Detection of Genomic Regions with Pleiotropic Effects for Growth and Carcass Quality Traits in the Rubia Gallega Cattle Breed.

The breeding scheme in the Rubia Gallega cattle population is based upon traits measured in farms and slaughterhouses. In recent years, genomic evaluation has been implemented by using a ssGBLUP (single-step Genomic Best Linear Unbiased Prediction). This procedure can reparameterized to perform ssGWAS (single-step Genome Wide Association Studies) by backsolving the SNP (single nucleotide polymorphisms) effects. Therefore, the objective of this study was to identify genomic regions associated with the genetic variability in growth and carcass quality traits. We implemented a ssGBLUP by using a database that included records for Birth Weight (BW-327,350 records-), Weaning Weight (WW-83,818-), Cold Carcass Weight (CCW-91,621-), Fatness (FAT-91,475-) and Conformation (CON-91,609-). The pedigree included 464,373 individuals, 2449 of which were genotyped. After a process of filtering, we ended up using 43,211 SNP markers. We used the GBLUP and SNPBLUP model equivalences to obtain the effects of the SNPs and then calculated the percentage of variance explained by the regions of the genome between 1 Mb. We identified 7 regions of the genome for CCW; 8 regions for BW, WW, FAT and 9 regions for CON, which explained the percentage of variance above 0.5%. Furthermore, a number of the genome regions had pleiotropic effects, located at: BTA1 (131-132 Mb), BTA2 (1-11 Mb), BTA3 (32-33 Mb), BTA6 (36-38 Mb), BTA16 (24-26 Mb), and BTA 21 (56-57 Mb). These regions contain, amongst others, the following candidate genes: NCK1, MSTN, KCNA3, LCORL, NCAPG, and RIN3.

Bayesian inference of the inbreeding load variance for fertility traits in Brown Swiss cattle.

Our study investigated the inbreeding load for fertility traits in the Italian Brown Swiss dairy cattle breed. Fertility traits included continuous traits (i.e., interval from calving to first service, days open, and calving interval) and categorical traits (i.e., calving rate at first insemination and nonreturn date at d 56). We included only records of the first 3 parities of cows that calved between 2010 and 2018. We traced up the pedigree of the cows with records as far as possible, ending up with a total of 73,246 animals. The final data set consisted of 59,864 records from 34,921 cows. We analyzed all models using a Bayesian approach that included a covariate with total inbreeding in addition to systematic, permanent environment, additive genetic, and inbreeding load effects. We then evaluated the trends in heritabilities and ratios of the inbreeding load using a continuum of partial inbreeding coefficients from 0.001 to 0.100 as reference. Posterior estimates of heritabilities tended to decrease across the continuum, whereas ratios of the inbreeding load tended to increase, more noticeably in categorical traits (calving rate at first insemination and nonreturn date at d 56). From the results obtained, we confirmed the presence of heterogeneity in inbreeding depression. We then predicted the inbreeding load effects, which had a low reliability of prediction, explained by having only 513 ancestors generating inbreeding. However, reliability of prediction was high enough for some of the individuals, obtaining a favorable prediction of inbreeding load for a relevant percentage, which improved the phenotypic performance of their inbred descendants. These results make it feasible to implement breeding and management strategies that select ancestors with a favorable inbreeding load prediction. In addition, it opens the possibility to define a global index for the expected consequences of the inbreeding generated by each individual.

Additive and Dominance Genomic Analysis for Litter Size in Purebred and Crossbred Iberian Pigs.

INGA FOOD S. A., as a Spanish company that produces and commercializes fattened pigs, has produced a hybrid Iberian sow called CASTÚA by crossing the Retinto and Entrepelado varieties. The selection of the parental populations is based on selection criteria calculated from purebred information, under the assumption that the genetic correlation between purebred and crossbred performance is high; however, these correlations can be less than one because of a GxE interaction or the presence of non-additive genetic effects. This study estimated the additive and dominance variances of the purebred and crossbred populations for litter size, and calculated the additive genetic correlations between the purebred and crossbred performances. The dataset consisted of 2030 litters from the Entrepelado population, 1977 litters from the Retinto population, and 1958 litters from the crossbred population. The individuals were genotyped with a GeneSeek® GGP Porcine70K HDchip. The model of analysis was a 'biological' multivariate mixed model that included additive and dominance SNP effects. The estimates of the additive genotypic variance for the total number born (TNB) were 0.248, 0.282 and 0.546 for the Entrepelado, Retinto and Crossbred populations, respectively. The estimates of the dominance genotypic variances were 0.177, 0.172 and 0.262 for the Entrepelado, Retinto and Crossbred populations. The results for the number born alive (NBA) were similar. The genetic correlations between the purebred and crossbred performance for TNB and NBA-between the brackets-were 0.663 in the Entrepelado and 0.881 in Retinto poplulations. After backsolving to obtain estimates of the SNP effects, the additive genetic variance associated with genomic regions containing 30 SNPs was estimated, and we identified four genomic regions that each explained > 2% of the additive genetic variance in chromosomes (SSC) 6, 8 and 12: one region in SSC6, two regions in SSC8, and one region in SSC12.

A multivariate analysis with direct additive and inbreeding depression load effects.

BACKGROUND: Inbreeding is caused by mating between related individuals and its most common consequence is inbreeding depression. Several studies have detected heterogeneity in inbreeding depression among founder individuals, and recently a procedure for predicting hidden inbreeding depression loads associated with founders and the Mendelian sampling of non-founders has been developed. The objectives of our study were to expand this model to predict the inbreeding loads for all individuals in the pedigree and to estimate the covariance between the inbreeding loads and the additive genetic effects for the trait of interest. We tested the proposed approach with simulated data and with two datasets of records on weaning weight from the Spanish Pirenaica and Rubia Gallega beef cattle breeds. RESULTS: The posterior estimates of the variance components with the simulated datasets did not differ significantly from the simulation parameters. In addition, the correlation between the predicted and simulated inbreeding loads were always positive and ranged from 0.27 to 0.82. The beef cattle datasets comprised 35,126 and 75,194 records on weights between 170 and 250 days of age, and pedigrees of 308,836 and 384,434 individual-sire-dam entries for the Pirenaica and Rubia Gallega breeds, respectively. The posterior mean estimates of the variance of inbreeding depression loads were 29,967.8 and 28,222.4 for the Pirenaica and Rubia Gallega breeds, respectively. They were larger than those of the additive variance (695.0 and 439.8 for Pirenaica and Rubia Gallega, respectively), because they should be understood as the variance of the inbreeding depression achieved by a fully inbred (100%) descendant. Therefore, the inbreeding loads have to be rescaled for smaller inbreeding coefficients. In addition, a strong negative correlation (- 0.43 ± 0.10) between additive effects and inbreeding loads was detected in the Pirenaica, but not in the Rubia Gallega breed. CONCLUSIONS: The results of the simulation study confirmed the ability of the proposed procedure to predict inbreeding depression loads for all individuals in the populations. Furthermore, the results obtained from the two real datasets confirmed the variability in the inbreeding depression loads in both breeds and suggested a negative correlation of the inbreeding loads with the additive genetic effects in the Pirenaica breed.