In order to gain insight into the genetic architecture of economically important traits in pigs and to derive suitable genetic markers to improve these traits in breeding programs, many studies have been conducted to map quantitative trait loci. Shortcomings of these studies were low mapping resolution, large confidence intervals for quantitative trait loci-positions and large linkage disequilibrium blocks. Here, we overcome these shortcomings by pooling four large F2 designs to produce smaller linkage disequilibrium blocks and by resequencing the founder generation at high coverage and the F1 generation at low coverage for subsequent imputation of the F2 generation to whole genome sequencing marker density. This lead to the discovery of more than 32 million variants, 8 million of which have not been previously reported. The pooling of the four F2 designs enabled us to perform a joint genome-wide association study, which lead to the identification of numerous significantly associated variant clusters on chromosomes 1, 2, 4, 7, 17 and 18 for the growth and carcass traits average daily gain, back fat thickness, meat fat ratio, and carcass length. We could not only confirm previously reported, but also discovered new quantitative trait loci. As a result, several new candidate genes are discussed, among them BMP2 (bone morphogenetic protein 2), which we recently discovered in a related study. Variant effect prediction revealed that 15 high impact variants for the traits back fat thickness, meat fat ratio and carcass length were among the statistically significantly associated variants.

Genome-wide association studies (GWAS) have been widely used in the genetic dissection of complex traits. As more genomic data is being generated within different commercial or resource pig populations, the challenge which arises is how to collectively investigate the data with the purpose to increase sample size and implicitly the statistical power. This study performs an individual population GWAS, a joint population GWAS and a meta-analysis in three pig F2 populations. D1 is derived from European type breeds (Piétrain, Large White and Landrace), D2 is obtained from an Asian breed (Meishan) and Piétrain, and D3 stems from a European Wild Boar and Piétrain, which is the common founder breed. The traits investigated are average daily gain, backfat thickness, meat to fat ratio and carcass length. The joint and the meta-analysis did not identify additional genomic clusters besides the ones discovered via the individual population GWAS. However, the benefit was an increased mapping resolution which pinpointed to narrower clusters harboring causative variants. The joint analysis identified a higher number of clusters as compared to the meta-analysis; nevertheless, the significance levels and the number of significant variants in the meta-analysis were generally higher. Both types of analysis had similar outputs suggesting that the two strategies can complement each other and that the meta-analysis approach can be a valuable tool whenever access to raw datasets is limited. Overall, a total of 20 genomic clusters that predominantly overlapped at various extents, were identified on chromosomes 2, 7 and 17, many confirming previously identified quantitative trait loci. Several new candidate genes are being proposed and, among them, a strong candidate gene to be taken into account for subsequent analysis is BMP2 (bone morphogenetic protein 2).

Reliability of genomic predictions is influenced by the size and genetic composition of the reference population. For German Warmblood horses, compilation of a reference population has been enabled through the cooperation of five German breeding associations. In this study, preliminary data from this joint reference population were used to genetically and genomically characterize withers height and to apply single-step methodology for estimating genomic breeding values for withers height. Using data on 2113 mares and their genomic information considering about 62,000 single nucleotide polymorphisms (SNPs), analysis of the genomic relationship revealed substructures reflecting breed origin and different breeding goals of the contributing breeding associations. A genome-wide association study confirmed a known quantitative trait locus (QTL) for withers height on equine chromosome (ECA) 3 close to LCORL and identified a further significant peak on ECA 1. Using a single-step approach with a combined relationship matrix, the estimated heritability for withers height was 0.31 (SE = 0.08) and the corresponding genomic breeding values ranged from − 2.94 to 2.96 cm. A mean reliability of 0.38 was realized for these breeding values. The analyses of withers height showed that compiling a reference population across breeds is a suitable strategy for German Warmblood horses. The single-step method is an appealing approach for practical genomic prediction in horses, because not many genotypes are available yet and animals without genotypes can by this way directly contribute to the estimation system.

In the present study, data from four F2 crosses were analysed and used to study the linkage disequilibrium (LD) structure within and across the crosses. Genome-wide association analyses (GWASes) for conductivity and dressing out meat traits were conducted using single-marker and Bayesian multi-marker models using the pooled data from all F2 crosses. Porcine F2 crosses generated from the distantly related founder breeds Wild Boar, Piétrain and Meishan, as well as from a porcine F2 cross from the closely related founder breed Piétrain and an F1 Large White × Landrace cross were pooled. A total of 2572 F2 animals were genotyped using a 62K SNP chip. The positions of the SNPs were based on genome assembly Sscrofa11.1. After post-alignment and genotype filtering, approximately 50K SNPs were usable for LD studies and GWASes. The main findings of the present study are that the breakdown of LD was faster in crosses from closely related founder breeds compared to crosses from distantly related founders. The fastest breakdown of LD was observed by pooling the data. Based on the single-marker results and LD structure, clusters and windows were built for 1-Mb intervals. For conductivity and dressing out, 183 and 191 nominal significant associations respectively and six and five clusters respectively were found. Dominance was important for conductivity, and considering dominance in GWASes improved the mapping signals. Most clear signals were found for conductivity on SSC6, 8 and 15 and for dressing out on SSC2 and 7. Considering dominance might contribute to the accuracy of genomic selection and serve as a guide for choosing mating pairs with good combining abilities. However, further research is needed to investigate if dominance is also important in crossbreed pig breeding schemes.

Background Structural variants and tandem repeats are relevant sources of genomic variation that are not routinely analyzed in genome wide association studies mainly due to challenging identification and genotyping. Here, we profiled these variants via state-of-the-art strategies in the founder animals of four F2 pig crosses using whole-genome sequence data (20x coverage). The variants were compared at a founder level with the commonly screened SNPs and small indels. At the F2 level, we carried out an association study using imputed structural variants and tandem repeats with four growth and carcass traits followed by a comparison with a previously conducted SNPs and small indels based association study. Results A total of 13,201 high confidence structural variants and 103,730 polymorphic tandem repeats (with a repeat length of 2-20 bp) were profiled in the founders. We observed a moderate to high (r from 0.48 to 0.57) level of co-localization between SNPs or small indels and structural variants or tandem repeats. In the association step 56.56% of the significant variants were not in high LD with significantly associated SNPs and small indels identified for the same traits in the earlier study and thus presumably not tagged in case of a standard association study. For the four growth and carcass traits investigated, many of the already proposed candidate genes in our previous studies were confirmed and additional ones were identified. Interestingly, a common pattern on how structural variants or tandem repeats regulate the phenotypic traits emerged. Many of the significant variants were embedded or nearby long non-coding RNAs drawing attention to their functional importance. Through which specific mechanisms the identified long non-coding RNAs and their associated structural variants or tandem repeats contribute to quantitative trait variation will need further investigation. Conclusions The current study provides insights into the characteristics of structural variants and tandem repeats and their role in association studies. A systematic incorporation of these variants into genome wide association studies is advised. While not of immediate interest for genomic prediction purposes, this will be particularly beneficial for elucidating biological mechanisms driving the complex trait variation.