Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture

M E Goddard; K E Kemper; I M MacLeod; A J Chamberlain; B J Hayes

doi:10.1098/rspb.2016.0569

Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture

Proc Biol Sci. 2016 Jul 27;283(1835):20160569. doi: 10.1098/rspb.2016.0569.

Authors

M E Goddard¹, K E Kemper², I M MacLeod³, A J Chamberlain⁴, B J Hayes⁵

Affiliations

¹ Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, Victoria 3010, Australia Department of Economic Development, Jobs, Transport and Resources, AgriBio, La Trobe University, Bundoora, Victoria 3083, Australia mike.goddard@ecodev.vic.gov.au.
² Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, Victoria 3010, Australia.
³ Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, Victoria 3010, Australia Department of Economic Development, Jobs, Transport and Resources, AgriBio, La Trobe University, Bundoora, Victoria 3083, Australia Dairy Futures Cooperative Research Centre, AgriBio, La Trobe University, Bundoora, Victoria 3083, Australia.
⁴ Department of Economic Development, Jobs, Transport and Resources, AgriBio, La Trobe University, Bundoora, Victoria 3083, Australia.
⁵ Department of Economic Development, Jobs, Transport and Resources, AgriBio, La Trobe University, Bundoora, Victoria 3083, Australia School of Applied System Biology, La Trobe University, Agribiosciences Building, Bundoora, Australia.

Abstract

Complex or quantitative traits are important in medicine, agriculture and evolution, yet, until recently, few of the polymorphisms that cause variation in these traits were known. Genome-wide association studies (GWAS), based on the ability to assay thousands of single nucleotide polymorphisms (SNPs), have revolutionized our understanding of the genetics of complex traits. We advocate the analysis of GWAS data by a statistical method that fits all SNP effects simultaneously, assuming that these effects are drawn from a prior distribution. We illustrate how this method can be used to predict future phenotypes, to map and identify the causal mutations, and to study the genetic architecture of complex traits. The genetic architecture of complex traits is even more complex than previously thought: in almost every trait studied there are thousands of polymorphisms that explain genetic variation. Methods of predicting future phenotypes, collectively known as genomic selection or genomic prediction, have been widely adopted in livestock and crop breeding, leading to increased rates of genetic improvement.

Keywords: complex traits; genome-wide association studies; genomic selection.

Publication types

Review

MeSH terms

Breeding
Genetic Association Studies*
Genomics
Models, Genetic*
Models, Statistical
Phenotype*
Polymorphism, Single Nucleotide*
Quantitative Trait Loci