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The difficulty in finding causative mutations has hampered their use in genomic prediction. Here, we present a methodology to fine-map potentially causal variants genome-wide by integrating the functional, evolutionary and pleiotropic information of variants using GWAS, variant clustering and Bayesian mixture models. Our analysis of 17 million sequence variants in 44,000+ Australian dairy cattle for 34 traits suggests, on average, one pleiotropic QTL existing in each 50 kb chromosome-segment. We selected a set of 80k variants representing potentially causal variants within each chromosome segment to develop a bovine XT-50K genotyping array. The custom array contains many pleiotropic variants with biological functions, including splicing QTLs and variants at conserved sites across 100 vertebrate species. This biology-informed custom array outperformed the standard array in predicting genetic value of multiple traits across populations in independent datasets of 90,000+ dairy cattle from the USA, Australia and New Zealand. Genomic prediction of phenotype may be improved by using DNA mutations with functional, evolutionary, and pleiotropic consequences. Here the authors describe a method for genome-wide fine-mapping of QTLs and develop a genotyping array for improved prediction of genetic values for cattle traits.

The Bovine Pangenome Consortium (BPC) is an international collaboration dedicated to the assembly of cattle genomes to develop a more complete representation of cattle genomic diversity. The goal of the BPC is to provide genome assemblies and a community-agreed pangenome representation to replace breed-specific reference assemblies for cattle genomics. The BPC invites partners sharing our vision to participate in the production of these assemblies and the development of a common, community-approved, pangenome reference as a public resource for the research community ( https://bovinepangenome.github.io/ ). This community-driven resource will provide the context for comparison between studies and the future foundation for cattle genomic selection.

Prediction of genetic merit using dense SNP genotypes can be used for estimation of breeding values for selection of livestock, crops, and forage species; for prediction of disease risk; and for forensics. The accuracy of these genomic predictions depends in part on the genetic architecture of the trait, in particular number of loci affecting the trait and distribution of their effects. Here we investigate the difference among three traits in distribution of effects and the consequences for the accuracy of genomic predictions. Proportion of black coat colour in Holstein cattle was used as one model complex trait. Three loci, KIT, MITF, and a locus on chromosome 8, together explain 24% of the variation of proportion of black. However, a surprisingly large number of loci of small effect are necessary to capture the remaining variation. A second trait, fat concentration in milk, had one locus of large effect and a host of loci with very small effects. Both these distributions of effects were in contrast to that for a third trait, an index of scores for a number of aspects of cow confirmation (\"overall type\"), which had only loci of small effect. The differences in distribution of effects among the three traits were quantified by estimating the distribution of variance explained by chromosome segments containing 50 SNPs. This approach was taken to account for the imperfect linkage disequilibrium between the SNPs and the QTL affecting the traits. We also show that the accuracy of predicting genetic values is higher for traits with a proportion of large effects (proportion black and fat percentage) than for a trait with no loci of large effect (overall type), provided the method of analysis takes advantage of the distribution of loci effects.

Background Short tandem repeats (STRs) are repetitive DNA sequences with 1–6 nucleotide repeat units, exhibiting high polymorphism due to varying repeat counts. STRs are more variable than SNPs and can cause genetic disorders. With population-scale cattle whole-genome sequencing data available, whole-genome STR identification has attracted new interest, but challenges remain due to the lack of standardized methods, sequencing data limitations, and the diversity of STR-calling tools. This study compared six STR-calling tools: HipSTR, GangSTR, and ExpansionHunter for short-read data, and Straglr, RepeatHMM, and LongTR for Oxford Nanopore (ONT) long-read data—using sequences from five Holstein cattle (two parent–offspring trios with a shared sire). This is the first cattle study to evaluate short- and long-read STR callers using both data types from the same animals. Results In short-read data, ExpansionHunter identified the highest number of polymorphic STRs (pSTRs) (327,690), followed by HipSTR (205,900) and GangSTR (110,680), with 93,023 loci detected by all three tools. In long-read data, LongTR detected 470,250 pSTRs, RepeatHMM 224,185, and Straglr 90,275, with only 33,253 loci shared among them. Mendelian consistency of STR genotypes in the trio offspring was high (> 0.8) for all short-read tools, with HipSTR and GangSTR highest at 0.98. LongTR was the only long-read tool with high consistency (0.88). Short-read tools also showed higher concordance in STR genotypes among themselves than was observed among long-read tools. However, long-read tools had a clear advantage in detecting large STRs. Relative to computational efficiency, HipSTR and GangSTR (short-reads), and LongTR (long-reads) required less memory and shorter runtimes than the other tools. Conclusions Tool selection is critical for accurate whole-genome STR identification in cattle. For short-read data, HipSTR showed relatively high Mendelian consistency and concordance compared to the other tools, while ExpansionHunter was able to detect longer STRs but with lower Mendelian consistency. For long-read data, LongTR demonstrated higher consistency and computational efficiency relative to the other tools. Based on these results, HipSTR and LongTR are suggested as preferred options for short-read and ONT long-read datasets, respectively, in cattle STR analysis. These recommendations are based on the metrics observed in this study, and confirmatory analyses across additional breeds, larger sample sizes, and validated truth sets are encouraged.

Background Causal variants for complex traits, such as eQTL are often found in non-coding regions of the genome, where they are hypothesised to influence phenotypes by regulating gene expression. Many regulatory regions are marked by histone modifications, which can be assayed by chromatin immunoprecipitation followed by sequencing (ChIP-seq). Sequence reads from ChIP-seq form peaks at putative regulatory regions, which may reflect the amount of regulatory activity at this region. Therefore, eQTL which are also associated with differences in histone modifications are excellent candidate causal variants. Results We assayed the histone modifications H3K4Me3, H3K4Me1 and H3K27ac and mRNA in the mammary gland of up to 400 animals. We identified QTL for peak height (histone QTL), exon expression (eeQTL), allele specific expression (aseQTL) and allele specific binding (asbQTL). By intersecting these results, we identify variants which may influence gene expression by altering regulatory regions of the genome, and may be causal variants for other traits. Lastly, we find that these variants are found in putative transcription factor binding sites, identifying a mechanism for the effect of many eQTL. Conclusions We find that allele specific and traditional QTL analysis often identify the same genetic variants and provide evidence that many eQTL are regulatory variants which alter activity at regulatory regions of the bovine genome. Our work provides methodological and biological updates on how regulatory mechanisms interplay at multi-omics levels.

Genome-wide association studies (GWAS) have identified many quantitative trait loci (QTL) associated with complex traits, predominantly in non-coding regions, posing challenges in pinpointing the causal variants and their target genes. Three types of evidence can help identify the gene through which QTL acts: (1) proximity to the most significant GWAS variant, (2) correlation of gene expression with the trait, and (3) the gene’s physiological role in the trait. However, there is still uncertainty about the success of these methods in identifying the correct genes. Here, we test the ability of these methods in a comparatively simple series of traits associated with the concentration of polar lipids in milk. We conducted single-trait GWAS for ~14 million imputed variants and 56 individual milk polar lipid (PL) phenotypes in 336 cows. A multi-trait meta-analysis of GWAS identified 10,063 significant SNPs at FDR ≤ 10% ( P  ≤ 7.15E-5). Transcriptome data from blood (~12.5K genes, 143 cows) and mammary tissue (~12.2K genes, 169 cows) were analyzed using the genetic score omics regression (GSOR) method. This method links observed gene expression to genetically predicted phenotypes and was used to find associations between gene expression and 56 PL phenotypes. GSOR identified 2,186 genes in blood and 1,404 in mammary tissue associated with at least one PL phenotype (FDR ≤ 1%). We partitioned the genome into non-overlapping windows of 100 Kb to test for overlap between GSOR-identified genes and GWAS signals. We found a significant overlap between these two datasets, indicating that GSOR-significant genes were more likely to be located within 100 Kb windows that include GWAS signals than those that do not ( P  = 0.01; odds ratio = 1.47). These windows included 70 significant genes expressed in mammary tissue and 95 in blood. Compared to all expressed genes in each tissue, these genes were enriched for lipid metabolism gene ontology (GO). That is, seven of the 70 significant mammary transcriptome genes ( P  < 0.01; odds ratio = 3.98) and five of the 95 significant blood genes ( P  < 0.10; odds ratio = 2.24) were involved in lipid metabolism GO. The candidate causal genes include DGAT1 , ACSM5 , SERINC5 , ABHD3 , CYP2U1 , PIGL , ARV1 , SMPD5 , and NPC2 , with some overlap between the two tissues. The overlap between GWAS, GSOR, and GO analyses suggests that together, these methods are more likely to identify genes mediating QTL, though their power remains limited, as reflected by modest odds ratios. Larger sample sizes would enhance the power of these analyses, but issues like linkage disequilibrium would remain.

Mastitis is a disease that incurs significant costs in the dairy industry. A promising approach to mitigate its negative effects is to genetically improve the resistance of dairy cattle to mastitis. A meta-analysis of genome-wide association studies (GWAS) across multiple breeds for clinical mastitis (CM) and its indicator trait, somatic cell score (SCS), is a powerful method to identify functional genetic variants that impact mastitis resistance. We conducted meta-analyses of eight and fourteen GWAS on CM and SCS, respectively, using 30,689 and 119,438 animals from six dairy cattle breeds. Methods for the meta-analyses were selected to properly account for the multi-breed structure of the GWAS data. Our study revealed 58 lead markers that were associated with mastitis incidence, including 16 loci that did not overlap with previously identified quantitative trait loci (QTL), as curated at the Animal QTLdb. Post-GWAS analysis techniques such as gene-based analysis and genomic feature enrichment analysis enabled prioritization of 31 candidate genes and 14 credible candidate causal variants that affect mastitis. Our list of candidate genes can help to elucidate the genetic architecture underlying mastitis resistance and provide better tools for the prevention or treatment of mastitis, ultimately contributing to more sustainable animal production.

Background Genome-wide association studies (GWASs) have identified thousands of loci for complex traits, but pinpointing causal variants and linking them to target genes remains challenging. Several strategies have been proposed to address these challenges, e.g., comparisons across the genome, using larger and multi-breed datasets, multi-trait analyses, leveraging multi-omics data, etc. Results We used a multi-breed dataset of over 81,000 cows from Australia, including Holstein, Jersey, and Australian Red, with phenotypes for milk lactose percentage (LP) and imputed sequence genotypes. LD pruning excluded SNPs with r2 > 0.95. We used BayesR to estimate SNP effects for LP (~ 1.1 million SNPs remained after LD pruning); These SNP effects were used to predict local genomic breeding values (GEBVs) for ~ 400 mammary RNA-sequenced cows from New Zealand. Then, genetic score omics regression (GSOR) was applied to test associations between observed gene expression and local GEBVs, identifying 711 significant genes (FDR ≤ 0.1) out of 12,000 genes expressed in the mammary gland. We developed a window-based test to investigate the significance of colocalization between GSOR results and GWAS summary statistics obtained from an independent study. We found 30 windows containing both GWAS signals and GSOR-significant genes (i.e., 34 genes); this overlap was significantly higher than chance expectation ( P Fisher = 2.96 × 10⁻⁹). Among the 34 genes analyzed, 20 contributed to the significantly enriched gene ontology term ‘transmembrane transport’ and its child terms (FDR < 0.05). These terms are relevant to the physiology of lactose production in the mammary gland. Conclusions We hypothesized that the 20 genes are the most likely causal genes for the trait because: mammary expression of these genes was associated with GEBV for the trait, they were significantly colocalized with GWAS signals, and they were enriched in gene ontology terms relevant to physiology of the trait. Our approach provides strong evidence for causal genes supported by multiple lines of evidence (GWAS, GSOR, and functional enrichment) and demonstrates the power of multi-omics data integration.

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 (Rsq soft ). 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 Rsq soft , this differed considerably for Minimac4 compared to Beagle 5.2 and IMPUTE5. We found that the Rsq soft 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 Rsq soft 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 Rsq soft 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.

Maternal diversity based on a sub-region of mitochondrial genome or variants were commonly used to understand past demographic events in livestock. Additionally, there is growing evidence of direct association of mitochondrial genetic variants with a range of phenotypes. Therefore, this study used complete bovine mitogenomes from a large sequence database to explore the full spectrum of maternal diversity. Mitogenome diversity was evaluated among 1883 animals representing 156 globally important cattle breeds. Overall, the mitogenomes were diverse: presenting 11 major haplogroups, expanding to 1309 unique haplotypes, with nucleotide diversity 0.011 and haplotype diversity 0.999. A small proportion of African taurine (3.5%) and indicine (1.3%) haplogroups were found among the European taurine breeds and composites. The haplogrouping was largely consistent with the population structure derived from alternate clustering methods (e.g. PCA and hierarchical clustering). Further, we present evidence confirming a new indicine subgroup (I1a, 64 animals) mainly consisting of breeds originating from China and characterised by two private mutations within the I1 haplogroup. The total genetic variation was attributed mainly to within-breed variance (96.9%). The accuracy of the imputation of missing genotypes was high (99.8%) except for the relatively rare heteroplasmic genotypes, suggesting the potential for trait association studies within a breed.