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Quantitative genetic methods depending on the nature of the phenotypic trait
Author(s) -
Villemereuil Pierre
Publication year - 2018
Publication title -
annals of the new york academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.712
H-Index - 248
eISSN - 1749-6632
pISSN - 0077-8923
DOI - 10.1111/nyas.13571
Subject(s) - quantitative genetics , biology , quantitative trait locus , phenotypic trait , trait , evolutionary biology , phenotype , context (archaeology) , genetics , computational biology , gene , genetic variation , computer science , paleontology , programming language
A consequence of the assumptions of the infinitesimal model, one of the most important theoretical foundations of quantitative genetics, is that phenotypic traits are predicted to be most often normally distributed (so‐called Gaussian traits). But phenotypic traits, especially those interesting for evolutionary biology, might be shaped according to very diverse distributions. Here, I show how quantitative genetics tools have been extended to account for a wider diversity of phenotypic traits using first the threshold model and then more recently using generalized linear mixed models. I explore the assumptions behind these models and how they can be used to study the genetics of non‐Gaussian complex traits. I also comment on three recent methodological advances in quantitative genetics that widen our ability to study new kinds of traits: the use of “modular” hierarchical modeling (e.g., to study survival in the context of capture–recapture approaches for wild populations); the use of aster models to study a set of traits with conditional relationships (e.g., life‐history traits); and, finally, the study of high‐dimensional traits, such as gene expression.