Genome-wide association study of superovulatory response traits in Canadian Holsteins (preliminary study) C. Jaton12, M. Sargolzaei13, M.K. Abo-Ismail14, F. Miglior15, C. A. Price6, A. Koeck1 and F. S. Schenkel1 1Centre for Genetic Improvement of Livestock (CGIL), University of Guelph, Guelph, Ontario, Canada, N1G 2W1, 2Centre d’insémination artificielle du Québec (Ciaq), Saint-Hyacinthe, Québec, Canada, J2S 7B8, 3Semex Alliance, Guelph, Ontario, Canada, N1G 3Z2, 4Damanhour University, Egypt, 5Canadian Dairy Network (CDN), Guelph, Ontario, Canada, N1K 1E5, 6Université de Montréal, Faculté de médecine vétérinaire, St-Hyacinthe, Québec, Canada, J2S 2M2 INTRODUCTION Superovulation and embryo transfer are frequently used in the Canadian dairy industry to produce more offspring from elite donor cows. The technical aspect of superovulation in dairy cattle has been described by many authors around the world, but the genetic and genomic aspect of this technique has still to be discovered. Superovulatory response is moderately heritable in Holstein dairy cattle (Jaton et al., 2015), so that it could be possible to genetically select donors that would produce more embryos. Moreover, finding genes that impact the superovulatory response may also help with to select of a donors that would respond well to superovulation. To our knowledge, very few studies have performed a genome-wide association study (GWAS) in dairy cattle for traits related to superovulatory response. One study performed a GWAS for fertility traits such as fertilization and blastocyst rate of Holstein cows and found significant SNPs associated with those traits (Huang et al., 2010). Another study done on Japanese black cattle identified a genetic variant in the Glutamate Receptor AMPA 1 (GRIA1) gene that has an impact on the ovulation rate (Sugimoto et al., 2010). This gene located on chromosome 7 of Bos Taurus. On the other hand, using candidate gene approach, some studies looked at SNPs in specific genes and tried to find associations with superovulatory response (Cory et al., 2012; Yang et al., 2010). One study performed in Canada found a SNP in the Follicle-stimulating hormone receptor (FSHR) gene located on chromosome 11 to identify good and bad donors (Cory et al., 2012). They concluded that this finding would need to be confirmed in a larger population. Another study done in a Chinese Holstein population found similar results for FSHR gene (Yang et al., 2010). This gene has been reported to play a role in the mediation of FSH signal transduction and follicle maturation (Cory et al., 2012). Also, a study demonstrated that the IGF1R (insulin-like growth factor 1 receptor) gene, which plays a role in ovulation, in the pre- implantation embryo development and in pregnancy rate, could be a potential marker to select superovulated donors (Yang et al., 2013). The main objectives of this study were 1) to perform a genome-wide association study in order to find SNPs associated with superovulatory response traits, and 2) to identify candidate genes corresponding to these associations and retrieve the biological pathways and mechanisms linked to superovulation and embryo transfer. 1 MATERIAL AND METHODS Data Holstein Canada (www.holstein.ca) provided a data set containing records of all donors that had been superovulated over the last 35 years. After editing, 137,446 records from 54,463 donors were considered for the analysis, with one record corresponding to one superovulatory protocol (Jaton et al., 2015). Traits. The two superovulatory response traits that were analyzed from the data set provided were the total number of embryos per flush and the number of viable embryos per flush. The difference between them is that the number of viable embryos does not include degenerated or dead embryos recovered from a flush. Pedigree. An animal pedigree file containing 197,246 animals was generated by tracing the pedigrees of the donors with records 7 generations back. EBV. Genetic parameters and estimated breeding values (EBV) for both superovulatory response traits were already estimated from univariate analyses (Jaton et al., 2015). Breeding values from logarithmic transformation were considered for the GWAS. Overall, 57,976 donors and their sires had EBVs available for both traits. EBVs were de-regressed using VanRaden’s simplified method for de-regression (VanRaden and Sullivan, 2010). Animals with a reliability of de-regressed EBV lower than 10% were not considered for further analysis. Genotypes and imputation. Genotypes were available for 7,925 donors and sires that had de- regressed EBVs. Of that number, 5,582 animals were genotyped with at least a 50K SNP panel and all those were imputed to high density genotypes using FImpute software (Sargolzaei et al., 2014). After accounting for the reliability threshold, 4,589 (803 M, 3,486 F) and 4,172 (758 M, 3,414 F) individuals were considered for further analyses of the total number of embryos and the number of viable embryos, respectively. Genome-wide association study Quality control. The quality control measures that were applied included the exclusion of SNPs having a minor allele frequency lower than 1%, a call rate lower than 90%, an excess of heterozygosity higher than 15% and a Mendelian error larger than 5%. The SNPs that were out of Hardy-Weinberg equilibrium with very low probability (1 x 10-8) and the individuals with a call rate lower than 90% were also excluded. Parentage verification was performed in snp1101. Overall, 657,932 SNPs on 29 autosomal chromosomes were considered for the association analysis. Method. Univariate single SNP generalized mixed linear model (SSR) was used to perform the GWAS using snp1101 software (Mehdi Sargolzaei, personal communication). Considering that there was a large variation in the reliabilities of the de-regressed EBVs, SSR method was chosen because it weights the reliabilities. In order to account for population structure, the genomic relationship matrix was built in snp1101 using VanRaden’s method. Correction for multiple-testing. The false discovery rate (FDR) at genome-wise level was used to correct for multiple testing. SNPs were considered to be significantly associated with the traits if they were above the 5% FDR significance level. 2 Enrichment analysis Significant SNP at 5% FDR were mapped to the nearby genes using NGS-SNP (Grant et al., 2011). Genes located in a distance of 100,000 base pairs (bp) on each side of the SNPs were considered for the enrichment analysis. Then, the gene list was submitted to the Database for Annotation, Visualization, and Integrated Discovery (DAVID) 6.7 tool to perform the enrichment analysis (Huang et al., 2009a, 2009b). Biological processes and pathways were retrieved for both superovulatory response traits. RESULTS AND DISCUSSION Genome-wide association study After accounting for quality control criteria, 595,074 and 594,955 SNPs were considered for the total number of embryos and the number of viable embryos, respectively. The Quantile-Quantile (QQ) plots are presented for both traits in figure 1 a and b. After accounting for multiple comparisons, a total of 57 and 47 SNPs were significantly associated at 5% FDR with the total number of embryos and the number of viable embryos, respectively. Figures 2 and 3 show the distribution of the significant SNPs for superovulatory response traits across the 29 chromosomes. For both traits there was a major peak on chromosome 11 and the majority of the significant SNPs were located on that chromosome. For the total number of embryos, 81% of the significant SNPs (46/57) were located on chromosome 11. The other significant SNPs were located on chromosomes 2 (4/57), 22 (2/57), 25 (1/57) and 29 (4/57). For the number of viable embryos, 46 out of 47 significant SNPs were located on chromosome 11 whereas the other significant SNP was located on chromosome 9. Thirty-three significant SNPs on chromosome 11 were having pleotropic effect on the total number of embryos and the number of viable embryos. This makes sense considering that the two superovulatory response traits considered in this study are very similar. Gene Identification and Enrichment analysis Total number of embryos. Table 1 lists the identified genes within 100k base of the 10 most significant SNPs for the total number of embryos. The most significant SNP (BovineHD1100027188) was located nearby (60,957 bases) prostaglandin-endoperoxide synthase 1 (PTGS1) gene. The PTGS1 protein coding gene is responsible for the conversion of arachidonic acid into PGH2, which is a precursor of different form of prostaglandins such as PGE2 and PGF2α (Arosh, 2002). Prostaglandins are, among other things, responsible for the ovulation of oocyte (Ball and Peters, 2004). All the other genes nearby this SNP were referenced as olfactory factor 1L8 protein coding gene (NCBI). We don’t know yet how this gene could be linked to superovulatory response. Also, several potential genes were found nearby significant SNPs. For example, NADH dehydrogenase (ubiquinone) 1 alpha subcomplex (NDUAF8) was reported to transfer electrons with a high redox potential from NADH to ubiquinone and its RNA being mostly expressed in human heart, skeletal muscle, and fetal heart (Triepels et al., 1998). The study identified tubulin tyrosine ligase-like family, member 11 (TTLL11) gene that is involved in the polyglutamylation of microtubules, which “are major constituents of the cytoskeleton” (van Dijk et al., 2007). Furthermore, this study identified LIM homeobox protein 6 (LHX6). A study performed on mouse demonstrated that LHX6 plays “an important role in the maturation of cortical interneurons and the formation of inhibitory circuits in the 3 mammalian cortex” (Neves et al., 2013). RNA binding motif protein 18 (RMB18) is a protein coding gene. RNA-binding proteins have been reported to be associated with the building of the cerebral cortex during the embryonic development (Pilaz and Silver, 2015). The DENN/MADD domain containing 1A (DENND1A) regulates Rab GTPases, which is, among other things, required for GnRH-induced gonadotropin release (Welt et al., 2012).