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Osteoporosis and bone fractures are a severe problem for the welfare of laying hens, with genetics and environment, such as housing system, each making substantial contributions to bone strength. In this work, we performed genetic analyses of bone strength, bone mineral density, and bone composition, as well as body weight, in 860 commercial crossbred laying hens from 2 different companies, kept in either furnished cages or floor pens. We compared bone traits between housing systems and crossbreds and performed a genome-wide association study of bone properties and body weight. As expected, the 2 housing systems produced a large difference in bone strength, with layers housed in floor pens having stronger bones. These differences were accompanied by differences in bone geometry, mineralization, and chemical composition. Genome scans either combining or independently analyzing the 2 housing systems revealed no genome-wide significant loci for bone breaking strength. We detected 3 loci for body weight that were shared between the housing systems on chromosomes 4, 6, and 27 (either genome-wide significant or suggestive) and these coincide with associations for bone length. In summary, we found substantial differences in bone strength, content, and composition between hens kept in floor pens and furnished cages that could be attributed to greater physical activity in pen housing. We found little evidence for large-effect loci for bone strength in commercial crossbred hens, consistent with a highly polygenic architecture for bone strength in the production environment. The lack of consistent genetic associations between housing systems in combination with the differences in bone phenotypes could be due to gene-by-environment interactions with housing system or a lack of power to detect shared associations for bone strength., Competing Interests: Conflicts of interest The authors declare no conflict of interest., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Genetics Society of America.)

Arctic charr ( Salvelinus alpinus ) is a species of high economic value for the aquaculture industry, and of high ecological value due to its Holarctic distribution in both marine and freshwater environments. Novel genome sequencing approaches enable the study of population and quantitative genetic parameters even on species with limited or no prior genomic resources. Low coverage genotyping by sequencing (GBS) was applied in a selected strain of Arctic charr in Sweden originating from a landlocked freshwater population. For the needs of the current study, animals from year classes 2013 (171 animals, parental population) and 2017 (759 animals; 13 full sib families) were used as a template for identifying genome wide single nucleotide polymorphisms (SNPs). GBS libraries were constructed using the PstI and MspI restriction enzymes. Approximately 14.5K SNPs passed quality control and were used for estimating a genomic relationship matrix. Thereafter a wide range of analyses were conducted in order to gain insights regarding genetic diversity and investigate the efficiency of the genomic information for parentage assignment and breeding value estimation. Heterozygosity estimates for both year classes suggested a slight excess of heterozygotes. Furthermore, F ST estimates among the families of year class 2017 ranged between 0.009 - 0.066. Principal components analysis (PCA) and discriminant analysis of principal components (DAPC) were applied aiming to identify the existence of genetic clusters among the studied population. Results obtained were in accordance with pedigree records allowing the identification of individual families. Additionally, DNA parentage verification was performed, with results in accordance with the pedigree records with the exception of a putative dam where full sib genotypes suggested a potential recording error. Breeding value estimation for juvenile growth through the usage of the estimated genomic relationship matrix clearly outperformed the pedigree equivalent in terms of prediction accuracy (0.51 opposed to 0.31). Overall, low coverage GBS has proven to be a cost-effective genotyping platform that is expected to boost the selection efficiency of the Arctic charr breeding program., (Copyright © 2020 Palaiokostas et al.)

Using targeted genetical genomics, a quantitative trait locus (QTL) affecting the initial postmortem pH value of chicken breast muscle (Pectoralis major) on chromosome 1 (GGA1) recently was fine-mapped. Thirteen genes were present in the QTL region of approximately 1 Mb. In this study, 10 birds that were inferred to be homozygous for either the high (QQ) or low (qq) QTL allele were selected for resequencing. After enrichment for 1 Mb around the QTL region, >500 × coverage for the QTL region in each of the 10 birds was obtained. In total 5056 single-nucleotide polymorphisms (SNPs) were identified for which the genotypes were consistent with one of the QTL genotypes. We used custom tools to identify putative causal mutations in the mapped QTL region from these SNPs. Four nonsynonymous SNPs differentiating the two QTL genotype groups were identified within four local genes (PRDX4, EIF2S3, PCYT1B, and E1BTD2). Although these are likely candidate SNPs to explain the QTL effect, 54 additional consensus SNPs were detected within gene-related regions (untranslated regions, splicing sites CpG island, and promoter regions) for the QQ birds and 71 for the qq birds. These could also play a role explaining the observed QTL effect. The results provide an important step for prioritizing among a large amount of candidate mutations and significantly contribute to the understanding of the genetic mechanisms affecting the initial postmortem pH value of chicken muscle., (Copyright © 2015 Li et al.)

Genomic selection has been widely implemented in dairy cattle breeding when the aim is to improve performance of purebred animals. In pigs, however, the final product is a crossbred animal. This may affect the efficiency of methods that are currently implemented for dairy cattle. Therefore, the objective of this study was to determine the accuracy of predicted breeding values in crossbred pigs using purebred genomic and phenotypic data. A second objective was to compare the predictive ability of SNPs when training is done in either single or multiple populations for four traits: age at first insemination (AFI); total number of piglets born (TNB); litter birth weight (LBW); and litter variation (LVR). We performed marker-based and pedigree-based predictions. Within-population predictions for the four traits ranged from 0.21 to 0.72. Multi-population prediction yielded accuracies ranging from 0.18 to 0.67. Predictions across purebred populations as well as predicting genetic merit of crossbreds from their purebred parental lines for AFI performed poorly (not significantly different from zero). In contrast, accuracies of across-population predictions and accuracies of purebred to crossbred predictions for LBW and LVR ranged from 0.08 to 0.31 and 0.11 to 0.31, respectively. Accuracy for TNB was zero for across-population prediction, whereas for purebred to crossbred prediction it ranged from 0.08 to 0.22. In general, marker-based outperformed pedigree-based prediction across populations and traits. However, in some cases pedigree-based prediction performed similarly or outperformed marker-based prediction. There was predictive ability when purebred populations were used to predict crossbred genetic merit using an additive model in the populations studied. AFI was the only exception, indicating that predictive ability depends largely on the genetic correlation between PB and CB performance, which was 0.31 for AFI. Multi-population prediction was no better than within-population prediction for the purebred validation set. Accuracy of prediction was very trait-dependent., (Copyright © 2015 Hidalgo et al.)

In animal breeding, the genetic potential of an animal is summarized as its estimated breeding value, which is derived from its own performance as well as the performance of related individuals. Here, we illustrate why estimated breeding values are not suitable as a phenotype for genome-wide association studies. We simulated human-type and pig-type pedigrees with a range of quantitative trait loci (QTL) effects (0.5-3% of phenotypic variance) and heritabilities (0.3-0.8). We analyzed 1000 replicates of each scenario with four models: (a) a full mixed model including a polygenic effect, (b) a regression analysis using the residual of a mixed model as a trait score (so called GRAMMAR approach), (c) a regression analysis using the estimated breeding value as a trait score, and (d) a regression analysis that uses the raw phenotype as a trait score. We show that using breeding values as a trait score gives very high false-positive rates (up 14% in human pedigrees and >60% in pig pedigrees). Simulations based on a real pedigree show that additional generations of pedigree increase the type I error. Including the family relationship as a random effect provides the greatest power to detect QTL while controlling for type I error at the desired level and providing the most accurate estimates of the QTL effect. Both the use of residuals and the use of breeding values result in deflated estimates of the QTL effect. We derive the contributions of QTL effects to the breeding value and residual and show how this affects the estimates.