without consanguineous mating
4 Exclude variants at which both affected and unaffected subjects have the same heterozygous genotypes. Dominant with full penetrance causal mutation(s) in the sample
5 Exclude variants at which affected subjects have alternative homozygous genotypes. Dominant without heterogeneity
6 Exclude variants at which affected family members have NO shared alleles. Full penetrance causal mutation(s) in the sample
7 ONLY include variants at which an offspring has one or two non-inherited alleles.

Note Used as --genotype-filter 4,7 to filter out the de novo mutations where both affected and unaffected subjects have the same heterozygous genotypes.
Note Used as --ignore-homo to ignore the de novo mutations where affected and unaffected subjects have the same ALTERNATIVE homozygous genotypes.
Detect de novo mutations which have nearly full penetrance.
In the main output file, there is a column named DenovoMutationEvent to record the genotypes of a child and his or her parents.

Example: N140_0:0/1:46,59&N140_1:0/0:57,0&N140_2:0/0:68,0
The child N140_0 has genotype 0/1 with 46 and 59 reads carrying reference alleles and alternative alleles respectively. The father N140_1 and mother N140_2 are homozygous 0/0.
8 ONLY include variants at which 1) the paired non-tumor (unaffected) and tumor (affected) samples have different genotypes.

Note The tag --indiv-pair has to be used together to specify the matched non-tumor and tumor samples, e.g.
--indiv-pair tumorIndivID1:normalIndivID1,tumorIndivID2:normalIndivID2
Detect somatic mutation(s) among matched tumor and non-tumor samples.
Note Multiple filtration codes have logical OR relationship.

Note The inheritance mode based filtration is proposed under strong assumptions for rare Mendelian disease with clear inheritance mode. If the inheritance mode is elusive, such filtration is not suggested as it may lead to the miss of genuine causal mutation(s). This is particularly true when one has no sufficient information to distinguish the compound-heterozygosity diseases from the recessive diseases.

Extract mutations affecting both copies of a gene in a diploid: --double-hit-gene-trio-filter or --double-hit-gene-phased-filter

java -jar ./kggseq.jar --buildver hg19 --vcf-file path/to/file1 --ped-file path/to/file2 --db-gene refgene --gene-feature-in 0,1,2,3,4,5,6 --db-filter 1kg201204,dbsnp137,ESP6500AA,ESP6500EA --rare-allele-freq 0.033 --db-score dbnsfp --mendel-causing-predict best --double-hit-gene-trio-filter (OR --double-hit-gene-phased-filter)

This function is designed for a disorder suspected to be under compound-heterozygous or recessive inheritance mode, in which both copies of a gene on the two homologous chromosomes of a patient are damaged by two different mutations or the same mutation. For recessive mode, it simply checks variants with homozygous genotypes in patients. For the compound-heterozygous mode, it can use two different input data, phased genotypes of a patient or unphased genotypes in a trio. Here the trio refers to the two parents and an offspring. When these alleles causing a disease at one locus, it follows the recessive model; and when they are at two loci, it follows the compound-heterozygosity model. In both cases, a gene is hit twice. This is the reason why it has the name 'double-hit gene'.

Besides the file storing prioritized sequence variants, this analysis with --double-hit-gene-trio-filter option will lead to a list of double-hit genes in at least one subject. The counts of double-hit genes of each subject are saved in *.doublehit.gene.trios.flt.count.xls (or *.doublehit.gene.trios.flt.count.txt) and the genotypes of involved sequence variants are saved in *.doublehit.gene.trios.flt.gty.xls (or *.doublehit.gene.trios.flt.gty.txt).

The '*doublehit.gene.trios.flt.count*' and the '*doublehit.gene.trios.flt.gty*' files or sheets contain multiple columns:

Gene: Gene symbols
PubMed: The PumMed ID(s) of papers co-mentioning the gene and phenotypes(or diseases) specified by '--phenotype-term'.
ExonLen: The length of total exons in kb. The overlapped regions are ignored.
CountHitCase: Total number of subjects having double-hit variants in the gene.
<Subjects>: Whether a subject carry the double-hit variants,1 for yes and 0 for no in the 'flt.count' file or sheet. In the 'gty.count' file or sheet, the variants positions and genotypes are shown.
Example:
17797224:0/0,0/1,0/1;17827193:0/1,0/0,0/1: The genotypes of father, mother, and child are 0/0, 0/1 and 0/1 at the variant with coordinate 17797224. At the other variant, the genotypes of father, mother, and child are 0/1, 0/0 and 0/1. Apparently, both copies of this gene in the child are hit by the two mutations.

The corresponding files produced by the analysis with --double-hit-gene-phased-filter option are *.doublehit.gene.phased.flt.count.xls (or *.doublehit.gene.phased.flt.count.txt) and *.doublehit.gene.phased.flt.gty.xls (or *.doublehit.gene.phased.flt.gty.txt).

The '*doublehit.gene.phased.flt.count*' and the '*doublehit.gene.phased.flt.gty*' files or sheets contain multiple columns:

Gene: Gene symbols
PubMed: The PumMed ID(s) of papers co-mentioning the gene and phenotypes(or diseases) specified by '--phenotype-term'.
P: The p-values of chi-square test to examine whether the double-hit mutations are associated with a disease.
CountCtl: Total number of controls having double-hit variants in the gene.
CountCase: Total number of patients having double-hit variants in the gene.
<Subjects>: Whether a subject carry the double-hit variants,1 for yes and 0 for no in the 'flt.count' file or sheet. In the 'gty.count' file or sheet, the variants positions and genotypes are shown.
Example:
105352884:0|1;105353305:1|0: The genotypes of a subject are 0|1 and 1|0 at the variants with coordinate 105352884 and 105353305. Apparently, both copies of this gene in the subject are hit by the two mutations.

Note Both filters have a strong assumption of full penetrance.

Note The --double-hit-gene-trio-filter or --double-hit-gene-phased-filter only works for inputs specified by --vcf-file and requires parents' genotypes to infer the phases of the alleles. And sequence variants with missed parents' genotypes are ignored.

Note To ignore homozygous mutation (i.e., implicating the autosomal recessive model), please add the tag --ignore-homo.

Extract a gene on which EVERY affected subject has at least one case-unique alternative allele. But these alleles may be from different variants: --unique-gene-filter

java -jar ./kggseq.jar --buildver hg19 --vcf-file path/to/file1 --ped-file path/to/file2 --db-filter 1kg201204,dbsnp137,ESP6500AA,ESP6500EA --rare-allele-freq 0.02 --db-score dbnsfp --mendel-causing-predict best --unique-gene-filter

Sequence variants beyond this type of genes will be filtered out.

Note --unique-gene-filter only works for inputs specified by --vcf-file.

Filter sequence variants by Identical by state (IBS): --ibs-case-filter X

java -jar ./kggseq.jar --buildver hg19 --vcf-file path/to/file1 --ped-file path/to/file2 --ibs-case-filter X

Search the longest region (in minimal length X kb) of each interesting variant, in which there is at least one allele identical (IBS>0) among all cases at all consecutive variants. This function can be used to highlight potential causal mutations or exclude non-disease-relevant sequence variants as the causal mutation(s) is very likely to have long shared regions among all affected family members of Mendelian dominant and recessive diseases. However, IBS checking is just a quick but over-estimation of identical by descent (IBD), which is a more accurate measure of shared regions among related individuals. KGGSeq currently has no function to estimate IBD regions but allow users to input the IBD information estimated by third-party tools into KGGSeq for annotation.

Note --ibs-case-filter only works for inputs specified by --vcf-file.

In the main output file, two columns will be appended:

LongestIBSRegion:The longest region in which all consecutive variants hae IBS over 0 in patients.
Example: 'chr2:46259552-57303414#Var:26469StartAtTailEndAtTail' means in the IBS region chr2:46259552-57303414 there are 26469 consecutive variants. This region start with and end at the boundaries of the input regions or chromosomes
LongestIBSRegionLength(bp): The length of the region.

Identical by descent (IBD) annotation: --ibd-annot

java -jar ./kggseq.jar --buildver hg19 --vcf-file path/to/file1 --ped-file path/to/file2 --ibd-annot path/to/file3

User can also provide IBD (i.e., the two alleles arose from the same allele in an earlier generation) or significant linkage regions, which are estimated by a third party statistical genetics tools (such as BEAGLE, Merlin and SimWalk2). Variants within these regions will be highlighted. For an ibd or linkage annotation file, title line is needed, and the file format is like:

CHR START END
1 6134174 10082841
1 202855724 207862411

Note Title line is needed.

In the main output file, two columns will be appended:

IBDRegion: The region with IBD information.
IBDRegionAnnot: The annotation information at the region.

Filter sequence variants by homozygosity: --homozygosity-case-filter X

java -jar ./kggseq.jar --buildver hg19 --vcf-file path/to/file1 --ped-file path/to/file2 --homozygosity-case-filter X

Search the longest region (in minimal length X kb) of consecutive homozygous genotype for each interesting variant with minimal length X kb in cases but not in controls. For cancer diseases, this function can be used to examine the loss of heterozygosity straightforwardly. For rare recessive diseases, it can be used to examine the runs of homozygosity straightforwardly. But KGGSeq currently does not have methods to test the statistical significance.

Note --homozygosity-case-filter only works for inputs specified by --vcf-file.

In the main output file, two columns will be appended:

LongestHomozygosityRegion longest region in which all consecutive variants hae IBS over 0 in patients.
Example: 'chr2:46259552-57303414#Var:26469StartAtTailEndAtTail' means in the IBS region chr2:46259552-57303414 there are 26469 consecutive variants. This region start with and end at the boundaries of the input regions or chromosomes
LongestHomozygosityRegionLength(bp): The length of the region.

Gene feature filtering

Filtration according to gene features: --db-gene & --gene-feature-in

java -jar ./kggseq.jar --buildver hg19 --vcf-file path/to/file1 --ped-file path/to/file2 --dgv-cnv-annot --splicing 6 --db-gene refgene,gencode,knowngene --gene-feature-in 0,1,2,3,4,5,6

Note --gene-feature-context X allows KGGSeq to jointly annoate non-synonymous variants and splicing variants within X (say, X=5) bp for each subject. However, this requires phased genotypes in VCF format as specified by --vcf-phased.

The '--dbgene' sets database(s) to annotate and filter variants.

Gene database label Supported reference genome Description
refgene hg18, hg19 and hg38 The RefGene database compiled by UCSC from hg18 refGene and hg19 refGene
Note: RefSeq has NO mitochondria gene definition.
gencode hg19 and hg38 The GENCODE gene sets, gencode.v19.annotation.gtf for hg19 and gencode.v23.annotation.gtf for hg38.See home page and the paper of GENCODE for details.
Note: GENCODE contains similar number of coding genes but more transcripts than RefGene. It HAS the mitochondria gene definition.
knowngene hg18, hg19 and hg38 The UCSC knonwGene database compiled by UCSC from hg18 knownGene and hg19 knownGene
path/to/file - KGGSeq also accepts a gene dataset customized by users. The example can be seen here. The following are the column names:
bin: The bin ID number
name: The name of a transcript
chrom: The chromosome name
strand: The strand information of the transcript
txStart: The start site of a transcript
txEnd: The end site of a transcript
cdsStart: The start site of coding DNA sequence
cdsEnd: The end site of coding DNA sequence
exonCount: The number of exons
exonStarts: The start sites of all exons
exonEnds: The end sites of all exons
score: mapping score
name2: Gene name
cdsStartStat: Un-used
cdsEndStat: Un-used
exonFrames: Un-used
sequences: DNA sequences of exons
delSites: Deletion sites on the human reference genome, compared to the latest cDNA
insSites: Insertion sites on the human reference genome, compared to the latest cDNA

With this filter, variants beyond regions sepcified by '--gene-feature-in' will be excluded. It is '--gene-feature-in 0,1,2,3,4,5...,15' by default.

Illustration of number codes for the gene features:
Feature Code Explanation
Frameshift 0 Short insertion or deletion result in a completely different translation from the original.
Nonframeshift 1 Short insertion or deletion results in loss of amino acids in the translated proteins.
Startloss 2 Indels or nucleotide substitution result in the loss of start codon(ATG) (mutated into a non-start codon).
Stoploss 3 Indels or nucleotide substitution result in the loss of stop codons (TAG, TAA, TGA)
Stopgain 4 Indels or nucleotide substitution result in the new stop codons (TAG, TAA, TGA), which may truncate the protein.
Splicing 5 variant is within 2-bp of a splicing junction (use --splicing x to change this, the unit of x is base-pair)
Missense 6 Variants result in a codon coding for a different amino acid (missense)
Synonymous 7 Nucleotide substitution does not change amino acid.
Exonic 8 Due to loss of sequences in reference database, this variant can only be mapped into exonic region without more precise annotation.
UTR5 9 Within a 5' untranslated region
UTR3 10 Within a 3' untranslated region
Intronic 11 Within an intron
Upstream 12 Within 1-kb region upstream of transcription start site (use --neargene x to change this, the unit of x is base-pair)
Downstream 13 Within 1-kb region downtream of transcription end site (use --neargene x to change this, the unit of x is base-pair)
ncRNA 14 Within a transcript without protein-coding annotation in the gene definition (see Notes below for more explanation)
Intergenic 15 variant is in intergenic region
Monomorphic 16 It is not a sequence variation actually, which may be resulted from bugs in reference genome in variant calling.
Unknown 17 Variants has no annotation.
The output of this function is a list of variables providing all involved gene features of a considered variant.

MostImportantFeatureGene: The gene corresponds to the variant’s smallest gene feature code.
MostImportantGeneFeature: The variant’s smallest gene feature code.
RefGeneFeatures: All matched gene features in the reference gene database.
GENCODEFeatures: All matched gene features in the GENCODE gene database.



The gene feature annotations are described in accordance with the Human Genome Variation Society (HGVS) recommendations. The followings are some examples:

GBP4:NM_052941:c.1633A.G>C.C:p.M545L:(11Exons):exon10:missense
Means: At nucleotide 1633 and 1635 of gene GBP4's transcript NM_052941, the nucleotides A and G are changed to a C and C respectively, which jointly changes the 545th amino-acid M to be L in this transcript's protein. This transcript has 11 exons in total and this variant is in the 10th exon. The . denotes reference allele.
KLHL17:NM_198317:c.1918A>C:p.T640P:(12Exons):exon12:missense
Means: At nucleotide 1918 of gene KLHL17's transcript NM_198317, an A is changed to a C, which changes the 640th amino-acid T to be P in this transcript's protein. This transcript has 12 exons in total and this variant is in the 12th exon.
SYNGR1:NM_004711:(4Exons):exon4:c.C605ins+CAA:p.P202ins??
Means: At nucleotide 605 of gene SYNGR1's transcript NM_004711, there is a 3bp insertion. This transcript has 4 exons in total and this variant is in the 4th exon. The '+' denotes an insertion. The affected peptide is unknown ('??')
PKD1L2:NM_001076780:c.A705delAA-:p.N236:(18Exons):exon4:frameshift
Means: At nucleotide 705 of gene PKD1L2's transcript NM_001076780, there is a 2bp deletion. This transcript has 18 exons in total and this variant is in the 4th exon. The '-' denotes an deletion. The variant results in a frameshift of protein-coding .
DNAJC8:NM_014280:c.237+2T>G:(9Exons):exon3GTdonor
Means:This is a T to G substitution of the 2nd nucleotide of the 2 nd intron (from its 5' part) positioned between coding DNA nucleotides 237 and 238, which will affect the GT splicing donor.
HNRNPCL2:NM_001136561:c.-169C>G:(2Exons):5UTR
Means: This is a C to G substitution of nucleotide, 169 nucleotides upstream of the ATG translation initiation codon (coding DNA -169).
PRAMEF4:NM_001009611:c.*62C>T:(4Exons):3UTR
Means: This is a C to T substitution of nucleotide coding DNA 62, located in the 3' UTR.
C1orf159:NM_017891:c.311-23T>C:(10Exons):intronic6
Means: This is a T to C substitution of the 23 nucleotide of the 6 th intron (from its 3' part) positioned between coding DNA nucleotides 310 and 311.
HNRNPCL1:NM_001013631:c.*138G>A:(2Exons):downstream
Means: This is a C to A substitution of nucleotide located 293 nucleotides downstream of the gene, i.e. the polyA-addition site.
TNFRSF18:NM_148901:c.-61C>T:(4Exons):upstream
Means: This is a C to T substitution of nucleotide, 61 nucleotides upstream of the ATG translation initiation codon (coding DNA -61).


Table: Compare non-synonymous gene feature annotation of three popular tools and two gene models for variants discovered in 1000 Genomes Project AFR panel

Note: The RefGene model was provided by UCSC on Oct 10, 2015 for KGGSeq; and for ANNOVAR and the latest RefGene model (by Oct 10, 2015) from their websites was used. a: Because ANNOVAR has no startloss category but annotates startloss as missense, no comparison was made for this category. b: Because dbSNP has no startloss category, no consistency was calculated for this category. c: rate of consistency with annotations in the dbSNP. The default parameters settings of each tool were used for the annotation.

Allele frequency trimming by 4 ways

1. Allele frequency trimming according to database: --db-filter

java -jar ./kggseq.jar --buildver hg19 --vcf-file path/to/file1 --ped-file path/to/file2 --db-filter 1kg201204,dbsnp141,ESP6500AA,ESP6500EA --rare-allele-freq 0.005 --db-filter-hard dbsnp138nf This mode enables variants filtering according to allele frequencies recorded in public data sources.
The '--db-filter' is use to set databases for filtration. In the above case, variants with alternative allele frequency EQUAL to or over 0.005 will be excluded. No spaces are allowed between and within database names.
Moreover, '--db-filter-hard' is use to filter ALL existing variants in a database. But it should be always careful to use this hard filtering.

Note1. To keep variants in a frequency range,[a,b], please use '--allele-freq a,b' option. The boundaries a and b are included.
Note2. The options, '--allele-freq' and '--rare-allele-freq', are mutually exclusive and the latter has higher priority.
Note3. By default, '--rare-allele-freq 0.01', will be used if allele frequency cutoff is set.
Note4. Difference between '--rare-allele-freq' and '--allele-freq': The '--rare-allele-freq 0,0.01' does not consider the variants with missing frequency in public databases, but '--rare-allele-freq 0.01' does. So '--rare-allele-freq 0.01' returns much more variants than '--rare-allele-freq 0,0.01'.


List of public databases for allele frequency trimming
DB Label Explanation
1kgeur201305 495 subjects in the EUR panel of 1000 Genomes Project release in 2013 May (around 24.0 million sequence variants)
1kgeas201305 496 subjects in the EAS panel of 1000 Genomes Project release in 2013 May (around 23.5 million sequence variants)
1kgafr201305 645 subjects in the AFR panel of 1000 Genomes Project release in 2013 May (around 41.7 million sequence variants)
1kgsas201305 485 subjects in the SAS panel of 1000 Genomes Project release in 2013 May (around 26.7 million sequence variants)
1kgamr201305 346 subjects in the AMR panel of 1000 Genomes Project release in 2013 May (around 28.2 million sequence variants)
1kg201305 All sample of 1000 Genomes Project release 2013 May (over 81 million sequence variants from 2504 subjects)
1kg201204 All sample of 1000 Genomes Project release 2012 April
1kgafr201204 African descendent samples of 1000 Genomes Project release in 2012 April
1kgeur201204 European descendent samples of 1000 Genomes Project release in 2012 April
1kgamr201204 American descendent samples of 1000 Genomes Project release in 2012 April
1kgasn201204 Asian descendent samples of 1000 Genomes Project release in 2012 April
dbsnp135 dbSNP version 135, compiled from UCSC.
dbsnp137 dbSNP version 137, compiled from UCSC.
dbsnp138 dbSNP version 138, compiled from UCSC.
dbsnp138nf dbSNP version 138 without the flagged SNPs by UCSC (so nonflagged: nf). In UCSC the flagged SNPs include SNPs clinically associated by dbSNP, mapped to a single location in the reference genome assembly, and not known to have a minor allele frequency of at least 1%.
dbsnp141 dbSNP version 141, compiled from NCBI dbSNP V141. Note Indels are ingored.
ESP5400 a public variants dataset from NHLBI GO Exome Sequencing Project (ESP)
ESP6500AA a public variants dataset of African American from NHLBI GO Exome Sequencing Project (ESP)
ESP6500EA a public variants dataset of European American from NHLBI GO Exome Sequencing Project (ESP)
exac Exome Aggregation Consortium (ExAC r0.3.1): Variants from 60,706 unrelated individuals sequenced. The samples are partitioned in to 7 ancestrally different panels, East Asian(eas, n=4327), South Asian (sas, n=8256), African/African American(afr, n= 5203), American(amr, n=5789 ), Finnish(fin, n=3307), Non-Finnish European(nfe, n=33370), Other (oth, n=454). By default (--db-filter exac), KGGSeq will use frequencies of all of the 7 panels for annotation and filtering. You can also specify the panels of your interests by the abbreviations. For example, you can specify the frequencies by of East Asian and Non-Finnish European by --db-filter exac.eas.nfe.
ehr DiscovEHR: More than 4 million Variants from more than 50,000 MyCode® participants. The DiscovEHR Collaboration between the Regeneron Genetics Center and Geisinger Health System brings together high throughput DNA sequencing with longitudinal electronic health records for discovery of genetic variation important for human disease and therapeutic response.
gadexome Genome Aggregation Database: More than 15 million Variants from more than 123,136 participants sequenced in exomes. The samples are partitioned in to 8 ancestrally different panels, East Asian(eas, n=8,624), South Asian (sas, n=15,391), African/African American(afr, n=7,652), Latino (amr, n=16,791), Finnish(fin, n=11,150), Non-Finnish European(nfe, n=55,860),Ashkenazi Jewish (asj, n=4,925), Other (oth, n=2,743). By default (--db-filter gadexome), KGGSeq will use frequencies of all of the 8 panels for annotation and filtering. You can also specify the panels of your interests by the abbreviations. For example, you can specify the frequencies by of East Asian and Non-Finnish European by --db-filter gadexome.eas.nfe.
gadgenome Genome Aggregation Database: More than 229 million Variants from more than 15,496 participants sequenced in whole genomes. The samples are partitioned in to 7 ancestrally different panels, East Asian(eas, n=881), African/African American(afr, n=4,368), Latino (amr, n=419), Finnish(fin, n=1,747), Non-Finnish European(nfe, n=7,509),Ashkenazi Jewish (asj, n=151), Other (oth, n=491). By default (--db-filter gadgenome), KGGSeq will use frequencies of all of the 7 panels for annotation and filtering. You can also specify the panels of your interests by the abbreviations. For example, you can specify the frequencies by of East Asian and Non-Finnish European by --db-filter gadgenome.eas.nfe.
The output of this function is a list of variables providing all involved gene features of a considered variant.

MaxDBAltAF: The maximum alternative allele frequency across datasets specified.
altFreq@hgXX_XXXXX: The alternative allele frequency at a dataset hgXX_XXXXX.
NoteVariants without allele frequencies in reference databases are marked by '.' in the results. Moreover, Variants not EXISTING in reference database are marked by 'N'.


2. Users can also provide local filters to filter variants. --local-filter

java -jar ./kggseq.jar --buildver hg19 --vcf-file path/to/file1 --ped-file path/to/file2 --local-filter path/to/file3 --rare-allele-freq 0.005 --local-filter-hard path/to/file4

Moreover, '--local-filter-hard' is use to filter ALL existing variants in a database. Multiple local datasets can be specified for filtering at a time, '--local-filter path/to/file3.1,path/to/file3.2'.

The '--local-filter' option sets local datasets for filterating. KGGSeq accepts local dataset files in the following format: each file has 5 columns [Chromosome, Physical Position, Reference Allele, Alternative Allele(s), and Frequency of alternative allele(s)], separated by tab character. KGGSeq provides a function to convert data in VCF format to that in the local dataset format:

java -jar ./kggseq.jar --make-filter --vcf-file path/to/file --out test.var An example file is like:

Chrom Position Reference Alternative Freq
1 469 C G 0.150
1 492 C T 0.175
1 519 G C 0.067
1 874290 - 0ACAGAG 0.809
1 875913 CAG 3 0.8431

Note The title line is needed and Freq is optional.

3. An in-house VCF file can be used for allele frequency trimming: --local-filter-vcf

java -jar ./kggseq.jar --buildver hg19 --vcf-file path/to/file1 --ped-file path/to/file2 --local-filter-vcf path/to/file3 --rare-allele-freq 0.005 --local-filter-vcf-hard path/to/file4

If the data were stored in different files chromosome by chromosome, you can use ' _CHROM_' to denote the chromosome names [1...Y] or directly specify [1,2,X] in the file name so that KGGSeq can read data in multiple files in a run.

Similarly, '--local-filter-vcf-hard' is use to filter ALL existing variants in a database.

4. An in-house VCF file without genotypes can also be used for allele frequency trimming: --local-filter-no-gty-vcf

java -jar ./kggseq.jar --buildver hg19 --vcf-file path/to/file1 --ped-file path/to/file2 --local-filter-no-gty-vcf path/to/file3 --rare-allele-freq 0.005 --local-filter-no-gty-vcf-hard path/to/file4

Similarly, '--local-filter-no-gty-vcf-hard' is use to filter ALL existing variants in a database.

5. Filter variants by minor allele frequency in the input sample: --filter-sample-maf-le or --filter-sample-maf-oe

java -jar ./kggseq.jar --buildver hg19 --vcf-file path/to/file1 --ped-file path/to/file2 --filter-sample-maf-le 0.01

--filter-sample-maf-le: Filter out variants with minor allele frequency less than OR equal to a cutoff (x) in the input samples.
--filter-sample-maf-oe: Filter out variants with minor allele frequency over OR equal to a cutoff (x) in the input samples.

Variant type, region and gene filtering

Ingore insertion and deletion sequence variants (indels): --ignore-indel

java -jar ./kggseq.jar --buildver hg19 --vcf-file path/to/file1 --ped-file path/to/file2 --ignore-indel

Accordingly, users can also ask KGGSeq to ingore Single nucleotide variants (SNVs) : --ignore-snv

java -jar ./kggseq.jar --buildver hg19 --vcf-file path/to/file1 --ped-file path/to/file2 --ignore-snv

By default, KGGSeq will process both SNV and Indel.

Ignore some genomic regions: --regions-out

java -jar ./kggseq.jar --buildver hg19 --vcf-file path/to/file1 --ped-file path/to/file2 --regions-out chrX,chrY:1-10000 Alternatively, one can also exclusively process sequence variants at some genome regions: java -jar ./kggseq.jar --buildver hg19 --vcf-file path/to/file1 --ped-file path/to/file2 --regions-in chr2,chr4:21212-233454

Ignore variants within some genes: --genes-out

java -jar ./kggseq.jar --buildver hg19 --vcf-file path/to/file1 --ped-file path/to/file2 --genes-out TCF4,CNNM2,ANK3 Alternatively, one can KEEP variants only within some genes: java -jar ./kggseq.jar --buildver hg19 --vcf-file path/to/file1 --ped-file path/to/file2 --genes-in TCF4,CNNM2,ANK3

Super duplicate region filtering

Filter out variants in super duplicate regions: --superdup-filter

java -jar ./kggseq.jar --buildver hg19 --vcf-file path/to/file1 --ped-file path/to/file2 --superdup-filter

KGGSeq can eliminate sequence variants within putative super-duplicate genomic regions defined in a dataset (genomicSuperDups) from UCSC datasets , which have higher genotyping error rate. (See more ).

Note Another option --superdup-annot allows you to just mark (but not to remove) the variants in the super duplicate regions. A column named SuperDupKValue (K-value calculated with Jukes-Cantor model) will be added in the resulting dataset

Gene's Variant number

Filter out genes with too many variants: --gene-var-filter

java -jar ./kggseq.jar --buildver hg19 --vcf-file path/to/file1 --ped-file path/to/file2 [options to filter out common and neutral variants] --gene-var-filter 4 After a number of hard-filtering by allele frequencies and pathogenic prediction, you may see some genes harboring candidate variants. Empirically, these genes are usually not interesting and their mutations are often produced by some unknown factors such pseudogene or platform bias. As a rule of thumb, it is safe to set a cutoff 4 or more.

Case-control Phenotype


Filter for case or control unique variants: --filter-model case-unique or --filter-model control-unique java -jar ./kggseq.jar --buildver hg19 --vcf-file path/to/file1 --ped-file path/to/file2 --filter-model case-unique Case unique: extract variants with alternative alleles only present among affected individuals.
Control unique: extract variants with alternative alleles only present among unaffected individuals.

Note The --filter-model only works for inputs specified by --vcf-file .
Note This function can work with options to flexibly set the sample size and missingness of genotypes, detalied in the Variant QC part.

 

Annotation

dbSNP RSID


Assign dbSNP RSID to variants:--rsid
java -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 --rsid KGGSeq can update the variants' dbSNP rsID (if available) according to their genomic coordinates. The resource data used are from SNPChrPosOnRef (hg19/hg38) [help].

Alternative splicing

Potential of altering splicing--scsnv-annot
java -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 --scsnv-annot Annotate a SNV with predicted potential of altering splicing from dbscSNV , which include all possible human SNVs within splicing consensus regions (-3 to +8 at the 5' splice site and -12 to +2 at the 3' splice site).
The command will append a field to the output file.

ada_score@dbScSNV: The prediction score by ADA approach which combines multiple existing tools. See more in Jian et al (2014)

Structure variation

Map a variant against known structure variation --dgv-cnv-annot
java -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 --dgv-cnv-annot Annotate a variant whether it is within a known insertion or deletion registered in the Database of Genomic Variants , which will help exclude candidates which are not interesting.
The command will append a field to the output file.

DGVIDs: the ID in the Database of Genomic Variants.
CNVSampleSize: the size of sample, used to derive the structure.
LossCNV: Number of subjects having loss of copy number variation.
GainCNV: Number of subjects having excessive copy number variation.

Shared protein-protein interactions (PPIs)


Examine whether variants share the same PPIs with some genes of interest: --candi-list gene1,gene2,...,geneN --ppi-annot ppiDatabase

java -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 --candi-list gene1,gene2,...,geneN --ppi-annot string --ppi-depth 1

Note Currently, the only supported PPI database is STRING (i.e., string).

Note Instead of specifying the gene list using the --candi-list option, users can specify a file containing the list using the --candi-file option.

In the file containing candidate genes, each row can only have one gene. The format is like the following:

GeneSymbol1
GeneSymbol2
GeneSymbol3
GeneSymbol4

Note The maximum distance of a PPI between a specifified candidate genes and a gene containing the potential causal variants can be adjusted using the --ppi-depth option. The defaul is --ppi-depth 1.

The option will append the following fields to the output file:

IsWithinCandidateGene: Whether the variant is within a candidate gene
PPI: PPI shared by at least one gene of interest and the gene containing the variant

Shared genesets


Examine whether variants share the same genesets with some genes of interest: --candi-list gene1,gene2,...,geneN --geneset-annot GenesetDatabase

java -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 --candi-list gene1,gene2,...,geneN --geneset-annot GeneSetDatabase

Currently, users can choose between 5 geneset databases from GSEA. The following are brief descriptions of the 5 datasets COPIED from the page of GSEA.

GeneSetDatabase Description Version
cano Gene sets from the geneset databases. Usually, these gene sets are canonical representations of a biological process compiled by domain experts. (1452 gene sets) MSigDB 3.1
cura Curated gene sets: Gene sets collected from various sources such as online geneset databases, publications in PubMed, and knowledge of domain experts. The gene set page for each gene set lists its source. (4850 gene sets) MSigDB 3.1
onco Oncogenic signatures: Gene sets represent signatures of cellular genesets which are often dis-regulated in cancer. The majority of signatures were generated directly from microarray data from NCBI GEO or from internal unpublished profiling experiments which involved perturbation of known cancer genes. In addition, a small number of oncogenic signatures were curated from scientific publications. (189 gene sets) MSigDB 3.1
cmop Computational gene sets: Computational gene sets defined by mining large collections of cancer-oriented microarray data. (858 gene sets) MSigDB 3.1
onto GO gene sets Gene sets are named by GO term and contain genes annotated by that term. GSEA users: Gene set enrichment analysis identifies gene sets consisting of co-regulated genes; GO gene sets are based on ontologies and do not necessarily comprise co-regulated genes. (1454 gene sets) MSigDB 3.1

Note The --candi-list and --candi-file options can be shared with --ppi-annot.

The option will append the following fields to the output file:

IsWithinCandidateGene: Whether the variant is within a candidate gene
SharedGeneSet: GeneSet shared by at least one gene of interest and the gene containing the variant

Mouse Phenotype


Annotate the genes with known mouse phenotypes as reference: --mouse-pheno. java -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 --mouse-pheno The genes harboring interested mutations will be annotated by known mouse phenotypes of ortholog genes in the file of Mouse/Human Orthology with Phenotype Annotations from Mouse Genome Informatics(MGI) and in the file of phenotype-genotype from International Mouse Phenotyping Consortium (IMPC)
An column will be appended in the main output file:

MousePhenotype (MGI):mouse phenotypes of ortholog genes. Multiple phenotypes are separated by '||'.
MousePhenotype (IMPC):mouse phenotypes of ortholog genes. Multiple phenotypes are separated by '||'.


Zebrafish Phenotype


Annotate the genes with known zebrafish phenotypes as reference: --zebrafish-pheno. java -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 --zebrafish-pheno The genes harboring interested mutations will be annotated by known zebrafish phenotypes of ortholog genes in the file of Phenotypic Zebrafish genes with Human Orthology from The Zebrafish Model Organism Database.
An column will be appended in the main output file:

ZebrafishPhenotype :Affected structure or process subterm after the knock-out of the human ortholog genes in Zebrafish.


DDD Study


Annotate by disease names in Deciphering Developmental Disorders (DDD) study: --ddd-annot. java -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 --ddd-annot The genes harboring interested mutations will be annotated by the documented disease names in Gene-based genotype to phenotype of Deciphering Developmental Disorders (DDD) study .
An column will be appended in the main output file:

DDDPhenotype : confirmation status;disease;pubmed_ids


PubMed literature


Assign PubMed ID to a gene:--phenotype-term searchTerm and --pubmed-mining
java -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 --phenotype-term search+Term1,search+Term2 --pubmed-mining KGGSeq can mine the titles and abstracts of published papers in PubMed via the NCBI E-utilities to find a co-mention with the searched term(s) of interest (i.e., disease) and the genes/cytogeneic regions in which the variants are located. The option --pubmed-mining will append the following two fields to the output file.

PubMedIDIdeogram : PubMed ID of articles in which the term and the cytogeneic position of the variant are co-mentioned
PubMedIDGene : PubMed ID of articles in which the term and the gene containing the variant are co-mentioned


IMPORTANT Please use + instead of blank characters within a search term.

OMIM

Assign OMIM term to a gene --omim-annot
java -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 --omim-annot KGGSeq can extract all available disorder names linked to a gene in which the variants are located from the OMIM dataset morbidmap. The command will append two extra fields to the output file:

DiseaseName(s)MIMid : Disorder, <disorder MIM no.> (<phene mapping key>)
Phenotype mapping method <phene mapping key>:
1 - the disorder is placed on the map based on its association with a gene, but the underlying defect is not known.
2 - the disorder has been placed on the map by linkage; no mutation has been found.
3 - the molecular basis for the disorder is known; a mutation has been found in the gene.
4 - a contiguous gene deletion or duplication syndrome, multiple genes are deleted or duplicated causing the phenotype.
GeneMIMid : Gene/locus MIM no.

COSMIC cancer information

Assign COSMIC cancer annotation to a gene: --cosmic-annot
java -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 --indiv-pair NONTUMOR.1:TUMOR.1,NONTUMOR.2:TUMOR.2 --genotype-filter 8 --cosmic-annot KGGSeq can retrieve the confirmed somatic mutation information registered for variants and genes in the COSMIC database. The command will append a field to the output file. e.g.,

COSMICCancerInfo : known cancer and occurrence time of the somatic mutation in COSMIC dataset. e.g., {serous_carcinoma=3, astrocytoma_Grade_IV=1}
This somatic mutation occurred 3 times in samples of 'serous_carcinoma' and once in samples of 'astrocytoma_Grade_IV'.

 

Prediction at variant

KGGSeq provides a series of methods to combine existing functional scores of DNA variants from multiple algorithms (i.e., SIFT, PolyPhen2, GWAVA and CADD) for a more accurate pathogenic prediction at coding and non-coding variants.

Predicting Mendelian disease-causing variants


Predict Mendelian disease-causing variants: --db-score dbnsfp --mendel-causing-predict
java -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 --db-score dbnsfp --mendel-causing-predict best --filter-nondisease-variant Use functional or deleterious scores (listed below) collected in dbNSFP v3.0+ database to RE-predict whether a nonsynonymous single nucleotide variant (SNV) will potentially be Mendelian disease causal or not by Logistic regression model (See methods in our paper, Li et al. PLoS Genet. 2013 Jan;9(1):e1003143). By default, KGGSeq try the top 10 subsets of the scores (according to their area under the curves (AUC) of receiver operating characteristic (ROC)) for a combinatorial prediction until a positive prediction occurs. This is a way to relief influence of missing values. Users can try more subsets by a number on the option, say --mendel-causing-predict best30. However, a larger number will increase the false positive rate. In conjunction with the prediction model, the --filter-nondisease-variant tag will be used to filter out the nondisease-causative variants predicted by Logistic regression model (See methods in Li et al. PLoS Genet. 2013 Jan;9(1):e1003143). .Note that this trying will increase the false positive rate although it decreases the false negative rate. So you may need more information to justify the involved gene(s).


Figure: Receiver operating characteristic (ROC) and Precision recall(PR) curves and area under the curves (AUC) of individual scores and combined score by Logistic regression model
Note: this is an updated plot of Figure 1 in our paper . Here we combined more individual functional impact scores, which is the only difference.

Although the logistic regression model was proposed more than 6 years ago,it has comparable performance with two recent methods, REVEL (Am J Hum Genet. 2016) and M-CAP (Nat Genet. 2016) .
Figure: The performance comparison of KGGSeq´s pathogenic prediction with REVEL and M-CAP. Note: A) area under the curve of receiver operating characteristic, B)area under the curve of precision-recall. This plot is from KGGSeq's reference paper. Please read details in producing the plot in the paper .

On the other hand, one can FIX the prediction using a specified subset or full set of the 5 impact scores by option like --mendel-causing-predict 4,6,7
The coding for the functional or deleterious scores used in'--mendel-causing-predict'options is listed below:

Coding Method Description (Mostly copied from dbNSFP)
1 SIFT SIFT uses the 'Sorting Tolerant From Intolerant' (SIFT) algorithm to predict whether a single amino acid substitution affects protein function or not, based on the assumption that important amino acids in a protein sequence should be conserved throughout evolution and substitutions at highly conserved sites are expected to affect protein function.A small scoreindicates a high chance for a substitutionto damage the protein function.
2 Polyphen2_HDIV Polyphen2 score based on HumDiv, i.e. hdiv_prob. The score ranges from 0 to 1, and the corresponding prediction is "probably damaging" if it is in [0.957,1]; "possibly damaging" if it is in [0.453,0.956]; "benign" if it is in [0,0.452]. Score cutoff for binary classification is 0.5, i.e. the prediction is "neutral" if the score is smaller than 0.5 and "deleterious" if the score is larger than 0.5. Multiple entries separated by ";"
3 Polyphen2_HVAR Polyphen2 predicts the possible impact of an amino acid substitution on the structure and function of a human protein using straightforward physical and comparative considerations by an iterative greedy algorithm. In the present study, we use the original scores generated by the HumVar (instead ofHumDiv) trained model as it is preferred for the diagnosis of Mendelian diseases. The scores range from 0 to 1. A substitution with larger score has a higher possibility to damage the protein function.
4 LRT LRT employed a likelihood ratio test to assess variant deleteriousness based on a comparative genomics data set of 32 vertebrate species. The identified deleterious mutations could disrupt highly conserved amino acids within protein-coding sequences, which are likely to be unconditionally deleterious.The scores range from 0 to 1. A larger score indicates a larger deleterious effect.
5 MutationTaster MutationTaster assesses the impact of the disease-causing potential of a sequence variant by a naive Bayes classifier using multiple resources such as evolutionary conservation, splice-site changes, loss of protein features and changes that might affect mRNA level. The scores range from 0 to 1. The larger score suggests a higher probability to cause a human disease.
6 MutationAssessor MutationAssessor "functional impact of a variant : predicted functional (high, medium), predicted non-functional (low, neutral)" Please refer to Reva et al. Nucl. Acids Res. (2011) 39(17):e118 for details
7 FATHMM FATHMM default score (weighted for human inherited-disease mutations with Disease Ontology); If a score is smaller than -1.5 the corresponding NS is predicted as "D(AMAGING)"; otherwise it is predicted as "T(OLERATED)". If there's more than one scores associated with the same NS due to isoforms, the smallest score (most damaging) was used. Please refer to Shihab et al Hum. Mut. (2013) 34(1):57-65 for details
8 VEST3 VEST 3.0 score. Score ranges from 0 to 1. The larger the score the more likely the mutation may cause functional change. In case there are multiple scores for the same variant, the largest score (most damaging) is presented. Please refer to Carter et al., (2013) BMC Genomics. 14(3) 1-16 for details. Please note this score is free for non-commercial use. For more details please refer to http://wiki.chasmsoftware.org/index.php/SoftwareLicense. Commercial users should contact the Johns Hopkins Technology Transfer office.
9 PROVEAN PROVEAN score (PROVEANori). Scores range from -14 to 14. The smaller the score the more likely the SNP has damaging effect..
10 CADD Combined Annotation Dependent Depletion (CADD) score for funtional prediction of a SNP. Please refer to Kircher et al. (2014) Nature Genetics 46(3):310-5 for details. The larger the score the more likely the SNP has damaging effect.
11 GERP++_NR Neutral rate
12 GERP++_RS RS score, the larger the score, the more conserved the site
13 phastCons7way_vertebrate phastCons conservation score based on the multiple alignments of 7 vertebrate genomes (including human). The larger the score, the more conserved the site. Scores range from 0 to 1.
14 SiPhy_29way_logOdds SiPhy score based on 29 mammals genomes. The larger the score,he more conserved the site. Scores range from 0 to 37.9718 in dbNSFP.
Note Predictions at variants with missing scores at specified methods will be ignored!
Note If you use this feature in any published work, please cite the following manuscript: Li et al. Predicting Mendelian disease-causing non-synonymous single nucleotide variants in exome sequencing studies. PLoS Genet. 2013 Jan;9(1):e1003143

The option will append the following fields to the output file:

...
Polyphen2_HDIV_pred: Polyphen2 prediction based on HumDiv, "D" ("porobably damaging"), "P" ("possibly damaging") and "B" ("benign"). Multiple entries separated by ";"
Polyphen2_HVAR_pred: Polyphen2 prediction based on HumVar, "D" ("porobably damaging"), "P" ("possibly damaging") and "B" ("benign"). Multiple entries separated by ";"
LRT_pred: Classification using LRT (D = deleterious, N = neutral, or U = unknown)
MutationTaster_pred: Classification using MutationTaster (A = disease_causing_automatic, D = disease_causing, N = polymorphism, or P = polymorphism_automatic)
MutationAssessor_pred: MutationAssessor "functional impact of a variant : predicted functional (high, medium), predicted non-functional (low, neutral)"
MetaSVM_pred: Prediction of an SVM based ensemble prediction score by Dong et al. [(2015) Human Molecular Genetics 24(8):2125-2137]."T(olerated)" or"D(amaging)".
MetaLR_pred: Prediction of a MetaLR based ensemble prediction score by Dong et al. [(2015) Human Molecular Genetics 24(8):2125-2137]."T(olerated)" or "D(amaging)".
clinvar_clnsig: clinical significance as to the clinvar data set; 2 - Benign, 3 - Likely benign, 4 - Likely pathogenic, 5 - Pathogenic, 6 - drug response, 7 - histocompatibility. A negative score means the the score is for the ref allele clinvar_trait: the trait/disease the clinvar_clnsig referring to (Please see more description about the attributes at https://drive.google.com/file/d/0B60wROKy6OqcWllRZjZXM2Vuc2c/view
[...]
DiseaseCausalProb_ExoVarTrainedModel: Conditional probability of being Mendelian disease-causing given the above prediction scores under a logistic regression model trained by our dataset ExoVar.
IsRareDiseaseCausal_ExoVarTrainedModel: Classification using the logistic regression model (Y = disease-causing or N = neutral)
BestCombinedTools:OptimalCutoff:TP:TN : The subset of original prediction tools (out of the 13 tools) used for the combined prediction by our Logistic Regression model which have the largest posterior probability among all possible combinatorial subsets: the cutoff leads to the maximal Matthews correlation coefficient (MCC): the corresponding true positive and true negative at the maximal MCC.

Note By default the option --mendel-causing-predict best is always switched on unless other prediction types are specified.

Predicting driver mutations of cancers


Predict non-synonyms cancer-driver somatic-mutations of cancers by an integrative approach based on : --db-score dbnsfp --cancer-driver-predict
java -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 --indiv-pair NONTUMOR.1:TUMOR.1,NONTUMOR.2:TUMOR.2 --genotype-filter 8 --cosmic-annot --db-score dbnsfp --cancer-driver-predict --filter-nondisease-variant A driver mutation is defined as the mutation that leads to selective advantage to a cancer clone by either increasing the cancer cells' survival or reproduction. Use the above mentioned 14 functional or deleterious scores collected in dbNSFP (dbNSFP v3.0+) database to jointly predict whether a nonsynonymous single nucleotide variant (SNV) will potentially be the driver mutations by a standard random forest model trained under COSMIC database. After the prediction, the --filter-nondisease-variant tag can be used to filter out all predicted non-driver missense variants.


Table: The area under the curves (AUC) of receiver operating characteristic (ROC) and Precision recall(PR) curves and Matthews correlation coefficient (MCC) of individual scores and combined score by the random forest model and Logistic regression model in a 10-fold cross-validation. As shown in the above table, the combined prediction by the random forest outperformed that by the Logistic regression.

The option will append the following fields to the output file:

...
IsCancerDriver_COSMICTrainedModel: Classification results by the random forest model (Y = cancer-driver or N = passenger)
RandomForestScore:Proportion of positive predictions in the random forest model in the benchmark dataset. It ranges from 0 to 1. The larger value, the more confident a positive prediction (Y).

These predicted cancer-driver somatic mutations are evaluated by our statistical approach.

Kwan JS, Ng OL, Sham PC and Li MX. Functional prediction of driver mutations in cancer genomes. (In preparation)
Benchmark dataset: This dataset is composed of 3,479 cancer-causing somatic nsSNVs that occurred at least twice in the Catalogue of Somatic Mutations in Cancer (COSMIC) database and located in genes from the Cancer Gene Census (CGC), which are treated as positive control variants, together with an equal number of somatic mutations randomly sampled without replacement from a set of neutral mutations (i.e., mutations occurred only once in COSMIC and not located in CGC genes), which are treated as negative control variants.


Predicting complex disease-causing variants

Predict regulatory or pathogenic potential of complex diseases at non-coding variants by different models.

Predicting noncoding regulatory variants based on composite strategy

Predict regulatory variants by 10 functional scores at non-coding variants: --db-score dbncfp_known [or dbncfp_all] --regulatory-causing-predict
java -jar kggseq.jar --vcf-file path/to/file1 --db-score dbncfp_known --regulatory-causing-predict all Use 10 existing functional prediction scores (See above table) to RE-predict whether a SNV will potentially be regulatory. By default, KGGSeq uses all scores for a combinatorial prediction. On the other hand, one can FIX the prediction using any specified subset (>2) or full set (all) of the impact scores by option like --regulatory-causing-predict 1,6,8,10.

The existing functional prediction scores at non-coding variants are listed below:

Coding Method Description
1 GWAVA_Region GWAVA uses the random forest algorithm to build three classifiers using all available annotations to discriminate between the disease variants and variants from each of the three control sets. This control set first was composed of all 1KG variants in the 1 kb surrounding each of the HGMD variants.
2 GWAVA_TSS GWAVA uses the random forest algorithm to build three classifiers using all available annotations to discriminate between the disease variants and variants from each of the three control sets. This control set first was matched for distance to the nearest TSS genome-wide.
3 GWAVA_Unmatched GWAVA uses the random forest algorithm to build three classifiers using all available annotations to discriminate between the disease variants and variants from each of the three control sets. This control set first was constructed from a random selection of SNVs from across the genome in order to sample overall background.
4 CADD_CScore "Raw" CADD scores come straight from the CADD model, and are interpretable as the extent to which the annotation profile for a given variant suggests that that variant is likely to be "observed" (negative values) vs "simulated" (positive values). These values have no absolute unit of meaning and are incomparable across distinct annotation combinations, training sets, or model parameters. However, raw values do have relative meaning, with higher values indicating that a variant is more likely to be simulated (or "not observed") and therefore more likely to have deleterious effects.
5 DANN DANN uses the same feature set and training data as CADD to train a deep neural network (DNN). DNNs can capture nonlinear relationships among features and are better suited than SVMs for problems with a large number of samples and features.
6 FATHMM-MKL FATHMM-MKL uses MKL classifier to predict the functional consequences of both coding and non-coding sequence variants from various genomic annotations and weights the significance of each component annotation source.
7 FunSeq FunSeq filters mutations overlapping 1000 Genomes variants and then prioritizes those in regions under strong selection (sensitive and ultrasensitive), breaking TF motifs, and those associated with hubs. It can score the deleterious potential of variants in single or multiple genomes. The scores for each noncoding variant vary from 0 to 6, with 6 corresponding to maximum deleterious effect. When multiple tumor genomes are given as input, FunSeq also identifies recurrent mutations in the same element.
8 FunSeq2 FunSeq2 is originally to annotate and prioritize somatic alterations integrating various resources from genomic and cancer studies. The framework consists of two components: (1) data context from uniformly processing large-scale datasets; and (2) a high-throughput variant prioritization pipeline. FunSeq2 can also be used to prioritize noncoding genetic variants.
9 GWAS3D GWAS3D systematically assesses the genetic variants that could affect regulatory elements, by integrating annotations from cell type-specific chromatin states, epigenetic modifications, sequence motifs and cross-species conservation. It combines the original GWAS signal, risk haplotype, binding affinity significance and conservation information to prioritize the leading variants, and infer the putative causal variant in the LD of leading variant.
10 SuRFR SuRFR integrates functional annotation and prior biological knowledge to prioritise candidate functional variants by regression model. It introduces novel training and validation datasets that i) capture the regional heterogeneity of genomic annotation better than previously applied approaches, and ii) facilitate understanding of which annotations are most important for discriminating different classes of functionally relevant variants from background variants.

If you have a lot of variants without annotation of existing tools, you can use the whole genome annotation dataset, --db-score dbncfp_all. However, you have to manually download the file from http://jjwanglab.org/ftp/MX/score/ANN/hgXX_all_SNV_dbNCFP.gz by efficient tools (e.g. FileZilla) and put it into the folder of KGGSeqFolder/resources/hgXX/hgXX_all_SNV_dbNCFP.gz.

Besides the above existing functional prediction scores, this option will append the following fields to the output file:

BF:Bayes factor (BF) that compares the two probability models, in which the null hypothesis is that the variant is neutral, and the alternative hypothesis is that the variant is causal.
Composite_Prob: the overall probability of the variant being regulatory-causal, which is calculated by multiplying the probabilities of being causal of multiple scores.


Reference: Mulin Jun Li et al. Predicting regulatory variants with composite statistic. Bioinformatics. 2016 Sep 15;32(18):2729-36.


Predicting context-dependent noncoding regulatory variants based on composite strategy and tissue/cell type-specific model

Note  <argument>: --regulatory-causing-predict is described in last selection (composite strategy).

Predict pathogenic variants by adding cell-type specific regulatory epigenomic markers: --db-score dbncfp_known [or dbncfp_all] --regulatory-causing-predict all --cell
java -jar kggseq.jar --vcf-file path/to/file1 --db-score dbncfp_known --regulatory-causing-predict all --cell GM12878 Assigning a cell type, KGGSeq uses a logit model to measure the probability of regulatory causality for given variants in selected condition. It finally combine the above composite model and context-dependent model into an unified model to estimate the posterior regulatory probability in a specific cell type/tissue. ftp://jjwanglab.org/PRVCS/dbNCFP/dbNCFP_hg19.ANN.bgz

If you have a lot of variants without annotation of existing tools (rare/de novo/somatic variants), you can use the whole genome annotation dataset, --db-score dbncfp_all. However, you have to manually download the file from http://jjwanglab.org/ftp/MX/score/ANN/hgXX_all_SNV_dbNCFP.gz by efficient tools (e.g. FileZilla) and put it into the folder of KGGSeqFolder/resources/hgXX/hgXX_all_SNV_dbNCFP.gz.

KGGSeq supports 16 ENCODE cell types as follows:
Coding Tissue Description
A549 Epithelium epithelial cell line derived from a lung carcinoma tissue. (PMID: 175022), "This line was initiated in 1972 by D.J. Giard, et al. through explant culture of lung carcinomatous tissue from a 58-year-old caucasian male." - ATCC, newly promoted to tier 2: not in 2011 analysis.
CD14 Monocytes Monocytes-CD14+ are CD14-positive cells from human leukapheresis production, from donor RO 01746 (draw 1 ID is RO 01746, draw 2 ID is RO 01826), newly promoted to tier 2: not in 2011 analysis.
GM12878 Blood B-lymphocyte, lymphoblastoid, International HapMap Project - CEPH/Utah - European Caucasion, Epstein-Barr Virus.
H1-hESC Embryonic stem cell embryonic stem cells.
HMEC Breast mammary epithelial cells.
HSMM Muscle skeletal muscle myoblasts.
HSMMT Muscle HSMM cell derived skeletal muscle myotubes cell line.
HUVEC Vessel umbilical vein endothelial cells.
HeLa-S3 Cervix cervical carcinoma.
HepG2 Liver hepatocellular carcinoma.
IMR90 Lung fetal lung fibroblasts, newly promoted to tier 2: not in 2011 analysis.
K562 Blood leukemia, "The continuous cell line K-562 was established by Lozzio and Lozzio from the pleural effusion of a 53-year-old female with chronic myelogenous leukemia in terminal blast crises." - ATCC.
NH-A Brain astrocytes (also called Astrocy).
NHDF Skin dermal fibroblasts from temple / breast
NHEK Skin epidermal keratinocytes.
NHLF Lung lung fibroblasts.
NoteBy default, KGGSeq will use GM12878 for context-dependent prioritization.

KGGSeq also supports 127 RoadMap human reference epigenomes : java -jar kggseq.jar --vcf-file path/to/file1 --db-score dbncfp_known --regulatory-causing-predict all --cell E116
Coding Lineage Group Epigenome Mnemonic Epigenome Name Anatomy Type
E001 ESC ESC.I3 ES-I3 Cell Line ESC CellLine
E002 ESC ESC.WA7 ES-WA7 Cell Line ESC CellLine
E003 ESC ESC.H1 H1 Cell Line ESC CellLine
E004 ES-deriv ESDR.H1.BMP4.MESO H1 BMP4 Derived Mesendoderm Cultured Cells ESC_DERIVED CellLineDerived
E005 ES-deriv ESDR.H1.BMP4.TROP H1 BMP4 Derived Trophoblast Cultured Cells ESC_DERIVED CellLineDerived
E006 ES-deriv ESDR.H1.MSC H1 Derived Mesenchymal Stem Cells ESC_DERIVED CellLineDerived
E007 ES-deriv ESDR.H1.NEUR.PROG H1 Derived Neuronal Progenitor Cultured Cells ESC_DERIVED CellLineDerived
E008 ESC ESC.H9 H9 Cell Line ESC CellLine
E009 ES-deriv ESDR.H9.NEUR.PROG H9 Derived Neuronal Progenitor Cultured Cells ESC_DERIVED CellLineDerived
E010 ES-deriv ESDR.H9.NEUR H9 Derived Neuron Cultured Cells ESC_DERIVED CellLineDerived
E011 ES-deriv ESDR.CD184.ENDO hESC Derived CD184+ Endoderm Cultured Cells ESC_DERIVED CellLineDerived
E012 ES-deriv ESDR.CD56.ECTO hESC Derived CD56+ Ectoderm Cultured Cells ESC_DERIVED CellLineDerived
E013 ES-deriv ESDR.CD56.MESO hESC Derived CD56+ Mesoderm Cultured Cells ESC_DERIVED CellLineDerived
E014 ESC ESC.HUES48 HUES48 Cell Line ESC CellLine
E015 ESC ESC.HUES6 HUES6 Cell Line ESC CellLine
E016 ESC ESC.HUES64 HUES64 Cell Line ESC CellLine
E017 IMR90 LNG.IMR90 IMR90 fetal lung fibroblasts Cell Line LUNG CellLine
E018 iPSC IPSC.15b iPS-15b Cell Line IPSC CellLine
E019 iPSC IPSC.18 iPS-18 Cell Line IPSC CellLine
E020 iPSC IPSC.20B iPS-20b Cell Line IPSC CellLine
E021 iPSC IPSC.DF.6.9 iPS DF 6.9 Cell Line IPSC CellLine
E022 iPSC IPSC.DF.19.11 iPS DF 19.11 Cell Line IPSC CellLine
E023 Mesench FAT.MSC.DR.ADIP Mesenchymal Stem Cell Derived Adipocyte Cultured Cells FAT CellLineDerived
E024 ESC ESC.4STAR   Cell Line ESC CellLine
E025 Mesench FAT.ADIP.DR.MSC Adipose Derived Mesenchymal Stem Cell Cultured Cells FAT PrimaryCell
E026 Mesench STRM.MRW.MSC Bone Marrow Derived Cultured Mesenchymal Stem Cells NECTIVE PrimaryCell
E027 Epithelial BRST.MYO Breast Myoepithelial Primary Cells BREAST PrimaryCell
E028 Epithelial BRST.HMEC.35 Breast variant Human Mammary Epithelial Cells (vHMEC) BREAST PrimaryCell
E029 HSC & B-cell BLD.CD14.PC Primary monocytes from peripheral blood BLOOD PrimaryCell
E030 HSC & B-cell BLD.CD15.PC Primary neutrophils from peripheral blood BLOOD PrimaryCell
E031 HSC & B-cell BLD.CD19.CPC Primary B cells from cord blood BLOOD PrimaryCell
E032 HSC & B-cell BLD.CD19.PPC Primary B cells from peripheral blood BLOOD PrimaryCell
E033 Blood & T-cell BLD.CD3.CPC Primary T cells from cord blood BLOOD PrimaryCell
E034 Blood & T-cell BLD.CD3.PPC Primary T cells from peripheral blood BLOOD PrimaryCell
E035 HSC & B-cell BLD.CD34.PC Primary hematopoietic stem cells BLOOD PrimaryCell
E036 HSC & B-cell BLD.CD34.CC Primary hematopoietic stem cells short term culture BLOOD PrimaryCell
E037 Blood & T-cell BLD.CD4.MPC Primary T helper memory cells from peripheral blood 2 BLOOD PrimaryCell
E038 Blood & T-cell BLD.CD4.NPC Primary T helper naive cells from peripheral blood BLOOD PrimaryCell
E039 Blood & T-cell BLD.CD4.CD25M.CD45RA.NPC Primary T helper naive cells from peripheral blood BLOOD PrimaryCell
E040 Blood & T-cell BLD.CD4.CD25M.CD45RO.MPC Primary T helper memory cells from peripheral blood 1 BLOOD PrimaryCell
E041 Blood & T-cell BLD.CD4.CD25M.IL17M.PL.TPC Primary T helper cells PMA-I stimulated BLOOD PrimaryCell
E042 Blood & T-cell BLD.CD4.CD25M.IL17P.PL.TPC Primary T helper 17 cells PMA-I stimulated BLOOD PrimaryCell
E043 Blood & T-cell BLD.CD4.CD25M.TPC Primary T helper cells from peripheral blood BLOOD PrimaryCell
E044 Blood & T-cell BLD.CD4.CD25.CD127M.TREGPC Primary T regulatory cells from peripheral blood BLOOD PrimaryCell
E045 Blood & T-cell BLD.CD4.CD25I.CD127.TMEMPC Primary T cells effector/memory enriched from peripheral blood BLOOD PrimaryCell
E046 HSC & B-cell BLD.CD56.PC Primary Natural Killer cells from peripheral blood BLOOD PrimaryCell
E047 Blood & T-cell BLD.CD8.NPC Primary T killer naive cells from peripheral blood BLOOD PrimaryCell
E048 Blood & T-cell BLD.CD8.MPC Primary T killer memory cells from peripheral blood BLOOD PrimaryCell
E049 Mesench STRM.CHON.MRW.DR.MSC Mesenchymal Stem Cell Derived Chondrocyte Cultured Cells NECTIVE PrimaryCell
E050 HSC & B-cell BLD.MOB.CD34.PC.F Primary hematopoietic stem cells G-CSF-mobilized Female BLOOD PrimaryCell
E051 HSC & B-cell BLD.MOB.CD34.PC.M Primary hematopoietic stem cells G-CSF-mobilized Male BLOOD PrimaryCell
E052 Myosat MUS.SAT Muscle Satellite Cultured Cells MUSCLE PrimaryCell
E053 Neurosph BRN.CRTX.DR.NRSPHR Cortex derived primary cultured neurospheres BRAIN PrimaryCell
E054 Neurosph BRN.GANGEM.DR.NRSPHR Ganglion Eminence derived primary cultured neurospheres BRAIN PrimaryCell
E055 Epithelial SKIN.PEN.FRSK.FIB.01 Foreskin Fibroblast Primary Cells skin01 SKIN PrimaryCell
E056 Epithelial SKIN.PEN.FRSK.FIB.02 Foreskin Fibroblast Primary Cells skin02 SKIN PrimaryCell
E057 Epithelial SKIN.PEN.FRSK.KER.02 Foreskin Keratinocyte Primary Cells skin02 SKIN PrimaryCell
E058 Epithelial SKIN.PEN.FRSK.KER.03 Foreskin Keratinocyte Primary Cells skin03 SKIN PrimaryCell
E059 Epithelial SKIN.PEN.FRSK.MEL.01 Foreskin Melanocyte Primary Cells skin01 SKIN PrimaryCell
E061 Epithelial SKIN.PEN.FRSK.MEL.03 Foreskin Melanocyte Primary Cells skin03 SKIN PrimaryCell
E062 Blood & T-cell BLD.PER.MONUC.PC Primary mononuclear cells from peripheral blood BLOOD PrimaryCell
E063 Adipose FAT.ADIP.NUC Adipose Nuclei FAT PrimaryTissue
E065 Heart VAS.AOR Aorta VASCULAR PrimaryTissue
E066 Other LIV.ADLT Liver LIVER PrimaryTissue
E067 Brain BRN.ANG.GYR Brain Angular Gyrus BRAIN PrimaryTissue
E068 Brain BRN.ANT.CAUD Brain Anterior Caudate BRAIN PrimaryTissue
E069 Brain BRN.CING.GYR Brain Cingulate Gyrus BRAIN PrimaryTissue
E070 Brain BRN.GRM.MTRX Brain Germinal Matrix BRAIN PrimaryTissue
E071 Brain BRN.HIPP.MID Brain Hippocampus Middle BRAIN PrimaryTissue
E072 Brain BRN.INF.TMP Brain Inferior Temporal Lobe BRAIN PrimaryTissue
E073 Brain BRN.DL.PRFRNTL.CRTX Brain Dorsolateral Prefrontal Cortex BRAIN PrimaryTissue
E074 Brain BRN.SUB.NIG Brain Substantia Nigra BRAIN PrimaryTissue
E075 Digestive GI.CLN.MUC Colonic Mucosa GI_COLON PrimaryTissue
E076 Sm. Muscle GI.CLN.SM.MUS Colon Smooth Muscle GI_COLON PrimaryTissue
E077 Digestive GI.DUO.MUC Duodenum Mucosa GI_DUODENUM PrimaryTissue
E078 Sm. Muscle GI.DUO.SM.MUS Duodenum Smooth Muscle GI_DUODENUM PrimaryTissue
E079 Digestive GI.ESO Esophagus S PrimaryTissue
E080 Other ADRL.GLND.FET Fetal Adrenal Gland ADRENAL PrimaryTissue
E081 Brain BRN.FET.M Fetal Brain Male BRAIN PrimaryTissue
E082 Brain BRN.FET.F Fetal Brain Female BRAIN PrimaryTissue
E083 Heart HRT.FET Fetal Heart HEART PrimaryTissue
E084 Digestive GI.L.INT.FET Fetal Intestine Large GI_INTESTINE PrimaryTissue
E085 Digestive GI.S.INT.FET Fetal Intestine Small GI_INTESTINE PrimaryTissue
E086 Other KID.FET Fetal Kidney KIDNEY PrimaryTissue
E087 Other PANC.ISLT Pancreatic Islets PANCREAS PrimaryTissue
E088 Other LNG.FET Fetal Lung LUNG PrimaryTissue
E089 Muscle MUS.TRNK.FET Fetal Muscle Trunk MUSCLE PrimaryTissue
E090 Muscle MUS.LEG.FET Fetal Muscle Leg MUSCLE_LEG PrimaryTissue
E091 Other PLCNT.FET Placenta PLACENTA PrimaryTissue
E092 Digestive GI.STMC.FET Fetal Stomach GI_STOMACH PrimaryTissue
E093 Thymus THYM.FET Fetal Thymus THYMUS PrimaryTissue
E094 Digestive GI.STMC.GAST Gastric GI_STOMACH PrimaryTissue
E095 Heart HRT.VENT.L Left Ventricle HEART PrimaryTissue
E096 Other LNG Lung LUNG PrimaryTissue
E097 Other OVRY Ovary OVARY PrimaryTissue
E098 Other PANC Pancreas PANCREAS PrimaryTissue
E099 Other PLCNT.AMN Placenta Amnion PLACENTA PrimaryTissue
E100 Muscle MUS.PSOAS Psoas Muscle MUSCLE PrimaryTissue
E101 Digestive GI.RECT.MUC.29 Rectal Mucosa Donor 29 GI_RECTUM PrimaryTissue
E102 Digestive GI.RECT.MUC.31 Rectal Mucosa Donor 31 GI_RECTUM PrimaryTissue
E103 Sm. Muscle GI.RECT.SM.MUS Rectal Smooth Muscle GI_RECTUM PrimaryTissue
E104 Heart HRT.ATR.R Right Atrium HEART PrimaryTissue
E105 Heart HRT.VNT.R Right Ventricle HEART PrimaryTissue
E106 Digestive GI.CLN.SIG Sigmoid Colon GI_COLON PrimaryTissue
E107 Muscle MUS.SKLT.M Skeletal Muscle Male MUSCLE PrimaryTissue
E108 Muscle MUS.SKLT.F Skeletal Muscle Female MUSCLE PrimaryTissue
E109 Digestive GI.S.INT Small Intestine GI_INTESTINE PrimaryTissue
E110 Digestive GI.STMC.MUC Stomach Mucosa GI_STOMACH PrimaryTissue
E111 Sm. Muscle GI.STMC.MUS Stomach Smooth Muscle GI_STOMACH PrimaryTissue
E112 Thymus THYM Thymus THYMUS PrimaryTissue
E113 Other SPLN Spleen SPLEEN PrimaryTissue
E114 ENCODE LNG.A549.ETOH002.CNCR A549 EtOH 0.02pct Lung Carcinoma Cell Line LUNG CellLine_Cancer
E115 ENCODE BLD.DND41.CNCR Dnd41 TCell Leukemia Cell Line BLOOD CellLine_Cancer
E116 ENCODE BLD.GM12878 GM12878 Lymphoblastoid Cell Line BLOOD CellLine
E117 ENCODE CRVX.HELAS3.CNCR HeLa-S3 Cervical Carcinoma Cell Line CERVIX CellLine_Cancer
E118 ENCODE LIV.HEPG2.CNCR HepG2 Hepatocellular Carcinoma Cell Line LIVER CellLine_Cancer
E119 ENCODE BRST.HMEC HMEC Mammary Epithelial Primary Cells BREAST CellLine
E120 ENCODE MUS.HSMM HSMM Skeletal Muscle Myoblasts Cell Line MUSCLE CellLine
E121 ENCODE MUS.HSMMT HSMM cell derived Skeletal Muscle Myotubes Cell Line MUSCLE CellLine
E122 ENCODE VAS.HUVEC HUVEC Umbilical Vein Endothelial Cells Cell Line VASCULAR CellLine
E123 ENCODE BLD.K562.CNCR K562 Leukemia Cell Line BLOOD CellLine
E124 ENCODE BLD.CD14.MONO Monocytes-CD14+ RO01746 Cell Line BLOOD CellLine
E125 ENCODE BRN.NHA NH-A Astrocytes Cell Line BRAIN CellLine
E126 ENCODE SKIN.NHDFAD NHDF-Ad Adult Dermal Fibroblast Primary Cells SKIN CellLine
E127 ENCODE SKIN.NHEK NHEK-Epidermal Keratinocyte Primary Cells SKIN CellLine
E128 ENCODE LNG.NHLF NHLF Lung Fibroblast Primary Cells LUNG CellLine
E129 ENCODE BONE.OSTEO Osteoblast Primary Cells BONE CellLine
The option will append the following fields to the output file:

BF: Bayes factor
Composite_Prob:Posterior probability of being regualtory given selected functional prediction scores.
Cell_Prob: Cell type-specific regulatory potential.
Combine_Prob: Combined posterior regulatory probability.

Note Epigenomes of some specific marks are missing in some tissues/cells, which may affect the prediction. We will use imputed epigenomes to handle this missing data problem soon!

Figure: Using GWAS fine mapped SNPs and 127 recently profiled human epigenomes, we showed that our context-dependent score can comprehensively depict the phenotypic cell type specificity.



Reference: Mulin Jun Li et al. cepip: context-dependent epigenomic weighting for prioritization of regulatory variants and disease-associated genes, Genome Biology (2017) 18:52

 

Gene feature specific model --db-score dbncfp_known [or dbncfp_all] --ldgf-func-predict java -jar kggseq.jar --vcf-file path/to/file1 --db-score dbncfp_known --ldgf-func-predict --buildver hg19

1) Annotate non-coding variants by above 10 existing functional prediction scores (See above table) in a known variant dataset and 2) jointly predict the functions importance by a new statistical model (linkage disequilibrium expanded and gene feature specific model). The known dataset contains around 110 million variants from multiple high throughput sequencing resources, including the 1000 Genomes Project Phase 3, UK10K Project, ESP6500 Project and ExAC database. The joint prediction also uses over 700 epigenomic markers for open chromatin, transcription factor binding, histone modification, RNA polymerase binding, and DNA methylation.

If you have a lot of variants without annotation of existing tools, you can use the whole genome annotation dataset, --db-score dbncfp_all. However, you have to manually download the file from http://jjwanglab.org/ftp/MX/score/ANN/hgXX_all_SNV_dbNCFP.gz by efficient tools (e.g. FileZilla) and put it into the folder of KGGSeqFolder/resources/hgXX/hgXX_all_SNV_dbNCFP.gz. Note Predictions at variants with missing scores at specified methods will use population mean of corresponding method!

Note Add the option tag --verbose-noncoding to show all detailed features used in the pathogenic prediction. However, this will consume a lot of memory!

Figure: Receiver operating characteristic (ROC) curves for performances from different gene feature model.

Besides the above existing functional prediction scores, this option will append the following fields to the output file:

IsComplexDiseasePathogenic: whether the variant is predicted to be pathogenic by the model trained with known pathogenic variants and neutral control variants.
RandomForestScore: the pathogenic scores of the random forest prediction model.


Reference: Pan et al. An improved functional prediction by linkage disequilibrium expanded and gene feature specific scores at non-coding variants. (Submitted)



Prediction at gene

Pathogenicity

Predict genes’ pathogenicity: --patho-gene-predict java -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 --out path/to/prefixname --excel --db-gene refgene --patho-gene-predict Gene-based pathogenicity estimation was developed for Autosomal Dominant (AD), Autosomal Recessive (AR), X chromosome linked (XL) and Paediatric diseases (PAE). By using random forest approach to combine various important gene characteristics with six existing gene prioritization methods for disease causal genes in Clinical Genomic Database, the inheritance-specific pathogenicity prioritization (ISPP) could be estimated. The higher score indicates the more similar molecular characteristics to the known disease causal genes under the corresponding inheritance mode. The six existing gene prioritization methods are Haploinsufficiency (HI), Recessive (REC), Network centrality (NET), Genic Intolerance (RVIS), Gene Damage Index (GDI) and Gene Constraint score (CONS).

The output is a series of pathogenic prediction scores for each gene. By comparing all ISPP scores of each gene, the possible inheritance mode and corresponding pathogenicity could be estimated. Please see the example on Table 1 of ISPP paper

ISPP_AD: Inheritance specific gene pathogenicity for Dominant diseases
ISPP_AR: Inheritance specific gene pathogenicity for Recessive diseases
ISPP_XL: Inheritance specific gene pathogenicity for X chromosome linked diseases
HI: Gene level haploinsufficiency estimation by Ni Huang et al
REC: A model to estimate the probability that a mutation causes a recessive disease by Daniel G. MacArthur et al.
NET: Gene centrality and indispensability in various protein-protein interactions and regulatory networks by Ekta Khurana et al.
RVIS: Residual Variation Intolerance Score , a gene score based module intended to help in the interpretation of human sequence data by Slave Petrovski et al.
GDI: Gene Damage Index (GDI) by Itan Y et al.
CONS: De novo mutation effectiveness prediction by Kaitlin E Samocha et al.


Reference: Jacob Hsu et al. Inheritance Modes Specific Pathogenicity Prioritization (ISPP) for Human Protein Coding Genes. Bioinformatics. 2016 Oct 15;32(20):3065-3071.

Phenotype Mining

Mine the phenotype of disease: --phenotype-term searchTerm and --phenolyzer-prediction java -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 --out path/to/prefixname --excel --db-gene refgene --phenotype-term searchTerm1,searchTerm2 --phenolyzer-prediction KGGseq has integrated the advanced tool phenolyzer as a plugin to implement the function of phenoytpe mining and predection. The score marked by phenolyer will by used to annotate the related genes in the output file.

NoteTo run Phenolyzer, your computer need install Perl. Please read the installation instructions.

Tissue-specific Expression

Tissues or cell-types where the transcripts of genes are specifically expressed: --tissue-spec-annot java -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 --out path/to/prefixname --excel --db-gene refgene --tissue-spec-annot Annotate genes with tissues or cell-types in which the transcripts of genes are specifically expressed. The tissue and cell-types are produced by a robust z-score approach for tissue-specific expression with the usage of GTEx RNA-sequencing data (version 7) at 52 tissues/cell-types. The tissues or cell-types are collected here as their tissue or cell-type specific expression p-values are less than 1E-6. See detail in TEA.

The output of this function is a list of tissues or cell-types in which the transcripts of genes are specifically expressed.

TissueWithSpecificExpression: the transcripts and tissues or cell-types where it is pecifically expressed. Multiple tissues and cell-types are delimited by commas and multiple transcripts are delimited by semicolons.

Statistical tests

Association at variants

Conventional statistical association tests at each individual variants: --var-assoc

java -jar ./kggseq.jar --buildver hg19 --var-assoc --p-value-cutoff 0.01 --multiple-testing benfdr --qqplot Calculate p-values (by chi-squared test) for case-control association and odds ratio. The '--p-value-cutoff' sets p-value cutoffs for filtration; the '--multiple-testing' sets the approaches for multiple testing comparisons; and the '--qqplot' enables Q-Q plot of the association results.

Multiple testing method Description
no No multiple testing methods. Set a fixed p value cutoff by --p-value-cutoff.
benfdr Benjamini-Hochberg method, which aims to control the FDR (specified by --p-value-cutoff), or "False Discovery Rate," rather than the FWE or "FamilyWise Error rate".
bonhol Bonferroni-Holm method, which is less conservative and more powerful than the Standard Bonferroni method given a family-wise error rate specified by --p-value-cutoff.
bonf Standard Bonferroni correction given a family-wise error rate specified by --p-value-cutoff.
The output of this function is a list of p-values and odds ratios at a variant.

PValueAllelic: The p-value calculated by chi-square test for allelic association between case-control status.
OddAllelic: The odds ratio of alleles in cases and controls.
PValueDom: The p-value calculated by chi-square test for genotype association between case-control status under dominant mode.
OddDom: The odds ratio of genotypes in cases and controls under dominant mode.
PValueDom: The p-value calculated by chi-square test for genotype association between case-control status under recessive mode.
OddDom: The odds ratio of genotypes in cases and controls under recessive mode.
PValueGeno: The p-value calculated by chi-square test for genotype association between case-control status.

Note Currently, this function only work for case-control study without confounding factors as covariables.

Variant-based association test by Rvtests: --rvtest-var

java -jar ./kggseq.jar --rvtest-var score,wald,exact,... --rvtest-vcf --rvtest-remove-set --vcf-file path/to/vcf/file --ped-file path/to/pedigree/file --phe phenotypeName --cov covariateName1,covariateName2,...,covariateNameN --out path/to/prefixname --excel --db-gene refgene KGGSeq will automatically download the C++ sources codes from Github and un-compress them into a folder named "plugin/rvtests-master" under the KGGSeq working folder. Users are required go into the "rvtests-master" to make an executable file from the C++ source codes. See more in a demonstration video .

Arguments/values Annotation
--rvtest-var model names Initiate the calling of RVTest for gene based association analysis. The model names are defined in Rvtests models. Currently, it supports score, wald, exact, dominantExact, famLRT, famScore, famGrammarGamma and firthc models. The default models are "score,wald".
--rvtest-vcf Ask KGGSeq to produce a new simplified VCF file, in which only genotypes are included in the FORMAT column, as the input of RVTests.
--rvtest-remove-set Remove the intermediate file defining sets of variants for RVTests group-wise test in your hard disk.

An extra file with the suffix ".rvtest.variant.xlsx" will be created in the targeted output path.

Association at gene

Gene-based association test by SKAT: --skat-gene

java -jar ./kggseq.jar --skat-gene --vcf-file path/to/vcf/file --ped-file path/to/pedigree/file --phe phenotypeName --cov covariateName1,covariateName2,...,covariateNameN --out path/to/prefixname --excel --db-gene refgene [--p-value-cutoff 0.01 --multiple-testing benfdr --qqplot] The gene-based analysis will be carried out by SKAT which was integrated as a plugin in KGGseq. The '--p-value-cutoff' sets p-value cutoffs for filtration; the '--multiple-testing' sets the approaches for multiple testing comparisons; and the '--qqplot' enables Q-Q plot of the association results.

Multiple testing method Description
no No multiple testing methods. Set a fixed p value cutoff by --p-value-cutoff.
benfdr Benjamini-Hochberg method, which aims to control the FDR (specified by --p-value-cutoff), or "False Discovery Rate," rather than the FWE or "FamilyWise Error rate".
bonhol Bonferroni-Holm method, which is less conservative and more powerful than the Standard Bonferroni method given a family-wise error rate specified by --p-value-cutoff.
bonf Standard Bonferroni correction given a family-wise error rate specified by --p-value-cutoff.

An extra file with the suffix ".skat.gene.xlsx" will be created in the targeted output path. The '--skat-gene' will provide the p values of SKAT,SKATO and Burden test. If the phenotype is binary, the option '--skat-gene binary' can be used, but the speed will decrease.

SKAT:The p-value calculated by SKAT.
SKATO:The p-value calculated by SKAT-O.
Burden:The p-value calculated by Burden test.

Note To use this function, the user should install R language platform first.

Note When total minor allele count (MAC) is very small in a gene, SKATBinary ( '--skat-gene binary' ) has better performance than SKAT( '--skat-gene' ). However, because SKATBinary uses a resampling approach to get a p-value, it is slower (sometimes quite slower) than SKAT function. When MAC is large, the performance of the two tests is similar.

Note The names of phenotype ( '--phe' ) and co-variables ( '--cov' ) are defined in the pedigree file .

Note If the option --skat-cutoff N is added, the genes with variants less than N will not be involved into SKAT. The default value of N is 0.

Note If the option --perm-pheno is added, phenotypes will be permuted prior to association analysis.

Gene-based association test by Rvtests: --rvtest-gene

java -jar ./kggseq.jar --rvtest-gene cmc,cmat,price,skat[nPerm=1000:alpha=0.001:beta1=1:beta2=20],... --rvtest-vcf --rvtest-remove-set --vcf-file path/to/vcf/file --ped-file path/to/pedigree/file --phe phenotypeName --cov covariateName1,covariateName2,...,covariateNameN --out path/to/prefixname --excel --db-gene refgene KGGSeq will automatically download the C++ sources codes from Github and un-compress them into a folder named "plugin/rvtests-master" under the KGGSeq working folder. Users are required go into the "rvtests-master" to make an executable file from the C++ source codes. See more in a demonstration video .

Arguments/values Annotation
--rvtest-gene model names Initiate the calling of RVTest for gene based association analysis. The model names are defined in Rvtests models. Currently, it supports cmc, zeggini, mb, fp, exactCMC, cmcWald, rarecover and cmat models.The default models are "cmc,skato[nPerm=1000:alpha=0.001:beta1=1:beta2=20]".
--rvtest-vcf Ask KGGSeq to produce a new VCF file as the input of RVTests. However, this is very time-consuming for large sample. It is advised to use --o-vcf to produce a VCF file with good quality once and to use that cleaned VCF file as the new KGGSeq input. In this scenario, the --rvtest-vcf is not needed, which may substantially reduce the analysis time.
--rvtest-remove-set Remove the intermediate file defining sets of variants for RVTests group-wise test in your hard disk.

An extra file with the suffix ".rvtest.gene.xlsx" will be created in the targeted output path.

Association at geneset

Geneset-based association test by SKAT: --skat-geneset

java -jar ./kggseq.jar --skat-geneset --geneset-file path/to/geneset/file --vcf-file path/to/vcf/file --ped-file path/to/pedigree/file --phe phenotypeName --cov covariateName1,covariateName2,...,covariateNameN --out path/to/prefixname --excel --db-gene refgene [--p-value-cutoff 0.01 --multiple-testing benfdr --qqplot] The gene-based analysis will be carried out by SKAT which was integrated as a plugin in KGGseq. The '--p-value-cutoff' sets p-value cutoffs for filtration; the '--multiple-testing' sets the approaches for multiple testing comparisons; and the '--qqplot' enables Q-Q plot of the association results.

Note, the pathway gene sets are specified in a text file in th following format:

[Column 1: GeneSet ID]
[Column 2: GeneSet URL]
[Column 3..N: Gene Symbols in the geneset; separated by spaces].
No title row is required!!!


e.g.,
----------------------------------------------
KEGG_GLYCOLYSIS_GLUCONEOGENESIS http://www.broadinstitute.org/KEGG_GLYCOLYSIS_GLUCONEOGENESIS.html LDHC LDHB LDHA
KEGG_CITRATE_CYCLE_TCA_CYCLE http://www.broadinstitute.org/KEGG_CITRATE_CYCLE_TCA_CYCLE.html GJB1 OGDHL OGDH PDHB IDH3G LDHB
...
...

An extra file with the suffix ".skat.geneset.xlsx" will be created in the targeted output path. The '--skat-geneset' will provide the p values of SKAT,SKATO and Burden test. If the phenotype is binary, the option '--skat-gene binary' can be used, but the speed will decrease.

SKAT:The p-value calculated by SKAT.
SKATO:The p-value calculated by SKAT-O.
Burden:The p-value calculated by Burden test.

Note To use this function, the user should install R language platform first.

Note When total minor allele count (MAC) is very small in a gene, SKATBinary ( '--skat-gene binary' ) has better performance than SKAT( '--skat-gene' ). However, because SKATBinary uses a resampling approach to get a p-value, it is slower (sometimes quite slower) than SKAT function. When MAC is large, the performance of the two tests is similar.

Note The names of phenotype ( '--phe' ) and co-variables ( '--cov' ) are defined in the pedigree file .

Note If the option --skat-cutoff N is added, the genes with variants less than N will not be involved into SKAT. The default value of N is 0.

Note If the option --perm-pheno is added, phenotypes will be permuted prior to association analysis.

Geneset-based association test by Rvtests: --rvtest-geneset

java -jar ./kggseq.jar --rvtest-geneset cmc,skato[nPerm=1000:alpha=0.001:beta1=1:beta2=20],... --rvtest-vcf --rvtest-remove-set --geneset-file path/to/geneset/file --vcf-file path/to/vcf/file --ped-file path/to/pedigree/file --phe phenotypeName --cov covariateName1,covariateName2,...,covariateNameN --out path/to/prefixname --excel --db-gene refgene [--p-value-cutoff 0.01 --multiple-testing benfdr --qqplot] KGGSeq will automatically download the C++ sources codes from Github and un-compress them into a folder named "plugin/rvtests-master" under the KGGSeq working folder. Users are required go into the "rvtests-master" to make an executable file from the C++ source codes. See more in a demonstration video .

Arguments/values Annotation
--rvtest-gene model names Initiate the calling of RVTest for gene based association analysis. The model names are defined in Rvtests models. Currently, it supports cmc, zeggini, mb, fp, exactCMC, cmcWald, rarecover and cmat models.The default models are "cmc,skato[nPerm=1000:alpha=0.001:beta1=1:beta2=20]".
--rvtest-vcf Ask KGGSeq to produce a new VCF file as the input of RVTests. However, this is very time-consuming for large sample. It is advised to use --o-vcf to produce a VCF file with good quality once and to use that cleaned VCF file as the new KGGSeq input. In this scenario, the --rvtest-vcf is not needed, which may substantially reduce the analysis time.
--rvtest-remove-set Remove the intermediate file defining set of variants for RVTests group-wise test.

An extra file with the suffix ".rvtest.geneset.xlsx" will be created in the targeted output path. The '--rvtest-geneset' will provide the p values of multiple set-based tests for rare variants.

Enrichment at geneset

Test whether a set of genes highly ranked in an ordered list of all genes by Wilcoxon rank sum test: --geneset-enrichment-test java -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 --out path/to/prefixname [other tags] --geneset-enrichment-test Basically, this analysis is to examine whether a set of genes are more highly ranked in an ordered list of all genes than would be expected by chance. Therefore,as the rank of gene is a prerequisite, it should be used together with a gene-based statistical test.
Currently, this enrichment test can only be used together with the gene-based mutation rate test: java -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 --out path/to/prefixname --db-filter 1kg201204,dbsnp137,ESP6500AA,ESP6500EA --rare-allele-freq 0.01 --db-gene refgene --gene-mutation-rate-test --geneset-enrichment-test --geneset-db <GenesetDatabase>

There are 5 geneset databases from GSEA. The following are brief descriptions of the 5 datasets COPIED from the page of GSEA.

GeneSetDatabase Description Version
cano Gene sets from the geneset databases. Usually, these gene sets are canonical representations of a biological process compiled by domain experts. (1452 gene sets) MSigDB 3.1
cura Curated gene sets: Gene sets collected from various sources such as online geneset databases, publications in PubMed, and knowledge of domain experts. The gene set page for each gene set lists its source. (4850 gene sets) MSigDB 3.1
onco Oncogenic signatures: Gene sets represent signatures of cellular pathways which are often dis-regulated in cancer. The majority of signatures were generated directly from microarray data from NCBI GEO or from internal unpublished profiling experiments which involved perturbation of known cancer genes. In addition, a small number of oncogenic signatures were curated from scientific publications. (189 gene sets) MSigDB 3.1
cmop Computational gene sets: Computational gene sets defined by mining large collections of cancer-oriented microarray data. (858 gene sets) MSigDB 3.1
onto GO gene sets Gene sets are named by GO term and contain genes annotated by that term. GSEA users: Gene set enrichment analysis identifies gene sets consisting of co-regulated genes; GO gene sets are based on ontologies and do not necessarily comprise co-regulated genes. (1454 gene sets) MSigDB 3.1

Alternatively, uses can specify the own pathway gene sets in a text file by --geneset-file path/to/geneset/file.
Note , please use --min-geneset X to ignore gene sets with less X genes and --max-geneset to ignore gene sets with over X genes.
In the output file of the enrichment analysis (*.enrichment.geneset.xls), there are detailed statistical evaluation results of each geneset:

GeneSetID:the input geneset name or id
WilcoxonP:the p-values of the enrichment test
Gene:the gene of the gene set
GeneP:the p-value of the gene used for the enrichment test


 

Plotting

Generate a histogram of minor allele frequency (MAF) according to allele frequencies in reference dataset --mafplot-ref or in sample --mafplot-sample. java -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 --out path/to/prefixname --db-filter 1kg201204,dbsnp141,ESP6500AA,ESP6500EA --allele-freq 0,1 --mafplot-ref java -jar kggseq.jar --vcf-file path/to/file1 --ped-file path/to/file2 --out path/to/prefixname --mafplot-sample Note Currently, --mafplot-sample only supports the inputs specified by --vcf-file.

Generate a Quantile-Quantile (Q-Q) plot for association p-values --qqplot java -jar kggseq.jar --vcf-file path/to/file --out path/to/prefixname --qqplot --skat-gene [or] --var-assoc

Miscellaneous

Calculate pairwise the linkage disequilibrium or genotypic correlation --calc-ld. java -Xmx15g -jar kggseq.jar --vcf-file path/to/file --ped-file path/to/file --out path/to/prefixname --calc-ld --db-filter 1kg201204,dbsnp141,ESP6500AA,ESP6500EA --allele-freq 0.05,0.95 --regions-in chr4:21212-233454 If the genotypes are phased, KGGSeq will calculate the linkage disequilibrium coefficient r-square. For uphased genotypes, KGGSeq will calculate the Pearson genotypic correlation of variants, in which the genotypes are encoded by the number of alternative alleles.
Note Currently, --calc-ld only supports variants with two alleles.
Note By default, KGGSeq will only calculate LD between variants within a window of 100,000,000 base-pairs. The window size can be changed by --ld-win X , in which X denotes a distance in base-pair.
Note By default, only the r-square or correlation square >0.01 will be saved in an output file. This can be changed by --ld-out X, in which X denotes r-square or correlation square. .

The results will be outputted in a text file with the suffix ".ld" or ".ld.gz". The following is an exmaple of the output format:

[chromsome] [coordinate of the first variant] [coordinate of the second variant] [r-square OR correlation]
22 16051249 16052962 0.8680002633575351
22 16051249 16053659 0.13732498950210686
22 16051249 16053862 0.984494959737711

Prune variants in high LD (r or genotypic correlation square >= x) --ld-prune X. java -Xmx15g -jar kggseq.jar --vcf-file path/to/file --ped-file path/to/file --out path/to/prefixname --ld-prune 0.9 --filter-sample-maf-le 0.05 --o-svcf KGGSeq will calculate the pairwise LD between variants within a window of 100,000,000 base-pairs. If a pair of variants have r-square or correlation square over 0.9, the variation with larger coordinate will be pruned. The variants with minor allele frequency <=5% are excluded (See details on --filter-sample-maf-le). The window size can be changed by --ld-win X, in which X denotes a distance in base-pair.


FAQ?

1) Can a compressed VCF file be processed in parallel?

Yes, it can. However, the files has to be compressed in a blocked GNU Zip format or Burrows–Wheeler format. See more here.

2) Does the KGGSeq binary genotype format support variants of over two alleles and/or phased genotypes?

Yes, it does support variants of over two alleles and/or phased genotypes. See more here.

3) How are the settings '--rare-allele-freq c' and '--allele-freq 0,c' different?

'--rare-allele-freq c' will excluded variants with alternative allele frequency EQUAL to or over c in the reference datasets; and '--allele-freq 0,c' will also excluded variants having no allele frequency in the reference datasets beside the variants excluded by '--rare-allele-freq c'

4) Does KGGSeq provide gene expression information in the output?

A project aiming to harness tissue-specific expression information to prioritize candidate genes is underway.

5) How shall I prepare the candidate gene list for protein-interaction and geneset annotation? What is the gene list for?

The candidate gene list can include any gene interesting to the user. It may be verified causal gene for the disease, or potential causal gene. The gene list is used to fish more hidden interesting genes in order to narrow down the searching region. If no gene list provided, KGGSeq can still perform routine ‘filtration + sharing’ strategy for sequencing data.

6)Suppose I have in-house control genomes in VCF format, how can I transform it into KGGSeq acceptable file?

There are two alternative ways to allow you use the in-house VCF data (containing genotypes of multiple individuals) as filter.

1. Convert the VCF data into a standard KGGSeq local filter format by the command: --make-filter --vcf-file path/to/vcffile --out test.var first. And then set the --local-filter test.hg19.var

2. Directly set --local-filter-vcf path/to/vcffile. However, this way is less efficient than the above way as it will take additional time to parse the VCF data.

7) Is there a practical guidance for MAF selection?

MAF selection is an open question, but generally three suggestions can be adopted: for rare dominant Mendelian disease, MAF cut-off at 0 or 0.005 is reasonable; for rare recessive Mendelian disease, MAF cut-off at 0 or 0.03 is reasonable; for common complex disease of relatively rare alleles with large effect size, MAF cut-off is dependent on disease prevalence, penetrance and genetic model, so a higher cut-off like 0.05 might be more reasonable.

8) Can I use ANNOVAR and KGGSeq together on my dataset?

KGGSeq is quite flexible for interacting with other sequence-oriented analytical programs/software (including SOAPSNP, ANNOVAR, PLINK/seq, VAAST, etc). It can accept various input formats, and output different kinds of sequence data. In case of ANNOVAR, KGGSeq can read ANNOVAR-formatted sequence variants, and write the final remaining variants in ANNOVAR format.

9) Can I run KGGSeq on my Lenovo or Mac laptop? Is it time consuming to run a complete KGGSeq process?

Normally, KGGSeq run well and fast with >=1 GB RAM memory. Hence current laptop are certainty affordable for running KGGSeq. The whole process need only <10 minutes, unless PubMed searching is set in the parameter file. For PubMed searching, it really depends on the speed of the network and also the loading of the NCBI PubMed database.

10) How do I report an error or bugs to KGGSeq?

You are welcomed to join our google group (http://groups.google.com/group/kggseq_user?hl=en). This site is used for communication and discussion of KGGSeq usage and functions. You can also directly write an email to limx54@yahoo.com, or kggseq_user@googlegroups.com.