Genetic Architecture of Complex Traits and Accuracy of Genomic Prediction: Coat Colour, Milk-Fat Percentage, and Type in Holstein Cattle as Contrasting Model Traits

Abstract

Prediction of genetic merit using dense SNP genotypes can be used for estimation of breeding values for selection of livestock, crops, and forage species; for prediction of disease risk; and for forensics. The accuracy of these genomic predictions depends in part on the genetic architecture of the trait, in particular number of loci affecting the trait and distribution of their effects. Here we investigate the difference among three traits in distribution of effects and the consequences for the accuracy of genomic predictions. Proportion of black coat colour in Holstein cattle was used as one model complex trait. Three loci, KIT, MITF, and a locus on chromosome 8, together explain 24% of the variation of proportion of black. However, a surprisingly large number of loci of small effect are necessary to capture the remaining variation. A second trait, fat concentration in milk, had one locus of large effect and a host of loci with very small effects. Both these distributions of effects were in contrast to that for a third trait, an index of scores for a number of aspects of cow confirmation (“overall type”), which had only loci of small effect. The differences in distribution of effects among the three traits were quantified by estimating the distribution of variance explained by chromosome segments containing 50 SNPs. This approach was taken to account for the imperfect linkage disequilibrium between the SNPs and the QTL affecting the traits. We also show that the accuracy of predicting genetic values is higher for traits with a proportion of large effects (proportion black and fat percentage) than for a trait with no loci of large effect (overall type), provided the method of analysis takes advantage of the distribution of loci effects.

The distribution of SNP marker effects for faecal worm egg count in sheep, and the feasibility of using these markers to predict genetic merit for resistance to worm infections

“…This is potentially a common architecture for many complex traits (e.g. Hayes et al, 2010;Yang et al, 2010). The distribution of marker effects has implications for the findings of this genome-wide association study, and other QTL detection studies in the literature.…”

“…However, the genetic architecture underlying such traits is poorly understood. Genetic architecture, that is, the size and distribution of polymorphisms affecting the trait, influences the success of association studies and the ability to predict future phenotypes, such as when predicting genetic risk to disease or when selecting livestock for breeding (Wray et al, 2007 ;Hayes et al, 2010). From an evolutionary perspective, knowledge of the genetic architecture in disease traits may help to elucidate the evolutionary influence of disease on hosts (Dawkins & Krebs, 1979).…”

Finding genes for economically important traits: Brahman cattle puberty

“…For AGE26, the calibration dataset comprised those bulls born until December 2005 (n = 565) and the bulls in the validation dataset were born from January 2006 onwards (n = 550). Using birth date to define calibration and validation datasets is a tested approach (Hayes et al 2010). A GWAS for AGECL using the full dataset has been reported previously (Hawken et al 2012), but the results reserving 363 heifers for a validation exercise are reported only here.…”

Genome-wide association and regional heritability mapping to identify loci underlying variation in nematode resistance and body weight in Scottish Blackface lambs

“…Attempts to increase the power of association studies have focused either on increasing the number of markers or the number of observations for a trait. Few have attempted to formally estimate the distribution of marker effects with dense SNP markers (for example, Hayes et al, 2010;Kemper et al, 2011) and thus the required power of experiments for complex disease traits, such as nematode resistance, is still unknown. An alternate approach exploiting dense SNP chip data, known as Regional Genomic Relationship Mapping or Regional Heritability Mapping (RHM) (Nagamine et al, 2012), has been advanced as a better approach to capture more of the underlying genetic effects.…”