TY - JOUR
T1 - Genomic Quantitative Genetics to Study Evolution in the Wild
AU - Gienapp, Phillip
AU - Fior, Simone
AU - Guillaume, Frédéric
AU - Lasky, Jesse R.
AU - Sork, Victoria L.
AU - Csilléry, Katalin
N1 - Funding Information:
We thank Thomas Mitchell-Olds, Jean-Luc Jannink, Marcel Visser and two anonymous reviewers for constructive comments on the manuscript. The idea for this manuscript developed at a Monte Verità Conference in 2016 on ‘The genomic basis of eco-evolutionary change’ organized by the ETH Zurich’s center for Adaptation to a Changing Environment (ACE). We would like to acknowledge the organizers for providing such a fruitful, scientific atmosphere. Camillo Bérénos, Josephine Pemberton, John Stanton-Geddes and Peter Tiffin kindly allowed us to re-use their figures and provided the data to do so. SF was partly supported by the Swiss National Science Foundation (project 31003A_160123). FG is supported by grant PP00P3_144846 from the Swiss National Science Foundation . KC was supported by an ACE Fellowship while working on the manuscript.
Publisher Copyright:
© 2017 Elsevier Ltd
PY - 2017/12
Y1 - 2017/12
N2 - Quantitative genetic theory provides a means of estimating the evolutionary potential of natural populations. However, this approach was previously only feasible in systems where the genetic relatedness between individuals could be inferred from pedigrees or experimental crosses. The genomic revolution opened up the possibility of obtaining the realized proportion of genome shared among individuals in natural populations of virtually any species, which could promise (more) accurate estimates of quantitative genetic parameters in virtually any species. Such a ‘genomic’ quantitative genetics approach relies on fewer assumptions, offers a greater methodological flexibility, and is thus expected to greatly enhance our understanding of evolution in natural populations, for example, in the context of adaptation to environmental change, eco-evolutionary dynamics, and biodiversity conservation. Information about genetic (co)variances of traits is essential to understand and predict evolutionary change. This has gained additional importance in the light of contemporary human-induced environmental change to which species and populations need to adapt. Quantitative genetics studies genetic (co)variances based on the phenotypic resemblance of related individuals. Relatedness among individuals can be known from observed pedigrees or breeding experiments but such information can only be gathered for a limited range of species. The advances in genomics tools now allow us to genotype virtually any species for thousands of genomic markers. Relatedness estimates from such genomic markers have been used in human genetics as well as animal and plant breeding, but doing so also in wild populations has the potential to considerably advance our understanding of adaptation in the wild.
AB - Quantitative genetic theory provides a means of estimating the evolutionary potential of natural populations. However, this approach was previously only feasible in systems where the genetic relatedness between individuals could be inferred from pedigrees or experimental crosses. The genomic revolution opened up the possibility of obtaining the realized proportion of genome shared among individuals in natural populations of virtually any species, which could promise (more) accurate estimates of quantitative genetic parameters in virtually any species. Such a ‘genomic’ quantitative genetics approach relies on fewer assumptions, offers a greater methodological flexibility, and is thus expected to greatly enhance our understanding of evolution in natural populations, for example, in the context of adaptation to environmental change, eco-evolutionary dynamics, and biodiversity conservation. Information about genetic (co)variances of traits is essential to understand and predict evolutionary change. This has gained additional importance in the light of contemporary human-induced environmental change to which species and populations need to adapt. Quantitative genetics studies genetic (co)variances based on the phenotypic resemblance of related individuals. Relatedness among individuals can be known from observed pedigrees or breeding experiments but such information can only be gathered for a limited range of species. The advances in genomics tools now allow us to genotype virtually any species for thousands of genomic markers. Relatedness estimates from such genomic markers have been used in human genetics as well as animal and plant breeding, but doing so also in wild populations has the potential to considerably advance our understanding of adaptation in the wild.
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U2 - 10.1016/j.tree.2017.09.004
DO - 10.1016/j.tree.2017.09.004
M3 - Review article
C2 - 29050794
AN - SCOPUS:85031494915
VL - 32
SP - 897
EP - 908
JO - Trends in Ecology and Evolution
JF - Trends in Ecology and Evolution
SN - 0169-5347
IS - 12
ER -