Genetics Selection Evolution最新文献

{"title":"Merging metabolomics and genomics provides a catalog of genetic factors that influence molecular phenotypes in pigs linking relevant metabolic pathways","authors":"Samuele Bovo, Anisa Ribani, Flaminia Fanelli, Giuliano Galimberti, Pier Luigi Martelli, Paolo Trevisi, Francesca Bertolini, Matteo Bolner, Rita Casadio, Stefania Dall’Olio, Maurizio Gallo, Diana Luise, Gianluca Mazzoni, Giuseppina Schiavo, Valeria Taurisano, Paolo Zambonelli, Paolo Bosi, Uberto Pagotto, Luca Fontanesi","doi":"10.1186/s12711-025-00960-8","DOIUrl":"https://doi.org/10.1186/s12711-025-00960-8","url":null,"abstract":"Metabolomics opens novel avenues to study the basic biological mechanisms underlying complex traits, starting from characterization of metabolites. Metabolites and their levels in a biofluid represent simple molecular phenotypes (metabotypes) that are direct products of enzyme activities and relate to all metabolic pathways, including catabolism and anabolism of nutrients. In this study, we demonstrated the utility of merging metabolomics and genomics in pigs to uncover a large list of genetic factors that influence mammalian metabolism. We obtained targeted characterization of the plasma metabolome of more than 1300 pigs from two populations of Large White and Duroc pig breeds. The metabolomic profiles of these pigs were used to identify genetically influenced metabolites by estimating the heritability of the level of 188 metabolites. Then, combining breed-specific genome-wide association studies of single metabolites and their ratios and across breed meta-analyses, we identified a total of 97 metabolite quantitative trait loci (mQTL), associated with 126 metabolites. Using these results, we constructed a human-pig comparative catalog of genetic factors influencing the metabolomic profile. Whole genome resequencing data identified several putative causative mutations for these mQTL. Additionally, based on a major mQTL for kynurenine level, we designed a nutrigenetic study feeding piglets that carried different genotypes at the candidate gene kynurenine 3-monooxygenase (KMO) varying levels of tryptophan and demonstrated the effect of this genetic factor on the kynurenine pathway. Furthermore, we used metabolomic profiles of Large White and Duroc pigs to reconstruct metabolic pathways using Gaussian Graphical Models, which included perturbation of the identified mQTL. This study has provided the first catalog of genetic factors affecting molecular phenotypes that describe the pig blood metabolome, with links to important metabolic pathways, opening novel avenues to merge genetics and nutrition in this livestock species. The obtained results are relevant for basic and applied biology and to evaluate the pig as a biomedical model. Genetically influenced metabolites can be further exploited in nutrigenetic approaches in pigs. The described molecular phenotypes can be useful to dissect complex traits and design novel feeding, breeding and selection programs in pigs.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"36 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of genomic selection models using whole genome sequence data and functional annotation in Belgian Blue cattle","authors":"Can Yuan, Alain Gillon, José Luis Gualdrón Duarte, Haruko Takeda, Wouter Coppieters, Michel Georges, Tom Druet","doi":"10.1186/s12711-025-00955-5","DOIUrl":"https://doi.org/10.1186/s12711-025-00955-5","url":null,"abstract":"The availability of large cohorts of whole-genome sequenced individuals, combined with functional annotation, is expected to provide opportunities to improve the accuracy of genomic selection (GS). However, such benefits have not often been observed in initial applications. The reference population for GS in Belgian Blue Cattle (BBC) continues to grow. Combined with the availability of reference panels of sequenced individuals, it provides an opportunity to evaluate GS models using whole genome sequence (WGS) data and functional annotation. Here, we used data from 16,508 cows, with phenotypes for five muscular development traits and imputed at the WGS level, in combination with in silico functional annotation and catalogs of putative regulatory variants obtained from experimental data. We evaluated first GS models using the entire WGS data, with or without functional annotation. At this marker density, we were able to run two approaches, assuming either a highly polygenic architecture (GBLUP) or allowing some variants to have larger effects (BayesRR-RC, a Bayesian mixture model), and observed an increased reliability compared to the official GBLUP model at medium marker density (on average 0.016 and 0.018 for GBLUP and BayesRR-RC, respectively). When functional annotation was used, we observed slightly higher reliabilities with an extension of GBLUP that included multiple polygenic terms (one per functional group), while reliabilities decreased with BayesRR-RC. We then used large subsets of variants selected based on functional information or with a linkage disequilibrium (LD) pruning approach, which allowed us to evaluate two additional approaches, BayesCπ and Bayesian Sparse Linear Mixed Model (BSLMM). Reliabilities were higher for these panels than for the WGS data, with the highest accuracies obtained when markers were selected based on functional information. In our setting, BSLMM systematically achieved higher reliabilities than other methods. GS with large panels of functional variants selected from WGS data allowed a significant increase in reliability compared to the official genomic evaluation approach. However, the benefits of using WGS and functional data remained modest, indicating that there is still room for improvement, for example by further refining the functional annotation in the BBC breed.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"34 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143538512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genome-wide association analysis using multiple Atlantic salmon populations","authors":"Afees A. Ajasa, Hans M. Gjøen, Solomon A. Boison, Marie Lillehammer","doi":"10.1186/s12711-025-00959-1","DOIUrl":"https://doi.org/10.1186/s12711-025-00959-1","url":null,"abstract":"In a previous study, we found low persistence of linkage disequilibrium (LD) phase across breeding populations of Atlantic salmon. Accordingly, we observed no increase in accuracy from combining these populations for genomic prediction. In this study, we aimed to examine if the same were true for detection power in genome-wide association studies (GWAS), in terms of reduction in p-values, and if the precision of mapping quantitative trait loci (QTL) would improve from such analysis. Since individual records may not always be available, e.g. due to proprietorship or confidentiality, we also compared mega-analysis and meta-analysis. Mega-analysis needs access to all individual records, whereas meta-analysis utilizes parameters, such as p-values or allele substitution effects, from multiple studies or populations. Furthermore, different methods for determining the presence or absence of independent or secondary signals, such as conditional association analysis, approximate conditional and joint analysis (COJO), and the clumping approach, were assessed. Mega-analysis resulted in increased detection power, in terms of reduction in p-values, and increased precision, compared to the within-population GWAS. Only one QTL was detected using conditional association analysis, both within populations and in mega-analysis, while the number of QTL detected with COJO and the clumping approach ranged from 1 to 19. The allele substitution effect and -log10p-values obtained from mega-analysis were highly correlated with the corresponding values from various meta-analysis methods. Compared to mega-analysis, a higher detection power and reduced precision were obtained with the meta-analysis methods. Our results show that combining multiple datasets or populations in a mega-analysis can increase detection power and mapping precision. With meta-analysis, a higher detection power was obtained compared to mega-analysis. However, care must be taken in the interpretation of the meta-analysis results from multiple populations because their test statistics might be inflated due to population structure or cryptic relatedness.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"210 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143506911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The ability to race barefoot is a heritable trait in Standardbred and Coldblooded trotters","authors":"Paulina Berglund, Sreten Andonov, Anna Jansson, Christina Olsson, Therese Lundqvist, Erling Strandberg, Susanne Eriksson","doi":"10.1186/s12711-025-00958-2","DOIUrl":"https://doi.org/10.1186/s12711-025-00958-2","url":null,"abstract":"In equine sports, shoes are used to protect the hooves from wear and tear. In Swedish trotting races, pulling off the shoes to race barefoot is popular because it improves racing time. Good hoof quality is essential for high-performance horses, but not all trotting horses have hooves that tolerate barefoot racing. The ability to race barefoot is a complex trait that is known to be influenced by environmental factors, but the genetic basis of this trait has not been studied. The aim of this study was to estimate genetic parameters and correlations between estimated breeding values for three novel traits: two related to the proportion of barefoot races and “barefoot status”, a binary trait that reflects the probability of racing unshod in a race, in Swedish Standardbred trotters (SB) and Swedish-Norwegian Coldblooded trotters (CB). For the two traits describing the proportion of barefoot races, single-trait mixed linear animal models were used to estimate variance components for up to 24,958 SB and up to 4050 CB. Estimates of heritability ranged from 0.17 to 0.28. For barefoot status, a binary trait with repeated measurements, 875,056 observations from 25,973 SB, and 93,376 observations from 3384 CB were included. Using a single-trait mixed animal threshold model estimates of heritability for barefoot status were 0.07 and 0.08. The Pearson correlation coefficient between the estimated breeding values for barefoot status and each of the traits describing the proportion of barefoot races for breeding stallions was 0.63 and 0.64 for SB and 0.82 and 0.76 for CB. The traits analyzed reflecting the ability to race barefoot are heritable, with the traits for the proportion of barefoot races showing higher heritability estimates for both breeds than barefoot status. Estimated breeding values for breeding stallions were moderately to strongly correlated for the three traits. The average accuracy of estimated breeding values for breeding stallions was moderate to high for all traits. To breed for the ability to race barefoot, further studies on the genetic correlation of the ability to race barefoot with performance traits and the impact of racing barefoot on career length, are necessary.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"3 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143485956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mendelian randomisation to uncover causal associations between conformation, metabolism, and production as potential exposure to reproduction in German Holstein dairy cattle","authors":"Leopold Schwarz, Johannes Heise, Zengting Liu, Jörn Bennewitz, Georg Thaller, Jens Tetens","doi":"10.1186/s12711-025-00950-w","DOIUrl":"https://doi.org/10.1186/s12711-025-00950-w","url":null,"abstract":"Reproduction is vital to welfare, health, and economics in animal husbandry and breeding. Health and reproduction are increasingly being considered because of the observed genetic correlations between reproduction, health, conformation, and performance traits in dairy cattle. Understanding the detailed genetic architecture underlying these traits would represent a major step in comprehending their interplay. Identifying known, putative or novel associations in genomics could improve animal health, welfare, and performance while allowing further adjustments in animal breeding. We conducted genome-wide association studies for 25 different traits belonging to four different complexes, namely reproduction (n = 13), conformation (n = 6), production (n = 3), and metabolism (n = 3), using a cohort of over 235,000 dairy cows. As a result, we identified genome-wide significant signals for all the studied traits. The obtained summary statistics collected served as the input for a Mendelian randomisation approach (GSMR) to infer causal associations between putative exposure and reproduction traits. The study considered conformation, production, and metabolism as exposure and reproduction as outcome. A range of 139 to 252 genome-wide significant SNPs per combination were identified as instrumental variables (IVs). Out of 156 trait combinations, 135 demonstrated statistically significant effects, thereby enabling the identification of the responsible IVs. Combinations of traits related to metabolism (38 out of 39), conformation (68 out of 78), or production (29 out of 39) were found to have significant effects on reproduction. These relationships were partially non-linear. Moreover, a separate variance component estimation supported these findings, strongly correlating with the GSMR results and offering suggestions for improvement. Downstream analyses of selected representative traits per complex resulted in identifying and investigating potential physiological mechanisms. Notably, we identified both trait-specific SNPs and genes that appeared to influence specific traits per complex, as well as more general SNPs that were common between exposure and outcome traits. Our study confirms the known genetic associations between reproduction traits and the three complexes tested. It provides new insights into causality, indicating a non-linear relationship between conformation and reproduction. In addition, the downstream analyses have identified several clustered genes that may mediate this association.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"14 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143485964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of crossbreeding strategies for improved adaptation and productivity in African smallholder cattle farms","authors":"Sèyi Fridaïus Ulrich Vanvanhossou, Tong Yin, Gregor Gorjanc, Sven König","doi":"10.1186/s12711-025-00952-8","DOIUrl":"https://doi.org/10.1186/s12711-025-00952-8","url":null,"abstract":"Crossbreeding is successfully implemented worldwide to improve animal productivity and adaptability. However, recurrent failures of crossbreeding programmes in African countries imply the need to design effective strategies for the predominant smallholder production systems. A comprehensive simulation procedure mimicked body weight (BWL) and tick count (TCL) incidence in a local taurine cattle breed and in an exotic indicine beef cattle breed (BWE and TCE, respectively). The two breeds were crossed to produce F1 and rotational animals. Additionally, synthetic breeds were created by applying four schemes defined as farm bull (FB), intra-village bull (IVB), exchanged-village bull (EVB), and population-wide bull (PWB) scheme. These schemes reflect different strategies to select and allocate bulls to smallholder farms. The different crosses were compared with the local breed over 20 generations by varying the genetic correlation between the traits ( $${r}_{g}$$ = − 0.4, 0, 0.4), genotype-by-environment effects (GxE) between local and exotic environment ( $${r}_{gtimes e}$$ = 0.4, 0.6, 0.8), and the relative emphasis of TCL compared to BWL in a selection index (SI_TCL10%, SI_TCL30%, SI_TCL50%). Regardless of $${r}_{g}$$ and $${r}_{gtimes e}$$ , EVB achieved the highest phenotypic and genetic gains for BWL and TCL over the 20 generations with SI_TCL50%. However, EVB displayed lower phenotypic means than F1 crosses in the first seven generations due to the loss of heterosis. Additive genetic variances were generally larger in synthetic crosses than in F1 and local animals, explaining the larger responses to selection. In addition, the EVB was the most effective strategy to stabilize inbreeding and retain heterosis in the advanced generations of synthetic animals. Low emphasis on TCL (SI_TCL30%, SI_TCL10%) resulted in negative phenotypic gain for TCL in synthetic animals when rg = − 0.4. In contrast to F1 and rotational crosses, GxE effects did not affect phenotypic gain in synthetic crosses. The study demonstrates opportunities for long-term genetic improvement of adaptive and productive performances in smallholder cattle farms using synthetic breeding. Extensive exchange of semen between villages or regions controls inbreeding and additionally contributes to increasing genetic gain. Furthermore, the definition of a suitable selection index prevents antagonistic selection responses caused by negative correlations between traits and GxE effects.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"130 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143451927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Is there an advantage of using genomic information to estimate gametic variances and improve recurrent selection in animal populations?","authors":"Jean-Michel Elsen, Jérôme Raoul, Hélène Gilbert","doi":"10.1186/s12711-025-00953-7","DOIUrl":"https://doi.org/10.1186/s12711-025-00953-7","url":null,"abstract":"Gametic variances can be predicted from the outcomes of a genomic prediction for any genotyped individual. This is widely used in plant breeding, applying the utility criterion (UC). This paper aims to examine the conditions to use UC for recurrent selection in livestock. Here, the UC for a selection candidate is the linear combination of the expected value of the future progeny (half of the candidate’s breeding value) and its predicted gametic variance weighted by a coefficient $$theta$$ to be optimized. First, generalizing previous results, we derived analytically the ratio of the variance of the candidate’s gametic variance and that of half of the candidate’s breeding value. This ratio depends strongly on the number of quantitative trait loci (QTL) affecting the trait and, to a lesser extent, on the distribution of QTL allele frequencies: highly unbalanced frequencies and a limited number of QTL (< 10) favor higher values of the ratio. Then, changes in average breeding values and genetic variances when recurrent selection in a population of infinite size is applied were analytically derived and analyzed for selection up to 15 generations: in this ideal situation, after 5 to 10 generations (depending on $$theta$$ ), the expected breeding values were higher with selection on UC and the genetic variance was always higher than with selection on estimated breeding values. To describe the potential of the UC in more general situations, simulations were applied to a population of 1000 males and 1000 females, with various selection rates, numbers and allele frequencies of QTL, and $$theta$$ . These simulations were performed assuming independent QTL with known positions and effects. The best values for $$theta$$ (i.e. providing the best genetic progress) were generally lower than 1, limiting the weight on the gametic variance. As expected from the analytical derivations, the gain in genetic progress from using UC was greatest when there were few QTL and allele frequencies were unbalanced, but they barely exceeded 5%. We conclude that the key factor to choose selection on UC rather than on estimated breeding values is the ratio between the variance of the gametic standard deviations and the variance of the breeding values (GEBV), which should be carefully evaluated.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"1 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic parameters and parental and early-life effects of boar semen traits","authors":"Pedro Sá, Rodrigo M. Godinho, Marta Gòdia, Claudia A. Sevillano, Barbara Harlizius, Ole Madsen, Henk Bovenhuis","doi":"10.1186/s12711-025-00954-6","DOIUrl":"https://doi.org/10.1186/s12711-025-00954-6","url":null,"abstract":"The objectives of this study were to estimate genetic parameters and studying the influence of early-life and parental factors on the semen traits of boars. The dataset included measurements on 449,966 ejaculates evaluated using a Computer-Assisted Sperm Analysis (CASA) system from 5692 artificial insemination (AI) boars. In total, we considered 16 semen traits measured on fresh semen and 6 sperm motility traits measured on semen after storage. Early-life effects included the dam’s parity, ages of the dam and sire, gestation length, litter size, litter sex ratio, number of piglets born alive, number of litter mates at weaning, rearing length, and weight gain. A repeatability model accounting for effects at collection was used to (1) estimate heritabilities and repeatabilities for semen traits and genetic and phenotypic correlations between traits, (2) test the significance of early-life effects, (3) quantify the contribution of exclusive dam and sire inheritances to the phenotypic variation, i.e., mitochondrial DNA and the Y chromosome, identified using a pedigree-based approach, and (4) quantify the contribution of maternal and paternal environment effects to the phenotypic variation of semen traits. We reported heritabilities between 0.11 and 0.27 and repeatabilities between 0.20 and 0.65 for semen traits. Semen quality traits showed a skewed distribution, and their transformation significantly reduced their repeatability estimates. Motility traits measured after storage were genetically different from motility traits measured on fresh semen. Early-life had suggestive effects on a limited number of semen traits. Mitochondrial DNA and the Y chromosome did not explain a discerning proportion of the phenotypic variance and the effect of the paternal environment was also negligible. We estimated a significant maternal environment effect predominantly on sperm motility traits, explaining between 2.3 and 4.6% of the phenotypic variance. Including maternal environmental effects in the model reduced heritability estimates for sperm motility traits and total morphological abnormalities. Our findings indicate that trait transformation has a large effect on repeatability estimates of semen traits. Sperm motility traits measured on fresh semen are genetically different from sperm motility traits measured after storage. Early-life conditions can have an effect on later semen quantity and quality traits. Mitochondrial DNA and Y chromosome inheritances showed no effect on semen traits. Finally, we emphasize the importance of considering maternal effects when analysing semen traits, which results in lower heritability estimates.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"9 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sequence-based GWAS in 180,000 German Holstein cattle reveals new candidate variants for milk production traits","authors":"Ana-Marija Križanac, Christian Reimer, Johannes Heise, Zengting Liu, Jennie E. Pryce, Jörn Bennewitz, Georg Thaller, Clemens Falker-Gieske, Jens Tetens","doi":"10.1186/s12711-025-00951-9","DOIUrl":"https://doi.org/10.1186/s12711-025-00951-9","url":null,"abstract":"Milk production traits are complex and influenced by many genetic and environmental factors. Although extensive research has been performed for these traits, with many associations unveiled thus far, due to their crucial economic importance, complex genetic architecture, and the fact that causal variants in cattle are still scarce, there is a need for a better understanding of their genetic background. In this study, we aimed to identify new candidate loci associated with milk production traits in German Holstein cattle, the most important dairy breed in Germany and worldwide. For that purpose, 180,217 cattle were imputed to the sequence level and large-scale genome-wide association study (GWAS) followed by fine-mapping and evolutionary and functional annotation were carried out to identify and prioritize new association signals. Using the imputed sequence data of a large cattle dataset, we identified 50,876 significant variants, confirming many known and identifying previously unreported candidate variants for milk (MY), fat (FY), and protein yield (PY). Genome-wide significant signals were fine-mapped with the Bayesian approach that determines the credible variant sets and generates the probability of causality for each signal. The variants with the highest probabilities of being causal were further classified using external information about the function and evolution, making the prioritization for subsequent validation experiments easier. The top potential causal variants determined with fine-mapping explained a large percentage of genetic variance compared to random ones; 178 variants explained 11.5%, 104 explained 7.7%, and 68 variants explained 3.9% of the variance for MY, FY, and PY, respectively, demonstrating the potential for causality. Our findings proved the power of large samples and sequence-based GWAS in detecting new association signals. In order to fully exploit the power of GWAS, one should aim at very large samples combined with whole-genome sequence data. These can also come with both computational and time burdens, as presented in our study. Although milk production traits in cattle are comprehensively investigated, the genetic background of these traits is still not fully understood, with the potential for many new associations to be revealed, as shown. With constantly growing sample sizes, we expect more insights into the genetic architecture of milk production traits in the future.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"25 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143125318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genomic selection strategies to overcome genotype by environment interactions in biosecurity-based aquaculture breeding programs","authors":"Ziyi Kang, Jie Kong, Qi Li, Juan Sui, Ping Dai, Kun Luo, Xianhong Meng, Baolong Chen, Jiawang Cao, Jian Tan, Qiang Fu, Qun Xing, Sheng Luan","doi":"10.1186/s12711-025-00949-3","DOIUrl":"https://doi.org/10.1186/s12711-025-00949-3","url":null,"abstract":"Family-based selective breeding programs typically employ both between-family and within-family selection in aquaculture. However, these programs may exhibit a reduced genetic gain in the presence of a genotype by environment interactions (G × E) when employing biosecurity-based breeding schemes (BS), compared to non-biosecurity-based breeding schemes (NBS). Fortunately, genomic selection shows promise in improving genetic gain by taking within-family variance into account. Stochastic simulation was employed to evaluate genetic gain and G × E trends in BS for improving the body weight of L. vannamei, considering selective genotyping strategies for test group (TG) at a commercial farm environment (CE), the number individuals of the selection group (SG) genotyped at nucleus breeding center (NE), and varying levels of G × E. The loss of genetic gain in BS ranged from 9.4 to 38.9% in pedigree-based selection and was more pronounced when G × E was stronger, as quantified by a lower genetic correlation for body weight between NE and CE. Genomic selection, particularly with selective genotyping of TG individuals with extreme performance, effectively offset the loss of genetic gain. With a genetic correlation of 0.8, genotyping 20 SG individuals in each candidate family achieved 93.2% of the genetic gain observed for NBS. However, when the genetic correlation fell below 0.5, the number of genotyped SG individuals per family had to be increased to 50 or more. Genetic gain improved by on average 9.4% when the number of genotyped SG individuals rose from 20 to 50, but the increase in genetic gain averaged only 2.4% when expanding from 50 to 80 individuals genotyped. In addition, the genetic correlation decreased by on average 0.13 over 30 generations of selection when performing BS and the genetic correlation fluctuated across generations. Genomic selection can effectively compensate for the loss of genetic gain in BS due to G × E. However, the number of genotyped SG individuals and the level of G × E significantly affected the extra genetic gain from genomic selection. A family-based BS selective breeding program should monitor the level of G × E and genotyping 50 SG individuals per candidate family to minimize the loss of genetic gain due to G × E, unless the level of G × E is confirmed to be low.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"17 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143020654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}