Genome wide association study of body weight and feed efficiency traits in a commercial broiler chicken population, a re-visitation

Abstract

Genome wide association study was conducted using a mixed linear model (MLM) approach that accounted for family structure to identify single nucleotide polymorphisms (SNPs) and candidate genes associated with body weight (BW) and feed efficiency (FE) traits in a broiler chicken population. The results of the MLM approach were compared with the results of a general linear model approach that does not take family structure in to account. In total, 11 quantitative trait loci (QTL) and 21 SNPs, were identified to be significantly associated with BW traits and 5 QTL and 5 SNPs were found associated with FE traits using MLM approach. Besides some overlaps between the results of the two GWAS approaches, there are considerable differences in the detected QTL. Even though the genomic inflation factor (λ) values indicate that there is no strong family structure in this population, using models that account for the existing family structure may reduce bias and increase accuracy of the estimated SNP effects in the association analysis. The SNPs and candidate genes identified in this study provide information on the genetic background of BW and FE traits in broiler chickens and might be used as prior information for genomic selection.

The genetics of body weight and feed efficiency in broiler chickens

“…Hence, the increase in the heritability estimates across the selection trajectory attest the increase in genetic variance is not only due to scale effects rather it may be attributed to accumulation of new mutuations or other genetic effects that are not properly accounted in the statistical model. This is consistent with the reported results in chapter 5 (Mebratie et al, 2019) in which we have detected a number of QTL regions and candidate genes associated with BW and FE traits even though birds were pre-selected for BW and undergone several generations of selection for FE. In fact, the increase in genetic variance of quantitative traits from the accumulation of new mutations has been known for quite some time (Hill, 2000;.…”

“…The genetic correlations of BW between different ages is also shown to decrease as the distance between BW measurements increased suggesting that BW at "early age" and "late age" is partly controlled by different genes which might be related to change in the physiological system of birds with age as explained by stating that there could be genes that "turn on" and "turn off" as an animal ages causing changes in physiology and performance. This is consistent with our results in chapter 5 (Mebratie et al, 2019) where we have identified age specific Quantitative trait locus (QTL) regions; different QTLs and candidate genes were identified to be associated with BW at three different ages with only a little overlap between the identified QTL regions and candidate genes across the three ages. The reported overlap in QTL and candidate genes were for BW measurements that were taken closer together than those recorded far apart.…”

“…In fact, results from long term selection experiments provide little evidence of long term genetic plateaus, and suggest that plateaus are only temporary when they do occur . This is also in line with our results in chapter 5 (Mebratie et al, 2019) where we have identified several loci and candidate genes associated with BW and FE traits, even though birds were pre-selected for BW and undergone several generations of selection for FE. Indeed, BW and FE traits are quantitative traits influenced by many genes which therefore do not go to fixation quickly.…”

“…Body weight and feed efficiency traits are quantitaive traits which are controlled by many genes, each having small effects. In chapter 5 (Mebratie et al, 2019), a number of QTL regions have been reported to be associated with BW and FE traits. This is a remarkable finding, given the fact that the broiler population were pre-selected for body weight and undergone several generations of selection for feed efficiency, one may not expect to detect a considerable number of QTL.…”

“…Mixed models offer a practical and comprehensive approach for simultaneously addressing confounding due to population stratification, family structure and cryptic relatedness (Gialona et al, 2018;. In chapter 5 (Mebratie et al, 2019), we have conducted GWAS using a statistical model (Mixed linear model) that takes in to account family structure by using the genomic relationship matrix and compared the results with a general linear model (GLM) which does not take family structure in to account. Although there were overlaps in the detected QTL regions in the two methods, most of the identified QTL regions for BW and FE traits were different suggesting that careful consideration of statistical models in GWAS is important.…”