Genome-wide association study for economic important traits in Japanese quail-comparison of multi-step BayesB and the single-step GBLUP methods

 Applying the appropriate statistical method to genome wide association studies (GWAS (is one of the major factors influencing the identify chromosome regions effect of quantitative traits. The single-step genomic best linear unbiased prediction (ssGBLUP) approach, a quite common procedure in GWAS, has the advantage of simultaneously using the phenotypes of genotyped and non-genotyped animals, pedigrees, and genotypes; therefore, there is no need to calculate pseudo-phenotypes. It has been reported that the use of ssGBLUP procedure increased the accuracy of genetic evaluation in many contexts and species compared with pedigree-based BLUP. However, the ssGBLUP assumes that all SNPs explain the same amount of genetic variance, which is unlikely in the case of traits whose major genes or QTL are segregating. The weighted single-step genome wide association studies (WssGWAS) approach allows the use of different weights for each SNP according to their trait-relevant importance and improves the accuracy of genetic evaluation and the precision of estimates of SNP effects. Thus, The aim of the present study was to compare the explained genetic variance from multi-step Bayes B (MS-BayesB) method in the different values of π with weighted single-step genome wide association study (WssGWAS (method related to some economically important traits in 920 Japanese quails.

 For each bird, a total of three traits including body weight gain (BWG), feed intake (FI) and feed conversion ratio (FCR) were recorded and by using Illumnia iSelect 4K Japanese quail SNP Bead chip. For associations between traits and effective SNPs using the GenSel and BLUPF90 family software. The effects of markers and the genomic estimated breeding values of the traits were obtained by five iterations of WssGWAS. The proportion of additive genetic variance (agv) for each of 1.5-Mb genomic window (adjacent SNPs) was used to identify informative genomic regions and candidate genes, accounting for more than 1% of the agv. Also, to estimate SNP marker effects, the Bayes-B method was used (Meuwissen et al., 2009) with set π 0.90, 0.95, 0.99 The Bayes-B method assumes that some proportion (π) of SNP markers has zero effects. The posterior distributions of the parameters and effects were obtained using Gibbs sampling. We performed a Markov chain Monte Carlo (MCMC) simulation of 41,000 rounds with Gibbs sampling, of which the first 1000 iterations were discarded as burn-in. To estimate posterior means and variances of marker effects, Metropolis–Hastings samples were run for 10 iterations. The QTL windows were identified and located for candidate genes using the Coturnix_japonica_2.0 assembly. DAVID v6.8 Functional Annotation Tool (Huang et al. 2009) was used for gene ontology (GO) enrichment in order to detect biological terms associated with genomic regions and gene networks identified in the analysis. Enrichment analysis of gene function was performed using implementation of the Bonferroni test of overrepresentation.

These unknown genotype individuals can supply additional information to improve the statistical power of QTL detection. Sample size can influence the power of GWAS. In general, the results showed that the Bayes A method performed better in explained additive genetic variance compared to BayesB method with π=90. A total of 15 significant windows over 1% explained genetic variance on 10 chromosomes were found for the BWG and explained 23.1% of agv. For FI, we identified 14 informative windows across 9 chromosomes, and explained 28.3% of the agv. Also, for the FCR, 12 significant windows were identified on 9 chromosomes and explained 27.4% of agv. The detected candidate genes in genomic regions played an important role in muscle development, feed intake and residual feed intake. Results of this study showed that use single-step Bayesian methods of phenotype, genotype and pedigree information simultaneously, had outperform in comparison than other multi-step BayesB method. Moreover, considering the identification of new genome regions and the key role of the mentioned genes in development of body weight and feed efficiency, the WssGWAS method can be validated for GWAS for economic traits in Japanese quail.

 In the present study, we identified a wide range of genomic regions associated with body weight gain and feed efficiency traits. The findings of this study provide an important foundation for future fine-mapping studies to more precisely elucidate the mutations affecting production traits in Japanese quail. Future studies should establish causative links between candidate variants and economically important phenotypes using functional analyses.