Genomic patterns of selection in morphometric traits across diverse Indian cattle breeds

This study seeks a comprehensive exploration of genome-wide selective processes impacting morphometric traits across diverse cattle breeds, utilizing an array of statistical methods. Morphometric traits, encompassing both qualitative and quantitative variables, play a pivotal role in characterizing and selecting livestock breeds based on their external appearance, size, and physical attributes. While qualitative traits, such as color, horn structure, and coat type, contribute to adaptive features and breed identification, quantitative traits like body weight and conformation measurements bear a closer correlation with production characteristics. This study employs advanced genotyping technologies, including the Illumina BovineSNP50 Bead Chip and next-generation sequencing methods like Reduced Representation sequencing, to identify genomic signatures associated with these traits. We applied four intra-population methods to find evidence of selection, such as Tajima's D, CLR, iHS, and ROH. We found a total of 40 genes under the selection signature, that were associated with morphometric traits in five cattle breeds (Kankrej, Tharparkar, Nelore, Sahiwal, and Gir). Crucial genes such as ADIPDQ, DPP6, INSIG1, SLC35D2 in Kankrej, LPL, ATP6V1B2, CDC14B in Tharparkar, HPSE2, PLAG1 in Nelore, PCSK1, PRKD1 in Sahiwal, and GNAQ, HPCAL1 in Gir were identified in our study. This approach provides valuable insights into the genetic basis of variations in body weight and conformation traits, facilitating informed selection processes and offering a deeper understanding of the evolutionary and domestication processes in diverse cattle breeds.

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.

Understanding the underlying genetic mechanisms for age at first calving, inter-calving period and scrotal circumference in Bonsmara cattle

BACKGROUND

Reproduction is a key feature of the sustainability of a species and thus represents an important component in livestock genetic improvement programs. Most reproductive traits are lowly heritable. In order to gain a better understanding of the underlying genetic basis of these traits, a genome-wide association was conducted for age at first calving (AFC), first inter-calving period (ICP) and scrotal circumference (SC) within the South African Bonsmara breed. Phenotypes and genotypes (120,692 single nucleotide polymorphisms (SNPs) post editing) were available on 7,128 South African Bonsmara cattle; the association analyses were undertaken using linear mixed models.

RESULTS

Genomic restricted maximum likelihood analysis of the 7,128 SA Bonsmara cattle yielded genomic heritability's of 0.183 (SE = 0.021) for AFC, 0.207 (SE = 0.022) for ICP and 0.209 (SE = 0.019) for SC. A total of 16, 23 and 51 suggestive (P ≤ 4 × 10-6) SNPs were associated with AFC, ICP and SC, while 11, 11 and 44 significant (P ≤ 4 × 10-7) SNPs were associated with AFC, ICP and SC respectively. A total of 11 quantitative trait loci (QTL) and 11 candidate genes were co-located with these associated SNPs for AFC, with 10 QTL harbouring 11 candidate genes for ICP and 41 QTL containing 40 candidate genes for SC. The QTL identified were close to genes previously associated with carcass, fertility, growth and milk-related traits. The biological pathways influenced by these genes include carbohydrate catabolic processes, cellular development, iron homeostasis, lipid metabolism and storage, immune response, ovarian follicle development and the regulation of DNA transcription and RNA translation.

CONCLUSIONS

This was the first attempt to study the underlying polymorphisms associated with reproduction in South African beef cattle. Genes previously reported in cattle breeds for numerous traits bar AFC, ICP or SC were detected in this study. Over 20 different genes have not been previously reported in beef cattle populations and may have been associated due to the unique genetic composite background of the SA Bonsmara breed.

Invited review: Good practices in genome-wide association studies to identify candidate sequence variants in dairy cattle

Genotype data from dairy cattle selection programs have greatly facilitated GWAS to identify variants related to economic traits. Results can enhance the accuracy of genomic prediction, analyze more complex models that go beyond additive effects, elucidate the genetic architecture of a trait, and finally, decipher the underlying biology of traits. The entire process, comprising data generation, quality control, statistical analyses, interpretation of association results, and linking results to biology should be designed and executed to minimize the generation of false-positive and false-negative associations and misleading links to biological processes. This review aims to provide general guidelines for data analysis that address data quality control, association tests, adjustment for population stratification, and significance evaluation to improve the reliability of conclusions. We also provide guidance on post-GWAS strategy and the interpretation of results. These guidelines are tailored to dairy cattle, which are characterized by long-range linkage disequilibrium, large half-sib families, and routinely collected phenotypes, requiring different approaches than those applied in human GWAS. We discuss common limitations and challenges that have been overlooked in the analysis and interpretation of GWAS to identify candidate sequence variants in dairy cattle.

Genomic diversity and relationship analyses of endangered German Black Pied cattle (DSN) to 68 other taurine breeds based on whole-genome sequencing

German Black Pied cattle (Deutsches Schwarzbuntes Niederungsrind, DSN) are an endangered dual-purpose cattle breed originating from the North Sea region. The population comprises about 2,500 cattle and is considered one of the ancestral populations of the modern Holstein breed. The current study aimed at defining the breeds closest related to DSN cattle, characterizing their genomic diversity and inbreeding. In addition, the detection of selection signatures between DSN and Holstein was a goal. Relationship analyses using fixation index (F_ST), phylogenetic, and admixture analyses were performed between DSN and 68 other breeds from the 1000 Bull Genomes Project. Nucleotide diversity, observed heterozygosity, and expected heterozygosity were calculated as metrics for genomic diversity. Inbreeding was measured as excess of homozygosity (F_Hom) and genomic inbreeding (F_RoH) through runs of homozygosity (RoHs). Region-wide F_ST and cross-population-extended haplotype homozygosity (XP-EHH) between DSN and Holstein were used to detect selection signatures between the two breeds, and RoH islands were used to detect selection signatures within DSN and Holstein. DSN showed a close genetic relationship with breeds from the Netherlands, Belgium, Northern Germany, and Scandinavia, such as Dutch Friesian Red, Dutch Improved Red, Belgian Red White Campine, Red White Dual Purpose, Modern Angler, Modern Danish Red, and Holstein. The nucleotide diversity in DSN (0.151%) was higher than in Holstein (0.147%) and other breeds, e.g., Norwegian Red (0.149%), Red White Dual Purpose (0.149%), Swedish Red (0.149%), Hereford (0.145%), Angus (0.143%), and Jersey (0.136%). The F_Hom and F_RoH values in DSN were among the lowest. Regions with high F_ST between DSN and Holstein, significant XP-EHH regions, and RoH islands detected in both breeds harbor candidate genes that were previously reported for milk, meat, fertility, production, and health traits, including one QTL detected in DSN for endoparasite infection resistance. The selection signatures between DSN and Holstein provide evidence of regions responsible for the dual-purpose properties of DSN and the milk type of Holstein. Despite the small population size, DSN has a high level of diversity and low inbreeding. F_ST supports its relatedness to breeds from the same geographic origin and provides information on potential gene pools that could be used to maintain diversity in DSN.

Genome-wide scans for signatures of selection in Mangalarga Marchador horses using high-throughput SNP genotyping

BACKGROUND

The detection of signatures of selection in genomic regions provides insights into the evolutionary process, enabling discoveries regarding complex phenotypic traits. In this research, we focused on identifying genomic regions affected by different selection pressures, mainly highlighting the recent positive selection, as well as understanding the candidate genes and functional pathways associated with the signatures of selection in the Mangalarga Marchador genome. Besides, we seek to direct the discussion about genes and traits of importance in this breed, especially traits related to the type and quality of gait, temperament, conformation, and locomotor system.

RESULTS

Three different methods were used to search for signals of selection: Tajima's D (TD), the integrated haplotype score (iHS), and runs of homozygosity (ROH). The samples were composed of males (n = 62) and females (n = 130) that were initially chosen considering well-defined phenotypes for gait: picada (n = 86) and batida (n = 106). All horses were genotyped using a 670 k Axiom® Equine Genotyping Array​ (Axiom MNEC670). In total, 27, 104 (chosen), and 38 candidate genes were observed within the signatures of selection identified in TD, iHS, and ROH analyses, respectively. The genes are acting in essential biological processes. The enrichment analysis highlighted the following functions: anterior/posterior pattern for the set of genes (GLI3, HOXC9, HOXC6, HOXC5, HOXC4, HOXC13, HOXC11, and HOXC10); limb morphogenesis, skeletal system, proximal/distal pattern formation, JUN kinase activity (CCL19 and MAP3K6); and muscle stretch response (MAPK14). Other candidate genes were associated with energy metabolism, bronchodilator response, NADH regeneration, reproduction, keratinization, and the immunological system.

CONCLUSIONS

Our findings revealed evidence of signatures of selection in the MM breed that encompass genes acting on athletic performance, limb development, and energy to muscle activity, with the particular involvement of the HOX family genes. The genome of MM is marked by recent positive selection. However, Tajima's D and iHS results point also to the presence of balancing selection in specific regions of the genome.

Exploring the Potential Genetic Heterogeneity in the Incidence of Hoof Disorders in Austrian Fleckvieh and Braunvieh Cattle

Genetic heterogeneity denotes the situation when different genetic architectures underlying diverse populations result in the same phenotype. In this study, we explore the genetic background underlying differences in the incidence of hoof disorders between Braunvieh and Fleckvieh cattle in the context of genetic heterogeneity between the breeds. Despite potentially higher power of testing due to twice as large sample size, none of the SNPs was significantly associated with the total number of hoof disorders in Fleckvieh, while 15 SNPs were significant in Braunvieh. The most promising candidate genes in Braunvieh were as follows: CBLB on BTA1, which causes arthritis in rats; CAV2 on BTA4, which affects skeletal muscles in mice; PTHLH on BTA5, which causes disease phenotypes related to the skeleton in humans, mice, and zebrafish; and SORCS2 on BTA6, which causes decreased susceptibility to injury in mice. Some of the significant SNPs (BTA1, BTA4, BTA5, BTA13, and BTA16) revealed allelic heterogeneity-i.e., different allele frequencies between Fleckvieh and Braunvieh. Some of the significant regions (BTA1, BTA5, BTA13, and BTA16) correlated to inter-breed differences in linkage disequilibrium (LD) structure and may thus represent false-positive heterogeneity. However, positions on BTA6 (SORCS2), BTA14, and BTA24 mark Braunvieh-specific regions. We hypothesize that the observed genetic heterogeneity of hoof disorders is a by-product of different selection goals defined for the analyzed breeds-toward dairy production in Braunvieh and toward beef production in Fleckvieh. Based on the current dataset, it is not possible to unequivocally confirm or exclude the hypothesis of genetic heterogeneity in the susceptibility to hoof disorders between Fleckvieh and Braunvieh. The main reason for the problem is that the potential heterogeneity was explored through SNP-phenotype associations and not through causal mutations, due to a limited SNP density offered by the SNP-chip. The rationale against genetic heterogeneity comprises a limited power of detection of true associations as well as differences in the length of LD blocks and in linkage phase between breeds. On the other hand, different selection goals defined for the analyzed breeds accompanied by no systematic, genome-wide differences in LD structure between the breeds favor the heterogeneity hypothesis at some smaller genomic regions.

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.

Two-stage genome-wide association study for the identification of causal variants underlying hoof disorders in cattle

Feet and legs disorders influence dairy cattle breeding by their effect on animal welfare, economic losses due to lower production and fertility, costs of treatment, and problems with herd management. In our study, we estimated heritabilities and performed a 2-step GWAS for 3 traits describing hoof health: hoof health status defined by a veterinarian (HSV), hoof health status defined by a claw trimmer (HSC), and the total number of hoof disorders (NHD), scored in 1,998 Fleckvieh and 979 Braunvieh cows. The individuals were genotyped with a high-density (HD) panel consisting of 76,934 SNP. For significant genomic regions, the SNP information was enhanced by SNP imputed from the whole-genome sequence of Fleckvieh and Braunvieh bulls from the 1000 Bulls Genome project. The heritabilities were estimated to be 0.035 for HSV, 0.249 for HSC, and 0.279 for NHD. Based on the first-stage GWAS with SNP from the HD panel, 7 significant genomic regions on 6 chromosomes were defined: (1) 120 SNP spanning 15,522 bp on BTA1, including the TOPBP1 gene; (2) 4,139 SNP spanning 1,426,046 bp on BTA7, including the RIOK2 and RGMB genes; (3) 167 SNP spanning 167,352 bp on BTA13, including the C13H20orf194 gene; (4) 2 regions on BTA14, one harboring 1,071 SNP spanning 380,024 bp, including RRM2B and NCALD, and the other comprising 632 SNP spanning 385,111 bp, including STK3; (5) 328 SNP on BTA15, spanning 235,567 bp between FAM168A and PLEKHB1; and (6) 1,549 SNP on BTA22, spanning 596,101 bp in the neighborhood of PTPRG. Then, we conducted a second-stage GWAS based on SNP from whole-genome sequences within the significant regions obtained in the first stage of the analysis. For HSV, the highest additive effect was estimated for 23 SNP located within a region on BTA15, close to FAM168A, corresponding to a predicted gene sequence. For HSC, the highest additive effect was attributed to 44 SNP located within a region of BTA22 corresponding to 4 predicted gene sequences, with rs135082893 within a sequence encoding a microRNA. Another potential causal mutation for HSC was rs134142607 on BTA13, within the exon of C13H20orf194. For NHD, 33 SNP with the highest estimated effect were located on BTA7 within a region of a predicted gene positioned between RIOK2 and RGMB. On BTA14, all significant SNP were located in introns of STK3, which is responsible for the "abnormal gait" phenotype in mice.

Assessing selection signatures within and between selected lines of dual-purpose black and white and German Holstein cattle

The aim of this study was to detect selection signatures considering cows from the German Holstein (GH) and the local dual-purpose black and white (DSN) population, as well as from generated sub-populations. The 4654 GH and 261 DSN cows were genotyped with the BovineSNP50 Genotyping BeadChip. The geographical herd location was used as an environmental descriptor to create the East-DSN and West-DSN sub-populations. In addition, two further sub-populations of GH cows were generated, using the extreme values for solutions of residual effects of cows for the claw disorder dermatitis digitalis. These groups represented the most susceptible and most resistant cows. We used cross-population extended haplotype homozygosity methodology (XP-EHH) to identify the most recent selection signatures. Furthermore, we calculated Wright's fixation index (F_ST ). Chromosomal segments for the top 0.1 percentile of negative or positive XP-EHH scores were studied in detail. For gene annotations, we used the Ensembl database and we considered a window of 250 kbp downstream and upstream of each core SNP corresponding to peaks of XP-EHH. In addition, functional interactions among potential candidate genes were inferred via gene network analyses. The most outstanding XP-EHH score was on chromosome 12 (at 77.34 Mb) for DSN and on chromosome 20 (at 36.29-38.42 Mb) for GH. Selection signature locations harbored QTL for several economically important milk and meat quality traits, reflecting the different breeding goals for GH and DSN. The average F_ST value between GH and DSN was quite low (0.068), indicating shared founders. For group stratifications according to cow health, several identified potential candidate genes influence disease resistance, especially to dermatitis digitalis.

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.

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.

Electronic supplementary material

The online version of this article (10.1186/s12711-019-0463-9) contains supplementary material, which is available to authorized users.

Genomic analysis of claw lesions in Holstein cows: Opportunities for genomic selection, quantitative trait locus detection, and gene identification

Claw lesions are the third most important health issue in dairy cattle, after mastitis and reproductive disorders, and genomic selection is a key component for long-term improvement of claw health. The objectives of this study were to assess the feasibility of a genomic evaluation for claw health in French Holstein cows, explore possibilities to increase evaluation accuracy, and gain a better understanding of the genetic determinism of claw health traits. The data set consisted of 48,685 trimmed Holstein cows, including 9,646 that were genotyped; 478 genotyped sires were also used. Seven claw lesion traits were evaluated using BLUP, genomic BLUP, BayesC, and single-step genomic BLUP, and the accuracies obtained using these approaches were measured through a validation study. The BayesC approach was used to detect quantitative trait locus (QTL) regions associated with the 7 individual traits (digital dermatitis, heel horn erosion, interdigital hyperplasia, sole hemorrhage circumscribed, sole hemorrhage diffused, sole ulcer, and white line fissure) based on their Bayes factor. Annotated genes on these regions were reported. Genomic evaluation approaches generally did not allow for greater accuracies than BLUP, except for single-step genomic BLUP. Accuracies were moderate, but best and worst validation animals were correctly discriminated and showed significant differences in lesion frequencies. A total of 192 QTL regions were identified, including 13 with major evidence or involved for 2 of the traits. A high number of genes were present on these regions, and several had functions associated with the immune system. In particular, the EPYC gene is located close to a major evidence QTL for resistance to digital dermatitis that is also a QTL for interdigital hyperplasia (on chromosome 5, around 20.9 MB) and has been associated with Ehlers-Danlos syndrome in cattle. Genomic selection can be used to improve resistance to individual claw lesions, and several possibilities exist to improve accuracies of genomic evaluations.

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.

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.

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.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5050-x) contains supplementary material, which is available to authorized users.

MicroRNA-guided prioritization of genome-wide association signals reveals the importance of microRNA-target gene networks for complex traits in cattle

MicroRNAs (miRNA) are key modulators of gene expression and so act as putative fine-tuners of complex phenotypes. Here, we hypothesized that causal variants of complex traits are enriched in miRNAs and miRNA-target networks. First, we conducted a genome-wide association study (GWAS) for seven functional and milk production traits using imputed sequence variants (13~15 million) and >10,000 animals from three dairy cattle breeds, i.e., Holstein (HOL), Nordic red cattle (RDC) and Jersey (JER). Second, we analyzed for enrichments of association signals in miRNAs and their miRNA-target networks. Our results demonstrated that genomic regions harboring miRNA genes were significantly (P < 0.05) enriched with GWAS signals for milk production traits and mastitis, and that enrichments within miRNA-target gene networks were significantly higher than in random gene-sets for the majority of traits. Furthermore, most between-trait and across-breed correlations of enrichments with miRNA-target networks were significantly greater than with random gene-sets, suggesting pleiotropic effects of miRNAs. Intriguingly, genes that were differentially expressed in response to mammary gland infections were significantly enriched in the miRNA-target networks associated with mastitis. All these findings were consistent across three breeds. Collectively, our observations demonstrate the importance of miRNAs and their targets for the expression of complex 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.

Electronic supplementary material

The online version of this article (10.1186/s12863-018-0620-0) contains supplementary material, which is available to authorized users.

Genomic breeding values, SNP effects and gene identification for disease traits in cow training sets

Holstein Friesian cow training sets were created according to disease incidences. The different datasets were used to investigate the impact of random forest (RF) and genomic BLUP (GBLUP) methodology on genomic prediction accuracies. In addition, for further verifications of some specific scenarios, single-step genomic BLUP was applied. Disease traits included the overall trait categories of (i) claw disorders, (ii) clinical mastitis and (iii) infertility from 80 741 first lactation Holstein cows kept in 58 large-scale herds. A subset of 6744 cows was genotyped (50K SNP panel). Response variables for all scenarios were de-regressed proofs (DRPs) and pre-corrected phenotypes (PCPs). Initially, all sick cows were allocated to the testing set, and healthy cows represented the training set. For the ongoing cow allocation schemes, the number of sick cows in the training set increased stepwise by moving 10% of the sick cows from the testing to the training set in each step. The size of training and testing sets was kept constant by replacing the same number of cows in the testing set with (randomly selected) healthy cows from the training set. For both the RF and GBLUP methods, prediction accuracies were larger for DRPs compared to PCPs. For PCPs as a response variable, the largest prediction accuracies were observed when the disease incidences in training sets reflected the disease incidence in the whole population. A further increase in prediction accuracies for some selected cow allocation schemes (i.e. larger prediction accuracies compared to corresponding scenarios with RF or GBLUB) was achieved via single-step GBLUP applications. Correlations between genome-wide association study SNP effects and RF importance criteria for single SNPs were in a moderate range, from 0.42 to 0.57, when considering SNPs from all chromosomes or from specific chromosome segments. RF identified significant SNPs close to potential positional candidate genes: GAS1, GPAT3 and CYP2R1 for clinical mastitis; SPINK5 and SLC26A2 for laminitis; and FGF12 for endometritis.

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 Studies for Female Fertility Traits in Chinese and Nordic Holsteins

Reduced female fertility could cause considerable economic loss and has become a worldwide problem in the modern dairy industry. The objective of this study was to detect quantitative trait loci (QTL) for female fertility traits in Chinese and Nordic Holsteins using various strategies. First, single-trait association analyses were performed for female fertility traits in Chinese and Nordic Holsteins. Second, the SNPs with P-value < 0.005 discovered in Chinese Holsteins were validated in Nordic Holsteins. Third, the summary statistics from single-trait association analyses were combined into meta-analyses to: (1) identify common QTL for multiple fertility traits within each Holstein population; (2) detect SNPs which were associated with a female fertility trait across two Holstein populations. A large numbers of QTL were discovered or confirmed for female fertility traits. The QTL segregating at 31.4~34.1 Mb on BTA13, 48.3~51.9 Mb on BTA23 and 34.0~37.6 Mb on BTA28 shared between Chinese and Nordic Holsteins were further ascertained using a validation approach and meta-analyses. Furthermore, multiple novel variants identified in Chinese Holsteins were validated with Nordic data as well as meta-analyses. The genes IL6R, SLC39A12, CACNB2, ZEB1, ZMIZ1 and FAM213A were concluded to be strong candidate genes for female fertility in Holsteins.

Use of biological priors enhances understanding of genetic architecture and genomic prediction of complex traits within and between dairy cattle breeds

Background

A better understanding of the genetic architecture underlying complex traits (e.g., the distribution of causal variants and their effects) may aid in the genomic prediction. Here, we hypothesized that the genomic variants of complex traits might be enriched in a subset of genomic regions defined by genes grouped on the basis of “Gene Ontology” (GO), and that incorporating this independent biological information into genomic prediction models might improve their predictive ability.

Results

Four complex traits (i.e., milk, fat and protein yields, and mastitis) together with imputed sequence variants in Holstein (HOL) and Jersey (JER) cattle were analysed. We first carried out a post-GWAS analysis in a HOL training population to assess the degree of enrichment of the association signals in the gene regions defined by each GO term. We then extended the genomic best linear unbiased prediction model (GBLUP) to a genomic feature BLUP (GFBLUP) model, including an additional genomic effect quantifying the joint effect of a group of variants located in a genomic feature. The GBLUP model using a single random effect assumes that all genomic variants contribute to the genomic relationship equally, whereas GFBLUP attributes different weights to the individual genomic relationships in the prediction equation based on the estimated genomic parameters. Our results demonstrate that the immune-relevant GO terms were more associated with mastitis than milk production, and several biologically meaningful GO terms improved the prediction accuracy with GFBLUP for the four traits, as compared with GBLUP. The improvement of the genomic prediction between breeds (the average increase across the four traits was 0.161) was more apparent than that it was within the HOL (the average increase across the four traits was 0.020).

Conclusions

Our genomic feature modelling approaches provide a framework to simultaneously explore the genetic architecture and genomic prediction of complex traits by taking advantage of independent biological knowledge.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-4004-z) contains supplementary material, which is available to authorized users.

Association analysis for young stock survival index with imputed whole-genome sequence variants in Nordic Holstein cattle

Identification of the genetic variants associated with calf survival in dairy cattle will aid in the elimination of harmful mutations from the cattle population and the reduction of calf and young stock mortality rates. We used de-regressed estimated breeding values for the young stock survival (YSS) index as response variables in a genome-wide association study with imputed whole-genome sequence variants. A total of 4,610 bulls with estimated breeding values were genotyped with the Illumina BovineSNP50 (Illumina, San Diego, CA) single nucleotide polymorphism (SNP) genotyping array. Genotypes were imputed to whole-genome sequence variants. After quality control, 15,419,550 SNP on 29 Bos taurus autosomes (BTA) were used for association analysis. A modified mixed-model association analysis was used for a genome scan, followed by a linear mixed-model analysis for selected genetic variants. We identified 498 SNP on BTA5 and BTA18 that were associated with the YSS index in Nordic Holstein. The SNP rs440345507 (Chr5:94721790) on BTA5 was the putative causal mutation affecting YSS. Two haplotype-based models were used to identify haplotypes with the largest detrimental effects on YSS index. For each association signal, 1 haplotype region with harmful effects and the lead associated SNP were identified. Detected haplotypes on BTA5 and BTA18 explained 1.16 and 1.20%, respectively, of genetic variance for the YSS index. We examined whether YSS quantitative trait loci (QTL) on BTA5 and BTA18 were associated with stillbirth. YSS QTL on BTA18 overlapped a QTL region for stillbirth, but most likely 2 different causal variants were responsible for these 2 QTL. Four component traits of the YSS index, defined by sex and age, were analyzed separately by the modified mixed-model approach. The same genomic regions were associated with both bull and heifer calf mortality. Several genes (EPS8, LOC100138951, and KLK family genes) contained a lead associated SNP or were included in haplotypes with large detrimental effects on YSS in Nordic Holstein cattle.

Exploring the genetic architecture and improving genomic prediction accuracy for mastitis and milk production traits in dairy cattle by mapping variants to hepatic transcriptomic regions responsive to intra-mammary infection

BACKGROUND

A better understanding of the genetic architecture of complex traits can contribute to improve genomic prediction. We hypothesized that genomic variants associated with mastitis and milk production traits in dairy cattle are enriched in hepatic transcriptomic regions that are responsive to intra-mammary infection (IMI). Genomic markers [e.g. single nucleotide polymorphisms (SNPs)] from those regions, if included, may improve the predictive ability of a genomic model.

RESULTS

We applied a genomic feature best linear unbiased prediction model (GFBLUP) to implement the above strategy by considering the hepatic transcriptomic regions responsive to IMI as genomic features. GFBLUP, an extension of GBLUP, includes a separate genomic effect of SNPs within a genomic feature, and allows differential weighting of the individual marker relationships in the prediction equation. Since GFBLUP is computationally intensive, we investigated whether a SNP set test could be a computationally fast way to preselect predictive genomic features. The SNP set test assesses the association between a genomic feature and a trait based on single-SNP genome-wide association studies. We applied these two approaches to mastitis and milk production traits (milk, fat and protein yield) in Holstein (HOL, n = 5056) and Jersey (JER, n = 1231) cattle. We observed that a majority of genomic features were enriched in genomic variants that were associated with mastitis and milk production traits. Compared to GBLUP, the accuracy of genomic prediction with GFBLUP was marginally improved (3.2 to 3.9%) in within-breed prediction. The highest increase (164.4%) in prediction accuracy was observed in across-breed prediction. The significance of genomic features based on the SNP set test were correlated with changes in prediction accuracy of GFBLUP (P < 0.05).

CONCLUSIONS

GFBLUP provides a framework for integrating multiple layers of biological knowledge to provide novel insights into the biological basis of complex traits, and to improve the accuracy of genomic prediction. The SNP set test might be used as a first-step to improve GFBLUP models. Approaches like GFBLUP and SNP set test will become increasingly useful, as the functional annotations of genomes keep accumulating for a range of species and traits.

Integrating Sequence-based GWAS and RNA-Seq Provides Novel Insights into the Genetic Basis of Mastitis and Milk Production in Dairy Cattle

Connecting genome-wide association study (GWAS) to biological mechanisms underlying complex traits is a major challenge. Mastitis resistance and milk production are complex traits of economic importance in the dairy sector and are associated with intra-mammary infection (IMI). Here, we integrated IMI-relevant RNA-Seq data from Holstein cattle and sequence-based GWAS data from three dairy cattle breeds (i.e., Holstein, Nordic red cattle, and Jersey) to explore the genetic basis of mastitis resistance and milk production using post-GWAS analyses and a genomic feature linear mixed model. At 24 h post-IMI, genes responsive to IMI in the mammary gland were preferentially enriched for genetic variants associated with mastitis resistance rather than milk production. Response genes in the liver were mainly enriched for variants associated with mastitis resistance at an early time point (3 h) post-IMI, whereas responsive genes at later stages were enriched for associated variants with milk production. The up- and down-regulated genes were enriched for associated variants with mastitis resistance and milk production, respectively. The patterns were consistent across breeds, indicating that different breeds shared similarities in the genetic basis of these traits. Our approaches provide a framework for integrating multiple layers of data to understand the genetic architecture underlying complex traits.