Frequentist p-values for large-scale-single step genome-wide association, with an application to birth weight in American Angus cattle

Genome-wide association study for milk yield, frame, and udder conformation traits of Gir dairy cattle

Genome-wide association studies (GWAS) are employed to identify genomic regions and candidate genes associated with several traits. The aim of this study was to perform a GWAS to identify causative variants and genes associated with milk yield, frame, and udder conformation traits in Gir dairy cattle. Body conformation traits were classified as "frame," and "udder" traits for this study. After genotyping imputation and quality control 42,105 polymorphisms were available for analyses and 24,489 cows with pedigree information had phenotypes. First, P-value was calculated based on the variance of the prediction error of the SNP-effects on the first iteration. After that, 2 more iterations were performed to carry out the weighted single-step genome-wide association methodology, performed using genomic moving windows defined based on linkage disequilibrium. The significant SNPs and top 10 windows explaining the highest percentage of additive genetic variance were selected and used for QTL and gene annotation. The variants identified in our work overlapped with QTLs from the animal QTL database on chromosomes 1 to 23, except for chromosome 4. The Gir breed is less studied than the Holstein breed and as such the animal QTL database is biased to Holstein results. Hence it is noteworthy that our GWAS had similarities with previously described QTLs. These previously known QTLs were related to milk yield, body height, rump angle, udder width, and udder depth. In total, 5 genes were annotated. Of these genes, FAM13A and CMSS1 had been previously related to bone and carcass weight in cattle.

Comparison of genomic prediction accuracy using different models for egg production traits in Taiwan country chicken

In local chickens targeted for niche markets, genotyping costs are relatively high due to the small population size and diverse breeding goals. The single-step genomic best linear unbiased prediction (ssGBLUP) model, which combines pedigree and genomic information, has been introduced to increase the accuracy of genomic estimated breeding value (GEBV). Therefore, this model may be more beneficial than the genomic BLUP (GBLUP) model for genomic selection in local chickens. Additionally, the single-step genome-wide association study (ssGWAS) can be used to extend the ssGBLUP model results to animals with available phenotypic information but without genotypic data. In this study, we compared the accuracy of (G)EBVs using the pedigree-based BLUP (PBLUP), GBLUP, and ssGBLUP models. Moreover, we conducted single-SNP GWAS (SNP-GWAS), GBLUP-GWAS, and ssGWAS methods to identify genes associated with egg production traits in the NCHU-G101 chicken to understand the feasibility of using genomic selection in a small population. The average prediction accuracy of (G)EBV for egg production traits using the PBLUP, GBLUP, and ssGBLUP models is 0.536, 0.531, and 0.555, respectively. In total, 22 suggestive- and 5% Bonferroni genome-wide significant-level SNPs for total egg number (EN), average laying rate (LR), average clutch length, and total clutch number are detected using 3 GWAS methods. These SNPs are mapped onto Gallus gallus chromosomes (GGA) 4, 6, 10, 18, and 25 in NCHU-G101 chicken. Furthermore, through SNP-GWAS and ssGWAS methods, we identify 2 genes on GGA4 associated with EN and LR: ENSGALG00000023172 and PPARGC1A. In conclusion, the ssGBLUP model demonstrates superior prediction accuracy, performing on average 3.41% than the PBLUP model. The implications of our gene results may guide future selection strategies for Taiwan Country chickens. Our results highlight the applicability of the ssGBLUP model for egg production traits selection in a small population, specifically NCHU-G101 chicken in Taiwan.

Genome-Wide Association Study for Weight Loss at the End of Dry-Curing of Hams Produced from Purebred Heavy Pigs

Dissecting the genetics of production traits in livestock is of outmost importance, both to understand biological mechanisms underlying those traits and to facilitate the design of selection programs incorporating that information. For the pig industry, traits related to curing are key for protected designation of origin productions. In particular, appropriate ham weight loss after dry-curing ensures high quality of the final product and avoids economic losses. In this study, we analyzed data (N = 410) of ham weight loss after approximately 20 months of dry-curing. The animals used for ham production were purebred pigs belonging to a commercial line. A genome-wide association study (GWAS) of 29,844 SNP markers revealed the polygenic nature of the trait: 221 loci explaining a small percentage of the variance (0.3-1.65%) were identified on almost all Sus scrofa chromosomes. Post-GWAS analyses revealed 32 windows located within regulatory regions and 94 windows located in intronic regions of specific genes. In total, 30 candidate genes encoding receptors and enzymes associated with ham weight loss (MTHFD1L, DUSP8), proteolysis (SPARCL1, MYH8), drip loss (TNNI2), growth (CDCA3, LSP1, CSMD1, AP2A2, TSPAN4), and fat metabolism (AGPAT4, IGF2R, PTDSS2, HRAS, TALDO1, BRSK2, TNNI2, SYT8, GTF2I, GTF2IRD1, LPCAT3, ATN1, GNB3, CMIP, SORCS2, CCSER1, SPP1) were detected.

Genetic background of walking ability and its relationship with leg defects, mortality, and performance traits in turkeys (Meleagris gallopavo)

This study aimed to explore the genetic basis of walking ability and potentially related performance traits in turkey purebred populations. Phenotypic, pedigree, and genomic datasets from 2 turkey lines hatched between 2010 and 2023 were included in the study. Walking ability data, defined based on a scoring system ranging from 1 (worst) to 6 (best), were collected on 192,019 animals of a female line and 235,461 animals of a male line. Genomic information was obtained for 46,427 turkeys (22,302 from a female line and 24,125 from a male line) using a 65K single nucleotide polymorphism (SNP) panel. Variance components and heritability for walking ability were estimated. Furthermore, genetic and phenotypic correlations among walking ability, mortality and disorders, and performance traits were calculated. A genome-wide association study (GWAS) was also conducted to identify SNPs associated with walking ability. Walking ability is moderately heritable (0.23 ± 0.01) in both turkey lines. The genetic correlations between walking ability and the other evaluated traits ranged from -0.02 to -0.78, with leg defects exhibiting the strongest negative correlation with walking ability. In the female line, 31 SNPs were associated with walking ability and overlapped with 116 genes. These positional genes are linked to 6 gene ontology (GO) terms. Notably, genes such as CSRP2, DDX1, RHBDL1, SEZ6L, and CTSK are involved in growth, development, locomotion, and bone disorders. GO terms, including fibronectin binding (GO:0001968), peptide cross-linking (GO:0018149), and catabolic process (GO:0009057), are directly linked with mobility. In the male line, 66 markers associated with walking ability were identified and overlapped with 281 genes. These genes are linked to 12 GO terms. Genes such as RB1CC1, TNNI1, MSTN, FN1, SIK3, PADI2, ERBB4, B3GNT2, and BACE1 are associated with cell growth, myostatin development, and disorders. GO terms in the male line are predominantly related to lipid metabolism. In conclusion, walking ability is moderately heritable in both populations. Furthermore, walking ability can be enhanced through targeted genetic selection, emphasizing its relevance to both animal welfare and productivity.

Leveraging genomics and temporal high-throughput phenotyping to enhance association mapping and yield prediction in sesame

Sesame (Sesamum indicum) is an important oilseed crop with rising demand owing to its nutritional and health benefits. There is an urgent need to develop and integrate new genomic-based breeding strategies to meet these future demands. While genomic resources have advanced genetic research in sesame, the implementation of high-throughput phenotyping and genetic analysis of longitudinal traits remains limited. Here, we combined high-throughput phenotyping and random regression models to investigate the dynamics of plant height, leaf area index, and five spectral vegetation indices throughout the sesame growing seasons in a diversity panel. Modeling the temporal phenotypic and additive genetic trajectories revealed distinct patterns corresponding to the sesame growth cycle. We also conducted longitudinal genomic prediction and association mapping of plant height using various models and cross-validation schemes. Moderate prediction accuracy was obtained when predicting new genotypes at each time point, and moderate to high values were obtained when forecasting future phenotypes. Association mapping revealed three genomic regions in linkage groups 6, 8, and 11, conferring trait variation over time and growth rate. Furthermore, we leveraged correlations between the temporal trait and seed-yield and applied multi-trait genomic prediction. We obtained an improvement over single-trait analysis, especially when phenotypes from earlier time points were used, highlighting the potential of using a high-throughput phenotyping platform as a selection tool. Our results shed light on the genetic control of longitudinal traits in sesame and underscore the potential of high-throughput phenotyping to detect a wide range of traits and genotypes that can inform sesame breeding efforts to enhance yield.

Genome-wide association study considering genotype-by-environment interaction for productive and reproductive traits using whole-genome sequencing in Nellore cattle

BACKGROUND

The genotype-by-environment interaction (GxE) in beef cattle can be investigated using reaction norm models to assess environmental sensitivity and, combined with genome-wide association studies (GWAS), to map genomic regions related to animal adaptation. Including genetic markers from whole-genome sequencing in reaction norm (RN) models allows us to identify high-resolution candidate genes across environmental gradients through GWAS. Hence, we performed a GWAS via the RN approach using whole-genome sequencing data, focusing on mapping candidate genes associated with the expression of reproductive and growth traits in Nellore cattle. For this purpose, we used phenotypic data for age at first calving (AFC), scrotal circumference (SC), post-weaning weight gain (PWG), and yearling weight (YW). A total of 20,000 males and 7,159 females genotyped with 770k were imputed to the whole sequence (29 M). After quality control and linkage disequilibrium (LD) pruning, there remained ∼ 2.41 M SNPs for SC, PWG, and YW and ∼ 5.06 M SNPs for AFC.

RESULTS

Significant SNPs were identified on Bos taurus autosomes (BTA) 10, 11, 14, 18, 19, 20, 21, 24, 25 and 27 for AFC and on BTA 4, 5 and 8 for SC. For growth traits, significant SNP markers were identified on BTA 3, 5 and 20 for YW and PWG. A total of 56 positional candidate genes were identified for AFC, 9 for SC, 3 for PWG, and 24 for YW. The significant SNPs detected for the reaction norm coefficients in Nellore cattle were found to be associated with growth, adaptative, and reproductive traits. These candidate genes are involved in biological mechanisms related to lipid metabolism, immune response, mitogen-activated protein kinase (MAPK) signaling pathway, and energy and phosphate metabolism.

CONCLUSIONS

GWAS results highlighted differences in the physiological processes linked to lipid metabolism, immune response, MAPK signaling pathway, and energy and phosphate metabolism, providing insights into how different environmental conditions interact with specific genes affecting animal adaptation, productivity, and reproductive performance. The shared genomic regions between the intercept and slope are directly implicated in the regulation of growth and reproductive traits in Nellore cattle raised under different environmental conditions.

Genome-Wide Gene-Environment Interaction Analysis Identifies Novel Candidate Variants for Growth Traits in Beef Cattle

Complex traits are widely considered to be the result of a compound regulation of genes, environmental factors, and genotype-by-environment interaction (G × E). The inclusion of G × E in genome-wide association analyses is essential to understand animal environmental adaptations and improve the efficiency of breeding decisions. Here, we systematically investigated the G × E of growth traits (including weaning weight, yearling weight, 18-month body weight, and 24-month body weight) with environmental factors (farm and temperature) using genome-wide genotype-by-environment interaction association studies (GWEIS) with a dataset of 1350 cattle. We validated the robust estimator's effectiveness in GWEIS and detected 29 independent interacting SNPs with a significance threshold of 1.67 × 10-6, indicating that these SNPs, which do not show main effects in traditional genome-wide association studies (GWAS), may have non-additive effects across genotypes but are obliterated by environmental means. The gene-based analysis using MAGMA identified three genes that overlapped with the GEWIS results exhibiting G × E, namely SMAD2, PALMD, and MECOM. Further, the results of functional exploration in gene-set analysis revealed the bio-mechanisms of how cattle growth responds to environmental changes, such as mitotic or cytokinesis, fatty acid β-oxidation, neurotransmitter activity, gap junction, and keratan sulfate degradation. This study not only reveals novel genetic loci and underlying mechanisms influencing growth traits but also transforms our understanding of environmental adaptation in beef cattle, thereby paving the way for more targeted and efficient breeding strategies.

Genetic factors underlying host resistance to Rhipicephalus microplus tick infestation in Braford cattle: a systems biology perspective

Approximately 80% of the world's cattle are raised in regions with a high risk of tick-borne diseases, resulting in significant economic losses due to parasitism by Rhipicephalus (Boophilus) microplus. However, the lack of a systemic biology approach hampers a comprehensive understanding of tick-host interactions that mediate tick resistance phenotypes. Here, we conducted a genome-wide association study (GWAS) of 2933 Braford cattle and found 340 single-nucleotide polymorphisms (SNPs) associated with tick counts. Gene expression analyses were performed on skin samples obtained from previously tick-exposed heifers with extremely high or low estimated breeding values for R. microplus counts. Evaluations were performed both before and after artificial infestation with ticks. Differentially expressed genes were found within 1-Mb windows centered at significant SNPs from GWAS. A total of 330 genes were related to the breakdown of homeostasis that was induced by larval attachment to bovine skin. Enrichment analysis pointed to a key role of proteolysis and signal transduction via JAK/STAT, NFKB and WNT/beta catenin signaling pathways. Integrative analysis on matrixEQTL revealed two cis-eQTLs and four significant SNPs in the genes peptidyl arginine deiminase type IV (PADI4) and LOC11449251. The integration of genomic data from QTL maps and transcriptome analyses has identified a set of twelve key genes that show significant associations with tick loads. These genes could be key candidates to improve the accuracy of genomic predictions for tick resistance in Braford cattle.

Single-step genome-wide association analyses for milk urea concentration in Walloon Holstein cows

The aims of this study were to conduct a single-step genome-wide association to identify genomic regions associated with milk urea (MU) and to estimate genetic correlations between MU and milk yield (MY), milk composition (calcium content [CC], fat percentage [FP], protein percentage [PP], and casein percentage [CNP]), and cheese-making properties (CMP; coagulation time [CT], curd firmness after 30 min from rennet addition [a30], and titratable acidity [TA]). The used data have been collected from 2015 to 2020 on 78,073 first-parity (485,218 test-day records) and 48,766 second-parity (284,942 test-day records) Holstein cows distributed in 671 herds in the Walloon Region of Belgium. Data of 565,533 SNP located on 29 BTA of 6,617 animals (1,712 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 BLUP approach. The proportion of the total additive genetic variance explained by windows of 50 consecutive SNPs (with an average size of ∼216 kb) was calculated, and the top-3 genomic regions explaining the largest rate of the genetic variance were considered promising regions and used to identify potential candidate genes. Mean (SD) MU was 25.38 (8.02) mg/dL and 25.03 (8.06) mg/dL in the first and second lactation, respectively. Mean heritability estimates for daily MU were 0.21 and 0.23 for the first and second lactation, respectively. The genetic correlations estimated between MU and MY, milk composition, and CMP were quite low (ranged from -0.10 [CC] to 0.10 [TA] and -0.05 [CT] to 0.13 [TA] for the first and second lactations, respectively). The top-3 regions associated with MU were located from 80.61 to 80.74 Mb on BTA6, 103.26 to 103.41 Mb on BTA11, and 1.59 to 2.15 Mb on BTA14. Genes including KCNT1, MROH1, SHARPIN, TSSK5, CPSF1, HSF1, TONSL, DGAT1, SCX, and MAF1 were identified as positional candidate genes for MU. The findings of this study can be used for a better understanding of the genomic architecture underlying MU in Holstein cattle.

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.

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.

Genome-wide association and functional annotation analysis for the calving interval in Nellore cattle

Calving interval (CI) measures the number of days between two consecutive calves of the same cow, and previous studies based on phenotype and pedigree data reported low heritability for this trait. However, the genetic architecture of CI in the Nellore breed was not evaluated based on genomic data. Thus, this study aimed to estimate the heritability based on genomic data and carry out a genome-wide association study (GWAS) for CI in the Nellore breed, using 12,599 pedigree records, 5078 CI records, and 3818 animals genotyped with 50k SNPchip panel. Both quality control and GWAS were performed in BLUPF90 family packages, which use the single-step genomic best linear unbiased predictor (ssGBLUP) method. The average CI was 427.6 days, with a standard deviation of 106.9 and a total range of 270-730 days. The heritability estimate was 0.04 ± 0.04. The p-values of GWAS analysis resulted in a genomic inflation factor (lambda) of 1.08. The only significant SNP (rs136725686) at the genome-wide level (p-value = 1.53E-06) was located on BTA13. Other 19 SNPs were significant at the chromosome-wide level, distributed on BTA1, 2, 3, 6, 10, 13, 14, 17, 18, 22, and 26. Functional annotation analysis found thirty-six protein-coding genes, including genes related to cell cycle (RAD21, BCAR3), oocyte function (LHX8, CLPX, UTP23), immune system (TXK, TEC, NFATC2), endocrine function (LRRFIP2, GPR158), estrous cycle (SLC38A7), and female fertility (CCK, LYZL4, TRAK1, FOXP1, STAC). Therefore, CI is a complex trait with small heritability in Nellore cattle, and various biological processes may be involved with the genetic architecture of CI in Nellore cattle.

Efficient genomics-based 'end-to-end' selective tree breeding framework

Since their initiation in the 1950s, worldwide selective tree breeding programs followed the recurrent selection scheme of repeated cycles of selection, breeding (mating), and testing phases and essentially remained unchanged to accelerate this process or address environmental contingencies and concerns. Here, we introduce an "end-to-end" selective tree breeding framework that: (1) leverages strategically preselected GWAS-based sequence data capturing trait architecture information, (2) generates unprecedented resolution of genealogical relationships among tested individuals, and (3) leads to the elimination of the breeding phase through the utilization of readily available wind-pollinated (OP) families. Individuals' breeding values generated from multi-trait multi-site analysis were also used in an optimum contribution selection protocol to effectively manage genetic gain/co-ancestry trade-offs and traits' correlated response to selection. The proof-of-concept study involved a 40-year-old spruce OP testing population growing on three sites in British Columbia, Canada, clearly demonstrating our method's superiority in capturing most of the available genetic gains in a substantially reduced timeline relative to the traditional approach. The proposed framework is expected to increase the efficiency of existing selective breeding programs, accelerate the start of new programs for ecologically and environmentally important tree species, and address climate-change caused biotic and abiotic stress concerns more effectively.

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.

Heritability estimates and genome-wide association study of methane emission traits in Nellore cattle

The objectives of the present study were to estimate the heritability for daily methane emission (CH4) and residual daily methane emission (CH4res) in Nellore cattle, as well as to perform genome-wide association studies (GWAS) to identify genomic regions and candidate genes influencing the genetic variation of CH4 and CH4res. Methane emission phenotypes of 743 Nellore animals belonging to 3 breeding programs were evaluated. CH4 was measured using the sulfur hexafluoride (SF6) tracer technique (which involves an SF6 permeation tube introduced into the rumen, and an appropriate apparatus on each animal), and CH4res was obtained as the difference between observed CH4 and CH4 adjusted for dry matter intake. A total of 6,252 genotyped individuals were used for genomic analyses. Data were analyzed with a univariate animal model by the single-step GBLUP method using the average information restricted maximum likelihood (AIREML) algorithm. The effects of single nucleotide polymorphisms (SNPs) were obtained using a single-step GWAS approach. Candidate genes were identified based on genomic windows associated with quantitative trait loci (QTLs) related to the 2 traits. Annotation of QTLs and identification of candidate genes were based on the initial and final coordinates of each genomic window considering the bovine genome ARS-UCD1.2 assembly. Heritability estimates were of moderate to high magnitude, being 0.42 ± 0.09 for CH4 and 0.21 ± 0.09 for CH4res, indicating that these traits will respond rapidly to genetic selection. GWAS revealed 11 and 15 SNPs that were significantly associated (P < 10-6) with genetic variation of CH4 and CH4res, respectively. QTLs associated with feed efficiency, residual feed intake, body weight, and height overlapped with significant markers for the traits evaluated. Ten candidate genes were present in the regions of significant SNPs; 3 were associated with CH4 and 7 with CH4res. The identified genes are related to different functions such as modulation of the rumen microbiota, fatty acid production, and lipid metabolism. CH4 and CH4res presented sufficient genetic variation and may respond rapidly to selection. Therefore, these traits can be included in animal breeding programs aimed at reducing enteric methane emissions across generations.

Genome-wide association study using a single-step approach for teat number in Duroc, Landrace and Yorkshire pigs in Korea

We investigated the genetic basis of teat number in sows, which is an important factor in their reproductive performance. We collected genotyping data from 20 353 pigs of three breeds (Duroc, Landrace and Yorkshire) using the Porcine SNP60K Bead Chip, and analyzed phenotypic data from 240 603 pigs. The heritability values of total teat number were 0.33 ± 0.02, 0.51 ± 0.01 and 0.50 ± 0.01 in Duroc, Landrace and Yorkshire pigs, respectively. A genome-wide association study was used to identify significant chromosomal regions associated with teat number in SSC7 and SSC9 in Duroc pig, SSC3, SSC7 and SSC18 in Landrace pig, and SSC7, SSC8 and SSC10 in Yorkshire pig. Among the markers, MARC0038565, located between the vertnin (VRTN) and synapse differentiation-inducing 1-like (SYNDIG1L) genes, showed the strongest association in the Duroc pig and was significant in all breeds. In Landrace and Yorkshire pigs, the most significant markers were located within the apoptosis resistant E3 ubiquitin protein ligase 1 (AREL1) and latent transforming growth factor beta-binding protein 2 (LTBP2) genes in SSC7, respectively. VRTN is a candidate gene regulating the teat number. Most markers were located in SSC7, indicating their significance in determining teat number and their potential as valuable genomic selection targets for improving this trait. Extensive linkage disequilibrium blocks were identified in SSC7, supporting their use in genomic selection strategies. Our study provides valuable insights into the genetic architecture of teat numbers in pigs, and helps identify candidate genes and genomic regions that may contribute to this economically important trait.

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

BACKGROUND

Hoof 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.

RESULTS

There 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.

CONCLUSIONS

This 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.

Single-step genome-wide association analyses for selected infrared-predicted cheese-making traits in Walloon Holstein cows

This study aimed to perform genome-wide association study to identify genomic regions associated with milk production and cheese-making properties (CMP) in Walloon Holstein cows. The studied traits were milk yield, fat percentage, protein percentage, casein percentage (CNP), calcium content, somatic cell score (SCS), coagulation time, curd firmness after 30 min from rennet addition, and titratable acidity. The used data have been collected from 2014 to 2020 on 78,073 first-parity (485,218 test-day records), 48,766 second-parity (284,942 test-day records), and 21,948 third-parity (105,112 test-day records) Holstein cows distributed in 671 herds in the Walloon Region of Belgium. Data of 565,533 single nucleotide polymorphisms (SNP), located on 29 Bos taurus autosomes (BTA) of 6,617 animals (1,712 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 BLUP approach. The proportion of the total additive genetic variance explained by windows of 50 consecutive SNPs (with an average size of ∼216 KB) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for positional candidate genes. Heritability estimates for the studied traits ranged from 0.10 (SCS) to 0.53 (CNP), 0.10 (SCS) to 0.50 (CNP), and 0.12 (SCS) to 0.49 (CNP) in the first, second, and third parity, respectively. Genome-wide association analyses identified 6 genomic regions (BTA1, BTA14 [4 regions], and BTA20) associated with the considered traits. Genes including the SLC37A1 (BTA1), SHARPIN, MROH1, DGAT1, FAM83H, TIGD5, MROH6, NAPRT, ADGRB1, GML, LYPD2, JRK (BTA14), and TRIO (BTA20) were identified as positional candidate genes for the studied CMP. The findings of this study help to unravel the genomic background of a cow's ability for cheese production and can be used for the future implementation and use of genomic evaluation to improve the cheese-making traits in Walloon Holstein cows.

Genetic analysis of geometric morphometric 3D visuals of French jumping horses

BACKGROUND

For centuries, morphology has been the most commonly selected trait in horses. A 3D video recording enabled us to obtain the coordinates of 43 anatomical landmarks of 2089 jumping horses. Generalized Procrustes analysis provided centered and scaled coordinates that were independent of volume, i.e., centroid size. Genetic analysis of these coordinates (mixed model; 17,994 horses in the pedigree) allowed us to estimate a variance-covariance matrix. New phenotypes were then defined: the "summarized shapes". They were obtained by linear combinations of Procrustes coordinates with, as coefficients, the eigenvectors of the genetic variance-covariance matrix. These new phenotypes were used in genome-wide association analyses (GWAS) and multitrait genetic analysis that included judges' scores and competition results of the horses.

RESULTS

We defined ten shapes that represented 86% of the variance, with heritabilities ranging from 0.14 to 0.42. Only one of the shapes was found to be genetically correlated with competition success (rg = - 0.12, standard error = 0.07). Positive and negative genetic correlations between judges' scores and shapes were found. This means that the breeding objective defined by judges involves improvement of anatomical parts of the body that are negatively correlated with each other. Known single nucleotide polymorphisms (SNPs) on chromosomes 1 and 3 for height at withers were significant for centroid size but not for any of the shapes. As these SNPs were not associated with the shape that distinguished rectangular horses from square horses (with height at withers greater than body length), we hypothesize that these SNPs play a role in the overall development of horses, i.e. in height, width, and length but not in height at withers when standardized to unit centroid size. Several other SNPs were found significant for other shapes.

CONCLUSIONS

The main application of 3D morphometric analysis is the ability to define the estimated breeding value (EBV) of a sire based on the shape of its potential progeny, which is easier for breeders to visualize in a single synthetic image than a full description based on linear profiling. However, the acceptance of these new phenotypes by breeders and the complex nature of summarized shapes may be challenging. Due to the low genetic correlations of the summarized shapes with jumping performance, the methodology did not allow indirect performance selection criteria to be defined.

Longitudinal genome-wide association analysis using a single-step random regression model for height in Japanese Holstein cattle

Growth traits, such as body weight and height, are essential in the design of genetic improvement programs of dairy cattle due to their relationship with feeding efficiency, longevity, and health. We investigated genomic regions influencing height across growth stages in Japanese Holstein cattle using a single-step random regression model. We used 72,921 records from birth to 60 mo of age with 4,111 animals born between 2000 and 2016. The analysis included 1,410 genotyped animals with 35,319 single nucleotide polymorphisms, consisting of 883 females with records and 527 bulls, and 30,745 animals with pedigree information. A single genomic region at the 58.4 megabase pair on chromosome 18 was consistently identified across 6 age points of 10, 20, 30, 40, 50, and 60 mo after multiple testing corrections for the significance threshold. Twelve candidate genes, previously reported for longevity and gestation length, were found near the identified genomic region. Another location near the identified region was also previously associated with body conformation, fertility, and calving difficulty. Functional Gene Ontology enrichment analysis suggested that the candidate genes regulate dephosphorylation and phosphatase activity. Our findings show that further study of the identified candidate genes will contribute to a better understanding of the genetic basis of height in Japanese Holstein cattle.

Single-step genome-wide association for selected milk fatty acids in Dual-Purpose Belgian Blue cows

The aim of this study was to estimate genetic parameters and identify genomic regions associated with selected individual and groups of milk fatty acids (FA) predicted by milk mid-infrared spectrometry in Dual-Purpose Belgian Blue cows. The used data were 69,349 test-day records of milk yield, fat percentage, and protein percentage along with selected individual and groups FA of milk (g/dL milk) collected from 2007 to 2020 on 7,392 first-parity (40,903 test-day records), and 5,185 second-parity (28,446 test-day records) cows distributed in 104 herds in the Walloon Region of Belgium. Data of 28,466 SNPs, located on 29 Bos taurus autosomes (BTA), of 1,699 animals (639 males and 1,060 females) 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 25-SNP sliding window (with an average size of ~2 Mb) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for candidate genes. Average daily heritability estimated for the included milk FA traits ranged from 0.01 (C4:0) to 0.48 (C12:0) and 0.01 (C4:0) to 0.42 (C12:0) in the first and second parities, respectively. Genetic correlations found between milk yield and the studied individual milk FA, except for C18:0, C18:1 trans, C18:1 cis-9, were positive. The results showed that fat percentage and protein percentage were positively genetically correlated with all studied individual milk FA. Genome-wide association analyses identified 11 genomic regions distributed over 8 chromosomes [BTA1, BTA4, BTA10, BTA14 (4 regions), BTA19, BTA22, BTA24, and BTA26] associated with the studied FA traits, though those found on BTA14 partly overlapped. The genomic regions identified differed between parities and lactation stages. Although these differences in genomic regions detected may be due to the power of quantitative trait locus detection, it also suggests that candidate genes underlie the phenotypic expression of the studied traits may vary between parities and lactation stages. These findings increase our understanding about the genetic background of milk FA and can be used for the future implementation of genomic evaluation to improve milk FA profile in Dual-Purpose Belgian Blue cows.

Genomic Prediction and Genome-Wide Association Study for Boar Taint Compounds

With a perspective future ban on surgical castration in Europe, selecting pigs with reduced ability to accumulate boar taint (BT) compounds (androstenone, indole, skatole) in their tissues seems a promising strategy. BT compound concentrations were quantified in the adipose tissue of 1075 boars genotyped at 29,844 SNPs. Traditional and SNP-based breeding values were estimated using pedigree-based BLUP (PBLUP) and genomic BLUP (GBLUP), respectively. Heritabilities for BT compounds were moderate (0.30-0.52). The accuracies of GBLUP and PBLUP were significantly different for androstenone (0.58 and 0.36, respectively), but comparable for indole and skatole (~0.43 and ~0.47, respectively). Several SNP windows, each explaining a small percentage of the variance of BT compound concentrations, were identified in a genome-wide association study (GWAS). A total of 18 candidate genes previously associated with BT (MX1), reproduction traits (TCF21, NME5, PTGFR, KCNQ1, UMODL1), and fat metabolism (CTSD, SYT8, TNNI2, CD81, EGR1, GIPC2, MIGA1, NEGR1, CCSER1, MTMR2, LPL, ERFE) were identified in the post-GWAS analysis. The large number of genes related to fat metabolism might be explained by the relationship between sexual steroid levels and fat deposition and be partially ascribed to the pig line investigated, which is selected for ham quality and not for lean growth.

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

BACKGROUND

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.

RESULTS

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.

CONCLUSIONS

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.

Dimensionality of genomic information and its impact on genome-wide associations and variant selection for genomic prediction: a simulation study

BACKGROUND

Identifying true positive variants in genome-wide associations (GWA) depends on several factors, including the number of genotyped individuals. The limited dimensionality of genomic information may give insights into the optimal number of individuals to be used in GWA. This study investigated different discovery set sizes based on the number of largest eigenvalues explaining a certain proportion of variance in the genomic relationship matrix (G). In addition, we investigated the impact on the prediction accuracy by adding variants, which were selected based on different set sizes, to the regular single nucleotide polymorphism (SNP) chips used for genomic prediction.

METHODS

We simulated sequence data that included 500k SNPs with 200 or 2000 quantitative trait nucleotides (QTN). A regular 50k panel included one in every ten simulated SNPs. Effective population size (Ne) was set to 20 or 200. GWA were performed using a number of genotyped animals equivalent to the number of largest eigenvalues of G (EIG) explaining 50, 60, 70, 80, 90, 95, 98, and 99% of the variance. In addition, the largest discovery set consisted of 30k genotyped animals. Limited or extensive phenotypic information was mimicked by changing the trait heritability. Significant and large-effect size SNPs were added to the 50k panel and used for single-step genomic best linear unbiased prediction (ssGBLUP).

RESULTS

Using a number of genotyped animals corresponding to at least EIG98 allowed the identification of QTN with the largest effect sizes when Ne was large. Populations with smaller Ne required more than EIG98. Furthermore, including genotyped animals with a higher reliability (i.e., a higher trait heritability) improved the identification of the most informative QTN. Prediction accuracy was highest when the significant or the large-effect SNPs representing twice the number of simulated QTN were added to the 50k panel.

CONCLUSIONS

Accurately identifying causative variants from sequence data depends on the effective population size and, therefore, on the dimensionality of genomic information. This dimensionality can help identify the most suitable sample size for GWA and could be considered for variant selection, especially when resources are restricted. Even when variants are accurately identified, their inclusion in prediction models has limited benefits.

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

Introduction

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.

Methods

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).

Results

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.

Discussion

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.

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

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

Comparison of two multi-trait association testing methods and sequence-based fine mapping of six additive QTL in Swiss Large White pigs

BACKGROUND

Genetic correlations between complex traits suggest that pleiotropic variants contribute to trait variation. Genome-wide association studies (GWAS) aim to uncover the genetic underpinnings of traits. Multivariate association testing and the meta-analysis of summary statistics from single-trait GWAS enable detecting variants associated with multiple phenotypes. In this study, we used array-derived genotypes and phenotypes for 24 reproduction, production, and conformation traits to explore differences between the two methods and used imputed sequence variant genotypes to fine-map six quantitative trait loci (QTL).

RESULTS

We considered genotypes at 44,733 SNPs for 5,753 pigs from the Swiss Large White breed that had deregressed breeding values for 24 traits. Single-trait association analyses revealed eleven QTL that affected 15 traits. Multi-trait association testing and the meta-analysis of the single-trait GWAS revealed between 3 and 6 QTL, respectively, in three groups of traits. The multi-trait methods revealed three loci that were not detected in the single-trait GWAS. Four QTL that were identified in the single-trait GWAS, remained undetected in the multi-trait analyses. To pinpoint candidate causal variants for the QTL, we imputed the array-derived genotypes to the sequence level using a sequenced reference panel consisting of 421 pigs. This approach provided genotypes at 16 million imputed sequence variants with a mean accuracy of imputation of 0.94. The fine-mapping of six QTL with imputed sequence variant genotypes revealed four previously proposed causal mutations among the top variants.

CONCLUSIONS

Our findings in a medium-size cohort of pigs suggest that multivariate association testing and the meta-analysis of summary statistics from single-trait GWAS provide very similar results. Although multi-trait association methods provide a useful overview of pleiotropic loci segregating in mapping populations, the investigation of single-trait association studies is still advised, as multi-trait methods may miss QTL that are uncovered in single-trait GWAS.

Understanding the phenotypic and genetic background of the lactose content in Sarda dairy sheep

Lactose, the principal carbohydrate found in milk, plays an important role in the physiological processes of milk production because it is related to milk volume, and it is responsible for the osmotic equilibrium between blood and milk in the mammary gland. In this study, factors affecting lactose content (LC) in sheep milk are investigated. For this purpose, 2,358 test-day records were sampled from 509 ewes (3-7 records per animal). The LC and other main milk traits were analyzed using a mixed linear model that included days in milk (DIM) class, parity, lambing month, and type of lambing as fixed effects and animal, permanent environment, and flock test day as random effects. The pedigree-based approach was used to estimate the heritability and repeatability of LC. Moreover, the genomic background of LC was investigated through a GWAS. The LC was affected by all tested factors (i.e., DIM class, parity, lambing month, and type of lambing). Low heritability (0.10 ± 0.05) and moderate repeatability (0.42 ± 0.02) were estimated for LC. High negative genetic correlations were estimated between LC and NaCl (-0.99 ± 0.01) and between LC and somatic cell count (-0.94 ± 0.05). Only 2 markers passed the chromosome-wide Bonferroni threshold. Results of the present study, although obtained on a relatively small sample, suggest the possibility to include LC in the breeding programs, particularly because of its strong relationship with NaCl and somatic cell count.

Single-step genome-wide association analyses of claw horn lesions in Holstein cattle using linear and threshold models

BACKGROUND

Lameness in dairy cattle is primarily caused by foot lesions including the claw horn lesions (CHL) of sole haemorrhage (SH), sole ulcers (SU), and white line disease (WL). This study investigated the genetic architecture of the three CHL based on detailed animal phenotypes of CHL susceptibility and severity. Estimation of genetic parameters and breeding values, single-step genome-wide association analyses, and functional enrichment analyses were performed.

RESULTS

The studied traits were under genetic control with a low to moderate heritability. Heritability estimates of SH and SU susceptibility on the liability scale were 0.29 and 0.35, respectively. Heritability of SH and SU severity were 0.12 and 0.07, respectively. Heritability of WL was relatively lower, indicating stronger environmental influence on the presence and development of WL than the other two CHL. Genetic correlations between SH and SU were high (0.98 for lesion susceptibility and 0.59 for lesion severity), whereas genetic correlations of SH and SU with WL also tended to be positive. Candidate quantitative trait loci (QTL) were identified for all CHL, including some on Bos taurus chromosome (BTA) 3 and 18 with potential pleiotropic effects associated with multiple foot lesion traits. A genomic window of 0.65 Mb on BTA3 explained 0.41, 0.50, 0.38, and 0.49% of the genetic variance for SH susceptibility, SH severity, WL susceptibility, and WL severity, respectively. Another window on BTA18 explained 0.66, 0.41, and 0.70% of the genetic variance for SH susceptibility, SU susceptibility, and SU severity, respectively. The candidate genomic regions associated with CHL harbour annotated genes that are linked to immune system function and inflammation responses, lipid metabolism, calcium ion activities, and neuronal excitability.

CONCLUSIONS

The studied CHL are complex traits with a polygenic mode of inheritance. Most traits exhibited genetic variation suggesting that animal resistance to CHL can be improved with breeding. The CHL traits were positively correlated, which will facilitate genetic improvement for resistance to CHL as a whole. Candidate genomic regions associated with lesion susceptibility and severity of SH, SU, and WL provide insights into a global profile of the genetic background underlying CHL and inform genetic improvement programmes aiming at enhancing foot health in dairy cattle.

Genome-wide association study and functional annotation analyses for nitrogen efficiency index and its composition traits in dairy cattle

The aims of this study were (1) to identify genomic regions associated with a N efficiency index (NEI) and its composition traits and (2) to analyze the functional annotation of identified genomic regions. The NEI included N intake (NINT1), milk true protein N (MTPN1), milk urea N yield (MUNY1) in primiparous cattle, and N intake (NINT2+), milk true protein N (MTPN2+), and milk urea N yield (MUNY2+) in multiparous cattle (2 to 5 parities). The edited data included 1,043,171 records on 342,847 cows distributed in 1,931 herds. The pedigree consisted of 505,125 animals (17,797 males). Data of 565,049 SNPs were available for 6,998 animals included in the pedigree (5,251 females and 1,747 males). The SNP effects were estimated using a single-step genomic BLUP approach. The proportion of the total additive genetic variance explained by windows of 50 consecutive SNPs (with an average size of about 240 kb) was calculated. The top 3 genomic regions explaining the largest rate of the total additive genetic variance of the NEI and its composition traits were selected for candidate gene identification and quantitative trait loci (QTL) annotation. The selected genomic regions explained from 0.17% (MTPN2+) to 0.58% (NEI) of the total additive genetic variance. The largest explanatory genomic regions of NEI, NINT1, NINT2+, MTPN1, MTPN2+, MUNY1, and MUNY2+ were Bos taurus autosome 14 (1.52-2.09 Mb), 26 (9.24-9.66 Mb), 16 (75.41-75.51 Mb), 6 (8.73-88.92 Mb), 6 (8.73-88.92 Mb), 11 (103.26-103.41 Mb), 11 (103.26-103.41 Mb). Based on the literature, gene ontology, Kyoto Encyclopedia of Genes and Genomes, and protein-protein interaction, 16 key candidate genes were identified for NEI and its composition traits, which are mainly expressed in the milk cell, mammary, and liver tissues. The number of enriched QTL related to NEI, NINT1, NINT2+, MTPN1, and MTPN2+ were 41, 6, 4, 11, 36, 32, and 32, respectively, and most of them were related to the milk, health, and production classes. In conclusion, this study identified genomic regions associated with NEI and its composition traits, and identified key candidate genes describing the genetic mechanisms of N use efficiency-related traits. Furthermore, the NEI reflects not only its composition traits but also the interactions among them.

Genomic selection models substantially improve the accuracy of genetic merit predictions for fillet yield and body weight in rainbow trout using a multi-trait model and multi-generation progeny testing

BACKGROUND

In aquaculture, the proportion of edible meat (FY = fillet yield) is of major economic importance, and breeding animals of superior genetic merit for this trait can improve efficiency and profitability. Achieving genetic gains for fillet yield is possible using a pedigree-based best linear unbiased prediction (PBLUP) model with direct and indirect selection. To investigate the feasibility of using genomic selection (GS) to improve FY and body weight (BW) in rainbow trout, the prediction accuracy of GS models was compared to that of PBLUP. In addition, a genome-wide association study (GWAS) was conducted to identify quantitative trait loci (QTL) for the traits. All analyses were performed using a two-trait model with FY and BW, and variance components, heritability, and genetic correlations were estimated without genomic information. The data used included 14,165 fish in the pedigree, of which 2742 and 12,890 had FY and BW phenotypic records, respectively, and 2484 had genotypes from the 57K single nucleotide polymorphism (SNP) array.

RESULTS

The heritabilities were moderate, at 0.41 and 0.33 for FY and BW, respectively. Both traits were lowly but positively correlated (genetic correlation; r = 0.24), which suggests potential favourable correlated genetic gains. GS models increased prediction accuracy compared to PBLUP by up to 50% for FY and 44% for BW. Evaluations were found to be biased when validation was performed on future performances but not when it was performed on future genomic estimated breeding values.

CONCLUSIONS

The low but positive genetic correlation between fillet yield and body weight indicates that some improvement in fillet yield may be achieved through indirect selection for body weight. Genomic information increases the prediction accuracy of breeding values and is an important tool to accelerate genetic progress for fillet yield and growth in the current rainbow trout population. No significant QTL were found for either trait, indicating that both traits are polygenic, and that marker-assisted selection will not be helpful to improve these traits in this population.

Genetics of rearing success in four pure laying hen lines during the first 17 weeks of age

This study aimed to investigate the genetics of rearing success (RS) in laying hens. Four rearing traits: clutch size (CS), first week mortality (FWM), rearing abnormalities (RA), and natural death (ND), were included as factors determining RS. Pedigree, genotypic, and phenotypic records of 4 purebred genetic lines of White Leghorn layers were available for 23,000 rearing batches obtained between 2010 and 2020. FWM and ND showed little or no variation amongst the 4 genetic lines over the years 2010-2020, whereas an increase was observed for CS and a decrease for RA. To determine whether these traits were heritable, genetic parameters for each trait were estimated, using a Linear Mixed Model. Heritabilities within lines were low (0.05-0.19 for CS, 0.01-0.04 for FWM, 0.02-0.06 for RA, 0.02-0.04 for ND, and 0.01-0.07 for RS). Additionally, genome wide association study was done to scan the genomes of the breeders to reveal single nucleotide polymorphisms (SNPs) associated with these traits. Manhattan plots indicated the existence of 12 different SNPs having a significant effect on RS. Thus, the identified SNPs will increase the understanding of the genetics of RS in laying hens.

Genome-wide association study for milk production and conformation traits in Canadian Alpine and Saanen dairy goats

Increasing the productivity of Canadian dairy goats is critical to the competitiveness of the sector; however, little is known about the underlying genetic architecture of economically important traits in these populations. Consequently, the objectives of this study were as follows: (1) to perform a single-step GWAS for milk production traits (milk, protein, and fat yields, and protein and fat percentages in first and later lactations) and conformation traits (body capacity, dairy character, feet and legs, fore udder, general appearance, rear udder, suspensory ligament, and teats) in the Canadian Alpine and Saanen breeds; and (2) to identify positional and functional candidate genes related to these traits. The data available for analysis included 305-d milk production records for 6,409 Alpine and 3,434 Saanen does in first lactation and 5,827 Alpine and 2,632 Saanen does in later lactations; as well as linear type conformation records for 5,158 Alpine and 2,342 Saanen does. Genotypes were available for 833 Alpine and 874 Saanen animals. Both single-breed and multiple-breed GWAS were performed using single-trait animal models. Positional and functional candidate genes were then identified in downstream analyses. The GWAS identified 189 unique SNP that were significant at the chromosomal level, corresponding to 271 unique positional candidate genes within 50 kb up- and downstream, across breeds and traits. This study provides evidence for the economic importance of several candidate genes (e.g., CSN1S1, CSN2, CSN1S2, CSN3, DGAT1, and ZNF16) in the Canadian Alpine and Saanen populations that have been previously reported in other dairy goat populations. Moreover, several novel positional and functional candidate genes (e.g., RPL8, DCK, and MOB1B) were also identified. Overall, the results of this study have provided greater insight into the genetic architecture of milk production and conformation traits in the Canadian Alpine and Saanen populations. Greater understanding of these traits will help to improve dairy goat breeding programs.

Effects of common full-sib families on accuracy of genomic prediction for tagging weight in striped catfish Pangasianodon hypophthalmus

Common full-sib families (c ² ) make up a substantial proportion of total phenotypic variation in traits of commercial importance in aquaculture species and omission or inclusion of the c ² resulted in possible changes in genetic parameter estimates and re-ranking of estimated breeding values. However, the impacts of common full-sib families on accuracy of genomic prediction for commercial traits of economic importance are not well known in many species, including aquatic animals. This research explored the impacts of common full-sib families on accuracy of genomic prediction for tagging weight in a population of striped catfish comprising 11,918 fish traced back to the base population (four generations), in which 560 individuals had genotype records of 14,154 SNPs. Our single step genomic best linear unbiased prediction (ssGLBUP) showed that the accuracy of genomic prediction for tagging weight was reduced by 96.5%-130.3% when the common full-sib families were included in statistical models. The reduction in the prediction accuracy was to a smaller extent in multivariate analysis than in univariate models. Imputation of missing genotypes somewhat reduced the upward biases in the prediction accuracy for tagging weight. It is therefore suggested that genomic evaluation models for traits recorded during the early phase of growth development should account for the common full-sib families to minimise possible biases in the accuracy of genomic prediction and hence, selection response.

Identification of genomic regions associated with total and progressive sperm motility in Italian Holstein bulls

Sperm motility is directly related to the ability of sperm to move through the female reproductive tract to reach the ovum. Sperm motility is a complex trait that is influenced by environmental and genetic factors and is associated with male fertility, oocyte penetration rate, and reproductive success of cattle. In this study we carried out a GWAS in Italian Holstein bulls to identify candidate regions and genes associated with variations in progressive and total motility (PM and TM, respectively). After quality control, the final data set consisted of 5,960 records from 949 bulls having semen collected in 10 artificial insemination stations and genotyped at 412,737 SNPs (call rate >95%; minor allele frequency >5%). (Co)variance components were estimated using single trait mixed models, and associations between SNPs and phenotypes were assessed using a genomic BLUP approach. Ten windows that explained the greatest percentage of genetic variance were located on Bos taurus autosomes 1, 2, 4, 6, 7, 23, and 26 for TM and Bos taurus autosomes 1, 2, 4, 6, 8, 16, 23, and 26 for PM. A total of 150 genes for TM and 72 genes for PM were identified within these genomic regions. Gene Ontology enrichment analyses identified significant Gene Ontology terms involved with energy homeostasis, membrane functions, sperm-egg interactions, protection against oxidative stress, olfactory receptors, and immune system. There was significant enrichment of quantitative trait loci for fertility, calving ease, immune response, feed intake, and carcass weight within the candidate windows. These results contribute to understanding the architecture of the genetic control of sperm motility and may aid in the development of strategies to identify subfertile bulls and improve reproductive success.

Genome-Wide Association Study for Body Conformation Traits and Fitness in Czech Holsteins

The aim of this study was a genome-wide association study (GWAS) on conformation traits using 25,486 genotyped Czech Holsteins, with 35,227 common SNPs for each genotype. Linear trait records were collected between 1995 and 2020. The Interbull information from Multiple Across Country Evaluation (MACE) was included for bulls that mostly had daughter records in a foreign country. When using the Bonferroni correction, the number of SNPs that were either significant or approached the significance threshold was low-dairy capacity composite on BTA4, feet and legs composite BTA21, total score BTA10, stature BTA24, body depth BTA6, angularity BTA20, fore udder attachment BTA10. Without the Bonferroni correction, the total number of significant or near of significance SNPs was 32. The SNPs were localized on BTA1,2,4,5,6,7,8,18,22,25,26,28 for dairy capacity composite, BTA15,21 for feet and legs composite, BTA10 for total score, BTA24 stature, BTA6,23 body depth, BTA20 angularity, BTA2 rump angle, BTA9,10 rear legs rear view, BTA2,19 rear legs side view, BTA10 fore udder attachment, BTA2 udder depth, BTA10 rear udder height, BTA12 central alignment, BTA24 rear teat placement, BTA8,29 rear udder width. The results provide biological information for the improvement of body conformation and fitness in the Holstein population.

Host genetic control on rumen microbiota and its impact on dairy traits in sheep

BACKGROUND

Milk yield and fine composition in sheep depend on the volatile and long-chain fatty acids, microbial proteins, vitamins produced through feedstuff digestion by the rumen microbiota. In cattle, the host genome has been shown to have a low to moderate genetic control on rumen microbiota abundance but a high control on dairy traits with heritabilities higher than 0.30. There is little information on the genetic correlations and quantitative trait loci (QTL) that simultaneously affect rumen microbiota abundance and dairy traits in ruminants, especially in sheep. Thus, our aim was to quantify the effect of the host genetics on rumen bacterial abundance and the genetic correlations between rumen bacterial abundance and several dairy traits, and to identify QTL that are associated with both rumen bacterial abundance and milk traits.

RESULTS

Our results in Lacaune sheep show that the heritability of rumen bacterial abundance ranges from 0 to 0.29 and that the heritability of 306 operational taxonomic units (OTU) is significantly different from 0. Of these 306 OTU, 96 that belong mainly to the Prevotellaceae, Lachnospiraceae and Ruminococcaceae bacterial families show strong genetic correlations with milk fatty acids and proteins (absolute values ranging from 0.33 to 0.99). Genome-wide association studies revealed a QTL for alpha-lactalbumin concentration in milk on Ovis aries chromosome (OAR) 11, and six QTL for rumen bacterial abundances i.e., for two OTU belonging to the genera Prevotella (OAR3 and 5), Rikeneleaceae_RC9_gut_group (OAR5), Ruminococcus (OAR5), an unknown genus of order Clostridia UCG-014 (OAR10), and CAG-352 (OAR11). None of these detected regions are simultaneously associated with rumen bacterial abundance and dairy traits, but the bacterial families Prevotellaceae, Lachnospiraceae and F082 show colocalized signals on OAR3, 5, 15 and 26.

CONCLUSIONS

In Lacaune dairy sheep, rumen microbiota abundance is partially controlled by the host genetics and is poorly genetically linked with milk protein and fatty acid compositions, and three main bacterial families, Prevotellaceae, Lachnospiraceae and F082, show specific associations with OAR3, 5, 15 and 26.

New insights into the genetic resistance to paratuberculosis in Holstein cattle via single-step genomic evaluation

Background

Bovine paratuberculosis, or Johne’s disease (JD), is a contagious and incurable disease caused by Mycobacterium avium subsp. paratuberculosis (MAP). It has adverse effects on animal welfare and is very difficult to control, leading to serious economic consequences. An important line of defense to this disease is host genetic resistance to MAP, which, when it will be more fully understood, could be improved through selective breeding. Using a large dataset of Holstein cows (161,253 animals including 56,766 cows with ELISA serological phenotypes and 12,431 animals with genotypes), we applied a single-step single nucleotide polymorphism (SNP) best linear unbiased prediction approach to investigate the genetic determinism underlying resistance to this disease (heritability estimate and identification of relevant genomic regions) and estimated genetic trends, reliability, and relative risk factors associated with genomic predictions.

Results

Resistance to JD was moderately heritable (0.14) and 16 genomic regions were detected that accounted for at least 0.05% of the breeding values variance (GV) in resistance to JD, and were located on chromosomes 1, 3, 5, 6, 7, 19, 20, 21, 23, 25, and 27, with the highest percentage of variance explained by regions on chromosomes 23 (0.36% GV), 5 (0.22% GV), 1 (0.14% GV), and 3 (0.13% GV). When estimated for the whole chromosomes, the autosomes with the largest overall contributions were chromosomes 3 (5.3% GV), 10 (4.8%), 23 (4.7%), 1 (3.6%), 7 (3.4%), 5 (2.9%), 12 (2.5%), 11 (2.2%), and 13 (2%). We estimated a slightly favorable genetic trend in resistance to JD over the last two decades, which can be explained by a low positive genetic correlation between resistance to JD and total merit index (+ 0.06). Finally, in a validation population of 907 cows, relatively reliable genomic predictions (reliability = 0.55) were obtained, which allowed the identification of cows at high risk of infection.

Conclusions

This study provides new insights into the genetic determinism of resistance to JD and shows that this trait can be predicted from SNP genotypes. It has led to the implementation of a single-step genomic evaluation that should rapidly become an effective tool for controlling paratuberculosis on French Holstein farms.

Correlation scan: identifying genomic regions that affect genetic correlations applied to fertility traits

Although the genetic correlations between complex traits have been estimated for more than a century, only recently we have started to map and understand the precise localization of the genomic region(s) that underpin these correlations. Reproductive traits are often genetically correlated. Yet, we don't fully understand the complexities, synergism, or trade-offs between male and female fertility. In this study, we used reproductive traits in two cattle populations (Brahman; BB, Tropical Composite; TC) to develop a novel framework termed correlation scan (CS). This framework was used to identify local regions associated with the genetic correlations between male and female fertility traits. Animals were genotyped with bovine high-density single nucleotide polymorphisms (SNPs) chip assay. The data used consisted of ~1000 individual records measured through frequent ovarian scanning for age at first corpus luteum (AGECL) and a laboratory assay for serum levels of insulin growth hormone (IGF1 measured in bulls, IGF1b, or cows, IGF1c). The methodology developed herein used correlations of 500-SNP effects in a 100-SNPs sliding window in each chromosome to identify local genomic regions that either drive or antagonize the genetic correlations between traits. We used Fisher's Z-statistics through a permutation method to confirm which regions of the genome harboured significant correlations. About 30% of the total genomic regions were identified as driving and antagonizing genetic correlations between male and female fertility traits in the two populations. These regions confirmed the polygenic nature of the traits being studied and pointed to genes of interest. For BB, the most important chromosome in terms of local regions is often located on bovine chromosome (BTA) 14. However, the important regions are spread across few different BTA's in TC. Quantitative trait loci (QTLs) and functional enrichment analysis revealed many significant windows co-localized with known QTLs related to milk production and fertility traits, especially puberty. In general, the enriched reproductive QTLs driving the genetic correlations between male and female fertility are the same for both cattle populations, while the antagonizing regions were population specific. Moreover, most of the antagonizing regions were mapped to chromosome X. These results suggest regions of chromosome X for further investigation into the trade-offs between male and female fertility. We compared the CS with two other recently proposed methods that map local genomic correlations. Some genomic regions were significant across methods. Yet, many significant regions identified with the CS were overlooked by other methods.

Theoretical accuracy for indirect predictions based on SNP effects from single-step GBLUP

Background

Although single-step GBLUP (ssGBLUP) is an animal model, SNP effects can be backsolved from genomic estimated breeding values (GEBV). Predicted SNP effects allow to compute indirect prediction (IP) per individual as the sum of the SNP effects multiplied by its gene content, which is helpful when the number of genotyped animals is large, for genotyped animals not in the official evaluations, and when interim evaluations are needed. Typically, IP are obtained for new batches of genotyped individuals, all of them young and without phenotypes. Individual (theoretical) accuracies for IP are rarely reported, but they are nevertheless of interest. Our first objective was to present equations to compute individual accuracy of IP, based on prediction error covariance (PEC) of SNP effects, and in turn, are obtained from PEC of GEBV in ssGBLUP. The second objective was to test the algorithm for proven and young (APY) in PEC computations. With large datasets, it is impossible to handle the full PEC matrix, thus the third objective was to examine the minimum number of genotyped animals needed in PEC computations to achieve IP accuracies that are equivalent to GEBV accuracies.

Results

Correlations between GEBV and IP for the validation animals using SNP effects from ssGBLUP evaluations were ≥ 0.99. When all available genotyped animals were used for PEC computations, correlations between GEBV and IP accuracy were ≥ 0.99. In addition, IP accuracies were compatible with GEBV accuracies either with direct inversion of the genomic relationship matrix (G) or using the algorithm for proven and young (APY) to obtain the inverse of G. As the number of genotyped animals included in the PEC computations decreased from around 55,000 to 15,000, correlations were still ≥ 0.96, but IP accuracies were biased downwards.

Conclusions

Theoretical accuracy of indirect prediction can be successfully obtained by computing SNP PEC out of GEBV PEC from ssGBLUP equations using direct or APY G inverse. It is possible to reduce the number of genotyped animals in PEC computations, but accuracies may be underestimated. Further research is needed to approximate SNP PEC from ssGBLUP to limit the computational requirements with many genotyped animals.

Genome-Wide Association Study of Parasite Resistance to Gastrointestinal Nematodes in Corriedale Sheep

Selection of genetically resistant animals is one alternative to reduce the negative impact of gastrointestinal nematodes (GIN) on sheep production. The aim of this study was to identify genomic regions associated with GIN resistance in Corriedale sheep by single-step genome-wide association studies (ssGWAS) using 170, 507 and 50K single nucleotide polymorphisms (SNPs). Analysis included 19,547 lambs with faecal egg counts (FEC) records, a pedigree file of 40,056 animals and 454, 711 and 383 genotypes from 170, 507 and 50K SNPs, respectively. Genomic estimated breeding values (GEBV) were obtained with single-step genomic BLUP methodology (ssGBLUP), using a univariate animal model, which included contemporary group, type of birth and age of dam as class fixed effects and age at FEC recording as covariate. The SNP effects as wells as p-values were estimated with POSTGSF90 program. Significance level was defined by a chromosome-wise False Discovery Rate of 5%. Significant genomic regions were identified in chromosomes 1, 3, 12 and 19 with the 170 SNP set, in chromosomes 7, 12 and 24 using the 507 SNP chip and only in chromosome 7 with the 50K SNP chip. Candidate genes located in these regions, using Oar_v4.0 as reference genome, were TIMP3, TLR5, LEPR and TLR9 (170 SNPs), SYNDIG1L and MGRN1 (507 SNP chip) and INO80, TLN2, TSHR and EIF2AK4 (50K SNP chip). These results validate genomic regions associated with FEC previously identified in Corriedale and other breeds and report new candidate regions for further investigation.

Genetic parameters and genome-wide association study of digital cushion thickness in Holstein cows

The digital cushion is linked to the development of claw horn lesions (CHL) in dairy cattle. The objectives of this study were to (1) estimate genetic parameters for digital cushion thickness (DCT), (2) estimate the genetic correlation between DCT and CHL, and (3) identify candidate genes associated with DCT. A cohort of 2,352 Holstein dairy cows were prospectively enrolled on 4 farms and assessed at 4 time points: before calving, immediately after calving, in early lactation, and in late lactation. At each time point, CHL was recorded by veterinary surgeons, and ultrasonographic images of the digital cushion were stored and retrospectively measured at 2 anatomical locations. Animals were genotyped and pedigree details extracted from the national database. Genetic parameters were estimated following a single-step approach implemented in AIREMLF90. Four traits were analyzed: the 2 DCT measurements, sole lesions (sole hemorrhage and sole ulcers), and white line lesions. All traits were analyzed with univariate linear mixed models; bivariate models were fit to estimate the genetic correlation between traits within and between time points. Single-marker and window-based genome-wide association analyses of DCT traits were conducted at each time point; candidate genes were mapped near (<0.2 Mb) or within the genomic markers or windows with the largest effects. Heritability estimates of DCT ranged from 0.14 to 0.44 depending on the location of DCT measurement and assessment time point. The genetic correlation between DCT and sole lesions was generally negative, notably between DCT immediately after calving and sole lesions in early or late lactation, and between DCT in early or late lactation and sole lesion severity in early or late lactation. Digital cushion thickness was not genetically correlated with white line lesions. A polygenic background to DCT was found; genes associated with inflammation, fat metabolism, and bone development were mapped near or within the top markers and windows. The moderate heritability of DCT provides an opportunity to use selective breeding to change DCT in a population. The negative genetic correlation between DCT and sole lesions at different stages of production lends support to current hypotheses of sole lesion pathogenesis. Highlighted candidate genes provide information regarding the complex genetic background of DCT in Holstein cows, but further studies are needed to explore and corroborate these findings.

Application of Bayesian genomic prediction methods to genome-wide association analyses

Background

Bayesian genomic prediction methods were developed to simultaneously fit all genotyped markers to a set of available phenotypes for prediction of breeding values for quantitative traits, allowing for differences in the genetic architecture (distribution of marker effects) of traits. These methods also provide a flexible and reliable framework for genome-wide association (GWA) studies. The objective here was to review developments in Bayesian hierarchical and variable selection models for GWA analyses.

Results

By fitting all genotyped markers simultaneously, Bayesian GWA methods implicitly account for population structure and the multiple-testing problem of classical single-marker GWA. Implemented using Markov chain Monte Carlo methods, Bayesian GWA methods allow for control of error rates using probabilities obtained from posterior distributions. Power of GWA studies using Bayesian methods can be enhanced by using informative priors based on previous association studies, gene expression analyses, or functional annotation information. Applied to multiple traits, Bayesian GWA analyses can give insight into pleiotropic effects by multi-trait, structural equation, or graphical models. Bayesian methods can also be used to combine genomic, transcriptomic, proteomic, and other -omics data to infer causal genotype to phenotype relationships and to suggest external interventions that can improve performance.

Conclusions

Bayesian hierarchical and variable selection methods provide a unified and powerful framework for genomic prediction, GWA, integration of prior information, and integration of information from other -omics platforms to identify causal mutations for complex quantitative traits.

Multivariate and Genome-Wide Analysis of Mid-Infrared Spectra of Non-Coagulating Milk of Sarda Sheep Breed

Milk coagulation ability is crucial for the dairy sheep industry since the whole amount of milk is processed into cheese. Non-coagulating milk (NCM) is defined as milk not forming a curd within the testing time. In sheep milk, it has been reported in literature that up to 20% of milk is NCM. Although the clotting properties of individual milk have been widely studied, little attention has been given to NCM and genomic dissection of this trait. Mid-infrared (MIR) spectra can be exploited both to predict cheese-making aptitude and to discriminate between coagulating milk and NCM. The main goals of this work were (i) to assess the predictivity of MIR spectra for NCM classification and (ii) to conduct a genome-wide association study on coagulation ability. Milk samples from 949 Sarda ewes genotyped and phenotyped for milk coagulation properties (MCPs) served as the training dataset. The validation dataset included 662 ewes. Three classical MCPs were measured: rennet coagulation time (RCT), curd firmness (a30), and curd firming time (k20). Moreover, MIR spectra were acquired and stored in the region between 925.92 and 5,011.54 cm−1. The probability of a sample to be NCM was modeled by step-wise logistic regression on milk spectral information (LR-W), logistic regression on principal component (LR-PC), and canonical discriminant analysis of spectral wave number (DA-W). About 9% of the samples did not coagulate at 30 min. The use of LR-W gave a poorer classification of NCM. The use of LR-PC improved the percentage of correct assignment (45 ± 9%). The DA-W method allows us to reach 75.1 ± 10.3 and 76.5 ± 18.4% of correct assignments of the inner and external validation datasets, respectively. As far as GWA of NCM, 458 SNP associations and 45 candidate genes were detected. The genes retrieved from public databases were mostly linked to mammary gland metabolism, udder health status, and a milk compound also known to affect the ability of milk to coagulate. In particular, the potential involvement of CAPNs deserves further investigation.

Identifying genetic variants and pathways influencing daughter averages for twinning in North American Holstein cattle and evaluating the potential for genomic selection

Multiple birth in dairy cattle is a detrimental trait both economically for producers and for animal health. Genetics of twinning is complex and has led to several quantitative trait loci regions being associated with increased twinning. To identify variants associated with this trait, calving records from 2 time periods were used to estimate daughter averages for twinning for Holstein bulls. Multiple analyses were conducted and compared including GWAS, genomic prediction, and gene set enrichment analysis for pathway detection. Although pathway analysis did not yield many congruent pathways of interest between data sets, it did indicate two of interest. Both pathways have ties to the strong candidate region on BTA11 from the genome-wide association analysis across data sets. This region does not overlap with previously identified quantitative trait loci regions for twinning or ovulation rate in cattle. The strongest associated SNPs were upstream from 2 candidate genes LHCGR and FSHR, which are involved in folliculogenesis. Genomic prediction showed a moderate correlation accuracy (0.43) when predicting genomic breeding values for bulls with estimates from calving records from 2010 to 2016. Future analysis of the region on BTA11 and the relation of the candidate genes could improve this accuracy.

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.