Detection of QTL for traits related to adaptation to sub-optimal climatic conditions in chickens

Abstract

BackgroundGrowth traits can be used as indicators of adaptation to sub-optimal conditions. The current study aimed at identifying quantitative trait loci (QTL) that control performance under variable temperature conditions in chickens.MethodsAn F2 population was produced by crossing the Taiwan Country chicken L2 line (selected for body weight, comb area, and egg production) with an experimental line of Rhode Island Red layer R- (selected for low residual feed consumption). A total of 844 animals were genotyped with the 60 K Illumina single nucleotide polymorphism (SNP) chip. Whole-genome interval linkage mapping and a genome-wide association study (GWAS) were performed for body weight at 0, 4, 8, 12, and 16 weeks of age, shank length at 8 weeks of age, size of comb area at 16 weeks of age, and antibody response to sheep red blood cells at 11 weeks of age (7 and 14 days after primary immunization). Relevant genes were identified based on functional annotation of candidate genes and potentially relevant SNPs were detected by comparing whole-genome sequences of several birds between the parental lines.ResultsWhole-genome QTL analysis revealed 47 QTL and 714 effects associated with 178 SNPs were identified by GWAS with 5% Bonferroni genome-wide significance. Little overlap was observed between the QTL and GWAS results, with only two chromosomal regions detected by both approaches, i.e. one on GGA24 (GGA for Gallus gallus chromosome) for BW04 and one on GGAZ for six growth-related traits. Based on whole-genome sequence, differences between the parental lines based on several birds were screened in the genome-wide QTL regions and in a region detected by both methods, resulting in the identification of 106 putative candidate genes with a total of 15,443 SNPs, of which 41 were missense and 1698 were not described in the dbSNP archive.ConclusionsThe QTL detected in this study for growth and morphological traits likely influence adaptation of chickens to sub-tropical climate. Using whole-genome sequence data, we identified candidate SNPs for further confirmation of QTL in the F2 design. A strong QTL effect found on GGAZ underlines the importance of sex-linked inheritance for growth traits in chickens.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-017-0314-5) contains supplementary material, which is available to authorized users.

Genome-wide association scan for QTL and their positional candidate genes associated with internal organ traits in chickens

“…Additionally, different QTL can be detected depending on the method and the density of markers used. In this context, in a study performed to identify QTL that control performance under variable temperature conditions in chickens, Lien et al [44] compared QTL detected in an F2 chicken population by GWAS using univariate linear mixed model (GEMMA software) and using interval mapping method (QTLMap software). This study showed that eight QTL were detected by QTLMap, 47 were detected by GEMMA and from those, only two were common to both approaches [44].…”

Identification of Major Loci and Candidate Genes for Meat Production-Related Traits in Broilers

“…A 24.08 Kb QTL on GGA24 (GGA24: 5.73–5.75 Mb) was identified for the six weight traits. According to the Animal QTL Database 8 , this region has been recorded as a QTL (GGA24: 4.3–6.0 Mb) for BW08 in an F2 population (Taiwan local chicken line L2 × experimental Rhode Island Red line; Lien et al, 2017 ). This region contains only one candidate gene ( DRD2 ), which encodes the D2 subtype of the dopamine receptor.…”

Genome-Wide Association Study Using Whole-Genome Sequencing Identifies a Genomic Region on Chromosome 6 Associated With Comb Traits in Nandan-Yao Chicken

“…To date, relatively few GWAS have been performed on comb traits, among which only two studies are established on the quantitative characters of combs. One was conducted on CL, CH, and CW using 600 K SNP arrays ( Shen et al, 2016 ) and the other on CA using a 60 K SNP chip ( Lien et al, 2017 ). Compared with these two studies, the magnitude of SNPs used in our study was much larger.…”