ArticlePLoS genetics2021
RAFFI: Accurate and fast familial relationship inference in large scale biobank studies using RaPID.
Article in PLoS genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Private detection of relatives in forensic genomics using homomorphic encryption.BMC medical genomics · 2024Article
- Scalable analysis of large multi-ancestry biobanks by leveraging sparse ancestry-adjusted sample-relatedness.Research square · 2024Article
- Article
- Secure discovery of genetic relatives across large-scale and distributed genomic data sets.Genome research · 2024Article
- FiMAP: A fast identity-by-descent mapping test for biobank-scale cohorts.PLoS genetics · 2023Article
- Open-source benchmarking of IBD segment detection methods for biobank-scale cohorts.GigaScience · 2022Article
- Privacy-aware estimation of relatedness in admixed populations.Briefings in bioinformatics · 2022Article
- Evaluating the utility of identity-by-descent segment numbers for relatedness inference via information theory and classification.G3 (Bethesda, Md.) · 2022Article
- Secure Discovery of Genetic Relatives across Large-Scale and Distributed Genomic Datasets.Research in computational molecular biology : ... Annual International Conference, RECOMB ... : proceedings. RECOMB (Conference : 2005- )Article
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5 authors.
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Abstract
Inference of relationships from whole-genome genetic data of a cohort is a crucial prerequisite for genome-wide association studies. Typically, relationships are inferred by computing the kinship coefficients (ϕ) and the genome-wide probability of zero IBD sharing (π0) among all pairs of individuals. Current leading methods are based on pairwise comparisons, which may not scale up to very large cohorts (e.g., sample size >1 million). Here, we propose an efficient relationship inference method, RAFFI. RAFFI leverages the efficient RaPID method to call IBD segments first, then estimate the ϕ and π0 from detected IBD segments. This inference is achieved by a data-driven approach that adjusts the estimation based on phasing quality and genotyping quality. Using simulations, we showed that RAFFI is robust against phasing/genotyping errors, admix events, and varying marker densities, and achieves higher accuracy compared to KING, the current leading method, especially for more distant relatives. When applied to the phased UK Biobank data with ~500K individuals, RAFFI is approximately 18 times faster than KING. We expect RAFFI will offer fast and accurate relatedness inference for even larger cohorts.
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