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Background: Use of whole-genome sequence data (WGS) is expected to improve identification of quantitative trait loci (QTL). However, this requires imputation to WGS, often with a limited number of sequenced animals for the target population. The objective of this study was to investigate imputation to WGS in two pig lines using a multi-line reference population and, subsequently, to investigate the effect of using these imputed WGS (iWGS) for GWAS. Methods: Phenotypes and genotypes were available on 12,184 Large White pigs (LW-line) and 4943 Dutch Landrace pigs (DL-line). Imputed 660 K and 80 K genotypes for the LW-line and DL-line, respectively, were imputed to iWGS using Beagle v.4.1. Since only 32 LW-line and 12 DL-line boars were sequenced, 142 animals from eight commercial lines were added. GWAS were performed for each line using the 80 K and 660 K SNPs, the genotype scores of iWGS SNPs that had an imputation accuracy (Beagle R2) higher than 0.6, and the dosage scores of all iWGS SNPs. Results: For the DL-line (LW-line), imputation of 80 K genotypes to iWGS resulted in an average Beagle R2 of 0.39 (0.49). After quality control, 2.5 × 106 (3.5 × 106) SNPs had a Beagle R2 higher than 0.6, resulting in an average Beagle R2 of 0.83 (0.93). Compared to the 80 K and 660 K genotypes, using iWGS led to the identification of 48.9 and 64.4% more QTL regions, for the DL-line and LW-line, respectively, and the most significant SNPs in the QTL regions explained a higher proportion of phenotypic variance. Using dosage instead of genotype scores improved the identification of QTL, because the model accounted for uncertainty of imputation, and all SNPs were used in the analysis. Conclusions: Imputation to WGS using the multi-line reference population resulted in relatively poor imputation, especially when imputing from 80 K (DL-line). In spite of the poor imputation accuracies, using iWGS instead of a lower density SNP chip increased the number of detected QTL and the estimated proportion of phenotypic variance explained by these QTL, especially when dosage scores were used instead of genotype scores. Thus, iWGS, even with poor imputation accuracy, can be used to identify possible interesting regions for fine mapping. [ABSTRACT FROM AUTHOR]

Background: Use of whole-genome sequence data is expected to increase persistency of genomic prediction across generations and breeds but affects model performance and requires increased computing time. In this study, we investigated whether the split-and-merge Bayesian stochastic search variable selection (BSSVS) model could overcome these issues. BSSVS is performed first on subsets of sequence-based variants and then on a merged dataset containing variants selected in the first step. Results: We used a dataset that included 4,154,064 variants after editing and de-regressed proofs for 3415 reference and 2138 validation bulls for somatic cell score, protein yield and interval first to last insemination. In the first step, BSSVS was performed on 106 subsets each containing ~39,189 variants. In the second step, 1060 up to 472,492 variants, selected from the first step, were included to estimate the accuracy of genomic prediction. Accuracies were at best equal to those achieved with the commonly used Bovine 50k-SNP chip, although the number of variants within a few well-known quantitative trait loci regions was considerably enriched. When variant selection and the final genomic prediction were performed on the same data, predictions were biased. Predictions computed as the average of the predictions computed for each subset achieved the highest accuracies, i.e. 0.5 to 1.1 % higher than the accuracies obtained with the 50k-SNP chip, and yielded the least biased predictions. Finally, the accuracy of genomic predictions obtained when all sequence-based variants were included was similar or up to 1.4 % lower compared to that based on the average predictions across the subsets. By applying parallelization, the split-and-merge procedure was completed in 5 days, while the standard analysis including all sequence-based variants took more than three months. Conclusions: The split-and-merge approach splits one large computational task into many much smaller ones, which allows the use of parallel processing and thus efficient genomic prediction based on whole-genome sequence data. The split-and-merge approach did not improve prediction accuracy, probably because we used data on a single breed for which relationships between individuals were high. Nevertheless, the split-and-merge approach may have potential for applications on data from multiple breeds. [ABSTRACT FROM AUTHOR]

Background The aim of this study was to determine the consequences of splitting sequencing effort over multiple breeds for imputation accuracy from a high-density SNP chip towards wholegenome sequence. Such information would assist for instance numerical smaller cattle breeds, but also pig and chicken breeders, who have to choose wisely how to spend their sequencing efforts over all the breeds or lines they evaluate. Sequence data from cattle breeds was used, because there are currently relatively many individuals from several breeds sequenced within the 1,000 Bull Genomes project. The advantage of whole-genome sequence data is that it carries the causal mutations, but the question is whether it is possible to impute the causal variants accurately. This study therefore focussed on imputation accuracy of variants with low minor allele frequency and breed specific variants. Results Imputation accuracy was assessed for chromosome 1 and 29 as the correlation between observed and imputed genotypes. For chromosome 1, the average imputation accuracy was 0.70 with a reference population of 20 Holstein, and increased to 0.83 when the reference population was increased by including 3 other dairy breeds with 20 animals each. When the same amount of animals from the Holstein breed were added the accuracy improved to 0.88, while adding the 3 other breeds to the reference population of 80 Holstein improved the average imputation accuracy marginally to 0.89. For chromosome 29, the average imputation accuracy was lower. Some variants benefitted from the inclusion of other breeds in the reference population, initially determined by the MAF of the variant in each breed, but even Holstein specific variants did gain imputation accuracy from the multi-breed reference population. Conclusions This study shows that splitting sequencing effort over multiple breeds and combining the reference populations is a good strategy for imputation from high-density SNP panels towards whole-genome sequence when reference populations are small and sequencing effort is limiting. When sequencing effort is limiting and interest lays in multiple breeds or lines this provides imputation of each breed. [ABSTRACT FROM AUTHOR]

A major quantitative trait locus (QTL) for milk fat content and fatty acids in both milk and adipose tissue has been detected on Bos taurus autosome 19 (BTA19) in several cattle breeds. The objective of this study was to refine the location of the QTL on BTA19 for bovine milk fat composition using a denser set of markers. Opportunities for fine mapping were provided by imputation from 50,000 genotyped single nucleotide polymorphisms (SNP) toward a high-density SNP panel with up to 777,000 SNP. The QTL region was narrowed down to a linkage disequilibrium block formed by 22 SNP covering 85,007 bp, from 51,303,322 to 51,388,329 bp on BTA19. This linkage disequilibrium block contained 2 genes: coiled-coil domain containing 57 (CCDC57) and fatty acid synthase (FASN). The gene CCDC57 is minimally characterized and has not been associated with bovine milk fat previously, but is expressed in the mammary gland. The gene FASN has been associated with bovine milk fat and fat in adipose tissue before. This gene is a likely candidate for the QTL on BTA19 because of its involvement in de novo fat synthesis. Future studies using sequence data of both CCDC57 and FASN, and eventually functional studies, will have to be pursued to assign the causal variant(s). [ABSTRACT FROM AUTHOR]

Background: In this study we perform a genome-wide association study (GWAS) for bovine milk fatty acids from summer milk samples. This study replicates a previous study where we performed a GWAS for bovine milk fatty acids based on winter milk samples from the same population. Fatty acids from summer and winter milk are genetically similar traits and we therefore compare the regions detected in summer milk to the regions previously detected in winter milk GWAS to discover regions that explain genetic variation in both summer and winter milk. Results: The GWAS of summer milk samples resulted in 51 regions associated with one or more milk fatty acids. Results are in agreement with most associations that were previously detected in a GWAS of fatty acids from winter milk samples, including eight 'new' regions that were not considered in the individual studies. The high correlation between the -log10(P-values) and effects of SNPs that were found significant in both GWAS imply that the effects of the SNPs were similar on winter and summer milk fatty acids. Conclusions: The GWAS of fatty acids based on summer milk samples was in agreement with most of the associations detected in the GWAS of fatty acids based on winter milk samples. Associations that were in agreement between both GWAS are more likely to be involved in fatty acid synthesis compared to regions detected in only one GWAS and are therefore worthwhile to pursue in fine-mapping studies. [ABSTRACT FROM AUTHOR]