Large scale sequence-based screen for recessive variants allows for identification and monitoring of rare deleterious variants in pigs

Abstract

AbstractMost deleterious variants are recessive and segregate at relatively low frequency. Therefore, high sample sizes are required to identify these variants. In this study we report a large-scale sequence based genome-wide association study (GWAS) in pigs, with a total of 120,000 Large White and 80,000 Synthetic breed animals imputed to sequence using a reference population of approximately 1,100 whole genome sequenced pigs. We imputed over 20 million variants with high accuracies (R2>0.9) even for low frequency variants (1-5% minor allele frequency). This sequence-based analysis revealed a total of 13 additive and 8 non-additive significant quantitative trait loci (QTLs) for growth rate and backfat thickness. With the non-additive (recessive) model, we identified a deleterious missense SNP in theCDHR2gene reducing growth rate and backfat in homozygous Large White animals. For the Synthetic breed, we revealed a QTL on chromosome 15 with a frameshift variant in theOBSL1gene. This QTL has a major impact on both growth rate and backfat, resembling human 3M-syndrome 2 which is related to the same gene. With the additive model, we confirmed known QTLs on chromosomes 1 and 5 for both breeds, including variants in theMC4RandCCND2genes. On chromosome 1, we disentangled a complex QTL region with multiple variants affecting both traits, harboring 4 independent QTLs in the span of 5 Mb. Together we present a large scale sequence-based association study that provides a key resource to scan for novel variants at high resolution for breeding and to further reduce the frequency of deleterious alleles at an early stage in the breeding program.Author SummaryIn this study we investigated the effect of over 20 million genetic variants on the growth rate and backfat thickness of approximately 140,000 pigs across two commercial breeds, with specific focus on recessive harmful variation. We identified 14 regions with a significant additive effect and 8 regions with a significant recessive effect on these traits. By looking at recessive effects we identified several rare deleterious variants with high impacts on animal fitness. These include a deletion on chromosome 15 in theOBSL1gene, which leads to a growth reduction of 100 grams a day on average. Interestingly, loss-of-function mutations inOBSL1are associated with short stature in humans. Looking at additive effects with this high-resolution dataset allowed us to gain more insight into the locus around theMC4Rgene on chromosome 1. Here we found a small complex region containing several independent variants affecting both growth rate and backfat. With this study we have shown that by using several gene models and a large dataset, we can identify novel genetic variants at high resolution (<0.01 frequency) with significant impact on animal fitness and production. These results can help us in further eradicating deleterious genetic variants from pig populations.