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Abstract The study of ecological speciation is inherently linked to the study of selection. Methods for estimating phenotypic selection within a generation based on associations between trait values and fitness (e.g. survival) of individuals are established. These methods attempt to disentangle selection acting directly on a trait from indirect selection caused by correlations with other traits via multivariate statistical approaches (i.e. inference of selection gradients). The estimation of selection on genotypic or genomic variation could also benefit from disentangling direct and indirect selection on genetic loci. However, achieving this goal is difficult with genomic data because the number of potentially correlated genetic loci (p) is very large relative to the number of individuals sampled (n). In other words, the number of model parameters exceeds the number of observations (p ≫ n). We present simulations examining the utility of whole‐genome regression approaches (i.e. Bayesian sparse linear mixed models) for quantifying direct selection in cases wherep ≫ n. Such models have been used for genome‐wide association mapping and are common in artificial breeding. Our results show they hold promise for studies of natural selection in the wild and thus of ecological speciation. But we also demonstrate important limitations to the approach and discuss study designs required for more robust inferences.
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A two‐phase Bayesian methodology for the analysis of binary phenotypes in genome‐wide association studies
Abstract Recent advances in sequencing and genotyping technologies are contributing to a data revolution in genome‐wide association studies that is characterized by the challenging largepsmallnproblem in statistics. That is, given these advances, many such studies now consider evaluating an extremely large number of genetic markers (p) genotyped on a small number of subjects (n). Given the dimension of the data, a joint analysis of the markers is often fraught with many challenges, while a marginal analysis is not sufficient. To overcome these obstacles, herein, we propose a Bayesian two‐phase methodology that can be used to jointly relate genetic markers to binary traits while controlling for confounding. The first phase of our approach makes use of a marginal scan to identify a reduced set of candidate markers that are then evaluated jointly via a hierarchical model in the second phase. Final marker selection is accomplished through identifying a sparse estimator via a novel and computationally efficient maximum a posteriori estimation technique. We evaluate the performance of the proposed approach through extensive numerical studies, and consider a genome‐wide application involving colorectal cancer.
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- Award ID(s):
- 1826715
- PAR ID:
- 10458722
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Biometrical Journal
- Volume:
- 62
- Issue:
- 1
- ISSN:
- 0323-3847
- Page Range / eLocation ID:
- p. 191-201
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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