Background: Identifying true-positive variants in genome-wide associations (GWA) depends on several factors, including the number of genotyped individuals. The limited dimensionality of the genomic information may give insights into the optimal number of individuals to use in GWA. This study investigated different discovery set sizes in GWA based on the number of largest eigenvalues explaining a certain proportion of variance in the genomic relationship matrix (G). An additional investigation included the change in accuracy by adding variants, selected based on different set sizes, to the regular SNP chips used for genomic prediction. Methods: Sequence data were simulated containing 500k SNP with 200 or 2000 quantitative trait nucleotides (QTN). A regular 50k panel included one every ten simulated SNP. Effective population size (Ne) was 20 and 200. The GWA was performed with the number of genotyped animals equivalent to the number of largest eigenvalues of G (EIG) explaining 50, 60, 70, 80, 90, 95, 98, and 99% of the variance. In addition, the largest discovery set consisted of 30k genotyped animals. Limited or extensive phenotypic information was mimicked by changing the trait heritability. Significant and high effect size SNP were added to the 50k panel and used for single-step GBLUP with and without weights. Results: Using the number of genotyped animals corresponding to at least EIG98 enabled the identification of QTN with the largest effect sizes when Ne was large. Smaller populations required more than EIG98. Furthermore, using genotyped animals with higher reliability (i.e., higher trait heritability) helped better identify the most informative QTN. The greatest prediction accuracy was obtained when the significant or the high effect SNP representing twice the number of simulated QTN were added to the 50k panel. Weighting SNP differently did not increase prediction accuracy, mainly because of the size of the genotyped population. Conclusions: Accurately identifying causative variants from sequence data depends on the effective population size and, therefore, the dimensionality of genomic information. This dimensionality can help identify the suitable sample size for GWA and could be considered for variant selection. Even when variants are accurately identified, their inclusion in prediction models has limited implications.

Pig survival is an economically important trait with relevant social welfare implications, thus standing out as an important selection criterion for the current pig farming system. We aimed to estimate (co)variance components for survival in different production phases in a crossbred pig population, as well as to investigate the benefit of including genomic information through single-step genomic BLUP (ssGBLUP) on the prediction accuracy of survival traits compared to results from traditional BLUP. Individual survival records on, at most, 64,894 crossbred piglets were evaluated under two multi-trait threshold models. The first model included farrowing, lactation, and combined post-weaning survival, whereas the second model included nursery and finishing survival. Direct and maternal breeding values were estimated using BLUP and ssGBLUP methods. Further, prediction accuracy, bias, and dispersion were accessed using the Linear Regression validation method. Direct heritability estimates for survival in all studied phases were low (from 0.02 to 0.08). Survival in pre-weaning phases (farrowing and lactation) was controlled by the dam and piglet additive genetic effects, although the maternal side was more important. Post-weaning phases (nursery, finishing, and the combination of both) showed the same or higher direct heritabilities compared to pre-weaning phases. The genetic correlations between survival traits within pre- and post-weaning phases were favorable and strong, but correlations between pre- and post-weaning phases were moderate. The prediction accuracy of survival traits was low, although it increased by including genomic information through ssGBLUP, compared to the prediction accuracy from BLUP. Direct and maternal breeding values were similarly accurate with BLUP, but direct breeding values benefited more from genomic information. Overall, a slight increase in bias was observed when genomic information was included, whereas dispersion of breeding values was greatly reduced. Post-weaning survival (POST) presented higher direct heritability than in the pre-weaning phases and the highest prediction accuracy among all evaluated production phases, therefore standing out as a candidate trait for improving survival. Survival is a complex trait with low heritability; however, important genetic gains can still be obtained, especially under a genomic prediction framework.

RESUMO O objetivo do presente trabalho foi avaliar a variabilidade genética de larvas e alevinos de piracanjuba em programa de repovoamento. Foram coletadas 180 larvas de piracanjuba de três dias e 90 alevinos de três meses de idade. Foram avaliados cinco loci microssatélites, os quais produziram 19 alelos. Não houve presença de alelos raros nem perdas de alelos ao longo do período. A heterozigosidade observada foi superior nas larvas em relação aos alevinos. Houve desvio no equilíbrio de Hardy-Weinberg na maioria dos loci em ambos os grupos. O coeficiente de endogamia foi positivo em ambos os grupos, sendo a média dos alevinos superior em relação às larvas. O excesso de heterozigotos foi significativo no modelo Stepwise Mutation Model para os alevinos, indicando a possibilidade de efeito gargalo recente. Conclui-se que, apesar da adequada variabilidade genética encontrada, os valores do coeficiente de endogamia e a possibilidade de efeito gargalo nos alevinos atentam para a necessidade de constante monitoramento genético desses estoques antes da liberação no ambiente.

The swine inflammation and necrosis syndrome (SINS) is a syndrome visually characterized by the presence of inflamed and necrotic skin at extreme body parts, such as the teats, tail, ears, and claw coronary bands. This syndrome is associated with several environmental causes, but knowledge of the role of genetics is still limited. Moreover, piglets affected by SINS are believed to be phenotypically more susceptible to chewing and biting behaviors from pen mates, which could cause a chronic reduction in their welfare throughout the production process. Our objectives were to 1) investigate the genetic basis of SINS expressed on piglets’ different body parts and 2) estimate SINS genetic relationship with post-weaning skin damage and pre and post-weaning production traits. A total of 5,960 two to three-day-old piglets were scored for SINS on the teats, claws, tails, and ears as a binary phenotype. Later, those binary records were combined into a trait defined as TOTAL_SINS. For TOTAL_SINS, animals presenting no signs of SINS were scored as 1, whereas animals showing at least one affected part were scored as 2. Apart from SINS traits, piglets had their birth weight (BW) and weaning weight (WW) recorded, and up to 4,132 piglets were later evaluated for combined skin damage (CSD), carcass backfat (BF), and loin depth (LOD). In the first set of analyses, the heritability of SINS on different body parts was estimated with single-trait animal-maternal models, and pairwise genetic correlations between body parts were obtained from two-trait models. Later, we used four three-trait animal models with TOTAL_SINS, CSD, and an alternative production trait (i.e., BW, WW, LOD, BF) to access trait heritabilities and genetic correlations between SINS and production traits. The maternal effect was included in the BW, WW, and TOTAL_SINS models. The direct heritability of SINS on different body parts ranged from 0.08 to 0.34, indicating that reducing SINS incidence through genetic selection is feasible. The direct genetic correlation between TOTAL_SINS and pre-weaning growth traits (BW and WW) was favorable and negative (from -0.40 to -0.30), indicating that selection for animals genetically less prone to present signs of SINS will positively affect the piglet’s genetics for heavier weight at birth and weaning. The genetic correlations between TOTAL_SINS and BF and between TOTAL_SINS and LOD were weak or not significant (-0.16 to 0.05). However, the selection against SINS was shown to be genetically correlated with CSD, with estimates ranging from 0.19 to 0.50. That means that piglets genetically less likely to present SINS signs are also more unlikely to suffer CSD after weaning, having a long-term increase in their welfare throughout the production system.

Background Identifying true positive variants in genome-wide associations (GWA) depends on several factors, including the number of genotyped individuals. The limited dimensionality of genomic information may give insights into the optimal number of individuals to be used in GWA. This study investigated different discovery set sizes based on the number of largest eigenvalues explaining a certain proportion of variance in the genomic relationship matrix (G). In addition, we investigated the impact on the prediction accuracy by adding variants, which were selected based on different set sizes, to the regular single nucleotide polymorphism (SNP) chips used for genomic prediction. Methods We simulated sequence data that included 500k SNPs with 200 or 2000 quantitative trait nucleotides (QTN). A regular 50k panel included one in every ten simulated SNPs. Effective population size (Ne) was set to 20 or 200. GWA were performed using a number of genotyped animals equivalent to the number of largest eigenvalues of G (EIG) explaining 50, 60, 70, 80, 90, 95, 98, and 99% of the variance. In addition, the largest discovery set consisted of 30k genotyped animals. Limited or extensive phenotypic information was mimicked by changing the trait heritability. Significant and large-effect size SNPs were added to the 50k panel and used for single-step genomic best linear unbiased prediction (ssGBLUP). Results Using a number of genotyped animals corresponding to at least EIG98 allowed the identification of QTN with the largest effect sizes when Ne was large. Populations with smaller Ne required more than EIG98. Furthermore, including genotyped animals with a higher reliability (i.e., a higher trait heritability) improved the identification of the most informative QTN. Prediction accuracy was highest when the significant or the large-effect SNPs representing twice the number of simulated QTN were added to the 50k panel. Conclusions Accurately identifying causative variants from sequence data depends on the effective population size and, therefore, on the dimensionality of genomic information. This dimensionality can help identify the most suitable sample size for GWA and could be considered for variant selection, especially when resources are restricted. Even when variants are accurately identified, their inclusion in prediction models has limited benefits.