Genome-wide association study to identify genomic regions and positional candidate genes associated with male fertility in beef cattle

The study addresses declining reproductive efficiency in beef cattle and the need to genetically identify and select bulls with superior fertility. Bull fertility traits such as sperm motility (SM) and scrotal circumference (SC) are heritable and correlated with semen quality and reproductive performance, making them useful indicator traits. However, selection for production traits has negatively impacted fertility through pleiotropic effects. The research aims to clarify genomic regions and genes influencing SC and SM, particularly in crossbred beef cattle where fewer GWAS have been conducted. Objectives: (1) identify 1 Mb SNP windows significantly associated with SC and SM in crossbred beef bulls using a weighted single-step genomic BLUP (WssGBLUP) approach, and (2) identify positional candidate genes within those windows, prioritize them based on functional evidence, and evaluate their roles in male fertility.

Prior work has shown that female fertility traits have low heritability (0.01–0.10), impeding genetic progress, while male traits such as SC and semen characteristics, including SM, are moderately heritable (SC and semen traits ~0.05–0.22; SM 0.29–0.60). SC correlates with testes weight, sperm output, and semen quality, and is positively associated with live sperm percentage, sperm number, and SM, though negatively correlated with some performance traits like feed efficiency. Previous GWAS have focused largely on dairy or purebred populations; fewer studies exist for crossbred beef cattle despite their industry prevalence. Associations on BTA9 have been reported for SC and testicular hypoplasia. Prior studies with similar sample sizes have identified meaningful fertility-related loci and genes, supporting the feasibility of detecting genetic signals with modest cohorts.

Population: 265 crossbred beef bulls from the Ontario Beef Research Centre (University of Guelph, Elora, ON, Canada). Predominant breed composition included Angus (~52%), Simmental (~24%), Piedmontese (~6.6%), Gelbvieh (~6.3%), Charolais (~3.8%), and Limousine (~1.1%). Mean age 384 days; mean weight 555 kg at collection. Traits and phenotyping: SC measured by palpating testes to the scrotal base and recording greatest circumference with a looped tape. Semen collected between 12–15 months via electroejaculation immediately after SC measurement. SM visually estimated immediately; ejaculates extended, cryopreserved, and assessed using CASA (IVOS system) at 37 °C with standardized concentration (1×10^7 sperm/mL) and slide chambers. Genotyping and QC: Affymetrix Genechip Bovine Genome High Density Array (648,874 SNPs); coordinates lifted to ARS-UCD1.2. SNP exclusion criteria: non-autosomal, MAF < 0.05, call rate < 95%. After QC, 379,591 markers remained. GWAS framework: WssGBLUP using BLUPF90/AIREMLF90. Proportion of genetic variance calculated for non-overlapping 1 Mb windows using PostGSf90. Model: y = Xb + Za + e, with y as phenotype (SC or SM). Fixed effects for SC: 25 herd-year-season (HYS) levels, body weight, age (cubic polynomial), and breed composition (AN, SM, PI, GV, CH, LM). Fixed effects for SM: 25 HYS levels, age, and breed composition. Random additive genetic effects modeled via ssGBLUP. Iterative weighting: initialize D=I; compute G=ZDZ′; estimate breeding values; derive SNP effects û; update weights d_i = û_i^2 / [2 p_i (1-p_i)]; normalize D by its trace; recompute G and iterate (two iterations) to obtain final SNP effects and window variances. Annotation and enrichment: Windows explaining >1% of genetic variance were selected for gene and QTL annotation using R and the GALLO package with ARS-UCD1.2 gene (.gtf) and QTLdb (.gff) resources. QTL enrichment used a chromosome-based bootstrap (1000 iterations) to compare observed vs expected QTL counts; p-values FDR-corrected at 5%. Gene prioritization: ToppGene Suite with a trained list of 100 fertility-related genes from GUILDify based on terms including scrotal/testis/sperm/semen/spermatogenesis/fertility. Positional genes within significant windows served as the test list. Multivariate annotation-based prioritization with FDR 5% selected significant prioritized genes. Network and GO analysis: Prioritized genes (SC and SM combined) were analyzed in NetworkAnalyst 3.0 using STRING interactome (confidence ≥900) to build a second-order PPI network. GO enrichment (BP, MF, CC) performed on network genes; significant terms (FDR ≤0.05) related to reproduction were used to extract Module 1. Module 2 comprised prioritized genes and their direct neighbors; enriched GO terms were evaluated for reproductive relevance.

- GWAS signals: - Scrotal circumference (SC): Eight 1 Mb windows on BTA9, BTA10, BTA20, BTA24, and BTA29 explained >1% each, totaling 13.19% of genetic variance. BTA29: 29.35–30.35 Mb was the largest contributor (3.47%). Thirty-two positional candidate genes were annotated; 14 were functionally prioritized, and their windows accounted for 9.76% of variance. - Sperm motility (SM): Five windows on BTA9, BTA13, BTA20, and BTA24 explained >1% each, totaling 7.17% of genetic variance. Twenty-eight positional genes were annotated; 14 were prioritized and their windows accounted for 7.16% of variance. - BTA9 explained the highest chromosome-wise variance for both SC (4.03%) and SM (2.76%). - Prioritized candidate genes: - SC (examples): MAP3K1 and VIP previously implicated in male reproduction; others included SASH1, FBXO5, MTRF1L, RGS17, SLC24A5, SEMA6D, SRPRA, TIRAP, DCPS, ST3GAL4, KIRREL3, CDON. - SM (examples): Five genes with prior evidence in male fertility: SOD2, TCP1, PACRG, SPEF2, PRLR; additional prioritized genes included MAS1, EZR, IL7R, SKP2, PLCB4, PAK5. - QTL annotation and enrichment: - In SC candidate windows, reproduction QTLs comprised 7.85% of annotated QTLs. Enrichment (FDR ≤0.05) identified 38 significant QTLs on BTA9 and BTA10, largely for exterior conformation (e.g., udder depth/attachment/cleft, teat placement-rear, strength, stature, body depth, feet and leg conformation). Example enriched traits and FDR-corrected p-values: udder depth (p=9.38×10^-3), body depth (9.38×10^-3), PTA type (9.38×10^-3), stature on BTA10 (9.38×10^-3), body weight gain (9.00×10^-3). - In SM candidate windows, reproduction QTLs comprised 8.11% of annotated QTLs; 13 significant QTLs enriched on BTA9 and BTA13 included daughter pregnancy rate and interval to first estrus after calving, and exterior traits (e.g., stature). - Functional analyses (NetworkAnalyst/GO): - The combined prioritized gene network (1442 nodes) yielded 120 significant GO biological processes (BP), 75 significant GO molecular functions (MF), and 55 significant GO cellular components (CC) (all FDR ≤0.05), many related to male fertility. - Module emphasizing prioritized genes and direct neighbors highlighted reproductive-relevant terms, including regulation of MAPK cascade, spermatid differentiation, and regulation of hormone secretion (not all significant in this reduced module), and significant MF/CC terms such as acetyltransferase activity (FDR 8.88×10^-4), zinc ion binding (2.12×10^-3), lipase activity (2.55×10^-3), endonuclease/nuclease activity (2.55×10^-3 / 4.58×10^-3), cation channel activity (9.51×10^-3), kinesin complex and spindle microtubule (both 1.53×10^-2), and cytosol (4.42×10^-2). - Biological interpretation: - SC: MAP3K1 relates to apoptotic regulation in germ cells; VIP influences testicular steroidogenesis and reproductive hormones. - SM: SOD2 mitigates oxidative stress damaging sperm chromatin; TCP1 chaperonin involved in spermatogenesis; PACRG and SPEF2 critical for flagellar structure and motility; PRLR implicated in spermatogenesis and fertility outcomes. - Several prioritized genes (SEMA6D, SRPRA, KIRREL3, ACAT2, MAS1) also have roles in female fertility, suggesting pleiotropic reproductive pathways.

The study’s single-step GWAS approach in crossbred beef bulls successfully identified genomic regions and positional genes associated with SC and SM, addressing the need to improve bull fertility through genetic selection. BTA9 emerged as a key chromosome harboring multiple windows explaining substantial variance for both traits and overlapping with prior reports for SC and testicular hypoplasia, reinforcing its relevance to male reproductive biology. Prioritized candidate genes provide plausible functional links to spermatogenesis, hormone regulation, apoptosis, and sperm structure/motility, supported by GO enrichment and literature. QTL enrichment for reproductive and exterior conformation traits within candidate windows indicates potential pleiotropy between fertility and body conformation, consistent with known genetic correlations (e.g., SC with growth/body size). Findings advance understanding of the genetic architecture of SC and SM in crossbred populations and highlight targets (e.g., MAP3K1, VIP, SOD2, TCP1, PACRG, SPEF2, PRLR) for further validation and potential incorporation into genomic prediction models, while indicating that selected regions may also influence female fertility-related traits.

This work identifies multiple genomic windows and positional candidate genes associated with scrotal circumference and sperm motility in crossbred beef bulls, with strong functional support from gene prioritization, QTL enrichment, and GO analyses. Notable candidates include MAP3K1 and VIP for SC and SOD2, TCP1, PACRG, SPEF2, and PRLR for SM. The enriched functional categories implicate pathways central to spermatogenesis, hormone regulation, oxidative stress defense, and cytoskeletal/flagellar function. These results enhance the understanding of male fertility genetics in beef cattle and can inform genomic selection strategies. Future research should validate these candidates in larger and independent populations, fine-map causal variants, assess pleiotropic effects with production and conformation traits, and integrate functional genomics to elucidate mechanisms.

- Sample size was modest (n=265), warranting cautious interpretation and necessitating validation in larger cohorts. - The crossbred nature of the cohort may introduce additional genetic complexity and heterogeneity. - Sperm motility phenotyping in beef cattle is less routine than in dairy, potentially limiting trait measurement depth. - The window-based threshold (>1% variance) may miss smaller-effect loci. - Potential bias in QTL databases was addressed via enrichment analyses, but residual biases may remain.