Genome-wide association study and accuracy of genomic prediction for teat number in Duroc pigs using genotyping-by-sequencing

Abstract

Background: The number of teats in pigs is related to a sow's ability to rear piglets to weaning age. Several studies have identified genes and genomic regions that affect teat number in swine but few common results were reported. The objective of this study was to identify genetic factors that affect teat number in pigs, evaluate the accuracy of genomic prediction, and evaluate the contribution of significant genes and genomic regions to genomic broad-sense heritability and prediction accuracy using 41,108 autosomal single nucleotide polymorphisms (SNPs) from genotyping-by-sequencing on 2936 Duroc boars.

Results: Narrow-sense heritability and dominance heritability of teat number estimated by genomic restricted maximum likelihood were 0.365 ± 0.030 and 0.035 ± 0.019, respectively. The accuracy of genomic predictions, calculated as the average correlation between the genomic best linear unbiased prediction and phenotype in a tenfold validation study, was 0.437 ± 0.064 for the model with additive and dominance effects and 0.435 ± 0.064 for the model with additive effects only. Genome-wide association studies (GWAS) using three methods of analysis identified 85 significant SNP effects for teat number on chromosomes 1, 6, 7, 10, 11, 12 and 14. The region between 102.9 and 106.0 Mb on chromosome 7, which was reported in several studies, had the most significant SNP effects in or near the PTGR2, FAM161B, LIN52, VRTN, FCF1, AREL1 and LRRC74A genes. This region accounted for 10.0% of the genomic additive heritability and 8.0% of the accuracy of prediction. The second most significant chromosome region not reported by previous GWAS was the region between 77.7 and 79.7 Mb on chromosome 11, where SNPs in the FGF14 gene had the most significant effect and accounted for 5.1% of the genomic additive heritability and 5.2% of the accuracy of prediction. The 85 significant SNPs accounted for 28.5 to 28.8% of the genomic additive heritability and 35.8 to 36.8% of the accuracy of prediction.

Conclusions: The three methods used for the GWAS identified 85 significant SNPs with additive effects on teat number, including SNPs in a previously reported chromosomal region and SNPs in novel chromosomal regions. Most significant SNPs with larger estimated effects also had larger contributions to the total genomic heritability and accuracy of prediction than other SNPs.

Fig. 1

Phenotypic distribution of total teat number in Duroc boars (N = 2936)

Fig. 2

Effect of the multidimensional scaling (MDS) dimensions on the genomic inflation factor and on Manhattan plots of SNP significance. a Genomic inflation factor remained relatively unchanged as the number of MDS dimensions increased beyond the first 35 dimensions. b–e GWAS significance from PLINK using the first 35 to 50 MDS dimensions, showing that the significance patterns were virtually unchanged, with the exception of those for chromosome 12, which displayed decreasing significance as the number of MDS dimensions increased. All p values in the figures are on the log(1/p) scale

Fig. 3

Manhattan plots from three methods of genome-wide association analysis. a Manhattan plot of p values for testing additive SNP effects using the generalized least squares (GLS) analysis of EPISNP2. b Manhattan plot of p values for testing dominance SNP effects using the generalized least squares (GLS) analysis of EPISNP2. c Manhattan plot of p values for testing additive SNP effects using the least squares (LS) analysis of PLINK with the first 35 dimensions of multidimensional scaling (MDS) as fixed effects. d Manhattan plot of p values for testing additive SNP effects using the LS analysis of EPISNP1 with the first 35 MDS dimensions as fixed effects. The horizontal green line indicates the genome-wide significance with the Bonferroni correction (p < 10^−5.91). All p values in the figures are in log(1/p) scale

Fig. 4

Analysis of the region between 102.9 and 106.0 Mb on chromosome 7. a Additive SNP effects by the generalized least squares analysis of EPISNP2 and by the least squares analysis of PLINK and EPISNP1, with stratification correction using the first 35 dimensions of multidimensional scaling. b Removal of the genotypic effects of the 14 SNPs with genome-wide significance by fitting these SNPs as fixed effects in the model completely removed all significant effects in this region and also removed the significant effects in the 116-Mb region on chromosome 7. c SNP contribution to genomic heritability and prediction accuracy of the 70 SNPs that are located within the region between 102.9 and 106.0 Mb, showing that the largest contributions originated from SNPs that were within or near the AREL1 and PTGR2 genes. d Linkage disequilibrium between the 21 significant SNPs in the region between 102.9 and 106.0 Mb on chromosome 7 by Haploview

Fig. 5

SNP partial heritability in the region between 102.9 and 106.0 Mb on chromosome 7 from two models with 20.5 and 41 K SNPs. The results show that partial heritability estimates were nearly unaffected by the number of SNPs in the model