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https://doi.org/10.1371/journal.pgen.1007021
Title: | Estimation of kinship coefficient in structured and admixed populations using sparse sequencing data | Authors: | Dou J. Sun B. Sim X. Hughes J.D. Reilly D.F. Tai E.S. Liu J. Wang C. |
Keywords: | gene linkage disequilibrium genetic marker genome genotype heritability human Malay (people) phenotype population structure sampling simulation Singapore uncertainty whole exome sequencing Asian continental ancestry group biological model biology DNA sequence exome genetic association study genetic database genetics genotyping technique human genome population genetics procedures software Asian Continental Ancestry Group Computational Biology Databases, Genetic Exome Genetic Association Studies Genetics, Population Genome, Human Genotype Genotyping Techniques Humans Linkage Disequilibrium Models, Genetic Sequence Analysis, DNA Software |
Issue Date: | 2017 | Publisher: | Public Library of Science | Citation: | Dou J., Sun B., Sim X., Hughes J.D., Reilly D.F., Tai E.S., Liu J., Wang C. (2017). Estimation of kinship coefficient in structured and admixed populations using sparse sequencing data. PLoS Genetics 13 (9) : e1007021. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pgen.1007021 | 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. © 2017 Dou et al. | Source Title: | PLoS Genetics | URI: | https://scholarbank.nus.edu.sg/handle/10635/165369 | ISSN: | 15537390 | DOI: | 10.1371/journal.pgen.1007021 |
Appears in Collections: | Staff Publications Elements |
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