Authors: Jinzhuang Dou, Chaolong Wang
SEEKIN is a software program for estimating pairwise kinship coefficients using either shallow sequencing data or genotyping data. The method was initially developed for shallow sequencing data, such as off-target sequencing data from target sequencing experiments (typically ~0.1-1X). SEEKIN, together with the LASER software [1,2] for ancestry estimation, enables control of population structure and cryptic relatedness in target/exome sequencing studies.
To address the missing data and genotype uncertainty issues that are intrinsic to shallow sequencing data, we use the LD-based genotype calling algorithm in BEAGLE [3] to process sequencing data and develop kinship estimators that explicitly model the genotype uncertainty via the Rsq metric output by BEAGLE.
SEEKIN includes a homogeneous estimator for samples from the sample population (SEEKIN-hom) and a heterogeneous estimator for samples with population structure and admixture (SEEKIN-het). For SEEKIN-het, we use the LASER program to estimate individual ancestry for each study individual, and then derive individual-specific allele frequencies for kinship estimation in a way similar to existing methods [4,5].
Our SEEKIN estimators are also applicable to high-quality genotyping data, such as those from array-genotyping or deep whole-genome sequencing studies. In this special case of no genotype uncertainty, SEEKIN performs similarly to existing kinship estimation programs.
In terms of implementation, SEEKIN utilizes a "single producer/consumer" design for parallel computation and takes the standard gzipped VCF file as the input. SEEKIN is both computationally and memory efficient and is scalable to estimate pairwise kinship between 10,000s of individuals on a typical computing cluster.
If you have questions or find any bug in the software, please email Jinzhuang Dou at [email protected] or Chaolong Wang at [email protected].
Details of the SEEKIN method can be found in our paper:
- J Dou, B Sun, X Sim, JD Hughes, DF Reilly, ES Tai, J Liu, C Wang (2017). Estimation of kinship coefficient in structured and admixed populations using sparse sequencing data. PLOS Genetics, 13: e1007021.
You can download SEEKIN by typing the following command in a terminal.
git clone https://github.com/chaolongwang/SEEKIN.git
This command will create a folder named "SEEKIN" in the current directory. The downloaded package contains a statically linked binary executable named seekin (in the bin/ folder), which was pre-compiled and tested on a 64-bit Linux machine.
If you want to compile your own version, please enter the src/ folder, change the library paths in the beginning of the Makefile accordingly, and type make to compile.
You can type the following command to get the help information.
seekin -h
SEEKIN provides three modules
- modelAF Model allele frequencies as linear functions of PCs using reference individuals
- getAF Estimate individual-specific allele frequencies for study individuals
- kinship Estimate pairwise kinship coefficients between study individuals
To get the detailed information on available options for each module (for example kinship), you can type:
seekin kinship -h
We will go through instructions of the software using examples in the following section.
Here we provide demo of SEEKIN based on data provided in the example/ folder, which include:
-
Study.10K.vcf.gz This file includes genotypes of ~10,000 randomly selected SNP for 50 Chinese and 50 Malays. The genotypes were called from off-target data in a WES study using BEAGLE with a reference panel from the 1000 Genomes Project. This example dataset contains noisy genotype data and is only for illustration purpose.
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SGVP.12K.vcf.gz This file includes array genotyping data of 268 individuals (Chinese, Malay, and Indian) on ~12,000 SNPs, including ~9,000 SNPs overlapping with the Study.10K.vcf.gz file. This dataset is a subset of the Singapore Genome Variation Project (SGVP) [6]. This dataset is intended to be the ancestry reference panel for estimating individual-specific allele frequencies for the SEEKIN-het estimator.
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SGVP.RefPC.coord This file contains PCA coordinates of the top 10 PCs for the reference individuals in SGVP.12K.vcf.gz. This file was generated by the LASER software.
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Study.onSGVP.PC.coord This file contains the top 2 PCs for the study individuals in the SGVP reference ancestry space (
SGVP.RefPC.coord). This file can be prepared using the LASER software with either sequence reads or genotypes.
To generate the above files, you can use our companion pipeline in the folder ./data_pipeline/.
When assuming no population structure, we only need the genotype file of study samples (Study.10K.vcf.gz) to estimate kinship using SEEKIN.
../bin/seekin kinship -i ./Study.10K.vcf.gz -r 0.3 -m 0.05 -d DS -p hom -l 2000 -t 3 -w 1 -o Study.hom
The meaning of all command line options are listed below (which can be found by typing seekin kinship -h):
Usage: seekin kinship [options]
Options:
-i File name of genotypes or dosages of study individuals (gzipped VCF). [no default]
-f File name of allele frequencies for study individuals (gzipped VCF). [default "indivAF.vcf.gz"]
-r Exclude SNPs with Rsq lower than the -r value. [default 0.5]
-m Exclude SNPs with MAF lower than the -m value. [default 0.05]
-d Data used for kinship estimation. GT: genotype values in the GT field;
DS: dosage values in the DS field. [default "DS"]
-p Kinship estimator. hom: homogeneous estimator; het: heterogeneous estimator. [default "het"]
-w Weighting scheme to combine estimates from multiple SNPs.
1: MAF(1-MAF)Rsq^2; 2: Rsq^2. [default 1]
-s Specify whether a mode for small sample size (typically < 10 ) would be used.
0: false; 1: true. [default "0"]
-l Number of SNPs in each computing block. [default 10,000]
-t Number of threads for parallel computation. [default 10]
-o Prefix of output file names.
Note: the option -f is NOT effective when -p is set to "hom".
Note: when the input file (-i) does not contain Rsq values (e.g. array genotyping data),
-d will be set to "GT" and Rsq will be treated as 1.
The output includes 5 files with prefix Study.10K.hom (specified by -o).
- _.log
This is the log file to monitor and record the progress for each module when running SEEKIN.
- _.kin
This file provides the kinship estimation for all pairs of individuals. The columns are individual IDs for the first and second individual of pair, number of SNPs used, and the estimated kinship coefficient, respectively. Example:
ind1 ind2 nsnp kinship
CH-44 CH-49 8911 -0.0002
CH-44 CH-43 8911 -0.0121
CH-44 CH-45 8911 -0.0052
CH-44 CH-40 8911 -0.0162
CH-44 CH-50 8911 0.0031
CH-44 CH-16 8911 -0.0082
CH-44 CH-15 8911 -0.0066
CH-44 CH-6 8911 -0.0117
CH-44 CH-14 8911 -0.0117
CH-44 CH-20 8911 0.0024
CH-44 CH-27 8911 -0.0020
CH-44 CH-3 8911 0.0042
- _.inbreed
This file provides the estimated inbreeding coefficient for each individual. The first column is individual ID and the second column is the estimated inbreeding coefficient. Example:
indivID inbreeding
CH-44 -0.0230
CH-49 0.0001
CH-43 0.0081
CH-45 0.0040
CH-40 0.0147
CH-50 -0.0034
CH-16 -0.0815
CH-15 -0.0097
CH-6 -0.0021
CH-14 -0.0627
CH-20 0.0004
- _.matrix and _.matrixID
The _.matrix file contains an N × N matrix of 2, where
is the estimated kinship matrix of N study individuals. The
.matrixID file contains individual IDs listed by the order in the _.matrix file and the input VCF file.
To estimate kinship coefficients for samples with population structure and admixture, we need to first estimate the ancestry background of each individual and derive individual-specific allele frequencies. We use LASER to estimate individual ancestry background, which applicable to both shallow sequencing data and genotyping data under a unified framework given an external ancestry reference panel [1,2]. Details of LASER can be found on the LASER website. Here, we assume the ancestry coordinates for both the reference individuals and the study individuals have been properly generated by LASER. We focus on describing the usage of SEEKIN below.
We model allele frequencies as linear functions of PCs based on the ancestry reference panel. The regression coefficients can be estimated using the modelAF module in SEEKIN:
../bin/seekin modelAF -i SGVP.12K.vcf.gz -c SGVP.RefPC.coord -k 2 -o SGVP.beta
This command will generate a file, named SGVP.beta (specified by –o), which contains the intercepts and regression coefficients of first two PCs (specified by -k) based on genotypes (specified by -i) and PC coordinates (specified by -c) of the reference individuals. In this file, the first five columns are chromosome, position, reference allele, alternative allele, and allele frequencies of the alternative allele in the reference dataset, respectively. The remaining columns are the estimated intercept and regression coefficients for each PC. This file is tab-delimited. Example:
CHROM POS REF ALT AF beta0 beta1 beta2
1 740857 G A 0.013 0.026418 -4.93102e-05 -0.000123628
1 1017197 C T 0.711 1.421560 -0.000896592 0.000878442
1 1241964 G A 0.500 0.999935 -0.00123432 0.000292638
1 1462766 C T 0.351 0.701477 -0.00028479 8.94892e-05
1 1715011 C A 0.118 0.236869 2.67180000 -0.000551193
1 1979724 C A 0.226 0.451491 -0.000218917 -0.000157946
1 2251160 T C 0.397 0.794793 0.000322257 -7.75743e-05
1 2505713 C T 0.093 0.186592 0.000425398 -0.000163982
1 2749716 G T 0.308 0.615128 -5.39846e-05 -0.000770491
The individual-specific allele frequencies of study individuals can be generated by the following command:
../bin/seekin getAF -i Study.onSGVP.PC.coord -b SGVP.beta -k 2 -o Study.10K.indvAF.vcf
This command estimates individual-specific allele frequencies based on the top 2 PCs (specified by –k) in the coordinate file (specified by -i) and the regression coefficients (specified by –b). The outputs are stored in a gzipped VCF file (specified by –o). Example:
##fileformat=VCFv4.2
##INFO=<ID=AF,Number=A,Type=Float,Description="Estimated allele frequencies averaged across all individuals">
##FORMAT=<ID=AF1,Number=A,Type=Float,Description="Estimated individual-specific allele frequencies">
#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT MA-1 MA-2 CH-1 MA-3 MA-4 MA-5 CH-2 CH-3
1 740857 . G A . . AF=0.0163 AF1 0.0247 0.0065 0.0269 0.0063 0.0057 0.0160 0.0271
1 1017197 . C T . . AF=0.7902 AF1 0.7653 0.8410 0.7530 0.8531 0.8508 0.7571 0.7492
1 1241964 . G A . . AF=0.6022 AF1 0.6205 0.6051 0.6188 0.6171 0.6114 0.5651 0.6158
1 1462766 . C T . . AF=0.3745 AF1 0.3775 0.3768 0.3767 0.3796 0.3784 0.3657 0.3760
1 1715011 . C A . . AF=0.1120 AF1 0.1428 0.0721 0.1519 0.0690 0.0677 0.1179 0.1534
1 1979724 . C A . . AF=0.2423 AF1 0.2575 0.2274 0.2607 0.2285 0.2269 0.2376 0.2607
1 2251160 . T C . . AF=0.3707 AF1 0.3660 0.3699 0.3664 0.3667 0.3682 0.3804 0.3672
1 2505713 . C T . . AF=0.0576 AF1 0.0549 0.0520 0.0565 0.0475 0.0493 0.0709 0.0577
1 2749716 . G T . . AF=0.3060 AF1 0.3517 0.2488 0.3646 0.2453 0.2430 0.3116 0.3665
To avoid out of boundary values, we force allele frequencies to be 0.001 or 0.999 when AF1<0.001 or AF1>0.999, respectively.
With the estimated individual-specific allele frequencies, Stdudy.10k.indvAF.vcf.gz, we can run the kinship module to estimate kinship coefficients using the following command:
tabix Study.10K.indvAF.vcf.gz # indexing the file
../bin/seekin kinship -i Study.10K.vcf.gz -f Study.10K.indvAF.vcf.gz -p het -l 2000 -t 3 -o Study.10K.het
Note that it is important to set –p to 'het' so that the SEEKIN-het estimator will be used and the –f option will be effective to take the individual-specific allele frequencies. Other options can be found by typing ../bin/seekin kinship -h.
The output files have the same format as described in Section 6.1.
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Version 1.0 (June 15, 2017)
- Initial release of SEEKIN.
-
Version 1.01 (Oct 10, 2017)
- Add the data processing piepline.
- Add an algorithm mode for small study sample (e.g. n<10).
- Wang C et al. (2014) Ancestry estimation and control of population stratification for sequence-based association studies. Nat Genet 46, 409-415.
- Wang C et al. (2015) Improved ancestry estimation for both genotyping and sequencing data using projection Procrustes analysis and genotype imputation. Am J Hum Genet 96, 926-937.
- Browning BL & Browning SR (2009) A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Am J Hum Genet 84, 210-223.
- Thornton T et al. (2012) Estimating kinship in admixed populations. Am J Hum Genet 91, 122-138.
- Conomos MP et al. (2016) Model-free estimation of recent genetic relatedness. Am J Hum Genet 98, 127-148.
- Teo YY et al. (2009) Singapore Genome Variation Project: a haplotype map of three Southeast Asian populations. Genome Res 19, 2154-2162.