Single-step Genome-wide Association Study Research Articles

Exploring the genetic control of sweat gland characteristics in beef cattle for enhanced heat tolerance

BackgroundThermal stress in subtropical regions is a major limiting factor in beef cattle production systems with around $369 million being lost annually due to reduced performance. Heat stress causes numerous physiological and behavioral disturbances including reduced feed intake and decreased production levels. Cattle utilize various physiological mechanisms such as sweating to regulate internal heat. Variation in these traits can help identify genetic variants that control sweat gland properties and subsequently allow for genetic selection of cattle with greater thermotolerance.MethodsThis study used 2,401 Brangus cattle from two commercial ranches in Florida. Precise phenotypes that contribute to an animal’s ability to manage heat stress were calculated from skin biopsies and included sweat gland area, sweat gland depth, and sweat gland length. All animals were genotyped with the Bovine GGP F250K, and BLUPF90 software was used to estimate genetic parameters and for Genome Wide Association Study.ResultsSweat gland phenotypes heritability ranged from 0.17 to 0.42 indicating a moderate amount of the phenotypic variation is due to genetics, allowing producers the ability to select for favorable sweat gland properties. A weighted single-step GWAS using sliding 10 kb windows identified multiple quantitative trait loci (QTLs) explaining a significant amount of genetic variation. QTLs located on BTA7 and BTA12 explained over 1.0% of genetic variance and overlap the ADGRV1 and CCDC168 genes, respectively. The variants identified in this study are implicated in processes related to immune function and cellular proliferation which could be relevant to heat management. Breed of Origin Alleles (BOA) were predicted using local ancestry in admixed populations (LAMP-LD), allowing for identification of markers’ origin from either Brahman or Angus ancestry. A BOA GWAS was performed to identify regions inherited from particular ancestral breeds that might have a significant impact on sweat gland phenotypes.ConclusionsThe results of the BOA GWAS indicate that both Brahman and Angus alleles contribute positively to sweat gland traits, as evidenced by favorable marker effects observed from both genetic backgrounds. Understanding and utilizing genetic traits that confer better heat tolerance is a proactive approach to managing the impacts of climate change on livestock farming.

Identification of genomic regions and pathways associated with traits related to rumen acidosis in feedlot Nellore cattle.

There may be an increased risk of metabolic disorders, such as rumen acidosis, in cattle fed high-concentrate diets, particularly those from Bos taurus indicus genotypes, which have shown to be more sensitive to ruminal acidification. Therefore, this study aimed to estimate (co)variance components and identify genomic regions and pathways associated with ruminal acidosis in feedlot Nellore cattle fed high-concentrate diets. It was utilized a dataset containing a total of 642 Nellore bulls that were genotyped from seven feedlot nutrition studies. The GGP Indicus 35k panel was used with the single step genome-wide association study methodology in which the effects of the markers were obtained from the genomic values estimated by the GBLUP model. A bivariate model to estimate genetic correlations between the economically important traits and indicator traits for acidosis was used. The traits evaluated in this study that were nutritionally related to rumen acidosis included average daily gain (ADG), final body weight, time spent eating (TSE), time spent ruminating, rumenitis score (RUM), rumen absorptive surface area (ASA), rumen keratinized layer thickness (KER) and hot carcass weight (HCW). The identified candidate genes were mainly involved in the negative or non-regulation of the apoptotic process, salivary secretion, and transmembrane transport. The genetic correlation between HCW and ASA was low positive (0.27 ± 0.23), and between ADG and ASA was high moderate (0.58 ± 0.59). A positive genetic correlation between RUM and all performance traits was observed, and TSE correlated negatively with HCW (-0.33 ± 0.21), ASA (-0.75 ± 0.48), and KER (-0.40 ± 0.27). The genetic association between economically important traits and indicator traits for acidosis suggested that Nellore cattle may be more sensitive to acidosis in feedlot systems.

Identification of Candidate Genes and Pathways Linked to the Temperament Trait in Sheep.

Temperament can be defined as the emotional variability among animals of the same species in response to the same stimulus, grouping animals by their reactivity as nervous, intermediate, or calm. Our goal was to identify genomic regions with the temperament phenotype measured by the Isolation Box Test (IBT) by single-step genome-wide association studies (ssGWAS). The database consisted of 4317 animals with temperament records, and 1697 genotyped animals with 38,268 effective Single Nucleotide Polymorphism (SNP) after quality control. We identified three genomic regions that explained the greatest percentage of the genetic variance, resulting in 25 SNP associated with candidate genes on chromosomes 6, 10, and 21. A total of nine candidate genes are reported for the temperament trait, which is: PYGM, SYVN1, CAPN1, FADS1, SYT7, GRID2, GPRIN3, EEF1A1 and FRY, linked to the energetic activity of the organism, synaptic transmission, meat tenderness, and calcium associated activities. This is the first study to identify these genetic variants associated with temperament in sheep, which could be used as molecular markers in future behavioral research.

Estimation of genetic parameters and single-step genome-wide association studies for eating time and rumination time in Holstein dairy cows

The aims of this study were to estimate genetic parameters and to identify genomic regions associated with eating time (ET) and rumination time (RUT) in Holstein dairy cows. Genetic correlations among ET, RUT, and milk yield traits were also estimated. The data were collected from 2019 to 2022 in 6 dairy herds located in the Walloon Region of Belgium. The dataset consisted of daily ET and RUT records on 284 Holstein cows, from which 41 cows had records only for the first parity (P1), 101 cows had records from both the first and second parities, and 142 cows had records only for the second parity (P2). The number of daily ET and RUT records in the P1 and P2 cows were 18,569 (on 142 cows) and 34,464 (on 243 cows), respectively. Data on 28,994 SNPs located on 29 Bos taurus autosomes (BTA) of 747 animals (435 males) were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single-step genomic best linear unbiased prediction approach. The proportion of genetic variance explained by each 20-SNP sliding window (with an average size of 1.52 Mb) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for candidate genes. Mean (standard deviation; SD) averaged daily ET and RUT were 327.0 (85.66) and 559.4 (77.69) min/d for cows in P1 and 316.0 (82.24) and 574.2 (75.42) min/d for cows in P2, respectively. Mean (standard deviation; SD) heritability estimates for daily ET and RUT were 0.42 (0.09) and 0.45 (0.06) for cows in P1 and 0.45 (0.04) and 0.43 (0.02) for cows in P2, respectively. Mean (SD) daily genetic correlations between daily ET and RUT were 0.27 (0.07) for P1 and 0.34 (0.08) for P2. Genome-wide association analyses identified 6 genomic regions distributed over 5 chromosomes (BTA1, BTA4, BTA11, 2 regions of BTA14, and BTA17) associated with ET or RUT. The findings of this study increase our preliminary understanding of the genetic background of feeding behavior in dairy cows; however, larger datasets are needed to determine whether ET and RUT might have the potential to be used in selection programs.

Genetic analysis of milk citrate predicted by milk mid-infrared spectra of Holstein cows in early lactation

Milk citrate is regarded as an early biomarker of negative energy balance in dairy cows during early lactation and serves as a suitable candidate phenotype for genomic selection due to its wide availability across a large number of cows through milk mid-infrared spectra prediction. However, its genetic background is not well known. Therefore, the objectives of this study were to (1) analyze the genetic parameters of milk citrate; (2) identify genomic regions associated with milk citrate; and (3) analyze the functional annotation of candidate genes and quantitative trait loci (QTL) related to milk citrate in Walloon Holstein cows. In total, 134,517 test-day milk-citrate phenotypes (mmol/L) collected within the first 50 d in milk on 52,198 Holstein cows were used. These milk-citrate phenotypes, predicted by milk mid-infrared spectra, were divided into 3 traits according to the first (citrate1), second (citrate2), and third to fifth parity (citrate3+). Genomic information for 566,170 SNPs was available for 4,479 animals. A multiple-trait repeatability model was used to estimate genetic parameters. A single-step GWAS was used to identify candidate genes for citrate and post-GWAS analysis was done to investigate the relationship and function of the identified candidate genes. The heritabilities estimated for citrate1, citrate2, and citrate3+ were 0.40, 0.37, and 0.35, respectively. The genetic correlations among the 3 traits ranged from 0.98 to 0.99. The genomic correlations among the 3 traits were also close to 1.00 across the genomic regions (1 Mb) in the whole genome, which means that citrate can be considered as a single trait in the first 5 parities. In total, 603 significant SNPs located on 3 genomic regions (chromosome 7, 68.569-68.575 Mb; chromosome 14, 0.15-1.90 Mb; and chromosome 20, 54.00-64.28 Mb), were identified to be associated with milk citrate. We identified 89 candidate genes including GPT, ANKH, PPP1R16A, and 32 QTL reported in the literature related to the identified significant SNPs. These identified QTL were mainly reported associated with milk fatty acids and metabolic diseases in dairy cows. This study suggests that milk citrate in Holstein cows is highly heritable and has the potential to be used as an early proxy for the negative energy balance of Holstein cows in a breeding objective.

Weighted single-step GWAS identified candidate genes associated with carcass traits in a Chinese yellow-feathered chicken population

Carcass traits in broiler chickens are complex traits that are influenced by multiple genes. To gain deeper insights into the genetic mechanisms underlying carcass traits, here we conducted a weighted single-step genome-wide association study (wssGWAS) in a population of Chinese yellow-feathered chicken. The objective was to identify genomic regions and candidate genes associated with carcass weight (CW), eviscerated weight with giblets (EWG), eviscerated weight (EW), breast muscle weight (BMW), drumstick weight (DW), abdominal fat weight (AFW), abdominal fat percentage (AFP), gizzard weight (GW), and intestine length (IL). A total of 1,338 broiler chickens with phenotypic and pedigree information were included in this study. Of these, 435 chickens were genotyped using a 600K single nucleotide polymorphism chip for association analysis. The results indicate that the most significant regions for 9 traits explained 2.38% to 5.09% of the phenotypic variation, from which the region of 194.53 to 194.63Mb on chromosome 1 with the gene RELT and FAM168A identified on it was significantly associated with CW, EWG, EW, BMW, and DW. Meanwhile, the 5 traits have a strong genetic correlation, indicating that the region and the genes can be used for further research. In addition, some candidate genes associated with skeletal muscle development, fat deposition regulation, intestinal repair, and protection were identified. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses suggested that the genes are involved in processes such as vascular development (CD34, FGF7, FGFR3, ITGB1BP1, SEMA5A, LOXL2), bone formation (FGFR3, MATN1, MEF2D, DHRS3, SKI, STC1, HOXB1, HOXB3, TIPARP), and anatomical size regulation (ADD2, AKT1, CFTR, EDN3, FLII, HCLS1, ITGB1BP1, SEMA5A, SHC1, ULK1, DSTN, GSK3B, BORCS8, GRIP2). In conclusion, the integration of phenotype, genotype, and pedigree information without creating pseudo-phenotype will facilitate the genetic improvement of carcass traits in chickens, providing valuable insights into the genetic architecture and potential candidate genes underlying carcass traits, enriching our understanding and contributing to the breeding of high-quality broiler chickens.

Genomic predictions and GWAS for heat tolerance in pigs based on reaction norm models with performance records and data from public weather stations considering alternative temperature thresholds.

Genetic improvement of livestock productivity has resulted in greater production of metabolic heat and potentially greater susceptibility to heat stress. Various studies have demonstrated that there is genetic variability for heat tolerance and genetic selection for more heat tolerant individuals is possible. The rate of genetic progress tends to be greater when genomic information is incorporated into the analyses as more accurate breeding values can be obtained for young individuals. Therefore, this study aimed (1) to evaluate the predictive ability of genomic breeding values for heat tolerance based on routinely recorded traits, and (2) to investigate the genetic background of heat tolerance based on single-step genome-wide association studies for economically important traits related to body composition, growth and reproduction in Large White pigs. Pedigree information was available for 265,943 animals and genotypes for 8686 animals. The studied traits included ultrasound backfat thickness (BFT), ultrasound muscle depth (MDP), piglet weaning weight (WW), off-test weight (OTW), interval between farrowing (IBF), total number of piglets born (TNB), number of piglets born alive (NBA), number of piglets born dead (NBD), number of piglets weaned (WN) and weaning-to-estrus interval (IWE). The number of phenotypic records ranged from 6059 (WN) to 172,984 (TNB). Single-step genomic reaction norm predictions were used to calculate the genomic estimated breeding values for each individual. Predictions of breeding values for the validation population individuals were compared between datasets containing phenotypic records measured in the whole range of temperatures (WR) and datasets containing only phenotypic records measured when the weather station temperature was above 10°C (10C) or 15°C (15C), to evaluate the usefulness of these datasets that may better reflect the within-barn temperature. The use of homogeneous or heterogeneous residual variance was found to be trait-dependent, where homogeneous variance presented the best fit for MDP, BFT, OTW, TNB, NBA, WN and IBF, while the other traits (WW and IWE) had better fit with heterogeneous variance. The average prediction accuracy, dispersion and bias values considering all traits for WR were 0.36 ± 0.05, -0.07 ± 0.13 and 0.76 ± 0.10, respectively; for 10C were 0.39 ± 0.02, -0.05 ± 0.07 and 0.81 ± 0.05, respectively; and for 15C were 0.32 ± 0.05, -0.05 ± 0.11 and 0.84 ± 0.10, respectively. Based on the studied traits, using phenotypic records collected when the outside temperature (from public weather stations) was above 10°C provided better predictions for most of the traits. Forty-three and 62 candidate genomic regions were associated with the intercept (overall performance level) and slope term (specific biological mechanisms related to environmental sensitivity), respectively. Our results contribute to improve genomic predictions using existing datasets and better understand the genetic background of heat tolerance in pigs. Furthermore, the genomic regions and candidate genes identified will contribute to future genomic studies and breeding applications.

Quantitative trait loci exploration and characterization of gestation length in Holstein cattle

Gestation length (GL) is a moderately heritable trait in cattle with economic and management implications. This study aimed to characterize the gestation length of an Argentinian Holstein cattle population, understand contributing factors, and explore the GL effect on production performance. Further objectives were to estimate direct and maternal heritabilities for this trait and to identify genomic regions affecting it. Data consisted of GL records from 45,738 births corresponding to 17,004 Holstein cows and heifers. The effects of age and calving season over GL were analyzed using a Student's t-test for homoscedastic samples. The effects of the GL category (GL shorter than 1.5 SD, within ±1.5 SD, and longer than 1.5 SD from the mean) on production performance were studied by analysis of variance. A single-step genome-wide association study was performed using the BLUPF90 suite of programs with genotypes from 654 Holstein animals on 40,339 SNP. The results showed that the younger the age at calving, the shorter the GL. Moreover, gestations ending in warmer seasons were, in general, statistically shorter than those ending in colder seasons for both heifers and cows. Regarding the effect of GL on production performance, cows with gestation periods within ±1.5 SD from the population mean exhibited the highest 305-day cumulative milk, fat, and protein productions. Direct and maternal heritabilities for GL were 0.42 and 0.03, respectively. We detected a SNP suggestively associated with direct gestation length at 57.7 Mb on Bos taurus autosome 18, a locus included in a region described in the literature as associated with the trait. The information obtained on the environmental and genetic factors affecting GL in Argentinian Holstein cows contributes to characterizing the population in pursuit of improving the performance of national dairy cattle breeding systems.

PSVI-11 Genome-Wide Association Study for 4 Behavior Traits in a Population of Labrador Retrievers Bred as Guide Dogs

Abstract The objective of this study was to assess the genetic architecture and investigate genomic regions associated with Body Handling, Harness sensitivity, Self-modulation, and Noise sensitivity in Labrador Retrievers used as guide dogs. A single-step genome-wide association study (ssGWAS) was used for this purpose. Phenotypic data were collected in 4,841 Labrador Retrievers with ages ranging from 3 months to 2.5 years, for all four traits. Phenotypes were selected from the Behavior Checklist, which is a scoring tool used by a skilled scorer to describe aspects of behavior observed during a variety of assessments including formal tests and while training or observing another handler working with the dog. The pedigree file contained 23,593 animals with birth years ranging from 1991 to 2019 from two populations with related ancestors. Genomic data were available for 457 individuals and were obtained by selecting 250K SNPs from whole genome sequences. Associations were calculated as the percentage of variance explained by windows of 80 adjacent SNPs. SNP variances were calculated based on SNP effects that were obtained by back-solving genomic estimated breeding values obtained with the single-step GBLUP method in a genomic selection program with the same population. Additionally, the genomic relationship matrix was modified under a weighted ssGBLUP (wssGBLUP) approach that allowed SNPs to have different distributions. Manhattan plots were generated with the variance explained by SNP windows for 3 iterations of wssGBLUP. After visual inspection of the Manhattan plots, the R package GALLO was used to annotate the genes located within the peaks that explained 0.6% or more of the genetic variance. The maximum amount of variance explained by a window of 80 SNPs was 1.1% for Body Handling. This trait presented three other regions explaining more than 0.6% of the genetic variance. Harness sensitivity presented four peaks above the threshold, with the major one explaining 0.75% of the genetic variance. Self-modulation and Noise sensitivity both had their major peak explaining 0.95% of the genetic variance and presented four and three regions above the threshold, respectively. The genes annotated in the candidate regions were not specifically related to behavior, nor were they involved in any pathway with biological relevance to the phenotypes. Moreover, some of the regions were not annotated, and no associated genes were found. The traits studied in this project were shown to be polygenic and complex, and that explains the lack of major genes. It is expected that the quality of associations will improve as the sample size increases. The next steps of this project are to increase the number of genotyped animals, increase the number of markers, and adopt alternative annotation and enrichment tools for the post-GWAS analysis.

Across-country genetic and genomic analyses of foot score traits in American and Australian Angus cattle

BackgroundHoof structure and health are essential for the welfare and productivity of beef cattle. Therefore, we assessed the genetic and genomic background of foot score traits in American (US) and Australian (AU) Angus cattle and investigated the feasibility of performing genomic evaluations combining data for foot score traits recorded in US and AU Angus cattle. The traits evaluated were foot angle (FA) and claw set (CS). In total, 109,294 and ~ 1.12 million animals had phenotypic and genomic information, respectively. Four sets of analyses were performed: (1) genomic connectedness between US and AU Angus cattle populations and population structure, (2) estimation of genetic parameters, (3) single-step genomic prediction of breeding values, and (4) single-step genome-wide association studies for FA and CS.ResultsThere was no clear genetic differentiation between US and AU Angus populations. Similar heritability estimates (FA: 0.22–0.24 and CS: 0.22–0.27) and moderate-to-high genetic correlations between US and AU foot scores (FA: 0.61 and CS: 0.76) were obtained. A joint-genomic prediction using data from both populations outperformed within-country genomic evaluations. A genomic prediction model considering US and AU datasets as a single population performed similarly to the scenario accounting for genotype-by-environment interactions (i.e., multiple-trait model considering US and AU records as different traits), even though the genetic correlations between countries were lower than 0.80. Common significant genomic regions were observed between US and AU for FA and CS. Significant single nucleotide polymorphisms were identified on the Bos taurus (BTA) chromosomes BTA1, BTA5, BTA11, BTA13, BTA19, BTA20, and BTA23. The candidate genes identified were primarily from growth factor gene families, including FGF12 and GDF5, which were previously associated with bone structure and repair.ConclusionsThis study presents comprehensive population structure and genetic and genomic analyses of foot scores in US and AU Angus cattle populations, which are essential for optimizing the implementation of genomic selection for improved foot scores in Angus cattle breeding programs. We have also identified candidate genes associated with foot scores in the largest Angus cattle populations in the world and made recommendations for genomic evaluations for improved foot score traits in the US and AU.

Weighted single-step genome-wide association study and functional enrichment analyses for gastrointestinal nematode resistance traits in Santa Ines sheep

This study aimed to identify genomic regions, pathways, and putative candidate genes associated with resistance to gastrointestinal nematode in Santa Ines sheep. The phenotypic information comprised 5529 records from 1703 naturally infected animals. After genomic data quality control, 37,511 SNPs from 589 animals were available. The weighted single-step approach for genome-wide association study was performed to estimate the SNP effects and variances accounted by 10–SNP sliding windows. Confirming the polygenic nature of the studied traits, 20, 22, 21, and 19 genomic windows that explained more than 0.5% of the additive genetic variance were identified for fecal egg counts (FEC), Famacha© (FAM), packed cell volume (PCV), and total plasma protein (TPP), respectively. A total of 81, 122, 106, and 101 protein-coding genes were found in windows associated with FEC, FAM, PCV, and TPP, respectively. Several protein‐coding genes related to the immune system and inflammatory response functions were identified within those genomic regions, such as ADCY9, ADRB2, BRAF, CADM1, CCL20, CD70, CREBBP, FNBP1, HTR4, IL16, IL22, IL26, MAPK8, NDFIP1, NLRC3, PAK5, PLCB1, PLCB4, ROCK1, TEK, TNFRSF12A, and VAV1. Functional enrichment analysis by DAVID tool also revealed many significant (P < 0.05) pathways and Gene Ontology terms that could be related to resistance to gastrointestinal nematode in Santa Ines sheep, such as chemokine signaling pathway (oas04062), cAMP signaling pathway (oas04024), cGMP-PKG signaling pathway (Oas04022), platelet activation (Oas04611), Rap1 signaling pathway (oas04015), and oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen (GO:0016705). These results contribute to improving the knowledge of the genetic architecture of resistance to gastrointestinal nematode in Santa Ines sheep.

Weighted single-step genome-wide association study (WssGWAS) to reveal new candidate genes for productive traits of Landrace pig in Korea

Weighted single-step genome-wide association study (WssGWAS) to reveal new candidate genes for productive traits of Landrace pig in Korea

CYP24A1 is associated with fetal mummification in pigs

Mummified piglets are among the leading causes of fertility loss and severely hamper reproductive performance in pigs. However, the contributions of genomic variation to the emergence of mummified piglets (MUM) have rarely been studied. This study aims to (1) elucidate the genetic architecture of MUM in sows of parity 1 - 3 using a single-step genome-wide association study (ssGWAS). The ssGWAS involved genotyping-by-sequencing of Large White and Landrace pig breeds. (2) Explore the biological role of the candidate genes at the cellular level. A total of 185 and 48 genome-wide significant SNPs are associated with MUM in Large White and Landrace pigs, explaining 0.01–36.52% genetic variance for different significant loci, respectively. All the significant SNPs are parity-specific, and the numerous, consecutive significant loci likely generated the nine significant peaks in different parities. Multiple candidate genes (including CYP24A1, FBXO30, and ARHGEF28) are associated with fetal congenital and maternal diseases. Collectively, CYP24A1 regulation contributes to steady-state levels of embryo development genes. CYP24A1 is involved in reproduction and, immune and gestational disorders. Thus, it is associated with known newborn death traits and MUM in Large White sows. Altogether, these results improve the current understanding of the genetic architecture of MUM and expand the knowledge on genetic variations for selecting against mummified piglets in pig breeding.

Investigation of potential genetic factors for growth traits in yellow-feather broilers using weighted single-step genome-wide association study

Yellow-feather broilers take a large portion of poultry industry in China due to its meat characteristics. Improving the growth traits of yellow-feathered broilers will have great significance for the Chinese poultry market. The current study was designed to investigate the potential genetic factors using the weighted single-step genome-wide association study (wssGWAS) method, which takes consideration of more factors including pedigree, sex, environment and has more accuracy than traditional GWAS. The yellow-feather dwarf chickens from Wens Nanfang Poultry Breeding Co. Ltd. were revolved to recode 9 growth traits: Average daily gain (ADG), body weight (BW) at 45 d, 49 d, 56 d, 63 d, 70 d, 77 d, 84 d, 91 d for analysis. For the results, the region 4.63 to 5.03 Mb on chromosome 15, which was the QTL overlapped in BW45, BW49, BW56, BW63, BW84, might be the crucial genetic region for growth traits. Seven GO terms and 3 KEGG pathways, GO:0005200, GO:0005882, GO:0045111, GO:0099513, GO:0099081, GO:0099512, GO:0099080, KEGG:gga04020, KEGG:gga04540, KEGG:gga04210, were detected to relevant with growth traits. The genes enriched in these biological processes (NRAS, TUBB1, ADORA2B, NTRK3, NGF, TNNC2, F-KER, LOC429492, LOC431325, LOC431324, LOC396480) might have the function in growth of yellow-feather broilers.

Using social interaction models for genetic analysis of skin damage in gilts.

Skin damage is a trait of economic and welfare importance that results from social interactions between animals. These interactions may produce wound signs on the gilt's skin as a result of damage behavior (i.e., fighting), biting syndromes (i.e., tail, vulva, or ear biting), and swine inflammation and necrosis syndrome. Although current selection for traits that are affected by social interactions primarily focuses on improving direct genetic effects, combined selection on direct and social genetic effects could increase genetic gain and avoid a negative response to selection in cases of competitive behavior. The objectives of this study were to (1) estimate variance components for combined skin damage (CSD), with or without accounting for social genetic effects, (2) investigate the impact of including genomic information on the prediction accuracy, bias, and dispersion of CSD estimated breeding values, and (3) perform a single-step genome-wide association study (ssGWAS) of CSD under a classical and a social interaction model. Our results show that CSD is heritable and affected by social genetic effects. Modeling CSD with social interaction models increased the total heritable variance relative to the phenotypic variance by three-fold compared to the classical model. Including genomic information increased the prediction accuracy of direct, social, and total estimated breeding values for purebred sires by at least 21.2%. Bias and dispersion of estimated breeding values were reduced by including genomic information in classical and social interaction models but remained present. The ssGWAS did not identify any single nucleotide polymorphism that was significantly associated with social or direct genetic effects for CSD. Combined skin damage is heritable, and genetic selection against this trait will increase the welfare of animals in the long term. Combined skin damage is affected by social genetic effects, and modeling this trait with a social interaction model increases the potential for genetic improvement. Including genomic information increases the prediction accuracy of estimated breeding values and reduces their bias and dispersion, although some biases persist. The results of the genome-wide association study indicate that CSD has a polygenic architecture and no major quantitative trait locus was detected.

Single-step genome-wide association studies and post-GWAS analyses for the number of oocytes and embryos in Gir cattle.

Genome-Wide Association Studies (GWAS) are used for identification of quantitate trait loci (QTL) and genes associated with several traits. We aimed to identify genomic regions, genes, and biological processes associated with number of total and viable oocytes, and number of embryos in Gir dairy cattle. A dataset with 17,526 follicular aspirations, including the following traits: number of viable oocytes (VO), number of total oocytes (TO), and number of embryos (EMBR) from 1641 Gir donors was provided by five different stock farms. A genotype file with 2093 animals and 395,524 SNP markers was used to perform a single-step GWAS analysis for each trait. The top 10 windows with the highest percentage of additive genetic variance explained by 100 adjacent SNPs were selected. The genomic regions identified in our work were overlapped with QTLs from QTL database on chromosomes 1, 2, 5, 6, 7, 8, 9, 13, 17, 18, 20, 21, 22, 24, and 29. These QTLs were classified as External, Health, Meat and carcass, Production or Reproduction traits, and about 38% were related to Reproduction. In total, 117 genes were identified, of which 111 were protein-coding genes. Exclusively associations were observed for 42 genes with EMBR, and 1 with TO. Also, 42 genes were in common between VO and TO, 28 between VO and EMBR, and four genes were in common among all traits. In conclusion, great part of the identified genes plays a functional role in initial embryo development or general cell functions. The protein-coding genes ARNT, EGR1, HIF1A, AHR, and PAX2 are good markers for the production of oocytes and embryos in Gir cattle.

Single-step genome-wide association study for susceptibility to Teratosphaeria nubilosa and precocity of vegetative phase change in Eucalyptus globulus.

Mycosphaerella leaf disease (MLD) is one of the most prevalent foliar diseases of Eucalyptus globulus plantations around the world. Since resistance management strategies have not been effective in commercial plantations, breeding to develop more resistant genotypes is the most promising strategy. Available genomic information can be used to detect genomic regions associated with resistance to MLD, which could significantly speed up the process of genetic improvement. We investigated the genetic basis of MLD resistance in a breeding population of E. globulus which was genotyped with the EUChip60K SNP array. Resistance to MLD was evaluated through resistance of the juvenile foliage, as defoliation and leaf spot severity, and through precocity of change to resistant adult foliage. Genome-wide association studies (GWAS) were carried out applying four Single-SNP models, a Genomic Best Linear Unbiased Prediction (GBLUP-GWAS) approach, and a Single-step genome-wide association study (ssGWAS). The Single-SNP (model K) and GBLUP-GWAS models detected 13 and 16 SNP-trait associations in chromosomes 2, 3 y 11; whereas the ssGWAS detected 66 SNP-trait associations in the same chromosomes, and additional significant SNP-trait associations in chromosomes 5 to 9 for the precocity of phase change (proportion of adult foliage). For this trait, the two main regions in chromosomes 3 and 11 were identified for the three approaches. The SNPs identified in these regions were positioned near the key miRNA genes, miR156.5 and miR157.4, which have a main role in the regulation of the timing of vegetative change, and also in the response to environmental stresses in plants. Our results demonstrated that ssGWAS was more powerful in detecting regions that affect resistance than conventional GWAS approaches. Additionally, the results suggest a polygenic genetic architecture for the heteroblastic transition in E. globulus and identified useful SNP markers for the development of marker-assisted selection strategies for resistance to MLD.

Scaling factor assessment in single-step GBLUP evaluations for small genotyped populations: A case study on Iranian Holstein cattle

The estimation of genomic breeding values (GEBV) with the single-step genomic BLUP (ssGBLUP), which combines genomic (G) and pedigree relationship matrices in the single-step relationships matrix (H), may require the specification of two scaling parameters, τ and ω. In this study, we evaluate different scenarios to determine the best combinations of these scaling factors (τ for G−1 and ω for A22−1), and emphasize their relevance for single-step genome-wide association studies (ssGWAS). We use a dataset of about 3.1 million Iranian Holstein cattle, including 827,396 and 469,676 primiparous cows with records on milk yield (MY) and somatic cell score (SCS), respectively, and 2,189 genotyped bulls. The choice of the scaling factors was in particular relevant for dispersion (measured by regression coefficient b1 in a linear regression). Using default value s of τ=ω=1 resulted in a slight dispersion in SCS (b1=1.038) compared to MY (b1=0.88). Optimal combinations of scaling factors for each trait were selected based on the validation metrics, accuracy and b1, and the outputs of post genomic analysis (like ssGWAS). Concerning the latter, the behavior of P-values and especially prediction error variance (PEV) of SNP effects when changing τ and ω, provided helpful information complementing the metrics accuracy and b1. Overall, our results confirm the importance of using the τ, and ω parameters in both (ss)GBLUP and (ss)GWAS, and suggest that the combinations of (τ, ω) equal to (1.2, 0.7) and (1, 1) are the best choices for MY and SCS on the considered dataset.

GWAS and genetic and phenotypic correlations of plasma metabolites with complete blood count traits in healthy young pigs reveal implications for pig immune response.

Introduction: In this study estimated genetic and phenotypic correlations between fifteen complete blood count (CBC) traits and thirty-three heritable plasma metabolites in young healthy nursery pigs. In addition, it provided an opportunity to identify candidate genes associated with variation in metabolite concentration and their potential association with immune response, disease resilience, and production traits. Methods: The blood samples were collected from healthy young pigs and Nuclear Magnetic Resonance (NMR) was used to quantify plasma metabolites. CBC was determined using the ADVIA® 2120i Hematology System. Genetic correlations of metabolite with CBC traits and single step genome-wide association study (ssGWAS) were estimated using the BLUPF90 programs. Results: Results showed low phenotypic correlation estimates between plasma metabolites and CBC traits. The highest phenotypic correlation was observed between lactic acid and plasma basophil concentration (0.36 ± 0.04; p < 0.05). Several significant genetic correlations were found between metabolites and CBC traits. The plasma concentration of proline was genetically positively correlated with hemoglobin concentration (0.94 ± 0.03; p < 0.05) and L-tyrosine was negatively correlated with mean corpuscular hemoglobin (MCH; -0.92 ± 0.74; p < 0.05). The genomic regions identified in this study only explained a small percentage of the genetic variance of metabolites levels that were genetically correlated with CBC, resilience, and production traits. Discussion: The results of this systems approach suggest that several plasma metabolite phenotypes are phenotypically and genetically correlated with CBC traits, suggesting that they may be potential genetic indicators of immune response following disease challenge. Genomic analysis revealed genes and pathways that might interact to modulate CBC, resilience, and production traits.

Weighted genomic prediction for growth and carcass-related traits in Nelore cattle.

This study aimed to assess the impact of differential weighting in genomic regions harboring candidate causal loci on the genomic prediction accuracy and dispersion for growth and carcass-related traits in Nelore cattle. The dataset contained 168 793 phenotypic records for adjusted weight at 450 days of age (W450), 83 624 for rib eye area (REA), 24 480 for marbling (MAR) and 82 981 for subcutaneous backfat thickness (BFT) and rump fat thickness (RFT). The pedigree harbored information from 244 254 animals born between 1977 and 2016, including 6283 sires and 50 742 dams. Animals (n=7769) were genotyped with the low-density panel (Clarifide® Nelore 3.0), and the genotypes were imputed to a panel containing 735 044 markers. A linear animal model was applied to estimate the genetic parameters and to perform the weighted single-step genome-wide association study (WssGWAS). A total of seven models for genomic prediction were evaluated combining the SNP weights obtained in the iterations of the WssGWAS and the candidate QTL. The heritability estimated for W450 (0.35) was moderate, and for carcass-related traits, the estimates were moderate for REA (0.27), MAR (0.28) and RFT (0.28), and low for BFT (0.18). The prediction accuracy for W450 incorporating reported QTL previously described in the literature along with different SNPs weights was like those described for the default ssGBLUP model. The use of the ssGWAS to weight the SNP effects displayed limited advantages for the REA prediction accuracy. Comparing the ssGBLUP with the BLUP model, a meaningful improvement in the prediction accuracy from 0.09 to 0.63 (700%) was observed for MAR. The highest prediction accuracy was obtained for BFT and RFT in all evaluated models. The application of information obtained from the WssGWAS is an alternative to reduce the genomic prediction dispersion for growth and carcass-related traits, except for MAR. Furthermore, the results obtained herein pointed out that is possible to improve the prediction accuracy and reduce the genomic prediction dispersion for growth and carcass-related traits in young animals.