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

Abstract

BackgroundThe 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.ResultsNarrow-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.ConclusionsThe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-017-0311-8) contains supplementary material, which is available to authorized users.

Genomic analysis reveals genes affecting distinct phenotypes among different Chinese and western pig breeds

“… 76 LRRC74A Teat number Tan, et al . 94 ENSSSCG00000024537 Lean production Yu, et al . 95 ENSSSCG00000015700 Lean production Yang, et al .…”

GVCHAP: A Computing Pipeline for Genomic Prediction and Variance Component Estimation Using Haplotypes and SNP Markers

“…Three potential applications involve the use of fixed effects. These include estimation of SNP contributions to prediction accuracy and genomic heritability (Tan et al, 2017), genomic prediction using fixed SNP effects for traits with large SNP effects (Spindel et al, 2015), and prediction of phenotypic values using genomic and non-genetic prediction (GNG) that combines genomic prediction and estimates of non-genetic fixed effects such as age and gender. For GNG, the current version of GVCHAP supports samples with one phenotypic observation per individual (N = n), and the general expression of the GNG values under this assumption is:ŷ = Xb +ĝ (27) whereĝ =â +d +â h for Model 1, =â +â h for Model 2, =d + a h for Model 3, =â h for Model 4, =â +d for Model 5, =â for Model 6, and =d for Model 7.…”

High-Throughput Sequencing With the Preselection of Markers Is a Good Alternative to SNP Chips for Genomic Prediction in Broilers

“…The choice of marker genotyping platform is a key factor affecting the accuracy of genomic estimated breeding values (GEBV) (Tan et al, 2017;Wang et al, 2019;Whalen et al, 2019). Single nucleotide polymorphism (SNP) chips are currently the most common choice in livestock and poultry (Wolc et al, 2011;Wang et al, 2013;Zhang et al, 2017).…”

“…RRGS can produce a large number of genomic markers at a low price and thus be used as an alternative genotyping platform, but the genotype quality of the markers is lower than that obtained with SNP chips (Gorjanc et al, 2015). In animal breeding, the application of RRGS in genomic prediction has attracted great attention and led to many studies (Tan et al, 2017;Wang et al, 2019;Whalen et al, 2019). Gorjanc et al (Gorjanc et al, 2015) used simulation data to show that the use of GBS for genotyping has great potential for genomic selection in livestock populations.…”

“…Gorjanc et al (Gorjanc et al, 2015) used simulation data to show that the use of GBS for genotyping has great potential for genomic selection in livestock populations. Tan et al (2017) used GBS for genotyping in a Duroc boar population, and the accuracy of genomic prediction for teat number was 0.435, but this study did not compare their results to those from SNP chips.…”