PADRE: Pedigree-Aware Distant-Relationship Estimation

Staples, Jeffrey; Witherspoon, David J.; Jorde, Lynn B.; Nickerson, Deborah A.; Below, Jennifer E.; Huff, Chad

doi:10.1016/j.ajhg.2016.05.020

Cited by 36 publications

(45 citation statements)

References 29 publications

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“…Inferring identical by descent (IBD) segments and estimating relatedness are classical problems in human genetics [1], with recent work motivated by the abundance of close relatives in large samples [2][3][4][5][6]. In order to study individuals with a known relationship, many investigators have performed simulations, both to evaluate novel methods [4][5][6][7][8], and to characterize the properties of IBD sharing rates among relatives [9,10]. Additionally, direct-to-consumer genetic testing companies-now with data from several million individualsrely on simulated data to infer relationships by matching relatedness statistics from their customers to those from simulations [11,12].…”

Section: Introductionmentioning

confidence: 99%

Crossover interference and sex-specific genetic maps shape identical by descent sharing in close relatives

Caballero

Seidman

Sannerud

et al. 2019

Preprint

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Simulations of close relatives and identical by descent (IBD) segments are common in genetic studies, yet most past efforts have utilized sex averaged genetic maps and ignored crossover interference, thus omitting features known to affect the breakpoints of IBD segments. We developed Ped-sim, a method for simulating relatives that can utilize either sex-specific or sex averaged genetic maps and also either a model of crossover interference or the traditional Poisson model for inter-crossover distances. To characterize the impact of previously ignored mechanisms, we simulated data for all four combinations of these factors. We found that modeling crossover interference decreases the standard deviation of the IBD proportion by 10.4% on average in full siblings through second cousins. By contrast, sex-specific maps increase this standard deviation by 4.2% on average, and also impact the number of segments relatives share. Most notably, using sex-specific maps, the number of segments half-siblings share is bimodal; and when combined with interference modeling, the probability that sixth cousins have non-zero IBD ranges from 9.0 to 13.1%, depending on the sexes of the individuals through which they are related. We present new analytical results for the distributions of IBD segments under these models and show they match results from simulations. Finally, we compared IBD sharing rates between simulated and real relatives and find that the combination of sex-specific maps and interference modeling most accurately captures IBD rates in real data. Ped-sim is open source and available from https://github.com/williamslab/ped-sim. Short title: Crossover interference and sex-specific maps shape IBD distributionsSimulations are ubiquitous throughout statistical genetics in order to generate data with known properties, enabling tests of inference methods and analyses of real world processes in settings where experimental data are challenging to collect. Simulating genetic data for relatives in a pedigree requires the synthesis of chromosomes parents transmit to their children. These chromosomes form as a mosaic of a given parent's two chromosomes, with the location of switches between the two parental chromosomes known as crossovers. Detailed information about crossover generation based on real data from humans now exists, including the fact that men and women have overall different rates (women produce ∼1.6 times more crossovers) and that real crossovers are subject to interference-whereby crossovers are further apart from one another than expected under a model that selects their locations randomly. Our new method, Ped-sim, can simulate pedigree data using these less commonly modeled crossover features, and we used it to evaluate the importance of sex-specific rates and interference in real data. These comparisons show that both factors shape the amount of DNA two relatives share identically, and that their inclusion in models of crossover better fit data from real relatives.

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Section: Introductionmentioning

confidence: 99%

Crossover interference and sex-specific genetic maps shape identical by descent sharing in close relatives

Caballero

Seidman

Sannerud

et al. 2019

Preprint

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“…Given these applications, several methods exist for pedigree reconstruction, and for confirming or disproving reported pedigree relationships [10][11][12]16,21,36 . However, differentiating among the relationships that map to a given degree of relatedness has remained challenging.…”

Section: Discussionmentioning

confidence: 99%

Distinguishing pedigree relationships using multi-way identical by descent sharing and sex-specific genetic maps

Qiao

Sannerud

Basu-Roy

et al. 2019

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The proportion of samples with one or more close relatives in a genetic dataset increases rapidly with sample size, necessitating relatedness modeling and enabling pedigree-based analyses. Despite this, relatives are generally unreported and current inference methods typically detect only the degree of relatedness of sample pairs and not pedigree relationships. We developed CREST, an accurate and fast method that identifies the pedigree relationships of close relatives. CREST utilizes identical by descent (IBD) segments shared between a pair of samples and their mutual relatives, leveraging the fact that sharing rates among these individuals differ across pedigree configurations. Furthermore, CREST exploits the profound differences in sex-specific genetic maps to classify pairs as maternally or paternally related-e.g., paternal half-siblings-using the locations of autosomal IBD segments shared between the pair. In simulated data, CREST correctly classifies 91.5-99.5% of grandparent-grandchild (GP) pairs, 70.5-97.0% of avuncular (AV) pairs, and 79.0-98.0% of half-siblings (HS) pairs compared to PADRE's rates of 38.5-76.0% of GP, 60.5-92.0% of AV, 73.0-95.0% of HS pairs. Turning to the real 20,032 sample Generation Scotland (GS) dataset, CREST correctly determines the relationship of 99.0% of GP, 85.7% of AV, and 95.0% of HS pairs that have sufficient mutual relative data, completing this analysis in 10.1 CPU hours including IBD detection. CREST's maternal and paternal relationship inference is also accurate, as it flagged five pairs as incorrectly labeled in the GS pedigreesthree of which we confirmed as mistakes, and two with an uncertain relationship-yielding 99.7% of HS and 93.5% of GP pairs correctly classified.

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“…In order to make use of the multi-way relatedness present in large samples, a key question is how best to combine information among the individuals. Two recently developed methods use composite likelihood approaches that enable them to outperform pairwise relatedness inference 21,22 . Results from these methods demonstrate that leveraging multi-way relatedness signals does improve accuracy, but their reliance upon composite likelihoods may be suboptimal.…”

Section: Introductionmentioning

confidence: 99%

“…We compared DRUID to the multi-way relatedness method PADRE 21 and to a pairwise inference approach that uses IBD segments called by Refined IBD 23,26 . Both PADRE and DRUID perform much better than pairwise analysis: using simulated data for two sets of full siblings that are distantly related to each other, the multi-way methods classify 5.0-35.4% more samples to their exact degree of relatedness.…”

Section: Introductionmentioning

confidence: 99%

Inferring identical by descent sharing of sample ancestors promotes high resolution relative detection

Ramstetter

Shenoy

Dyer

et al. 2018

Preprint

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As genetic datasets increase in size, the fraction of samples with one or more close relatives increases rapidly, resulting in sets of mutually related individuals. We present DRUID-Deep Relatedness Utilizing Identity by Descent-a method that works by inferring the identical by descent (IBD) sharing profile of an ungenotyped ancestor of a set of close relatives. Using this IBD profile, DRUID infers relatedness between these unobserved ancestors and more distant relatives, thereby combining information from multiple samples to remove one or more generations between the deep relationships to be identified. DRUID constructs sets of close relatives by detecting full siblings and also uses a novel approach to identify the aunts/uncles of two or more siblings, recovering 95.4% of real aunts/uncles with zero false positives. We used DRUID to infer relatedness among individuals in both real and simulated data by applying it to close relatives consisting of full siblings or siblings and their aunts/uncles. Compared to PADRE, DRUID correctly infers up to 10% more relatives when using data from two sets of distantly related siblings, and 10-30% more relatives given two sets of siblings and their aunts/uncles. DRUID frequently infers relationships either correctly or within one degree of the truth, with PADRE classifying 43-58% of tenth degree relatives in this way compared to 78-96% using DRUID.

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PADRE: Pedigree-Aware Distant-Relationship Estimation

Cited by 36 publications

References 29 publications

Crossover interference and sex-specific genetic maps shape identical by descent sharing in close relatives

Crossover interference and sex-specific genetic maps shape identical by descent sharing in close relatives

Distinguishing pedigree relationships using multi-way identical by descent sharing and sex-specific genetic maps

Inferring identical by descent sharing of sample ancestors promotes high resolution relative detection

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