
==== Front
J Anim Sci
J Anim Sci
jansci
Journal of Animal Science
0021-8812
1525-3163
Oxford University Press US

38545844
10.1093/jas/skae085
skae085
Animal Genetics and Genomics
AcademicSubjects/SCI00960
Genomic regions and biological pathways associated with sex-limited reproductive traits in bovine species
https://orcid.org/0000-0002-4941-9991
Olasege Babatunde S The University of Queensland, School of Chemistry and Molecular Biosciences, Saint Lucia Campus, Brisbane, QLD, 4072, Australia
Ag and Food, CSIRO Agriculture and Food, Saint Lucia, QLD, 4067, Australia

Oh Zhen Yin The University of Queensland, School of Chemistry and Molecular Biosciences, Saint Lucia Campus, Brisbane, QLD, 4072, Australia

Tahir Muhammad S The University of Queensland, School of Chemistry and Molecular Biosciences, Saint Lucia Campus, Brisbane, QLD, 4072, Australia
Ag and Food, CSIRO Agriculture and Food, Saint Lucia, QLD, 4067, Australia

Porto-Neto Laercio R Ag and Food, CSIRO Agriculture and Food, Saint Lucia, QLD, 4067, Australia

Hayes Ben J The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Saint Lucia Campus, Brisbane, QLD, 4072, Australia

https://orcid.org/0000-0002-7254-1960
Fortes Marina R S The University of Queensland, School of Chemistry and Molecular Biosciences, Saint Lucia Campus, Brisbane, QLD, 4072, Australia
The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Saint Lucia Campus, Brisbane, QLD, 4072, Australia

Corresponding author: m.fortes@uq.edu.au
2024
28 3 2024
102 skae08531 10 2023
25 3 2024
29 5 2024
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2024
https://academic.oup.com/pages/standard-publication-reuse-rights This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/pages/standard-publication-reuse-rights)

Abstract

Many animal species exhibit sex-limited traits, where certain phenotypes are exclusively expressed in one sex. Yet, the genomic regions that contribute to these sex-limited traits in males and females remain a subject of debate. Reproductive traits are ideal phenotypes to study sexual differences since they are mostly expressed in a sex-limited way. Therefore, this study aims to use local correlation analyses to identify genomic regions and biological pathways significantly associated with male and female sex-limited traits in two distinct cattle breeds (Brahman [BB] and Tropical Composite [TC]). We used the Correlation Scan method to perform local correlation analysis on 42 trait pairs consisting of six female and seven male reproductive traits recorded on ~1,000 animals for each sex in each breed. To pinpoint a specific region associated with these sex-limited reproductive traits, we investigated the genomic region(s) consistently identified as significant across the 42 trait pairs in each breed. The genes found in the identified regions were subjected to Quantitative Trait Loci (QTL) colocalization, QTL enrichment analyses, and functional analyses to gain biological insight into sexual differences. We found that the genomic regions associated with the sex-limited reproductive phenotypes are widely distributed across all the chromosomes. However, no single region across the genome was associated with all the 42 reproductive trait pairs in the two breeds. Nevertheless, we found a region on the X-chromosome to be most significant for 80% to 90% (BB: 33 and TC: 38) of the total 42 trait pairs. A considerable number of the genes in this region were regulatory genes. By considering only genomic regions that were significant for at least 50% of the 42 trait pairs, we observed more regions spread across the autosomes and the X-chromosome. All genomic regions identified were highly enriched for trait-specific QTL linked to sex-limited traits (percentage of normal sperm, metabolic weight, average daily gain, carcass weight, age at puberty, etc.). The gene list created from these identified regions was enriched for biological pathways that contribute to the observed differences between sexes. Our results demonstrate that genomic regions associated with male and female sex-limited reproductive traits are distributed across the genome. Yet, chromosome X seems to exert a relatively larger effect on the phenotypic variation observed between the sexes.

Delve into our latest study as we decode the sex chromosome’s pivotal role in shaping the intriguing differences between male and female cattle.

genomics
local correlation analyses
reproduction
sex-limited traits
X-chromosome
==== Body
pmcIntroduction

Reproductive success, which can be measured as the quantity of offspring produced by a population, is widely used as a measure of fitness (Strassmann and Gillespie, 2003; Stearns et al., 2010). Individuals with a bigger genetic contribution to future generations are considered of higher fitness. In most species, including livestock, males and females often employ unique reproductive strategies to increase their reproductive success (Andersson, 1994; Lidborg et al., 2022). The divergent reproductive strategies between the two sexes often give rise to different optimal phenotypes (Parker et al., 1979; Schärer et al., 2015). Natural selection tends to exert forces in contrasting directions creating divergence between sexes. This scenario is typically referred to as sexual conflict (Mank, 2017a). The evidence of this conflict is observable in sex-limited traits exhibited by males and females, especially reproductive traits, which are considered the hallmark of intrasexual conflict due to their impact on fitness and sex-limited expression (Pick et al., 2017). Such conflict is thought to have a broad genomic influence, potentially associated with a large number of loci (Chippindale et al., 2001; Wright et al., 2018). Nevertheless, our knowledge of the precise genomic regions that contribute to sex-limited traits in males and females is still inadequate.

The evolution of sexual differences between male and female traits requires that the across-sex genetic correlations are less than 1 (Robertson, 1959; Lande, 1980). In such cases, the selection of traits expressed in one sex will either reduce the expression in the other sex or completely be disfavored (Connallon and Matthews, 2019). Most of the estimates of genetic correlations between reproductive traits in males and females are generally weak and often negative (Brommer et al., 2007; Poissant et al., 2010; Punzalan et al., 2014; Hendry et al., 2018; Connallon and Matthews, 2019). Nevertheless, some traits can have high correlation estimates such that they could be used as predictors of traits measured on the opposite sex; for example, scrotal circumference (SC) is considered an indicator trait correlated to the female age of puberty (Olasege et al., 2021). Conventional estimates of genetic correlations (termed global genetic correlations) only provide a summary statistic of the genetic similarities between sexes; they do not point to the genomic regions that may contribute to the global genetic correlation (Doyle et al., 2021; Gerring et al., 2022). To identify regions of significance for global genetic correlations, local correlation analyses are needed (Werme et al., 2021; Zhang et al., 2021; Olasege et al., 2022). Local correlation analysis quantifies the correlation between two or more traits at a specific region in the genome, which can provide biological insights into shared genetic architecture between traits (Gerring et al., 2022; Reay et al., 2022). Therefore, local correlation analyses could be used to identify the genomic regions associated with sex-limited reproductive traits in males and females.

In this study, we used the Correlation Scan (CS) method (Olasege et al., 2022) to perform local correlation analysis to pinpoint the exact regions in the genome associated with across-sex genetic correlations between male and female traits. To pinpoint these genomic regions, we investigated 42 pairs of sex-limited reproductive traits measured either in males or in females of two distinct cattle breeds: Brahman (BB) and Tropical Composite (TC). First, we identified the genomic regions associated with each trait pair across sex for the two breeds. Subsequently, we examined the region(s) that were significant consistently associated with the majority of the 42 trait pairs investigated. These analyses identified genomic regions, which were then subjected to quantitative trait loci (QTL) colocalization, QTL enrichment, and functional enrichment analyses. Overall, we aimed to identify candidate genes and provide biological insights about the genetic components underlying male and female reproductive traits.

Materials and Methods

Animal Care and Use Committee approval was not required for this study because the data were obtained from existing phenotypic and genotypic databases, collected by the Cooperative Research Centre for Beef Genetic Technologies, or Beef CRC (Griffith et al., 2013; Griffith and Burrow, 2015).

Phenotypes and genotypes

The reproductive traits used in this current study have been previously described in detail (Olasege et al., 2021). In brief, six female and seven male reproductive traits were available for ~1,000 animals in BB and TC breeds. These traits are important reproductive phenotypes in the beef industry. The description of the traits and the number of animals recorded in each sex are provided in Table 1. The pairwise global genetic correlations between the male and female traits listed above in the two populations have been reported in our previous study (Olasege et al., 2021). As expected, these genetic correlations ranged from positive to negative and in most cases negative. No perfect correlation (e.g., global genetic correlation = ± 1) was observed between the studied traits (Olasege et al., 2021).

Table 1. Trait description for the female and male reproductive traits used in the study

Traits	Description	No of individuals	
		BB	TC	
Cow phenotypes	
AGECL	Age at detection of first corpus luteum (d)	980	996	
PPAI	Postpartum anestrus interval (d)	618	822	
IGF1c	Cows’ blood concentration of IGF1 at 18 mo of age (ng/mL)	995	1,015	
DC1	Days-to-calving first breeding opportunity (d)	996	1,091	
DC5	Days-to-calving averaged for 5 breeding opportunities (d)	794	922	
AFC	Age at first calving (yr)	944	1,058	
Bull phenotypes	
IGF1b	Bulls’ blood concentration of IGF1 at 6 mo of age (ng/mL)	964	998	
MAS24	Sperm mass activity at 24 mo of age (%)	986	970	
MOT24	Sperm motility at 24 mo of age (%)	990	970	
PNS24	Percentage of normal sperm at 24 mo of age (%)	938	993	
SC12	Scrotal circumference at 12 mo of age (cm)	1,020	998	
SC18	Scrotal circumference at 18 mo of age (cm)	1,022	998	
SC24	Scrotal circumference at 24 mo of age (cm)	1,022	998	
BB, Brahman; TC, Tropical Composite.

The animals were either genotyped using the BovineSNP50 (Matukumalli et al., 2009) or the BovineHD (Illumina Inc., San Diego, CA, USA) chips. Animals genotyped with the BovineSNP50 single nucleotide polymorphism (SNP) panels had their genotypes imputed to BovineHD using a mixed breed imputation reference set as described by Bolormaa et al. (2013). Before imputation, all the genotypes were mapped to the new bovine reference genome (ARS_UCD1.2; Rosen et al., 2020) and were phased using Eagle v.2.4.1 software (Loh et al., 2016). Next, we used Minimac3 to impute the BovineSNP50 genotypes for all autosomes and Minimac4 for the X-chromosome (Das et al., 2016). Notably, the X-chromosome was imputed after separating the pseudoautosomal regions (PAR) and the nonpseudoautosomal region (nPAR) as recently defined by Johnson et al. (2019). For each sex per breed, we performed quality control by filtering for minor allele frequency (MAF < 5%) and imputation accuracy (Minimac R2 ≥ 0.8). Subsequently, we merged both male and female genotypes, resulting in 550,127 and 659,443 SNPs for BB and TC animals, respectively.

Local correlation analysis

We used the CS method (Olasege et al., 2022) to estimate bivariate local correlations between the 42 reproductive trait pairs in both BB and TC populations. Briefly, the CS utilizes the SNP effects between two trait pairs to identify genomic regions significantly associated with the global genetic correlation between traits. The framework uses a window of 500 SNP effects to estimate local correlations between traits in 100 SNP-sliding windows across each chromosome. The resulting local correlation estimates are then subjected to a permutation test in order to identify significant genomic regions associated with the global genetic correlations. Only the genomic regions with Bonferroni-corrected P value <0.05 are considered significant. Further details about the CS methodology and about estimating P values in this framework were provided in the original publication for this methodology, by Olasege et al. (2022).

Estimation of SNP effects for the CS framework

The SNP effects for each trait in the two sexes for the two populations were derived as described in the CS framework. For each trait, the genomic breeding values (GEBVs) of all the animals within the breed were estimated using the genomic best linear unbiased prediction (GBLUP) model available in GCTA (Yang et al., 2011).

y=Xb+Za+e

where y is the vector of phenotypic values, X is the incidence matrix of fixed effects, b is the vector of fixed effects, Z is the design matrix assigned to the GEBVs, g is the vector of GEBVs for all animals, and e is the vector of residuals. Vectors g and e are assumed to follow a normal distribution, thus e~N(0, Iσe2). Matrix G is the genomic relationship matrix (GRM), constructed following Yang et al. (2011). Notably, we treated the X-chromosome as nonpseudo autosomal region with genotype coding of 0 and 2 for males (see Olasege et al. (2021) for details). The fixed effects (e.g., contemporary group [CG], batch effect of the IGF1 assay, age of the animal, age of dam) included in the model were specific for each trait and were deemed significant for the phenotypic variations observed in either BB or TC populations. A linear mixed model implemented in R software v4.2.1 (R Core Team, 2019) was used to determine the level of significance. The CG effect was relevant for most traits, except IGF1b in BB and TC. The CG represents a cohort of animals born in the same year and raised under the same management conditions. The batch effect and age of dam were only significant for IG1Fb in both BB and TC. The age of the animal at the time of trait recording was included as linear covariates. In addition to the GRM, we used the first two principal components to account for the, quite varied, breed composition in the TC population for all the traits (see Olasege et al. (2021) for details).

Following the estimation of GEBVs for each trait in BB and TC, we obtained the SNP effects by back-solving the GEBVs using the approach described by Strandén and Garrick (2009).

Identifying significant genomic regions associated with the 42 trait pairs

Genomic regions with a significant local correlation (Bonferroni-corrected P value <0.05) for each reproductive trait pair across sex were identified for both BB and TC populations. In addition, we investigated the genomic regions with significant local correlations across all 42 trait pairs to pinpoint the exact regions in the genome associated with across-sex genetic correlations between male and female traits. As complex traits like fertility are likely to be influenced by many regions across the genome (Gaddis et al., 2016; Goddard et al., 2016), and different traits may be influenced by different regions, it might the difficult to pinpoint regions associated with all across-sex genetic correlations. Therefore, we relaxed our criteria to also consider investigating genomic regions with significant local correlation for at least 50% (21 out of 42), 60% (~25 out of 42), and 70% (~29 out of 42) of the total trait pairs.

Gene annotation, QTL colocalization, and QTL enrichment analysis

The coordinates of the significant genomic regions consistently identified as significant across the trait pairs (either all or the 50%, 60%, and 70%) in BB and TC populations were used for gene annotation and QTL colocalization using GALLO R package (Fonseca et al., 2020). Firstly, the bovine gene annotation from ARS-UCD1.2 assembly (Rosen et al., 2020) was used to map the genes within the coordinates. Next, we used the annotation information to query the Cattle QTLdb (https://www.animalgenome.org/cgi-bin/QTLdb/index; Hu et al., 2016) (accessed November 24, 2022) for genes colocalizing with QTL classes and QTL related to specific traits. Here, QTL classes are defined as QTL categorized into 6 different trait groups, namely: milk, reproduction, production, meat and carcass, health, and exterior QTL class (as defined in the Cattle QTLdb).

In addition, we also used the annotation information to investigate the trait-specific QTL, e.g., QTL related to milk yield, conception rate, dry matter intake, length of productive life, and clinical mastitis, among others. There is a higher probability that trait-specific QTL will be biased toward milk traits due to the large number of QTL related to milk in the Cattle QTLdb. To avoid this bias, we performed QTL enrichment analysis (Fonseca et al., 2020) using a chromosome-wide approach for the annotated genes. This analysis tests if QTL identified were associated with specific traits more than expected by chance. Briefly, a hypergeometric test approach was used to estimate whether the number of observed records, for a specific trait, in a chromosome is larger than expected by chance based on the frequency of recurrence of such trait. Finally, the P values were corrected for multiple tests using a Bonferroni correction of 5% (Fonseca et al., 2020).

It is important to acknowledge a limitation and a bias in all QTL enrichment analyses. The QTL database is biased towards autosomes since many GWAS did not include X-chromosomes (Irano et al., 2016; Regatieri et al., 2017). However, the inclusion of the X-chromosome in GWAS is now gaining momentum (Fortes et al., 2020; Sanchez et al., 2023). Also, the QTL database includes studies published by our group and others that used the Beef CRC populations and thus this will influence the enrichment results. Nonetheless, the cattle QTL database is the most complete annotation available.

Functional analyses

The genes annotated as associated with male and female correlations were subjected to functional analyses using DAVID (Dennis et al., 2003) with the Bos taurus background list (accessed November 30, 2022). The enriched biological pathways and biological processes with Bonferroni P values <0.05 were considered significant. We also analyzed the candidate gene list using g:Profiler (Raudvere et al., 2019; Kolberg et al., 2023) to provide additional evidence supporting the discussions about candidate genes and their biological functions.

Results

Genomic regions associated with male and female reproduction traits

The total number of regions (i.e., windows) generated for all 42 reproductive trait pairs was 5366 and 6460 for BB and TC, respectively. The local correlation estimates and their corresponding P values, chromosome positions, and genomic coordinates of these regions for each trait pair are provided in Supplementary File 1 (Tables S1 to S42; https://doi.org/10.6084/m9.figshare.22313434.v2) for BB and Supplementary File 2 (Tables S43 to S84; https://doi.org/10.6084/m9.figshare.22313434.v2) for TC. For each trait pair studied, several regions across each chromosome were significantly found to be associated with the across-sex correlation between male and female traits, for both breeds. Across all 42 trait pairs, in both BB and TC, no single region significantly was found to be associated with the genetic correlation between male and female reproductive traits. However, a specific region on the X-chromosome (position: 40510902 to 45610392 Bp) was found to be significant for 33 trait pairs out of 42 in BB (~80%). Similarly, a region on the X-chromosome (position: 66781657 to 69364019 Bp) is significantly associated with ~90% (38 out of 42) of the total trait pairs in TC (Table 2). There was no overlap between the X-chromosome region found in BB and TC for these positions. The number of genes in the significant X-chromosome region in BB and TC was 11 and 16, respectively. In BB, the gene type present in the significant X region was 45% for protein-coding, 45% for regulatory genes, while pseudogenes accounted for the remaining 9.1% (Figure 1A). In the TC significant X region, however, protein-coding genes accounted for 81.25%, while 18.75% were regulatory genes (Figure 1B).

Table 2. The most significant windows associated with across-sex correlation in BB and TC breeds

Breed	Chr	Window	Start	End	No. of genes	No of significant trait pairs	
BB	X	W5213	40510902	45610392	11	33 of 42	
TC	X	w6335	66715002	69782169	16	38 of 42	
Chr = chromosome; BB = Brahman; TC = Tropical Composite.

Figure 1. The proportion of gene biotype for the most significant genomic regions affecting the 42 reproductive trait pairs; (A) BB (Chr X: 40.51 to 45.61 Mb); (B) TC (Chr X: 66.71 to 69.78 Mb).

Although no single region was associated with all the 42 trait pairs in the two breeds, we found several regions across the Bos taurus genome that were significantly associated with at least 50% (BB: 103 and TC: 184), 60% (BB: 29 and TC: 45), or 70% (BB: 8 and TC: 12) of the total number of trait pairs investigated for each breed. The downstream results from the analyses of the regions with at least 50%, 60%, and 70% look almost identical. Thus, we present only the result of the 50% in the main text while the results for alternative thresholds (60% and 70%) in Supplementary File 3 (https://doi.org/10.6084/m9.figshare.22313434.v2). We found 103 (BB) and 184 (TC) genomic regions that were significantly associated with at least 50% of the 42 trait pairs. These significant regions were distributed across the Bos taurus genome, including the X-chromosome (further details are provided in Supplementary File 4; Tables S86 and S87; https://doi.org/10.6084/m9.figshare.22313434.v2). The number of annotated genes in the significant regions was 1,492 and 2,021 for BB and TC, respectively. Out of this gene list, the number of shared genes between the two breeds was 659. In both BB and TC, protein-coding genes account for about 81% of the shared gene list, while 17% were regulatory genes and 2% were pseudogene (Figure 2).

Figure 2. The proportion of gene biotype associated with at least 50% of the 42 reproductive trait pairs at the genomic regions identified; (A) BB; (B) TC.

QTL colocalization and QTL enrichment analyses

We identified QTL classes and trait-specific QTL for the genes within the most significant regions (as per Table 2) associated with most of the reproductive trait pairs in BB and TC breeds. For clarification, the most significant region is the X-chromosome (positions 40510902 to 45610392 Bp in BB and 66781657 to 69364019 Bp in TC) mentioned above, which was associated with 33 trait pairs out of 42 in BB and 38 out of 42 trait pairs in TC. The genes found in the X region for BB colocalized only with reproduction QTLs (100%), whereas for TC, the genes colocalized with reproduction (99.83%) and production (0.17%) QTLs. The QTL enrichment analysis identified percentage of normal sperm as the only significantly enriched trait-specific QTL for both BB (Bonferroni-corrected P value of 1.6 × 10−67) and TC (Bonferroni-corrected P value of 1.9 × 10−4).

For the regions significantly associated with at least 50% of all the trait pairs, the genes in BB and TC mostly colocalize with reproduction QTL (BB: 84.19% and TC: 86.17%) (Figure 3A and C). The QTL enrichment analysis revealed 45 (BB) and 58 (TC) significant trait-specific QTL (Bonferroni-corrected P value <0.05) for the two breeds (Supplementary File 5; Tables S88 and S89; https://doi.org/10.6084/m9.figshare.22313434.v2). The most significant trait-specific QTLs for both BB and TC was metabolic body weight on chromosome 14. The top 10 most enriched traits for the two breeds are provided (Figure 3B and D).

Figure 3. The pie chart of the percentage QTL classes (left) and bubble plot (right) of the enriched trait-specific QTL for the genomic regions that are associated with at least 50% of the 42 reproductive trait pairs in Brahman (top) and TC (bottom) breeds. For the bubble plot, the darker the shade in the circles, the more significant the enrichment. The area of the circles is proportional to the number of QTLs. The x-axis shows a richness factor obtained by the ratio of the number of QTLs annotated in the candidate regions and the total number of each QTL (and chromosome in the case of this plot) in the reference database.

Biological pathways and processes

Firstly, we focused on the biological pathways and processes enriched for the X regions identified as the most significantly (as per Table 2) associated with most of the 42 reproductive trait pairs in BB and TC. The results from DAVID and g:Profiler often look similar. Hence, we provided the results from DAVID in the main text and g:Profiler in Supplementary File 6; https://doi.org/10.6084/m9.figshare.22313434.v2). The 11 genes within the X region detected in BB were enriched for three biological pathways and biological processes (Bonferroni-corrected P value <0.05). The enriched biological pathways were: p53 signaling pathway, ubiquitin-mediated proteolysis, and Wnt signaling pathway. The biological processes include female gamete generation, actin filament organization, and proteasome-mediated ubiquitin-dependent protein catabolic process. In TC, no biological pathway was significant for the 16 genes within the most significant X region. The only significant biological process was involved in spliceosome snRNP assembly. Supplementary File 7 (Tables S90 and S91; https://doi.org/10.6084/m9.figshare.22313434.v2) provides full details of the functional annotation of each gene in both BB and TC populations. Some of the genes found in BB have known biological pathways and GO terms associated with fertility-related processes. A small proportion of these genes are uncharacterized (ENSBTAG00000048495 and ENSBTAG00000053314) while some are well known (i.e., DIAPH2). Although most of the TC genes were not enriched for any biological pathways, they are highly enriched for biological processes associated with fertility (i.e., CYCL1, POU3F4, RPS6KA6)

Examining the regions associated with at least 50% of the 42 reproductive trait pairs, the most significant biological pathway for the two breeds was olfactory transduction, with Bonferroni-corrected P value of 3.68 × 10−38 (BB) and 2.4 × 10−11 (TC). The hydrogen peroxide catabolic process was the only significant biological process in BB, whereas for TC, three biological processes were enriched: metabolic process, cytolysis, and homophilic cell adhesion via plasma membrane adhesion molecules. These results are displayed in Figure 4.

Figure 4. The bar charts of the enriched term for the biological pathways (left) and biological processes (right) for the genomic regions associated with at least 50% of the 42 reproductive trait pairs in BB (top) and TC (bottom). The y-axis represents the enriched terms, and the x-axis represents the number of genes. The color of the plot indicates the level of significance. Prot. denotes proteolysis, while UDPCP represents the ubiquitin-dependent protein catabolic process.

Discussion

Using 42 trait pairs that capture reproductive characteristics measured either in male or female cattle, we investigated the genomic regions that significantly affect the genetic correlations between these traits. The traits targeted were sex-limited reproductive traits, and we used two cattle breeds. Identifying these significant genomic regions helps to 1) better understand the genomic hotspot(s) associated with sex-limited reproductive traits and 2) gain biological insight regarding the genetics underlying sex-limited traits in cattle.

Genomic regions and biological pathways associated with male and female reproduction traits

Across each trait pair studied, the genomic regions associated with the reproductive traits are distributed across all chromosomes. This result demonstrates that both the autosomes and the X-chromosome contribute to the phenotypic variation in sex-limited reproductive traits (Long and Rice, 2007; Ruzicka and Connallon, 2020). The Y-chromosome was not investigated in this study. Our results confirm the understanding that most reproductive traits exhibit a complex genetic architecture, with multiple loci across the genome implicated (Randall et al., 2013; Cui et al., 2014; Mank, 2017b). Our investigation of the significant regions across the 42 trait pairs studied in two diverse breeds led to the observation that the X-chromosome plays an important role in underlying genetic correlations between sex-limited traits. Our results corroborate the established architecture of complex reproductive traits (Gaddis et al., 2016; Goddard et al., 2016), making it difficult to pinpoint a single region for all 42 trait pairs. Instead, we found regions on the X-chromosome that influenced 80% to 90% of the total trait pairs in the two cattle breeds. Thus, although both autosomal and X-chromosome genes contribute to the observed difference in male and female traits, it appears that the X-chromosome exerts a considerable effect on the correlations between such traits. This finding aligns with the notion that the X-chromosome serves as a hotspot for phenotypic variation and divergence between the sexes (Rice, 1984; Gibson et al., 2002). The hotspot on the X-chromosome was not the same for the two breeds. Breed differences in the genetic basis of sexual difference have been previously observed in a study with Piemontese and Belgian Blue cattle (Bittante et al., 2018). Despite the different hotspots between BB and TC, all the genes mapped to the X-chromosome hotspots were related to reproduction, and a considerable number of them were regulatory genes (such as microRNA and long noncoding RNA). Thus we contribute to the emerging body of evidence that suggests pivotal roles for these regulatory genes in sex determination and differentiation; this knowledge seems to be gathering momentum (Burgos et al., 2021; Yan et al., 2021). Moreover, regulatory genes on the X-chromosome exert their phenotypic effect by modifying autosomal gene expression. Thus, both the autosomes and the X-chromosome are likely contributing to sexual differences between males and females (Ruzicka and Connallon, 2020). Besides, the genes identified on the X-chromosome for the two breeds were enriched for the same trait-specific QTL (i.e., percentage of normal sperm). Females are known to be biased toward sperm use, which poses serious intense selective pressure on males to produce effective and sufficient sperm cells, given that the sperm must compete against many obstacles to fertilize an egg (Fitzpatrick and Lüpold, 2014; Rowley et al., 2019). This intense selective pressure from females to improve their reproductive success has shaped the evolution of testicular size and function to ensure male fertility (Ramm et al., 2014). The struggle for reproductive success has driven the two sexes down different evolutionary paths and promoted sexual differences (Darwin, 1871; Andersson, 1994). Furthermore, the functional analyses of the genes identified within the X-chromosome also support the sexual difference between the two sexes. The three biological pathways identified in BB (i.e., the p53 signaling pathway, Ubiquitin-mediated proteolysis, and Wnt signaling pathway) have been linked to sex-specific differences between males and females (Kim and Casaccia‐Bonnefil, 2009; Lane and Levine, 2010; Deshpande et al., 2016; Bayer et al., 2020; Salzberg et al., 2020). The biological processes enriched for female gamete generation, actin filament organization, and spliceosomal snRNP assembly further highlight the major role of the X-chromosome in the phenotypic differences observed between sexes.

Genomic regions and biological pathways associated with at least 50% of male and female reproduction traits.

About 84% to 87% of the regions that were significant for 50% or more of the trait pairs colocalized with reproductive These genes spread across different chromosomes, and many were regulatory genes. Although the genes were mostly related to reproduction, the most enriched trait-specific QTL were related to metabolic body weight, average daily gain, and carcass weight on chromosome 14, including the PLAG1 region for both breeds. The PLAG1 region is known as a pleiotropic region that affects both fertility and stature traits (Fortes et al., 2013a; Juma et al., 2016). Pleiotropic genes emerging from the local correlation analyses seem logical as they are signals arising from male and female trait pairs.

The functional enrichment analyses pointed to the olfactory transduction pathways as relevant for both breeds. The olfactory system plays a key role in reproductive physiology and behavior in most mammals (Boehm et al., 2005). Males and females emit sex-specific pheromones to convey specific signals related to reproduction and social behavior (Cherry and Baum, 2020). For example, females produce chemical signals to induce sexual receptivity, advertise territory ownership, and facilitate suckling behavior in offspring (Nowak et al., 2000; Schaal et al., 2003; Heymann, 2006; Stockley et al., 2013). On the other hand, males use scent markings to advertise their location and territorial dominance, providing specific information to a potential female (Hurst and Beynon, 2004; Zala and Penn, 2004; Hurst, 2009). These sex-specific signals, when produced by one sex, have a physiological priming effect on the reproductive physiology of the other sex (Koyama, 2004, 2016; Coombes et al., 2018). All these sex differences in chemical communication, detection, and response in the struggle for reproductive success have driven the two sexes down different evolutionary paths that further promote sexual differences (Darwin, 1871; Andersson, 1994). Thus, identifying olfactory transduction pathways as relevant to sex-limited traits and thus the sexual differences between male and female cattle seems plausible.

Key candidate genes and their biological functions

The X-chromosome region found to be significantly associated with most of the studied traits is highly enriched for key candidate genes with biological functions related to reproduction. For example, DIAPH2, LOC100336539 (ENSBTAG00000048495, ENSBTAG00000053993), RPS6KA6, CYCL1, and POU3F4 are highly enriched in biological pathways and processes involved in reproduction. Several studies have also identified the biological function of these genes. For instance, the DIAPH2 and its surrounding genes have been found to be associated with percentage of normal sperm in cattle and largely involved in controlling meiosis and follicular development in male and female sheep (Mandon-Pépin et al., 2003; Fortes et al., 2013b). The CYCL1 gene is known to play a key role in spermiogenic remodeling and male fertility (Schneider et al., 2023). The POU3F4 gene has been associated with SC in cattle (Fortes et al., 2013b), suggesting its role in testicular development. Although some of the genes in the X-chromosome regions in the two populations are yet to be characterized, these X-chromosome regions might be key candidate regions contributing to sex-limited reproductive traits in male and female cattle.

Conclusions

Our results demonstrate that the genomic regions associated with sex-limited reproductive traits between males and females are distributed across all autosomes and the X-chromosome. However, X is the only chromosome that seems to exert an effect across most trait pairs for both breeds. While most of the candidate genes identified were protein-coding, a considerable number were regulatory genes (i.e., miRNA and lncRNA). The significant genomic regions identified were enriched for trait-specific QTL and harbored biological pathways that promote differences between sexes.

Supplementary Material

skae085_suppl_Supplementary_Materials

Acknowledgments

This research was conducted using the legacy database of the Cooperative Centre for Beef Genetics Technologies and their core partners including Meat and Livestock Australia. The authors acknowledge all the participants of the Cooperative Centre for Beef Genetics Technologies for their efforts in conducting the field trials and recording the phenotypes. BSO was supported by funding from Meat and Livestock Australia (L.GEN.1710; Female Fertility Phenobank and L.GEN.1818; Bull fertility update: historical data, new cohort, and advanced genomics) and top-up scholarship from CSIRO.

Abbreviations

AFC age at first calving

AGECL age at detection of first corpus luteum

BB Brahman

CG contemporary group

CS correlation scan

DC1 days-to-calving first breeding opportunity

DC5 days-to-calving averaged for five breeding opportunities

GBLUP genomic best linear unbiased prediction

GEBVS genomic breeding values

IGF1b bulls’ blood concentration of IGF1 at 6 mo of age

IGF1c cows’ blood concentration of IGF1 at 18 mo of age

MAS24 sperm mass activity at 24 mo of age

MOT24 sperm motility at 24 mo of age

PPAI postpartum anestrus interval

PNS24 percentage of normal sperm at 24 mo of age

QTL quantitative trait loci

SC12 scrotal circumference at 12 mo of age

SC18 scrotal circumference at 18 mo of age

SC24 scrotal circumference at 24 mo of age

SNP single nucleotide polymorphism

TC Tropical Composite

Conflict of interest statement.

All authors declare no financial or personal conflicts of interest.
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