ArticlePLoS genetics2017
Estimation of kinship coefficient in structured and admixed populations using sparse sequencing data.
Article in PLoS genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Genetically Informed Research Designs in Perinatal Pharmacoepidemiology: A Methodological Overview.Drug safety · 2026Review
- Evaluating the performance of ancient DNA genetic relatedness estimation methods using high-fidelity pedigree simulations.Genome biology · 2026Article
- Domain mapping of disease mutations reveals pathogenic SORL1 variants in Alzheimer's disease.Molecular neurodegeneration · 2025Article
- COMPADRE: Combined pedigree-aware distant relatedness estimation for improved pedigree reconstruction.American journal of human genetics · 2025Article
- Kinship Decouple and Phenotype Selection (KDPS): a tool for phenotype-aware decoupling of related subjects.Briefings in bioinformatics · 2025Article
- Genome-Wide Association Study of Gallstone Disease Identifies Novel Candidate Genomic Variants in a Latino Community of Southwest USA.Journal of racial and ethnic health disparities · 2025Article
- Private detection of relatives in forensic genomics using homomorphic encryption.BMC medical genomics · 2024Article
- A brief guide to analyzing expression quantitative trait loci.Molecules and cells · 2024Review
- Continental-scale associations of Arabidopsis thaliana phyllosphere members with host genotype and drought.Nature microbiology · 2024Article
- MethylGenotyper: Accurate Estimation of SNP Genotypes and Genetic Relatedness from DNA Methylation Data.Genomics, proteomics & bioinformatics · 2024Article
- Taking identity-by-descent analysis into the wild: Estimating realized relatedness in free-ranging macaques.bioRxiv : the preprint server for biology · 2024Article
- An unbiased kinship estimation method for genetic data analysis.BMC bioinformatics · 2022Article
- Privacy-aware estimation of relatedness in admixed populations.Briefings in bioinformatics · 2022Article
- A comparative analysis of genomic and phenomic predictions of growth-related traits in 3-way coffee hybrids.G3 (Bethesda, Md.) · 2022Article
- Moment estimators of relatedness from low-depth whole-genome sequencing data.BMC bioinformatics · 2022Article
- Article
- Association ofBMJ open diabetes research & care · 2020Article
- Allele frequency-free inference of close familial relationships from genotypes or low-depth sequencing data.Molecular ecology · 2019Article
- Article
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8 authors.
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No grant is acknowledged in the PubMed record.
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.
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