Genome wide association study for milk production traits in domestic goat (Capra hircus)

Abstract

Economically important milk production traits including milk volume, milk fat and protein content vary considerably in the New Zealand dairy goat herd. A significant portion of the variation is attributable to genetic variation. This study focuses on the discovery of genetic loci linked to milk production traits that can be used for selection across the goat population to increase production. Prior to this study, there had not been any genetic investigation of the NZ dairy goat population and because of this, there are no marker assisted selection breeding programmes. A genome wide association study with an applied mixed model to account for differences in population structure was performed in 3165 NZ dairy goats. The study highlighted eight quantitative trait loci (QTL) associated with milk production traits at the Bonferroni-corrected p-value threshold of significance (P value < 1 x 10-2). Two significant QTL on chromosomes 14 and 19 became the focus of the majority of the study reported here. The QTL on chromosome 14 associated with milk fat content was thought to be due to the effects of DGAT1, a well-established lactation gene. Two previously identified DGAT1polymorphisms (R251Land R396W) known to impact milk fat content were detected in the NZ dairy goat population. Additionally, several previously unknown DGAT1 polymorphisms were also detected. The QTL on chromosome 19 associated with multiple milk production traits was found to be also associated with live weight. The findings indicate heavier animals produced more milk. A CCG deletion in the growth gene KDM6B, resulting in a deletion of a proline amino acid was thought to be responsible for the chromosome 19 QTL. The top linked SNP for the chromosome 19 QTL are now progressively being used to select the next generation of high performance animals. The data set is currently being genotyped with a new set of markers including the candidate KDM6BCCG deletion and this data will help confirm or otherwise this candidate as causal. Overall this project has delivered an initial selection tool which will provide considerable economic benefit and as the work progresses, has the potential to deliver additional markers for selection.