Skip to main content

Genome-wide assessment and mapping of inbreeding depression identifies candidate genes associated with semen traits in Holstein bulls



The reduction in phenotypic performance of a population due to mating between close relatives is called inbreeding depression. The genetic background of inbreeding depression for semen traits is poorly understood. Thus, the objectives were to estimate the effect of inbreeding and to identify genomic regions underlying inbreeding depression of semen traits including ejaculate volume (EV), sperm concentration (SC), and sperm motility (SM). The dataset comprised ~ 330 K semen records from ~ 1.5 K Holstein bulls genotyped with 50 K single nucleotide polymorphism (SNP) BeadChip. Genomic inbreeding coefficients were estimated using runs of homozygosity (i.e., FROH > 1 Mb) and excess of SNP homozygosity (FSNP). The effect of inbreeding was estimated by regressing phenotypes of semen traits on inbreeding coefficients. Associated variants with inbreeding depression were also detected by regressing phenotypes on ROH state of the variants.


Significant inbreeding depression was observed for SC and SM (p < 0.01). A 1% increase in FROH reduced SM and SC by 0.28% and 0.42% of the population mean, respectively. By splitting FROH into different lengths, we found significant reduction in SC and SM due to longer ROH, which is indicative of more recent inbreeding. A genome-wide association study revealed two signals positioned on BTA 8 associated with inbreeding depression of SC (p < 0.00001; FDR < 0.02). Three candidate genes of GALNTL6, HMGB2, and ADAM29, located in these regions, have established and conserved connections with reproduction and/or male fertility. Moreover, six genomic regions on BTA 3, 9, 21 and 28 were associated with SM (p < 0.0001; FDR < 0.08). These genomic regions contained genes including PRMT6, SCAPER, EDC3, and LIN28B with established connections to spermatogenesis or fertility.


Inbreeding depression adversely affects SC and SM, with evidence that longer ROH, or more recent inbreeding, being especially detrimental. There are genomic regions associated with semen traits that seems to be especially sensitive to homozygosity, and evidence to support some from other studies. Breeding companies may wish to consider avoiding homozygosity in these regions for potential artificial insemination sires.

Peer Review reports


A reduction in the mean performance of a quantitative trait owing to mating between relatives is known as inbreeding depression. The genetic background of inbreeding depression is increased homozygosity because of descending two copy of the same allele from a common ancestor [1]. Inbreeding depression has traditionally been investigated by regressing phenotypes on pedigree-derived inbreeding coefficients [2]. Nowadays the accessibility of single nucleotide polymorphism (SNP) genotypes, distributed throughout the genome, enables researchers to estimate inbreeding coefficient (F) based on genomic data and provides them with some benefits. Genomic inbreeding does not depend on the quality and completeness of the pedigree [3], and is expected to be more accurate than pedigree-based inbreeding because of considering Mendelian sampling effect [4]. Genomic inbreeding coefficients can be estimated by SNP-based methods (e.g., excess of homozygosity), or alternatively, by runs of homozygosity (ROH) [5]. Genomic ROH-based inbreeding has the merit of providing an estimation regarding the age of inbreeding by distinguishing between identical by state (IBS) and identical by descent (IBD) segments [6, 7].

The approximate age of inbreeding can be estimated by the \(E\left({L}_{IBD-H}|gcA\right)=\frac{100}{2gcA}\) equation, where \(E\left({L}_{IBD-H}|gcA\right)\) is the expected length of an IBD haplotype given \(gcA\), which is the number of generations from a common ancestor [8]. Indeed, shorter ROHs display more ancient inbreeding, while longer ROHs are indicative of inbreeding events that have occurred in recent generations [6, 9]. Recent inbreeding is considered to have a more significant adverse effect compared with ancient inbreeding. This phenomenon is explained by genetic purging, in that selection operates against deleterious alleles, because most of them are recessive or partially recessive [10, 11]. Consequently, through selection, the frequency of favourable alleles increases in the population. Only a few experiments have studied the impact of recent and old inbreeding on livestock performance, and the findings were sometimes conflicting [12, 13].

Another relative merit of ROH-based inbreeding is that it enables researchers to identify genes and genomic regions underlying inbreeding depression. However, only a few researchers have used ROH to do so. Pryce et al. [14] used a statistical model to identify genes and genomic regions contributing to inbreeding depression of calving interval and milk yield in dairy cattle. Ferenčaković et al. [15] applied a similar model to find the associated regions with semen quality traits in beef cattle. These authors concluded that inbreeding depression could be reduced by avoiding the mating which would result in homozygous offspring for deleterious haplotypes causing inbreeding depression [14, 15].

Recent studies have shown that inbreeding influences many quantitative traits in livestock including fertility (e.g., Doekes et al. [16]). Fertility is a complex trait being influenced by both female and male components as well as the interaction between them [17]. However, male component of fertility has largely been ignored, probably because of the assumption that the artificial insemination (AI) centres monitor and standardize the quality of semen before it is distributed [18, 19]. However, this comes at a cost to the AI company and in extreme circumstances may mean that bulls are not marketable at all. In the dairy cattle industry, one bull is usually mated with thousands of cows. Therefore, using bulls with improved male fertility can immediately affect the overall herd fertility [18]. Non-return rates (NRR) are generally used as indicators of male fertility as data are abundantly available, whereas sperm quality data is usually the domain of AI companies. A moderate to high genetic and phenotypic correlation between semen quality traits and service sire NRR has been reported [20]. For many AI companies semen quality traits are used as a screening tool (i.e., this data is available on most sires), while only those bulls that pass quality checks end up with NRR data.

Based on this background information, the objectives of the current study were: (i) to estimate genomic inbreeding coefficients and inbreeding depression; (ii) to explore inbreeding depression resulted from old and recent inbreeding; and (iii) to identify genetic variants contributing to inbreeding depression, for semen quality traits in Holstein bulls.

Material and methods

Data description

Phenotypic records of three semen quality traits including EV, SC, and SM belonging to 1,508 Holstein bulls genotyped using the Illumina Bovine SNP50 BeadChip were used in this study. This dataset was published in a study by Yin et al. [21]. Phenotypic records were already collected from bulls born between 1996 and 2016. Briefly, the semen quality traits were obtained from 12 AI centres across China [21]. EV (in ml) was read directly from a graduated collection tube. SC was measured using a spectrophotometer as 108 spermatozoa per ml [22]. SM was estimated as the proportion of forward-moving sperm by microscopic examination by experienced technicians [21]. Phenotypic records were filtered to eliminate possible outliers from the analysis. After quality control, a total of 332,531 records ranged between 1 and 25 mL for EV, 330,270 records ranged between 1 and 30 × 108 mL for SC, and 330,199 records for SM ranged between 0.1 and 0.98, remained for further analyses. Across the three semen quality traits, the minimum and maximum number of records per bull were 6 and 1,415, respectively.

A total of 52,886 SNPs across 29 autosomes were available in the initial dataset. There were no missing genotypes in the dataset as they were already imputed [21]. Genotype data was converted to ARS-UCD1.2 reference genome [23] using liftOver tool ( and the default options. In this process, 231 SNPs were removed, which were duplicated or with unknown positions in ARS-UCD1.2 reference genome; therefore, 52,655 SNPs remained for the following analyses.

Genomic inbreeding coefficients

All SNPs, even those with very low minor allele frequency (MAF) were used to calculate inbreeding as suggested by Meyermans et al. [24]. Two estimates of genomic F were used.

Inbreeding coefficient based on excess of homozygosity (FSNP)

FSNP was defined as the excess of homozygosity [7], which is estimated as follows:


in which, \(\mathrm{s}\) is the total number of SNPs, \({\mathrm{OH}}_{\mathrm{i}}\) (observed homozygosity) in the \({\mathrm{animal}}_{\mathrm{i}}\) equals to \({\sum }_{\mathrm{j}=1}^{\mathrm{s}}{\mathrm{X}}_{\mathrm{ij}}\), in which \({\mathrm{X}}_{\mathrm{ij}}\) is 0 or 1 if the \({\mathrm{animal}}_{\mathrm{i}}\) for \({\mathrm{SNP}}_{\mathrm{j}}\) is heterozygous or homozygous, respectively. The expected homozygosity in the population (i.e., \(\mathrm{EH}\)) can be calculated as \({\sum }_{\mathrm{j}=1}^{\mathrm{s}}\left[1-2{\mathrm{m}}_{\mathrm{j}}(1-{\mathrm{m}}_{\mathrm{j}})\right]\), where \({\mathrm{m}}_{\mathrm{j}}\) shows the MAF for \({\mathrm{SNP}}_{\mathrm{j}}\). FSNP can take positive or negative values when \(\mathrm{EH}\) is smaller or larger than \({\mathrm{OH}}_{i}\), respectively.

Inbreeding coefficients based on runs of homozygosity (FROH)

This inbreeding coefficient represents the proportion of the genome that is covered with ROH segments, and is calculated as follows [5]:

$${F}_{{\mathrm{ROH}}_{\mathrm{i}}}=\frac{{\sum }_{\mathrm{k}=1}^{\mathrm{n}}{\mathrm{L}}_{{\mathrm{ROH}}_{\mathrm{ik}}}}{{\mathrm{L}}_{\mathrm{g}}}$$

where \(\mathrm{n}\) shows the total number of ROH identified for the \({\mathrm{animal}}_{\mathrm{i}}\), \({\mathrm{L}}_{{\mathrm{ROH}}_{\mathrm{ik}}}\) represents the length of the \({\mathrm{ROH}}_{\mathrm{k}}\) identified for the \({\mathrm{animal}}_{\mathrm{i}}\), and \({\mathrm{L}}_{\mathrm{g}}\) is the length of the autosomal genome covered by SNPs. The scanning approach implemented in the PLINK 1.9 [25] software was used. In this study we used similar parameters to those used by Doekes et al. [12]. To define ROH we allowed: (a) a minimum ROH length of 1 Mb; (b) a minimum number of 15 SNP per ROH; (c) an average SNP density of 1 SNP per 100 Kb; (d) a maximum of one heterozygous call within a ROH; and (e) a maximum gap of 500 kb between adjacent SNPs.

To study the contribution of length of ROH on inbreeding depression, we classified ROH into five groups: (i) 1 to 2 Mb, (ii) 2 to 4 Mb, (iii) 4 to 8 Mb, (iv) 8 to 16 Mb, and (v) > 16 Mb (abbreviated to FROH1−2, FROH2−4, FROH48, FROH8−16, and FROH>16 respectively). Inbreeding coefficients relevant to different length classes were calculated by summing up ROH segments of that class for each animal divided by \({\mathrm{L}}_{\mathrm{g}}\).

Data quality control and model fitting

The SNPs with MAF < 0.01 and Hardy–Weinberg equilibrium chi-square test p value < 1e−6 were discarded using PLINK 1.9 [25]. We utilised the same thresholds employed in the study by Yin et al. [21], as we analysed publicly available data provided by the authors. A total of 43,886 SNPs passed the quality control steps. These SNPs were used to construct the genomic relationship matrix (G) to fit the inbreeding depression models. We adapted the model of Yin et al. [21] and included inbreeding coefficients (FROH or FSNP) in the model to study the effect of inbreeding depression on semen traits:

$${\mathrm{y}}_{\mathrm{ijklm}}=\upmu +{\mathrm{year}\_\mathrm{season}}_{\mathrm{i}}+{\mathrm{insemin}\_\mathrm{center}}_{\mathrm{j}}+{\mathrm{interval}}_{\mathrm{k}}+{\mathrm{n}\_\mathrm{sample}}_{\mathrm{l}}+{\mathrm{b}}_{1}\times {\mathrm{age}}_{\mathrm{ijklm}}+{\mathrm{b}}_{2}\times {F}_{\mathrm{m}}+{\mathrm{animal}}_{\mathrm{m}}+{\mathrm{perm}}_{\mathrm{m}}+{\mathrm{e}}_{\mathrm{ijklm}}$$

where, \({\mathrm{y}}_{\mathrm{ijklm}}\) is the dependent variable (i.e., phenotypic records of EV, SC, and SM), \(\upmu\) is the overall mean, \({\mathrm{year}\_\mathrm{season}}_{\mathrm{i}}\) is the combined fixed effect of the \({\mathrm{i}}^{\mathrm{th}}\) year and season (60 levels), \({\mathrm{insemin}\_\mathrm{center}}_{\mathrm{j}}\) is fixed effect of the \({\mathrm{j}}^{\mathrm{th}}\) AI centre (12 levels), \({\mathrm{interval}}_{\mathrm{k}}\) is fixed effect of the interval between two subsequent semen collections (four levels), \({\mathrm{n}\_\mathrm{sample}}_{\mathrm{l}}\) is fixed effect of the number of sample collections on the respective collection day (three levels), \({\mathrm{animal}}_{\mathrm{m}}\) and \({\mathrm{perm}}_{\mathrm{m}}\) are, respectively, the random effects and permanent environment of the \({\mathrm{m}}^{\mathrm{th}}\) bull; and \({\mathrm{e}}_{\mathrm{ijklm}}\) is the random error term. In this model, \({\mathrm{b}}_{1}\) is the regression coefficient on \({\mathrm{age}}_{\mathrm{ijklm}}\) (i.e., age of the \({\mathrm{m}}^{\mathrm{th}}\) bull in months at the time of phenotype recording); and \({\mathrm{b}}_{2}\) is the regression coefficient on \({F}_{\mathrm{m}}\) (i.e., inbreeding coefficient of the \({\mathrm{m}}^{\mathrm{th}}\) bull). The bull effect was assumed to follow N(0,\(\mathbf{G}{\upsigma }_{\mathrm{u}}^{2}\)), where \(\mathbf{G}\) is the genomic relationship matrix, and \({\upsigma }_{\mathrm{u}}^{2}\) is additive genetic variance.

Model fitting of different ROH length

FROH1−2, FROH2−4, FROH48, FROH8−16, and FROH>16 were fitted simultaneously in the model, as follows:

$${\mathrm{y}}_{\mathrm{ijklm}}=\upmu +{\mathrm{year}\_\mathrm{season}}_{\mathrm{i}}+{\mathrm{insemin}\_\mathrm{center}}_{\mathrm{j}}+{\mathrm{interval}}_{\mathrm{k}}+{\mathrm{n}\_\mathrm{sample}}_{\mathrm{l}}+{\mathrm{b}}_{1}{\times \mathrm{age}}_{\mathrm{ijklm}}+\sum_{\mathrm{q}=1}^{\mathrm{n}=5}{(\mathrm{b}}_{2\mathrm{q}}\times {F}_{\mathrm{mq}})+{\mathrm{animal}}_{\mathrm{m}}+{\mathrm{perm}}_{\mathrm{m}}+{\mathrm{e}}_{\mathrm{ijklm}}$$

This model is similar to the Model (1) except \(\sum_{\mathrm{q}=1}^{\mathrm{n}=5}{(\mathrm{b}}_{2\mathrm{q}}\times {\mathrm{F}}_{\mathrm{mq}})\), in which \({\mathrm{b}}_{2\mathrm{q}}\) represents the regression coefficient of the \({\mathrm{q}}^{\mathrm{th}}\) inbreeding class and \({\mathrm{F}}_{\mathrm{mq}}\) is the inbreeding coefficient of the \({\mathrm{m}}^{\mathrm{th}}\) bull for that ROH length class.

Mapping genomic regions associated with inbreeding depression

SC and SM were used in the association analyses since we found significant inbreeding (i.e., FROH) depression for these traits in Model (1). To map genomic regions underlying inbreeding depression, each of the autosomal SNPs (n = 52,655) were fitted one at a time and sequentially in the model (3):

$${\mathrm{y}}_{\mathrm{ijklm}}=\upmu +{\mathrm{year}\_\mathrm{season}}_{\mathrm{i}}+{\mathrm{insemin}\_\mathrm{center}}_{\mathrm{j}}+{\mathrm{interval}}_{\mathrm{k}}+{\mathrm{n}\_\mathrm{sample}}_{\mathrm{l}}+{\mathrm{b}}_{1}{\times \mathrm{age}}_{\mathrm{ijklm}}+{\mathrm{b}}_{2}{ \times \mathrm{SNP}}_{\mathrm{m}}+{\mathrm{b}}_{3}{\times \mathrm{ROH}}_{\mathrm{m}}+{\mathrm{animal}}_{\mathrm{m}}+{\mathrm{perm}}_{\mathrm{m}}+{\mathrm{e}}_{\mathrm{ijklm}}$$

which is similar to the model (1), but two new covariates were fitted instead of F: (i) the regression coefficient \({\mathrm{b}}_{2}\) on \({\mathrm{SNP}}_{\mathrm{m}}\) which represents the SNP genotype for the \({\mathrm{m}}^{\mathrm{th}}\) bull and was included to adjust for the additive genetic effect of the presence of one more copy of the same allele. Thus, \({\mathrm{SNP}}_{\mathrm{m}}\) was coded as 0 for homozygous genotypes (AA) or 2 for alternative homozygous genotypes (aa) and coded as 1 for heterozygous genotypes (Aa); (ii) the regression coefficient \({\mathrm{b}}_{3}\) on \({\mathrm{ROH}}_{\mathrm{m}}\), which represents ROH state of the SNP on bull m, was coded as 1 when the \({\mathrm{SNP}}_{\mathrm{m}}\) is present within an ROH and coded as 0 otherwise. Thus, after correcting for the additive effect of the SNP, the effect of homozygosity was estimated at that position.

We used AIREMLF90 1.148 [26] to estimate the variance components and fit the above models. To investigate whether FSNP and FROH, in the model (1), FROH1−2, FROH2−4, FROH4−8, FROH8−16, and FROH>16 in the model (2), and \({\mathrm{ROH}}_{\mathrm{m}}\) in the model (3) had a statistically significant adverse effect on semen traits, we estimated the regression coefficients and corresponding standard errors (SE) for inbreeding measures. A t-test was then conducted in R [27] to test the significance of the regression coefficients.

Model (3) was run for each individual SNP at a time because the aim was to map genomic regions associated with inbreeding depression. To consider multiple testing problem, a method proposed by Bolormaa et al. [28] was used to find FDR for the p value significance threshold. Assuming \(\mathrm{FDR}=\frac{\mathrm{P}(1-\mathrm{N})}{\mathrm{N}(1-\mathrm{P})}\), where P is the p value threshold, and N is the proportion of significant SNPs. Different p value thresholds were tested to find the p value with the minimum FDR for SC and SM.

Results and discussion

Descriptive statistics and heritability

The descriptive statistics and estimated heritability and repeatability are shown in Table 1. Heritability estimates for EV, SM, and SC were 0.13 ± 0.02, 0.08 ± 0.02, and 0.11 ± 0.02, respectively. The heritability was estimated using a GBLUP (i.e., model 1) while excluding the inbreeding effect from the model. To estimate variance components, we used the priors which were reported by using a BLUP model in the study by Yin et al. [29]. Our heritability estimates were comparable to the pooled mean heritability estimates (± SE) obtained from across different studies, which were 0.2 (0.02), 0.05 (0.01) and 0.17 (0.03), for semen volume, sperm motility and concentration respectively [30].

Table 1 Descriptive statistics and the estimated heritability (h2) and repeatability (r) of the three semen quality traits

Inbreeding coefficients

Figure 1a shows the average ± SD of FSNP, FROH, FROH1−2, FROH2−4, FROH4−8, FROH8−16, and FROH>16, and Fig. 1b represents the distribution of the proportion of different lengths of ROH class. The mean inbreeding coefficient of FROH was 0.09 ± 0.02. In a previous study by Bjelland et al. [31] FROH of 0.04 ± 0.02 was reported for US Holstein cows, which was less than the amount we obtained for Holstein bulls of China. The average ± SD of FSNP -0.006 ± 0.03 in our study was less than FROH (Fig. 1a). This is because FSNP can also be negative when the observed homozygosity is lower than the expected homozygosity. However, we observed a strong correlation of 0.91 between FSNP and FROH (Fig. 2). Our finding was in line with previous works; in which, a correlation of 0.96 and 0.86 were reported between FROH and FSNP in pigs [32] and sheep [33], respectively. The high correlation between FROH and FSNP can probably be caused by the fact that a large amount of the total inbreeding captured by FSNP was related to SNPs in ROH state. Keller et al. [7] suggested that as Ne decreases, the similarity between molecular inbreeding measures (e.g., FROH and FSNP) increases. Therefore, the difference in the correlation reported by previous studies in pig and sheep breeds [32, 33] compared with our correlation (i.e., 0.96 and 0.86 versus 0.91) is more likely to be due to differences in Ne.

Fig. 1
figure 1

Box plot of the estimated inbreeding coefficients (a); and proportion of different ROH length (b)

Footnote: Seven FSNP values between -0.1 and -0.3 in Fig. 1a were not shown

Fig. 2
figure 2

Correlogram of Pearson correlations between different inbreeding measures. The genomic inbreeding coefficients are estimated based on FSNP; FROH; FROH 1-2 Mb; FROH 2-4 Mb; FROH 4-8 Mb; FROH 8-16 Mb; and FROH > 16 Mb

Recent inbreeding (i.e., FROH>16 Mb) had the highest correlation with FSNP (0.75) and FROH (0.81), while more distant inbreeding (i.e., FROH1-2 Mb) had the lowest correlation (~ 0.09) with the same measures (Fig. 2). The high correlation between recent and total inbreeding was in accordance with previous reports in Holstein cattle [12, 13] and pig [32]. According to Fig. 1b, ROH of short length (i.e., 2–4 Mb) had the highest frequency. However, it seems that only a small proportion of FROH is explained by FROH1-2 Mb and FROH2-4 Mb (Fig. 1a). Therefore, longer ROHs raised from more recent inbreeding covered a higher proportion of the genome compared with the short ROHs, and contributed more to the total inbreeding. This could explain the higher correlation between total and recent inbreeding to some extent.

Inbreeding depression of semen quality traits

In this study, we hypothesized that inbreeding would negatively affect semen quality traits. Inbreeding depression estimates of the semen traits are shown in Table 2. Neither FSNP nor FROH was significant for EV (p > 0.05). However, both FROH and FSNP had significant effects on SM (p < 0.01). A 1% increase in FROH, reduces SM by 0.28% of the mean population. Inbreeding depression for SM has been documented in livestock species including cattle [2, 34], sheep [33], as well as in horses [35, 36], endangered ungulates [37] and zebra finch [38]. Dorado et al. [34] studying the impact of inbreeding on sperm quality found features in sperm motility (i.e., erratic tracks, unexpected change in direction, non-progressive but highly active spermatozoa) in highly inbreed bulls that were associated with hyperactivate spermatozoa pattern [39, 40]. In inbred mice, Carey and Olds-Clarke [41] reported an increase in epididymal motility of spermatozoa (i.e., early sperm hyperactivation) which was accompanied by a reduction in fertility. A similar case was also reported in donkeys where males having a higher proportion of hyperactive spermatozoa were less fertile [42]. Therefore, it is plausible that transportation of premature hyperactive spermatozoa along the lower female reproductive tract and the spermatozoas’ access to the fertilization spot could be impaired in inbred animals, leading to a reduction in fertility.

Table 2 Regression coefficients and standard errors (SE) of different inbreeding measures on the three semen traits

Our result show that the effect of FROH on SC was significantly different from zero (p < 0.01), and a 1% increase in FROH decreased SC about 0.42% of the population mean (Table 2). In a dairy sheep breed, Antonios et al. [33] observed that the effect of FROH on SC was null. However, in Oldfield mice (Peromyscus polionotus), significant effect of inbreeding depression on testicular SC was reported [43]. To the extent of our knowledge, the relationship between shift in sperm count and fertility has not been studied in livestock species. In human, this relationship was largely depended on the median SC and onset and type of shift such that a dramatic decline from the high level slightly changed the fertility, whereas a minor decay from the low level remarkably deteriorated fertility [44]. A similar conclusion was also drawn in a captive population of mice [43]. Therefore, considering the adverse effect of inbreeding on sperm count or concentration, it seems to become a challenge in populations with endangered species or populations with small Ne, which are subject to some strong levels of inbreeding.

ROH length and inbreeding depression

We also hypothesized that recent inbreeding would have a more detrimental effect on SM and SC. All the ROH length classes were simultaneously fitted in the model to account for the correlations between classes [33]. No significant inbreeding depression was found for the old ROH classes of length 1–2 Mb and 2–4 Mb. These ROH classes represent a common ancestor back to about 50–25 and 25–12.5 generations ago, respectively. However, we found a significant adverse effect of recent inbreeding (i.e., long ROH) on the SM and SC. The effect of FROH>16 Mb was significant on SM (p < 0.01) and the effect of FROH8-16 Mb was significant on SC (p < 0.05). This level of inbreeding traces back to common ancestors approximately 3 and 6 to 3 generations ago, respectively. Our findings regarding the importance of more recent inbreeding on semen traits were in line with previous studies on semen and other traits. Antonios et al. [33] reported significant effects of recent inbreeding (i.e., ROH > 17 Mb) on SM, while old inbreeding (i.e., ROH 4–17 Mb) was not significant. Makanjuola et al. [13] studied inbreeding depression on production and fertility traits in Holstein dairy cows. They reported that recent inbreeding had a greater detrimental effect, while old inbreeding had a favourable effect on the studied traits. Our results could be explained by the purging effect of deleterious alleles in older generations. Genetic purging occurs when long-lasting selection for a given trait causes the harmful alleles to be eliminated [45]. Studying inbreeding depression in a Holstein–Friesian population, Mc Parland et al. [46] observed genetic purging effects for the production traits (i.e., milk, fat, and protein); but not for female fertility traits (i.e., age at first calving, calving interval, and survival). Doekes et al. [12] showed a purging effect of inbreeding depression for most of the milk production and female fertility traits by using pedigree-based inbreeding coefficients. Unfortunately, we were unable to confirm the purging effect since the pedigree data only went back one generation. In contrast to our findings regarding the importance of recent inbreeding, Doekes et al. [12] reported that both old and recent components of inbreeding can cause inbreeding depression across production and female fertility traits. The inconsistency between Doekes et al. [12] and our results may be due to differences in the genetic architecture of the traits studied. Moreover, there may be different representations of high-use sires, which are typically used in artificial insemination to produce large numbers of offspring, and therefore can have a disproportionate impact on the genetic makeup of a population.

Identification of genomic regions contributing to inbreeding depression

To link inbreeding depression of semen traits to genomic regions, a statistical test was performed for every autosomal SNP, considering the potential additive effect and ROH state of the SNP in the model. The distributions of -log10 p values for the association between SNP in ROH state and inbreeding depression for semen quality traits across chromosomes are shown in Fig. 3.

Fig. 3
figure 3

Manhattan plots of -log10 p values, illustrating inbreeding depression of sperm concentration (p < 0.00001; FDR < 0.02) (a); and sperm motility (p < 0.0001; FDR < 0.08) (b)

Identification of genomic regions contributing to inbreeding depression of female fertility and semen quality traits has already been reported in cattle [14, 15]. Yet, to our knowledge, this is the first study to apply the same approach to sperm quality traits in dairy cattle. We calculated the FDR for each trait at various p value thresholds. As shown in Fig. 3a, there were two signals positioned at 4.21–4.66 Mb and at 5.02–6.74 Mb on BTA 8. These inbreeding depression signals contained 52 SNP significantly associated (p < 0.00001; FDR < 2%) with the SC trait (Additional file 1 Table S1). There were 12 protein coding genes within 500 Kb upstream and downstream of these regions: GALNTL6, GALNT7, HMGB2, SAP30, SCRG1, HAND2, FBXO8, CEP44, HPGD, GLRA3, and ADAM29. Three candidate genes of GALNTL6, HMGB2, and ADAM29 have established connections with reproduction and/or male fertility. GALNTL6 is a member of membrane-bound polypeptide N-acetyl galactosaminyl transferase family that catalyses the first step in mucin-type O-glycosylation of peptides in the Golgi apparatus. The GALNTL6 gene was reported as a candidate gene for sperm characteristics in Holstein dairy cattle [47]. Parker Gaddis et al. [48] did a genome-wide association and gene network analyses of Holstein female fertility traits, the largest gene network in their study included 24 genes, with a regulatory role of GALNTL6 gene. The same authors mentioned that among those 24 genes, GALNTL6 was significantly associated with cow and heifer conception rate and daughter pregnancy rate. HMGB2 (High Mobility Group Box 2) is a member of the HMGB protein family, which includes the ubiquitous HMGB1 and the embryo-specific HMGB3. It has been reported in mice that adult males lacking HMGB2 (Hmgb2/) have reduced fertility, that correlates with Sertoli and germ cell degeneration in seminiferous tubules and immotile spermatozoa [49]. The protein encoded by ADAM29 is highly expressed in the testis and involved in human spermatogenesis [50]. Gene Ontology related to this gene include spermatogenesis GO:0,007,283 and male gonad development GO:0,008,584. In addition, three genes of ADAM29, GLRA3 and HPGD were reported to be associated with early pregnancy in Nelore heifers [51]. The functions of the remaining genes are currently unknown, and further investigation is needed to determine their role in male fertility.

We also identified inbreeding depression signals with significant adverse effects on SM trait (Fig. 3b; Additional file 2 Table S2). These regions were in clusters on BTA 3 (36.7–36.9), BTA 9 (42.3–43.2 Mb, 44.8–46.3 Mb, and 92.1–92.2 Mb), BTA 21 (31.6–31.7 Mb), and an obvious peak on BTA 28, with the most significant SNP (i.e., 28:45,649,746) passing our significance threshold criterion. Overall, 71 SNPs were significantly associated with ROH-based inbreeding depression for SM (p < 0.0001; FDR < 8%). There were 74 genes within the 500 Kb upstream and downstream of the significant SNPs such as PRMT6, SCAPER, EDC3 and LIN28B (Additional file 3 Table S3). PRMT6 (Protein Arginine Methyltransferase 6) on BTA 3 at the region 36.73–36.94 is highly expressed in testis and influences cell migration and apoptosis of germ cells ( [52]. Gene Ontology annotations related to PRMT6 include methyltransferase activity and protein methyltransferase activity, and the diseases associated with PRMT6 include male infertility in humans [52,53,54]. PRMT6 was previously reported to be associated with sperm concentration in Holstein bulls (P = 6.5E-6) [55]. In the present study, however, it was significantly associated with inbreeding depression of SM. We found a significant gene on BTA 21 at the region 31.6–31.7 Mb. The SCAPER (S Phase Cyclin A-Associated Protein in The Endoplasmic Reticulum) gene in this region is highly expressed in testis and involved in spermatogenesis (GO: 0,007,283) ( SCAPER, originally identified as a cell cycle regulator, was also suggested to be a ciliary protein. It has been reported to be associated with sterility in male and reduced fertility in female mice [56]. The same authors hypothesized that SCAPER is a crucial component in both the male and female reproductive systems [56]. In human patients homozygous for a mutation which disturbs SCAPER expression in spermatogonia are azoospermic due to early defects in spermatogenesis [56].

There were also other genes associated with inbreeding depression of SC in our study which could be associated with male fertility in bulls. EDEC3 is overexpressed in testis and may play a role in spermiogenesis and oogenesis ( LIN28B is another gene that is overexpressed in testis ( and is reported to be associated with globozoospermia, which is a rare but severe cause of male infertility in mammals [56, 57]. Moreover, CLDN20, AFGL1, TIAM2, RCN2, ODF3L1, and ARID3B are other genes that overexpressed in testis (, suggesting a potential role for these genes in relation to the SM trait. However, FDR for SM trait was calculated 7.5%, so these candidate genes need to be validated by using a different dataset to confirm their role in bull fertility.


We aimed to address: (i) which semen traits are more susceptible to inbreeding depression (ii) Whether the detected inbreeding depression is due to recent or ancient inbreeding (iii) Where the associated genes with these traits are in the genome. In summary, we found sperm concentration and sperm motility were unfavourably affected by inbreeding. The effect of recent inbreeding on semen traits was more harmful, probably because of genetic purging effect reducing its impact in older inbreeding. Our results provide novel insights into where genes causing inbreeding depression of semen traits are in the genome. Breeding companies may wish to consider avoiding homozygosity in these regions for potential artificial insemination sires. Validation of these regions and additional research into candidate genes located in these regions using larger sample sizes and denser markers may shed light on the molecular mechanisms and the causal variants underlying inbreeding depression of semen traits in dairy cattle.

Availability of data and materials

The datasets analysed during the current study are available in the figshare repository,



Ejaculate volume


Sperm concentration


Sperm motility


Single nucleotide polymorphism


Runs of homozygosity


Bos Taurus autosomes


False discovery rate




Identical by state


Identical by descent


Artificial insemination


Non-return rate


Minor allele frequency


  1. Charlesworth D, Willis JH. The genetics of inbreeding depression. Nat Rev Genet. 2009;10(11):783–96.

    Article  CAS  PubMed  Google Scholar 

  2. Maximini L, Fuerst-Waltl B, Gredler B, Baumung R. Inbreeding depression on semen quality in Austrian dual-purpose simmental bulls. Reprod Domest Anim. 2011;46(1):e102-104.

    Article  CAS  PubMed  Google Scholar 

  3. Ghoreishifar SM, Moradi-Shahrbabak H, Fallahi MH, Jalil Sarghale A, Moradi-Shahrbabak M, Abdollahi-Arpanahi R, Khansefid M. Genomic measures of inbreeding coefficients and genome-wide scan for runs of homozygosity islands in Iranian river buffalo, Bubalus bubalis. BMC Genet. 2020;21(1):16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Hill WG, Weir BS. Variation in actual relationship as a consequence of Mendelian sampling and linkage. Genetics research. 2011;93(1):47–64.

    Article  CAS  PubMed  Google Scholar 

  5. McQuillan R, Leutenegger A-L, Abdel-Rahman R, Franklin CS, Pericic M, Barac-Lauc L, Smolej-Narancic N, Janicijevic B, Polasek O, Tenesa A, et al. Runs of homozygosity in European populations. Am J Hum Genet. 2008;83(3):359–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Curik I, Ferenčaković M, Sölkner J. Inbreeding and runs of homozygosity: A possible solution to an old problem. Livest Sci. 2014;166:26–34.

    Article  Google Scholar 

  7. Keller MC, Visscher PM, Goddard ME. Quantification of inbreeding due to distant ancestors and its detection using dense single nucleotide polymorphism data. Genetics. 2011;189(1):237–49.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Browning SR, Browning BL. Identity by Descent Between Distant Relatives: Detection and Applications. Annu Rev Genet. 2012;46(1):617–33.

    Article  CAS  PubMed  Google Scholar 

  9. Speed D, Balding DJ. Relatedness in the post-genomic era: is it still useful? Nat Rev Genet. 2015;16(1):33–44.

    Article  CAS  PubMed  Google Scholar 

  10. Boakes E, Wang J. A simulation study on detecting purging of inbreeding depression in captive populations. Genetics Research. 2005;86(2):139–48.

    Article  Google Scholar 

  11. Hedrick PW, Garcia-Dorado A. Understanding Inbreeding Depression, Purging, and Genetic Rescue. Trends Ecol Evol. 2016;31(12):940–52.

    Article  PubMed  Google Scholar 

  12. Doekes HP, Veerkamp RF, Bijma P, de Jong G, Hiemstra SJ, Windig JJ. Inbreeding depression due to recent and ancient inbreeding in Dutch Holstein-Friesian dairy cattle. Genet Sel Evol. 2019;51(1):54.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Makanjuola BO, Maltecca C, Miglior F, Schenkel FS, Baes CF. Effect of recent and ancient inbreeding on production and fertility traits in Canadian Holsteins. BMC Genomics. 2020;21(1):605.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Pryce JE, Haile-Mariam M, Goddard ME, Hayes BJ. Identification of genomic regions associated with inbreeding depression in Holstein and Jersey dairy cattle. Genet Sel Evol. 2014;46(1):71.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Ferenčaković M, Sölkner J, Kapš M, Curik I. Genome-wide mapping and estimation of inbreeding depression of semen quality traits in a cattle population. J Dairy Sci. 2017;100(6):4721–30.

    Article  PubMed  Google Scholar 

  16. Doekes HP, Bijma P, Windig JJ. How Depressing Is Inbreeding? A Meta-Analysis of 30 Years of Research on the Effects of Inbreeding in Livestock. Genes. 2021;12(6):926.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Abdollahi-Arpanahi R, Morota G, Peñagaricano F. Predicting bull fertility using genomic data and biological information. J Dairy Sci. 2017;100(12):9656–66.

    Article  CAS  PubMed  Google Scholar 

  18. Haile-Mariam M, Pryce JE. Use of insemination data for joint evaluation of male and female fertility in predominantly seasonal-calving dairy herds. J Dairy Sci. 2021;104(11):11807–19.

    Article  CAS  PubMed  Google Scholar 

  19. Rezende FM, Haile-Mariam M, Pryce JE, Peñagaricano F. Across-country genomic prediction of bull fertility in Jersey dairy cattle. J Dairy Sci. 2020;103(12):11618–27.

    Article  CAS  PubMed  Google Scholar 

  20. Gebreyesus G, Lund MS, Kupisiewicz K, Su G. Genetic parameters of semen quality traits and genetic correlations with service sire nonreturn rate in Nordic Holstein bulls. J Dairy Sci. 2021;104(9):10010–9.

    Article  CAS  PubMed  Google Scholar 

  21. Yin H, Zhou C, Shi S, Fang L, Liu J, Sun D, Jiang L, Zhang S. Weighted Single-Step Genome-Wide Association Study of Semen Traits in Holstein Bulls of China. Front Genet. 2019;10:1053.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Qin C, Yin H, Zhang X, Sun D, Zhang Q, Liu J, Ding X, Zhang Y, Zhang S. Genome-wide association study for semen traits of the bulls in Chinese Holstein. Anim Genet. 2017;48(1):80–4.

    Article  CAS  PubMed  Google Scholar 

  23. Rosen BD, Bickhart DM, Schnabel RD, Koren S, Elsik CG, Tseng E, Rowan TN, Low WY, Zimin A, Couldrey C, et al. De novo assembly of the cattle reference genome with single-molecule sequencing. GigaScience. 2020;9(3):giaa021.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Meyermans R, Gorssen W, Buys N, Janssens S. How to study runs of homozygosity using PLINK? A guide for analyzing medium density SNP data in livestock and pet species. BMC Genomics. 2020;21(1):94.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ: Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 2015, 4(1).

  26. Misztal I, Lawlor TJ, Gengler N. Relationships among estimates of inbreeding depression, dominance and additive variance for linear traits in Holsteins. Genet Sel Evol. 1997;29(3):319.

    Article  PubMed Central  Google Scholar 

  27. Team RC: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. 2021.

  28. Bolormaa S, Hayes BJ, Savin K, Hawken R, Barendse W, Arthur PF, Herd RM, Goddard ME. Genome-wide association studies for feedlot and growth traits in cattle. J Anim Sci. 2011;89(6):1684–97.

    Article  CAS  PubMed  Google Scholar 

  29. Yin H, Fang L, Qin C, Zhang S. Estimation of the genetic parameters for semen traits in Chinese Holstein bulls. BMC Genet. 2019;20(1):51.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Berry DP, Wall E, Pryce JE. Genetics and genomics of reproductive performance in dairy and beef cattle. Animal. 2014;8:105–21.

    Article  PubMed  Google Scholar 

  31. Bjelland DW, Weigel KA, Vukasinovic N, Nkrumah JD. Evaluation of inbreeding depression in Holstein cattle using whole-genome SNP markers and alternative measures of genomic inbreeding. J Dairy Sci. 2013;96(7):4697–706.

    Article  CAS  PubMed  Google Scholar 

  32. Saura M, Fernández A, Varona L, Fernández AI, de Cara MÁR, Barragán C, Villanueva B. Detecting inbreeding depression for reproductive traits in Iberian pigs using genome-wide data. Genet Sel Evol. 2015;47(1):1.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Antonios S, Rodríguez-Ramilo ST, Aguilar I, Astruc JM, Legarra A, Vitezica ZG. Genomic and pedigree estimation of inbreeding depression for semen traits in the Basco-Béarnaise dairy sheep breed. J Dairy Sci. 2021;104(3):3221–30.

    Article  CAS  PubMed  Google Scholar 

  34. Dorado J, Cid RM, Molina A, Hidalgo M, Ariza J, Moreno-Millán M, Demyda-Peyrás S. Effect of inbreeding depression on bull sperm quality and field fertility. Reprod Fertil Dev. 2017;29(4):712.

    Article  PubMed  Google Scholar 

  35. van Eldik P, van der Waaij EH, Ducro B, Kooper AW, Stout TAE, Colenbrander B. Possible negative effects of inbreeding on semen quality in Shetland pony stallions. Theriogenology. 2006;65(6):1159–70.

    Article  PubMed  Google Scholar 

  36. Pirosanto Y, Molina A, Valera M, Dorado J, Terán E, Azcona F, Sebastian DP. 17 Increased inbreeding levels negatively affect sperm kinetics and motility in Purebred Spanish horses. Reprod Fertil Dev. 2021;33(2):116–116.

    Article  Google Scholar 

  37. Ruiz-Lopez MJ, Evenson DP, Espeso G, Gomendio M, Roldan ERS. High Levels of DNA Fragmentation in Spermatozoa Are Associated with Inbreeding and Poor Sperm Quality in Endangered Ungulates1. Biol Reprod. 2010;83(3):332–8.

    Article  CAS  PubMed  Google Scholar 

  38. Opatová P, Ihle M, Albrechtová J, Tomášek O, Kempenaers B, Forstmeier W, Albrecht T. Inbreeding depression of sperm traits in the zebra finch Taeniopygia guttata. Ecol Evol. 2016;6(1):295–304.

    Article  PubMed  Google Scholar 

  39. Cancel AM, Lobdell D, Mendola P, Perreault SD. Objective evaluation of hyperactivated motility in rat spermatozoa using computer-assisted sperm analysis*. Hum Reprod. 2000;15(6):1322–8.

    Article  CAS  PubMed  Google Scholar 

  40. Muiño R, Tamargo C, Hidalgo CO, Peña AI. Identification of sperm subpopulations with defined motility characteristics in ejaculates from Holstein bulls: Effects of cryopreservation and between-bull variation. Anim Reprod Sci. 2008;109(1):27–39.

    Article  PubMed  Google Scholar 

  41. Carey JE, Olds-Clarke P. Differences in sperm function in vitro but not in vivo between inbred and random-bred mice. Gamete Res. 1980;3(1):9–15.

    Article  Google Scholar 

  42. Dorado J, Acha D, Ortiz I, Gálvez MJ, Carrasco JJ, Díaz B, Gómez-Arrones V, Calero-Carretero R, Hidalgo M. Relationship between conventional semen characteristics, sperm motility patterns and fertility of Andalusian donkeys (Equus asinus). Anim Reprod Sci. 2013;143(1):64–71.

    Article  CAS  PubMed  Google Scholar 

  43. Margulis SW, Walsh A. The effects of inbreeding on testicular sperm concentration in Peromyscus polionotus. Reprod Fertil Dev. 2002;14(1):63–7.

    Article  PubMed  Google Scholar 

  44. Bonde JPE, Hjollund NHI, Kolstad HA, Abell A, Larsen SB. Environmental semen studies - is infertility increased by a decline in sperm count? Scand J Work Environ Health. 1999;1:12–6.

    Google Scholar 

  45. Leroy G. Inbreeding depression in livestock species: review and meta-analysis. Anim Genet. 2014;45(5):618–28.

    Article  CAS  PubMed  Google Scholar 

  46. Mc Parland S, Kearney F, Berry DP. Purging of inbreeding depression within the Irish Holstein-Friesian population. Genet Sel Evol. 2009;41:16.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Borowska A, Szwaczkowski T, Kamiński S, Hering DM, Kordan W, Lecewicz M. Identification of genome regions determining semen quality in Holstein-Friesian bulls using information theory. Anim Reprod Sci. 2018;192:206–15.

    Article  CAS  PubMed  Google Scholar 

  48. Parker Gaddis KL, Null DJ, Cole JB. Explorations in genome-wide association studies and network analyses with dairy cattle fertility traits. J Dairy Sci. 2016;99(8):6420–35.

    Article  CAS  PubMed  Google Scholar 

  49. Ronfani L, Ferraguti M, Croci L, Ovitt CE, Scholer HR, Consalez GG, Bianchi ME. Reduced fertility and spermatogenesis defects in mice lacking chromosomal protein Hmgb2. Development. 2001;128(8):1265–73.

    Article  CAS  PubMed  Google Scholar 

  50. Xu R, Cai J, Xu T, Zhou W, Ying B, Deng K, Zhao S, Li C. Molecular Cloning and Mapping of a Novel ADAM Gene (ADAM29) to Human Chromosome 4. Genomics. 1999;62(3):537–9.

    Article  CAS  PubMed  Google Scholar 

  51. Júnior GAO, Perez BC, Cole JB, Santana MHA, Silveira J, Mazzoni G, Ventura RV, Júnior MLS, Kadarmideen HN, Garrick DJ, et al. Genomic study and Medical Subject Headings enrichment analysis of early pregnancy rate and antral follicle numbers in Nelore heifers. J Anim Sci. 2017;95(11):4796–812.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Luo M, Li Y, Guo H, Lin S, Chen J, Ma Q, Gu Y, Jiang Z, Gui Y. Protein Arginine Methyltransferase 6 Involved in Germ Cell Viability during Spermatogenesis and Down-Regulated by the Androgen Receptor. Int J Mol Sci. 2015;16(12):29467–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Hu Z, Xia Y, Guo X, Dai J, Li H, Hu H, Jiang Y, Lu F, Wu Y, Yang X, et al. A genome-wide association study in Chinese men identifies three risk loci for non-obstructive azoospermia. Nat Genet. 2011;44(2):183–6.

    Article  PubMed  Google Scholar 

  54. Zou S, Li Z, Wang Y, Chen T, Song P, Chen J, He X, Xu P, Liang M, Luo K, et al. Association study between polymorphisms of PRMT6, PEX10, SOX5, and nonobstructive azoospermia in the Han Chinese population. Biol Reprod. 2014;90(5):96.

    Article  PubMed  Google Scholar 

  55. Hering DM, Olenski K, Kaminski S. Genome-wide association study for poor sperm motility in Holstein-Friesian bulls. Anim Reprod Sci. 2014;146(3–4):89–97.

    Article  CAS  PubMed  Google Scholar 

  56. Tatour Y, Bar-Joseph H, Shalgi R, Ben-Yosef T. Male sterility and reduced female fertility in SCAPER-deficient mice. Hum Mol Genet. 2020;29(13):2240–9.

    Article  CAS  PubMed  Google Scholar 

  57. Wang X-X, Sun B-F, Jiao J, Chong Z-C, Chen Y-S, Wang X-L, Zhao Y, Zhou Y-M, Li D. Genome-wide 5-hydroxymethylcytosine modification pattern is a novel epigenetic feature of globozoospermia. Oncotarget. 2015;6(9):6535–43.

    Article  PubMed  PubMed Central  Google Scholar 

Download references


Not applicable.


MG and MK received financial support from DairyBio.

Author information

Authors and Affiliations



MG conceived and designed the experiments. SV and MG performed the analyses, interpreted the results, and prepared the manuscript. SV conducted visualization. SS, MK and JP participated in the interpretation of results and revision of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Mohammad Ghoreishifar.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

Authors declare no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1:

Table S1. Genome-wide association with inbreeding depression of sperm concentration.

Additional file 2:

Table S2. Genome-wide association with inbreeding depression of sperm motility.

Additional file 3:

Table S3. Genes associated with inbreeding depression of sperm motility trait.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ghoreishifar, M., Vahedi, S.M., Salek Ardestani, S. et al. Genome-wide assessment and mapping of inbreeding depression identifies candidate genes associated with semen traits in Holstein bulls. BMC Genomics 24, 230 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: