Evidence for and localization of proposed causative variants in cattle and pig genomes

Abstract

Background This paper reviews the localization of published potential causative variants in contemporary pig and cattle reference genomes, and the evidence for their causality. In spite of the difficulties inherent to the identification of causative variants from genetic mapping and genome-wide association studies, researchers in animal genetics have proposed putative causative variants for several traits relevant to livestock breeding. Results For this review, we read the literature that supports potential causative variants in 13 genes (ABCG2, DGAT1, GHR, IGF2, MC4R, MSTN, NR6A1, PHGK1, PRKAG3, PLRL, RYR1, SYNGR2 and VRTN) in cattle and pigs, and localized them in contemporary reference genomes. We review the evidence for their causality, by aiming to separate the evidence for the locus, the proposed causative gene and the proposed causative variant, and report the bioinformatic searches and tactics needed to localize the sequence variants in the cattle or pig genome. Conclusions Taken together, there is usually good evidence for the association at the locus level, some evidence for a specific causative gene at eight of the loci, and some experimental evidence for a specific causative variant at six of the loci. We recommend that researchers who report new potential causative variants use referenced coordinate systems, show local sequence context, and submit variants to repositories.

Genomic prediction with whole-genome sequence data in intensely selected pig lines

“…Whole-genome sequence data has already been applied in genome-wide association studies (GWAS) to identify variants associated with a variety of traits in livestock [ 2 , 32 – 34 ], including pigs [ 35 , 36 ]. However, the fine-mapping of causal variants remains challenging due to the pervasive long-range linkage disequilibrium across extremely dense variants [ 37 ].…”

Comparison of the choice of animals for re-sequencing in two maternal pig lines

“…Verification, validation and fine-mapping of quantitative trait loci (QTL) derived from genome-wide association studies can benefit from imputed sequence data [ 14 ], although the relatively high level of linkage disequilibrium in pig breeding populations makes detection of causative variants more difficult. Combining statistical, bioinformatic, and functional information, such as genetic associations, linkage disequilibrium, annotation, and functional genomic data, might optimize such detection (e.g., [ 16 – 18 ]) and, in addition, will increase the number of QTL and the proportion of variance explained by these QTL, compared to the use of a lower marker density [ 19 ].…”

Identification of a missense variant in the porcine AGPAT gene family associated with intramuscular fat content through whole‐genome sequencing

“…In contrast, we did not find any candidate variant in AGPAT1 or AGPAT3, and the few candidate variants in AGPAT2 and AGPAT4 were discarded because they were located in introns. A typical criterion for prioritizing candidate variants is to limit the search to coding and promoter regions, since the prediction of the potential effects of variants located in non-coding regions from DNA sequence is not straightforward (Johnsson & Jungnickel, 2021). However, in some instances non-coding variants, which may have regulatory functions, have been proposed as candidate variants (Ryan et al, 2012;Solé et al, 2021;Van Laere et al, 2003), while variants in coding regions have often been found to have a small impact on complex traits Abbreviations: C16:0, palmitic acid; C18:0, stearic acid; C16:1n-7, palmitoleic acid; C18:1n-7, vaccenic acid; C18:1n-9, oleic acid; C18:2n-6, linoleic acid; C18:3n-3, linolenic acid; C20:4n-6, arachidonic acid; MUFA, monounsaturated fatty acids; PUFA, polyunsaturated fatty acids; SFA, saturated fatty acids.…”