Understanding and predicting complex traits: knowledge from cattle

Abstract

The genetic architecture of complex traits in cattle includes very large numbers of loci affecting any given trait. Most of these loci have small effects but occasionally there are loci with moderate-to-large effects segregating due to recent selection for the mutant allele. Genomic markers capture most but not all of the additive genetic variance for traits, probably because there are causal mutations with low allele frequency and therefore in incomplete linkage disequilibrium with the markers. The prediction of genetic value from genomic markers can achieve high accuracy by using statistical models that include all markers and assuming that marker effects are random variables drawn from a specified prior distribution. Recent effective population size is in the order of 100 within cattle breeds and ≈ 2500 animals with genotypes and phenotypes are sufficient to predict the genetic value of animals with an accuracy of 0.65. Recent effective population size for humans is much larger, in the order of 10,000-15,000, and more than 145,000 records would be required to reach a similar accuracy for people. However, our calculations assume that genomic markers capture all the genetic variance. This may be possible in the future as causal polymorphisms are genotyped using genome sequence data.