Genomic Quantitative Genetics to Study Evolution in the Wild

Phillip Gienapp, Simone Fior, Frédéric Guillaume, Jesse R. Lasky, Victoria L. Sork, Katalin Csilléry

Research output: Contribution to journalReview articlepeer-review

91 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)897-908
Number of pages12
JournalTrends in Ecology and Evolution
Issue number12
StatePublished - Dec 2017

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics


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