4.8 Article

Global Genetic Heterogeneity in Adaptive Traits

Journal

MOLECULAR BIOLOGY AND EVOLUTION
Volume 38, Issue 11, Pages 4822-4831

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/molbev/msab208

Keywords

evolutionary genomics; GWAS; regulation of gene expression; genetic architecture

Funding

  1. COLCIENCIAS [860]
  2. DAAD (STIBET program)
  3. Open Access Publication Fund of the University of Wuerzburg

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The study demonstrates that sampling locations have an impact on GWAS results in the model plant Arabidopsis thaliana, showing that certain traits are influenced by unique genetic effects in local populations. While using gene expression as a molecular phenotype, some genes are affected by shared variants globally, while others are affected by variants specific to subpopulations.
Understanding the genetic architecture of complex traits is a major objective in biology. The standard approach for doing so is genome-wide association studies (GWAS), which aim to identify genetic polymorphisms responsible for variation in traits of interest. In human genetics, consistency across studies is commonly used as an indicator of reliability. However, if traits are involved in adaptation to the local environment, we do not necessarily expect reproducibility. On the contrary, results may depend on where you sample, and sampling across a wide range of environments may decrease the power of GWAS because of increased genetic heterogeneity. In this study, we examine how sampling affects GWAS in the model plant species Arabidopsis thaliana. We show that traits like flowering time are indeed influenced by distinct genetic effects in local populations. Furthermore, using gene expression as a molecular phenotype, we show that some genes are globally affected by shared variants, whereas others are affected by variants specific to subpopulations. Remarkably, the former are essentially all cis-regulated, whereas the latter are predominately affected by trans-acting variants. Our result illustrate that conclusions about genetic architecture can be extremely sensitive to sampling and population structure.

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