A genome‐wide association study using selective DNA pooling identifies candidate markers for fertility in Holstein cattle

The decline in the reproductive efficiency of dairy cows, especially those with high producing potential, has become a challenging problem. In this study, a selective DNA pooling approach was applied to a cow population whose oocytes were fertilized and cultured to obtain phenotypic records of fertilization rate and blastocyst rate. Using a stringent 5% genome‐wide significance level, 22 and five single nucleotide polymorphisms (SNPs) were found to be associated with fertilization rate and blastocyst rate, respectively. SNPs that showed significant association in selective DNA pooling were further evaluated by individual genotyping. Interestingly, the majority of the SNP associations were confirmed by individual genotyping, testifying to the effectiveness of selective DNA pooling using a high‐density SNP genotyping array. This study is the first application of the selective DNA pooling approach using the BovineSNP50 array in cattle.     

Genome‐wide association study for pigmentation traits in Chinese Holstein population

With the Illumina BovineSNP50K BeadChip, we performed a genome‐wide association study (GWAS) for two pigmentation traits in a Chinese Holstein population: proportion of black (PB) and teat colour (TC). A case–control design was used. Cases were the cows with PB <0.30 (n = 129) and TC <2 points (n = 140); controls were those with PB >0.90 (n = 58) and TC >4 points (n = 281). The RM test of roadtrips (version 1.2) was applied to detect SNPs for the two traits with 42 883 and 42 741 SNPs respectively. A total of nine and 12 genome‐wide significant (P < 0.05) SNPs associated with PB and TC respectively were identified. Of these, two SNPs for PB were located within the KIT and IGFBP7 genes, and the other four SNPs were 23~212 kb away from the PDGFRA gene on BTA6; nine SNPs associated with TC were located within or 21~78.8 kb away from known genes on chromosomes 4, 11, 22, 23 and 24. By combing through our GWAS results and the biological functions of the genes, we suggest that the KIT, IGFBP7, PDGFRA, MITF, ING3 and WNT16 genes are promising candidates for PB and TC in Holstein cattle, providing a basis for further investigation on the genetic mechanism of pigmentation formation.     

Genome‐wide association study reveals candidate genes associated with body measurement traits in Chinese Wagyu beef cattle

We performed a genome‐wide association study to identify candidate genes for body measurement traits in 463 Wagyu beef cattle typed with the Illumina Bovine HD 770K SNP array. At the genome‐wide level, we detected 18, five and one SNPs associated with hip height, body height and body length respectively. In total, these SNPs are within or near 11 genes, six of which (PENK, XKR4, IMPAD1, PLAG1, CCND2 and SNTG1) have been reported previously and five of which (CSMD3, LAP3, SYN3, FAM19A5 and TIMP3) are novel candidate genes that we found to be associated with body measurement traits. Further exploration of these candidate genes will facilitate genetic improvement in Chinese Wagyu beef cattle.     

A genome‐wide association study suggests several novel candidate genes for carcass traits in Chinese Simmental beef cattle

A genome‐wide association study (GWAS) was conducted for two carcass traits in Chinese Simmental beef cattle. The experimental population consisted of 1301 individuals genotyped with the Illumina BovineHD SNP BeadChip (770K). After quality control, 671 990 SNPs and 1217 individuals were retained for the GWAS. The phenotypic traits included carcass weight and bone weight, which were measured after the cattle were slaughtered at 16 to 18 months of age. Three statistical models—a fixed polygene model, a random polygene model and a composite interval mapping polygene model—were used for the GWAS. The genome‐wide significance threshold after Bonferroni correction was 7.44E‐08 (= 0.05/671 990). In this study, we detected eight and seven SNPs significantly associated with carcass weight and bone weight respectively. In total, 11 candidate genes were identified within or close to these significant SNPs. Of these, we found several novel candidate genes, including PBX1, GCNT4, ALDH1A2, LCORL and WDFY3, to be associated with carcass weight and bone weight in Chinese Simmental beef cattle, and their functional roles need to be verified in further studies.     

Genome‐wide association mapping for female fertility traits in Danish and Swedish Holstein cattle

A genome‐wide association study was conducted using a mixed model analysis for QTL for fertility traits in Danish and Swedish Holstein cattle. The analysis incorporated 2,531 progeny tested bulls, and a total of 36 387 SNP markers on 29 bovine autosomes were used. Eleven fertility traits were analyzed for SNP association. Furthermore, mixed model analysis was used for association analyses where a polygenic effect was fitted as a random effect, and genotypes at single SNPs were successively included as a fixed effect in the model. The Bonferroni correction for multiple testing was applied to adjust the significance threshold. Seventy‐four SNP‐trait combinations showed chromosome‐wide significance, and five of these were significant genome‐wide. Twenty‐four QTL regions on 14 chromosomes were detected. Strong evidence for the presence of QTL that affect fertility traits were observed on chromosomes 3, 5, 10, 13, 19, 20, and 24. The QTL intervals were generally smaller than those described in earlier linkage studies. The identification of fertility trait‐associated SNPs and mapping of the corresponding QTL in small chromosomal regions reported here will facilitate searches for candidate genes and candidate polymorphisms.     

Pleiotropy informed adaptive association test of multiple traits using genome‐wide association study summary data

Genetic variants associated with disease outcomes can be used to develop personalized treatment. To reach this precision medicine goal, hundreds of large‐scale genome‐wide association studies (GWAS) have been conducted in the past decade to search for promising genetic variants associated with various traits. They have successfully identified tens of thousands of disease‐related variants. However, in total these identified variants explain only part of the variation for most complex traits. There remain many genetic variants with small effect sizes to be discovered, which calls for the development of (a) GWAS with more samples and more comprehensively genotyped variants, for example, the NHLBI Trans‐Omics for Precision Medicine (TOPMed) Program is planning to conduct whole genome sequencing on over 100 000 individuals; and (b) novel and more powerful statistical analysis methods. The current dominating GWAS analysis approach is the “single trait” association test, despite the fact that many GWAS are conducted in deeply phenotyped cohorts including many correlated and well‐characterized outcomes, which can help improve the power to detect novel variants if properly analyzed, as suggested by increasing evidence that pleiotropy, where a genetic variant affects multiple traits, is the norm in genome‐phenome associations. We aim to develop pleiotropy informed powerful association test methods across multiple traits for GWAS. Since it is generally very hard to access individual‐level GWAS phenotype and genotype data for those existing GWAS, due to privacy concerns and various logistical considerations, we develop rigorous statistical methods for pleiotropy informed adaptive multitrait association test methods that need only summary association statistics publicly available from most GWAS. We first develop a pleiotropy test, which has powerful performance for truly pleiotropic variants but is sensitive to the pleiotropy assumption. We then develop a pleiotropy informed adaptive test that has robust and powerful performance under various genetic models. We develop accurate and efficient numerical algorithms to compute the analytical P‐value for the proposed adaptive test without the need of resampling or permutation. We illustrate the performance of proposed methods through application to joint association test of GWAS meta‐analysis summary data for several glycemic traits. Our proposed adaptive test identified several novel loci missed by individual trait based GWAS meta‐analysis. All the proposed methods are implemented in a publicly available R package.     

A genome‐wide association study reveals candidate genes for the supernumerary nipple phenotype in sheep (Ovis aries)

Genome‐wide association studies (GWASs) have been widely applied in livestock to identify genes associated with traits of economic interest. Here, we conducted the first GWAS of the supernumerary nipple phenotype in Wadi sheep, a native Chinese sheep breed, based on Ovine Infinium HD SNP BeadChip genotypes in a total of 144 ewes (75 cases with four teats, including two normal and two supernumerary teats, and 69 control cases with two teats). We detected 63 significant SNPs at the chromosome‐wise threshold. Additionally, one candidate region (chr1: 170.723–170.734 Mb) was identified by haplotype‐based association tests, with one SNP (rs413490006) surrounding functional genes BBX and CD47 on chromosome 1 being commonly identified as significant by the two mentioned analyses. Moreover, Gene Ontology enrichment for the significant SNPs identified by the GWAS analysis was functionally clustered into the categories of receptor activity and synaptic membrane. In addition, pathway mapping revealed four promising pathways (Wnt, oxytocin, MAPK and axon guidance) involved in the development of the supernumerary nipple phenotype. Our results provide novel and important insights into the genetic mechanisms underlying the phenotype of supernumerary nipples in mammals, including humans. These findings may be useful for future breeding and genetics in sheep and other livestock.     

Genome‐wide association studies for hematological traits in swine

Improving immune capacity may increase the profitability of animal production if it enables animals to better cope with infections. Hematological traits play pivotal roles in animal immune capacity and disease resistance. Thus far, few studies have been conducted using a high‐density swine SNP chip panel to unravel the genetic mechanism of the immune capability in domestic animals. In this study, using mixed model‐based single‐locus regression analyses, we carried out genome‐wide association studies, using the Porcine SNP60 BeadChip, for immune responses in piglets for 18 hematological traits (seven leukocyte traits, seven erythrocyte traits, and four platelet traits) after being immunized with classical swine fever vaccine. After adjusting for multiple testing based on permutations, 10, 24, and 77 chromosome‐wise significant SNPs were identified for the leukocyte traits, erythrocyte traits, and platelet traits respectively, of which 10 reached genome‐wise significance level. Among the 53 SNPs for mean platelet volume, 29 are located in a linkage disequilibrium block between 32.77 and 40.59 Mb on SSC6. Four genes of interest are located within the block, providing genetic evidence that this genomic segment may be considered a candidate region relevant to the platelet traits. Other candidate genes of interest for red blood cell, hemoglobin, and red blood cell volume distribution width also have been found near the significant SNPs. Our genome‐wide association study provides a list of significant SNPs and candidate genes that offer valuable information for future dissection of molecular mechanisms regulating hematological traits.     

A genome‐wide association analysis for carcass traits in a commercial Duroc pig population

We performed a genome‐wide association study to map the genetic determinants of carcass traits in 350 Duroc pigs typed with the Porcine SNP60 BeadChip. Association analyses were carried out using the gemma software. The proportion of phenotypic variance explained by the SNPs ranged between negligible to moderate (= 0.01–0.30) depending on the trait under consideration. At the genome‐wide level, we detected one significant association between backfat thickness between the 3^rd and 4^th ribs and six SNPs mapping to SSC12 (37–40 Mb). We also identified several chromosome‐wide significant associations for ham weight (SSC11: 51–53 Mb, three SNPs; 67–68 Mb, two SNPs), carcass weight (SSC11: 66–68 Mb, two SNPs), backfat thickness between the 3^rd and 4^th ribs (SSC12: 21 Mb, one SNP; 33–40 Mb, 17 SNPs; 51–58 Mb, two SNPs), backfat thickness in the last rib (SSC12: 37 Mb, one SNP; 40–41 Mb, nine SNPs) and lean meat content (SSC13: 34 Mb, three SNPs and SSC16: 45.1 Mb, one SNP; 62–63 Mb, 10 SNPs; 71–75 Mb, nine SNPs). The ham weight trait‐associated region on SSC11 contains two genes (UCHL3 and LMO7) related to muscle development. In addition, the ACACA gene, which encodes an enzyme for the catalysis of fatty acid synthesis, maps to the SSC12 (37–41 Mb) region harbouring trait‐associated regions for backfat thickness traits. Sequencing of these candidate genes may help to uncover the causal mutations responsible for the associations found in the present study.     

Genome-wide associations for investigating time-dependent genetic effects for milk production traits in dairy cattle

Phenotypic variation in milk production traits has been described over the course of a lactation as well as between different parities. The objective of this study was to investigate whether variation in production is affected by different loci across lactations. A genome-wide association study (GWAS) using a 50-k SNP chip was conducted in 152 divergent German Holstein Friesian cows to test for association with milk production traits over different lactations. The first four lactations were analysed regarding milk yield, fat, protein, lactose, milk urea nitrogen yield and content as well as somatic cell score. Two approaches were used: (i) Wilmink curve parameters were used to assess the genetic effects over the course of a lactation and (ii) test-day yield deviations (YD) were used as a normative approach for a GWAS. The significant effects were largest for markers affecting curve parameters for which there was a statistical power <0.8 of detection even in this small design. While significant markers for YDs were detected in this study, the power to detect effects of a similar magnitude was only 0.11, suggesting that many loci may have been missed with this approach in the present design. Furthermore, all significant effects were specific for a single lactation, leading to the conclusion that the variance explained by a certain locus changes from lactation to lactation. We confirm the common evidence that most production traits vary in the degree of persistency after the peak as a result of genetic influence.     

Genome‐wide association study uncovers four QTL predisposing to supernumerary teats in cattle

Supernumerary teats (hyperthelia, SNTs) are a common abnormality of the bovine udder with a medium to high heritability and a postulated oligogenic or polygenic inheritance pattern. SNTs not only negatively affect machine milking ability but also act as a reservoir for bacteria. A genome‐wide association study was carried out to identify genes involved in the development of SNTs in the dual‐purpose Fleckvieh breed. A total of 2467 progeny‐tested bulls were genotyped at 43 698 single nucleotide polymorphisms, and daughter yield deviations (DYDs) for ‘udder clearness' (UC) were used as high‐heritability phenotypes. Massive structuring of the study population was accounted for by principal components analysis‐based and mixed model‐based approaches. Four loci on BTA5, BTA6, BTA11 and BTA17 were significantly associated with the UC DYD. Three associated regions contain genes of the highly conserved Wnt signalling pathway. The four QTL together account for 10.7% of the variance of the UC DYD, whereas the major fraction of the DYD variance is attributable to chromosomes with no identified QTL. Our results support both an oligogenic and a polygenic inheritance pattern of SNTs in cattle. The identified candidate genes permit insights into the genetic architecture of teat malformations in cattle and provide clues to unravel the molecular mechanisms of mammary gland alterations in cattle and other species.     

Genome‐wide association mapping and pathway analysis of leukosis incidence in a US Holstein cattle population

Bovine leukosis virus is an oncogenic virus that infects B cells, causing bovine leukosis disease. This disease is known to have a negative impact on dairy cattle production and, because no treatment or vaccine is available, finding a possible genetic solution is important. Our objective was to perform a comprehensive genetic analysis of leukosis incidence in dairy cattle. Data on leukosis occurrence, pedigree and molecular information were combined into multitrait GBLUP models with milk yield (MY) and somatic cell score (SCS) to estimate genetic parameters and to perform whole‐genome scans and pathway analysis. Leukosis data were available for 11 554 Holsteins daughters of 3002 sires from 112 herds in 16 US states. Genotypes from a 60K SNP panel were available for 961 of those bulls as well as for 2039 additional bulls. Heritability for leukosis incidence was estimated at about 8%, and the genetic correlations of leukosis disease incidence with MY and SCS were moderate at 0.18 and 0.20 respectively. The genome‐wide scan indicated that leukosis is a complex trait, possibly modulated by many genes. The gene set analysis identified many functional terms that showed significant enrichment of genes associated with leukosis. Many of these terms, such as G‐Protein Coupled Receptor Signaling Pathway, Regulation of Nucleotide Metabolic Process and different calcium‐related processes, are known to be related to retrovirus infection. Overall, our findings contribute to a better understanding of the genetic architecture of this complex disease. The functional categories associated with leukosis may be useful in future studies on fine mapping of genes and development of dairy cattle breeding strategies.     

Genome‐wide association study identifies energy metabolism genes for resistance to ketosis in Chinese Holstein cattle

Ketosis (KET) is one common metabolic disorder that occurs mainly in early lactation and affects the dairy industry with significant economic losses. Cows with ketosis have lower milk yield and reproductive performance and greater risk of other periparturient diseases. As a metabolic disease, the pathogenesis of KET is multifactorial. To better understand the genetic background of KET, a genome‐wide association study was performed using the Illumina BovineSNP50 BeadChip. Single‐step genomic BLUP methodology was used to incorporate genomic data into a threshold‐liability model. Results of the GWAS are reported as the proportion of variance explained by 20‐SNP windows. Six genomic regions on Bos taurus autosomes 10, 13, 14 and 25 showed association with KET. Most interestingly, several candidate genes, including previously reported genes (BMP4, HNF4A and APOBR) and newly identified genes (SOCS4, GCH1, ATG14, RGS6, CYP7A1 and MAPK3), are involved in insulin metabolism or lipid metabolism, implicating the contribution of energy‐metabolism‐associated genes to the genetic basis of KET. Our results provide new information about the underlying biology and molecular mechanisms associated with KET. Future studies that combine genomic variation analysis and functional gene information may help elucidate the biology of KET.     

Pathway analysis of genome‐wide association datasets of personality traits

Although several genome‐wide association (GWA) studies of human personality have been recently published, genetic variants that are highly associated with certain personality traits remain unknown, due to difficulty reproducing results. To further investigate these genetic variants, we assessed biological pathways using GWA datasets. Pathway analysis using GWA data was performed on 1089 Korean women whose personality traits were measured with the Revised NEO Personality Inventory for the 5‐factor model of personality. A total of 1042 pathways containing 8297 genes were included in our study. Of these, 14 pathways were highly enriched with association signals that were validated in 1490 independent samples. These pathways include association of: Neuroticism with axon guidance [L1 cell adhesion molecule (L1CAM) interactions]; Extraversion with neuronal system and voltage‐gated potassium channels; Agreeableness with L1CAM interaction, neurotransmitter receptor binding and downstream transmission in postsynaptic cells; and Conscientiousness with the interferon‐gamma and platelet‐derived growth factor receptor beta polypeptide pathways. Several genes that contribute to top‐ranked pathways in this study were previously identified in GWA studies or by pathway analysis in schizophrenia or other neuropsychiatric disorders. Here we report the first pathway analysis of all five personality traits. Importantly, our analysis identified novel pathways that contribute to understanding the etiology of personality traits.     

Meta‐analysis of genome‐wide association from genomic prediction models

Genome‐wide association (GWA) studies based on GBLUP models are a common practice in animal breeding. However, effect sizes of GWA tests are small, requiring larger sample sizes to enhance power of detection of rare variants. Because of difficulties in increasing sample size in animal populations, one alternative is to implement a meta‐analysis (MA), combining information and results from independent GWA studies. Although this methodology has been used widely in human genetics, implementation in animal breeding has been limited. Thus, we present methods to implement a MA of GWA, describing the proper approach to compute weights derived from multiple genomic evaluations based on animal‐centric GBLUP models. Application to real datasets shows that MA increases power of detection of associations in comparison with population‐level GWA, allowing for population structure and heterogeneity of variance components across populations to be accounted for. Another advantage of MA is that it does not require access to genotype data that is required for a joint analysis. Scripts related to the implementation of this approach, which consider the strength of association as well as the sign, are distributed and thus account for heterogeneity in association phase between QTL and SNPs. Thus, MA of GWA is an attractive alternative to summarizing results from multiple genomic studies, avoiding restrictions with genotype data sharing, definition of fixed effects and different scales of measurement of evaluated traits.     

A genome‐wide association study reveals a quantitative trait locus for age at first calving in delta/notch‐like EGF repeat containing on chromosome 2 in Japanese Black cattle

Age at first calving (AFC) is an important trait for achieving earlier reproductive performance in cattle. To identify quantitative trait loci for AFC in Japanese Black cattle, we conducted a genome‐wide association study using 866 animals with extreme AFC values selected from a larger group of 52 009 animals. We identified single nucleotide polymorphisms (SNPs) on bovine chromosome 2 that were associated with AFC. These SNPs were located within 112.8‐kbp intronic region of delta/notch‐like EGF repeat containing (DNER) and proved to be in a state of high linkage disequilibrium. The association was replicated in an independent sample of 2963 animals. In the replicated population, the frequency of the reduced AFC allele (Q) was 0.463, and the allele accounts for 8% of the total genetic variance. The effect of allele substitution on AFC was a decrease of 11.54 days. The results suggest that the Q allele could serve as a useful marker in Japanese Black cattle to select animals with superior AFC performance.     

Systematic analyses for candidate genes of milk production traits in water buffalo (Bubalus Bubalis)

Water buffalo (Bubalus bubalis) is of great economic importance as a provider of milk and meat in many countries. However, the milk yield of buffalo is much lower than that of Holstein cows. Selection of candidate genes related to milk production traits can be applied to improve buffalo milk performance. A systematic review of studies of these candidate genes will be greatly beneficial for researchers to timely and efficiently understand the research development of molecular markers for buffalo milk production traits. Here, we identified and classified the candidate genes associated with buffalo milk production traits. A total of 517 candidate genes have been identified as being associated with milk performance in different buffalo breeds. Nineteen candidate genes containing 47 mutation sites have been identified using the candidate gene approach. In addition, 499 candidate genes have been identified in six genome‐wide association studies (GWASes) including two studies performed with the bovine SNP chip and four studies with the buffalo SNP chip. Genes CTNND2 (catenin delta 2), APOB (apolipoprotein B), FHIT (fragile histidine triad) and ESRRG (estrogen related receptor gamma) were identified in at least two GWASes. These four genes, especially APOB, deserve further study to explore regulatory roles in buffalo milk production. With growth in the number of buffalo genomic studies, more candidate genes associated with buffalo milk production traits will be identified. Therefore, future studies, such as those investigating gene location and functional analyses, are necessary to facilitate the exploitation of genetic potential and the improvement of buffalo milk performance.