A Genome-Wide Association Study Identifying Genetic Variants Associated with Growth, Carcass and Meat Quality Traits in Rabbits

Abstract

Growth, carcass characteristics and meat quality are the most important traits used in the rabbit industry. Identification of the candidate markers and genes significantly associated with these traits will be beneficial in rabbit breeding. In this study, we enrolled 465 rabbits, including 16 male Californian rabbits and 17 female Kangda5 line rabbits as the parental generation, along with their offspring (232 male and 200 female), in a genome-wide association study (GWAS) based on SLAF-seq technology. Bodyweight at 35, 42, 49, 56, 63 and 70 d was recorded for growth traits; and slaughter liveweight (84 d) and dressing out percentage were measured as carcass traits; and cooking loss and drip loss were measured as meat quality traits. A total of 5,223,720 SLAF markers were obtained by digesting the rabbit genome using RsaI + EcoRV-HF® restriction enzymes. After quality control, a subset of 317,503 annotated single-nucleotide polymorphisms (SNPs) was retained for subsequent analysis. A total of 28, 81 and 10 SNPs for growth, carcass and meat quality traits, respectively, were identified based on genome-wide significance (p < 3.16 × 10−7). Additionally, 16, 71 and 9 candidate genes were identified within 100 kb upstream or downstream of these SNPs. Further analysis is required to determine the biological roles of these candidate genes in determining rabbit growth, carcass traits and meat quality.

Genome-Wide Association Studies for Growth Curves in Meat Rabbits Through the Single-Step Nonlinear Mixed Model

“…The absence of SNPs independently associated with either mature weight or maturity rate would indicate the specific genetic architecture of growth performance for this studied population. Also, the number of significant SNPs identified in this study was also higher than our former observation (Yang et al, 2020) that was alternatively performed through the separate association analysis with BW at different time points. However, no growth curve parameter was estimated and used as pseudo-phenotype of association analysis by Yang et al (2020), which would disable the direct comparison.…”

“…One commercial crossbred population of meat rabbits, by crossing 22 Kangda5 rabbits (_) with 53 Californian rabbits (\), was subjected to collection of phenotypic records, which was described in our previous study (Yang et al, 2020). In brief, individual body weight (BW) was initially measured for 461 rabbits at seven time points, including 35,42,49,56,63,70,and 84 DOA,respectively. At each time point, the phenotypic records were set to missing values if they deviated by more than three standard deviations (SD) from the population mean.…”

“…For the initial SNP set that was generated from specific-locus amplified fragment sequencing approach (Yang et al, 2020), we reapplied more strict criterion of quality control (QC) using the filtering expression of "QualByDepth (QD) < 2.0 || FisherStrand (FS) > 60.0 || RMSMappingQuality (MQ) < 40.0" intrinsically implemented in GATK software v4.2 (McKenna et al, 2010). A total of 6,721,762 SNPs were obtained and subjected to additional QC steps using PLINK software v1.9 (Chang et al, 2015), which required the genotype missing rate lower than 0.1, individual missing rate lower than 0.2, minor allele frequency (MAF) higher than 0.05, and no extreme deviation from Hardy-Weinberg equilibrium (i.e., only retained SNPs with p > 1.0E−08).…”

“…One of the important reasons is the serious lack of relevant studies conducted in rabbits, such as the genome-wide association studies (GWAS) and genomic selection (GS) for the economically important traits. Recently, some pioneer studies were published about the GWAS (Sosa-Madrid et al, 2020;Yang et al, 2020;Bovo et al, 2021) and GS (Chen et al, 2021;Helal et al, 2021;Mancin et al, 2021) in rabbits.…”

“…Therefore, the relevant genetic studies on growth in livestock can be implemented through different approaches. The first approach is to select one or a few representative time points and subject them to separate analysis; for instance, the GWAS of growth traits were separately performed among multiple time points of age in meat rabbits (Yang et al, 2020). The second approach is to fit growth curves using nonlinear regression models and obtain the growth curve parameters (such as mature weight and maturity rate), and subsequently, these derived parameters are used as the pseudo-phenotypes for association analysis.…”