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Heredity (Edinb). 2008 Feb;100(2):158-70. Epub 2007 Feb 21.

Combining population genomics and quantitative genetics: finding the genes underlying ecologically important traits.

Author information

1
Department of Ecology and Evolutionary Biology, Centre for the Analysis of Genome Evolution and Function, University of Toronto, Toronto, Ontario, Canada. john.stinchcombe@utoronto.ca

Abstract

A central challenge in evolutionary biology is to identify genes underlying ecologically important traits and describe the fitness consequences of naturally occurring variation at these loci. To address this goal, several novel approaches have been developed, including 'population genomics,' where a large number of molecular markers are scored in individuals from different environments with the goal of identifying markers showing unusual patterns of variation, potentially due to selection at linked sites. Such approaches are appealing because of (1) the increasing ease of generating large numbers of genetic markers, (2) the ability to scan the genome without measuring phenotypes and (3) the simplicity of sampling individuals without knowledge of their breeding history. Although such approaches are inherently applicable to non-model systems, to date these studies have been limited in their ability to uncover functionally relevant genes. By contrast, quantitative genetics has a rich history, and more recently, quantitative trait locus (QTL) mapping has had some success in identifying genes underlying ecologically relevant variation even in novel systems. QTL mapping, however, requires (1) genetic markers that specifically differentiate parental forms, (2) a focus on a particular measurable phenotype and (3) controlled breeding and maintenance of large numbers of progeny. Here we present current advances and suggest future directions that take advantage of population genomics and quantitative genetic approaches - in both model and non-model systems. Specifically, we discuss advantages and limitations of each method and argue that a combination of the two provides a powerful approach to uncovering the molecular mechanisms responsible for adaptation.

PMID:
17314923
DOI:
10.1038/sj.hdy.6800937
[Indexed for MEDLINE]
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