Genome‐wide association analysis and genetic architecture of egg weight and egg uniformity in layer chickens

Abstract

The pioneering work by Professor Soller et al., among others, on the use of genetic markers to analyze quantitative traits has provided opportunities to discover their genetic architecture in livestock by identifying quantitative trait loci (QTL). The recent availability of high-density single nucleotide polymorphism (SNP) panels has advanced such studies by capitalizing on population-wide linkage disequilibrium at positions across the genome. In this study, genomic prediction model Bayes-B was used to identify genomic regions associated with the mean and standard deviation of egg weight at three ages in a commercial brown egg layer line. A total of 24,425 segregating SNPs were evaluated simultaneously using over 2900 genotyped individuals or families. The corresponding phenotypic records were represented as individual measurements or family means from full-sib progeny. A novel approach using the posterior distribution of window variances from the Monte Carlo Markov Chain samples was used to describe genetic architecture and to make statistical inferences about regions with the largest effects. A QTL region on chromosome 4 was found to explain a large proportion of the genetic variance for the mean (30%) and standard deviation (up to 16%) of the weight of eggs laid at specific ages. Additional regions with smaller effects on chromosomes 2, 5, 6, 8, 20, 23, 28 and Z showed suggestive associations with mean egg weight and a region on chromosome 13 with the standard deviation of egg weight at 26-28 weeks of age. The genetic architecture of the analyzed traits was characterized by a limited number of genes or genomic regions with large effects and many regions with small polygenic effects. The region on chromosome 4 can be used to improve both the mean and standard deviation of egg weight by marker-assisted selection.

Heritable Environmental Variance Causes Nonlinear Relationships Between Traits: Application to Birth Weight and Stillbirth of Pigs

“…In vertebrates, the FTO genotype was shown to be associated with phenotypic variability in body mass index in humans, with the two homozygotes differing 7% in standard deviation (SD) (Yang et al 2012). In laying hens, a QTL region on chromosome 4 was identified that explained 30% and 16% of the genetic variance in mean and within-individual SD, respectively, of egg weight (Wolc et al 2012); and in dairy cattle the phenotypic variability of somatic cell score, an indicator for mastitis, was shown to be determined by many loci, while that with the largest effect explained 3% of the genetic variance in V E (Mulder et al 2013a). It has been argued that findings such as those of Yang et al (2012) can also be explained in terms of a scale effect (Sun et al 2013), but the extreme transformations required are not biologically plausible (Shen and Rönnegård 2013).…”

Single nucleotide polymorphisms associated with growth traits in Jinghai yellow chickens

“…In chickens, many genome-wide association studies (GWAS) were conducted using the Illumina 60K SNP Beadchip. Using this method, major loci associated with growth (Gu et al, 2011;Xie et al, 2012), egg production Wolc et al, 2012), rumpless and ear-tufted traits (Noorai et al, 2012), body composition and meat quality (Liu et al, 2013), resistance to Marek's disease (Li et al, 2013), and immune responses to Newcastle disease virus (Luo et al, 2013) were identified.…”

Marker Assisted Selection-Applications and Evaluation for Commercial Poultry Breeding

“…In vitro amplify specific DNA sequences with the help of Recently in plant and animal breeding, the concept of QTL affecting the variance of quantitative traits (vQTL) has gained importance because of the increasing importance of uniformity (Wolc et al, 2012). The first of published studies on vQTL was on recombinant inbred lines of maize for economically important traits (Ordas et al, 2008), whereas in animals, researchers failed to detect significant vQTL while working on back fat thickness in pigs (Yang et al, 2011).…”

“…Because of lesser investment in the field of molecular markers, the full potential of MAS is not being capitalized in developing countries. A practical complication analysed for MAS is the loss of relationship between distinct marker allele and favourable trait, when marker is not in proximity of the region to be selected (Fulton, 2012).…”