A Meta-Analysis Including Pre-selected Sequence Variants Associated With Seven Traits in Three French Dairy Cattle Populations

Using expression data to fine map QTL associated with fertility in dairy cattle

BACKGROUND

Female fertility is an important trait in dairy cattle. Identifying putative causal variants associated with fertility may help to improve the accuracy of genomic prediction of fertility. Combining expression data (eQTL) of genes, exons, gene splicing and allele specific expression is a promising approach to fine map QTL to get closer to the causal mutations. Another approach is to identify genomic differences between cows selected for high and low fertility and a selection experiment in New Zealand has created exactly this resource. Our objective was to combine multiple types of expression data, fertility traits and allele frequency in high- (POS) and low-fertility (NEG) cows with a genome-wide association study (GWAS) on calving interval in Australian cows to fine-map QTL associated with fertility in both Australia and New Zealand dairy cattle populations.

RESULTS

Variants that were significantly associated with calving interval (CI) were strongly enriched for variants associated with gene, exon, gene splicing and allele-specific expression, indicating that there is substantial overlap between QTL associated with CI and eQTL. We identified 671 genes with significant differential expression between POS and NEG cows, with the largest fold change detected for the CCDC196 gene on chromosome 10. Our results provide numerous candidate genes associated with female fertility in dairy cattle, including GYS2 and TIGAR on chromosome 5 and SYT3 and HSD17B14 on chromosome 18. Multiple QTL regions were located in regions with large numbers of copy number variants (CNV). To identify the causal mutations for these variants, long read sequencing may be useful.

CONCLUSIONS

Variants that were significantly associated with CI were highly enriched for eQTL. We detected 671 genes that were differentially expressed between POS and NEG cows. Several QTL detected for CI overlapped with eQTL, providing candidate genes for fertility in dairy cattle.

Exploring genome-wide differentiation and signatures of selection in Italian and North American Holstein populations

Among Italian dairy cattle, the Holstein is the most reared breed for the production of Parmigiano Reggiano protected designation of origin cheese, which represents one of the most renowned products in the entire Italian dairy industry. In this work, we used a medium-density genome-wide data set consisting of 79,464 imputed SNPs to study the genetic structure of Italian Holstein breed, including the population reared in the area of Parmigiano Reggiano cheese production, and assessing its distinctiveness from the North American population. Multidimensional scaling and ADMIXTURE approaches were used to explore the genetic structure among populations. We also investigated putative genomic regions under selection among these 3 populations by combining 4 different statistical methods based either on allele frequencies (single marker and window-based) or extended haplotype homozygosity (EHH; standardized log-ratio of integrated EHH and cross-population EHH). The genetic structure results allowed us to clearly distinguish the 3 Holstein populations; however, the most remarkable difference was observed between Italian and North American stock. Selection signature analyses identified several significant SNPs falling within or closer to genes with known roles in several traits such as milk quality, resistance to disease, and fertility. In particular, a total of 22 genes related to milk production have been identified using the 2 allele frequency approaches. Among these, a convergent signal has been found in the VPS8 gene which resulted to be involved in milk traits, whereas other genes (CYP7B1, KSR2, C4A, LIPE, DCDC1, GPR20, and ST3GAL1) resulted to be associated with quantitative trait loci related to milk yield and composition in terms of fat and protein percentage. In contrast, a total of 7 genomic regions were identified combining the results of standardized log-ratio of integrated EHH and cross-population EHH. In these regions candidate genes for milk traits were also identified. Moreover, this was also confirmed by the enrichment analyses in which we found that the majority of the significantly enriched quantitative trait loci were linked to milk traits, whereas the gene ontology and pathway enrichment analysis pointed to molecular functions and biological processes involved in AA transmembrane transport and methane metabolism pathway. This study provides information on the genetic structure of the examined populations, showing that they are distinguishable from each other. Furthermore, the selection signature analyses can be considered as a starting point for future studies in the identification of causal mutations and consequent implementation of more practical application.

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.

Meta-analysis of genome-wide association studies for litter size in sheep

Litter size and ovulation rate are important reproduction traits in sheep and have important impacts on the profitability of farm animals. To investigate the genetic architecture of litter size, we report the first meta-analysis of genome-wide association studies (GWAS) using 522 ewes and 564,377 SNPs from six sheep breeds. We identified 29 significant associations for litter size which 27 of which have not been reported in individual GWAS for each population. However, we could confirm the role of BMPR1B in prolificacy. Our gene set analysis discovered biological pathways related to cell signaling, communication, and adhesion. Functional clustering and enrichment using protein databases identified epidermal growth factor-like domain affecting litter size. Through analyzing protein-protein interaction data, we could identify hub genes like CASK, PLCB4, RPTOR, GRIA2, and PLCB1 that were enriched in most of the significant pathways. These genes have a role in cell proliferation, cell adhesion, cell growth and survival, and autophagy. Notably, identified SNPs were scattered on several different chromosomes implying different genetic mechanisms underlying variation of prolificacy in each breed. Given the different layers that make up the follicles and the need for communication and transfer of hormones and nutrients through these layers to the oocyte, the significance of pathways related to cell signaling and communication seems logical. Our results provide genetic insights into the litter size variation in different sheep breeds.

Association of DGAT1 With Cattle, Buffalo, Goat, and Sheep Milk and Meat Production Traits

Milk fatty acids are essential for many dairy product productions, while intramuscular fat (IMF) is associated with the quality of meat. The triacylglycerols (TAGs) are the major components of IMF and milk fat. Therefore, understanding the polymorphisms and genes linked to fat synthesis is important for animal production. Identifying quantitative trait loci (QTLs) and genes associated with milk and meat production traits has been the objective of various mapping studies in the last decade. Consistently, the QTLs on chromosomes 14, 15, and 9 have been found to be associated with milk and meat production traits in cattle, goat, and buffalo and sheep, respectively. Diacylglycerol O-acyltransferase 1 (DGAT1) gene has been reported on chromosomes 14, 15, and 9 in cattle, goat, and buffalo and sheep, respectively. Being a key role in fat metabolism and TAG synthesis, the DGAT1 has obtained considerable attention especially in animal milk production. In addition to milk production, DGAT1 has also been a subject of interest in animal meat production. Several polymorphisms have been documented in DGAT1 in various animal species including cattle, buffalo, goat, and sheep for their association with milk production traits. In addition, the DGAT1 has also been studied for their role in meat production traits in cattle, sheep, and goat. However, very limited studies have been conducted in cattle for association of DGAT1 with meat production traits in cattle. Moreover, not a single study reported the association of DGAT1 with meat production traits in buffalo; thus, further studies are warranted to fulfill this huge gap. Keeping in view the important role of DGAT1 in animal production, the current review article was designed to highlight the major development and new insights on DGAT1 effect on milk and meat production traits in cattle, buffalo, sheep, and goat. Moreover, we have also highlighted the possible future contributions of DGAT1 for the studied species.

Genomic scans for selective sweeps through haplotype homozygosity and allelic fixation in 14 indigenous sheep breeds from Middle East and South Asia

The performance and productivity of livestock have consistently improved by natural and artificial selection over the centuries. Both these selections are expected to leave patterns on the genome and lead to changes in allele frequencies, but natural selection has played the major role among indigenous populations. Detecting selective sweeps in livestock may assist in understanding the processes involved in domestication, genome evolution and discovery of genomic regions associated with economically important traits. We investigated population genetic diversity and selection signals in this study using SNP genotype data of 14 indigenous sheep breeds from Middle East and South Asia, including six breeds from Iran, namely Iranian Balochi, Afshari, Moghani, Qezel, Zel, and Lori-Bakhtiari, three breeds from Afghanistan, namely Afghan Balochi, Arabi, and Gadik, three breeds from India, namely Indian Garole, Changthangi, and Deccani, and two breeds from Bangladesh, namely Bangladeshi Garole and Bangladesh East. The SNP genotype data were generated by the Illumina OvineSNP50 Genotyping BeadChip array. To detect genetic diversity and population structure, we used principal component analysis (PCA), admixture, phylogenetic analyses, and Runs of homozygosity. We applied four complementary statistical tests, FST (fixation index), xp-EHH (cross-population extended haplotype homozygosity), Rsb (extended haplotype homozygosity between-populations), and FLK (the extension of the Lewontin and Krakauer) to detect selective sweeps. Our results not only confirm the previous studies but also provide a suite of novel candidate genes involved in different traits in sheep. On average, FST, xp-EHH, Rsb, and FLK detected 128, 207, 222, and 252 genomic regions as candidates for selective sweeps, respectively. Furthermore, nine overlapping candidate genes were detected by these four tests, especially TNIK, DOCK1, USH2A, and TYW1B which associate with resistance to diseases and climate adaptation. Knowledge of candidate genomic regions in sheep populations may facilitate the identification and potential exploitation of the underlying genes in sheep breeding.

GWAS-Based Identification of New Loci for Milk Yield, Fat, and Protein in Holstein Cattle

Simple Summary

Understanding the genetic architecture underlying milk production traits in cattle is beneficial so that genetic variants can be targeted toward the genetic improvement. In this study, we performed a genome-wide association study for milk production and quality traits in Holstein cattle. In the total of ten significant single-nucleotide polymorphisms (SNPs) associated with milk fat and protein, six are located in previously reported quantitative traits locus (QTL) regions. The study not only identified the effect of DGAT1 gene on milk fat and protein but also found several novel candidate genes. In addition, some pleiotropic SNPs and QTLs were identified that associated with more than two traits, these results could provide some basis for molecular breeding in dairy cattle.

Abstract

High-yield and high-quality of milk are the primary goals of dairy production. Understanding the genetic architecture underlying these milk-related traits is beneficial so that genetic variants can be targeted toward the genetic improvement. In this study, we measured five milk production and quality traits in Holstein cattle population from China. These traits included milk yield, fat, and protein. We used the estimated breeding values as dependent variables to conduct the genome-wide association studies (GWAS). Breeding values were estimated through pedigree relationships by using a linear mixed model. Genotyping was carried out on the individuals with phenotypes by using the Illumina BovineSNP150 BeadChip. The association analyses were conducted by using the fixed and random model Circulating Probability Unification (FarmCPU) method. A total of ten single-nucleotide polymorphisms (SNPs) were detected above the genome-wide significant threshold (p < 4.0 × 10⁻⁷), including six located in previously reported quantitative traits locus (QTL) regions. We found eight candidate genes within distances of 120 kb upstream or downstream to the associated SNPs. The study not only identified the effect of DGAT1 gene on milk fat and protein, but also discovered novel genetic loci and candidate genes related to milk traits. These novel genetic loci would be an important basis for molecular breeding in dairy cattle.

Signatures of selection reveal candidate genes involved in economic traits and cold acclimation in five Swedish cattle breeds

Background

Thousands of years of natural and artificial selection have resulted in indigenous cattle breeds that are well-adapted to the environmental challenges of their local habitat and thereby are considered as valuable genetic resources. Understanding the genetic background of such adaptation processes can help us design effective breeding objectives to preserve local breeds and improve commercial cattle. To identify regions under putative selection, GGP HD 150 K single nucleotide polymorphism (SNP) arrays were used to genotype 106 individuals representing five Swedish breeds i.e. native to different regions and covering areas with a subarctic cold climate in the north and mountainous west, to those with a continental climate in the more densely populated south regions.

Results

Five statistics were incorporated within a framework, known as de-correlated composite of multiple signals (DCMS) to detect signatures of selection. The obtained p-values were adjusted for multiple testing (FDR < 5%), and significant genomic regions were identified. Annotation of genes in these regions revealed various verified and novel candidate genes that are associated with a diverse range of traits, including e.g. high altitude adaptation and response to hypoxia (DCAF8, PPP1R12A, SLC16A3, UCP2, UCP3, TIGAR), cold acclimation (AQP3, AQP7, HSPB8), body size and stature (PLAG1, KCNA6, NDUFA9, AKAP3, C5H12orf4, RAD51AP1, FGF6, TIGAR, CCND2, CSMD3), resistance to disease and bacterial infection (CHI3L2, GBP6, PPFIBP1, REP15, CYP4F2, TIGD2, PYURF, SLC10A2, FCHSD2, ARHGEF17, RELT, PRDM2, KDM5B), reproduction (PPP1R12A, ZFP36L2, CSPP1), milk yield and components (NPC1L1, NUDCD3, ACSS1, FCHSD2), growth and feed efficiency (TMEM68, TGS1, LYN, XKR4, FOXA2, GBP2, GBP5, FGD6), and polled phenotype (URB1, EVA1C).

Conclusions

We identified genomic regions that may provide background knowledge to understand the mechanisms that are involved in economic traits and adaptation to cold climate in cattle. Incorporating p-values of different statistics in a single DCMS framework may help select and prioritize candidate genes for further analyses.

Meta-analysis for milk fat and protein percentage using imputed sequence variant genotypes in 94,321 cattle from eight cattle breeds

Background

Sequence-based genome-wide association studies (GWAS) provide high statistical power to identify candidate causal mutations when a large number of individuals with both sequence variant genotypes and phenotypes is available. A meta-analysis combines summary statistics from multiple GWAS and increases the power to detect trait-associated variants without requiring access to data at the individual level of the GWAS mapping cohorts. Because linkage disequilibrium between adjacent markers is conserved only over short distances across breeds, a multi-breed meta-analysis can improve mapping precision.

Results

To maximise the power to identify quantitative trait loci (QTL), we combined the results of nine within-population GWAS that used imputed sequence variant genotypes of 94,321 cattle from eight breeds, to perform a large-scale meta-analysis for fat and protein percentage in cattle. The meta-analysis detected (p ≤ 10⁻⁸) 138 QTL for fat percentage and 176 QTL for protein percentage. This was more than the number of QTL detected in all within-population GWAS together (124 QTL for fat percentage and 104 QTL for protein percentage). Among all the lead variants, 100 QTL for fat percentage and 114 QTL for protein percentage had the same direction of effect in all within-population GWAS. This indicates either persistence of the linkage phase between the causal variant and the lead variant across breeds or that some of the lead variants might indeed be causal or tightly linked with causal variants. The percentage of intergenic variants was substantially lower for significant variants than for non-significant variants, and significant variants had mostly moderate to high minor allele frequencies. Significant variants were also clustered in genes that are known to be relevant for fat and protein percentages in milk.

Conclusions

Our study identified a large number of QTL associated with fat and protein percentage in dairy cattle. We demonstrated that large-scale multi-breed meta-analysis reveals more QTL at the nucleotide resolution than within-population GWAS. Significant variants were more often located in genic regions than non-significant variants and a large part of them was located in potentially regulatory regions.

Imputation for sequencing variants preselected to a customized low-density chip

The sequencing variants preselected from association analyses and bioinformatics analyses could improve genomic prediction. In this study, the imputation of sequencing SNPs preselected from major dairy breeds in Denmark-Finland-Sweden (DFS) and France (FRA) was investigated for both contemporary animals and old bulls in Danish Jersey. For contemporary animals, a two-step imputation which first imputed to 54 K and then to 54 K + DFS + FRA SNPs achieved highest accuracy. Correlations between observed and imputed genotypes were 91.6% for DFS SNPs and 87.6% for FRA SNPs, while concordance rates were 96.6% for DFS SNPs and 93.5% for FRA SNPs. The SNPs with lower minor allele frequency (MAF) tended to have lower correlations but higher concordance rates. For old bulls, imputation for DFS and FRA SNPs were relatively accurate even for bulls without progenies (correlations higher than 97.2% and concordance rates higher than 98.4%). For contemporary animals, given limited imputation accuracy of preselected sequencing SNPs especially for SNPs with low MAF, it would be a good strategy to directly genotype preselected sequencing SNPs with a customized SNP chip. For old bulls, given high imputation accuracy for preselected sequencing SNPs with all MAF ranges, it would be unnecessary to re-genotype preselected sequencing SNPs.

Genome-wide association study of milk production traits in a crossbred dairy sheep population using three statistical models

Milk production is one of the most important characteristics of dairy sheep, and the identification of genes affecting milk production traits is critical to understanding the genetics and improve milk production in future generations. Three statistical techniques, namely GWAS, ridge-regression BLUP and BayesC π , were used to identify SNPs in significant association with three milk production traits (milk yield, fat yield and protein yield) in a crossbred dairy sheep population. The results suggested that chromosomes 1, 3, 4, 5, 7 and 11 were likely to harbor genes important to milk production because these chromosomes had the greatest top-100-SNP variance contributions on the three milk production traits. The GWAS analysis identified between 74 and 288 genome-wide significant SNP (P < 0.05) whereas the BayesCπ model revealed between six and 63 SNPs, each with >95% posterior probability of inclusion as having a non-zero association effect on at least one of the three milk production traits. Positional candidate genes for milk production in sheep were searched, based on the sheep genomic assembly OAR version 3.1, such as those which map position coincided with or was located within 0.1 Mbp of a genome-wide suggestive or significant SNP. These identified SNPs and candidate genes supported some previous findings and also added new information about genetic markers for genetic improvement of lactation in dairy sheep, but keeping in mind that the majority of these positional candidate genes are not necessarily true causative loci for these traits and future validations are thus necessary.

GWAS and fine-mapping of livability and six disease traits in Holstein cattle

BACKGROUND

Health traits are of significant economic importance to the dairy industry due to their effects on milk production and associated treatment costs. Genome-wide association studies (GWAS) provide a means to identify associated genomic variants and thus reveal insights into the genetic architecture of complex traits and diseases. The objective of this study is to investigate the genetic basis of seven health traits in dairy cattle and to identify potential candidate genes associated with cattle health using GWAS, fine mapping, and analyses of multi-tissue transcriptome data.

RESULTS

We studied cow livability and six direct disease traits, mastitis, ketosis, hypocalcemia, displaced abomasum, metritis, and retained placenta, using de-regressed breeding values and more than three million imputed DNA sequence variants. After data edits and filtering on reliability, the number of bulls included in the analyses ranged from 11,880 (hypocalcemia) to 24,699 (livability). GWAS was performed using a mixed-model association test, and a Bayesian fine-mapping procedure was conducted to calculate a posterior probability of causality to each variant and gene in the candidate regions. The GWAS detected a total of eight genome-wide significant associations for three traits, cow livability, ketosis, and hypocalcemia, including the bovine Major Histocompatibility Complex (MHC) region associated with livability. Our fine-mapping of associated regions reported 20 candidate genes with the highest posterior probabilities of causality for cattle health. Combined with transcriptome data across multiple tissues in cattle, we further exploited these candidate genes to identify specific expression patterns in disease-related tissues and relevant biological explanations such as the expression of Group-specific Component (GC) in the liver and association with mastitis as well as the Coiled-Coil Domain Containing 88C (CCDC88C) expression in CD8 cells and association with cow livability.

CONCLUSIONS

Collectively, our analyses report six significant associations and 20 candidate genes of cattle health. With the integration of multi-tissue transcriptome data, our results provide useful information for future functional studies and better understanding of the biological relationship between genetics and disease susceptibility in cattle.

Identification of genetic markers associated with milk production traits in Chinese Holstein cattle based on post genome-wide association studies

With the rapid development of dairy industry, the breeding process of dairy cows has been accelerated. In previous genome-wide association studies (GWAS), a large number of genetic markers have been reported which may contribute to the selection of Holstein populations with superior milk-producing traits, but they remain to be further verified before practical application. In this study, 90 single nucleotide polymorphisms (SNPs) were selected, which were reported to be significantly associated with five milk production traits, including 305-day milk yield (305MY), 305-day milk fat percent (305FC), 305-day milk protein percent (305PC), 305-day milk fat yield (305FY) and 305-day milk protein yield (305PY). Effective 305-day data and fresh DNA samples were obtained from 295 healthy cows with gestational age of 1-4. Matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) was used to perform precise genotyping of these loci, followed by site association and haplotype analysis. Results showed that 36 out of 90 loci were supported to be used as genetic markers. In particular, several novel and effective haplotypes were also presented. Overall, our results verified tens of useful markers and provided a basis for further development of breeding strategies.

SNV discovery and functional candidate gene identification for milk composition based on whole genome resequencing of Holstein bulls with extremely high and low breeding values

We have sequenced the whole genomes of eight proven Holstein bulls from the four half-sib or full-sib families with extremely high and low estimated breeding values (EBV) for milk protein percentage (PP) and fat percentage (FP) using Illumina re-sequencing technology. Consequently, 2.3 billion raw reads were obtained with an average effective depth of 8.1×. After single nucleotide variant (SNV) calling, total 10,961,243 SNVs were identified, and 57,451 of them showed opposite fixed sites between the bulls with high and low EBVs within each family (called as common differential SNVs). Next, we annotated the common differential SNVs based on the bovine reference genome, and observed that 45,188 SNVs (78.70%) were located in the intergenic region of genes and merely 11,871 SNVs (20.67%) located within the protein-coding genes. Of them, 13,099 common differential SNVs that were within or close to protein-coding genes with less than 5 kb were chosen for identification of candidate genes for milk compositions in dairy cattle. By integrated analysis of the 2,657 genes with the GO terms and pathways related to protein and fat metabolism, and the known quantitative trait loci (QTLs) for milk protein and fat traits, we identified 17 promising candidate genes: ALG14, ATP2C1, PLD1, C3H1orf85, SNX7, MTHFD2L, CDKN2D, COL5A3, FDX1L, PIN1, FIG4, EXOC7, LASP1, PGS1, SAO, GPLD1 and MGEA5. Our findings provided an important foundation for further study and a prompt for molecular breeding of dairy cattle.