Correlation scan: identifying genomic regions that affect genetic correlations applied to fertility traits

Although the genetic correlation between complex traits have been estimated for more than a century, only recently we have started to map and understand the precise localization of the genomic region(s) that underpin these correlations. Reproductive traits are often genetically correlated, and yet we don't fully understand the complexities, synergism, or trade-offs between male and female fertility. In this study, we used reproductive traits in two cattle populations to develop a novel framework termed correlation scan. This framework was used to identify regions associated with the genetic correlations between male and female fertility traits across the bovine genome. The traits used were age at first corpus luteum (AGECL) and serum levels of insulin growth hormone (IGF1 measured in bulls, IGF1b, or cows, IGF1c). The methodology developed herein used correlations of 500-SNP (single nucleotide polymorphism) effects in a 100-SNPs sliding window in each chromosome to identify regions in the genome that either drive (i.e., SNP effects on the same direction) or antagonize (i.e., SNP effects in the opposite direction) the genetic correlations between traits. We used a permutation test to confirm which regions of the genome harboured significant correlations. Hence, this framework can also identify neutral genomic regions with no effect on the pairwise trait studied. About 40% of the total genomic regions were identified as driving and antagonizing genetic correlations between male and female fertility traits in the two population. These regions confirmed the polygenic nature of the traits being studied and pointed to genes of interest. Quantitative trait loci (QTL) and functional enrichment analysis revealed that many significant windows co-located with known QTLs related to milk production and fertility traits, especially puberty. In general, the enriched reproductive QTLs driving the genetic correlations between male and female fertility are the same for both cattle populations, while the antagonizing regions were population specific. Moreover, most of the antagonizing regions were mapped to the chromosome X. These results suggest regions of the chromosome X for further investigation into the trade-offs between male and female fertility. Although the methodology was applied to cattle phenotypes, using high-density SNP genotypes, the general framework developed can be applied to any species or traits, and it can easily accommodate genome sequence data.

Genetic inbreeding depression load for fertility traits in Pura Raza Española mares

Fertility is a key factor in the economic success of horse farms. However, it has received little attention due to the difficulty of measuring fertility objectively. Since its studbook creation (1912), the Pura Raza Española (PRE) breed has been a closed population and become high in-bred resulting in inbreeding depression (poor phenotypic values). Nevertheless, heterogeneous effects of inbreeding depression have been detected among founders and non-founders. The aims of this study were (1) to analyse the genetic parameters for reproductive traits in mares of the PRE horse breed, and (2) to estimate, for the first time, the inbreeding depression load associated with common ancestors of the breed. A total of 22,799 mares were analysed. Heritability estimates ranged from 0.05 (interval between first and second foaling) to 0.16 (age at first foaling), while inbreeding depression load ratios ranged from 0.06 (parturition efficiency at 6 th foaling) to 0.17 (age at first foaling), for a partial inbreeding coefficient of 10%. While heritability is related to the variability expressed in the population, inbreeding depression load ratios measure the potential variability, whether expressed in the population or not. Most correlations between additive and inbreeding depression load genetic values were significant (P-values <0.001), and of low to moderate magnitude. Our results confirm that individual inbreeding depression loads allow us to select horses that have a genetic value resistant to the deleterious effects of inbreeding.

PSXI-2 Genome-Wide association study and possibilities for genomic selection of Simmental cattle breed in Russia

The implementation of genomic selection methods in dairy cattle is limited due to the size of the reference population, quantitative traits variability and genetic structure of population. The Simmental cattle and related breeds in Russia occupy the third place by registered cows - 87.6 thousand, and are bred from the western borders of central Russia to Yakutia in Siberia. The study aim was to search for QTL using as a pseudo phenotype sires’ EBV to validate the effectiveness of genomic assessments for Simmental cattle in Russia. Reference group based on 358 bulls from different Russian regional populations was formed. By the dataset of 61976 cows, using BLUP Animal Model approach, animals’ EBV were calculated. The GBLUP procedure was used to get genomic prediction (DGV). Genotype quality control selected of 37143 SNPs. GWAS analysis was perform by Plink 1.90. To prove DGV we used verification procedure to comparison with EBV in training population dataset that showed slightly moderate repeatability for 305-milk yield - 37.6%, milk fat - 39.8% and milk protein - 43.6%. Significant SNPs associated with bulls EBV according to milk production and fertility traits revealed for: 305-milk yield on BTA5 (ACO2,p=0.0005), BTA29 (NAP1L4, P = 0.00004; KCNQ1, P = 0.00004); fat percentage on BTA3 (ROR1, P = 0.0003), BTA14 (CRH, P = 0.0003; TRIM55, P = 0.0003; DNAJC5B, P = 0.0003; REX2, P = 0.0020; ZFHX4, P = 0.0020) BTA17 (TLR2, P = 0.0011); days open on BTA1 (TNIK, P = 0.0007; FNDC3B, P = 0.0007), BTA21 (IGF1R, P = 0.0002). Using Cattle QTL Database, the identified genes were associated with milk yield, fat and protein percentage, protein yield, somatic cell score, protein-to-fat ratio, stearic fatty acid content, calving ease, daughter pregnancy rate, stillbirth and inseminations per conception. GWAS results identified significant SNPs associated with milk production and fertility traits that will allow in the nearest future improve reliability for genomic prediction of young bulls and boost genetic trend in the Russian Simmental cattle population. The study was funded by RFBR within project No.17-29-08030

166 Livestock Resiliency: Concepts and Approaches

Genetic improvement of health, welfare, efficiency, and fertility traits is challenging due to expensive and fuzzy phenotypes, the polygenic nature of traits, antagonistic genetic correlations to production traits and low heritabilities. Nevertheless, many organizations have introduced large-scale genetic evaluations for such traits in routine selection indexes. Medium and high-density arrays can be applied in genomic selection strategies to improve breeding value accuracy, and also in genome-wide association studies (GWAS) to identify causative mutations responsible for economically important traits. Genomic information is particularly helpful when traits have low heritability. The objective here is to provide a framework for including health, welfare, efficiency, and fertility traits taken from large-scale genetic and genomic analyses and identifying areas of potential improvement in terms of trait definition and performance testing. General tendencies between trait groups confirmed that a number of moderate unfavourable correlations (+/-0.20 or higher) exist between economically important trait complexes and health, welfare, and fertility traits. A number of trait complexes were identified in which “closer-to-biology” phenotypes could provide clear improvements to routine genetic and genomic selection programs. Here we outline development of these phenotypes and describe their collection. While conventional variance component estimation methods have underpinned the genomic component of some traits of economic interest, performance testing for health, welfare, efficiency, and fertility traits remains an elusive goal for breeding programs. Although our results are encouraging, there is much to be done in terms of trait definition and obtaining better measures of physiological parameters for wide-scale application in breeding programs. Close collaboration between veterinarians, physiologists, and geneticists is necessary to attain meaningful advancement in such areas. We would like to acknowledge the support and funding from all national and international partners involved in the RDGP project through the Large Scale Applied Research Project program from Genome Canada