A rapid, accurate approach to inferring pedigrees in endogamous populations

Williams, Cole; Scelza, Brooke A.; Daya, Michelle; Lange, Ethan M.; Gignoux, Christopher R.; Henn, Brenna M.

doi:10.1101/2020.02.25.965376

Cited by 20 publications

(25 citation statements)

References 40 publications

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“…This is because the real data are often blended with various artifacts, even though decision boundaries based on IBD1 and IBD2 are clear in theoretical settings. While we derived a data-driven approach for adjusting decision boundaries for RAFFI, we noticed that related work of [ 31 ] also adopted a data-driven approach for discerning various subtypes of 2 nd degree relatedness. We expect more data-driven approaches can be developed to make relatedness inference methods more practical.…”

Section: Discussionmentioning

confidence: 99%

RAFFI: Accurate and fast familial relationship inference in large scale biobank studies using RaPID

et al. 2021

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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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Section: Discussionmentioning

confidence: 99%

RAFFI: Accurate and fast familial relationship inference in large scale biobank studies using RaPID

et al. 2021

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“…Leveraging IBD shared by close relatives has the effect of increasing the amount of available data for inferring pairwise relationships. Ramstetter et al (2018) demonstrated that an approach based on summarizing IBD from close relatives can greatly improve the accuracy of estimates of distant degrees of relatedness compared with the approach of computing a composite likelihood over all pairs of individuals (Staples et al 2016). These two approaches are shown in Figure 5.…”

Section: Definingmentioning

confidence: 99%

“…In Panel (B), IBD information in the descendants of A 1 and A 2 is summarized to infer the degree of relatedness among these two common ancestors. The approach in Panel (A) is taken by the PADRE method (Staples et al 2016). The approach in Panel (B) is taken by the DRUID method (Ramstetter et al 2018).…”

Section: Definingmentioning

confidence: 99%

Bonsai: An efficient method for inferring large human pedigrees from genotype data

Jewett

McManus

Freyman

et al. 2021

Preprint

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Pedigree inference from genotype data is a challenging problem, particularly when pedigrees are sparsely sampled and individuals may be distantly related to their closest genotyped relatives. We present a new method that infers small pedigrees of close relatives and then assembles them into larger pedigrees. To assemble large pedigrees, we introduce several new formulas and tools including a new likelihood for the degree separating two small pedigrees, a method for detecting individuals who share background identity-by-descent (IBD) that does not reflect recent common ancestry, and a method for identifying the ancestral branches through which distant relatives are connected. Our method also takes several new approaches that help to improve the accuracy and efficiency of pedigree inference. In particular, we incorporate age information directly into the likelihood rather than using ages only for consistency checks and we employ a heuristic branch-and-bound-like approach to more efficiently explore the space of possible pedigrees. Together, these approaches make it possible to construct large pedigrees that are challenging or intractable for current inference methods. The new method, Bonsai, is available at https://github.com/23andMe/bonsaitree.

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“…IBD estimates that are phase aware can improve relationship and pedigree inference ( Ramstetter et al. 2017 , 2018 ; Williams et al. 2020 ), enable health and trait inheritance to be traced ( Browning and Thompson 2012 ; Lin et al 2013 ; Vacic et al.…”

Section: Introductionmentioning

confidence: 99%

Fast and Robust Identity-by-Descent Inference with the Templated Positional Burrows–Wheeler Transform

Freyman

McManus

Shringarpure

et al. 2020

Molecular Biology and Evolution

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Estimating the genomic location and length of identical-by-descent (IBD) segments among individuals is a crucial step in many genetic analyses. However, the exponential growth in the size of biobank and direct-to-consumer genetic data sets makes accurate IBD inference a significant computational challenge. Here we present the templated positional Burrows–Wheeler transform (TPBWT) to make fast IBD estimates robust to genotype and phasing errors. Using haplotype data simulated over pedigrees with realistic genotyping and phasing errors, we show that the TPBWT outperforms other state-of-the-art IBD inference algorithms in terms of speed and accuracy. For each phase-aware method, we explore the false positive and false negative rates of inferring IBD by segment length and characterize the types of error commonly found. Our results highlight the fragility of most phased IBD inference methods; the accuracy of IBD estimates can be highly sensitive to the quality of haplotype phasing. Additionally, we compare the performance of the TPBWT against a widely used phase-free IBD inference approach that is robust to phasing errors. We introduce both in-sample and out-of-sample TPBWT-based IBD inference algorithms and demonstrate their computational efficiency on massive-scale data sets with millions of samples. Furthermore, we describe the binary file format for TPBWT-compressed haplotypes that results in fast and efficient out-of-sample IBD computes against very large cohort panels. Finally, we demonstrate the utility of the TPBWT in a brief empirical analysis, exploring geographic patterns of haplotype sharing within Mexico. Hierarchical clustering of IBD shared across regions within Mexico reveals geographically structured haplotype sharing and a strong signal of isolation by distance. Our software implementation of the TPBWT is freely available for noncommercial use in the code repository (https://github.com/23andMe/phasedibd, last accessed January 11, 2021).

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A rapid, accurate approach to inferring pedigrees in endogamous populations

Cited by 20 publications

References 40 publications

RAFFI: Accurate and fast familial relationship inference in large scale biobank studies using RaPID

RAFFI: Accurate and fast familial relationship inference in large scale biobank studies using RaPID

Bonsai: An efficient method for inferring large human pedigrees from genotype data

Fast and Robust Identity-by-Descent Inference with the Templated Positional Burrows–Wheeler Transform

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