Single-step Genome-wide Association Study Research Articles

Single-step genome-wide association study and candidate genes networks affecting reproductive traits in Iranian Holstein cattle

The process of reproductive in domestic livestock is one of the most importantchallenges of animal husbandry. Fertility is an important trait that contributes to herd profitability, and it can be improved by genomic information. One of the best ways to examine the association between single nucleotide polymorphisms (SNPs) and phenotypic performance is genome-wide association study (GWAS). This study aimed to perform single-step GWAS (ssGWAS) on reproductive traits in Holstein heifers and cows (born from 1992 to 2018) which were obtained from Animal Breeding Center of Iran. Reproductive traits included: age at first service (AFS), age at first calving (AFC), interval between first and last insemination (IFL), calving interval (CI), days from calving to first service (DFS), gestation length (GL), days open (DO), pregnancy rate (PR) and number of services per conception (NS). The animals were genotyped using SNP panels of different densities (Illumina and GeenSeek Genomic companies) and imputed to a 50K SNP density. The number of genotyped animals after quality control was 1654 and the total number of animals in the pedigree file was 3452730. Gene set enrichment analysis (GSEA) as well as pathway analysis were performed to maximize ssGWAS potential. In total 32 significant SNPs (P < 0.05) were observed of which 11 and 21 SNPs were located within candidate genes of reproductive traits in heifers and cows, respectively. Novel potential candidate genes for reproductive traits included PSEN1, ENSBTAG00000050002, USH2A, DMXL1, AVEN, CCDC93, PARP16, MRPL11, PSMA5, PABPN1, CENPF, and MRPL40. Moreover, the most significant gene ontology term was proteolysis which is involved in cellular protein catabolic process (for gestation length). As a result, the SNPs and candidate genes identified in this study could be used in genomic testing to improve fertility performance of Iranian Holstein dairy cattle and provide new information about the genetic architecture of traits.

Weighted Single-Step Genome-Wide Association Study Uncovers Known and Novel Candidate Genomic Regions for Milk Production Traits and Somatic Cell Score in Valle del Belice Dairy Sheep.

Simple SummaryMilk production is the most economically crucial dairy sheep trait and constitutes the major genetic enhancement purpose via selective breeding. Also, mastitis is one of the most frequently encountered diseases, having a significant impact on animal welfare, milk yield, and quality. The aim of this study was to identify genomic region(s) associated with the milk production traits and somatic cell score (SCS) in Valle del Belice sheep using single-step genome-wide association (ssGWA) and genotyping data from medium density SNP panels. We identified several genomic regions (OAR1, OAR2, OAR3, OAR4, OAR6, OAR9, and OAR25) and candidate genes implicated in milk production traits and SCS. Our findings offer new insights into the genetic basis of milk production traits and SCS in dairy sheep.The objective of this study was to uncover genomic regions explaining a substantial proportion of the genetic variance in milk production traits and somatic cell score in a Valle del Belice dairy sheep. Weighted single-step genome-wide association studies (WssGWAS) were conducted for milk yield (MY), fat yield (FY), fat percentage (FAT%), protein yield (PY), protein percentage (PROT%), and somatic cell score (SCS). In addition, our aim was also to identify candidate genes within genomic regions that explained the highest proportions of genetic variance. Overall, the full pedigree consists of 5534 animals, of which 1813 ewes had milk data (15,008 records), and 481 ewes were genotyped with a 50 K single nucleotide polymorphism (SNP) array. The effects of markers and the genomic estimated breeding values (GEBV) of the animals were obtained by five iterations of WssGBLUP. We considered the top 10 genomic regions in terms of their explained genomic variants as candidate window regions for each trait. The results showed that top ranked genomic windows (1 Mb windows) explained 3.49, 4.04, 5.37, 4.09, 3.80, and 5.24% of the genetic variances for MY, FY, FAT%, PY, PROT%, and total SCS, respectively. Among the candidate genes found, some known associations were confirmed, while several novel candidate genes were also revealed, including PPARGC1A, LYPLA1, LEP, and MYH9 for MY; CACNA1C, PTPN1, ROBO2, CHRM3, and ERCC6 for FY and FAT%; PCSK5 and ANGPT1 for PY and PROT%; and IL26, IFNG, PEX26, NEGR1, LAP3, and MED28 for SCS. These findings increase our understanding of the genetic architecture of six examined traits and provide guidance for subsequent genetic improvement through genome selection.

Revealing the genetic basis of eyelid pigmentation in Hereford cattle.

Ocular squamous cell carcinoma and infectious keratoconjunctivitis are common ocular pathologies in Hereford cattle with considerable economic impact. Both pathologies have been associated with low eyelid pigmentation, and thus, genetic selection for higher eyelid pigmentation could reduce their incidence. The objective of the present study was to reveal the genetic basis of eyelid pigmentation in Hereford cattle. The analysis included a single-step genome-wide association study (ssGWAS) and a subsequent gene-set analysis in order to identify individual genes, genetic mechanisms, and biological pathways implicated in this trait. Data consisted of eyelid pigmentation records in 1,165 Hereford bulls and steers, visually assessed in five categories between 0% and 100%. Genotypic data for 774,660 single-nucleotide polymorphism markers were available for 886 animals with pigmentation records. Pedigree information of three generations of ancestors of animals with phenotype was considered in this study, with a total of 4,929 animals. Our analyses revealed that eyelid pigmentation is a moderately heritable trait, with heritability estimates around 0.41. The ssGWAS identified at least eight regions, located on BTA1, BTA3, BTA5, BTA14, BTA16, BTA18, BTA19, and BTA24, associated with eyelid pigmentation. These regions harbor genes that are directly implicated in melanocyte biology and skin pigmentation, such as ADCY8, PLD1, KITLG, and PRKCA. The gene-set analysis revealed several functional terms closely related to melanogenesis, such as positive regulation of melanocyte differentiation and regulation of ERK1 and ERK2 cascade. Overall, our findings provide evidence that eyelid pigmentation is a heritable trait influenced by many loci. Indeed, the ssGWAS detected several candidate genes that are directly implicated in melanocyte biology, including melanogenesis. This study contributes to a better understanding of the genetic and biological basis of eyelid pigmentation and presents novel information that could aid to design breeding strategies for reducing the incidence of ocular pathologies in cattle. Additional research on the genetic link between eyelid pigmentation and ocular pathologies is needed.

Weighted Single-Step GWAS for Body Mass Index and Scans for Recent Signatures of Selection in Yorkshire Pigs.

Controlling extra fat deposition is economically favorable in modern swine industry. Understanding the genetic architecture of fat deposition traits such as body mass index (BMI) can help in improving genomic selection for such traits. We utilized a weighted single-step genome-wide association study (WssGWAS) to detect genetic regions and candidate genes associated with BMI in a Yorkshire pig population. Three extended haplotype homozygosity (EHH)-related statistics were also incorporated within a de-correlated composite of multiple signals (DCMS) framework to detect recent selection signatures signals. Overall, the full pedigree consisted of 7016 pigs, of which 5561 had BMI records and 598 pigs were genotyped with an 80K single nucleotide polymorphism (SNP) array. Results showed that the most significant windows (top 15) explained 9.35% of BMI genetic variance. Several genes were detected in regions previously associated with pig fat deposition traits and treated as potential candidate genes for BMI in Yorkshire pigs: FTMT, SRFBP1, KHDRBS3, FOXG1, SOD3, LRRC32, TSKU, ACER3, B3GNT6, CCDC201, ADCY1, RAMP3, TBRG4, CCM2. Signature of selection analysis revealed multiple candidate genes previously associated with various economic traits. However, BMI genetic variance explained by regions under selection pressure was minimal (1.31%). In conclusion, candidate genes associated with Yorkshire pigs' BMI trait were identified using WssGWAS. Gene enrichment analysis indicated that the identified candidate genes were enriched in the insulin secretion pathway. We anticipate that these results further advance our understanding of the genetic architecture of BMI in Yorkshire pigs and provide information for genomic selection for fat deposition in this breed.

Genome-wide association study of beef bull semen attributes

BackgroundCattle production is dependent upon fertility because it results in producing offspring to offset production costs. A number of semen attributes are believed to affect fertility and are frequently measured as part of routine breeding soundness exams or semen collection procedures. The objective of this study was to perform a single-step genome-wide association study (ssGWAS) for beef bull semen attributes. Beef bull fertility phenotypes including volume (VOL), concentration (CONC), number of spermatozoa (NSP), initial motility (IMot), post-thaw motility (PTMot), three-hour post-thaw motility (3HRPTMot), percentage of normal spermatozoa (%NORM), primary abnormalities (PRIM), and secondary abnormalities (SEC) were obtained from two artificial insemination (AI) centers. A total of 1819 Angus bulls with 50,624 collection records were used for ssGWAS. A five-generation pedigree was obtained from the American Angus Association and consisted of 6521 sires and 17,136 dams. Genotypes on 1163 bulls were also obtained from the American Angus Association and utilized in ssGWAS.ResultsA multi-trait animal model was used for the estimation of single nucleotide polymorphism (SNP) effects. Significant SNP were those with a -log10P-value threshold greater than 4.0. Volume, CONC, NSP, IMot, PTMot, 3HRPTMot, %NORM, PRIM, and SEC have five, three, six, seven, two, six, six, and two genome-wide significant SNP, respectively.ConclusionsSeveral significant SNP were determined to be near or within quantitative trait loci (QTL) associated with beef bull semen attributes. In addition, genes associated with fertility were found to contain or be near the significant SNP found in the study. The results indicate there are regions of the genome that impact fertility, proving inclusion of genomic information into genetic evaluation should be advantageous for genetic improvement of male fertility traits.

Weighted genome-wide association study reveals new candidate genes related to boar taint compounds 1

The weighted single-step genome-wide association study (WssGWAS) and post-GWAS analysis may be a useful tool to identify quantitative trait loci (QTL) and candidate genes linked to economically important phenotypes, such as boar taint in pigs. The boar taint is an unpleasant taste and smell observable at uncastrated pork cooking. Therefore, we aimed to identify QTL regions and pinpoint candidate genes associated with boar taint compounds applying a WssGWAS. In this study, we analyzed phenotypic records of androstenone, skatole and indole levels (AND, SKA and IND, respectively) recorded on 4,922 animals from three pig sire lines. From these animals, a total of 3,749 were genotyped. This dataset was used to perform a WssGWAS analysis. A total of 16 windows, explaining 0.5% or more of the genetic variance of boar taint compounds, were identified (5, 4 and 7 for AND, SKA and IND, respectively) at Sus scrofa chromosomes 1, 2, 3, 5, 6, 10 12, 15 and 17. Together, these windows explained 2.90%, 2.15% and 4.47% of the genetic variance of AND, SKA or IND, respectively. A total of 128 genes located in the significant QTL regions were identified. Five new candidate genes (CDK4, CRHBP, CTDSP2, CYP27B1 and SDR4E1) were identified as linked to boar taint compounds. In addition, we also find the HSD17B2 gene, which has already been described in literature as associated with the studied traits. These genes should be better studied looking to understand the boar taint occurrence and selection against that.

Genome Wide Association Study of Beef Traits in Local Alpine Breed Reveals the Diversity of the Pathways Involved and the Role of Time Stratification.

Knowledge of the genetic architecture of key growth and beef traits in livestock species has greatly improved worldwide thanks to genome-wide association studies (GWAS), which allow to link target phenotypes to Single Nucleotide Polymorphisms (SNPs) across the genome. Local dual-purpose breeds have rarely been the focus of such studies; recently, however, their value as a possible alternative to intensively farmed breeds has become clear, especially for their greater adaptability to environmental change and potential for survival in less productive areas. We performed single-step GWAS and post-GWAS analysis for body weight (BW), average daily gain (ADG), carcass fleshiness (CF) and dressing percentage (DP) in 1,690 individuals of local alpine cattle breed, Rendena. This breed is typical of alpine pastures, with a marked dual-purpose attitude and good genetic diversity. Moreover, we considered two of the target phenotypes (BW and ADG) at different times in the individuals’ life, a potentially important aspect in the study of the traits’ genetic architecture. We identified 8 significant and 47 suggestively associated SNPs, located in 14 autosomal chromosomes (BTA). Among the strongest signals, 3 significant and 16 suggestive SNPs were associated with ADG and were located on BTA10 (50–60 Mb), while the hotspot associated with CF and DP was on BTA18 (55–62 MB). Among the significant SNPs some were mapped within genes, such as SLC12A1, CGNL1, PRTG (ADG), LOC513941 (CF), NLRP2 (CF and DP), CDC155 (DP). Pathway analysis showed great diversity in the biological pathways linked to the different traits; several were associated with neurogenesis and synaptic transmission, but actin-related and transmembrane transport pathways were also represented. Time-stratification highlighted how the genetic architectures of the same traits were markedly different between different ages. The results from our GWAS of beef traits in Rendena led to the detection of a variety of genes both well-known and novel. We argue that our results show that expanding genomic research to local breeds can reveal hitherto undetected genetic architectures in livestock worldwide. This could greatly help efforts to map genomic complexity of the traits of interest and to make appropriate breeding decisions.

Haplotype-Based Single-Step GWAS for Yearling Temperament in American Angus Cattle.

Behavior is a complex trait and, therefore, understanding its genetic architecture is paramount for the development of effective breeding strategies. The objective of this study was to perform traditional and weighted single-step genome-wide association studies (ssGWAS and WssGWAS, respectively) for yearling temperament (YT) in North American Angus cattle using haplotypes. Approximately 266 K YT records and 70 K animals genotyped using a 50 K single nucleotide polymorphisms (SNP) panel were used. Linkage disequilibrium thresholds (LD) of 0.15, 0.50, and 0.80 were used to create the haploblocks, and the inclusion of non-LD-clustered SNPs (NCSNP) with the haplotypes in the genomic models was also evaluated. WssGWAS did not perform better than ssGWAS. Cattle YT was found to be a highly polygenic trait, with genes and quantitative trait loci (QTL) broadly distributed across the whole genome. Association studies using LD-based haplotypes should include NCSNPs and different LD thresholds to increase the likelihood of finding the relevant genomic regions affecting the trait of interest. The main candidate genes identified, i.e., ATXN10, ADAM10, VAX2, ATP6V1B1, CRISPLD1, CAPRIN1, FA2H, SPEF2, PLXNA1, and CACNA2D3, are involved in important biological processes and metabolic pathways related to behavioral traits, social interactions, and aggressiveness in cattle. Future studies should further investigate the role of these candidate genes.

Genome-wide association study on milk production and somatic cell score for Thai dairy cattle using weighted single-step approach with random regression test-day model

Genome-wide association studies are a powerful tool to identify genomic regions and variants associated with phenotypes. However, only limited mutual confirmation from different studies is available. The objectives of this study were to identify genomic regions as well as genes and pathways associated with the first-lactation milk, fat, protein, and total solid yields; fat, protein, and total solid percentage; and somatic cell score (SCS) in a Thai dairy cattle population. Effects of SNPs were estimated by a weighted single-step GWAS, which back-solved the genomic breeding values predicted using single-step genomic BLUP (ssGBLUP) fitting a single-trait random regression test-day model. Genomic regions that explained at least 0.5% of the total genetic variance were selected for further analyses of candidate genes. Despite the small number of genotyped animals, genomic predictions led to an improvement in the accuracy over the traditional BLUP. Genomic predictions using weighted ssGBLUP were slightly better than the ssGBLUP. The genomic regions associated with milk production traits contained 210 candidate genes on 19 chromosomes [Bos taurus autosome (BTA) 1 to 7, 9, 11 to 16, 20 to 21, 26 to 27 and 29], whereas 21 candidate genes on 3 chromosomes (BTA 11, 16, and 21) were associated with SCS. Many genomic regions explained a small fraction of the genetic variance, indicating polygenic inheritance of the studied traits. Several candidate genes coincided with previous reports for milk production traits in Holstein cattle, especially a large region of genes on BTA14. We identified 141 and 5 novel genes related to milk production and SCS, respectively. These novel genes were also found to be functionally related to heat tolerance (e.g., SLC45A2, IRAG1, and LOC101902172), longevity (e.g., SYT10 and LOC101903327), and fertility (e.g., PAG1). These findings may be attributed to indirect selection in our population. Identified biological networks including intracellular cell transportation and protein catabolism implicate milk production, whereas the immunological pathways such as lymphocyte activation are closely related to SCS. Further studies are required to validate our findings before exploiting them in genomic selection.

Single-step genome-wide association study uncovers known and novel candidate genomic regions for endocrine and classical fertility traits in Swedish Red and Holstein dairy cows

• In this study, the novel endocrine and classical fertility traits of Swedish dairy herds were evaluated using in-line milk progesterone profiles in ssGWAS approach. • The genomic regions associated with the endocrine and classical traits were identified. • A total of 20 QTLs, of which 18 of them are novel, were detected. The novel QTL regions embedded candidate genes ( ATG7, GPI, GJA1, PPP3CA, ECT2, ARHGAP20, PHLDB1, CACNA1D, CCNE1, CDH13, PLD1, FBN2, KIF3A, FGF12, KCNMB2, MAN1A1, KCNN2, SMAD6, MAPK8IP1, PHF21A, LPXN, MMRN1, KCNIP4, NID2, PCDHGA8, GRIA1, PCDHGB4, PHLDB2, STXBP5L, PTPRR, SRGAP1, SNX27, SPTA1, S100A10, TBC1D20 and ITCH ) that are associated with endocrine and classical fertility traits. • The candidate QTL regions provide insights into the genetic basis of endocrine and classical fertility in SR and Holstein dairy breeds. In a study aiming to identify candidate genomic regions associated with endocrine and classical fertility traits in Swedish Red (SR) and Holstein cows, data on 3955 lactations in 1164 SR and 1672 Holstein cows were examined. The dataset comprised milk progesterone (P4) levels ( n = 341,212) in 14 Swedish herds, automatically collected and analyzed in-line using the DeLaval Herd Navigator™. Endocrine traits studied were: days from calving to commencement of luteal activity (C-LA), first luteal phase length (LPL), length of first inter-luteal interval, length of first inter-ovulatory interval (IOI), luteal activity during the first 60 DIM, and proportion of samples with luteal activity during the first 60 DIM. Classical fertility traits based on insemination data were also investigated, such as days from calving to last insemination and calving interval. A total of 180 SR and 312 Holstein cows were genotyped with a low-density SNP chip and imputed to 50 K. Single-step genome-wide association (ssGWAS) was used to explore candidate genomic regions associated with fertility traits. A mixed linear single-trait animal model was fitted, considering season and parity as fixed effects and animal and permanent environment as random effects. The results revealed 990 and 415 SNPs above the threshold (-log ( p -value) ≥4) for SR and Holstein cows, respectively. The breeds shared only eight SNPs significantly associated with fertility traits. Annotation analysis revealed 281 SNPs located in 241 genes. Functional enrichment analysis using DAVID tools reduced the number to 80 genes, which were mediated in various biological processes and KEGG pathways in multiple functions, including folliculogenesis, embryogenesis, uterine growth and development, immune response, and ovarian cysts. Of the 80 genes, 67 were associated with fertility traits in SR cows and 13 in Holstein. Most genes were associated with LPL and IOI in SR cows, but in Holstein the only association with an endocrine trait was with C-LA. Twenty QTL regions that embedded 40 genes were associated with fertility traits in both breeds. All the QTLs detected, except at BTA2 and BTA19 are novel QTL regions that were not reported previously. These novel QTL regions embedded the candidate genes that include ARHGAP20, PHLDB1, CACNA1D, ATG7, CCNE1, GPI, CDH13, ECT2, PLD1, FBN2, KIF3A, FGF12, KCNMB2, GJA1, MAN1A1, KCNN2, SMAD6, MAPK8IP1, PHF21A, LPXN, MMRN1, KCNIP4, NID2, PCDHGA8, GRIA1, PCDHGB4, PHLDB2, STXBP5L, PPP3CA, PTPRR, SRGAP1, SNX27, SPTA1, S100A10, TBC1D20 and ITCH. The candidate regions may help to improve genetic progress in female fertility if used in selection decisions. A challenge for future research is to determine why different regions seem relevant for different traits and breeds, and the practical implications for genomic selection.

149 Integration of Selection Signatures Analyses and Weighted Single-step GWAS to Prioritize Candidate Genes for Body Conformation Traits in Pigs

Abstract Body conformation traits such as body height (BH) and body length (BL) have been included in the swine industry’s selection criteria. The objective of this study was to identify the quantitative trait loci (QTLs) and candidate genes for pig conformation traits using an integration of selection signatures analyses and weighted single-step GWAS (WssGWAS). Body measurement records of 5,593 Yorkshire pigs of which 598 animals were genotyped with Illumina 50K panel were used. Estimated breeding values (EBVs) for BH and BL were computed using univariate animal models. Genotyped animals were grouped into top 5% and bottom 5% based on their EBVs, and selection signatures analyses were performed using fixation index (Fst), FLK, hapFLK, and Rsb statistics, which were then combined as a Mahalanobis distance (Md) framework. The WssGWAS was conducted to detect the single nucleotide polymorphisms (SNPs) associated with the studied traits. The top 1% SNPs (n=530) from Md distribution that overlapped with the top 1% SNPs from WssGWAS (n = 530) were used to detect the candidate genes. A total of 31 and six overlapped SNPs were found to be associated with BH and BL, respectively. Several candidate genes were identified for BH (PARVA, DCDC1, SYT1, CASTOR2, RGSL1, RGS8, RBMS3, TGFBR2, and HS6ST1) and BL (SNTB1, AK7, PAPOLA, KSR1, CHODL, and BMP2), explaining 2.58% and 0.42% of the trait’s genetic variation, respectively. Our results indicated that integrating data from the signatures of selection tests with WssGWAS could help elucidate genomic regions underlying complex traits.

Genetic parameters and weighted single-step genome-wide association study for supernumerary teats in Holstein cattle

Supernumerary teats (SNT) are a common epidermal abnormality of udders in mammals. The SNT negatively affect machine milking ability, udder health, and animal welfare and sometimes act as reservoirs for undesirable bacteria, resulting in economic losses on calves and lactating cows due to the cost of SNT removal surgery, early culling, and low milk yield. This study aimed to analyze the incidence and genetic parameter of SNT and detect SNT-related genes in Chinese Holstein cattle. In this study, the incidence of SNT was recorded in 4,670 Chinese Holstein cattle (born between 2008 and 2017) from 2 farms, including 734 genotyped cows with 114,485 SNPs. The SNT had a total frequency of 9.8% and estimated heritability of 0.22 (SE = 0.07), which were obtained using a threshold model in the studied Chinese Holstein population. Furthermore, we calculated approximate genetic correlations between SNT and the following indicator traits: 12 milk production, 28 body conformation, 5 fertility and reproduction, 5 health, and 9 longevity. Generally, the estimated correlations, such as 305-d milk yield for third parity (-0.55; SE = 0.02) and age at first calving in heifer (0.19; SE = 0.03), were low to moderate. A single-step GWAS was implemented, and 10 genes associated with SNT located in BTA4 were identified. The region (112.70-112.90 Mb) on BTA4 showed the highest genetic variance for SNT. The quantitative trait loci on BTA4 was mapped into the RARRES2 gene, which was previously shown to affect adipogenesis and hormone secretion. The WIF1 gene, which was located in BTA5, was also considered as a candidate gene for SNT. Overall, these findings provide useful information for breeders who are interested in reducing SNT.

Genome-Wide Association Study for Body Length, Body Height, and Total Teat Number in Large White Pigs

Body length, body height, and total teat number are economically important traits in pig breeding, as these traits are usually associated with the growth, reproductivity, and longevity potential of piglets. Here, we report a genetic analysis of these traits using a population comprising 2,068 Large White pigs. A genotyping-by-sequencing (GBS) approach was used to provide high-density genome-wide SNP discovery and genotyping. Univariate and bivariate animal models were used to estimate heritability and genetic correlations. The results showed that heritability estimates for body length, body height, and total teat number were 0.25 ± 0.04, 0.11 ± 0.03, and 0.22 ± 0.04, respectively. The genetic correlation between body length and body height exhibited a strongly positive correlation (0.63 ± 0.15), while a positive but low genetic correlation was observed between total teat number and body length. Furthermore, we used two different genome-wide association study (GWAS) approaches: single-locus GWAS and weighted single-step GWAS (WssGWAS), to identify candidate genes for these traits. Single-locus GWAS detected 76, 13, and 29 significant single-nucleotide polymorphisms (SNPs) associated with body length, body height, and total teat number. Notably, the most significant SNP (S17_15781294), which is located 20 kb downstream of the BMP2 gene, explained 9.09% of the genetic variance for body length traits, and it also explained 9.57% of the genetic variance for body height traits. In addition, another significant SNP (S7_97595973), which is located in the ABCD4 gene, explained 8.92% of the genetic variance for total teat number traits. GWAS results for these traits identified some candidate genomic regions, such as SSC6: 14.96–15.02 Mb, SSC7: 97.18–98.18 Mb, SSC14: 128.29–131.15 Mb, SSC17: 15.39–17.27 Mb, and SSC17: 22.04–24.15 Mb, providing a starting point for further inheritance research. Most quantitative trait loci were detected by single-locus GWAS and WssGWAS. These findings reveal the complexity of the genetic mechanism of the three traits and provide guidance for subsequent genetic improvement through genome selection.

Single-step genomic best linear unbiased predictor genetic parameter estimations and genome-wide associations for milk fatty acid profiles, interval from calving to first insemination, and ketosis in Holstein cattle

Milk fatty acids (FA) have been suggested as biomarkers for early-lactation metabolic diseases and for female fertility status. The aim of the present study was to infer associations between FA, the metabolic disorder ketosis (KET), and the interval from calving to first insemination (ICF) genetically and genomically. In this regard, we focused on a single-step genomic BLUP approach, allowing consideration of genotyped and ungenotyped cows simultaneously. The phenotypic data set considered 38,375 first-lactation Holstein cows, kept in 45 large-scale co-operator herds from 2 federal states in Germany. The calving years for these cows were from 2014 to 2017. Concentrations in milk from the first official milk recording test-day for saturated, unsaturated (UFA), monounsaturated (MUFA), polyunsaturated, palmitic, and stearic (C18:0) FA were determined via Fourier-transform infrared spectroscopy. Ketosis was defined as a binary trait according to a veterinarian diagnosis key, considering diagnoses within a 6-wk interval after calving. A subset of 9,786 cows was genotyped for 40,989 SNP markers. Variance components and heritabilities for all Gaussian distributed FA and for ICF, and for binary KET were estimated by applying single-step genomic BLUP single-trait linear and threshold models, respectively. Genetic correlations were estimated in series of bivariate runs. Genomic breeding values for the single-step genomic BLUP estimations were dependent traits in single-step GWAS. Heritabilities for FA were moderate in the range from 0.09 to 0.20 (standard error = 0.02-0.03), but quite small for ICF (0.08, standard error = 0.01) and for KET (0.05 on the underlying liability scale, posterior standard deviation = 0.02). Genetic correlations between KET and UFA, MUFA, and C18:0 were large (0.74 to 0.85, posterior standard deviation = 0.14-0.19), and low positive between KET and ICF (0.17, posterior standard deviation = 0.22). Genetic correlations between UFA, MUFA, and C18:0 with ICF ranged from 0.34 to 0.46 (standard error = 0.12). In single-step GWAS, we identified a large proportion of overlapping genomic regions for the different FA, especially for UFA and MUFA, and for saturated and palmitic FA. One identical significantly associated SNP was identified for C18:0 and KET on BTA 15. However, there was no genomic segment simultaneously significantly affecting all trait categories ICF, FA, and KET. Nevertheless, some of the annotated potential candidate genes DGKA, IGFBP4, and CXCL8 play a role in lipid metabolism and fertility mechanisms, and influence production diseases in early lactation. Genetic and genomic associations indicate that Fourier-transform infrared spectroscopy FA concentrations in milk from the first official test-day are valuable predictors for KET and for ICF.

Investigating the genetic architecture of disease resilience in pigs by genome-wide association studies of complete blood count traits collected from a natural disease challenge model

BackgroundGenetic improvement for disease resilience is anticipated to be a practical method to improve efficiency and profitability of the pig industry, as resilient pigs maintain a relatively undepressed level of performance in the face of infection. However, multiple biological functions are known to be involved in disease resilience and this complexity means that the genetic architecture of disease resilience remains largely unknown. Here, we conducted genome-wide association studies (GWAS) of 465,910 autosomal SNPs for complete blood count (CBC) traits that are important in an animal’s disease response. The aim was to identify the genetic control of disease resilience.ResultsUnivariate and multivariate single-step GWAS were performed on 15 CBC traits measured from the blood samples of 2743 crossbred (Landrace × Yorkshire) barrows drawn at 2-weeks before, and at 2 and 6-weeks after exposure to a polymicrobial infectious challenge. Overall, at a genome-wise false discovery rate of 0.05, five genomic regions located on Sus scrofa chromosome (SSC) 2, SSC4, SSC9, SSC10, and SSC12, were significantly associated with white blood cell traits in response to the polymicrobial challenge, and nine genomic regions on multiple chromosomes (SSC1, SSC4, SSC5, SSC6, SSC8, SSC9, SSC11, SSC12, SSC17) were significantly associated with red blood cell and platelet traits collected before and after exposure to the challenge. By functional enrichment analyses using Ingenuity Pathway Analysis (IPA) and literature review of previous CBC studies, candidate genes located nearby significant single-nucleotide polymorphisms were found to be involved in immune response, hematopoiesis, red blood cell morphology, and platelet aggregation.ConclusionsThis study helps to improve our understanding of the genetic basis of CBC traits collected before and after exposure to a polymicrobial infectious challenge and provides a step forward to improve disease resilience.

A Single-Step Genome Wide Association Study on Body Size Traits Using Imputation-Based Whole-Genome Sequence Data in Yorkshire Pigs.

The body shape of a pig is the most direct production index, which can fully reflect the pig’s growth status and is closely related to important economic traits. In this study, a genome-wide association study on seven body size traits, the body length (BL), height (BH), chest circumference (CC), abdominal circumference (AC), cannon bone circumference (CBC), rump width (RW), and chest width (CW), were conducted in Yorkshire pigs. Illumina Porcine 80K SNP chips were used to genotype 589 of 5,572 Yorkshire pigs with body size records, and then the chip data was imputed to sequencing data. After quality control of imputed sequencing data, 784,267 SNPs were obtained, and the averaged linkage disequilibrium (r2) was 0.191. We used the single-trait model and the two-trait model to conduct single-step genome wide association study (ssGWAS) on seven body size traits; a total of 198 significant SNPS were finally identified according to the P-value and the contribution to the genetic variance of individual SNP. 11 candidate genes (CDH13, SIL1, CDC14A, TMRPSS15, TRAPPC9, CTNND2, KDM6B, CHD3, MUC13, MAPK4, and HMGA1) were found to be associated with body size traits in pigs; KDM6B and CHD3 jointly affect AC and CC, and MUC13 jointly affect RW and CW. These genes are involved in the regulation of bone growth and development as well as the absorption of nutrients and are associated with obesity. HMGA1 is proposed as a strong candidate gene for body size traits because of its important function and high consistency with other studies regarding the regulation of body size traits. Our results could provide valuable information for pig breeding based on molecular breeding.

Integration of a single-step genome-wide association study with a multi-tissue transcriptome analysis provides novel insights into the genetic basis of wool and weight traits in sheep

BackgroundGenetic improvement of wool and growth traits is a major goal in the sheep industry, but their underlying genetic architecture remains elusive. To improve our understanding of these mechanisms, we conducted a weighted single-step genome-wide association study (WssGWAS) and then integrated the results with large-scale transcriptome data for five wool traits and one growth trait in Merino sheep: mean fibre diameter (MFD), coefficient of variation of the fibre diameter (CVFD), crimp number (CN), mean staple length (MSL), greasy fleece weight (GFW), and live weight (LW).ResultsOur dataset comprised 7135 individuals with phenotype data, among which 1217 had high-density (HD) genotype data (n = 372,534). The genotypes of 707 of these animals were imputed from the Illumina Ovine single nucleotide polymorphism (SNP) 54 BeadChip to the HD Array. The heritability of these traits ranged from 0.05 (CVFD) to 0.36 (MFD), and between-trait genetic correlations ranged from − 0.44 (CN vs. LW) to 0.77 (GFW vs. LW). By integrating the GWAS signals with RNA-seq data from 500 samples (representing 87 tissue types from 16 animals), we detected tissues that were relevant to each of the six traits, e.g. liver, muscle and the gastrointestinal (GI) tract were the most relevant tissues for LW, and leukocytes and macrophages were the most relevant cells for CN. For the six traits, 54 quantitative trait loci (QTL) were identified covering 81 candidate genes on 21 ovine autosomes. Multiple candidate genes showed strong tissue-specific expression, e.g. BNC1 (associated with MFD) and CHRNB1 (LW) were specifically expressed in skin and muscle, respectively. By conducting phenome-wide association studies (PheWAS) in humans, we found that orthologues of several of these candidate genes were significantly (FDR < 0.05) associated with similar traits in humans, e.g. BNC1 was significantly associated with MFD in sheep and with hair colour in humans, and CHRNB1 was significantly associated with LW in sheep and with body mass index in humans.ConclusionsOur findings provide novel insights into the biological and genetic mechanisms underlying wool and growth traits, and thus will contribute to the genetic improvement and gene mapping of complex traits in sheep.

Genome-scale comparative analysis for host resistance against sea lice between Atlantic salmon and rainbow trout

Sea lice (Caligus rogercresseyi) is an ectoparasite which causes major production losses in the salmon aquaculture industry worldwide. Atlantic salmon (Salmo salar) and rainbow trout (Oncorhynchus mykiss) are two of the most susceptible salmonid species to sea lice infestation. The objectives of this study were to: (1) identify genomic regions associated with resistance to Caligus rogercresseyi in Atlantic salmon and rainbow trout by performing single-step Genome-Wide Association studies (ssGWAS), and (2) identify candidate genes related to trait variation based on exploring orthologous genes within the associated regions across species. A total of 2626 Atlantic salmon and 2643 rainbow trout were challenged and genotyped with 50 K and 57 K SNP panels, respectively. We ran two independent ssGWAS for sea lice resistance on each species and identified 7 and 13 regions explaining more than 1% of the genetic variance for the trait, with the most important regions explaining 3% and 2.7% for Atlantic salmon and rainbow trout, respectively. We identified genes associated with immune response, cytoskeleton function, and cell migration when focusing on important genomic regions for each species. Moreover, we found 15 common orthogroups which were present in more than one associated genomic region, within- or between-species; however, only one orthogroup showed a clear potential biological relevance in the response against sea lice. For instance, dual-specificity protein phosphatase 10-like (dusp10) and dual-specificity protein phosphatase 8 (dusp8) were found in genomic regions associated with lice density in Atlantic salmon and rainbow trout, respectively. Dusp10 and dusp8 are modulators of the MAPK pathway and might be involved in the differences of the inflammation response between lice resistant and susceptible fish from both species. Our results provide further knowledge on candidate genes related to sea lice resistance and may help establish better control for sea lice in fish populations.

Genotype-by-environment interactions for reproduction, body composition, and growth traits in maternal-line pigs based on single-step genomic reaction norms

BackgroundThere is an increasing need to account for genotype-by-environment (G × E) interactions in livestock breeding programs to improve productivity and animal welfare across environmental and management conditions. This is even more relevant for pigs because selection occurs in high-health nucleus farms, while commercial pigs are raised in more challenging environments. In this study, we used single-step homoscedastic and heteroscedastic genomic reaction norm models (RNM) to evaluate G × E interactions in Large White pigs, including 8686 genotyped animals, for reproduction (total number of piglets born, TNB; total number of piglets born alive, NBA; total number of piglets weaned, NW), growth (weaning weight, WW; off-test weight, OW), and body composition (ultrasound muscle depth, MD; ultrasound backfat thickness, BF) traits. Genetic parameter estimation and single-step genome-wide association studies (ssGWAS) were performed for each trait.ResultsThe average performance of contemporary groups (CG) was estimated and used as environmental gradient in the reaction norm analyses. We found that the need to consider heterogeneous residual variance in RNM models was trait dependent. Based on estimates of variance components of the RNM slope and of genetic correlations across environmental gradients, G × E interactions clearly existed for TNB and NBA, existed for WW but were of smaller magnitude, and were not detected for NW, OW, MD, and BF. Based on estimates of the genetic variance explained by the markers in sliding genomic windows in ssGWAS, several genomic regions were associated with the RNM slope for TNB, NBA, and WW, indicating specific biological mechanisms underlying environmental sensitivity, and dozens of novel candidate genes were identified. Our results also provided strong evidence that the X chromosome contributed to the intercept and slope of RNM for litter size traits in pigs.ConclusionsWe provide a comprehensive description of G × E interactions in Large White pigs for economically-relevant traits and identified important genomic regions and candidate genes associated with GxE interactions on several autosomes and the X chromosome. Implementation of these findings will contribute to more accurate genomic estimates of breeding values by considering G × E interactions, in order to genetically improve the environmental robustness of maternal-line pigs.

Detection of Genomic Regions with Pleiotropic Effects for Growth and Carcass Quality Traits in the Rubia Gallega Cattle Breed.

Simple SummaryThe breeding scheme in the Rubia Gallega cattle population is based upon traits measured in farms and slaughterhouses. We have developed a ssGWAS by backsolving the SNP effects after implementing a ssGBLUP. The results showed an apparent heterogeneity of the additive genetic variance across the genome. Some of the genomic regions explaining the most of this additive variance were shared across traits, indicating the presence of pleiotropic effects, which were reflected in their genetic correlations.The breeding scheme in the Rubia Gallega cattle population is based upon traits measured in farms and slaughterhouses. In recent years, genomic evaluation has been implemented by using a ssGBLUP (single-step Genomic Best Linear Unbiased Prediction). This procedure can reparameterized to perform ssGWAS (single-step Genome Wide Association Studies) by backsolving the SNP (single nucleotide polymorphisms) effects. Therefore, the objective of this study was to identify genomic regions associated with the genetic variability in growth and carcass quality traits. We implemented a ssGBLUP by using a database that included records for Birth Weight (BW-327,350 records-), Weaning Weight (WW-83,818-), Cold Carcass Weight (CCW-91,621-), Fatness (FAT-91,475-) and Conformation (CON-91,609-). The pedigree included 464,373 individuals, 2449 of which were genotyped. After a process of filtering, we ended up using 43,211 SNP markers. We used the GBLUP and SNPBLUP model equivalences to obtain the effects of the SNPs and then calculated the percentage of variance explained by the regions of the genome between 1 Mb. We identified 7 regions of the genome for CCW; 8 regions for BW, WW, FAT and 9 regions for CON, which explained the percentage of variance above 0.5%. Furthermore, a number of the genome regions had pleiotropic effects, located at: BTA1 (131–132 Mb), BTA2 (1–11 Mb), BTA3 (32–33 Mb), BTA6 (36–38 Mb), BTA16 (24–26 Mb), and BTA 21 (56–57 Mb). These regions contain, amongst others, the following candidate genes: NCK1, MSTN, KCNA3, LCORL, NCAPG, and RIN3.