Genomic variants associated with oocyte and embryo production in dairy Gir cattle

Genomic variants associated with oocyte and embryo production in dairy Gir cattle | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 25 August 2025 V1 Latest version Share on Genomic variants associated with oocyte and embryo production in dairy Gir cattle Authors : Renata de Fatima Bretanha Rocha 0000-0002-0552-7042 [email protected] , Haniel Cedraz de Oliveira , MARTA MARTINS 0000-0002-4260-5329 , Marco Machado , Joao Panetto , Marcos da Silva , and Simone Guimarães Authors Info & Affiliations https://doi.org/10.22541/au.175613235.56438074/v1 Published Animal Genetics Version of record Peer review timeline 174 views 86 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The combined use of in vitro fertilization and genomics contribute to increasing productive potential and genetic gain in dairy cattle. The aim of this study is to deepen the understanding of the genomic architecture of the dairy Gir breed by identifying variants associated with the number of oocytes and embryos. This study is a continuation of a previous family analysis study conducted in the dairy Gir, in which a region on chromosome 7 (BTA7) was associated with the number of oocytes and embryos. In the current study, a genomic variant call file (VCF) of 12 sires with positive Predicted Transmission Ability (PTA) for milk production was investigated. These bulls are widely used in dairy herds to produce Gir daughters, whose oocyte and embryo data were collected. This region on BTA 7 was analyzed using the online tool EnsemblVEP and four variants were classified as lead SNPs. Two lead SNPs were single-nucleotide substitutions and both caused the inclusion of a premature stop codon in XRCC4 and HAPLN1 genes. A third lead SNP regards the deletion of five sequential thymines in an intergenic region. The fourth lead SNP involves the deletion of one nucleotide in EDIL3 gene, causing frameshift. In conclusion, these lead SNPs may have a positive or negative impact on the production of oocytes and in vitro embryos in the dairy Gir, which can be verified through subsequent association studies. Therefore, it is extremely important to verify how genetic improvement and breeding programs can increase gains for the reproductive traits. INTRODUCTION Recently, the remarkable rise in the adoption of cattle assisted reproductive technologies in Brazil is globally recognized, underscored by an increase of in vitro embryo production (IVEP) from approximately 12,500 in 2000 to over 300,000 annually after 2010 (Sartori et al., 2016)⁠. According to the International Embryo Technology Society (IETS, 2025)⁠, Brazil is the second largest IVEP producer, surpassed only by the USA. In 2023, more than one million straws of embryo transfer (ET) were sold in Brazil. However, due to the growing market for low-cost embryos, not all ET are registered by the breeder associations, indicating that the numbers of IVEP and ET in Brazil are likely higher than official estimates (Viana, 2024)⁠. The joint use of reproductive technologies and genomic selection can enhance genetic gain by providing higher quantity and quality oocytes from females with high Predicted Transmission Ability (PTA) and by selecting young animals, which reduces the generation interval and accelerates genetic progress (Sirard, 2018; Hansen, 2023)⁠. With advances in reproductive biotechnologies in livestock, there has been an increase in studies exploring the genomic aspects of oocyte and embryo production in cattle (Cornelissen et al., 2017; Watanabe et al., 2017; Ferré et al., 2023)⁠. These studies are common in zebu breeds, such as the dairy Gir, which shows high potential for milk production in tropical climates, resistance to heat and parasites, and responds better to ovulation protocols, yielding a greater number of oocytes compared to taurine breeds (Feltes et al., 2022)⁠. In a recent study, a genomic region on Bos taurus autosome (BTA) 7, located between nucleotides 82,974,837 and 83,997,563 was identified in Gir sire families and associated with the number of total oocytes, viable oocytes, and embryos (Rocha et al., 2024)⁠. Although few GWAS studies lead to functional mutations, the current study aimed to uncover all genetic variants in this BTA7 interval and investigate their effects on the number of oocytes and embryos in the dairy Gir families under investigation. The hypothesis for our analysis was that mutations affecting oocyte and embryo production in dairy Gir cattle would be identified in this predetermined region on BTA7. MATERIALS AND METHODS Population and data For a comprehensive understanding of the current work, details of the previous research can be found in the Open Access article by Rocha et al. (2024). In brief, a family analysis was performed using daughter design approach (Weller et al., 1990)⁠. For that, 15 genotyped Gir sires with more than 20 daughters each were selected from a pedigree file containing 4,679 Gir animals. These sires showed positive PTA for milk production, meaning they are widely used in Brazil to produce Gir and Girolando cattle. Genomic information for the sires and their daughters was provided by EMBRAPA Dairy cattle and phenotypic information for the daughters was provided by 5 farms in Minas Gerais state, including the number of viable oocytes (VO), the number of total oocytes (TO) and the number of in vitro produced embryos (EMBR). The search for genomic regions associated with these traits was performed through GWAS analyses among and within families. Ultimately, a genomic region on BTA7, between nucleotides 82,974,837 and 83,997,563 ( Bos taurus genome assembly ARS-UCD1.2), was identified as the most commonly region inherited among the families. For the current study, a variant calling format (VCF) file containing substitutions and indel variants for all 29 autosomal chromosomes of 43 Gir sires was provided by the Embrapa Dairy Cattle Research Center. From that file, 12 sires from the previous research were recovered. Subsequently, variants between 82,974,837 and 83,997,563 nucleotides were extracted using BCFtools (Danecek et al., 2021)⁠ for further analyses. Variant check The extracted VCF file of the BTA7 region was analyzed using the online tool EnsemblVEP (McLaren et al., 2016)⁠ to determine the presence of insertions, deletions, substitutions, and their consequences. Variants with a high impact classification in EnsemblVEP were considered for further analysis as lead SNPs, and were visually reviewed and validated in the Integrative Genomics Viewer (IGV) (Robinson et al., 2023)⁠. The VCF file was phased using Beagle software (version 5.5, Browning et al., 2021)⁠, and then transformed into the PLINK program format (version 1.9; Chang et al., 2015) to calculate linkage disequilibrium (LD) between each lead SNP and other variants. Next, 20 upstream and 20 downstream variants from each lead SNP’s position with an r² > 0.8 were selected to build the haplotypes using vcfR package in R (Knaus and Grünwald, 2017). The daughters’ genotype file from the previous research (Rocha et al., 2024) containing 420,718 single nucleotide polymorphisms (SNPs) was used to visualize recombination events. For this, the “hsphase” package (Ferdosi and Gondro, 2025)⁠ was used to build graphs within the predetermined region. The haplotype blocks fro the graphs were based on the genotypes of half-sisters within each family for BTA7 and for the 82,974,837 to 83,997,563 nucleotide position intervals. Statistical analyses The genomic estimated breeding value (GEBV) and its accuracy for all the sires and their daughters were obtained from the previous research for TO, VO, and EMBR traits (Rocha et al., 2024). Briefly, these traits were transformed using a logarithmic scale, ln (X + 1) to obtain a normal distribution of the residuals, verified by the Anderson–Darling test (Stephens, 1986; Thode, 2002). GEBV was calculated using the BLUPF90 software family (Misztal et al., 2002), considering the following model: y = Xβ + Za + Wp + e where, y , β , a , p , and e are the vectors of observations; fixed effects; additive genetic random effects; permanent environment random effects and residual effects, respectively; X , Z , and W are the incidence matrices of fixed, additive genetic and permanent environment, respectively. For VO and TO, contemporary group (CG) was considered as fixed effect. GC included 5 farms, 8 companies that performed OvumPick Up (OPU), OPU year (2005 -2020) and season (1: Jan-Mar, 2: Apr-Jun, 3: Jul-Sep, and 4: Oct-Dec). For EMBR, the fixed effects of GC, bull, and bull breed used in the insemination process (Gir or Holstein) were considered. Donor’s age in days (linear and quadratic components) was included as covariates for all traits. OPU interval (linear component) was included as a covariate for VO and TO. The additive genetic, permanent environment and residual effects were assumed to be random. Then, daughters were grouped according to their sire’s genotype for each one of the lead SNPs found on EnsemblVEP. The normal distribution was verified with the “nortest” package (Gross and Ligges, 2015)⁠. Analyses of variance (ANOVA) were performed for GEBV of each trait, considering genotype groups (G) using the R software (version 4.4.3, R Core Team, 2025)⁠. To evaluate the traits, the proposed model was: where Yij is the GEBV of the i-th observation from the j-th genetic group, µ is the overall mean and e is the random error associated with the observation. Statistical significance was considered when p-value < 0.05 and the Student’s t-test was used to compare averages. RESULTS The number of daughters per sire ranged from 26 to 395 (Table 1). The impact of variants in EnsemblVEP was classified as modifier, low, moderate, and high impact. A high impact classification indicates that the variant has a disruptive effect on the protein sequence, such as protein truncation, loss of function, or triggering nonsense-mediated decay. In our study, four variants in the 82,974,837-83,997,563 interval were classified as high impact and were considered lead SNPs for further analyses. Lead SNPs 1 and 2 were single-nucleotide substitutions, and both resulted in a stop codon within the protein coding sequence, causing protein truncation. Lead SNP 1 (7:83178164) is located in the XRCC4 gene (ENSBTAT00000131234.1, exon 8/8), where a cytosine (C) was replaced by a guanine (G). Lead SNP 2 (7:83600471) is located in the HAPLN1 gene (ENSBTAT00000098839.1, exon 2/2), where a cytosine (C) was replaced by a thymine (T). Lead SNP 3 (7:83814505) showed two alternative alleles in the original VCF file, where alternative allele 1 is the deletion of five sequential thymines and alternative allele 2 is the deletion of four thymines. For the 12 sires in this research, only the alternative allele 1 was present. This is the only variant that occured in an intergenic region. Lead SNP 4 (7:83919309) is located in the EDIL3 gene (ENSBTAT00000121872.1, exon 11/11), and a (T) was deleted, causing a frameshift. The four lead SNPs identified matched the unique identifier number from the National Center for Biotechnology Information (NCBI, 2025)⁠ SNP data base: rs518509552 for lead SNP 1, rs438544900 for lead SNP 2, rs450555472 for lead SNP 3, and rs470818992 for lead SNP 4. The lead SNPs are displayed in Table 1 for all 12 sires and were visually confirmed using IGV (Supplementary Figs S1-S4). Exons and protein sequences of variants found in protein-coding regions were visually verified using Ensembl (Dyer et al., 2025) and NCBI (Supplementary Figs S5-S7). Haplotypes were built with 20 upstream and 20 downstream variants in LD from each lead SNP with r² > 0.8 (Supplementary Tables S1-S4). The association between the haplotypes and GEBV for TO, VO, and EMBR was investigated. The variant (7:83173097) in high LD with lead SNP 1 showed four alternate alleles, most of which are repetitions of adenines and thymines. For this variant, sires with positive GEBV for VO, TO and EMBR showed at least one copy of alternative allele 2 (A), while the other copy was either the reference allele (AAT), alternative allele 1 (AATAT) or the alternative allele 2 (A). For Lead SNP 2, four sires were heterozygous and eight were homozygous for the wild allele. However, significant haplotype differences for lead SNP 2 were not observed among bulls with high and low GEBV. For lead SNP 4, all three genotypes were identified among the 12 sires: homozygous for wild allele, heterozygous, and homozygous for the mutant allele. For lead SNP 3, all sires showed at least one copy of the mutant allele, and most of them were homozygous for this SNP. Plots showing recombination events view were built with approximately 226 SNP markers (Supplementary Figs S8–S19). ANOVA results for daughters’ GEBVs and phenotypes according to their sires’ genotypes are available in Table 2. All results were significant for each of the lead SNPs, except for the VO phenotype averages for lead SNP1 and the EMBR phenotype averages for lead SNP3. Most grouped daughters showed negative GEBV for the traits, similar to most of the sires’ GEBV. The exceptions were for number of embryos for the following genotypes: homozygous for the wild allele (C/C) of lead SNP 1, homozygous for the wild allele (C/C) of lead SNP 2, homozygous for the mutant allele (C/C) of lead SNP 3, and heterozygous (AT/A_) for lead SNP 4. Boxplots for the average GEBVs of the daughters according to the sires’ genotype for each lead SNP are available in Fig. 1. Boxplots for the average phenotype of the daughters according to the sires’ genotypes for each lead SNP are available in Fig. 2. DISCUSSION In this study, we investigated a specific region on BTA 7 in 12 Gir sires that was previously associated with the number of oocytes and in vitro embryos produced by their daughters. This study was conducted to identify mutations within this region in the sires’ genomes. Since these bulls show high PTA for milk production, they are widely used in dairy Gir and Girolando breeding programs and, therefore, they show very high number of daughters in herds of both breeds in Brazil. The study of these bulls’ genomes revealed four variants inherited by their daughters that may impact oocyte and embryo production in both breeds. Reproductive traits are polygenic and therefore affected by several genomic regions. On BTA 7, for example, associations have been made for the number of oocytes and embryos in dairy Gir cattle (Rocha et al., 2024)⁠, calving interval and gestation length in Hanwoo cows (Haque et al., 2024)⁠, and calving ease and survival in Holstein cattle attributed to the sire effect (Chen et al., 2022)⁠. These references suggest that BTA7 carries genes and mutations affecting several reproductive traits. Thus, the genetic correlation between oocyte/embryo production and other reproductive traits should be confirmed in future studies. Additionally, although the p-values were significant high in our study, this significance may be disproportionately influenced by a single bull with a large number of daughters. The low number of parents per genotypic group can reduce statistical power, so the ANOVA results should be interpreted as preliminary and indicative, and require validation in a larger population, without this limitation of low number of daughters for some bulls. In addition, there is a lack of studies exploring genomic regions associated with oocytes and embryos, which increases the need to explore such traits. In our study, we found mutations in the sequence of XRCC4 , HAPLN1, and EDIL3 genes, which were identified in our previous study as associated with the number of oocytes and embryos (Rocha et al., 2024). Comparing whole-genome sequence of Nellore, Gir and Hereford breeds, Thambiraja et al. (2024)⁠ uncovered 1,147 substitutions, 17 insertions, and 4 deletions exclusively in the Bos indicus EDIL3 gene, which was linked to innate immunity. These findings show the large number of mutations that can be found in a single gene, highlighting that, in addition of being affected by several genes, polygenic traits can also be influenced by several mutations within a single gene. It is also important to highlight the possibility of pleiotropic effects, where mutations within the same gene trigger different physiological events, impacting more than one trait. For example, Singh et al. (2022) found significant SNPs in the sequence of VCAN , HAPLN1 and EDIL3 genes related to calcium content in the milk of Vrindavani cattle, a taurine-indicine crossbred. This may suggest a genetic relationship between oocyte/embryo production and milk mineral content, which could be verified in future studies. Furthermore, VCAN gene was one of the genes uncovered in the predetermined region on BTA7 along with XRCC4 , HAPLN1 , and EDIL3 (Rocha et al., 2024). Although we did not find any mutations with high impact in VCAN gene on EsembleVEP, some mutations in this gene were in linkage disequilibrium with the lead SNPs found in this study (Supplementary Tables S1-S4). Despite the fact that the lead SNPs showed existing reference SNP cluster IDs (rsIDs), the literature on these specific variants is scarce, with most studies focusing on larger Copy Number Variations (CNVs) in cattle genomes that overlap many variants (Boussaha et al., 2015; Mesbah-Uddin et al., 2018)⁠. Regarding reproductive traits, limited information reports XRCC4 mutations related to gonadal failure in humans (de Villartay, 2015)⁠, and its downregulation was observed in women with endometriosis (Bane et al., 2022)⁠. Interestingly, XRCC4 phosphorylation suppresses DNA double-strand breaks repair to prevent genome instability during mitosis (Terasawa et al., 2014)⁠. A succession of mitotic processes occurs during the development from a fertilized oocyte to a fully formed embryo, which may explain the association of the XRCC4 gene with the studied traits, possibly by preventing genome instability. This suggests that the mutation in XRCC4 causing protein truncation may trigger instability during the mitotic and lead to DNA double-strand breaks, potentially impacting embryo development and the total number of embryos obtained. This hypothesis is supported by studies in rats, where Talibova et al. (2024)⁠ found strong expression of XRCC4 gene in granulosa cells and oocytes, and Gao et al. (1998)⁠ concluded that XRCC4 deficiency caused late embryonic lethality. A similar theory could apply to the HAPLN1 gene, where another stop-codon-causing variant was found in a recent study which uncovered a mutation on the sequence of this gene, causing the genuine empty follicle syndrome where a women undergoing ovarian stimulation develop normal follicle but yield no oocytes (Lledó et al., 2024)⁠. The identification of the stop codon by the translation machinery is essential for accurately terminating translation and ensuring correct protein production however, when a mutation generates a stop codon - nonsense mutation - and causes the protein truncation, as is the case with lead SNPs 1 and 2, it can negatively impact traits of economic interest. Examples of improper stop codons impacting reproductive traits in cattle include early pregnancy loss due to embryo homozygosity for certain haplotype (Häfliger et al., 2021, 2022)⁠. It is still worth mentioning a nonsense mutation in the APAF1 gene in Holstein sires that, when in homozygosis, results in a rare and fatal congenital anomaly that caused more than 500,000 spontaneous abortions worldwide over 30 years (Adams et al., 2016)⁠. One way to check the impact of a stop codon is to induce it through gene editing techniques to silence the gene expression (Wang et al., 2020)⁠, which could be performed in future studies to assess the impact of lead SNPs 1 and 2 on oocyte and embryo production. For lead SNP 4, a deletion occurred, that caused a frameshift, changing the reading frame of the genetic code and altering the entire amino acid sequence from that position. In addition to variants within genes, variants in intergenic regions, like lead SNP 3, can also affect the expression of phenotypes, as has been demonstrated for traits such as muscle development (Doyle et al., 2020)⁠ and milk production in cattle (Sanchez et al., 2017)⁠. Therefore, mutations in intergenic regions should not be ignored, as they can be located in regulatory regions. This suggests that the deletion of multiple thymines in lead SNP 3 should be explored further to verify its expression in the daughters of the sampled sires and its effect on the phenotypes. Chen et al. (2022)⁠ found a significant SNP (83,320,343 nucleotide, ARS-UCD1.2 assembly) on BTA7 which was associated with sire calving ease and calving survival for Holstein cows and heifers, indicating XRCC4 and VCAN as candidate genes for these traits since this location is situated between lead SNP 1 and lead SNP 2 found in our study. Since haplotypes in this region are largely conservated in our study, it is possible that the number of oocytes and embryos shows a genetic relationship with calving ease and survival. However, studies exploring this assumption are still limited. In an initial attempt to associate the effect of the lead SNPs with the studied traits, the average genomic values (GEBVs) of the daughters for each variant genetic group were analyzed. The results indicated that all the variants showed a significant impact on the production of oocytes and embryos at both the phenotypic and genomic levels. This opens opportunities for validation studies to confirm the presence of such variants in Gir and Girolando breeds and their actual effects. Regarding haplotype results, sire 1 was the only homozygous for the lead SNP 4 wild allele. This sire also showed different haplotypes for lead SNP 1 and lead SNP 4 when compared to all the other sires. Sire 1 showed a negative GEBV for oocytes and embryos and is the sire with the greatest number of daughters. Most part of the sires showed negative GEBV for TO, VO and EMBR but, as Sire 1 is currently one of the most used Gir sire in Brazil, he has a greater chance to propagate his variants through the herds, which might impact negatively the production of oocytes and embryos. Since literature examples of variants causing great economic impact due to homozygous alleles resulting in pregnancy failure and estrus repetition (Adams et al., 2016)⁠, it is important to pay attention to such details, especially when studying the genotypes of the daughters. By visualizing the region studied within BTA 7 in the graphs produced by the “hsphase package” in R, it is possible to observe almost no recombination events in the region between nucleotides 82,974,837 and 83,997,563, indicating how highly conserved this interval is. This is consistent with R² results, as most variants in linkage disequilibrium with the lead SNPs showed values near 1.0, meaning that there is complete or near-complete linkage disequilibrium which causes the loci to be inherited together as a haplotype block (Huang et a., 2022). This finding suggests that the haplotype combination identified in the bulls is most likely to be highly passed on to their daughters than an individual mutation. Therefore, future studies should disentangle the architecture of this region to better understand the effects of the entire haplotype, not only on oocyte and embryo production but also on other traits. Moreover, analyzing this BTA7 interval in other indicine and taurine breeds may clarify its effects in other cattle groups and even shed light on the evolution of this genomic region in bovines. CONCLUSIONS The variants rs518509552, rs438544900, rs450555472, and rs470818992 are significantly associated with oocyte and embryo production in the Gir breed and may have a positive or negative impact in this trait. It is important for genetic improvement and breeding programs to verify if these effects can increase gains for reproductive traits. Future studies should also evaluate the correlation between the production of oocytes and embryos and milk PTA, not only in Gir but also in other indicine and taurine breeds. Finally, the impact of the entire haplotype on oocyte and embryo production should be verified and compared to the effects of the individual variants. Funding statement and acknowledgments We thank the collaborating herds and Embrapa Dairy Cattle Research Center for providing the data for this study. This project was partially supported by Embrapa (Brazil) SEG 02.13.05.011.00.00 and FAPEMIG APQ- 02750–23 projects. Coordination and Improvement of Higher-Level Personnel (CAPES PROEX 88887.844747/2023-00), National Council for Scientific and Technological Development (CNPq, process number: 150320/2024-8), Ministério da Ciência Tecnologia e Inovação (MCTI) and Brazilian National Institute of Science and Technology in Animal Science (INCT-CA) provided financial support towards this study. Data availability The data sets analyzed during the current study are not publicly available because databases belong to private commercial farms. Ethics approval statement Ethics committee approval was not necessary, as we received the database ready for analysis. Conflict of interest statement The authors have not stated any conflicts of interest. ORCIDS Renata de Fátima Bretanha Rocha https://orcid.org/0000-0002-0552-7042 Haniel Cedraz de Oliveira https://orcid.org/0000-0001-7317-2004 Marcos Vinícius Barbosa da Silva https://orcid.org/0000-0001-5449-1413 Marta Fonseca Martins https://orcid.org/0000-0002-4260-5329 João Claudio do Carmo Panetto https://orcid.org/0000-0002-9198-9728 Simone Eliza Facioni Guimarães https://orcid.org/0000-0003-3704-8131 REFERENCES Adams, H.A., T.S. Sonstegard, P.M. VanRaden, D.J. Null, C.P. Van Tassell, D.M. Larkin, and H.A. Lewin. 2016. Identification of a nonsense mutation in APAF1 that is likely causal for a decrease in reproductive efficiency in Holstein dairy cattle. J. Dairy Sci. 99:6693–6701. doi:10.3168/jds.2015-10517. Bane, K., J. Desouza, A. Rojewale, R.R. Katkam, G. Fernandes, R. Sawant, U. Dudhedia, N. Warty, A. Chauhan, U. Chaudhari, R. Gajbhiye, and G. Sachdeva. 2022. Dysregulation of X-ray repair cross-complementing 4 expression in the eutopic endometrium of women with endometriosis. Reproduction 163:95–105. doi:10.1530/REP-21-0436. Boussaha, M., D. Esquerré, J. Barbieri, A. Djari, A. Pinton, R. Letaief, G. Salin, F. Escudié, A. Roulet, S. Fritz, F. Samson, C. Grohs, M. Bernard, C. Klopp, D. Boichard, and D. Rocha. 2015. Genome-Wide Study of Structural Variants in Bovine Holstein, Montbéliarde and Normande Dairy Breeds. PLoS One 10:e0135931. doi:10.1371/journal.pone.0135931. Browning, B.L., X. Tian, Y. Zhou, and S.R. Browning. 2021. Fast two-stage phasing of large-scale sequence data. Am. J. Hum. Genet. 108:1880–1890. doi:10.1016/j.ajhg.2021.08.005. Chang, C.C., C.C. Chow, L.C. Tellier, S. Vattikuti, S.M. Purcell, and J.J. Lee. 2015. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4. doi:10.1186/s13742-015-0047-8. Chen, S.-Y., F.S. Schenkel, A.L.P. Melo, H.R. Oliveira, V.B. Pedrosa, A.C. Araujo, M.G. Melka, and L.F. Brito. 2022. Identifying pleiotropic variants and candidate genes for fertility and reproduction traits in Holstein cattle via association studies based on imputed whole-genome sequence genotypes. BMC Genomics 23:331. doi:10.1186/s12864-022-08555-z. Cornelissen, M.A.M.C., E. Mullaart, C. Van der Linde, and H.A. Mulder. 2017. Estimating variance components and breeding values for number of oocytes and number of embryos in dairy cattle using a single-step genomic evaluation. J. Dairy Sci. 100:4698–4705. doi:10.3168/jds.2016-12075. Danecek, P., J.K. Bonfield, J. Liddle, J. Marshall, V. Ohan, M.O. Pollard, A. Whitwham, T. Keane, S.A. McCarthy, R.M. Davies, and H. Li. 2021. Twelve years of SAMtools and BCFtools. Gigascience 10. doi:10.1093/gigascience/giab008. Doyle, J.L., D.P. Berry, R.F. Veerkamp, T.R. Carthy, R.D. Evans, S.W. Walsh, and D.C. Purfield. 2020. Genomic regions associated with muscularity in beef cattle differ in five contrasting cattle breeds. Genet. Sel. Evol. 52:2. doi:10.1186/s12711-020-0523-1. Dyer, S.C., O. Austine-Orimoloye, A.G. Azov, M. Barba, I. Barnes, V.P. Barrera-Enriquez, A. Becker, R. Bennett, M. Beracochea, A. Berry, J. Bhai, S.K. Bhurji, S. Boddu, P.R.B. Lins, L. Brooks, S.B. Ramaraju, L.I. Campbell, M.C. Martinez, M. Charkhchi, L.A. Cortes, C. Davidson, S. Denni, K. Dodiya, S. Donaldson, B. El Houdaigui, T. El Naboulsi, O. Falola, R. Fatima, T. Genez, J.G. Martinez, T. Gurbich, M. Hardy, Z. Hollis, T. Hunt, M. Kay, V. Kaykala, D. Lemos, D. Lodha, N. Mathlouthi, G.A. Merino, R. Merritt, L.P. Mirabueno, A. Mushtaq, S.N. Hossain, J.G. Pérez-Silva, M. Perry, I. Piližota, D. Poppleton, I. Prosovetskaia, S. Raj, A.I.A. Salam, S. Saraf, N. Saraiva-Agostinho, S. Sinha, B. Sipos, V. Sitnik, E. Steed, M.-M. Suner, L. Surapaneni, K. Sutinen, F.F. Tricomi, I. Tsang, D. Urbina-Gómez, A. Veidenberg, T.A. Walsh, N.L. Willhoft, J. Allen, J. Alvarez-Jarreta, M. Chakiachvili, J. Cheema, J.B. da Rocha, N.H. de Silva, S. Giorgetti, L. Haggerty, G.R. Ilsley, J. Keatley, J.E. Loveland, B. Moore, J.M. Mudge, G. Naamati, J. Tate, S.J. Trevanion, A. Winterbottom, B. Flint, A. Frankish, S.E. Hunt, R.D. Finn, M.A. Freeberg, P.W. Harrison, F.J. Martin, A.D. Yates. 2025. Ensembl 2025. Nucleic Acids Res. 2025, 53(D1):D948–D957. doi: 10.1093/nar/gkae1071. Feltes, G.L., R. Negri, F.S.S. Raidan, L.F.R. Feres, V.M.P. Ribeiro, and J.A. Cobuci. 2022. Genetic evaluation of oocyte and embryo production in dairy Gir cattle using repeatability and random regression models. Rev. Bras. Zootec. 51. doi:10.37496/rbz5120220017. Ferdosi, M., and C. Gondro. 2025. hsphase – an R package for identification of recombination events, pedigree and haplotype reconstruction and imputation using SNP data from half-sib families. R Packag. vignette version R Packag. version 2.0.4. Ferré, L.B., H. Alvarez‐Gallardo, S. Romo, C. Fresno, T. Stroud, B. Stroud, B. Lindsey, and M.E. Kjelland. 2023. Transvaginal ultrasound‐guided oocyte retrieval in cattle: State‐of‐the‐art and its impact on the in vitro fertilization embryo production outcome. Reprod. Domest. Anim. 58:363–378. doi:10.1111/rda.14303. Gao, Y., Y. Sun, K.M. Frank, P. Dikkes, Y. Fujiwara, K.J. Seidl, J.M. Sekiguchi, G.A. Rathbun, W. Swat, J. Wang, R.T. Bronson, B.A. Malynn, M. Bryans, C. Zhu, J. Chaudhuri, L. Davidson, R. Ferrini, T. Stamato, S.H. Orkin, M.E. Greenberg, and F.W. Alt. 1998. A Critical Role for DNA End-Joining Proteins in Both Lymphogenesis and Neurogenesis. Cell 95:891–902. doi:10.1016/S0092-8674(00)81714-6. Gross, J., and U. Ligges. 2015. nortest: Tests for Normality. R package version 1.0-4. Häfliger, I.M., F.R. Seefried, M. Spengeler, and C. Drögemüller. 2021. Mining massive genomic data of two Swiss Braunvieh cattle populations reveals six novel candidate variants that impair reproductive success. Genet. Sel. Evol. 53:95. doi:10.1186/s12711-021-00686-3. Häfliger, I.M., M. Spengeler, F.R. Seefried, and C. Drögemüller. 2022. Four novel candidate causal variants for deficient homozygous haplotypes in Holstein cattle. Sci. Rep. 12:5435. doi:10.1038/s41598-022-09403-6. Hansen, P.J. 2023. Review: Some challenges and unrealized opportunities toward widespread use of the in vitro-produced embryo in cattle production. animal 17:100745. doi:10.1016/j.animal.2023.100745. Haque, M.A., Y.-M. Lee, J.-J. Ha, S. Jin, B. Park, N.-Y. Kim, J.-I. Won, and J.-J. Kim. 2024. Genome-wide association study identifies genomic regions associated with key reproductive traits in Korean Hanwoo cows. BMC Genomics 25:496. doi:10.1186/s12864-024-10401-3. Huang, K., D.W. Dunn, W. Li, D. Wang, and B. Li. 2022. Linkage disequilibrium under polysomic inheritance. Heredity (Edinb). 128:11–20. doi:10.1038/s41437-021-00482-1. IETS. 2025. International Embryo Technology Society. Accessed in 21 may 2025. Available in: https://www.iets.org/. Knaus, B.J., Grünwald, N.J. 2017. “VCFR: a package to manipulate and visualize variant call format data in R.” Mol. Ecol. Resour., 17:44–53. doi: 10.1111/1755-0998.12549. Lledó, B., J.J. Piqueras, F.M. Lozano, M. Hortal, R. Morales, J.A. Ortiz, J. Guerrero, A. Benabeu, and R. Bernabeu. 2024. Exome sequencing in genuine empty follicle syndrome: Novel candidate genes. Eur. J. Obstet. Gynecol. Reprod. Biol. 297:221–226. doi:10.1016/j.ejogrb.2024.04.029. McLaren, W., L. Gil, S.E. Hunt, H.S. Riat, G.R.S. Ritchie, A. Thormann, P. Flicek, and F. Cunningham. 2016. The Ensembl Variant Effect Predictor. Genome Biol. 17:122. doi:10.1186/s13059-016-0974-4. Mesbah-Uddin, M., B. Guldbrandtsen, T. Iso-Touru, J. Vilkki, D.-J. De Koning, D. Boichard, M.S. Lund, and G. Sahana. 2018. Genome-wide mapping of large deletions and their population-genetic properties in dairy cattle. DNA Res. 25:49–59. doi:10.1093/dnares/dsx037. Misztal, I., S. Tsuruta, T. Strabel, B. Auvray, T. Druet, and D.H. Lee. 2002. BLUPF90 and related programs (BGF90). Montpellier, France. NCBI. 2025. National Center for Biotechnology Information. Accessed in 05 of june 2025. Available in: https://www.ncbi.nlm.nih.gov/. R Core Team. 2025. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Robinson, J.T., H. Thorvaldsdottir, D. Turner, and J.P. Mesirov. 2023. igv.js: an embeddable JavaScript implementation of the Integrative Genomics Viewer (IGV). Bioinformatics 39. doi:10.1093/bioinformatics/btac830. Rocha, R.F.B., A.O. Garcia, M.G. dos Santos, P.I. Otto, M.V.B. da Silva, M.F. Martins, M.A. Machado, J.C.C. Panetto, M.P.L. Calus, and S.E.F. Guimarães. 2024. Inheritance of genomic regions and genes associated with number of oocytes and embryos in Gir cattle through daughter design. J. Dairy Sci. 107:3794–3801. doi:10.3168/jds.2023-24111. Sanchez, M.-P., A. Govignon-Gion, P. Croiseau, S. Fritz, C. Hozé, G. Miranda, P. Martin, A. Barbat-Leterrier, R. Letaïef, D. Rocha, M. Brochard, M. Boussaha, and D. Boichard. 2017. Within-breed and multi-breed GWAS on imputed whole-genome sequence variants reveal candidate mutations affecting milk protein composition in dairy cattle. Genet. Sel. Evol. 49:68. doi:10.1186/s12711-017-0344-z. Sartori, R., A.B. Prata, A.C.S. Figueiredo, B. V. Sanches, G.C.S. Pontes, J.H.M. Viana, J.H. Pontes, J.L.M. Vasconcelos, M.H.C. Pereira, M.A.N. Dode, P.L.J. Monteiro, and P.S. Baruselli. 2016. Update and overview on assisted reproductive technologies (ARTs) in Brazil. Anim. Reprod. 13:300–312. doi:10.21451/1984-3143-AR873. Singh, A., A. Kumar, C. Gondro, A.K. Pandey, T. Dutt, and B.P. Mishra. 2022. Genome Wide Scan to Identify Potential Genomic Regions Associated With Milk Protein and Minerals in Vrindavani Cattle. Front. Vet. Sci. 9. doi:10.3389/fvets.2022.760364. Sirard, M.-A. 2018. 40 years of bovine IVF in the new genomic selection context. Reproduction 156:R1–R7. doi:10.1530/REP-18-0008. Stephens, M.A. 1986. Tests based on EDF statistics. R.B. D’Agostino and M.A. Stephens, ed. Marcel Dekker, New York. Talibova, G., Y. Bilmez, B. Tire, and S. Ozturk. 2024. The DNA double-strand break repair proteins γH2AX, RAD51, BRCA1, RPA70, KU80, and XRCC4 exhibit follicle-specific expression differences in the postnatal mouse ovaries from early to older ages. J. Assist. Reprod. Genet. 41:2419–2439. doi:10.1007/s10815-024-03189-4. Terasawa, M., A. Shinohara, and M. Shinohara. 2014. Canonical Non-Homologous End Joining in Mitosis Induces Genome Instability and Is Suppressed by M-phase-Specific Phosphorylation of XRCC4. PLoS Genet. 10:e1004563. doi:10.1371/journal.pgen.1004563. Thambiraja, M., S.K. Iyengar, B. Satishkumar, S.R. Kavuru, A. Katari, D. Singh, S.K. Onteru, and R.M. Yennamalli. 2024. Genetic basis of immunity in Indian cattle as revealed by comparative analysis of Bos genome. doi:10.1101/2024.12.09.627532. Thode, H.C. 2002. Testing For Normality. 1st Edition. Marcel-Dekker, New York. Viana, J.H.M. 2024. 2023 Statistics of embryo production and transfer in domestic farm animals. de Villartay, J. 2015. When natural mutants do not fit our expectations: the intriguing case of patients with XRCC 4 mutations revealed by whole‐exome sequencing. EMBO Mol. Med. 7:862–864. doi:10.15252/emmm.201505307. Wang, X., Z. Liu, G. Li, L. Dang, S. Huang, L. He, Y. Ma, C. Li, M. Liu, G. Yang, X. Huang, F. Zhou, and X. Ma. 2020. Efficient Gene Silencing by Adenine Base Editor-Mediated Start Codon Mutation. Mol. Ther. 28:431–440. doi:10.1016/j.ymthe.2019.11.022. Watanabe, Y.F., H.A. Souza, R.D. Mingoti, R.M. Ferreira, E.O.S. Batista, A. Dayan, O.Y. Watanabe, F. V. Meirelles, M.F.G. Nogueira, J.B.S. Ferraz, and P.S. Baruselli. 2017. Number of oocytes retrieved per donor during OPU and its relationship with in vitro embryo production and field fertility following embryo transfer. Anim. Reprod. 14:635–644. doi:10.21451/1984-3143-AR1008. Weller, J.I., Y. Kashi, and M. Soller. 1990. Power of Daughter and Granddaughter Designs for Determining Linkage Between Marker Loci and Quantitative Trait Loci in Dairy Cattle. J. Dairy Sci. 73:2525–2537. doi:10.3168/jds.S0022-0302(90)78938-2. TABLES Table 1. Description¹ of the number of daughters, lead SNP genotype, genomic estimated breeding value (GEBV) and accuracy (r) for each trait per dairy Gir sire. Sires Number of Daughters Lead SNP 1 Lead SNP 2 Lead SNP 3 Lead SNP 4 Total oocytes Viable oocytes Embryos rs518509552 rs438544900 rs450555472 rs470818992 GEBV r GEBV r GEBV r s1 395 0/1 0/1 1/1 0/0 -0.13 0.89 -0.10 0.89 -0.06 0.89 s2 367 0/0 0/0 1/1 0/1 0.16 0.89 0.18 0.88 0.25 0.88 s3 110 0/0 0/1 1/1 1/1 -0.15 0.89 -0.12 0.89 -0.20 0.88 s4 73 0/0 0/1 0/1 1/1 -0.28 0.86 -0.32 0.86 -0.32 0.85 s5 62 0/0 0/0 1/1 1/1 -0.24 0.82 -0.31 0.81 -0.11 0.79 s6 55 0/0 0/0 1/1 1/1 0.46 0.87 0.59 0.87 0.10 0.86 s7 51 0/1 0/0 1/1 1/1 -0.61 0.87 -0.56 0.87 -0.31 0.86 s8 46 0/0 0/0 1/1 0/1 -0.38 0.84 -0.37 0.84 -0.38 0.83 s9 38 0/0 0/0 1/1 0/1 0.12 0.84 0.05 0.84 0.02 0.83 s10 37 0/0 0/0 1/1 0/1 -0.63 0.84 -0.55 0.83 -0.26 0.82 s11 30 0/0 0/0 1/1 0/1 -0.27 0.82 -0.23 0.81 -0.20 0.80 s12 26 0/0 0/1 0/1 1/1 0.04 0.82 0.09 0.82 -0.12 0.80 ¹Lead SNP: variant with high impact on chromosome 7:82.974.837-83.997.563 ( Bos taurus Genome Assembly ARS-UCD1.2); 0/0: homozygous for wild allele; 0/1: heterozygous; 1/1: homozygous for mutant allele. Table 2. Average Genomic Estimated Breeding Value (GEBV) of daughters and phenotype according to sires’ genotype for each lead SNP in dairy Gir cattle. Lead SNP 1 Daughters GEBV Daughters phenotype Daughters GEBV Daughters phenotype Daughters GEBV Daughters phenotype Genotype C/C (wild) C/G G/G (mutation) P-value GEBV P-value Phenotype Sires 2, 3, 4, 5, 6, 8, 9, 10, 11, 12 1, 7 - Number of daughters/data 844 7120 446 3309 - - Viable oocytes -0.007 16.977 -0.072 16.449 - - 8.81E-06 0.05393 Total oocytes -0.032 22.809 -0.098 22.096 - - 2.03E-06 0.03415 Embryos 0.008 3.013 -0.018 2.844 - - 0.02384 0.03329 Lead SNP 2 Daughters GEBV Daughters phenotype Daughters GEBV Daughters phenotype Daughters GEBV Daughters phenotype Genotype C/C (wild) C/T T/T (mutation) P-value GEBV P-value Phenotype Sires 2, 5, 6, 7, 8, 9, 10, 11 1, 3, 4, 12 - Number of daughters/data 686 5765 610 5106 - - Viable oocytes -0.008 16.973 -0.082 15.317 - - 2.19E-07 1.432e-11 Total oocytes -0.036 22.884 -0.109 20.595 - - 1.20E-07 2.299e-14 Embryos 0.026 3.067 -0.032 2.698 - - 5.26E-08 2.307e-07 Lead SNP 3 Daughters GEBV Daughters phenotype Daughters GEBV Daughters phenotype Daughters GEBV Daughters phenotype Genotype CTTTTT/CTTTTT (wild) CTTTTT/C C/C (mutation) P-value GEBV P-value Phenotype Sires - 4, 12 1, 2, 3, 5, 6, 7, 8, 9, 10, 11 Number of daughters/data - - 99 653 1191 9776 Viable oocytes - - -0.078 19.268 -0.025 16.645 0.04167 6.393e-07 Total oocytes - - -0.082 26.032 -0.053 22.352 0.2161 1.263e-08 Embryos - - -0.100 2.731 0.007 2.975 1.48E-07 0.1102 Lead SNP 4 Daughters GEBV Daughters phenotype Daughters GEBV Daughters phenotype Daughters GEBV Daughters phenotype Genotype AT/AT (wild) AT/A_ A_/A_ (mutation) P-value GEBV P-value Phenotype Sires 1 2, 8, 9, 10, 11 3, 4, 5, 6, 7, 12 Number of daughters/data 395 2780 518 4533 377 3116 Viable oocytes -0.044 17.710 -0.001 16.539 -0.052 16.400 0.003788 0.0001069 Total oocytes -0.071 23.580 -0.029 22.363 -0.074 22.013 0.005549 0.0004082 Embryos -0.002 2.962 0.049 3.167 -0.070 2.655 < 2.2e-16 4.133e-08 ¹G: Guanine; T: Thymine; C: Cytosine; A: Adenine; Slash (/): allele division for that genotype. FIGURES Figure 1 . Distribution of daughter GEBVs for reproductive traits, grouped by sire genotype for each lead SNP in dairy Gir cattle. Figure 2 . Distribution of daughter phenotypes for reproductive traits, grouped by sire genotype for each lead SNP in dairy Gir cattle. Information & Authors Information Version history V1 Version 1 25 August 2025 Peer review timeline Published Animal Genetics Version of Record 12 Apr 2026 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords dairy cattle mutations reproductive traits variant call file Authors Affiliations Renata de Fatima Bretanha Rocha 0000-0002-0552-7042 [email protected] Universidade Federal de Vicosa View all articles by this author Haniel Cedraz de Oliveira Universidade Federal de Vicosa View all articles by this author MARTA MARTINS 0000-0002-4260-5329 Embrapa Gado de Leite View all articles by this author Marco Machado Embrapa Gado de Leite View all articles by this author Joao Panetto Embrapa Gado de Leite View all articles by this author Marcos da Silva Embrapa Gado de Leite View all articles by this author Simone Guimarães Universidade Federal de Vicosa View all articles by this author Metrics & Citations Metrics Article Usage 174 views 86 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Renata de Fatima Bretanha Rocha, Haniel Cedraz de Oliveira, MARTA MARTINS, et al. Genomic variants associated with oocyte and embryo production in dairy Gir cattle. Authorea . 25 August 2025. DOI: https://doi.org/10.22541/au.175613235.56438074/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.175613235.56438074/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a026f4fb3b8e1640',t:'MTc3OTkwNTIyMg=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();