Statistical Genetics Tutorials

Association Analysis of Sequence Data using Variant Association Tools (VAT) for Complex Traits

Copyright (c) 2019 Gao Wang, Diana Cornejo Sánchez & Suzanne M. Leal

PURPOSE

Variant Association Tools [VAT, Wang et al (2014)] [1] was developed to perform quality control and association analysis of sequence data. It can also be used to analyze genotype data, e.g. exome chip data and imputed data. The software incorporates many rare variant association methods which include but not limited to Combined Multivariate Collapsing (CMC) [2], Burden of Rare Variants (BRV) [3], Weighted Sum Statistic (WSS) [4], Kernel Based Adaptive Cluster (KBAC) [5], Variable Threshold (VT) [6] and Sequence Kernel Association Test (SKAT) [7]. VAT inherits the intuitive command-line interface of Variant Tools (VTools) [8] with re-design and implementation of its infrastructure to accommodate the scale of dataset generated from current sequencing efforts on large populations. Features of VAT are implemented into VTools subcommand system.

RESOURCES

Basic concepts to handle sequence data using vtools can be found at:

http://varianttools.sourceforge.net/Main/Concepts

VAT Software documentation:

http://varianttools.sourceforge.net/Main/Documentation

Genotype data

Exome genotype data was downloaded from the 1000 Genomes pilot data July 2010 release for both the CEU and YRI populations. Only the autosomes are contained in the datasets accompanying this exercise. The data sets (CEU.exon.2010_03.genotypes.vcf.gz, YRI.exon.2010_03.genotypes.vcf.gz) are available from: ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/pilot_data/release/2010_07/exon/snps

Phenotype data

To demonstrate the association analysis, we simulated a quantitative trait phenotype (BMI). Please note that these phenotypes are NOT from the 1000 genome project.

Computation resources

Due to the nature of next-generation sequencing data, a reasonably powerful machine with high speed internet connection is needed to use this tool for real-world applications. For this reason, in this tutorial we will use a small demo dataset to demonstrate association analysis.

Part I: Data Quality Control, Annotation and Variant/sample Selection

1.1 Getting started

Please navigate to the exercise data directory, and check the available subcommands by typing:

In [2]:
vtools -h
usage: vtools [-h] [--version]
              {init,import,phenotype,show,liftover,use,update,select,exclude,compare,output,export,remove,associate,admin,execute}
              ...

A variant calling, processing, annotation and analysis tool for next-
generation sequencing studies.

optional arguments:
  -h, --help            show this help message and exit
  --version             show program's version number and exit

subcommands:
  {init,import,phenotype,show,liftover,use,update,select,exclude,compare,output,export,remove,associate,admin,execute}
    init                Create a new project, or a subproject from an existing
                        parent project, or merge several existing projects
                        into one
    import              Import variants and related sample genotype from files
                        in specified formats
    phenotype           Manage sample phenotypes
    show                Display content of a project
    liftover            Set alternative reference genome and update
                        alternative coordinates of all variant tables
    use                 Prepare (download or import if necessary) and use an
                        annotation database
    update              Add or update fields of existing variants and genotype
                        using information from specified existing fields,
                        sample genotype, or external files
    select              Output or save select variants that match specified
                        conditions
    exclude             Output or save variants after excluding variants that
                        match specified conditions
    compare             Compare sites, variants, or genotypes of variants in
                        two or more variant tables
    output              Output variants in tab or comma separated format
    export              Export samples (variants and genotypes) in specified
                        format
    remove              Remove project or its contents such as variant tables,
                        fields, and annotation databases.
    associate           Test association between variants and phenotypes
    admin               Perform various administrative tasks including merge
                        and rename samples.
    execute             Execute a SQL query

Use 'vtools cmd -h' for details about each command. Please contact Bo Peng
(bpeng at mdanderson.org) if you have any question.

Subcommand system is used for various data manipulation tasks (to check details of each subcommand use vtools <name of subcommand> -h). This tutorial is mission oriented and focuses on a subset of the commands that are relevant to variant-phenotype association analysis, rather than introducing them systematically. For additional functionality, please refer to documentation and tutorials online.

Initialize a project

In [3]:
vtools init VATDemo
INFO: variant tools 3.0.3 : Copyright (c) 2011 - 2016 Bo Peng
INFO: Please visit http://varianttools.sourceforge.net for more information.
INFO: Creating a new project VATDemo
WARNING: Resource file annoDB/dbNSFP-hg18_hg19_2_9.DB.gz has been updated. Please update it using command "vtools admin --update_resource existing".

Command vtools init creates a new project in the current directory. A directory can only have one project. After a project is created, subsequent vtools calls will automatically load the project in the current directory. Working from outside of a project directory is not allowed.

Import variant and genotype data

Import all vcf files under the current directory:

In [4]:
vtools import *.vcf.gz --var_info DP filter --geno_info DP_geno --build hg18 -j1
INFO: Importing variants from CEU.exon.2010_03.genotypes.vcf.gz (1/2)
CEU.exon.2010_03.genotypes.vcf.gz: 100% [============] 4,306 521.8/s in 00:00:08
INFO: 3,489 new variants (3,489 SNVs) from 3,500 lines are imported.
Importing genotypes: 100% [==========================] 3,489 18.1K/s in 00:00:00
INFO: Importing variants from YRI.exon.2010_03.genotypes.vcf.gz (2/2)
YRI.exon.2010_03.genotypes.vcf.gz: 100% [============] 5,967 864.0/s in 00:00:06
INFO: 3,498 new variants (5,175 SNVs) from 5,186 lines are imported.
Importing genotypes: 100% [==========================] 6,987 25.1K/s in 00:00:00

Command vtools import imports variants, sample genotypes and related information fields. The imported variants are saved to the master variant table for the project, along with their information fields.

The command above imports two vcf files sequentially into an empty vtools project. The second INFO message in the screen output shows that 3,489 variant sites are imported from the first vcf file, where 3,489 new means that all of them are new because prior to importing the first vcf the project was empty so there was 0 site. The fourth INFO message tells that 5,175 variant sites are imported from the second vcf file, but only 3,498 of them are new (which are not seen in the existing 3,489) because prior to importing the second vcf there were already 3,489 existing variant sites from first vcf.

Thus, 5,175 - 3,498 = 1,677 variant sites are overlapped sites between first and second vcfs. The last INFO message summarizes that the sum of variant sites contained in both vcfs is 8,664 = 3,489 + 5,175, where there are 6,987 variant sites after merging variants from both vcfs.

More details about vtools import command can be found at:

http://varianttools.sourceforge.net/Vtools/Import

Since the input VCF file uses hg18 as the reference genome while most modern annotation data sources are hg19-based, we need to "liftover" our project using hg19 in order to use various annotation sources in the analysis. Vtools provides a command which is based on the tool of USCS liftOver to map the variants from existing reference genome to an alternative build. More details about vtools liftover command can be found at:

http://varianttools.sourceforge.net/Vtools/Liftover

In [ ]:
vtools liftover hg19 --flip
INFO: Downloading liftOver tool from UCSC
--2019-03-22 11:26:06--  http://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64/liftOver
Resolving hgdownload.cse.ucsc.edu (hgdownload.cse.ucsc.edu)... 128.114.119.163
Connecting to hgdownload.cse.ucsc.edu (hgdownload.cse.ucsc.edu)|128.114.119.163|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 13279280 (13M) [text/plain]
Saving to: ‘/home/gaow/Documents/GIT/wiki/ismb-2018/data/.vtools_cache/liftOver_tmp2032’

/home/gaow/Document 100%[=====================>]  12.66M  1.97MB/s   in 6.4s   

2019-03-22 11:26:13 (1.99 MB/s) - ‘/home/gaow/Documents/GIT/wiki/ismb-2018/data/.vtools_cache/liftOver_tmp2032’ saved [13279280/13279280]

INFO: Downloading liftOver chain file from UCSC
--2019-03-22 11:26:13--  http://hgdownload.cse.ucsc.edu/goldenPath/hg18/liftOver/hg18ToHg19.over.chain.gz
Resolving hgdownload.cse.ucsc.edu (hgdownload.cse.ucsc.edu)... 128.114.119.163
Connecting to hgdownload.cse.ucsc.edu (hgdownload.cse.ucsc.edu)|128.114.119.163|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 140346 (137K) [application/x-gzip]
Saving to: ‘/home/gaow/Documents/GIT/wiki/ismb-2018/data/.vtools_cache/hg18ToHg19.over.chain_tmp2032.gz’

/home/gaow/Document 100%[=====================>] 137.06K   590KB/s   in 0.2s   

2019-03-22 11:26:13 (590 KB/s) - ‘/home/gaow/Documents/GIT/wiki/ismb-2018/data/.vtools_cache/hg18ToHg19.over.chain_tmp2032.gz’ saved [140346/140346]

INFO: Exporting variants in BED format
Exporting variants: 100% [==========================] 6,987 387.4K/s in 00:00:00
INFO: Running UCSC liftOver tool
INFO: Flipping primary and alternative reference genome

Import phenotype data

The aim of the association test is to find variants that modulate the phenotype BMI. We simulated BMI values for each of the individuals. The phenotype file must be in plain text format with sample names matching the sample IDs in the vcf file(s):

In [6]:
%preview phenotypes.csv -n -l 10
> phenotypes.csv (5.0 KiB):
  sample_name   panel   SEX   BMI  
0 NA06984 ILLUMINA 1 36.353
1 NA06985 NaN 2 21.415
2 NA06986 ABI_SOLID+ILLUMINA 1 26.898
3 NA06989 ILLUMINA 2 25.015
4 NA06994 ABI_SOLID+ILLUMINA 1 23.858
5 NA07000 ABI_SOLID+ILLUMINA 2 36.226
6 NA07037 ILLUMINA 1 32.513
7 NA07048 ILLUMINA 2 17.570
8 NA07051 ILLUMINA 1 37.142
9 NA07346 NaN 2 30.978

The phenotype file includes information for every individual, the sample name, sequencing panel, sex and BMI. To import the phenotype data:

In [7]:
vtools phenotype --from_file phenotypes.csv --delimiter ","
INFO: Adding phenotype panel of type VARCHAR(24)
INFO: Adding phenotype SEX of type INT
INFO: Adding phenotype BMI of type FLOAT
INFO: 3 field (3 new, 0 existing) phenotypes of 202 samples are updated.

Unlike vtools import, this command imports/adds properties to samples rather than to variants. More details about vtools phenotype command can be found at:

http://varianttools.sourceforge.net/Vtools/Phenotype

View imported data

Summary information for the project can be viewed anytime using the command vtools show, which displays various project and system information. More details about vtools show can be found at:

http://varianttools.sourceforge.net/Vtools/Show

Some useful data summary commands are:

In [8]:
vtools show project
Project name:                VATDemo
Created on:                  Fri Jul  6 10:56:25 2018
Primary reference genome:    hg19
Secondary reference genome:  hg18
Runtime options:             verbosity=1, shared_resource=/home/gaow/.variant_tools, local_resource=/home/gaow/.variant_tools
Variant tables:              variant
Annotation databases:        

In [9]:
vtools show tables
table      #variants     date message
variant        6,987    Jul06 Master variant table
In [10]:
vtools show table variant
Name:                   variant
Description:            Master variant table
Creation date:          Jul06
Command:
Fields:                 variant_id, bin, chr, pos, ref, alt, DP, filter,
                        alt_bin, alt_chr, alt_pos
Number of variants:     6987
In [11]:
vtools show fields
variant.chr (char)      Chromosome name (VARCHAR)
variant.pos (int)       Position (INT, 1-based)
variant.ref (char)      Reference allele (VARCHAR, - for missing allele of an
                        insertion)
variant.alt (char)      Alternative allele (VARCHAR, - for missing allele of an
                        deletion)
variant.DP (int)
variant.filter (char)
variant.alt_chr (char)
variant.alt_pos (int)

1.2 Overview of variant and genotype data

Total number of variants

The number of imported variants may be greater than number of lines in the vcf file, because when a variant has two alternative alleles (e.g. A->T/C) it is treated as two separate variants.

In [12]:
vtools select variant --count
Counting variants: 4 788.4/s in 00:00:00
6987

There are 6987 variants in our toy data-set.

vtools select table condition action selects from a variant table table a subset of variants satisfying a specified condition, and perform an action of

  • creating a new variant table if --to_table is specified.
  • counting the number of variants if --count is specified.
  • outputting selected variants if --output is specified.

The condition should be a SQL expression using one or more fields in a project (displayed in vtools show fields). If the condition argument is unspecified, then all variants in the table will be selected. An optional condition --samples [condition] can also be used to limit selected variants to specific samples. More details about vtools select command can be found at:

http://varianttools.sourceforge.net/Vtools/Select

Genotype Summary

The command vtools show genotypes displays the number of genotypes for each sample and names of the available genotype information fields for each sample, e.g. GT - genotype; DP geno - genotype read depth. Such information is useful for the calculation of summary statistics of genotypes (e.g. depth of coverage).

In [13]:
vtools show genotypes > genotype_summary.txt
In [14]:
%preview genotype_summary.txt -n -l 10
> genotype_summary.txt (12.5 KiB):
sample_name	filename                 	num_genotypes	sample_genotype_fields
NA06984    	CEU.exon...notypes.vcf.gz	3162         	GT,DP_geno
NA06985    	CEU.exon...notypes.vcf.gz	3144         	GT,DP_geno
NA06986    	CEU.exon...notypes.vcf.gz	3437         	GT,DP_geno
NA06989    	CEU.exon...notypes.vcf.gz	3130         	GT,DP_geno
NA06994    	CEU.exon...notypes.vcf.gz	3002         	GT,DP_geno
NA07000    	CEU.exon...notypes.vcf.gz	3388         	GT,DP_geno
NA07037    	CEU.exon...notypes.vcf.gz	3374         	GT,DP_geno
NA07048    	CEU.exon...notypes.vcf.gz	3373         	GT,DP_geno
NA07051    	CEU.exon...notypes.vcf.gz	3451         	GT,DP_geno

Variant Quality Overview

The following command calculates summary statistics on the variant site depth of coverage (DP). Below is the command to calculate depth of coverage information for all variant sites.

In [15]:
vtools output variant "max(DP)" "min(DP)" "avg(DP)" "stdev(DP)" "lower_quartile(DP)" "upper_quartile(DP)" --header
max_DP_	min_DP_	avg_DP_           	stdev_DP_         	lower_quartile_DP_	upper_quartile_DP_
25490  	13     	6815.7702876771145	3434.2804009099777	4301              	9143

In the test data, the maximum DP for variant sites is 25490, minimum DP 13, average DP about 6815, standard deviation of DP about 3434, lower quartile of DP 4301 and upper quartile of DP 9143.

The same syntax can be applied to other variant information or annotation information fields. The command vtools output <name of variant table> outputs properties of variants in a specified variant table. The properties include fields from annotation databases and variant tables, basically fields outputted from command vtools show fields, and SQL-supported functions and expressions. There are several freely available SQL resources on the web to learn more about SQL functions and expressions.

It is also possible to view variant level summary statistic for variants satisfying certain filtering criteria using vtools select <name of variant table> command, for example to count only variants having passed all quality filters:

In [16]:
vtools select variant "filter='PASS'" --count
Counting variants: 5 910.3/s in 00:00:00
6987

All 6987 variants have passed the quality filters. To combine variant filtering and summary statistics:

In [17]:
vtools select variant "filter='PASS'" -o "max(DP)" "min(DP)" "avg(DP)" "stdev(DP)" "lower_quartile(DP)" "upper_quartile(DP)" --header
max_DP_	min_DP_	avg_DP_           	stdev_DP_         	lower_quartile_DP_	upper_quartile_DP_
25490  	13     	6815.7702876771145	3434.2804009099777	4301              	9143

The output information of command above will be the same as the previous vtools output command, since all variants have passed quality filter.

1.3 Data exploration

Variant level summaries

The command below will calculate:

  • total: Total number of genotypes (GT) for a variant
  • num: Total number of alternative alleles across all samples
  • het: Total number of heterozygote genotypes 1/0
  • hom: Total number of homozygote genotypes 1/1
  • other: Total number of double-homozygotes 1/2
  • min/max/meanDP: Summaries for depth of coverage and genotype quality across samples
  • maf: Minor allele frequency
  • Add calculated variant level statistics to fields, which can be shown by commands vtools show fields and vtools show table variant
In [18]:
vtools update variant --from_stat 'total=#(GT)' 'num=#(alt)' 'het=#(het)' 'hom=#(hom)' 'other=#(other)' 'minDP=min(DP_geno)' 'maxDP=max(DP_geno)' 'meanDP=avg(DP_geno)' 'maf=maf()'
Counting variants: 100% [==============================] 202 102.7/s in 00:00:01
INFO: Adding variant info field num with type INT
INFO: Adding variant info field hom with type INT
INFO: Adding variant info field het with type INT
INFO: Adding variant info field other with type INT
INFO: Adding variant info field total with type INT
INFO: Adding variant info field maf with type FLOAT
INFO: Adding variant info field minDP with type INT
INFO: Adding variant info field maxDP with type INT
INFO: Adding variant info field meanDP with type FLOAT
Updating variant: 100% [============================] 6,987 103.9K/s in 00:00:00
INFO: 6987 records are updated
In [19]:
vtools show fields
variant.chr (char)      Chromosome name (VARCHAR)
variant.pos (int)       Position (INT, 1-based)
variant.ref (char)      Reference allele (VARCHAR, - for missing allele of an
                        insertion)
variant.alt (char)      Alternative allele (VARCHAR, - for missing allele of an
                        deletion)
variant.DP (int)
variant.filter (char)
variant.alt_chr (char)
variant.alt_pos (int)
variant.num (int)       Created from stat "#(alt)"  with type INT on Jul06
variant.hom (int)       Created from stat "#(hom)"  with type INT on Jul06
variant.het (int)       Created from stat "#(het)"  with type INT on Jul06
variant.other (int)     Created from stat "#(other)"  with type INT on Jul06
variant.total (int)     Created from stat "#(GT)"  with type INT on Jul06
variant.maf (float)     Created from stat "maf()"  with type FLOAT on Jul06
variant.minDP (int)     Created from stat "min(DP_geno)"  with type INT on Jul06
variant.maxDP (int)     Created from stat "max(DP_geno)"  with type INT on Jul06
variant.meanDP (float)  Created from stat "avg(DP_geno)"  with type FLOAT on
                        Jul06
In [20]:
vtools show table variant
Name:                   variant
Description:            Master variant table
Creation date:          Jul06
Command:
Fields:                 variant_id, bin, chr, pos, ref, alt, DP, filter,
                        alt_bin, alt_chr, alt_pos, num, hom, het, other, total,
                        maf, minDP, maxDP, meanDP
Number of variants:     6987

Command vtools update updates variant info fields (and to a lesser extend genotype info fields) by adding more fields or updating values at existing fields. It does not add any new variants or genotypes, and does not change existing variants, samples, or genotypes. Using three parameters --from_file, --from_stat, and --set, variant information fields could be updated from external file, sample genotypes, and existing fields. More details about vtools update command can be found at

http://varianttools.sourceforge.net/Vtools/Update

Summaries for different genotype depth (GD) and genotype quality (GQ) filters

The --genotypes CONDITION option restricts calculation to genotypes satisfying a given condition. Later we will remove individual genotypes by DP_geno filters. The command below will calculate summary statistics genotypes of all samples per variant site. It can assist us in determining filtering criteria for genotype call quality.

In [21]:
vtools update variant --from_stat 'totalGD10=#(GT)' 'numGD10=#(alt)' 'hetGD10=#(het)' 'homGD10=#(hom)' 'otherGD10=#(other)' 'mafGD10=maf()' --genotypes "DP_geno > 10"
Counting variants: 100% [==============================] 202 331.2/s in 00:00:00
INFO: Adding variant info field numGD10 with type INT
INFO: Adding variant info field homGD10 with type INT
INFO: Adding variant info field hetGD10 with type INT
INFO: Adding variant info field otherGD10 with type INT
INFO: Adding variant info field totalGD10 with type INT
INFO: Adding variant info field mafGD10 with type FLOAT
Updating variant: 100% [============================] 6,976 129.2K/s in 00:00:00
INFO: 6976 records are updated
In [22]:
vtools show fields
variant.chr (char)      Chromosome name (VARCHAR)
variant.pos (int)       Position (INT, 1-based)
variant.ref (char)      Reference allele (VARCHAR, - for missing allele of an
                        insertion)
variant.alt (char)      Alternative allele (VARCHAR, - for missing allele of an
                        deletion)
variant.DP (int)
variant.filter (char)
variant.alt_chr (char)
variant.alt_pos (int)
variant.num (int)       Created from stat "#(alt)"  with type INT on Jul06
variant.hom (int)       Created from stat "#(hom)"  with type INT on Jul06
variant.het (int)       Created from stat "#(het)"  with type INT on Jul06
variant.other (int)     Created from stat "#(other)"  with type INT on Jul06
variant.total (int)     Created from stat "#(GT)"  with type INT on Jul06
variant.maf (float)     Created from stat "maf()"  with type FLOAT on Jul06
variant.minDP (int)     Created from stat "min(DP_geno)"  with type INT on Jul06
variant.maxDP (int)     Created from stat "max(DP_geno)"  with type INT on Jul06
variant.meanDP (float)  Created from stat "avg(DP_geno)"  with type FLOAT on
                        Jul06
variant.numGD10 (int)   Created from stat "#(alt)"  with type INT on Jul06
variant.homGD10 (int)   Created from stat "#(hom)"  with type INT on Jul06
variant.hetGD10 (int)   Created from stat "#(het)"  with type INT on Jul06
variant.otherGD10 (int) 
                        Created from stat "#(other)"  with type INT on Jul06
variant.totalGD10 (int) 
                        Created from stat "#(GT)"  with type INT on Jul06
variant.mafGD10 (float) 
                        Created from stat "maf()"  with type FLOAT on Jul06
In [23]:
vtools show table variant
Name:                   variant
Description:            Master variant table
Creation date:          Jul06
Command:
Fields:                 variant_id, bin, chr, pos, ref, alt, DP, filter,
                        alt_bin, alt_chr, alt_pos, num, hom, het, other, total,
                        maf, minDP, maxDP, meanDP, numGD10, homGD10, hetGD10,
                        otherGD10, totalGD10, mafGD10
Number of variants:     6987

You will notice the change in genotype counts when applying the filter on genotype depth of coverage and only retaining those genotypes with a read depth greater than 10X. There are now 6976 variant sites after filtering on DP_geno>10. Note that some variant sites will become monomorphic after removing genotypes due to low read depth.

Minor allele frequencies (MAFs)

In previous steps, we calculated MAFs for each variant site before and after filtering on genotype read depth. Below is a summary of the results:

In [24]:
vtools output variant chr pos maf mafGD10 --header --limit 20
chr	pos     	maf                  	mafGD10
1  	1115503 	0.03508771929824561  	0.05128205128205128
1  	1115548 	0.009433962264150943 	0.01282051282051282
1  	1118275 	0.19230769230769232  	0.18023255813953487
1  	1120377 	0.0056179775280898875	0.0
1  	1120431 	0.228125             	0.2423076923076923
1  	3548136 	0.12012987012987009  	0.15217391304347827
1  	3548832 	0.041025641025641026 	0.043209876543209874
1  	3551737 	0.0056179775280898875	0.006172839506172839
1  	3551792 	0.044444444444444446 	0.05333333333333334
1  	3555351 	0.0056179775280898875	0.005813953488372093
1  	6524501 	0.13114754098360656  	0.14
1  	6524688 	0.05113636363636364  	0.056451612903225805
1  	6524703 	0.011494252873563218 	0.015625
1  	7838196 	0.0056179775280898875	0.006578947368421052
1  	10502369	0.005747126436781609 	0.006756756756756757
1  	11710561	0.1111111111111111   	0.10344827586206896
1  	17914057	0.0755813953488372   	0.0859375
1  	17914122	0.08235294117647059  	0.08064516129032258
1  	17928672	0.00684931506849315  	0.011363636363636364
1  	17949562	0.006172839506172839 	0.009615384615384616

Adding “> filename.txt” at the end of the above command will write the output to a file.

Next, we examine population specific MAFs. Our data is imported from two files, a CEU dataset (90 samples) and an YRI dataset (112 samples). To calculate allele frequency for each population, let us first assign an additional RACE phenotype (0 for YRI samples and 1 for CEU samples):

In [25]:
vtools phenotype --set "RACE=0" --samples "filename like 'YRI%'"
INFO: Adding phenotype RACE
INFO: 112 values of 1 phenotypes (1 new, 0 existing) of 112 samples are updated.
In [26]:
vtools phenotype --set "RACE=1" --samples "filename like 'CEU%'"
INFO: 90 values of 1 phenotypes (0 new, 1 existing) of 90 samples are updated.
In [27]:
vtools show samples --limit 10
sample_name	filename                 	panel             	SEX	BMI   	RACE
NA06984    	CEU.exon...notypes.vcf.gz	ILLUMINA          	1  	36.353	1
NA06985    	CEU.exon...notypes.vcf.gz	.                 	2  	21.415	1
NA06986    	CEU.exon...notypes.vcf.gz	ABI_SOLID+ILLUMINA	1  	26.898	1
NA06989    	CEU.exon...notypes.vcf.gz	ILLUMINA          	2  	25.015	1
NA06994    	CEU.exon...notypes.vcf.gz	ABI_SOLID+ILLUMINA	1  	23.858	1
NA07000    	CEU.exon...notypes.vcf.gz	ABI_SOLID+ILLUMINA	2  	36.226	1
NA07037    	CEU.exon...notypes.vcf.gz	ILLUMINA          	1  	32.513	1
NA07048    	CEU.exon...notypes.vcf.gz	ILLUMINA          	2  	17.57 	1
NA07051    	CEU.exon...notypes.vcf.gz	ILLUMINA          	1  	37.142	1
NA07346    	CEU.exon...notypes.vcf.gz	.                 	2  	30.978	1
(192 records omitted)

Population specific MAF calculations will be performed using those genotypes that passed the read depth filter (DP_geno>10).

In [28]:
vtools update variant --from_stat 'CEU_mafGD10=maf()' --genotypes 'DP_geno>10' --samples "RACE=1"
INFO: 90 samples are selected
Counting variants: 100% [===============================] 90 221.6/s in 00:00:00
INFO: Adding variant info field CEU_mafGD10 with type FLOAT
Updating variant: 100% [=============================] 3,483 93.3K/s in 00:00:00
INFO: 3483 records are updated
In [29]:
vtools update variant --from_stat 'YRI_mafGD10=maf()' --genotypes 'DP_geno>10' --samples "RACE=0"
INFO: 112 samples are selected
Counting variants: 100% [==============================] 112 274.8/s in 00:00:00
INFO: Adding variant info field YRI_mafGD10 with type FLOAT
Updating variant: 100% [============================] 5,167 113.1K/s in 00:00:00
INFO: 5167 records are updated
In [30]:
vtools output variant chr pos mafGD10 CEU_mafGD10 YRI_mafGD10 --header --limit 10
chr	pos    	mafGD10             	CEU_mafGD10         	YRI_mafGD10
1  	1115503	0.05128205128205128 	0.05128205128205128 	0.0
1  	1115548	0.01282051282051282 	0.01282051282051282 	0.0
1  	1118275	0.18023255813953487 	0.02127659574468085 	0.3717948717948718
1  	1120377	0.0                 	0.0                 	0.0
1  	1120431	0.2423076923076923  	0.025               	0.42857142857142855
1  	3548136	0.15217391304347827 	0.17045454545454541 	0.13541666666666663
1  	3548832	0.043209876543209874	0.08333333333333333 	0.005952380952380952
1  	3551737	0.006172839506172839	0.006172839506172839	0.0
1  	3551792	0.05333333333333334 	0.05333333333333334 	0.0
1  	3555351	0.005813953488372093	0.005813953488372093	0.0

You will observe zero values because some variant sites are monomorphic or they are population specific.

Sample level genotype summaries

Similar operations could be performed on a sample level instead of on a variant level. More details about obtaining genotype level summary information using vtools phenotype --from_stat can be found at

http://varianttools.sourceforge.net/Vtools/Phenotype

In [31]:
vtools phenotype --from_stat 'CEU_totalGD10=#(GT)' 'CEU_numGD10=#(alt)' --genotypes 'DP_geno>10' --samples "RACE=1"
Calculating phenotype: 100% [============================] 90 89.9/s in 00:00:01
INFO: 180 values of 2 phenotypes (2 new, 0 existing) of 90 samples are updated.
In [32]:
vtools phenotype --from_stat 'YRI_totalGD10=#(GT)' 'YRI_numGD10=#(alt)' --genotypes 'DP_geno>10' --samples "RACE=0"
Calculating phenotype: 100% [==========================] 112 111.9/s in 00:00:01
INFO: 224 values of 2 phenotypes (2 new, 0 existing) of 112 samples are updated.
In [33]:
vtools phenotype --output sample_name CEU_totalGD10 CEU_numGD10 YRI_totalGD10 YRI_numGD10 --header
sample_name	CEU_totalGD10	CEU_numGD10	YRI_totalGD10	YRI_numGD10
NA06984	2774	849	NA	NA
NA06985	1944	570	NA	NA
NA06986	3386	1029	NA	NA
NA06989	2659	819	NA	NA
NA06994	1730	486	NA	NA
NA07000	3089	979	NA	NA
NA07037	2990	931	NA	NA
NA07048	3305	1012	NA	NA
NA07051	3402	1130	NA	NA
NA07346	3356	1092	NA	NA
NA07347	3330	1121	NA	NA
NA07357	3373	1063	NA	NA
NA10847	2371	791	NA	NA
NA10851	2408	665	NA	NA
NA11829	3365	1087	NA	NA
NA11830	2935	939	NA	NA
NA11831	3379	1069	NA	NA
NA11832	3398	1149	NA	NA
NA11840	1886	615	NA	NA
NA11843	2400	790	NA	NA
NA11881	2273	698	NA	NA
NA11893	2951	921	NA	NA
NA11918	3297	1044	NA	NA
NA11919	2855	753	NA	NA
NA11920	3365	1129	NA	NA
NA11930	3336	1128	NA	NA
NA11992	3386	1111	NA	NA
NA11994	3370	1095	NA	NA
NA11995	1993	622	NA	NA
NA12003	3328	1062	NA	NA
NA12004	1613	449	NA	NA
NA12005	2973	923	NA	NA
NA12006	1656	484	NA	NA
NA12043	3323	1089	NA	NA
NA12044	2602	791	NA	NA
NA12045	3385	1052	NA	NA
NA12058	2664	837	NA	NA
NA12144	3316	993	NA	NA
NA12154	3114	1028	NA	NA
NA12155	3354	1126	NA	NA
NA12156	1390	380	NA	NA
NA12234	3333	1060	NA	NA
NA12249	2081	638	NA	NA
NA12272	2371	756	NA	NA
NA12273	2319	737	NA	NA
NA12275	2251	725	NA	NA
NA12282	1758	529	NA	NA
NA12283	2459	770	NA	NA
NA12286	2528	785	NA	NA
NA12287	3231	1059	NA	NA
NA12340	2648	820	NA	NA
NA12341	2266	634	NA	NA
NA12342	2666	825	NA	NA
NA12347	3056	927	NA	NA
NA12348	2751	794	NA	NA
NA12383	3356	1082	NA	NA
NA12400	2169	679	NA	NA
NA12413	3387	1095	NA	NA
NA12414	2709	800	NA	NA
NA12489	2888	870	NA	NA
NA12546	3389	1125	NA	NA
NA12716	2617	829	NA	NA
NA12717	2280	724	NA	NA
NA12718	2310	715	NA	NA
NA12748	3302	978	NA	NA
NA12749	3103	935	NA	NA
NA12750	2210	712	NA	NA
NA12751	2202	692	NA	NA
NA12760	2868	890	NA	NA
NA12761	1675	525	NA	NA
NA12762	3184	1026	NA	NA
NA12763	1634	526	NA	NA
NA12775	3228	960	NA	NA
NA12776	3186	1050	NA	NA
NA12812	2244	693	NA	NA
NA12814	2959	940	NA	NA
NA12815	1589	475	NA	NA
NA12828	3274	1051	NA	NA
NA12829	3227	1019	NA	NA
NA12830	3058	914	NA	NA
NA12842	1684	502	NA	NA
NA12843	2832	846	NA	NA
NA12872	1485	425	NA	NA
NA12873	1329	357	NA	NA
NA12874	1802	521	NA	NA
NA12878	3463	1125	NA	NA
NA12889	360	103	NA	NA
NA12890	3394	1089	NA	NA
NA12891	3435	1107	NA	NA
NA12892	3426	1055	NA	NA
NA18486	NA	NA	4718	1180
NA18488	NA	NA	4591	1150
NA18489	NA	NA	3350	685
NA18498	NA	NA	4058	926
NA18499	NA	NA	3408	642
NA18501	NA	NA	4267	1005
NA18504	NA	NA	38	7
NA18508	NA	NA	4036	912
NA18516	NA	NA	86	13
NA18519	NA	NA	4820	1163
NA18520	NA	NA	4886	1176
NA18522	NA	NA	27	3
NA18523	NA	NA	5027	1299
NA18853	NA	NA	4645	1169
NA18856	NA	NA	4958	1282
NA18858	NA	NA	5000	1323
NA18861	NA	NA	4525	1089
NA18865	NA	NA	1294	279
NA18867	NA	NA	4849	1211
NA18868	NA	NA	4430	1079
NA18870	NA	NA	42	5
NA18871	NA	NA	52	10
NA18877	NA	NA	4866	1236
NA18881	NA	NA	4484	1062
NA18907	NA	NA	3826	871
NA18909	NA	NA	3551	767
NA18910	NA	NA	4836	1216
NA18915	NA	NA	4394	1025
NA18916	NA	NA	4378	1009
NA18917	NA	NA	2835	688
NA18923	NA	NA	3042	697
NA18924	NA	NA	3086	697
NA18933	NA	NA	2772	654
NA18934	NA	NA	3079	704
NA19092	NA	NA	4762	1234
NA19095	NA	NA	4012	963
NA19096	NA	NA	4072	912
NA19098	NA	NA	2843	648
NA19102	NA	NA	1908	303
NA19105	NA	NA	4150	953
NA19108	NA	NA	5043	1214
NA19113	NA	NA	4049	987
NA19116	NA	NA	3590	721
NA19117	NA	NA	4092	978
NA19118	NA	NA	4189	998
NA19119	NA	NA	2866	665
NA19121	NA	NA	4364	1061
NA19122	NA	NA	4024	978
NA19130	NA	NA	4570	1153
NA19131	NA	NA	2827	694
NA19133	NA	NA	4688	1146
NA19135	NA	NA	4575	1158
NA19137	NA	NA	1695	381
NA19138	NA	NA	2897	697
NA19141	NA	NA	2615	584
NA19143	NA	NA	3260	772
NA19146	NA	NA	3934	965
NA19149	NA	NA	4187	967
NA19150	NA	NA	4064	940
NA19152	NA	NA	3238	715
NA19153	NA	NA	3279	795
NA19156	NA	NA	4516	1127
NA19157	NA	NA	4773	1166
NA19159	NA	NA	3122	744
NA19163	NA	NA	4371	1069
NA19166	NA	NA	4845	1220
NA19168	NA	NA	4479	1114
NA19171	NA	NA	3168	747
NA19172	NA	NA	4161	949
NA19175	NA	NA	4167	986
NA19179	NA	NA	3969	970
NA19181	NA	NA	2911	696
NA19182	NA	NA	4116	991
NA19184	NA	NA	4140	1004
NA19185	NA	NA	4315	1017
NA19187	NA	NA	4222	995
NA19189	NA	NA	5019	1279
NA19190	NA	NA	4603	1034
NA19195	NA	NA	4450	1093
NA19196	NA	NA	4450	1082
NA19197	NA	NA	3433	875
NA19198	NA	NA	3196	749
NA19200	NA	NA	2990	710
NA19201	NA	NA	2519	592
NA19204	NA	NA	3114	714
NA19206	NA	NA	3056	765
NA19207	NA	NA	2280	525
NA19209	NA	NA	2962	673
NA19210	NA	NA	1350	273
NA19213	NA	NA	4910	1206
NA19214	NA	NA	4214	1020
NA19216	NA	NA	4439	1098
NA19217	NA	NA	4230	1025
NA19220	NA	NA	2690	611
NA19222	NA	NA	5053	1261
NA19223	NA	NA	2720	628
NA19225	NA	NA	5047	1304
NA19229	NA	NA	4813	1228
NA19235	NA	NA	4466	1074
NA19236	NA	NA	4668	1174
NA19238	NA	NA	5027	1271
NA19239	NA	NA	5147	1379
NA19240	NA	NA	5145	1361
NA19247	NA	NA	4606	1108
NA19248	NA	NA	4698	1146
NA19250	NA	NA	4218	1025
NA19253	NA	NA	4964	1248
NA19257	NA	NA	4969	1229
NA19259	NA	NA	4182	1005
NA19260	NA	NA	4404	1076
NA19262	NA	NA	4308	1044
NA19266	NA	NA	4878	1211

1.4 Variant Annotation

For rare variant aggregated association tests, we want to focus on analyzing aggregating variants having potential functional contribution to a phenotype. Thus, each variant site needs to be annotated for its functionality. Annotation is performed using variant annotation tools [7] which implements an ANNOVAR pipeline for variant function annotation [9]. More details about the ANNOVAR pipeline can be found at

http://varianttools.sourceforge.net/Pipeline/Annovar

In [34]:
# You need to make sure `annovar` package & database are installed in the system
# This is already the case here.
vtools execute ANNOVAR geneanno
INFO: Executing ANNOVAR.geneanno_0: Load specified snapshot if a snapshot is specified. Otherwise use the existing project.
INFO: Executing ANNOVAR.geneanno_10: Check the existence of ANNOVAR's annotate_variation.pl command.
INFO: Command annotate_variation.pl is located.
INFO: Executing ANNOVAR.geneanno_11: Determine the humandb path of ANNOVAR
INFO: Running which annotate_variation.pl > /data/ismb-2018/data/.vtools_cache/annovar.path
INFO: Pipeline variable HUMANDB is set to /home/gaow/tmp/29-Jun-2018/annovar/humandb
INFO: Executing ANNOVAR.geneanno_14: Download gene database for specified --dbtype if they are unavailable
INFO: Reuse existing /home/gaow/tmp/29-Jun-2018/annovar/humandb/hg19_refGene.txt, /home/gaow/tmp/29-Jun-2018/annovar/humandb/hg19_refGeneMrna.fa
INFO: Executing ANNOVAR.geneanno_20: Export variants in ANNOVAR format
INFO: Running vtools export variant --format ANNOVAR --output /data/ismb-2018/data/.vtools_cache/annovar_input
INFO: Executing ANNOVAR.geneanno_30: Execute ANNOVAR annotate_variation.pl --geneanno
INFO: Running annotate_variation.pl --geneanno --dbtype refGene --buildver hg19 /data/ismb-2018/data/.vtools_cache/annovar_input /home/gaow/tmp/29-Jun-2018/annovar/humandb
INFO: Executing ANNOVAR.geneanno_40: Importing results from ANNOVAR output .variant_function if --variant_info is specified
INFO: Running vtools update variant --from_file /data/ismb-2018/data/.vtools_cache/annovar_input.variant_function --format ANNOVAR_variant_function --var_info region_type, region_name
INFO: Using primary reference genome hg19 of the project.
Getting existing variants: 100% [===================] 6,987 645.3K/s in 00:00:00
INFO: Updating variants from /data/ismb-2018/data/.vtools_cache/annovar_input.variant_function (1/1)
annovar_input.variant_function: 100% [================] 6,987 4.9K/s in 00:00:01
INFO: Fields region_type, region_name of 6,987 variants are updated
INFO: Executing ANNOVAR.geneanno_50: Importing results from ANNOVAR output .exonic_variant_function if --exonic_info is specified
INFO: Running vtools update variant --from_file /data/ismb-2018/data/.vtools_cache/annovar_input.exonic_variant_function --format ANNOVAR_exonic_variant_function --var_info mut_type, function
INFO: Using primary reference genome hg19 of the project.
Getting existing variants: 100% [===================] 6,987 656.9K/s in 00:00:00
INFO: Updating variants from /data/ismb-2018/data/.vtools_cache/annovar_input.exonic_variant_function (1/1)
annovar_input.exonic_variant_function: 100% [=========] 6,928 4.6K/s in 00:00:01
INFO: Fields mut_type, function of 6,928 variants are updated
INFO: Execution of pipeline ANNOVAR.geneanno is successful with output /data/ismb-2018/data/.vtools_cache/annovar_input.exonic_variant_function

The following command will output the annotated variant sites to the screen.

In [35]:
vtools output variant chr pos ref alt mut_type --limit 20 --header
chr	pos     	ref	alt	mut_type
1  	1115503 	T  	C  	nonsynonymous SNV
1  	1115548 	G  	A  	nonsynonymous SNV
1  	1118275 	C  	T  	synonymous SNV
1  	1120377 	T  	A  	nonsynonymous SNV
1  	1120431 	G  	A  	nonsynonymous SNV
1  	3548136 	T  	C  	synonymous SNV
1  	3548832 	G  	C  	nonsynonymous SNV
1  	3551737 	C  	T  	nonsynonymous SNV
1  	3551792 	G  	A  	synonymous SNV
1  	3555351 	G  	A  	synonymous SNV
1  	6524501 	T  	C  	nonsynonymous SNV
1  	6524688 	T  	C  	synonymous SNV
1  	6524703 	C  	T  	synonymous SNV
1  	7838196 	A  	G  	nonsynonymous SNV
1  	10502369	A  	G  	synonymous SNV
1  	11710561	T  	G  	nonsynonymous SNV
1  	17914057	G  	A  	nonsynonymous SNV
1  	17914122	G  	A  	nonsynonymous SNV
1  	17928672	G  	C  	nonsynonymous SNV
1  	17949562	C  	T  	synonymous SNV

Many more annotation sources are available which are not covered in this tutorial. Please read

http://varianttools.sourceforge.net/Annotation

for annotation databases, and

http://varianttools.sourceforge.net/Pipeline for annotation pipelines.

1.5 Data Quality Control (QC) and Variant Selection

Ti/Tv ratio evaluations

Before performing any data QC we examine the transition/transversion (Ti/Tv) ratio for all variant sites. Note that here we are obtaining Ti/Tv ratios for the entire sample, Ti/Tv ratios can also be obtained for each sample.

In [36]:
vtools_report trans_ratio variant -n num
num_of_transition	num_of_transversion	ratio
161,637          	44,641             	3.62082

The command above counts the number of transition and transversion variants and calculates its ratio. More details about vtools report trans_ratio command can be found at

http://varianttools.sourceforge.net/VtoolsReport/TransRatio

If only genotype calls having depth of coverage greater than 10 are considered:

In [37]:
vtools_report trans_ratio variant -n numGD10
num_of_transition	num_of_transversion	ratio
140,392          	38,710             	3.62676

We can see that Ti/Tv ratio has increase slightly if low depth of coverage calls are removed. There is only a small change in the Ti/Tv ratio since only a few variant sites become monomorphic and are no longer included in the calculation. In practice Ti/Tv ratios can be used to evaluate which threshold should be used in data QC.

Removal of low quality variant sites

We should not need to remove any variant site based on read depth because all variants passed the quality filter. To demonstrate removal of variant sites, let us remove those with a total read depth DP<15:

In [38]:
vtools select variant "DP<15" -t to_remove
Running: 2 1.1K/s in 00:00:00
INFO: 1 variants selected.
In [39]:
vtools show tables
table        #variants     date message
to_remove            1    Jul06
variant          6,987    Jul06 Master variant table
In [40]:
vtools remove variants to_remove -v0
In [41]:
vtools show tables
table      #variants     date message
variant        6,986    Jul06 Master variant table

We can see that one variant site has been removed from master variant table. The vtools remove command can remove various items from the current project. More details about vtools remove command can be found at:

http://varianttools.sourceforge.net/Vtools/Remove

Using a combination of select/remove subcommands low quality variant sites can be easily filtered out. The vtools show fields, vtools show tables, and vtools show table variant commands will allow you to see the new/updated fields and tables you have added/changed to the project.

Filter genotype calls by quality

We have calculated various summary statistics using the command --genotypes CONDITION but we have not yet removed genotypes having genotype read depth of coverage lower than 10X. The command below removes these genotypes.

In [42]:
vtools remove genotypes "DP_geno<10" -v0

Select variants by annotated functionality

To select potentially functional variants for association mapping:

In [43]:
vtools select variant "mut_type like 'non%' or mut_type like 'stop%' or region_type='splicing'" -t v_funct
Running: 10 2.3K/s in 00:00:00
INFO: 3524 variants selected.
In [44]:
vtools show tables
table      #variants     date message
v_funct        3,524    Jul06
variant        6,986    Jul06 Master variant table

The command above selects variant sites that are either nonsynonymous (by condition mut_type like ’non%’) or stop-gain/stop-loss (by condition mut_type like ’stop%’) or alternative splicing (by condition region-type=’splicing’)

3367 functional variant sites are selected.

Part II: Association Tests for Quantitative Traits

2.1 View phenotype data

In [45]:
vtools show samples --limit 5
sample_name	filename                 	panel             	SEX	BMI   	RACE	CEU_totalGD10	CEU_numGD10	YRI_totalGD10	YRI_numGD10
NA06984    	CEU.exon...notypes.vcf.gz	ILLUMINA          	1  	36.353	1   	2774         	849        	.            	.
NA06985    	CEU.exon...notypes.vcf.gz	.                 	2  	21.415	1   	1944         	570        	.            	.
NA06986    	CEU.exon...notypes.vcf.gz	ABI_SOLID+ILLUMINA	1  	26.898	1   	3386         	1029       	.            	.
NA06989    	CEU.exon...notypes.vcf.gz	ILLUMINA          	2  	25.015	1   	2659         	819        	.            	.
NA06994    	CEU.exon...notypes.vcf.gz	ABI_SOLID+ILLUMINA	1  	23.858	1   	1730         	486        	.            	.
(197 records omitted)

2.2 Analysis plan

We want to carry out the association analysis for CEU and YRI separately. For starters we demonstrate analysis of CEU samples; and the same commands will be applicable for YRI samples. After completing the analysis of CEU samples please use the same commands to analyze the YRI data set. You should not analyze the data from different populations together, once you have the p-values from each analysis, you may perform a meta-analysis.

2.3 Subset data by MAFs

To carry out association tests we need to treat common and rare variants separately. The dataset for our tutorial has very small sample size, but with large sample size it is reasonable to define rare variants as having observed MAF<0.01, and common variants as variants having observed MAF$\ge$0.05. First, we create variant tables based on calculated alternative allele frequencies for both populations

In [46]:
vtools select variant "CEU_mafGD10>=0.05" --samples "RACE=1" -t common_ceu
INFO: 90 samples are selected by condition: RACE=1
Collecting sample variants: 100% [=======================] 90 2.1K/s in 00:00:00
Running: 6 1.3K/s in 00:00:00
INFO: 1450 variants selected.
In [47]:
vtools select v_funct "CEU_mafGD10<0.01" --samples "RACE=1" -t rare_ceu
INFO: 90 samples are selected by condition: RACE=1
Collecting sample variants: 100% [=======================] 90 2.1K/s in 00:00:00
Running: 6 1.3K/s in 00:00:00
INFO: 602 variants selected.

Notice that for selection of rare variants we only keep those that are annotated as functional (chosen from v_funct table). There are 1450 and 604 variant sites selected for MAF0.05 and MAF<0.01, respectively.

2.4 Annotate variants to genes

For gene based rare variant analysis we need annotations that tell us the boundaries of genes. We use the refGene annotation database for this purpose.

In [48]:
vtools use refGene
Binning ranges: 100% [=============================] 41,302 211.3K/s in 00:00:00
INFO: Using annotation DB refGene as refGene in project VATDemo.
INFO: refseq Genes
In [49]:
vtools show annotation refGene
Annotation database refGene (version hg19_20110909)
Description:            refseq Genes
Database type:          range
Reference genome hg19:  chr, txStart, txEnd
  name (char)           Gene name
  chr (char)
  strand (char)         which DNA strand contains the observed alleles
  txStart (int)         Transcription start position
  txEnd (int)           Transcription end position
  cdsStart (int)        Coding region start
  cdsEnd (int)          Coding region end
  exonCount (int)       Number of exons
  score (int)           Score
  name2 (char)          Alternative name
  cdsStartStat (char)   cds start stat, can be 'non', 'unk', 'incompl', and
                        'cmp1'
  cdsEndStat (char)     cds end stat, can be 'non', 'unk', 'incompl', and 'cmp1'

The names of genes are contained in the refGene.name2 field. The vtools use command, attaches an annotation database to the project, effectively incorporating one or more attributes available to variants in the project. More details about vtools use command can be found at

http://varianttools.sourceforge.net/Vtools/Use

2.5 Association testing of common/rare variants

The association test program suite is implemented as the vtools associate subcommand. To list available association test options

In [50]:
vtools associate -h
usage: vtools associate [-h] [--covariates [COVARIATES [COVARIATES ...]]]
                        [--var_info [VAR_INFO [VAR_INFO ...]]]
                        [--geno_info [GENO_INFO [GENO_INFO ...]]]
                        [--geno_name GENO_NAME] [-m METHODS [METHODS ...]]
                        [-g [GROUP_BY [GROUP_BY ...]]] [-s [COND [COND ...]]]
                        [--genotypes [COND [COND ...]]]
                        [--discard_samples [EXPR [EXPR ...]]]
                        [--discard_variants [EXPR [EXPR ...]]]
                        [--to_db annoDB] [-d DELIMITER] [-f] [-j N]
                        [-v {0,1,2,3}]
                        variants phenotypes

Call one or more statistical association tests and return test results as
fields to variants tested.

optional arguments:
  -h, --help            show this help message and exit
  -j N, --jobs N        Number of processes to carry out association tests.
  -v {0,1,2,3}, --verbosity {0,1,2,3}
                        Output error and warning (0), info (1), debug (2) and
                        trace (3) information to standard output (default to
                        1).

Genotype, phenotype, and covariates:
  variants              Table of variants to be tested.
  phenotypes            A list of phenotypes that will be passed to the
                        association statistics calculator. Currently only a
                        single phenotype is allowed.
  --covariates [COVARIATES [COVARIATES ...]]
                        Optional phenotypes that will be passed to statistical
                        tests as covariates. Values of these phenotypes should
                        be integer or float.
  --var_info [VAR_INFO [VAR_INFO ...]]
                        Optional variant information fields (e.g. minor allele
                        frequency from 1000 genomes project) that will be
                        passed to statistical tests. The fields could be any
                        annotation fields of with integer or float values,
                        including those from used annotation databases (use
                        "vtools show fields" to see a list of usable fields).
  --geno_info [GENO_INFO [GENO_INFO ...]]
                        Optional genotype fields (e.g. quality score of
                        genotype calls, cf. "vtools show genotypes") that will
                        be passed to statistical tests. Note that the fields
                        should exist for all samples that are tested.
  --geno_name GENO_NAME
                        Field name of genotype, default to 'GT'. If another
                        field name is specified, for example if imputation
                        scores are available as 'DS' (dosage), then the given
                        field 'DS' will be used as genotype data for
                        association analysis.

Association tests:
  -m METHODS [METHODS ...], --methods METHODS [METHODS ...]
                        Method of one or more association tests. Parameters
                        for each method should be specified together as a
                        quoted long argument (e.g. --methods "m --alternative
                        2" "m1 --permute 1000"), although the common method
                        parameters can be specified separately, as long as
                        they do not conflict with command arguments. (e.g.
                        --methods m1 m2 -p 1000 is equivalent to --methods "m1
                        -p 1000" "m2 -p 1000".). You can use command 'vtools
                        show tests' for a list of association tests, and
                        'vtools show test TST' for details about a test.
                        Customized association tests can be specified as
                        mod_name.test_name where mod_name should be a Python
                        module (system wide or in the current directory), and
                        test_name should be a subclass of NullTest.
  -g [GROUP_BY [GROUP_BY ...]], --group_by [GROUP_BY [GROUP_BY ...]]
                        Group variants by fields. If specified, variants will
                        be separated into groups and are tested one by one.

Select and filter samples and genotypes:
  -s [COND [COND ...]], --samples [COND [COND ...]]
                        Limiting variants from samples that match conditions
                        that use columns shown in command 'vtools show sample'
                        (e.g. 'aff=1', 'filename like "MG%"'). Each line of
                        the sample table (vtools show samples) is considered
                        as samples. If genotype of a physical sample is
                        scattered into multiple samples (e.g. imported
                        chromosome by chromosome), they should be merged using
                        command vtools admin.
  --genotypes [COND [COND ...]]
                        Limiting genotypes to those matching conditions that
                        use columns shown in command 'vtools show genotypes'
                        (e.g. 'GQ>15'). Genotypes failing such conditions will
                        be regarded as missing genotypes.
  --discard_samples [EXPR [EXPR ...]]
                        Discard samples that match specified conditions within
                        each test group (defined by parameter --group_by).
                        Currently only expressions in the form of "%(NA)>p" is
                        providedted to remove samples that have more 100*p
                        percent of missing values.
  --discard_variants [EXPR [EXPR ...]]
                        Discard variant sites based on specified conditions
                        within each test group. Currently only expressions in
                        the form of '%(NA)>p' is provided to remove variant
                        sites that have more than 100*p percent of missing
                        genotypes. Note that this filter will be applied after
                        "--discard_samples" is applied, if the latter also is
                        specified.

Output of test statistics:
  --to_db annoDB        Name of a database to which results from association
                        tests will be written. Groups with existing results in
                        the database will be ignored unless parameter --force
                        is used.
  -d DELIMITER, --delimiter DELIMITER
                        Delimiter use to separate columns of output. The
                        default output uses multiple spaces to align columns
                        of output. Use '-d,' for csv output, or -d'\t' for
                        tab-delimited output.
  -f, --force           Analyze all groups including those that have recorded
                        results in the result database.
In [51]:
vtools show tests
BurdenBt                Burden test for disease traits, Morris & Zeggini 2009
BurdenQt                Burden test for quantitative traits, Morris & Zeggini
                        2009
CFisher                 Fisher's exact test on collapsed variant loci, Li & Leal
                        2008
Calpha                  c-alpha test for unusual distribution of variants
                        between cases and controls, Neale et al 2011
CollapseBt              Collapsing method for disease traits, Li & Leal 2008
CollapseQt              Collapsing method for quantitative traits, Li & Leal
                        2008
GroupStat               Calculates basic statistics for each testing group
GroupWrite              Write data to disk for each testing group
KBAC                    Kernel Based Adaptive Clustering method, Liu & Leal 2010
LinRegBurden            A versatile framework of association tests for
                        quantitative traits
LogitRegBurden          A versatile framework of association tests for disease
                        traits
RBT                     Replication Based Test for protective and deleterious
                        variants, Ionita-Laza et al 2011
RTest                   A general framework for association analysis using R
                        programs
RareCover               A "covering" method for detecting rare variants
                        association, Bhatia et al 2010.
SKAT                    SKAT (Wu et al 2011) and SKAT-O (Lee et al 2012)
SSeq_common             Score statistic / SCORE-Seq software (Tang & Lin 2011),
                        for common variants analysis
SSeq_rare               Score statistic / SCORE-Seq software (Tang & Lin 2011),
                        for rare variants analysis
VATStacking             VAT stacking with resampling-based p-value adjustment
                        for applying many algorithms
VTtest                  VT statistic for disease traits, Price et al 2010
VariableThresholdsBt    Variable thresholds method for disease traits, in the
                        spirit of Price et al 2010
VariableThresholdsQt    Variable thresholds method for quantitative traits, in
                        the spirit of Price et al 2010
WSSRankTest             Weighted sum method using rank test statistic, Madsen &
                        Browning 2009
WeightedBurdenBt        Weighted genotype burden tests for disease traits, using
                        one or many arbitrary external weights as well as one of
                        4 internal weighting themes
WeightedBurdenQt        Weighted genotype burden tests for quantitative traits,
                        using one or many arbitrary external weights as well as
                        one of 4 internal weighting themes
aSum                    Adaptive Sum score test for protective and deleterious
                        variants, Han & Pan 2010
In [52]:
vtools show test LinRegBurden
Name:          LinRegBurden
Description:   A versatile framework of association tests for quantitative traits
usage: vtools associate --method LinRegBurden [-h] [--name NAME]
                                              [-q1 MAFUPPER] [-q2 MAFLOWER]
                                              [--alternative TAILED]
                                              [--use_indicator] [-p N]
                                              [--permute_by XY] [--adaptive C]
                                              [--variable_thresholds]
                                              [--extern_weight [EXTERN_WEIGHT [EXTERN_WEIGHT ...]]]
                                              [--weight {Browning_all,Browning,KBAC,RBT}]
                                              [--NA_adjust]
                                              [--moi {additive,dominant,recessive}]

Linear regression test. p-value is based on the significance level of the
regression coefficient for genotypes. If --group_by option is specified, it
will collapse the variants within a group into a generic genotype score

optional arguments:
  -h, --help            show this help message and exit
  --name NAME           Name of the test that will be appended to names of
                        output fields, usually used to differentiate output of
                        different tests, or the same test with different
                        parameters.
  -q1 MAFUPPER, --mafupper MAFUPPER
                        Minor allele frequency upper limit. All variants
                        having sample MAF<=m1 will be included in analysis.
                        Default set to 1.0
  -q2 MAFLOWER, --maflower MAFLOWER
                        Minor allele frequency lower limit. All variants
                        having sample MAF>m2 will be included in analysis.
                        Default set to 0.0
  --alternative TAILED  Alternative hypothesis is one-sided ("1") or two-sided
                        ("2"). Default set to 1
  --use_indicator       This option, if evoked, will apply binary coding to
                        genotype groups (coding will be "1" if ANY locus in
                        the group has the alternative allele, "0" otherwise)
  -p N, --permutations N
                        Number of permutations
  --permute_by XY       Permute phenotypes ("Y") or genotypes ("X"). Default
                        is "Y"
  --adaptive C          Adaptive permutation using Edwin Wilson 95 percent
                        confidence interval for binomial distribution. The
                        program will compute a p-value every 1000 permutations
                        and compare the lower bound of the 95 percent CI of
                        p-value against "C", and quit permutations with the
                        p-value if it is larger than "C". It is recommended to
                        specify a "C" that is slightly larger than the
                        significance level for the study. To disable the
                        adaptive procedure, set C=1. Default is C=0.1
  --variable_thresholds
                        This option, if evoked, will apply variable thresholds
                        method to the permutation routine in burden test on
                        aggregated variant loci
  --extern_weight [EXTERN_WEIGHT [EXTERN_WEIGHT ...]]
                        External weights that will be directly applied to
                        genotype coding. Names of these weights should be in
                        one of '--var_info' or '--geno_info'. If multiple
                        weights are specified, they will be applied to
                        genotypes sequentially. Note that all weights will be
                        masked if --use_indicator is evoked.
  --weight {Browning_all,Browning,KBAC,RBT}
                        Internal weighting themes inspired by various
                        association methods. Valid choices are:
                        'Browning_all', 'Browning', 'KBAC' and 'RBT'. Except
                        for 'Browning_all' weighting, tests using all other
                        weighting themes has to calculate p-value via
                        permutation. For details of the weighting themes,
                        please refer to the online documentation.
  --NA_adjust           This option, if evoked, will replace missing genotype
                        values with a score relative to sample allele
                        frequencies. The association test will be adjusted to
                        incorporate the information. This is an effective
                        approach to control for type I error due to
                        differential degrees of missing genotypes among
                        samples.
  --moi {additive,dominant,recessive}
                        Mode of inheritance. Will code genotypes as 0/1/2/NA
                        for additive mode, 0/1/NA for dominant or recessive
                        mode. Default set to additive

Note that we use the quantitative trait BMI as the phenotype, and we will account for “SEX” as a covariate in the regression framework. More details about vtools associate command can be found at

http://varianttools.sourceforge.net/Vtools/Associate

Analysis of common variants

By default, the program will perform single variant tests using a simple linear model, and the Wald test statistic will be evaluated for p-values:

In [53]:
vtools associate common_ceu BMI --covariate SEX --samples "RACE=1" -m "LinRegBurden --alternative 2" -j1 --to_db EA_CV > EA_CV.asso.res
INFO: 90 samples are selected by condition: (RACE=1)
INFO: 1450 groups are found
Loading genotypes: 100% [================================] 90 79.2/s in 00:00:01
Testing for association: 100% [====================] 1,450/5 645.0/s in 00:00:02
INFO: Association tests on 1450 groups have completed. 5 failed.
INFO: Using annotation DB EA_CV as EA_CV in project VATDemo.
INFO: Annotation database used to record results of association tests. Created on Fri, 06 Jul 2018 16:03:42
INFO: 1450 out of 6986 variant.chr, variant.pos are annotated through annotation database EA_CV

Option -j1 specifies that 1 CPU core be used for association testing. You may use larger number of jobs for real world data analysis, e.g., use -j16 if your computational resources has 16 CPU cores available. Linux command cat /proc/cpuinfo shows the number of cores and other information related to the CPU on your computer.

The following command displays error messages about the failed tests. In each case, the sample size was too small to perform the regression analysis.

In [55]:
grep -i error *.log | tail -5
2018-07-06 11:03:47,381: DEBUG: An ERROR has occurred in process 0 while processing '6:29910604': Sample size too small (2) to be analyzed for '6:29910604'.
2018-07-06 11:03:47,386: DEBUG: An ERROR has occurred in process 0 while processing '6:29910742': Sample size too small (2) to be analyzed for '6:29910742'.
2018-07-06 11:03:47,607: DEBUG: An ERROR has occurred in process 0 while processing '7:148921732': Sample size too small (2) to be analyzed for '7:148921732'.
2018-07-06 11:03:47,700: DEBUG: An ERROR has occurred in process 0 while processing '8:145747920': Sample size too small (4) to be analyzed for '8:145747920'.
2018-07-06 11:03:47,707: DEBUG: An ERROR has occurred in process 0 while processing '9:215057': Sample size too small (4) to be analyzed for '9:215057'.

A summary from the association test is written to the file EA_CV.asso.res. The first column indicates the variant chromosome and base pair position so that you may follow up on the top signals using various annotation sources that we will not introduce in this tutorial. The result will be automatically built into annotation database if --to_db option is specified.

In [56]:
head EA_CV.asso.res
variant_chr	variant_pos	sample_size_LinRegBurden	num_variants_LinRegBurden	total_mac_LinRegBurden	beta_x_LinRegBurden	pvalue_LinRegBurden	wald_x_LinRegBurden	beta_2_LinRegBurden	beta_2_pvalue_LinRegBurden	wald_2_LinRegBurden
1          	1115503    	39                      	1                        	4                     	-3.79867           	0.303847           	-1.04312           	1.81933            	0.423273                  	0.809982
1          	3548136    	44                      	1                        	15                    	1.87087            	0.374567           	0.897738           	0.0423982          	0.984496                  	0.0195514
1          	3548832    	78                      	1                        	13                    	1.29502            	0.562724           	0.581386           	-0.753517          	0.651351                  	-0.453706
1          	3551792    	75                      	1                        	8                     	4.31445            	0.102654           	1.65315            	-1.38652           	0.3924                    	-0.860446
1          	6524501    	62                      	1                        	10                    	1.10259            	0.671892           	0.425678           	-1.16366           	0.544558                  	-0.609463
1          	6524688    	63                      	1                        	7                     	-1.34283           	0.632522           	-0.480637          	0.376518           	0.831142                  	0.214169
1          	11710561   	38                      	1                        	9                     	0.0203366          	0.992064           	0.0100182          	2.19027            	0.370985                  	0.906279
1          	17914057   	68                      	1                        	11                    	-2.23783           	0.387371           	-0.870241          	-1.0346            	0.588188                  	-0.544168
1          	17914122   	64                      	1                        	11                    	3.03457            	0.240427           	1.18548            	-1.02577           	0.600161                  	-0.526919

To sort the results by p-value and output the first 10 lines of the file use the command:

In [57]:
sort -g -k7 EA_CV.asso.res | head
variant_chr	variant_pos	sample_size_LinRegBurden	num_variants_LinRegBurden	total_mac_LinRegBurden	beta_x_LinRegBurden	pvalue_LinRegBurden	wald_x_LinRegBurden	beta_2_LinRegBurden	beta_2_pvalue_LinRegBurden	wald_2_LinRegBurden
11         	108383676  	88                      	1                        	25                    	6.53168            	0.000105185        	4.06922            	0.0735287          	0.961696                  	0.0481674
19         	16008257   	54                      	1                        	17                    	7.31337            	0.00038548         	3.80137            	1.45651            	0.466234                  	0.734125
16         	57735900   	71                      	1                        	41                    	-5.19002           	0.000386273        	-3.73498           	0.570017           	0.721588                  	0.357818
19         	16008388   	34                      	1                        	9                     	6.97057            	0.00279873         	3.24718            	2.8695             	0.200913                  	1.30674
19         	16006413   	47                      	1                        	13                    	6.7213             	0.002973           	3.14519            	0.614935           	0.775703                  	0.28668
9          	35792423   	32                      	1                        	15                    	6.60852            	0.00564457         	2.98954            	0.820153           	0.714935                  	0.368829
2          	49191041   	88                      	1                        	73                    	3.34503            	0.00656039         	2.78702            	0.947342           	0.552026                  	0.597102
17         	33768354   	44                      	1                        	42                    	-4.13311           	0.00686359         	-2.84707           	-2.08353           	0.319621                  	-1.00746
8          	121215991  	86                      	1                        	77                    	-3.34412           	0.00722408         	-2.75438           	0.63102            	0.697061                  	0.390644
sort: write failed: standard output: Broken pipe
sort: write error

If you obtain significant p-values be sure to also observe the accompanying sample size. Significant p-values from too small of a sample size may not be results you can trust.

Also, depending on your phenotype you may have to add additional covariates to your analysis. VAT allows you to test many different models for the various phenotypes and covariates. P-values for covariates are also reported.

Similar to using an annotation database, you can use the results from the association test to annotate the project and follow up variants of interest, for example:

In [58]:
vtools show fields
variant.chr (char)      Chromosome name (VARCHAR)
variant.pos (int)       Position (INT, 1-based)
variant.ref (char)      Reference allele (VARCHAR, - for missing allele of an
                        insertion)
variant.alt (char)      Alternative allele (VARCHAR, - for missing allele of an
                        deletion)
variant.DP (int)
variant.filter (char)
variant.alt_chr (char)
variant.alt_pos (int)
variant.num (int)       Created from stat "#(alt)"  with type INT on Jul06
variant.hom (int)       Created from stat "#(hom)"  with type INT on Jul06
variant.het (int)       Created from stat "#(het)"  with type INT on Jul06
variant.other (int)     Created from stat "#(other)"  with type INT on Jul06
variant.total (int)     Created from stat "#(GT)"  with type INT on Jul06
variant.maf (float)     Created from stat "maf()"  with type FLOAT on Jul06
variant.minDP (int)     Created from stat "min(DP_geno)"  with type INT on Jul06
variant.maxDP (int)     Created from stat "max(DP_geno)"  with type INT on Jul06
variant.meanDP (float)  Created from stat "avg(DP_geno)"  with type FLOAT on
                        Jul06
variant.numGD10 (int)   Created from stat "#(alt)"  with type INT on Jul06
variant.homGD10 (int)   Created from stat "#(hom)"  with type INT on Jul06
variant.hetGD10 (int)   Created from stat "#(het)"  with type INT on Jul06
variant.otherGD10 (int) 
                        Created from stat "#(other)"  with type INT on Jul06
variant.totalGD10 (int) 
                        Created from stat "#(GT)"  with type INT on Jul06
variant.mafGD10 (float) 
                        Created from stat "maf()"  with type FLOAT on Jul06
variant.CEU_mafGD10 (float) 
                        Created from stat "maf()" for samples ['RACE=1'] with
                        type FLOAT on Jul06
variant.YRI_mafGD10 (float) 
                        Created from stat "maf()" for samples ['RACE=0'] with
                        type FLOAT on Jul06
variant.region_type (char)
variant.region_name (char)
variant.mut_type (char)
variant.function (char)
refGene.name (char)     Gene name
refGene.chr (char)
refGene.strand (char)   which DNA strand contains the observed alleles
refGene.txStart (int)   Transcription start position
refGene.txEnd (int)     Transcription end position
refGene.cdsStart (int)  Coding region start
refGene.cdsEnd (int)    Coding region end
refGene.exonCount (int) Number of exons
refGene.score (int)     Score
refGene.name2 (char)    Alternative name
refGene.cdsStartStat (char)
                        cds start stat, can be 'non', 'unk', 'incompl', and
                        'cmp1'
refGene.cdsEndStat (char)
                        cds end stat, can be 'non', 'unk', 'incompl', and 'cmp1'
EA_CV.variant_chr (char)
                        variant_chr
EA_CV.variant_pos (int) variant_pos
EA_CV.sample_size_LinRegBurden (int)
                        sample size
EA_CV.num_variants_LinRegBurden (int)
                        number of variants in each group (adjusted for specified
                        MAF upper/lower bounds)
EA_CV.total_mac_LinRegBurden (int)
                        total minor allele counts in a group (adjusted for MOI)
EA_CV.beta_x_LinRegBurden (float)
                        test statistic. In the context of regression this is
                        estimate of effect size for x
EA_CV.pvalue_LinRegBurden (float)
                        p-value
EA_CV.wald_x_LinRegBurden (float)
                        Wald statistic for x (beta_x/SE(beta_x))
EA_CV.beta_2_LinRegBurden (float)
                        estimate of beta for covariate 2
EA_CV.beta_2_pvalue_LinRegBurden (float)
                        p-value for covariate 2
EA_CV.wald_2_LinRegBurden (float)
                        Wald statistic for covariate 2

You see additional annotation fields starting with EA CV, the name of the annotation database you just created from association test (if you used the --to db option mentioned above). You can use them to easily select/output variants of interest. More details about outputting annotation fields for significant findings can be found at

http://varianttools.sourceforge.net/Vtools/Output

Burden test for rare variants (BRV)

BRV method uses the count of rare variants in given genetic region for association analysis, regardless of the region length.

We use the -g option and use the ‘refGene.name2’ field to define the boundaries of a gene. By default, the test is a linear regression using aggregated counts of variants in a gene region as the regressor.

In [59]:
vtools associate rare_ceu BMI --covariate SEX --samples "RACE=1" -m "LinRegBurden --alternative 2" -g refGene.name2 -j1 --to_db EA_RV  > EA_RV.asso.res
INFO: 90 samples are selected by condition: (RACE=1)
INFO: 404 groups are found
Loading genotypes: 100% [===============================] 90 299.7/s in 00:00:00
Testing for association: 100% [=====================] 404/13 640.5/s in 00:00:00
INFO: Association tests on 404 groups have completed. 13 failed.
INFO: Using annotation DB EA_RV as EA_RV in project VATDemo.
INFO: Annotation database used to record results of association tests. Created on Fri, 06 Jul 2018 16:05:00
INFO: 404 out of 23269 refGene.refGene.name2 are annotated through annotation database EA_RV

Association tests on 404 groups have completed. 13 failed. To view failed tests:

In [72]:
grep -i error *.log | tail -10
2018-06-29 17:33:20,124: DEBUG: An ERROR has occurred in process 0 while processing 'BCL2L15': No variant found in genotype data for 'BCL2L15'.
2018-06-29 17:33:20,181: DEBUG: An ERROR has occurred in process 0 while processing 'CDHR5': No variant found in genotype data for 'CDHR5'.
2018-06-29 17:33:20,189: DEBUG: An ERROR has occurred in process 0 while processing 'CERS3': No variant found in genotype data for 'CERS3'.
2018-06-29 17:33:20,213: DEBUG: An ERROR has occurred in process 0 while processing 'CTSW': No variant found in genotype data for 'CTSW'.
2018-06-29 17:33:20,366: DEBUG: An ERROR has occurred in process 0 while processing 'LOC100287722': No variant found in genotype data for 'LOC100287722'.
2018-06-29 17:33:20,420: DEBUG: An ERROR has occurred in process 0 while processing 'NOM1': Sample size too small (1) to be analyzed for 'NOM1'.
2018-06-29 17:33:20,438: DEBUG: An ERROR has occurred in process 0 while processing 'OR10J1': No variant found in genotype data for 'OR10J1'.
2018-06-29 17:33:20,531: DEBUG: An ERROR has occurred in process 0 while processing 'PRG3': No variant found in genotype data for 'PRG3'.
2018-06-29 17:33:20,645: DEBUG: An ERROR has occurred in process 0 while processing 'SULT1A1': No variant found in genotype data for 'SULT1A1'.
2018-06-29 17:33:20,670: DEBUG: An ERROR has occurred in process 0 while processing 'TMCC1': No variant found in genotype data for 'TMCC1'.

The output file is EA_RV.asso.res. The first column is the gene name, with corresponding p-values in the sixth column for the entire gene.

In [60]:
head EA_RV.asso.res
refgene_name2	sample_size_LinRegBurden	num_variants_LinRegBurden	total_mac_LinRegBurden	beta_x_LinRegBurden	pvalue_LinRegBurden	wald_x_LinRegBurden	beta_2_LinRegBurden	beta_2_pvalue_LinRegBurden	wald_2_LinRegBurden
AATF         	89                      	3                        	3                     	4.06571            	0.371806           	0.897786           	0.819087           	0.617609                  	0.501059
ABCB9        	58                      	1                        	1                     	4.29374            	0.561422           	0.584278           	0.0901042          	0.962807                  	0.0468439
ABLIM3       	90                      	2                        	2                     	-7.83832           	0.158126           	-1.42364           	0.466136           	0.774715                  	0.287105
ACCN3        	56                      	1                        	1                     	9.84035            	0.17451            	1.37632            	-1.2081            	0.530485                  	-0.631412
ACHE         	76                      	1                        	1                     	1.51292            	0.845698           	0.195304           	-0.186314          	0.916242                  	-0.105534
ACTL8        	81                      	2                        	2                     	-4.82112           	0.378176           	-0.886308          	0.25399            	0.88081                   	0.150435
ADAM29       	88                      	1                        	1                     	-6.52372           	0.403205           	-0.840108          	0.850198           	0.606861                  	0.516479
ADAMTS4      	69                      	1                        	1                     	0.171944           	0.981712           	0.0230098          	-0.771931          	0.667839                  	-0.431047
AHNAK        	89                      	4                        	4                     	-1.18887           	0.764332           	-0.300749          	0.914744           	0.578008                  	0.55842

You can also sort these results by p-value using command:

In [61]:
sort -g -k6 EA_RV.asso.res | head
refgene_name2	sample_size_LinRegBurden	num_variants_LinRegBurden	total_mac_LinRegBurden	beta_x_LinRegBurden	pvalue_LinRegBurden	wald_x_LinRegBurden	beta_2_LinRegBurden	beta_2_pvalue_LinRegBurden	wald_2_LinRegBurden
CIDEA        	73                      	1                        	1                     	20.294             	0.00504822         	2.89536            	-0.235139          	0.885684                  	-0.144293
SPP2         	90                      	2                        	2                     	15.0031            	0.00549521         	2.8476             	0.792108           	0.611456                  	0.509838
WNT16        	88                      	1                        	1                     	20.703             	0.00683376         	2.77254            	1.17245            	0.460926                  	0.740684
CRTAP        	77                      	1                        	1                     	20.4367            	0.00723434         	2.76236            	0.580854           	0.730853                  	0.345294
CYP24A1      	83                      	1                        	1                     	20.5275            	0.00790623         	2.72451            	1.1321             	0.49334                   	0.688164
MFAP1        	86                      	1                        	1                     	18.4607            	0.0133889          	2.52736            	-0.228389          	0.884407                  	-0.145832
MBD5         	90                      	4                        	4                     	9.56169            	0.0144442          	2.49605            	0.362862           	0.818813                  	0.229766
SLA          	89                      	1                        	1                     	16.0687            	0.0380065          	2.10727            	0.548345           	0.73386                   	0.3411
THRB         	90                      	2                        	2                     	10.2182            	0.0617212          	1.89271            	0.796836           	0.617967                  	0.500528
sort: write failed: standard output: Broken pipe
sort: write error

Variable thresholds test for rare variants (VT)

The variable thresholds (VT) method will carry out multiple testing in the same gene region using groups of variants based on observed variant allele frequencies. This test will maximize over statistics thus obtain a final test statistic, and calculate the empirical p-value so that multiple comparisons are adjusted for correctly.

We will use adaptive permutation to obtain empirical p-values. Therefore, to avoid performing too large number of permutations we use a cutoff to limit the number of permutations when the p-value is greater than 0.0005, e.g. not all 100,000 permutations are performed. Generally, even more permutations are used but we limit it to 100,000 to save time for this exercise.

The command using variable thresholds method on our data is:

In [62]:
vtools associate rare_ceu BMI --covariate SEX --samples "RACE=1" -m "VariableThresholdsQt --alternative 2 -p 100000 --adaptive 0.0005" \
    -g refGene.name2 -j1 --to_db EA_RV > EA_RV_VT.asso.res
INFO: 90 samples are selected by condition: (RACE=1)
INFO: 404 groups are found
Loading genotypes: 100% [===============================] 90 173.9/s in 00:00:00
Testing for association: 100% [======================] 404/13 82.9/s in 00:00:04
INFO: Association tests on 404 groups have completed. 13 failed.
INFO: Using annotation DB EA_RV as EA_RV in project VATDemo.
INFO: Annotation database used to record results of association tests. Created on Fri, 06 Jul 2018 16:05:00
INFO: 404 out of 23269 refGene.refGene.name2 are annotated through annotation database EA_RV

To view test that failed,

In [63]:
grep -i error *.log | tail -10
2018-07-06 11:06:20,496: DEBUG: An ERROR has occurred in process 0 while processing 'BCL2L15': No variant found in genotype data for 'BCL2L15'.
2018-07-06 11:06:20,851: DEBUG: An ERROR has occurred in process 0 while processing 'CDHR5': No variant found in genotype data for 'CDHR5'.
2018-07-06 11:06:20,901: DEBUG: An ERROR has occurred in process 0 while processing 'CERS3': No variant found in genotype data for 'CERS3'.
2018-07-06 11:06:21,062: DEBUG: An ERROR has occurred in process 0 while processing 'CTSW': No variant found in genotype data for 'CTSW'.
2018-07-06 11:06:22,081: DEBUG: An ERROR has occurred in process 0 while processing 'LOC100287722': No variant found in genotype data for 'LOC100287722'.
2018-07-06 11:06:22,485: DEBUG: An ERROR has occurred in process 0 while processing 'NOM1': Sample size too small (1) to be analyzed for 'NOM1'.
2018-07-06 11:06:22,617: DEBUG: An ERROR has occurred in process 0 while processing 'OR10J1': No variant found in genotype data for 'OR10J1'.
2018-07-06 11:06:23,321: DEBUG: An ERROR has occurred in process 0 while processing 'PRG3': No variant found in genotype data for 'PRG3'.
2018-07-06 11:06:24,130: DEBUG: An ERROR has occurred in process 0 while processing 'SULT1A1': No variant found in genotype data for 'SULT1A1'.
2018-07-06 11:06:24,318: DEBUG: An ERROR has occurred in process 0 while processing 'TMCC1': No variant found in genotype data for 'TMCC1'.

To view results,

In [65]:
head EA_RV_VT.asso.res
refgene_name2	sample_size_VTQt	num_variants_VTQt	total_mac_VTQt	beta_x_VTQt	pvalue_VTQt	std_error_VTQt	num_permutations_VTQt	MAF_threshold_VTQt
AATF         	89              	3                	3             	4.06571    	0.405594   	4.50659       	1000                 	0.00561798
ABCB9        	58              	1                	1             	4.29374    	0.659341   	7.16459       	1000                 	0.00862069
ABLIM3       	90              	2                	2             	-7.83832   	0.135864   	5.4353        	1000                 	0.00555556
ACCN3        	56              	1                	1             	9.84035    	0.171828   	7.06708       	1000                 	0.00892857
ACHE         	76              	1                	1             	1.51292    	0.799201   	7.50048       	1000                 	0.00657895
ACTL8        	81              	2                	2             	-4.82112   	0.433566   	5.42058       	1000                 	0.00617284
ADAM29       	88              	1                	1             	-6.52372   	0.4995     	7.64897       	1000                 	0.00568182
ADAMTS4      	69              	1                	1             	0.171944   	0.935065   	7.28058       	1000                 	0.00724638
AHNAK        	89              	4                	4             	-1.18887   	0.761239   	3.91307       	1000                 	0.00561798

Sort and output the lowest p-values using the command:

In [66]:
sort -g -k6 EA_RV_VT.asso.res | head
refgene_name2	sample_size_VTQt	num_variants_VTQt	total_mac_VTQt	beta_x_VTQt	pvalue_VTQt	std_error_VTQt	num_permutations_VTQt	MAF_threshold_VTQt
SPP2         	90              	2                	2             	15.0031    	0.00999001 	5.5654        	1000                 	0.00555556
CRTAP        	77              	1                	1             	20.4367    	0.011988   	7.72247       	1000                 	0.00649351
CYP24A1      	83              	1                	1             	20.5275    	0.013986   	7.76257       	1000                 	0.0060241
CIDEA        	73              	1                	1             	20.294     	0.017982   	7.5195        	1000                 	0.00684932
NRG1         	87              	1                	1             	-11.5171   	0.017982   	7.99103       	1000                 	0.00574713
WNT16        	88              	1                	1             	20.703     	0.01998    	7.64807       	1000                 	0.00568182
FUCA2        	80              	1                	1             	-10.9701   	0.025974   	7.74197       	1000                 	0.00625
LRRC27       	79              	1                	1             	-11.6328   	0.031968   	7.62696       	1000                 	0.00632911
PDSS1        	55              	1                	1             	-11.7564   	0.033966   	7.79539       	1000                 	0.00909091

Why do some tests fail?

Notice that vtools associate command will fail on some association test units. Instances of failure are printed to terminal in red and are recorded in the project log file. Most failures occur due to an association test unit having too few samples or number of variants (for gene based analysis). You should view these error messages after each association scan is complete, e.g., using the Linux command grep -i error *.log and make sure you are informed of why failures occur.

In the variable thresholds analysis above, gene TMCC1 failed the association test. If we look at this gene more closely we can see which variants are being analyzed by our test:

In [70]:
vtools select rare_ceu "refGene.name2='TMCC1'" -o chr pos ref alt CEU_mafGD10 numGD10 mut_type --header
chr	pos      	ref	alt	CEU_mafGD10	numGD10	mut_type
3  	129546729	T  	C  	0.0        	339    	nonsynonymous SNV

After applying our QC filters we are left with one variant within the TMCC1 gene to analyze. Because the MAF for this variant is 0.0 there are no variants in the gene to analyze so that this gene is ignored. Note that all individuals are homozygous for the alternative allele for this variant site.

QQ and Manhattan plots for association results

The vtools report plot association command generates QQ and Manhattan plots from output of vtools associate command. More details about vtools report plot association can be found at

http://varianttools.sourceforge.net/VtoolsReport/PlotAssociation

In [71]:
vtools_report plot_association qq -o QQRV -b --label_top 2 -f 6 < EA_RV.asso.res
vtools_report plot_association manhattan -o MHRV -b --label_top 5 --color Dark2 --chrom_prefix None -f 6 < EA_RV.asso.res
INFO: Reading from standard input ...
INFO: Processing 78K of input data ...
INFO: Generating graph(s) ...
Genomic inflation factor for method 'LinRegBurden' is: 1.25185482052633
INFO: Complete!
INFO: Reading from standard input ...
INFO: Processing 78K of input data ...
INFO: Generating graph(s) ...
INFO: Complete!
In [78]:
%preview MHRV.pdf -s png --dpi 150
> MHRV.png (45.0 KiB):
In [80]:
%preview QQRV.pdf -s png --dpi 150
> QQRV.png (11.3 KiB):

QQ plots aid in evaluating if there is systematic inflation of test statistics. A common cause of inflation is population structure or batch effects. If you observe significant inflation of test you may consider including MDS components in the association test model.

MDS analysis and PC adjustment

This pipeline needs PLINK 1.9 and KING.

In [81]:
vtools execute KING
INFO: Executing KING.king_0: Load specified snapshot if a snapshot is specified. Otherwise use the existing project.
INFO: Executing KING.king_10: Check the existence of KING and PLINK command.
INFO: Command king is located.
INFO: Command plink is located.
INFO: Executing KING.king_20: Write selected variant and samples in tped format
INFO: Running vtools export variant --format tped --samples "1" | awk '{$2=$1"_"$4;$3=0;print $0}' > /data/ismb-2018/data/.vtools_cache/KING.tped
INFO: Executing KING.king_21: Rename tfam file to match tped file
INFO: Running mv variant.tfam /data/ismb-2018/data/.vtools_cache/KING.tfam
INFO: Executing KING.king_30: Calculate LD pruning candidate list with a cutoff of R^2=0.5
INFO: Running plink --noweb --tped KING.tped --tfam KING.tfam --indep-pairwise 50 5 0.5 --allow-no-sex --out KING.LD.50 under /data/ismb-2018/data/.vtools_cache
INFO: Executing KING.king_31: LD pruning from pre-calculated list
INFO: Running plink --noweb --tped KING.tped --tfam KING.tfam --extract KING.LD.50.prune.in --no-parents --no-sex --no-pheno --maf 0.01 --make-bed --out KING under /data/ismb-2018/data/.vtools_cache
INFO: Executing KING.king_41: Global ancestry inference
INFO: Running king -b KING.bed --mds --ibs --prefix KING- under /data/ismb-2018/data/.vtools_cache
INFO: Executing KING.king_42: Kinship inference
INFO: Running king -b KING.bed --kinship --related --degree 3 --prefix KING under /data/ismb-2018/data/.vtools_cache
INFO: Executing KING.king_51: Extract MDS result for vtools phenotype import
INFO: Running cut KING-pc.ped -f 2,`seq -s, 7 $((6+5))` -d " " | sed -e "s/ /\\t/g" | sed 1i"sample_name`seq -s, 1 5 | awk -F, '{if (NF>20) NF=20; for (i=1; i<=NF; ++i) printf("\\t%s", "KING_MDS"$i)}'`" > KING-mds.vtools.txt under /data/ismb-2018/data/.vtools_cache
INFO: Executing KING.king_52: Import phenotype from global ancestry analysis
INFO: Running vtools phenotype --from_file /data/ismb-2018/data/.vtools_cache/KING-mds.vtools.txt
INFO: Adding phenotype KING_MDS1 of type FLOAT
INFO: Adding phenotype KING_MDS2 of type FLOAT
INFO: Adding phenotype KING_MDS3 of type FLOAT
INFO: Adding phenotype KING_MDS4 of type FLOAT
INFO: Adding phenotype KING_MDS5 of type FLOAT
INFO: 5 field (5 new, 0 existing) phenotypes of 196 samples are updated.
INFO: Executing KING.king_61: Save global ancestry inference result to plot
INFO: Running vtools_report plot_pheno_fields KING_MDS1 KING_MDS2 --samples "1" --dot KING.mds.pdf --discrete_color Accent
INFO: Executing KING.king_62: Save kinship analysis result to text file
INFO: Running cat /data/ismb-2018/data/.vtools_cache/KING.kin0 | cut -f 2,4,6,7,8 | awk '{ if ($5>0.0442) print $0}' | awk '{if ($5>0.354) $6="MZ"; if ($5>=0.177 && $5<=0.354) $6="1st-degree"; if ($5>=0.0884 && $5<=0.177) $6="2nd-degree"; if ($5>=0.0442 && $5<=0.0884) $6="3rd-degree"; if ($5=="Kinship") $6="Relationship"; print $0}' > KING.RelatedIndividuals.txt
INFO: Execution of pipeline KING.king is successful with output KING.RelatedIndividuals.txt
In [83]:
%preview KING.mds.pdf -s png --dpi 150
> KING.mds.png (13.1 KiB):

You should not arbitrarily include MDS (or PCA) components in the analysis. Instead put in each MDS component and examine the lambda value, i.e. include MDS component 1 them MDS components 1 and 2, etc. Visualization of the QQ plot is also useful to determine if population substructure/admixture is controlled.

2.6 Association analysis of YRI samples

Procedures for YRI sample association analysis is the same as for CEU samples as previously has been described, thus is left as an extra exercise for you to work on your own. Commands to perform analysis for YRI are found below:

In [84]:
vtools associate rare_ceu BMI --covariate SEX KING_MDS1 KING_MDS2 -m "LinRegBurden --name RVMDS2 --alternative 2" -g refGene.name2 -j1 --to_db EA_RV > EA_RV_MDS2.asso.res
WARNING: Sample NA12889 is ignored due to missing value for phenotype KING_MDS1
WARNING: Sample NA12889 is ignored due to missing value for phenotype KING_MDS2
WARNING: Sample NA18504 is ignored due to missing value for phenotype KING_MDS1
WARNING: Sample NA18504 is ignored due to missing value for phenotype KING_MDS2
WARNING: Sample NA18516 is ignored due to missing value for phenotype KING_MDS1
WARNING: Sample NA18516 is ignored due to missing value for phenotype KING_MDS2
WARNING: Sample NA18522 is ignored due to missing value for phenotype KING_MDS1
WARNING: Sample NA18522 is ignored due to missing value for phenotype KING_MDS2
WARNING: Sample NA18870 is ignored due to missing value for phenotype KING_MDS1
WARNING: Sample NA18870 is ignored due to missing value for phenotype KING_MDS2
WARNING: Sample NA18871 is ignored due to missing value for phenotype KING_MDS1
WARNING: Sample NA18871 is ignored due to missing value for phenotype KING_MDS2
INFO: 196 samples are found
INFO: 404 groups are found
Loading genotypes: 100% [===============================] 196 93.2/s in 00:00:02
Testing for association: 100% [======================] 404/5 347.6/s in 00:00:01
INFO: Association tests on 404 groups have completed. 5 failed.
INFO: Using annotation DB EA_RV as EA_RV in project VATDemo.
INFO: Annotation database used to record results of association tests. Created on Fri, 06 Jul 2018 16:05:00
INFO: 404 out of 23269 refGene.refGene.name2 are annotated through annotation database EA_RV
In [86]:
vtools_report plot_association qq -o QQRV_MDS2 -b --label_top 2 -f 6 < EA_RV_MDS2.asso.res
INFO: Reading from standard input ...
INFO: Processing 99K of input data ...
INFO: Generating graph(s) ...
Genomic inflation factor for method 'RVMDS2' is: 1.2732420092267
INFO: Complete!
In [87]:
vtools select variant "YRI_mafGD10>=0.05" --samples "RACE=0" -t common_yri
INFO: 112 samples are selected by condition: RACE=0
Collecting sample variants: 100% [======================] 112 1.5K/s in 00:00:00
Running: 10 1.4K/s in 00:00:00
INFO: 1984 variants selected.
In [88]:
vtools select v_funct "YRI_mafGD10<0.01" --samples "RACE=0" -t rare_yri
INFO: 112 samples are selected by condition: RACE=0
Collecting sample variants: 100% [======================] 112 1.7K/s in 00:00:00
Running: 9 1.3K/s in 00:00:00
INFO: 746 variants selected.
In [89]:
vtools associate common_yri BMI --covariate SEX --samples "RACE=0" -m "LinRegBurden --alternative 2" -j1 --to_db YA_CV > YA_CV.asso.res
INFO: 112 samples are selected by condition: (RACE=0)
INFO: 1984 groups are found
Loading genotypes: 100% [==============================] 112 131.5/s in 00:00:00
Testing for association: 100% [===================] 1,984/12 568.0/s in 00:00:03
INFO: Association tests on 1984 groups have completed. 12 failed.
INFO: Using annotation DB YA_CV as YA_CV in project VATDemo.
INFO: Annotation database used to record results of association tests. Created on Fri, 06 Jul 2018 16:12:39
INFO: 1984 out of 6986 variant.chr, variant.pos are annotated through annotation database YA_CV
In [90]:
vtools associate rare_yri BMI --covariate SEX --samples "RACE=0" -m "LinRegBurden --alternative 2" -g refGene.name2 -j1 --to_db YA_RV > YA_RV.asso.res
INFO: 112 samples are selected by condition: (RACE=0)
INFO: 419 groups are found
Loading genotypes: 100% [==============================] 112 278.4/s in 00:00:00
Testing for association: 100% [=====================] 419/11 519.6/s in 00:00:00
INFO: Association tests on 419 groups have completed. 11 failed.
INFO: Using annotation DB YA_RV as YA_RV in project VATDemo.
INFO: Annotation database used to record results of association tests. Created on Fri, 06 Jul 2018 16:12:46
INFO: 419 out of 23269 refGene.refGene.name2 are annotated through annotation database YA_RV
In [91]:
vtools associate rare_yri BMI --covariate SEX --samples "RACE=0" -m "VariableThresholdsQt --alternative 2 -p 100000 --adaptive 0.0005" \
    -g refGene.name2 -j1 --to_db YA_RV > YA_RV_VT.asso.res
INFO: 112 samples are selected by condition: (RACE=0)
INFO: 419 groups are found
Loading genotypes: 100% [===============================] 112 64.6/s in 00:00:01
Testing for association: 100% [======================] 419/11 68.2/s in 00:00:06
INFO: Association tests on 419 groups have completed. 11 failed.
INFO: Using annotation DB YA_RV as YA_RV in project VATDemo.
INFO: Annotation database used to record results of association tests. Created on Fri, 06 Jul 2018 16:12:46
INFO: 419 out of 23269 refGene.refGene.name2 are annotated through annotation database YA_RV

2.7 Meta-analysis

Here we demonstrate the application of meta-analysis to combine association results from the two populations via vtools report meta_analysis. More details about vtools report meta_analysis command can be found at

http://varianttools.sourceforge.net/VtoolsReport/MetaAnalysis

The input to this command are the association results files generated from previous steps, for example:

In [92]:
vtools_report meta_analysis EA_RV_VT.asso.res YA_RV_VT.asso.res --beta 5 --pval 6 --se 7 -n 2 --link 1 > META_RV_VT.asso.res

To view the results,

In [96]:
cut -f1,3 META_RV_VT.asso.res | sort -g -k2 | head
refgene_name2	pvalue_meta
RREB1	1.497E-02
TTC26	2.743E-02
MBD5	3.152E-02
RAD51AP1	3.582E-02
SHKBP1	4.222E-02
CYP24A1	4.336E-02
VLDLR	4.664E-02
PPIG	4.731E-02
PLXDC1	5.296E-02

Note that for genes that only appears in one study but not the other, or only have a valid p-value in one study but not the other, will be ignored from meta-analysis.

2.8 Summary

Analyzing variants with VAT is much like any other analysis software with a general workflow of:

  • Variant level cleaning
  • Sample genotype cleaning
  • Variant annotation and phenotype information processing
  • Sample/variant selection
  • Association analysis
  • Interpreting the findings

The data cleaning and filtering conditions within this exercise should be considered as general guidelines. Your data may allow you to be laxer with certain criteria or force you to be more stringent with others.

Questions

Question 1

List the four lowest p-values and associated variants or gene regions for the EA CV.asso.res, EA RV.asso.res, and EA RV VT.asso.res test outputs, which are results from single variant Wald test, rare variant BRV and VT tests, respectively, using the European American (CEU) population. Also, list the results using Yoruba African (YRI) population from YA CV.asso.res, YA RV.asso.res and YA RV VT.asso.res.

EA CV.asso.res - single variant tests using CEU

1)

2)

3)

4)

EA RV.asso.res - BRV tests using CEU

1)

2)

3)

4)

EA RV VT.asso.res - VT tests using CEU

1)

2)

3)

4)

YA CV.asso.res - single variant tests using YRI

1)

2)

3)

4)

YA RV.asso.res - BRV tests using YRI

1)

2)

3)

4)

YA RV VT.asso.res - VT tests using YRI

1)

2)

3)

4)

Question 2

List any gene regions that show up in the lowest eight p-values for both the BRV and the VT tests. Why might the p-values for the VT tests be higher than the p-values for the BRV tests? Are any of the top p-value hits significant? Why or why not?

Answers

Question 1

EA CV.asso.res

  1. 107888886 0.000105185
  2. 15869257 0.00038548
  3. 56293401 0.000386273
  4. 15869388 0.00279873

EA RV.asso.res

  1. CIDEA 0.00504822
  2. UGT1A10 0.00549521
  3. UGT1A5 0.00549521
  4. UGT1A6 0.00549521

EA RV VT.asso.res

  1. UGT1A9 0.007996
  2. CPED1 0.00999001
  3. UGT1A10 0.00999001
  4. UGT1A6 0.011988

YA CV.asso.res

  1. 107888886 0.00000974
  2. 6003506 0.000211457
  3. 25901623 0.001329
  4. 3392651 0.00194995

YA RV.asso.res

  1. EMILIN2 0.00262487
  2. ASIC2 0.0551664
  3. MDN1 0.0593085
  4. BAZ2B 0.0607625

YA RV VT.asso.res

  1. EMILIN2 0.00533156
  2. MDN1 0.013986
  3. VLDLR 0.01998
  4. LRRC9 0.025974

Question 2

The p-values do not achieve significance based on the corrected p values above (Bonferroni correction for multiple tests). Since the BMI values were randomly generated for each individual it is unlikely that any of the p-values for the single variant and aggregation tests would have achieved significance. Also, because of the multiple testing, the p-values for the VT tests might be higher than the p-values for the BRV tests.

References

  1. Wang, G.T., Peng, B., and Leal, S.M. (2014). Variant Association Tools for Quality Control and Analysis of Large-Scale Sequence and Genotyping Array Data. Am. J. Hum. Genet. 94, 770783
  1. Li B, Leal SM. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am J Hum Genet 2008 83:311-21

  2. Auer PL, Wang G, Leal SM. Testing for rare variant associations in the presence of missing data. Genet Epidemiol 2013 37:529-38

  3. Liu DJ, Leal SM. A novel adaptive method for the analysis of next-generation sequencing data to detect com-plex trait associations with rare variants due to gene main effects and interactions. PLoS Genet 2010 6:e1001156

  4. Madsen BE, Browning SR. A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genet 2009 5:e1000384

  5. Price AL, Kryukov GV, de Bakker PI, Purcell SM, Staples J, Wei LJ, Sunyaev SR. Pooled association tests for rare variants in exon-resequencing studies. Am J Hum Genet 20010 86:832-8

  6. Wu MC, Lee S, Cai T, Li Y, Boehnke M, Lin X. Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet 2011 89:82-93

  7. Lucas FAS, Wang G, Scheet P, Peng B. Integrated annotation and analysis of genetic variants from next-generation sequencing studies with variant tools. Bioinformatics 2012 28:421-2

  8. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 2010 38:e164

  9. Manichaikul A, Mychaleckyj JC, Rich SS, Daly K, Sale M, Chen WM. Robust relationship inference in genome-wide association studies. Bioinformatics 2010 26(22):2867-2873

  10. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, de Bakker PIW, Daly MJ & Sham PC. PLINK: a toolset for whole-genome association and population-based linkage analysis. Am J Hum Genet, 2007 81:559-75


© 2019 Center for Statistical Genetics, Department of Neurology, Columbia University

Exported from notebooks/VAT.ipynb committed by Gao Wang on Mon Oct 28 08:30:48 2019 revision 12, 7af5e49