Imputation accuracy to whole-genome sequence in Nellore cattle.

BACKGROUND: A cost-effective strategy to explore the complete DNA sequence in animals for genetic evaluation purposes is to sequence key ancestors of a population, followed by imputation mechanisms to infer marker genotypes that were not originally reported in a target population of animals genotyped with single nucleotide polymorphism (SNP) panels. The feasibility of this process relies on the accuracy of the genotype imputation in that population, particularly for potential causal mutations which may be at low frequency and either within genes or regulatory regions. The objective of the present study was to investigate the imputation accuracy to the sequence level in a Nellore beef cattle population, including that for variants in annotation classes which are more likely to be functional. METHODS: Information of 151 key sequenced Nellore sires were used to assess the imputation accuracy from bovine HD BeadChip SNP (~ 777 k) to whole-genome sequence. The choice of the sires aimed at optimizing the imputation accuracy of a genotypic database, comprised of about 10,000 genotyped Nellore animals. Genotype imputation was performed using two computational approaches: FImpute3 and Minimac4 (after using Eagle for phasing). The accuracy of the imputation was evaluated using a fivefold cross-validation scheme and measured by the squared correlation between observed and imputed genotypes, calculated by individual and by SNP. SNPs were classified into a range of annotations, and the accuracy of imputation within each annotation classification was also evaluated. RESULTS: High average imputation accuracies per animal were achieved using both FImpute3 (0.94) and Minimac4 (0.95). On average, common variants (minor allele frequency (MAF) > 0.03) were more accurately imputed by Minimac4 and low-frequency variants (MAF ≤ 0.03) were more accurately imputed by FImpute3. The inherent Minimac4 Rsq imputation quality statistic appears to be a good indicator of the empirical Minimac4 imputation accuracy. Both software provided high average SNP-wise imputation accuracy for all classes of biological annotations. CONCLUSIONS: Our results indicate that imputation to whole-genome sequence is feasible in Nellore beef cattle since high imputation accuracies per individual are expected. SNP-wise imputation accuracy is software-dependent, especially for rare variants. The accuracy of imputation appears to be relatively independent of annotation classification.

Unraveling genetic sensitivity of beef cattle to environmental variation under tropical conditions.

BACKGROUND: Selection of cattle that are less sensitive to environmental variation in unfavorable environments and more adapted to harsh conditions is of primary importance for tropical beef cattle production systems. Understanding the genetic background of sensitivity to environmental variation is necessary for developing strategies and tools to increase efficiency and sustainability of beef production. We evaluated the degree of sensitivity of beef cattle performance to environmental variation, at the animal and molecular marker levels (412 K single nucleotide polymorphisms), by fitting and comparing the results of different reaction norm models (RNM), using a comprehensive dataset of Nellore cattle raised under diverse environmental conditions. RESULTS: Heteroscedastic RNM (with different residual variances for environmental level) provided better fit than homoscedastic RNM. In addition, spline and quadratic RNM outperformed linear RNM, which suggests the existence of a nonlinear genetic component affecting the performance of Nellore cattle. This nonlinearity indicates that within-animal sensitivity depends on the environmental gradient (EG) level and that animals may present different patterns of sensitivity according to the range of environmental variations. The spline RNM showed that sensitivity to environmental variation from harsh to average EG is lowly correlated with sensitivity from average to good EG, at both the animal and molecular marker levels. Although the genomic regions that affect sensitivity in harsher environments were not the same as those associated with less challenging environments, the candidate genes within those regions participate in common biological processes such as those related to inflammatory and immune response. Some plausible candidate genes were identified. CONCLUSIONS: Sensitivity of tropical beef cattle to environmental variation is not continuous along the environmental gradient, which implies that animals that are less sensitive to harsher conditions are not necessarily less responsive to variations in better environmental conditions, and vice versa. The same pattern was observed at the molecular marker level, i.e. genomic regions and, consequently, candidate genes associated with sensitivity to harsh conditions were not the same as those associated with sensitivity to less challenging conditions.

Genomic predictions combining SNP markers and copy number variations in Nellore cattle.

BACKGROUND: Due to the advancement in high throughput technology, single nucleotide polymorphism (SNP) is routinely being incorporated along with phenotypic information into genetic evaluation. However, this approach often cannot achieve high accuracy for some complex traits. It is possible that SNP markers are not sufficient to predict these traits due to the missing heritability caused by other genetic variations such as microsatellite and copy number variation (CNV), which have been shown to affect disease and complex traits in humans and other species. RESULTS: In this study, CNVs were included in a SNP based genomic selection framework. A Nellore cattle dataset consisting of 2230 animals genotyped on BovineHD SNP array was used, and 9 weight and carcass traits were analyzed. A total of six models were implemented and compared based on their prediction accuracy. For comparison, three models including only SNPs were implemented: 1) BayesA model, 2) Bayesian mixture model (BayesB), and 3) a GBLUP model without polygenic effects. The other three models incorporating both SNP and CNV included 4) a Bayesian model similar to BayesA (BayesA+CNV), 5) a Bayesian mixture model (BayesB+CNV), and 6) GBLUP with CNVs modeled as a covariable (GBLUP+CNV). Prediction accuracies were assessed based on Pearson's correlation between de-regressed EBVs (dEBVs) and direct genomic values (DGVs) in the validation dataset. For BayesA, BayesB and GBLUP, accuracy ranged from 0.12 to 0.62 across the nine traits. A minimal increase in prediction accuracy for some traits was noticed when including CNVs in the model (BayesA+CNV, BayesB+CNV, GBLUP+CNV). CONCLUSIONS: This study presents the first genomic prediction study integrating CNVs and SNPs in livestock. Combining CNV and SNP marker information proved to be beneficial for genomic prediction of some traits in Nellore cattle.

Assessing the value of phenotypic information from non-genotyped animals for QTL mapping of complex traits in real and simulated populations.

BACKGROUND: QTL mapping through genome-wide association studies (GWAS) is challenging, especially in the case of low heritability complex traits and when few animals possess genotypic and phenotypic information. When most of the phenotypic information is from non-genotyped animals, GWAS can be performed using the weighted single-step GBLUP (WssGBLUP) method, which permits to combine all available information, even that of non-genotyped animals. However, it is not clear to what extent phenotypic information from non-genotyped animals increases the power of QTL detection, and whether factors such as the extent of linkage disequilibrium (LD) in the population and weighting SNPs in WssGBLUP affect the importance of using information from non-genotyped animals in GWAS. These questions were investigated in this study using real and simulated data. RESULTS: Analysis of real data showed that the use of phenotypes of non-genotyped animals affected SNP effect estimates and, consequently, QTL mapping. Despite some coincidence, the most important genomic regions identified by the analyses, either using or ignoring phenotypes of non-genotyped animals, were not the same. The simulation results indicated that the inclusion of all available phenotypic information, even that of non-genotyped animals, tends to improve QTL detection for low heritability complex traits. For populations with low levels of LD, this trend of improvement was less pronounced. Stronger shrinkage on SNPs explaining lower variance was not necessarily associated with better QTL mapping. CONCLUSIONS: The use of phenotypic information from non-genotyped animals in GWAS may improve the ability to detect QTL for low heritability complex traits, especially in populations in which the level of LD is high.

Low levels of taurine introgression in the current Brazilian Nelore and Gir indicine cattle populations.

BACKGROUND: Nelore and Gir are the two most important indicine cattle breeds for production of beef and milk in Brazil. Historical records state that these breeds were introduced in Brazil from the Indian subcontinent, crossed to local taurine cattle in order to quickly increase the population size, and then backcrossed to the original breeds to recover indicine adaptive and productive traits. Previous investigations based on sparse DNA markers detected taurine admixture in these breeds. High-density genome-wide analyses can provide high-resolution information on the genetic composition of current Nelore and Gir populations, estimate more precisely the levels and nature of taurine introgression, and shed light on their history and the strategies that were used to expand these breeds. RESULTS: We used the high-density Illumina BovineHD BeadChip with more than 777 K single nucleotide polymorphisms (SNPs) that were reduced to 697 115 after quality control filtering to investigate the structure of Nelore and Gir populations and seven other worldwide populations for comparison. Multidimensional scaling and model-based ancestry estimation clearly separated the indicine, European taurine and African taurine ancestries. The average level of taurine introgression in the autosomal genome of Nelore and Gir breeds was less than 1% but was 9% for the Brahman breed. Analyses based on the mitochondrial SNPs present in the Illumina BovineHD BeadChip did not clearly differentiate taurine and indicine haplotype groupings. CONCLUSIONS: The low level of taurine ancestry observed for both Nelore and Gir breeds confirms the historical records of crossbreeding and supports a strong directional selection against taurine haplotypes via backcrossing. Random sampling in production herds across the country and subsequent genotyping would be useful for a more complete view of the admixture levels in the commercial Nelore and Gir populations.

Genome-wide association analysis of feed intake and residual feed intake in Nellore cattle.

BACKGROUND: Feed intake plays an important economic role in beef cattle, and is related with feed efficiency, weight gain and carcass traits. However, the phenotypes collected for dry matter intake and feed efficiency are scarce when compared with other measures such as weight gain and carcass traits. The use of genomic information can improve the power of inference of studies on these measures, identifying genomic regions that affect these phenotypes. This work performed the genome-wide association study (GWAS) for dry matter intake (DMI) and residual feed intake (RFI) of 720 Nellore cattle (Bos taurus indicus). RESULTS: In general, no genomic region extremely associated with both phenotypic traits was observed, as expected for the variables that have their regulation controlled by many genes. Three SNPs surpassed the threshold for the Bonferroni multiple test for DMI and two SNPs for RFI. These markers are located on chromosomes 4, 8, 14 and 21 in regions near genes regulating appetite and ion transport and close to important QTL as previously reported to RFI and DMI, thus corroborating the literature that points these two processes as important in the physiological regulation of intake and feed efficiency. CONCLUSIONS: This study showed the first GWAS of DMI to identify genomic regions associated with feed intake and efficiency in Nellore cattle. Some genes and QTLs previously described for DMI and RFI, in other subspecies (Bos taurus taurus), that influences these phenotypes are confirmed in this study.

Accuracy of genotype imputation in Nelore cattle.

BACKGROUND: Genotype imputation from low-density (LD) to high-density single nucleotide polymorphism (SNP) chips is an important step before applying genomic selection, since denser chips tend to provide more reliable genomic predictions. Imputation methods rely partially on linkage disequilibrium between markers to infer unobserved genotypes. Bos indicus cattle (e.g. Nelore breed) are characterized, in general, by lower levels of linkage disequilibrium between genetic markers at short distances, compared to taurine breeds. Thus, it is important to evaluate the accuracy of imputation to better define which imputation method and chip are most appropriate for genomic applications in indicine breeds. METHODS: Accuracy of genotype imputation in Nelore cattle was evaluated using different LD chips, imputation software and sets of animals. Twelve commercial and customized LD chips with densities ranging from 7 K to 75 K were tested. Customized LD chips were virtually designed taking into account minor allele frequency, linkage disequilibrium and distance between markers. Software programs FImpute and BEAGLE were applied to impute genotypes. From 995 bulls and 1247 cows that were genotyped with the Illumina® BovineHD chip (HD), 793 sires composed the reference set, and the remaining 202 younger sires and all the cows composed two separate validation sets for which genotypes were masked except for the SNPs of the LD chip that were to be tested. RESULTS: Imputation accuracy increased with the SNP density of the LD chip. However, the gain in accuracy with LD chips with more than 15 K SNPs was relatively small because accuracy was already high at this density. Commercial and customized LD chips with equivalent densities presented similar results. FImpute outperformed BEAGLE for all LD chips and validation sets. Regardless of the imputation software used, accuracy tended to increase as the relatedness between imputed and reference animals increased, especially for the 7 K chip. CONCLUSIONS: If the Illumina® BovineHD is considered as the target chip for genomic applications in the Nelore breed, cost-effectiveness can be improved by genotyping part of the animals with a chip containing around 15 K useful SNPs and imputing their high-density missing genotypes with FImpute.

Accuracy of genomic predictions in Bos indicus (Nellore) cattle.

BACKGROUND: Nellore cattle play an important role in beef production in tropical systems and there is great interest in determining if genomic selection can contribute to accelerate genetic improvement of production and fertility in this breed. We present the first results of the implementation of genomic prediction in a Bos indicus (Nellore) population. METHODS: Influential bulls were genotyped with the Illumina Bovine HD chip in order to assess genomic predictive ability for weight and carcass traits, gestation length, scrotal circumference and two selection indices. 685 samples and 320 238 single nucleotide polymorphisms (SNPs) were used in the analyses. A forward-prediction scheme was adopted to predict the genomic breeding values (DGV). In the training step, the estimated breeding values (EBV) of bulls were deregressed (dEBV) and used as pseudo-phenotypes to estimate marker effects using four methods: genomic BLUP with or without a residual polygenic effect (GBLUP20 and GBLUP0, respectively), a mixture model (Bayes C) and Bayesian LASSO (BLASSO). Empirical accuracies of the resulting genomic predictions were assessed based on the correlation between DGV and dEBV for the testing group. RESULTS: Accuracies of genomic predictions ranged from 0.17 (navel at weaning) to 0.74 (finishing precocity). Across traits, Bayesian regression models (Bayes C and BLASSO) were more accurate than GBLUP. The average empirical accuracies were 0.39 (GBLUP0), 0.40 (GBLUP20) and 0.44 (Bayes C and BLASSO). Bayes C and BLASSO tended to produce deflated predictions (i.e. slope of the regression of dEBV on DGV greater than 1). Further analyses suggested that higher-than-expected accuracies were observed for traits for which EBV means differed significantly between two breeding subgroups that were identified in a principal component analysis based on genomic relationships. CONCLUSIONS: Bayesian regression models are of interest for future applications of genomic selection in this population, but further improvements are needed to reduce deflation of their predictions. Recurrent updates of the training population would be required to enable accurate prediction of the genetic merit of young animals. The technical feasibility of applying genomic prediction in a Bos indicus (Nellore) population was demonstrated. Further research is needed to permit cost-effective selection decisions using genomic information.

Genome-wide association study for birth weight in Nellore cattle points to previously described orthologous genes affecting human and bovine height.

BACKGROUND: Birth weight (BW) is an economically important trait in beef cattle, and is associated with growth- and stature-related traits and calving difficulty. One region of the cattle genome, located on Bos primigenius taurus chromosome 14 (BTA14), has been previously shown to be associated with stature by multiple independent studies, and contains orthologous genes affecting human height. A genome-wide association study (GWAS) for BW in Brazilian Nellore cattle (Bos primigenius indicus) was performed using estimated breeding values (EBVs) of 654 progeny-tested bulls genotyped for over 777,000 single nucleotide polymorphisms (SNPs). RESULTS: The most significant SNP (rs133012258, PGC = 1.34 × 10-9), located at BTA14:25376827, explained 4.62% of the variance in BW EBVs. The surrounding 1 Mb region presented high identity with human, pig and mouse autosomes 8, 4 and 4, respectively, and contains the orthologous height genes PLAG1, CHCHD7, MOS, RPS20, LYN, RDHE2 (SDR16C5) and PENK. The region also overlapped 28 quantitative trait loci (QTLs) previously reported in literature by linkage mapping studies in cattle, including QTLs for birth weight, mature height, carcass weight, stature, pre-weaning average daily gain, calving ease, and gestation length. CONCLUSIONS: This study presents the first GWAS applying a high-density SNP panel to identify putative chromosome regions affecting birth weight in Nellore cattle. These results suggest that the QTLs on BTA14 associated with body size in taurine cattle (Bos primigenius taurus) also affect birth weight and size in zebu cattle (Bos primigenius indicus).

A comparison of statistical methods for genomic selection in a mice population.

BACKGROUND: The availability of high-density panels of SNP markers has opened new perspectives for marker-assisted selection strategies, such that genotypes for these markers are used to predict the genetic merit of selection candidates. Because the number of markers is often much larger than the number of phenotypes, marker effect estimation is not a trivial task. The objective of this research was to compare the predictive performance of ten different statistical methods employed in genomic selection, by analyzing data from a heterogeneous stock mice population. RESULTS: For the five traits analyzed (W6W: weight at six weeks, WGS: growth slope, BL: body length, %CD8+: percentage of CD8+ cells, CD4+/ CD8+: ratio between CD4+ and CD8+ cells), within-family predictions were more accurate than across-family predictions, although this superiority in accuracy varied markedly across traits. For within-family prediction, two kernel methods, Reproducing Kernel Hilbert Spaces Regression (RKHS) and Support Vector Regression (SVR), were the most accurate for W6W, while a polygenic model also had comparable performance. A form of ridge regression assuming that all markers contribute to the additive variance (RR_GBLUP) figured among the most accurate for WGS and BL, while two variable selection methods ( LASSO and Random Forest, RF) had the greatest predictive abilities for %CD8+ and CD4+/ CD8+. RF, RKHS, SVR and RR_GBLUP outperformed the remainder methods in terms of bias and inflation of predictions. CONCLUSIONS: Methods with large conceptual differences reached very similar predictive abilities and a clear re-ranking of methods was observed in function of the trait analyzed. Variable selection methods were more accurate than the remainder in the case of %CD8+ and CD4+/CD8+ and these traits are likely to be influenced by a smaller number of QTL than the remainder. Judged by their overall performance across traits and computational requirements, RR_GBLUP, RKHS and SVR are particularly appealing for application in genomic selection.

Genetic and environmental heterogeneity of residual variance of weight traits in Nellore beef cattle.

BACKGROUND: Many studies have provided evidence of the existence of genetic heterogeneity of environmental variance, suggesting that it could be exploited to improve robustness and uniformity of livestock by selection. However, little is known about the perspectives of such a selection strategy in beef cattle. METHODS: A two-step approach was applied to study the genetic heterogeneity of residual variance of weight gain from birth to weaning and long-yearling weight in a Nellore beef cattle population. First, an animal model was fitted to the data and second, the influence of additive and environmental effects on the residual variance of these traits was investigated with different models, in which the log squared estimated residuals for each phenotypic record were analyzed using the restricted maximum likelihood method. Monte Carlo simulation was performed to assess the reliability of variance component estimates from the second step and the accuracy of estimated breeding values for residual variation. RESULTS: The results suggest that both genetic and environmental factors have an effect on the residual variance of weight gain from birth to weaning and long-yearling in Nellore beef cattle and that uniformity of these traits could be improved by selecting for lower residual variance, when considering a large amount of information to predict genetic merit for this criterion. Simulations suggested that using the two-step approach would lead to biased estimates of variance components, such that more adequate methods are needed to study the genetic heterogeneity of residual variance in beef cattle.

Genetic and genomic analyses of testicular hypoplasia in Nellore cattle.

Reproductive performance is a key indicator of the long-term sustainability of any livestock production system. Testicular hypoplasia (TH) is a morphological and functional reproductive disorder that affects bulls around the world and consequently causes major economic losses due to reduced fertility rates. Despite the improvements in management practices to enhance performance of affected animals, the use of hypoplastic animals for reproduction might contribute to expand the prevalence of this disorder. The aim of this study was to identify genomic regions that are associated with TH in Nellore cattle by performing a genome-wide association study (GWAS) and functional analyses. Phenotypic and pedigree data from 47,563 animals and genotypes (500,689 Single Nucleotide Polymorphism, SNPs) from 265 sires were used in this study. TH was evaluated as a binary trait measured at 18 months of age. The estimated breeding values (EBVs) were calculated by fitting a single-trait threshold animal model using a Bayesian approach. The SNP effects were estimated using the Bayes C method and de-regressed EBVs for TH as the response variable (pseudo-phenotype). The top-15 ranking windows (5-adjacent SNPs) that explained the highest proportion of variance were identified for further functional and biological network analyses. The posterior mean (95% highest posterior density) of the heritability for TH was 0.16 (0.08; 0.23). The most important genomic windows were located on BTA1, BTA3, BTA4, BTA5, BTA9, BTA22, BTA23, and BTA25. These windows explained together 22.69% of the total additive genetic variance for TH. Strong candidate genes associated with metabolism and synthesis of steroids, cell survival, spermatogenesis process and sperm motility were identified, which might play an important role in the expression of TH. Our findings contribute to a better biological understanding of TH and future characterization of causal variants might enable improved genomic prediction of this trait in beef cattle.

Genome-wide association study and functional analysis of feet and leg conformation traits in Nellore cattle.

Feet and leg conformation is evaluated as a subset of conformational structure traits in dairy and beef cattle and is related to the feet and leg quality that can compromise the animals' productive performance and longevity. The aim of this study was to perform a genome-wide association study (GWAS) of two traits related to feet and leg conformation in Nellore cattle to identify chromosomal regions related to the expression of these traits. Phenotypic and pedigree data from 104,725 animals and genotypes from 1,435 animals and 407,730 SNPs were used. Feet and leg structure was evaluated as a binary trait (FL1) to identify yearling animals with feet and leg problems or as categorical score (FL2) to assess the overall quality of their feet and leg. The top ten 1-Mb windows that explained the largest proportion of the total genetic variance were identified and functional enrichment analyses were performed. The 10 windows with large effects obtained for FL1 are located on chromosomes 1, 2, 6, 7, 8, 10, and 14, and together explained 8.96% of the additive genetic variance. For FL2, these windows are located on chromosomes 1, 7, 10, 11, 18, 20, 22, 28, and 29, explaining 8.98% of the additive genetic variance. Several candidate genes were identified, including DLX2 which is associated with osteogenic differentiation, IL-1ß and IL-1A associated with some properties of articular cartilage, PiT1 which plays an important role in bone physiology, and CTSL associated with rheumatoid arthritis. The results presented here should contribute to a better understanding of the genetic and physiologic mechanisms regulating both traits, and identifies candidate genes for future investigation of causal mutations.

Pleiotropic Genes Affecting Carcass Traits in Bos indicus (Nellore) Cattle Are Modulators of Growth.

Two complementary methods, namely Multi-Trait Meta-Analysis and Versatile Gene-Based Test for Genome-wide Association Studies (VEGAS), were used to identify putative pleiotropic genes affecting carcass traits in Bos indicus (Nellore) cattle. The genotypic data comprised over 777,000 single-nucleotide polymorphism markers scored in 995 bulls, and the phenotypic data included deregressed breeding values (dEBV) for weight measurements at birth, weaning and yearling, as well visual scores taken at weaning and yearling for carcass finishing precocity, conformation and muscling. Both analyses pointed to the pleomorphic adenoma gene 1 (PLAG1) as a major pleiotropic gene. VEGAS analysis revealed 224 additional candidates. From these, 57 participated, together with PLAG1, in a network involved in the modulation of the function and expression of IGF1 (insulin like growth factor 1), IGF2 (insulin like growth factor 2), GH1 (growth hormone 1), IGF1R (insulin like growth factor 1 receptor) and GHR (growth hormone receptor), suggesting that those pleiotropic genes operate as satellite regulators of the growth pathway.

Assessment of autozygosity in Nellore cows (Bos indicus) through high-density SNP genotypes.

The use of relatively low numbers of sires in cattle breeding programs, particularly on those for carcass and weight traits in Nellore beef cattle (Bos indicus) in Brazil, has always raised concerns about inbreeding, which affects conservation of genetic resources and sustainability of this breed. Here, we investigated the distribution of autozygosity levels based on runs of homozygosity (ROH) in a sample of 1,278 Nellore cows, genotyped for over 777,000 SNPs. We found ROH segments larger than 10 Mb in over 70% of the samples, representing signatures most likely related to the recent massive use of few sires. However, the average genome coverage by ROH (>1 Mb) was lower than previously reported for other cattle breeds (4.58%). In spite of 99.98% of the SNPs being included within a ROH in at least one individual, only 19.37% of the markers were encompassed by common ROH, suggesting that the ongoing selection for weight, carcass and reproductive traits in this population is too recent to have produced selection signatures in the form of ROH. Three short-range highly prevalent ROH autosomal hotspots (occurring in over 50% of the samples) were observed, indicating candidate regions most likely under selection since before the foundation of Brazilian Nellore cattle. The putative signatures of selection on chromosomes 4, 7, and 12 may be involved in resistance to infectious diseases and fertility, and should be subject of future investigation.

Genome-wide mapping of loci explaining variance in scrotal circumference in Nellore cattle.

The reproductive performance of bulls has a high impact on the beef cattle industry. Scrotal circumference (SC) is the most recorded reproductive trait in beef herds, and is used as a major selection criterion to improve precocity and fertility. The characterization of genomic regions affecting SC can contribute to the identification of diagnostic markers for reproductive performance and uncover molecular mechanisms underlying complex aspects of bovine reproductive biology. In this paper, we report a genome-wide scan for chromosome segments explaining differences in SC, using data of 861 Nellore bulls (Bos indicus) genotyped for over 777,000 single nucleotide polymorphisms. Loci that excel from the genome background were identified on chromosomes 4, 6, 7, 10, 14, 18 and 21. The majority of these regions were previously found to be associated with reproductive and body size traits in cattle. The signal on chromosome 14 replicates the pleiotropic quantitative trait locus encompassing PLAG1 that affects male fertility in cattle and stature in several species. Based on intensive literature mining, SP4, MAGEL2, SH3RF2, PDE5A and SNAI2 are proposed as novel candidate genes for SC, as they affect growth and testicular size in other animal models. These findings contribute to linking reproductive phenotypes to gene functions, and may offer new insights on the molecular biology of male fertility.