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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.

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.

While understanding the genetic basis of heat tolerance is crucial in the context of global warming’s effect on humans, livestock, and wildlife, the specific genetic variants and biological features that confer thermotolerance in animals are still not well characterized. We used dairy cows as a model to study heat tolerance because they are lactating, and therefore often prone to thermal stress. The data comprised almost 0.5 million milk records (milk, fat, and proteins) of 29,107 Australian Holsteins, each having around 15 million imputed sequence variants. Dairy animals often reduce their milk production when temperature and humidity rise; thus, the phenotypes used to measure an individual’s heat tolerance were defined as the rate of milk production decline (slope traits) with a rising temperature–humidity index. With these slope traits, we performed a genome-wide association study (GWAS) using different approaches, including conditional analyses, to correct for the relationship between heat tolerance and level of milk production. The results revealed multiple novel loci for heat tolerance, including 61 potential functional variants at sites highly conserved across 100 vertebrate species. Moreover, it was interesting that specific candidate variants and genes are related to the neuronal system ( ITPR1, ITPR2, and GRIA4 ) and neuroactive ligand–receptor interaction functions for heat tolerance ( NPFFR2, CALCR, and GHR ), providing a novel insight that can help to develop genetic and management approaches to combat heat stress.

Selection decisions are generally based on estimated breeding values (EBV) for a combination of traits that are polygenic (e.g. milk production). However, in some cases, there is additional intense selection for a single allele, or SNP, for a perceived benefit, such as selection for polled or A2 milk. Using a dataset where the A2 mutation was imputed using a reference population with whole genome sequence, we tested the hypothesis that intense selection in Australian Holstein cattle for the A2 allele in the β-casein gene may have resulted in increased inbreeding. We also estimated the average difference in performance between animals homozygous for the A1 or A2 allele for a range of traits. Using high-density genotypes we compared differences in genome-wide and regional inbreeding between Holstein cows homozygous for either the A1 or A2 β-casein alleles i.e. A1/A1 or A2/A2. This study shows that between the years 2000 to 2017, the frequency of the A2/A2 genotype increased by 20% in Holstein cows (from 32% to 52%). Our results suggest that selection for homozygosity at the β-casein A2 allele has increased inbreeding both across the genome and on chromosome 6 in A2/A2 Holstein cows. Animals that were A2/A2 were twice as likely to have a run of homozygosity of at least 1Mb long across the β-casein locus compared to animals that were A1/A1. Cows that are homozygous for the A2 allele had an average protein yield EBV advantage of 0.24 genetic standard deviations (SD) compared to A1/A1 homozygous cows. In contrast, A2/A2 homozygous animals were on average 0.2 genetic SD inferior on fertility EBV. As a result, the difference in the overall economic index (that includes traits contributing to profitability) there was only a small advantage of 0.05 SD for A2/A2 cows compared to A1/A1 cows. However, strong selection for the A2 allele has likely led to a higher level of regional and overall inbreeding which in the long term could harm genetic progress for some or all economic traits. Therefore, applying approaches that mitigate rapid inbreeding while selecting for preferred alleles and quantitative traits may be desirable.

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.

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.

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.

Background Integrating germplasm populations genotyped by different genotyping platforms via genotype imputation is a way to utilize accumulated genetic resources. In this study, we used 278 canola samples genotyped via whole-genome sequencing (WGS) at 10× coverage to evaluate the imputation accuracy of three imputation approaches. The optimal imputation methods were used to impute and integrate two Canola genotype datasets: a diverse canola collection genotyped by genotyping-by-sequencing via transcriptome (GBS-t) and a double haploid (DH) line collection genotyped with low-coverage WGS (skim-WGS). The genomic predictive ability (GP) and detection power of marker‒trait association (GWAS) of the combined population for blackleg resistance were evaluated. Results The empirical imputation accuracy ( r 2 ) measured as the squared correlation between observed and imputed genotypes was moderate for Minimac3 when imputing from the GBS-t density to the WGS. The accuracy dramatically improved from 0.64 to 0.82 by removing SNPs with poor Minimac3-reported Rsq (Rsq < 0.2) quality statistics. The r 2 for GLIMPSE was higher than that for Beagle when imputing from different low-coverage to full-coverage WGS. We imputed and integrated the diverse canola collection and the DH lines, and the combined population showed similar or slightly greater predictive ability (PA) for blackleg resistance traits than did each of the single populations with ~ 921 K SNPs. Higher marker-trait association (MTA) detection powers were indicated with the combined population; however, similar numbers of MTAs were discovered when each single population was combined in a meta-GWAS. Conclusion It is feasible to impute and integrate germplasms from different sequencing platforms for downstream analyses. However, genetic heterogeneity across populations could add complexity to the analysis. Increasing the sample size by combining datasets showed slightly greater predictive ability and greater detection power in GWASs in the present study.

Background Whole-genome sequence (WGS) data could contain information on genetic variants at or in high linkage disequilibrium with causative mutations that underlie the genetic variation of polygenic traits. Thus far, genomic prediction accuracy has shown limited increase when using such information in dairy cattle studies, in which one or few breeds with limited diversity predominate. The objective of our study was to evaluate the accuracy of genomic prediction in a multi-breed Australian sheep population of relatively less related target individuals, when using information on imputed WGS genotypes. Methods Between 9626 and 26,657 animals with phenotypes were available for nine economically important sheep production traits and all had WGS imputed genotypes. About 30% of the data were used to discover predictive single nucleotide polymorphism (SNPs) based on a genome-wide association study (GWAS) and the remaining data were used for training and validation of genomic prediction. Prediction accuracy using selected variants from imputed sequence data was compared to that using a standard array of 50k SNP genotypes, thereby comparing genomic best linear prediction (GBLUP) and Bayesian methods (BayesR/BayesRC). Accuracy of genomic prediction was evaluated in two independent populations that were each lowly related to the training set, one being purebred Merino and the other crossbred Border Leicester x Merino sheep. Results A substantial improvement in prediction accuracy was observed when selected sequence variants were fitted alongside 50k genotypes as a separate variance component in GBLUP (2GBLUP) or in Bayesian analysis as a separate category of SNPs (BayesRC). From an average accuracy of 0.27 in both validation sets for the 50k array, the average absolute increase in accuracy across traits with 2GBLUP was 0.083 and 0.073 for purebred and crossbred animals, respectively, whereas with BayesRC it was 0.102 and 0.087. The average gain in accuracy was smaller when selected sequence variants were treated in the same category as 50k SNPs. Very little improvement over 50k prediction was observed when using all WGS variants. Conclusions Accuracy of genomic prediction in diverse sheep populations increased substantially by using variants selected from whole-genome sequence data based on an independent multi-breed GWAS, when compared to genomic prediction using standard 50K genotypes.