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

Ramstetter, Monica D.; Shenoy, Sushila; Dyer, Thomas D.; Lehman, Donna M.; Curran, Joanne E.; Duggirala, Ravindranath; Blangero, John; Mezey, Jason G.; Williams, Amy L.

doi:10.1101/243048

Cited by 13 publications

(32 citation statements)

References 46 publications

(44 reference statements)

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“…Modular extensions of pairwise methods to perform multi-way relatedness inference (e.g. [64]) have also been shown to outperform pairwise methods.…”

Section: Discussionmentioning

confidence: 99%

Estimating relatedness between malaria parasites

Taylor

Jacob

Neafsey

et al. 2019

Preprint

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Understanding the relatedness of individuals within or between populations is a common goal in biology. Increasingly, relatedness features in genetic epidemiology studies of pathogens. These studies are relatively new compared to those in humans and other organisms, but are important for designing interventions and understanding pathogen transmission. Only recently have researchers begun to routinely apply relatedness to apicomplexan eukaryotic malaria parasites, and to date have used a range of different approaches on an ad hoc basis. It remains unclear how to compare different studies, therefore, and which measures to use. Here, we systematically compare measures based on identity-by-state and identity-by-descent using a globally diverse data set of malaria parasites, Plasmodium falciparum and Plasmodium vivax, and provide marker requirements for estimates based on identity-by-descent. We formally show that the informativeness of polyallelic markers for relatedness inference is maximised when alleles are equifrequent. Estimates based on identity-bystate are sensitive to allele frequencies, which vary across populations and by experimental design. For portability across studies, we thus recommend estimates based on identity-by-descent. To generate reliable estimates, we recommend approximately 200 biallelic or 100 polyallelic markers. Confidence intervals illuminate inference across studies based on different sets of markers. These marker requirements, unlike many thus far reported, are immediately applicable to haploid malaria parasites and other haploid eukaryotes. This is the first attempt to provide rigorous analysis of the reliability of, and requirements for, relatedness inference in malaria genetic epidemiology, and will provide a basis for statistically informed prospective study design and surveillance strategies.

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“…Modular extensions of pairwise methods to perform multi-way relatedness inference (e.g. [64]) have also been shown to outperform pairwise methods.…”

Section: Discussionmentioning

confidence: 99%

Estimating relatedness between malaria parasites

Taylor

Jacob

Neafsey

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Most apparently, it can enable the companies to report specific relationship types, including which parent an individual is related through for the relationships CREST applies to. Additionally, while the mutual relatives of a target pair inform the pedigree structure between the pair, providing this pedigree structure to the method DRUID enables more exact detection of the distance between those close relatives and the mutual relatives 17 . Thus, an iterative procedure is possible, with mutual relatives of unknown relationship to a set of close relatives enabling the detection of the latter pairs' relationship types, and the resulting pedigrees enabling more precise characterization of their distance to the mutual relatives.…”

Section: Discussionmentioning

confidence: 99%

“…This inference is possible due to distinct features of male and female maps that lead to different patterns in the IBD segments paternal and maternal pairs share. It is common practice to use a sex-averaged genetic map when analyzing relatives 13,16,17 , but doing so overlooks the substantial differences between the sex-specific maps.…”

Section: Classifying Relationship Types Using Kernel Density Estimatimentioning

confidence: 99%

“…In all cases we used sex-specific genetic maps 18 and crossover interference 26 modeling in these simulations, and a collection of European descent samples 27 as the input phased data. (The latter were previously phased using Beagle 28 , and filtered so that no pair is more closely related than fifth degree 17 . )…”

Section: Simulationsmentioning

confidence: 99%

“…However, an existing method that leverages these pairwise signals provides limited ability to discriminate among second degree relationships 16 . Turning to multi-way IBD approaches, a recent method detects aunts/uncles of siblings 17 , but it requires at least two siblings to work and can only identify their aunts/uncles. We developed CREST (Classification of RElationShip Types), an approach for inferring sex-specific pedigree relationships that leverages multi-way IBD sharing and sex-specific genetic maps.…”

Section: Introductionmentioning

confidence: 99%

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

Qiao

Sannerud

Basu-Roy

et al. 2019

Preprint

Self Cite

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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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On the application, reporting, and sharing of in silico simulations for genetic studies

Riggs

Chen

Rotunno³

et al. 2020

Genetic Epidemiology

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In silico simulations play an indispensable role in the development and application of statistical models and methods for genetic studies. Simulation tools allow for the evaluation of methods and investigation of models in a controlled manner. With the growing popularity of evolutionary models and simulation-based statistical methods, genetic simulations have been applied to a wide variety of research disciplines such as population genetics, evolutionary genetics, genetic epidemiology, ecology, and conservation biology. In this review, we surveyed 1409 articles from five journals that publish on major application areas of genetic simula

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

Cited by 13 publications

References 46 publications

Estimating relatedness between malaria parasites

Estimating relatedness between malaria parasites

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

On the application, reporting, and sharing of in silico simulations for genetic studies

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