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Background: Genotype imputation is a cost-effective method for obtaining sequence genotypes for downstream analyses such as genome-wide association studies (GWAS). However, low imputation accuracy can increase the risk of false positives, so it is important to pre-filter data or at least assess the potential limitations due to imputation accuracy. In this study, we benchmarked three different imputation programs (Beagle 5.2, Minimac4 and IMPUTE5) and compared the empirical accuracy of imputation with the software estimated accuracy of imputation (Rsqsoft). We also tested the accuracy of imputation in cattle for autosomal and X chromosomes, SNP and INDEL, when imputing from either low-density or high-density genotypes. Results: The accuracy of imputing sequence variants from real high-density genotypes was higher than from low-density genotypes. In our software benchmark, all programs performed well with only minor differences in accuracy. While there was a close relationship between empirical imputation accuracy and the imputation Rsqsoft, this differed considerably for Minimac4 compared to Beagle 5.2 and IMPUTE5. We found that the Rsqsoft threshold for removing poorly imputed variants must be customised according to the software and this should be accounted for when merging data from multiple studies, such as in meta-GWAS studies. We also found that imposing an Rsqsoft filter has a positive impact on genomic regions with poor imputation accuracy due to large segmental duplications that are susceptible to error-prone alignment. Overall, our results showed that on average the imputation accuracy for INDEL was approximately 6% lower than SNP for all software programs. Importantly, the imputation accuracy for the non-PAR (non-Pseudo-Autosomal Region) of the X chromosome was comparable to autosomal imputation accuracy, while for the PAR it was substantially lower, particularly when starting from low-density genotypes. Conclusions: This study provides an empirically derived approach to apply customised software-specific Rsqsoft thresholds for downstream analyses of imputed variants, such as needed for a meta-GWAS. The very poor empirical imputation accuracy for variants on the PAR when starting from low density genotypes demonstrates that this region should be imputed starting from a higher density of real genotypes. [ABSTRACT FROM AUTHOR]

Background: Mitochondrial genomes differ from the nuclear genome and in humans it is known that mitochondrial variants contribute to genetic disorders. Prior to genomics, some livestock studies assessed the role of the mitochondrial genome but these were limited and inconclusive. Modern genome sequencing provides an opportunity to re-evaluate the potential impact of mitochondrial variation on livestock traits. This study first evaluated the empirical accuracy of mitochondrial sequence imputation and then used real and imputed mitochondrial sequence genotypes to study the role of mitochondrial variants on milk production traits of dairy cattle. Results: The empirical accuracy of imputation from Single Nucleotide Polymorphism (SNP) panels to mitochondrial sequence genotypes was assessed in 516 test animals of Holstein, Jersey and Red breeds using Beagle software and a sequence reference of 1883 animals. The overall accuracy estimated as the Pearson's correlation squared (R2) between all imputed and real genotypes across all animals was 0.454. The low accuracy was attributed partly to the majority of variants having low minor allele frequency (MAF < 0.005) but also due to variants in the hypervariable D-loop region showing poor imputation accuracy. Beagle software provides an internal estimate of imputation accuracy (DR2), and 10 percent of the total 1927 imputed positions showed DR2 greater than 0.9 (N = 201). There were 151 sites with empirical R2 > 0.9 (of 954 variants segregating in the test animals) and 138 of these overlapped the sites with DR2 > 0.9. This suggests that the DR2 statistic is a reasonable proxy to select sites that are imputed with higher accuracy for downstream analyses. Accordingly, in the second part of the study mitochondrial sequence variants were imputed from real mitochondrial SNP panel genotypes of 9515 Australian Holstein, Jersey and Red dairy cattle. Then, using only sites with DR2 > 0.900 and real genotypes, we undertook a genome-wide association study (GWAS) for milk, fat and protein yields. The GWAS mitochondrial SNP effects were not significant. Conclusion: The accuracy of imputation of mitochondrial genotypes from the SNP panel to sequence was generally low. The Beagle DR2 statistic enabled selection of sites imputed with higher empirical accuracy. We recommend building larger reference populations with mitochondrial sequence to improve the accuracy of imputing less common variants and ensuring that SNP panels include common variants in the D-loop region. [ABSTRACT FROM AUTHOR]

Background: Variants that regulate transcription, such as expression quantitative trait loci (eQTL), have shown enrichment in genome-wide association studies (GWAS) for mammalian complex traits. However, no study has reported eQTL in sheep, although it is an important agricultural species for which many GWAS of complex meat traits have been conducted. Using RNA sequence data produced from liver and muscle from 149 sheep and imputed whole-genome single nucleotide polymorphisms (SNPs), our aim was to dissect the genetic architecture of the transcriptome by associating sheep genotypes with three major molecular phenotypes including gene expression (geQTL), exon expression (eeQTL) and RNA splicing (sQTL). We also examined these three types of eQTL for their enrichment in GWAS of multi-meat traits and fatty acid profiles. Results: Whereas a relatively small number of molecular phenotypes were significantly heritable (h2 > 0, P < 0.05), their mean heritability ranged from 0.67 to 0.73 for liver and from 0.71 to 0.77 for muscle. Association analysis between molecular phenotypes and SNPs within ± 1 Mb identified many significant cis-eQTL (false discovery rate, FDR < 0.01). The median distance between the eQTL and transcription start sites (TSS) ranged from 68 to 153 kb across the three eQTL types. The number of common variants between geQTL, eeQTL and sQTL within each tissue, and the number of common variants between liver and muscle within each eQTL type were all significantly (P < 0.05) larger than expected by chance. The identified eQTL were significantly (P < 0.05) enriched in GWAS hits associated with 56 carcass traits and fatty acid profiles. For example, several geQTL in muscle mapped to the FAM184B gene, hundreds of sQTL in liver and muscle mapped to the CAST gene, and hundreds of sQTL in liver mapped to the C6 gene. These three genes are associated with body composition or fatty acid profiles. Conclusions: We detected a large number of significant eQTL and found that the overlap of variants between eQTL types and tissues was prevalent. Many eQTL were also QTL for meat traits. Our study fills a gap in the knowledge on the regulatory variants and their role in complex traits for the sheep model. [ABSTRACT FROM AUTHOR]

Background: Several examples of structural variation (SV) affecting phenotypic traits have been reported in cattle. Currently the identification of SV from whole-genome sequence data (WGS) suffers from a high false positive rate. Our aim was to construct a high quality set of SV calls in cattle using WGS data. First, we tested two SV detection programs, Breakdancer and Pindel, and the overlap of these methods, on simulated sequence data to determine their precision and sensitivity. We then identified population SV from WGS of 252 Holstein and 64 Jersey bulls based on the overlapping calls from the two programs. In addition, we validated an overlapped SV set in 28 twice-sequenced Holstein individuals, and in another two validated sets (one for each breed) that were transmitted from sire to son. We also tested whether highly conserved gene sets across eukaryotes and recently expanded gene families in bovine were depleted and enriched, respectively, for SV. Results: In empirical WGS data, 17,518 SV covering 27.36 Mb were found in the Holstein population and 4285 SV covering 8.74 Mb in the Jersey population, of which 4.62 Mb of SV overlapped between Holsteins and Jerseys. A total of 11,534 candidate SV covering 5.64 Mb were validated in the 28 twice-sequenced individuals, while 3.49 and 0.67 Mb of SV were validated from Holstein and Jersey sire-son transmission, respectively. Only eight of 237 core eukaryotic genes had at least a 50-bp overlap with an SV from our validated sets, suggesting that conserved genes are depleted for SV (p < 0.05). In addition, we observed that recently expanded gene families were significantly more associated with SV than other genes. Long interspersed nuclear elements-1 were enriched for deletions when compared to the rest of the genome (p = 0.0035). Conclusions: We reported SV from 252 Holstein and 64 Jersey individuals. A considerable proportion of Jersey population SV (53.5%) were also found in Holstein. In contrast, about 76.90% sire-son transmission validated SV were present in Jerseys and Holsteins. The enrichment of SV in expanding gene families suggests that SV can be a source of genetic variation for evolution. [ABSTRACT FROM AUTHOR]

Background: Genomic selection is increasingly widely practised, particularly in dairy cattle. However, the accuracy of current predictions using GBLUP (genomic best linear unbiased prediction) decays rapidly across generations, and also as selection candidates become less related to the reference population. This is likely caused by the effects of causative mutations being dispersed across many SNPs (single nucleotide polymorphisms) that span large genomic intervals. In this paper, we hypothesise that the use of a nonlinear method (BayesR), combined with a multi-breed (Holstein/Jersey) reference population will map causative mutations with more precision than GBLUP and this, in turn, will increase the accuracy of genomic predictions for selection candidates that are less related to the reference animals. Results: BayesR improved the across-breed prediction accuracy for Australian Red dairy cattle for five milk yield and composition traits by an average of 7% over the GBLUP approach (Australian Red animals were not included in the reference population). Using the multi-breed reference population with BayesR improved accuracy of prediction in Australian Red cattle by 2 - 5% compared to using BayesR with a single breed reference population. Inclusion of 8478 Holstein and 3917 Jersey cows in the reference population improved accuracy of predictions for these breeds by 4 and 5%. However, predictions for Holstein and Jersey cattle were similar using within-breed and multi-breed reference populations. We propose that the improvement in across-breed prediction achieved by BayesR with the multi-breed reference population is due to more precise mapping of quantitative trait loci (QTL), which was demonstrated for several regions. New candidate genes with functional links to milk synthesis were identified using differential gene expression in the mammary gland. Conclusions: QTL detection and genomic prediction are usually considered independently but persistence of genomic prediction accuracies across breeds requires accurate estimation of QTL effects. We show that accuracy of across-breed genomic predictions was higher with BayesR than with GBLUP and that BayesR mapped QTL more precisely. Further improvements of across-breed accuracy of genomic predictions and QTL mapping could be achieved by increasing the size of the reference population, including more breeds, and possibly by exploiting pleiotropic effects to improve mapping efficiency for QTL with small effects. [ABSTRACT FROM AUTHOR]