Meta-analysis of six dairy cattle breeds reveals biologically relevant candidate genes for mastitis resistance.

BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.

A breed-of-origin of alleles model that includes crossbred data improves predictive ability for crossbred animals in a multi-breed population.

BACKGROUND: Recently, crossbred animals have begun to be used as parents in the next generations of dairy and beef cattle systems, which has increased the interest in predicting the genetic merit of those animals. The primary objective of this study was to investigate three available methods for genomic prediction of crossbred animals. In the first two methods, SNP effects from within-breed evaluations are used by weighting them by the average breed proportions across the genome (BPM method) or by their breed-of-origin (BOM method). The third method differs from the BOM in that it estimates breed-specific SNP effects using purebred and crossbred data, considering the breed-of-origin of alleles (BOA method). For within-breed evaluations, and thus for BPM and BOM, 5948 Charolais, 6771 Limousin and 7552 Others (a combined population of other breeds) were used to estimate SNP effects separately within each breed. For the BOA, the purebreds' data were enhanced with data from ~ 4K, ~ 8K or ~ 18K crossbred animals. For each animal, its predictor of genetic merit (PGM) was estimated by considering the breed-specific SNP effects. Predictive ability and absence of bias were estimated for crossbreds and the Limousin and Charolais animals. Predictive ability was measured as the correlation between PGM and the adjusted phenotype, while the regression of the adjusted phenotype on PGM was estimated as a measure of bias. RESULTS: With BPM and BOM, the predictive abilities for crossbreds were 0.468 and 0.472, respectively, and with the BOA method, they ranged from 0.490 to 0.510. The performance of the BOA method improved as the number of crossbred animals in the reference increased and with the use of the correlated approach, in which the correlation of SNP effects across the genome of the different breeds was considered. The slopes of regression for PGM on adjusted phenotypes for crossbreds showed overdispersion of the genetic merits for all methods but this bias tended to be reduced by the use of the BOA method and by increasing the number of crossbred animals. CONCLUSIONS: For the estimation of the genetic merit of crossbred animals, the results from this study suggest that the BOA method that accommodates crossbred data can yield more accurate predictions than the methods that use SNP effects from separate within-breed evaluations.

Genome-wide association study identifies functional genomic variants associated with young stock survival in Nordic Red Dairy Cattle.

Identifying quantitative trait loci (QTL) associated with calf survival is essential for both reducing economic loss in cattle industry and understanding the genetic basis of the trait. To identify mutations and genes underlying young stock survival (YSS), we performed GWAS using de-regressed estimated breeding values of a YSS index and its component traits defined by sex and age in 3,077 Nordic Red Dairy Cattle (RDC) bulls and 2 stillbirth traits (first lactation and later lactations) in 5,141 RDC bulls. Two associated QTL regions on Bos taurus autosome (BTA) 4 and 6 were identified for the YSS index. The results of 4 YSS component traits indicate that same QTL regions were associated with bull and heifer calf mortality, but the effects were different over the growing period and suggested an additional QTL on BTA23. The GWAS on stillbirth identified 3 additional QTL regions on BTA5, 14, and 24 compared with YSS and its component traits. The conditional test of BTA6 showed at least 2 closely located QTL segregating for YSS component traits and stillbirth. We found 2 independent QTL for stillbirth on BTA23. The post-GWAS revealed LCORL, PPM1K, SSP1, MED28, and LAP3 are putative causal genes on BTA6, and a frame shift variant within LCORL, BTA6:37401770 (rs384548488) could be the putative causal variant. On BTA4, the GRB10 gene is the putative causal gene and BTA4:5296018 is the putative causal variant. In addition, NDUFA9 and FGF23 on BTA5, LYN on BTA14, and KCNK5 on BTA23 are putative causal genes for QTL for stillbirth. The gene analysis also proposed several candidate genes. Our findings shed new light on the candidate genes affecting calf survival, and the knowledge could be utilized to reduce calf mortality and thereby enhance welfare of dairy cattle.

Large-scale association study on daily weight gain in pigs reveals overlap of genetic factors for growth in humans.

BACKGROUND: Imputation from genotyping array to whole-genome sequence variants using resequencing of representative reference populations enhances our ability to map genetic factors affecting complex phenotypes in livestock species. The accumulation of knowledge about gene function in human and laboratory animals can provide substantial advantage for genomic research in livestock species. RESULTS: In this study, 201,388 pigs from three commercial Danish breeds genotyped with low to medium (8.5k to 70k) SNP arrays were imputed to whole genome sequence variants using a two-step approach. Both imputation steps achieved high accuracies, and in total this yielded 26,447,434 markers on 18 autosomes. The average estimated imputation accuracy of markers with minor allele frequency ≥ 0.05 was 0.94. To overcome the memory consumption of running genome-wide association study (GWAS) for each breed, we performed within-breed subpopulation GWAS then within-breed meta-analysis for average daily weight gain (ADG), followed by a multi-breed meta-analysis of GWAS summary statistics. We identified 15 quantitative trait loci (QTL). Our post-GWAS analysis strategy to prioritize of candidate genes including information like gene ontology, mammalian phenotype database, differential expression gene analysis of high and low feed efficiency pig and human GWAS catalog for height, obesity, and body mass index, we proposed MRAP2, LEPROT, PMAIP1, ENSSSCG00000036234, BMP2, ELFN1, LIG4 and FAM155A as the candidate genes with biological support for ADG in pigs. CONCLUSION: Our post-GWAS analysis strategy helped to identify candidate genes not just by distance to the lead SNP but also by multiple sources of biological evidence. Besides, the identified QTL overlap with genes which are known for their association with human growth-related traits. The GWAS with this large data set showed the power to map the genetic factors associated with ADG in pigs and have added to our understanding of the genetics of growth across mammalian species.

Genome-wide association study with imputed whole-genome sequence variants including large deletions for female fertility in 3 Nordic dairy cattle breeds.

Fertility is an economically important trait in livestock. Poor fertility in dairy cattle can be due to loss-of-function variants affecting any essential gene that causes early embryonic mortality in homozygotes. To identify fertility-associated quantitative trait loci, we performed single-marker association analyses for 8 fertility traits in Holstein, Jersey, and Nordic Red Dairy cattle using imputed whole-genome sequence variants including SNPs, indels, and large deletion. We then performed stepwise selection of independent markers from GWAS loci using conditional and joint association analyses. From single-marker analyses for fertility traits, we reported genome-wide significant associations of 30,384 SNPs, 178 indels, and 3 deletions in Holstein; 23,481 SNPs, 189 indels, and 13 deletions in Nordic Red; and 17 SNPs in Jersey cattle. Conditional and joint association analyses identified 37 and 23 independent associations in Holstein and Nordic Red Dairy cattle, respectively. Fertility-associated GWAS loci were enriched for developmental and cellular processes (Gene Ontology enrichment, false discovery rate < 0.05). For these quantitative trait loci regions (top marker and 500 kb of surrounding regions), we proposed several candidate genes with functional annotations corresponding to embryonic lethality and various fertility-related phenotypes in mouse and cattle. The inclusion of these top markers in future releases of the custom SNP chip used for genomic evaluations will enable their validation in independent populations and improve the accuracy of genomic predictions.

Improvement of genomic prediction by integrating additional single nucleotide polymorphisms selected from imputed whole genome sequencing data.

The availability of whole genome sequencing (WGS) data enables the discovery of causative single nucleotide polymorphisms (SNPs) or SNPs in high linkage disequilibrium with causative SNPs. This study investigated effects of integrating SNPs selected from imputed WGS data into the data of 54K chip on genomic prediction in Danish Jersey. The WGS SNPs, mainly including peaks of quantitative trait loci, structure variants, regulatory regions of genes, and SNPs within genes with strong effects predicted with variant effect predictor, were selected in previous analyses for dairy breeds in Denmark-Finland-Sweden (DFS) and France (FRA). Animals genotyped with 54K chip, standard LD chip, and customized LD chip which covered selected WGS SNPs and SNPs in the standard LD chip, were imputed to 54K together with DFS and FRA SNPs. Genomic best linear unbiased prediction (GBLUP) and Bayesian four-distribution mixture models considering 54K and selected WGS SNPs as one (a one-component model) or two separate genetic components (a two-component model) were used to predict breeding values. For milk production traits and mastitis, both DFS (0.025) and FRA (0.029) sets of additional WGS SNPs improved reliabilities, and inclusions of all selected WGS SNPs generally achieved highest improvements of reliabilities (0.034). A Bayesian four-distribution model yielded higher reliabilities than a GBLUP model for milk and protein, but extra gains in reliabilities from using selected WGS SNPs were smaller for a Bayesian four-distribution model than a GBLUP model. Generally, no significant difference was observed between one-component and two-component models, except for using GBLUP models for milk.

Distinguishing pleiotropy from linked QTL between milk production traits and mastitis resistance in Nordic Holstein cattle.

BACKGROUND: Production and health traits are central in cattle breeding. Advances in next-generation sequencing technologies and genotype imputation have increased the resolution of gene mapping based on genome-wide association studies (GWAS). Thus, numerous candidate genes that affect milk yield, milk composition, and mastitis resistance in dairy cattle are reported in the literature. Effect-bearing variants often affect multiple traits. Because the detection of overlapping quantitative trait loci (QTL) regions from single-trait GWAS is too inaccurate and subjective, multi-trait analysis is a better approach to detect pleiotropic effects of variants in candidate genes. However, large sample sizes are required to achieve sufficient power. Multi-trait meta-analysis is one approach to deal with this problem. Thus, we performed two multi-trait meta-analyses, one for three milk production traits (milk yield, protein yield and fat yield), and one for milk yield and mastitis resistance. RESULTS: For highly correlated traits, the power to detect pleiotropy was increased by multi-trait meta-analysis compared with the subjective assessment of overlapping of single-trait QTL confidence intervals. Pleiotropic effects of lead single nucleotide polymorphisms (SNPs) that were detected from the multi-trait meta-analysis were confirmed by bivariate association analysis. The previously reported pleiotropic effects of variants within the DGAT1 and MGST1 genes on three milk production traits, and pleiotropic effects of variants in GHR on milk yield and fat yield were confirmed. Furthermore, our results suggested that variants in KCTD16, KCNK18 and ENSBTAG00000023629 had pleiotropic effects on milk production traits. For milk yield and mastitis resistance, we identified possible pleiotropic effects of variants in two genes, GC and DGAT1. CONCLUSIONS: Multi-trait meta-analysis improves our ability to detect pleiotropic interactions between milk production traits and identifies variants with pleiotropic effects on milk production traits and mastitis resistance. In particular, this should contribute to better understand the biological mechanisms that underlie the unfavorable genetic correlation between milk yield and mastitis.

Weighted single-step genomic best linear unbiased prediction integrating variants selected from sequencing data by association and bioinformatics analyses.

BACKGROUND: Sequencing data enable the detection of causal loci or single nucleotide polymorphisms (SNPs) highly linked to causal loci to improve genomic prediction. However, until now, studies on integrating such SNPs using a single-step genomic best linear unbiased prediction (ssGBLUP) model are scarce. We investigated the integration of sequencing SNPs selected by association (1262 SNPs) and bioinformatics (2359 SNPs) analyses into the currently used 54K-SNP chip, using three ssGBLUP models which make different assumptions on the distribution of SNP effects: a basic ssGBLUP model, a so-called featured ssGBLUP (ssFGBLUP) model that considered selected sequencing SNPs as a feature genetic component, and a weighted ssGBLUP (ssWGBLUP) model in which the genomic relationship matrix was weighted by the SNP variances estimated from a Bayesian whole-genome regression model, with every 1, 30, or 100 adjacent SNPs within a chromosome region sharing the same variance. We used data on milk production and female fertility in Danish Jersey. In total, 15,823 genotyped and 528,981‬ non-genotyped females born between 1990 and 2013 were used as reference population and 7415 genotyped females and 33,040 non-genotyped females born between 2014 and 2016 were used as validation population. RESULTS: With basic ssGBLUP, integrating SNPs selected from sequencing data improved prediction reliabilities for milk and protein yields, but resulted in limited or no improvement for fat yield and female fertility. Model performances depended on the SNP set used. When using ssWGBLUP with the 54K SNPs, reliabilities for milk and protein yields improved by 0.028 for genotyped animals and by 0.006 for non-genotyped animals compared with ssGBLUP. However, with the SNP set that included SNPs selected from sequencing data, no statistically significant difference in prediction reliability was observed between the three ssGBLUP models. CONCLUSIONS: In summary, when using 54K SNPs, a ssWGBLUP model with a common weight on the SNPs in a given region is a feasible approach for single-trait genetic evaluation. Integrating relevant SNPs selected from sequencing data into the standard SNP chip can improve the reliability of genomic prediction. Based on such SNP data, a basic ssGBLUP model was suggested since no significant improvement was observed from using alternative models such as ssWGBLUP and ssFGBLUP.

Prioritizing candidate genes for fertility in dairy cows using gene-based analysis, functional annotation and differential gene expression.

BACKGROUND: An unfavorable genetic correlation between milk production and fertility makes simultaneous improvement of milk production and fertility difficult in cattle breeding. Rapid genetic improvement in milk production traits in dairy cattle has been accompanied by decline in cow fertility. The genetic basis of this correlation remains poorly understood. Expanded reference populations and large sets of sequenced animals make genome-wide association studies (GWAS) with imputed markers possible for large populations and thereby studying genetic architecture of complex traits. RESULTS: In this study, we associated 15,551,021 SNPs with female fertility index in 5038 Nordic Holstein cattle. We have identified seven quantitative trait loci (QTL) on six chromosomes in cattle. Along with nearest genes to GWAS hits, we used gene-based analysis and spread of linkage disequilibrium (LD) information to generate a list of potential candidate genes affecting fertility in cattle. Subsequently, we used prior knowledge on gene related to fertility from Gene Ontology terms, Kyoto Encyclopedia of Genes and Genomes pathway analysis, mammalian phenotype database, and public available RNA-seq data to refine the list of candidate genes for fertility. We used variant annotations to investigate candidate mutations within the prioritized candidate genes. Using multiple source of information, we proposed candidate genes with biological relevance underlying each of these seven QTL. On chromosome 1, we have identified ten candidate genes for two QTL. For the rest of chromosomes, we proposed one candidate gene for each QTL. In the candidate genes list, differentially expressed genes from different studies support FRAS1, ITGB5, ADCY5, and SEMA5B as candidate genes for cow fertility. CONCLUSION: The GWAS result not only confirmed previously mapped QTL, but also made new findings. Our findings contributes towards dissecting the genetics for female fertility in cattle. Moreover, this study shows the usefulness of adding independent information to pick candidate genes during post-GWAS analysis.

Whole genome sequence and de novo assembly revealed genomic architecture of Indian Mithun (Bos frontalis).

BACKGROUND: Mithun (Bos frontalis), also called gayal, is an endangered bovine species, under the tribe bovini with 2n = 58 XX chromosome complements and reared under the tropical rain forests region of India, China, Myanmar, Bhutan and Bangladesh. However, the origin of this species is still disputed and information on its genomic architecture is scanty so far. We trust that availability of its whole genome sequence data and assembly will greatly solve this problem and help to generate many information including phylogenetic status of mithun. Recently, the first genome assembly of gayal, mithun of Chinese origin, was published. However, an improved reference genome assembly would still benefit in understanding genetic variation in mithun populations reared under diverse geographical locations and for building a superior consensus assembly. We, therefore, performed deep sequencing of the genome of an adult female mithun from India, assembled and annotated its genome and performed extensive bioinformatic analyses to produce a superior de novo genome assembly of mithun. RESULTS: We generated ≈300 Gigabyte (Gb) raw reads from whole-genome deep sequencing platforms and assembled the sequence data using a hybrid assembly strategy to create a high quality de novo assembly of mithun with 96% recovered as per BUSCO analysis. The final genome assembly has a total length of 3.0 Gb, contains 5,015 scaffolds with an N50 value of 1 Mb. Repeat sequences constitute around 43.66% of the assembly. The genomic alignments between mithun to cattle showed that their genomes, as expected, are highly conserved. Gene annotation identified 28,044 protein-coding genes presented in mithun genome. The gene orthologous groups of mithun showed a high degree of similarity in comparison with other species, while fewer mithun specific coding sequences were found compared to those in cattle. CONCLUSION: Here we presented the first de novo draft genome assembly of Indian mithun having better coverage, less fragmented, better annotated, and constitutes a reasonably complete assembly compared to the previously published gayal genome. This comprehensive assembly unravelled the genomic architecture of mithun to a great extent and will provide a reference genome assembly to research community to elucidate the evolutionary history of mithun across its distinct geographical locations.

Dissecting closely linked association signals in combination with the mammalian phenotype database can identify candidate genes in dairy cattle.

BACKGROUND: Genome-wide association studies (GWAS) have been successfully implemented in cattle research and breeding. However, moving from the associations to identify the causal variants and reveal underlying mechanisms have proven complicated. In dairy cattle populations, we face a challenge due to long-range linkage disequilibrium (LD) arising from close familial relationships in the studied individuals. Long range LD makes it difficult to distinguish if one or multiple quantitative trait loci (QTL) are segregating in a genomic region showing association with a phenotype. We had two objectives in this study: 1) to distinguish between multiple QTL segregating in a genomic region, and 2) use of external information to prioritize candidate genes for a QTL along with the candidate variants. RESULTS: We observed fixing the lead SNP as a covariate can help to distinguish additional close association signal(s). Thereafter, using the mammalian phenotype database, we successfully found candidate genes, in concordance with previous studies, demonstrating the power of this strategy. Secondly, we used variant annotation information to search for causative variants in our candidate genes. The variant information successfully identified known causal mutations and showed the potential to pinpoint the causative mutation(s) which are located in coding regions. CONCLUSIONS: Our approach can distinguish multiple QTL segregating on the same chromosome in a single analysis without manual input. Moreover, utilizing information from the mammalian phenotype database and variant effect predictor as post-GWAS analysis could benefit in candidate genes and causative mutations finding in cattle. Our study not only identified additional candidate genes for milk traits, but also can serve as a routine method for GWAS in dairy cattle.

Weighting sequence variants based on their annotation increases the power of genome-wide association studies in dairy cattle.

BACKGROUND: Genome-wide association studies (GWAS) are widely used to identify regions of the genome that harbor genetic determinants of quantitative traits. However, the multiple-testing burden from scanning tens of millions of whole-genome sequence variants reduces the power to identify associated variants, especially if sample size is limited. In addition, factors such as inaccuracy of imputation, complex linkage disequilibrium structures, and multiple closely-located causal variants may result in an identified causative mutation not being the most significant single nucleotide polymorphism in a particular genomic region. Therefore, the use of information from different sources, particularly variant annotations, was proposed to enhance the fine-mapping of causal variants. Here, we tested whether applying significance thresholds based on variant annotation categories increases the power of GWAS compared with a flat Bonferroni multiple-testing correction. RESULTS: Whole-genome sequence variants in dairy cattle were categorized according to type and predicted impact. Then, GWAS between markers and 17 quantitative traits were analyzed for enrichment for association of each annotation category. By using annotation categories that were determined with the variants effect predictor software and datasets indicating regions of open chromatin, "low impact" variants were found to be highly enriched. Moreover, when the variants annotated as "modifier" and not located at open chromatin regions were further classified into different types of potential regulatory elements, the high impact variants, moderate impact variants, variants located in the 3' and 5' untranslated regions, and variants located in potential non-coding RNA regions exhibited relatively more enrichment. In contrast, a similar study on human GWAS data reported that enrichment of association signals was highest with high impact variants. We observed an increase in power when these variant category-based significance thresholds were applied for GWAS results on stature in Nordic Holstein cattle, as more candidate genes from previous large GWAS meta-analysis for cattle stature were confirmed. CONCLUSIONS: Use of variant category-based genome-wide significance thresholds can marginally increase the power to detect the candidate genes in cattle. With the continued improvements in annotation of the bovine genome, we anticipate that the growing usefulness of variant category-based significance thresholds will be demonstrated.

Joint imputation of whole-genome sequence variants and large chromosomal deletions in cattle.

Genotype imputation, often focused on SNP and small insertions and deletions (indels; size ≤50 bp), is a crucial step for association mapping and estimation of genomic breeding values. Here, we present strategies to impute genotypes for large chromosomal deletions (size >50 bp), along with SNP and indels in cattle. The pipelines include a strategy for extending the whole-genome sequence reference panel for large deletions, a 2-step genotype refinement approach using Beagle4 and SHAPEIT2 software, and finally, joint imputation of SNP, indels, and large deletions to the existing SNP array-typed population using Minimac3 software. Using these pipelines we achieved an imputation accuracy of the squared Pearson correlation (r2) > 0.6 at minor allele frequencies as low as 0.7% for SNP and indels, and 0.2% for large deletions. This highlights the potential of our approach to build a haplotype reference panel and impute different classes of sequence variants across a wide allele frequency spectrum with high accuracy.

Prioritizing candidate genes post-GWAS using multiple sources of data for mastitis resistance in dairy cattle.

BACKGROUND: Improving resistance to mastitis, one of the costliest diseases in dairy production, has become an important objective in dairy cattle breeding. However, mastitis resistance is influenced by many genes involved in multiple processes, including the response to infection, inflammation, and post-infection healing. Low genetic heritability, environmental variations, and farm management differences further complicate the identification of links between genetic variants and mastitis resistance. Consequently, studies of the genetics of variation in mastitis resistance in dairy cattle lack agreement about the responsible genes. RESULTS: We associated 15,552,968 imputed whole-genome sequencing markers for 5147 Nordic Holstein cattle with mastitis resistance in a genome-wide association study (GWAS). Next, we augmented P-values for markers in genes in the associated regions using Gene Ontology terms, Kyoto Encyclopedia of Genes and Genomes pathway analysis, and mammalian phenotype database. To confirm results of gene-based analyses, we used gene expression data from E. coli-challenged cow udders. We identified 22 independent quantitative trait loci (QTL) that collectively explained 14% of the variance in breeding values for resistance to clinical mastitis (CM). Using association test statistics with multiple pieces of independent information on gene function and differential expression during bacterial infection, we suggested putative causal genes with biological relevance for 12 QTL affecting resistance to CM in dairy cattle. CONCLUSION: Combining information on the nearest positional genes, gene-based analyses, and differential gene expression data from RNA-seq, we identified putative causal genes (candidate genes with biological evidence) in QTL for mastitis resistance in Nordic Holstein cattle. The same strategy can be applied for other traits.

Dissecting closely linked association signals in combination with the mammalian phenotype database can identify candidate genes in dairy cattle.

BACKGROUND: Genome-wide association studies (GWAS) have been successfully implemented in cattle research and breeding. However, moving from the associations to identifying the causal variants and revealing underlying mechanisms have proven complicated. In dairy cattle populations, we face a challenge due to long-range linkage disequilibrium (LD) arising from close familial relationships in the studied individuals. Long range LD makes it difficult to distinguish if one or multiple quantitative trait loci (QTL) are segregating in a genomic region showing association with a phenotype. We had two objectives in this study: 1) to distinguish between multiple QTL segregating in a genomic region, and 2) use of external information to prioritize candidate genes for a QTL along with the candidate variant. RESULTS: We observed fixing the lead SNP as a covariate can help to distinguish additional close association signal(s). Thereafter, using the mammalian phenotype database, we successfully found candidate genes, in concordance with previous studies, demonstrating the power of this strategy. Secondly, we used variant annotation information to search for causative variants in our candidate genes. The variant information successfully identified known causal mutations and showed the potential to pinpoint the causative mutation(s) which are located in coding regions. CONCLUSIONS: Our approach can distinguish multiple QTL segregating on the same chromosome in a single analysis without manual input. Moreover, utilizing information from the mammalian phenotype database and variant effect predictor as post-GWAS analysis could benefit in candidate genes and causative mutations finding in cattle. Our study not only identified additional candidate genes for milk traits, but also can serve as a routine method for GWAS in dairy cattle.

Retraction Note: Dissecting closely linked association signals in combination with the mammalian phenotype database can identify candidate genes in dairy cattle.

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SNP markers associated with body size and pelt length in American mink (Neovison vison).

BACKGROUND: Identification of genes underlying production traits is a key aim of the mink research community. Recent availability of genomic tools have opened the possibility for faster genetic progress in mink breeding. Availability of mink genome assembly allows genome-wide association studies in mink. RESULTS: In this study, we used genotyping-by-sequencing to obtain single nucleotide polymorphism (SNP) genotypes of 2496 mink. After multiple rounds of filtering, we retained 28,336 high quality SNPs and 2352 individuals for a genome-wide association study (GWAS). We performed the first GWAS for body weight, behavior, along with 10 traits related to fur quality in mink. CONCLUSIONS: Combining association results with existing functional information of genes and mammalian phenotype databases, we proposed WWC3, MAP2K4, SLC7A1 and USP22 as candidate genes for body weight and pelt length in mink.

Impact of rare and low-frequency sequence variants on reliability of genomic prediction in dairy cattle.

BACKGROUND: Availability of whole-genome sequence data for a large number of cattle and efficient imputation methodologies open a new opportunity to include rare and low-frequency variants (RLFV) in genomic prediction in dairy cattle. The objective of this study was to examine the impact of including RLFV that are within genes and selected from whole-genome sequence variants, on the reliability of genomic prediction for fertility, health and longevity in dairy cattle. RESULTS: All genic RLFV with a minor allele frequency lower than 0.05 were extracted from imputed sequence data and subsets were created using different strategies. These subsets were subsequently combined with Illumina 50 k single nucleotide polymorphism (SNP) data and used for genomic prediction. Reliability of prediction obtained by using 50 k SNP data alone was used as reference value and absolute changes in reliabilities are referred to as changes in percentage points. Adding a component that included either all the genic or a subset of selected RLFV into the model in addition to the 50 k component changed the reliability of predictions by - 2.2 to 1.1%, i.e. hardly no change in reliability of prediction was found, regardless of how the RLFV were selected. In addition to these empirical analyses, a simulation study was performed to evaluate the potential impact of adding RLFV in the model on the reliability of prediction. Three sets of causal RLFV (containing 21,468, 1348 and 235 RLFV) that were randomly selected from different numbers of genes were generated and accounted for 10% additional genetic variance of the estimated variance explained by the 50 k SNPs. When genic RLFV based on mapping results were included in the prediction model, reliabilities improved by up to 4.0% and when the causal RLFV were included they improved by up to 6.8%. CONCLUSIONS: Using selected RLFV from whole-genome sequence data had only a small impact on the empirical reliability of genomic prediction in dairy cattle. Our simulations revealed that for sequence data to bring a benefit, the key is to identify causal RLFV.

Association analysis for udder index and milking speed with imputed whole-genome sequence variants in Nordic Holstein cattle.

Genome-wide association testing facilitates the identification of genetic variants associated with complex traits. Mapping genes that promote genetic resistance to mastitis could reduce the cost of antibiotic use and enhance animal welfare and milk production by improving outcomes of breeding for udder health. Using imputed whole-genome sequence variants, we carried out association studies for 2 traits related to udder health, udder index, and milking speed in Nordic Holstein cattle. A total of 4,921 bulls genotyped with the BovineSNP50 BeadChip array were imputed to high-density genotypes (Illumina BovineHD BeadChip, Illumina, San Diego, CA) and, subsequently, to whole-genome sequence variants. An association analysis was carried out using a linear mixed model. Phenotypes used in the association analyses were deregressed breeding values. Multitrait meta-analysis was carried out for these 2 traits. We identified 10 and 8 chromosomes harboring markers that were significantly associated with udder index and milking speed, respectively. Strongest association signals were observed on chromosome 20 for udder index and chromosome 19 for milking speed. Multitrait meta-analysis identified 13 chromosomes harboring associated markers for the combination of udder index and milking speed. The associated region on chromosome 20 overlapped with earlier reported quantitative trait loci for similar traits in other cattle populations. Moreover, this region was located close to the FYB gene, which is involved in platelet activation and controls IL-2 expression; FYB is a strong candidate gene for udder health and worthy of further investigation.

Genome-wide association study for milking speed in French Holstein cows.

Using a combination of data from the BovineSNP50 BeadChip SNP array (Illumina, San Diego, CA) and a EuroGenomics (Amsterdam, the Netherlands) custom single nucleotide polymorphism (SNP) chip with SNP pre-selected from whole genome sequence data, we carried out an association study of milking speed in 32,491 French Holstein dairy cows. Milking speed was measured by a score given by the farmer. Phenotypes were yield deviations as obtained from the French evaluation system. They were analyzed with a linear mixed model for association studies. We identified SNP on 22 chromosomes significantly associated with milking speed. As clinical mastitis and somatic cell score have an unfavorable genetic correlation with milking speed, we tested whether the most significant SNP on these 22 chromosomes associated with milking speed were also associated with clinical mastitis or somatic cell score. Nine hundred seventy-one genome-wide significant SNP were associated with milking speed. Of these, 86 were associated with clinical mastitis and 198 with somatic cell score. The most significant association signals for milking speed were observed on chromosomes 7, 8, 10, 14, and 18. The most significant signal was located on chromosome 14 (ZFAT gene). Eleven novel milking speed quantitative trait loci (QTL) were observed on chromosomes 7, 10, 11, 14, 18, 25, and 26. Twelve candidate SNP for milking speed mapped directly within genes. Of these, 10 were QTL lead SNP, which mapped within the genes HMHA1, POLR2E, GNB5, KLHL29, ZFAT, KCNB2, CEACAM18, CCL24, and LHPP. Limited pleiotropy was observed between milking speed QTL and clinical mastitis.