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

Naseri, Ardalan; Shi, Junjie; Lin, Xihong; Zhang, Shaojie; Zhi, Degui

doi:10.1371/journal.pgen.1009315

Cited by 10 publications

(10 citation statements)

References 32 publications

(56 reference statements)

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“…The phased data may contain some long-range switch errors or blips, but they will not contribute to a noticeable reduction in the detection power except for strict and very long IBD cutoff thresholds. The possible reduction of power can also be alleviated for some downstream analysis, especially if the total shared IBDs between 2 individuals is of interest [ 36 ]. However, the impact of phasing errors could be more consequential if the number of segments is being considered.…”

Section: Resultsmentioning

confidence: 99%

Open-source benchmarking of IBD segment detection methods for biobank-scale cohorts

et al. 2022

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In the recent biobank era of genetics, the problem of identical-by-descent (IBD) segment detection received renewed interest, as IBD segments in large cohorts offer unprecedented opportunities in the study of population and genealogical history, as well as genetic association of long haplotypes. While a new generation of efficient methods for IBD segment detection becomes available, direct comparison of these methods is difficult: existing benchmarks were often evaluated in different datasets, with some not openly accessible; methods benchmarked were run under suboptimal parameters; and benchmark performance metrics were not defined consistently. Here, we developed a comprehensive and completely open-source evaluation of the power, accuracy, and resource consumption of these IBD segment detection methods using realistic population genetic simulations with various settings. Our results pave the road for fair evaluation of IBD segment detection methods and provide an practical guide for users.

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Section: Resultsmentioning

confidence: 99%

Open-source benchmarking of IBD segment detection methods for biobank-scale cohorts

et al. 2022

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“…An important factor in attempting to utilize IBD segment numbers is their accurate detection. Switch errors profoundly influence segment number estimates when using phase-based IBD detectors 12,13,16,28 . Our use of IBIS segments in our classifier was motivated by IBIS's ability to call IBD segments in unphased dataone of only a few methods to do so 13 -which is key to avoiding biased segment number estimates.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the utility of identity-by-descent segment numbers for relatedness inference via information theory and classification

Smith

Qiao

Williams

2021

Preprint

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Despite decades of methods development for classifying relatives in genetic studies, pairwise relatedness methods' recalls are above 90% only for first through third degree relatives. The top-performing approaches, which leverage identity-by-descent (IBD) segments, often use only kinship coefficients, while others, including ERSA, use the number of segments relatives share. To quantify the potential for using segment numbers in relatedness inference, we leveraged information theory measures to analyze exact (i.e., produced by a simulator) IBD segments from simulated relatives. Over a range of settings, we found that the mutual information between the relatives' degree of relatedness and a tuple of their kinship coefficient and segment number is on average 4.6% larger than between the degree and the kinship coefficient alone. We further evaluated IBD segment number utility by building a Bayes classifier to predict first through sixth degree relationships using different feature sets. When trained and tested with exact segments, the inclusion of segment numbers improves the recall by between 0.0028 and 0.030 for second through sixth degree relatives. However, the recalls improve by less than 0.018 per degree when using inferred segments, suggesting limitations due to IBD detection accuracy. Lastly, we compared our Bayes classifier that includes segment numbers with ERSA and IBIS and found comparable results, with the Bayes classifier and ERSA slightly outperforming each other across different degrees. Overall, this study shows that IBD segment numbers can improve relatedness inference but that errors from current SNP array-based detection methods yield dampened signals in practice.

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“…Maximum-likelihood methods (Such as RelateAdmix [44] and ERSA [45]) use expectation-maximization (EM) to jointly estimate the kinship statistics. Recent methods (such as RAFFI [46], IBDKin [47]) use fast algorithms to search for IBD matches from phased genotypes and estimate kinship from shared IBD estimates. There are also methods that estimate kinship from next-generation sequencing data, which are especially useful from low-coverage sequencing approaches (NGSRemix [48], LASER [49], SEEKIN [50]).…”

Section: Introductionmentioning

confidence: 99%

Privacy-Aware Kinship Inference in Admixed Populations using Projection on Reference Panels

Wang

Kim

et al. 2022

Preprint

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Estimation of genetic relatedness, or kinship, is used occasionally for recreational purposes and in forensic applications. While numerous methods were developed to estimate kinship, they suffer from high computational requirements and often make an untenable assumption of homogeneous population ancestry of the samples. Moreover, genetic privacy is generally overlooked in the usage of kinship estimation methods. There can be ethical concerns about finding unknown familial relationships in 3rd party databases. Similar ethical concerns may arise while estimating and reporting sensitive population-level statistics such as inbreeding coefficients for the concerns around marginalization and stigmatization. Here, we make use of existing reference panels with a projection-based approach that simplifies kinship estimation in the admixed populations. We use simulated and real datasets to demonstrate the accuracy and efficiency of kinship estimation. We present a secure federated kinship estimation framework and implement a secure kinship estimator using homomorphic encryption-based primitives for computing relatedness between samples in 2 different sites while genotype data is kept confidential.

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

Cited by 10 publications

References 32 publications

Open-source benchmarking of IBD segment detection methods for biobank-scale cohorts

Open-source benchmarking of IBD segment detection methods for biobank-scale cohorts

Evaluating the utility of identity-by-descent segment numbers for relatedness inference via information theory and classification

Privacy-Aware Kinship Inference in Admixed Populations using Projection on Reference Panels

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