Tree-based QTL mapping with expected local genetic relatedness matrices

Link, Vivian; Schraiber, Joshua G.; Fan, Caoqi; Dinh, Bryan; Mancuso, Nicholas; Chiang, Charleston W.K.; Edge, Michael D.

doi:10.1016/j.ajhg.2023.10.017

Cited by 3 publications

(2 citation statements)

References 102 publications

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“…There is substantial interest in using inferred ARGs to improve association testing methods (Zhang et al, 2023;Link et al, 2023;Nowbandegani et al, 2023), and there is a pressing need for a well-tested, efficient and user-friendly means of simulating phenotypes on ARGs. Highly realistic simulations conditioned on large pedigrees (Anderson-Trocmé et al, 2023) provide an exciting opportunity to test the effects of intricate population structure on GWAS, and we hope that tstrait will facilitate these investigations.…”

Section: Discussionmentioning

confidence: 99%

“…Recent breakthroughs in inferrence methods have made it possible to estimate ARGs at biobank scale (Kelleher et al, 2019; Zhang et al, 2023), and there is now intense interest in their practical application (Lewanski et al, 2024; Brandt et al, 2024). Statistical genetics has been a particular focus, and ARG-based methods have been shown to detect more ultra rare variants than conventional association testing methods (Zhang et al, 2023); to have better power to detect causal loci in quantitative-trait locus mapping (Link et al, 2023); and to provide a sparse and efficient model of linkage disequilibrium in GWAS and downstream applications (Nowbandegani et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

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tstrait: a quantitative trait simulator for ancestral recombination graphs

Tagami,

Bisschop,

Kelleher

2024

Preprint

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Ancestral recombination graphs (ARGs) encode the ensemble of correlated genealogical trees arising from recombination in a compact and efficient structure, and are of fundamental importance in population and statistical genetics. Recent breakthroughs have made it possible to simulate and infer ARGs at biobank scale, and there is now intense interest in using ARG-based methods across a broad range of applications, particularly in genome-wide association studies (GWAS). Sophisticated methods exist to simulate ARGs using population genetics models, but there is currently no software to simulate quantitative traits directly from these ARGs. To apply existing quantitative trait simulators users must export genotype data, losing important information about ancestral processes and producing prohibitively large files when applied to the biobank-scale datasets currently of interest in GWAS. We present tstrait, an open-source Python library to simulate quantitative traits on ARGs, and show how this user-friendly software can quickly simulate phenotypes for biobank-scale datasets on a laptop computer.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

tstrait: a quantitative trait simulator for ancestral recombination graphs

Tagami,

Bisschop,

Kelleher

2024

Preprint

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A general and efficient representation of ancestral recombination graphs

Wong,

Ignatieva,

Koskela

et al. 2023

Preprint

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As a result of recombination, adjacent nucleotides can have different paths of genetic inheri-tance and therefore the genealogical trees for a sample of DNA sequences vary along the genome. The structure capturing the details of these intricately interwoven paths of inheritance is referred to as an ancestral recombination graph (ARG). New developments have made it possible to infer ARGs at scale, enabling many new applications in population and statistical genetics. This rapid progress, however, has led to a substantial gap opening between theory and practice. Standard mathematical formalisms, based on exhaustively detailing the “events” that occur in the history of a sample, are insufficient to describe the outputs of current methods. Moreover, we argue that the underlying assumption that all events can be known and precisely estimated is fundamentally unsuited to the realities of modern, population-scale datasets. We propose an alternative mathematical formulation that encompasses the outputs of recent methods and can capture the full richness of modern large-scale datasets. By defining this ARG encoding in terms of specific genomes and their intervals of genetic inheritance, we avoid the need to exhaustively list (and estimate)allevents. The effects of multiple events can be aggregated in different ways, providing a natural way to express many forms of approximate and partial knowledge about the recombinant ancestry of a sample.

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Evaluating ARG-estimation methods in the context of estimating population-mean polygenic score histories

Peng,

Mulder,

Edge

2024

Preprint

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Scalable methods for estimating marginal coalescent trees across the genome present new opportunities for studying evolution and have generated considerable excitement, with new methods extending scalability to thousands of samples. Benchmarking of the available methods has revealed general tradeoffs between accuracy and scalability, but performance in downstream applications has not always been easily predictable from general performance measures, suggesting that specific features of the ARG may be important for specific downstream applications of estimated ARGs. To exemplify this point, we benchmark ARG estimation methods with respect to a specific set of methods for estimating the historical time course of a population-mean polygenic score (PGS) using the marginal coalescent trees encoded by the ancestral recombination graph (ARG). Here we examine the performance in simulation of six ARG estimationmethods: ARGweaver, RENT+, Relate, tsinfer+tsdate, ARG-Needle/ASMC-clust, andSINGER, using their estimated coalescent trees and examining bias, mean squared error (MSE), confidence interval coverage, and Type I and II error rates of the downstream methods. Although it does not scale to the sample sizes attainable by other new methods,SINGERproduced the most accurate estimated PGS histories in many instances, even whenRelate, tsinfer+tsdate, andARG-Needle/ASMC-clustused samples ten times as large as those used by SINGER. In general, the best choice of method depends on the number of samples available and the historical time period of interest. In particular, the unprecedented sample sizes allowed byRelate, tsinfer+tsdate, andARG-Needle/ASMC-clustare of greatest importance when the recent past is of interest—further back in time, most of the tree has coalesced, and differences in contemporary sample size are less salient.

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Tree-based QTL mapping with expected local genetic relatedness matrices

Cited by 3 publications

References 102 publications

tstrait: a quantitative trait simulator for ancestral recombination graphs

tstrait: a quantitative trait simulator for ancestral recombination graphs

A general and efficient representation of ancestral recombination graphs

Evaluating ARG-estimation methods in the context of estimating population-mean polygenic score histories

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