Estimation of kinship coefficient in structured and admixed populations using sparse sequencing data.

Abstract:

:Knowledge of biological relatedness between samples is important for many genetic studies. In large-scale human genetic association studies, the estimated kinship is used to remove cryptic relatedness, control for family structure, and estimate trait heritability. However, estimation of kinship is challenging for sparse sequencing data, such as those from off-target regions in target sequencing studies, where genotypes are largely uncertain or missing. Existing methods often assume accurate genotypes at a large number of markers across the genome. We show that these methods, without accounting for the genotype uncertainty in sparse sequencing data, can yield a strong downward bias in kinship estimation. We develop a computationally efficient method called SEEKIN to estimate kinship for both homogeneous samples and heterogeneous samples with population structure and admixture. Our method models genotype uncertainty and leverages linkage disequilibrium through imputation. We test SEEKIN on a whole exome sequencing dataset (WES) of Singapore Chinese and Malays, which involves substantial population structure and admixture. We show that SEEKIN can accurately estimate kinship coefficient and classify genetic relatedness using off-target sequencing data down sampled to ~0.15X depth. In application to the full WES dataset without down sampling, SEEKIN also outperforms existing methods by properly analyzing shallow off-target data (~0.75X). Using both simulated and real phenotypes, we further illustrate how our method improves estimation of trait heritability for WES studies.

journal_name

PLoS Genet

journal_title

PLoS genetics

authors

Dou J,Sun B,Sim X,Hughes JD,Reilly DF,Tai ES,Liu J,Wang C

doi

10.1371/journal.pgen.1007021

subject

Has Abstract

pub_date

2017-09-29 00:00:00

pages

e1007021

issue

9

eissn

1553-7390

issn

1553-7404

pii

PGENETICS-D-17-01193

journal_volume

13

pub_type

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