A QTL for Number of Teats Shows Breed Specific Effects on Number of Vertebrae in Pigs: Bridging the Gap Between Molecular and Quantitative Genetics

Abstract

Modern breeding schemes for livestock species accumulate a large amount of genotype and phenotype data which can be used for genome-wide association studies (GWAS). Many chromosomal regions harboring effects on quantitative traits have been reported from these studies, but the underlying causative mutations remain mostly undetected. In this study, we combine large genotype and phenotype data available from a commercial pig breeding scheme for three different breeds (Duroc, Landrace, and Large White) to pinpoint functional variation for a region on porcine chromosome 7 affecting number of teats (NTE). Our results show that refining trait definition by counting number of vertebrae (NVE) and ribs (RIB) helps to reduce noise from other genetic variation and increases heritability from 0.28 up to 0.62 NVE and 0.78 RIB in Duroc. However, in Landrace, the effect of the same QTL on NTE mainly affects NVE and not RIB, which is reflected in reduced heritability for RIB (0.24) compared to NVE (0.59). Further, differences in allele frequencies and accuracy of rib counting influence genetic parameters. Correction for the top SNP does not detect any other QTL effect on NTE, NVE, or RIB in Landrace or Duroc. At the molecular level, haplotypes derived from 660K SNP data detects a core haplotype of seven SNPs in Duroc. Sequence analysis of 16 Duroc animals shows that two functional mutations of the Vertnin ( VRTN ) gene known to increase number of thoracic vertebrae (ribs) reside on this haplotype. In Landrace, the linkage disequilibrium (LD) extends over a region of more than 3 Mb also containing both VRTN mutations. Here, other modifying loci are expected to cause the breed-specific effect. Additional variants found on the wildtype haplotype surrounding the VRTN region in all sequenced Landrace animals point toward breed specific differences which are expected to be present also across the whole genome. This Landrace specific haplotype contains two missense mutations in the ABCD4 gene, one of which is expected to have a negative effect on the protein function. Together, the integration of largescale genotype, phenotype and sequence data shows exemplarily how population parameters are influenced by underlying variation at the molecular level.

Use of whole-genome sequence data for genomic prediction across populations and species

“…Using the pipeline, ( Derks et al in preparation) identified causal variants that are already known in literature to affect complex traits in pigs. For example, they found a variant in the MC4R gene that is already known to affect production traits in pigs (Kim et al 2000), a causal variant in the VRTN gene affecting the number of teats in pigs (Van Son et al 2019), and a causal variant in the PRKAG3 gene affecting meat quality (Milan et al 2000). In addition to enhancing our understanding of the biology of complex traits, the pipeline can be used to pre-select likely causal variants that can continuously be added on to the array for GP.…”

From sequence to phenotype: the impact of deleterious variation in livestock

“…The impact of the splice mutations on the expression of the gene was assessed using RNA-seq data. The animals sequenced are frequently used artificial insemination boars selected based on extreme phenotypes all present in the genotyping data (van Son et al 2017b). The phenotypes are based on high and low sperm DNA fragmentation index, a measure of well packed double-stranded DNA vs singlestranded denatured DNA, which is an important indicator of boar fertility.…”

“…The combination of genotype and gene expression data provides an additional layer of evidence to find causal variation, as differences in expression of genes can be associated with a variant (expression quantitative trait loci; eQTL). In this study we use 59 RNA-sequenced samples (van Son et al 2017b) from Landrace (n=34) and Duroc (n=25) to test for differential expression between the genotype classes (homozygous reference, heterozygous, homozygous alternative) to associate the expression of genes with the genotypes. The samples were sequenced from testis tissue and further details about the sequenced samples and alignment depth are provided in Table S2.…”

“…Post processing was performed using bcftools (Li et al 2009). Variants with low phred quality score (<20), low call rate (<0.7) and variants within 3 bp of an indel are discarded, leaving a total of 21,648,132 (Landrace), 23,667,234 (Duroc),23,286,212 (Synthetic), and 25,709,552 (Large White) postfiltering variants, respectively. The average per variant call rate is above 98% for all breeds and the ratio transitions to transversions is between 2.33-2.35 (Table S11).…”

“…We used 25 Duroc and 34 Landrace RNA-sequenced boars selected based on high and low sperm DNA fragmentation index, a measure of well packed double-stranded DNA vs single-stranded denatured DNA, which is an important indicator of boar fertility (van Son et al 2017b). The boars were all born in the same period of time and a broad range of semen quality tests were conducted on ejaculates of these boars.…”