Genetics Selection Evolution最新文献

{"title":"Assessing the predictive potential of the Holstein bovine sperm methylome for bull fertility using machine learning.","authors":"A Asset, V Costes, C Fouéré, A Raja-Ravi-Shankar, C Le Danvic, F Jaffrézic, H Kiefer, D Makowski","doi":"10.1186/s12711-026-01097-y","DOIUrl":"https://doi.org/10.1186/s12711-026-01097-y","url":null,"abstract":"<p><strong>Background: </strong>Bull fertility is a key parameter in the dairy cattle industry, with significant economic implications. DNA methylation is an epigenetic mark essential for fertility, and recent works have highlighted sperm DNA methylation as a promising source of biomarkers for male fertility, but no operational tool has been developed for predicting bull fertility based on DNA methylation. Here, we leveraged existing methylation data to evaluate the potential of DNA methylation for predicting bull fertility with machine learning models.</p><p><strong>Results: </strong>Our study was conducted on a cohort of 351 Holstein commercial bulls, classified as subfertile, neutral and fertile based on a field fertility index (non-return rate of the inseminated cows after 56 days post-insemination). This dataset includes the largest cohort assembled for bull fertility prediction from epigenetic markers. We developed classification models to discriminate subfertile and fertile individuals using a subset of predictors derived from reduced representation bisulfite sequencing data obtained on frozen semen. Four machine learning algorithms were compared: Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest, Gradient Boosting, and Neural Networks. To optimize performances of each algorithm and conduct an unbiased assessment of classification accuracy, we used a three-fold cross-validation before final testing on an independent test set. To increase the robustness of the results, model performances were averaged over 20 partitions of the data into training and test sets, to account for variability in bull distribution across sets. All models performed better than random classification. The best model was LASSO according to classification accuracy (0.62) and Random Forest according to Area Under Curve (0.69) computed with the independent test set.</p><p><strong>Conclusions: </strong>Even with this moderate performance, our models would lead to a 12.4% point increase of the probability of identifying subfertile individuals. These results suggest that sperm DNA methylation holds potential as a relevant source of predictive markers for bull fertility.</p>","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"58 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149302230","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":"Genotype by environment interaction under two production systems utilizing a random regression model for milk production traits in multiparous South African Holstein dairy cattle.","authors":"Michiel Van Niekerk, Frederick W C Neser, Jacobus B Van Wyk, Vincent Ducrocq","doi":"10.1186/s12711-026-01098-x","DOIUrl":"https://doi.org/10.1186/s12711-026-01098-x","url":null,"abstract":"<p><strong>Background: </strong>Dairy cattle in South Africa (SA) have to produce in varying environments which can roughly be separated as total mixed ration (TMR) and pasture (PAST) production systems according to the level of rainfall in the herd area. Previous studies aimed to determine a possible genotype × environment interaction (GxE) using 305-day lactation records, finding no interaction, but different heritabilities. An alternative random regression (aRR) model was developed using SA Holstein test-day records of herds with known production system used (TMR or PAST, analysed independently). The aRR model separates the animal genetic (additive) effect into a general production level and a linear persistency effect and shows clear differences in the genetic variances and heritability estimates over the lactation between TMR and PAST. The aim of this study was to further investigate the existence of a possible GxE interaction, using comparable data, methods and RR model to differentiate each trait (milk, fat and protein production for the first 3 lactations) separately for the two production systems (TMR or PAST). The possible reranking of sires on resulting estimated breeding values from the analysis between TMR and PAST were also investigated.</p><p><strong>Results: </strong>Genetic correlations (0.83 to 0.93) of the genetic general production level effect between TMR and PAST were strong, while corresponding correlations of the genetic linear persistency effect were moderate (0.40 to 0.60) for all traits. Significant reranking of sires between TMR and PAST are predicted for genetic linear persistency, as well as genetic total production.</p><p><strong>Conclusions: </strong>Genetic correlations show a significant genotype by environment interaction for the genetic linear persistency effect between the two production systems in SA Holsteins. Genetic evaluations should analyse production traits in the different systems as genetically different traits, utilizing an applicable random regression model that can effectively dissociate genetic general production level from genetic linear persistency. Estimating separate breeding values for persistency for each production system is justified.</p>","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"58 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149302239","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":"A deregression-based approach to integrate multitrait information into a single pooled phenotype for genetic evaluation.","authors":"Alice Markey, Nicolas Gengler","doi":"10.1186/s12711-026-01096-z","DOIUrl":"10.1186/s12711-026-01096-z","url":null,"abstract":"<p><strong>Background: </strong>Genetic evaluation of complex traits is often challenged by heterogeneous, incomplete or costly phenotyping. Many biological processes require multiple traits to be characterized. Moreover, combining several traits becomes difficult when the configuration of available traits is complex (e.g., increasing number, complex missing patterns), leading to more demanding computations. To overcome these limitations, a deregression-based pooling method was developed to pre-process multitrait information into a single pseudo-phenotype of the target trait (i.e., a pooled phenotype). This method was assessed in a terminal crossbreeding situation in pigs.</p><p><strong>Results: </strong>The behavior of estimated breeding values and the accuracy of reliabilities systems were studied on different datasets (i.e., different data availability scenarios - a real and 3 simulated). Three modeling steps were used: (1) a multitrait model generating input for deregressions, (2) an adjusted scalar deregression, combining information into a pooled phenotype using (3) a single trait model applied to this pooled phenotype to assess how these results match the multitrait predictions from step (1). In all data structure scenarios, unadjusted deregression underestimates breeding values and distorts reliabilities. In contrast, applying expansion and reliability-alignment factors as adjustments restored the distributions and scale of estimated breeding values, yielding a near-perfect agreement with the multitrait model. Across all simulated scenarios, including the one with the complete absence of the target trait, the adjusted deregression process achieved the highest correlations between true and estimated breeding values.</p><p><strong>Conclusions: </strong>The deregression-based process provides an effective and flexible approach for combining heterogeneous multitrait information into a unique pooled phenotype in a single trait setting, while maintaining genetic scale and reliability of the target trait in a multiple trait setting. Unlike classical deregression, the adjusted deregression process prevents overshrinking, maintains accuracy and reproduces the behavior of a multitrait evaluation. This pre-processing would be a practical solution for integrating complex or partially recorded traits into routine genetic evaluation pipelines. This method is applicable even if the main target trait is limited or missing, provided that moderately to highly correlated traits are available. The strategy could be extended to other situations as long as any double-counting can be avoided.</p>","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"58 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13689062/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149247635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Expanding the pig metabolomics QTL catalog.","authors":"Emil Ibragimov, Malene Kjelin Morsing, Martin Peter Rydal, Zexi Cai, Hans Stenlund, Jens Peter Nielsen, Merete Fredholm, Peter Karlskov-Mortensen","doi":"10.1186/s12711-026-01095-0","DOIUrl":"10.1186/s12711-026-01095-0","url":null,"abstract":"<p><strong>Background: </strong>Understanding the genetic regulation of metabolism is essential for improving animal health and productivity and for strengthening the use of pigs as translational models for human metabolic diseases. Metabolomics, when integrated with genomic data, provides a powerful framework for identifying genetic factors that influence intermediate molecular phenotypes closely linked to physiology. While large-scale metabolomics genome‑wide association studies (GWAS) have been extensively performed in humans, comparable studies in pigs remain limited. Among omics disciplines, metabolomics, the systematic study of metabolites central to physiology, represents the omics layer most closely linked to end-point phenotypes in mammals, such as health and production traits (e.g. growth rate). Metabolite levels are influenced by genetics, diet, and microbiome composition, with human studies showing high heritability and identifying numerous metabolite quantitative trait loci (mQTLs). While such findings have advanced our understanding of disease biology in humans, comparable studies in pigs remain limited.</p><p><strong>Results: </strong>We performed GWAS of the serum metabolome in 213 three‑way crossbred pigs [(Landrace × Yorkshire) × Duroc], analyzing 248 metabolites and 30,628 metabolite ratios. We identified 41 significant metabolite-variant associations, which were consolidated into 18 mQTLs based on linkage disequilibrium (LD). Several regions were associated with multiple, biochemically related metabolites, while others showed highly specific effects. Cross-species comparison with the human GWAS catalog revealed candidate genes within seven mQTLs. Through an integrative prioritization strategy, putative causal genes-predominantly enzymes and transporters-were identified for 14 of 18 mQTLs.</p><p><strong>Conclusions: </strong>Our results expand the catalog of genetic loci influencing serum metabolite variation in pigs and highlight both conserved and pig specific components of metabolic regulation. These findings provide a foundation for incorporating metabolomics into pig breeding programs and reinforce the value of pigs as a translational model for studying metabolite-associated traits and diseases in humans.</p>","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"58 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13685178/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149240562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genomic architecture of non-nominal offspring risk in a simulated composite cattle breed.","authors":"Miguel Gallach","doi":"10.1186/s12711-026-01093-2","DOIUrl":"10.1186/s12711-026-01093-2","url":null,"abstract":"<p><strong>Background: </strong>Composite cattle breeds are designed to combine complementary attributes from different founder breeds while retaining part of the heterosis generated by crossbreeding. Brangus cattle are nominally defined as 5/8 Angus and 3/8 Brahman, but the same global breed composition can be produced by different genomic architectures. This distinction may be important because animals with the same Brahman ancestry proportion may differ in ancestry heterozygosity, homozygous ancestry composition and segregation behaviour when used as parents. The objective of this study was to use controlled simulations to evaluate whether genomic architecture affects the risk of producing offspring outside the nominal Brangus ancestry range.</p><p><strong>Results: </strong>We simulated two alternative pathways to the same expected Brahman ancestry proportion of 0.375. Although both pathways produced similar mean Brahman ancestry, the direct pathway generated a broader ancestry distribution than the indirect pathway, with a higher proportion of individuals outside the nominal interval. After filtering candidate sires to the nominal interval, formation-pathway-dependent genomic architectures remained evident. In a mixed inter se mating analysis extended to 100 generations and three population-size scenarios, pathway-specific ancestry-state differences were progressively eroded and the standard deviation of Brahman ancestry proportion declined after an early maximum, but non-nominal frequencies remained non-negligible over the simulated time horizon. Family-level simulations showed that matings between nominal parents differed substantially in their probability of producing non-nominal daughters. This risk depended on both the distance between the expected family mean and the target ancestry and the block-weighted segregation potential of the parental genomes. An exploratory quasibinomial model supported positive associations between non-nominal risk and displacement from the target and high-variance parental block structures. Finally, a heuristic-assisted simulation-informed sire allocation strategy increased the proportion of nominal daughters relative to random sire allocation, with the greatest gains obtained from broader sire catalogues.</p><p><strong>Conclusions: </strong>Nominal breed composition does not define a unique genomic state. In simulated Brangus cattle, animals that were equivalent in global ancestry could differ in genomic architecture and in the offspring ancestry distributions generated by specific matings. These results suggest that composite-breed management should consider not only the ancestry proportion of individual animals, but also the expected outcome of sire-dam pairs.</p>","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"58 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13673633/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149207436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating longitudinal metabolite profiles improves phenotype prediction in pigs in a trait- and timepoint-dependent manner.","authors":"Quazi Abir Hassan Roddur, Dingzhen Liang, Jiayi Qu, Eula Regina Carrara, Daniela Lourenco, Bruno Valente, Ching-Yi Chen, Justin Holl, Hao Cheng","doi":"10.1186/s12711-026-01092-3","DOIUrl":"10.1186/s12711-026-01092-3","url":null,"abstract":"<p><strong>Background: </strong>Accurate prediction of phenotypes is essential for assessing the actual productive performance of individual pigs, particularly for traits that are costly or difficult to measure directly. This study investigated the potential of integrating individual-level blood serum metabolite profiles sampled at two developmental stages (10-week and 20-week) into phenotype prediction models for five economically important traits: average daily feed intake (DFI), feed conversion ratio (FCR), backfat thickness (BF), test daily gain (TDG), and loin depth (LD). Using a BayesC modeling framework, we analyzed 1,637 pigs from a single purebred population with complete phenotype, genotype, and metabolite-profile data. We evaluated seven models, including a genotype-only model (G), metabolite-only models using metabolite-profile data from 10-week (M1), 20-week (M2), or both timepoints (M1+M2), and combined models integrating genotypes with metabolite-profile data (G+M1, G+M2, G+M1+M2).</p><p><strong>Results: </strong>Heritability estimates were generally low to moderate, ranging from 0.08 to 0.23 for the 10-week metabolite profiles and from 0.07 to 0.26 for the 20-week metabolite profiles. Prediction accuracy of phenotypes consistently improved when metabolite-profile data were integrated into prediction models, although the magnitude of improvement varied depending on the trait and the timepoint of metabolite sampling. Prediction accuracy increased from 0.33 (G) to 0.42 (G+M2; G+M1+M2) for DFI, and from 0.27 (G) to 0.32 (G+M2; G+M1+M2) for FCR. The latter two models also delivered the largest gains over G for BF (from 0.39 to 0.46) and TDG (from 0.30 to 0.32). However, LD benefited the most when both 10-week and 20-week metabolite profiles were combined (G+M1+M2: 0.44 compared to G: 0.42).</p><p><strong>Conclusions: </strong>Across all traits, models combining genotype data with metabolite profiles from one or multiple timepoints achieved the highest or equally high phenotype prediction accuracies compared to the genotype-only model, reflecting complementary biological insights captured by metabolite profiles. These findings highlight the potential value of metabolite-profile data as an intermediate omics layer to enhance phenotypic prediction, particularly when integration strategies are tailored to trait-specific biology and sampling timepoints.</p>","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"58 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13660076/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149186277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cellular mechanism of gain-of-function mutation I173M in sheep MC4R gene identified in year-round and seasonal estrus breeds through whole-genome resequencing.","authors":"Yuta Yang, Yuxin Kang, Peiyao Liu, Chunna Cao, Yang Li, Ran Li, Xianyong Lan, Chuanying Pan","doi":"10.1186/s12711-026-01091-4","DOIUrl":"10.1186/s12711-026-01091-4","url":null,"abstract":"<p><strong>Background: </strong>Investigating key genes regulating year-round estrus can improve sheep reproductive performance and industry efficiency. Although several candidate genes have been proposed, the key regulators underlying estrus seasonality remain unclear. Therefore, this study aimed to identify critical genetic variants associated with year-round estrus using whole-genome resequencing data from multiple sheep breeds.</p><p><strong>Results: </strong>In this study, we analyzed whole-genome resequencing data from 392 sheep across six year-round and ten seasonal estrus breeds, and identified the Melanocortin 4 receptor (MC4R) gene as a key regulator of estrus seasonality. A missense mutation in the MC4R gene, I173M (g.59480440G > C; p.Ile173Met), was significantly enriched in year-round estrus breeds. Transcriptome analysis following MC4R I173M transfection revealed differential expression of genes enriched in reproductive pathways. Subsequent functional experiments demonstrated that the I173M mutation increased MC4R constitutive activity, activated cAMP and MAPK/ERK signaling, and promoted both granulosa cell proliferation and the expression of steroidogenic regulatory genes. Cells harboring the I173M mutation exhibited substantially amplified transcription levels of gonadotropin signaling components. Specifically, follicle-stimulating hormone receptor (FSHR) and luteinizing hormone receptor (LHR) were expressed at much higher levels relative to wild-type (WT) controls.</p><p><strong>Conclusions: </strong>Our study provides evidence that MC4R functions as a molecular modulator of estrous cyclicity, with the I173M missense mutation identified as a critical functional variant driving constitutive estrous activity in ovine species.</p>","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"58 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13656466/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149170842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Genetic parameters via predictivity in large populations under strong genomic selection","authors":"Gopal Gowane,Jorge Hidalgo,Mary Kate Hollifield,Ignacy Misztal,Daniela Lourenco","doi":"10.1186/s12711-026-01089-y","DOIUrl":"https://doi.org/10.1186/s12711-026-01089-y","url":null,"abstract":"","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"102 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149021233","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 rate and spectrum of germline mutations in chicken from a commercial pedigree line.","authors":"Eugenio López-Cortegano, Bolívar Samuel Sosa-Madrid, Andreas Kranis","doi":"10.1186/s12711-026-01085-2","DOIUrl":"10.1186/s12711-026-01085-2","url":null,"abstract":"<p><strong>Background: </strong>Pedigree-based studies of germline mutations provide a direct understanding of the origin and consequences of genetic variation, and allow quantification of how mutations affect functional variation. Here, we use whole-genome sequencing data from 194 chicken (Gallus gallus) trios from a commercial pedigree line to estimate the germline mutation rate and spectrum, and to characterize patterns of mutational variation across the genome and genes, aiming to rank protein coding genes by their expected contributions to mutational variance (V_m).</p><p><strong>Results: </strong>We estimated a mutation rate of µ = 3.49 × 10^-9 mutations per nucleotide site per generation. A goodness-of-fit test (χ²₁₃ = 10.97, P = 0.61) indicated that the number of new mutations per generation follows a Poisson distribution (λ = 6.22). A read-based phasing approach revealed a strong sex bias, with males contributing approximately twice as many mutations as females. Microchromosomes showed a point estimate of mutation rate approximately 8% higher than macrochromosomes, likely owing to their higher GC content and a mutation spectrum dominated by C→T transitions. From this spectrum, we built a codon transition matrix and show that genes vary widely in their propensity to mutation based on codon composition, likely reflecting large differences in their contributions to V_m. Specifically, we found that genes involved in immunity and development rank among those most prone to non-silent mutations.</p><p><strong>Conclusions: </strong>Our results reveal predictable patterns of gene-level mutational variation and provide a framework for anticipating how new mutations generate functional variation. By bridging quantitative genetics and molecular biology, we provide a mechanistic basis for variation in V_m across the genome, showing that heterogeneity in V_m can arise from gene-specific features, particularly codon composition and mutational target size. These metrics offer a way to rank genes and prioritize genetic variants by their expected contributions to biological functions as a consequence of de novo mutation. This provides a new basis for informed management of genetic variation in breeding populations and for understanding the evolutionary dynamics of functionally important genes.</p>","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"58 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13591848/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149018925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"National genomic evaluation for a novel functional longevity trait.","authors":"Pedro Ramos, Shogo Tsuruta, Andre Garcia, Kelli Retallick, Stephen P Miller, Daniela Lourenco","doi":"10.1186/s12711-026-01087-0","DOIUrl":"10.1186/s12711-026-01087-0","url":null,"abstract":"<p><strong>Background: </strong>Cow longevity plays a major role in beef cattle industry profitability, reflecting the female's ability to remain productive in the herd. In this context, longitudinal models as random regression models (RRM) are the preferred method for genetic evaluation as they account for genetic and environmental effects over time. Traditionally, RRM longevity evaluations use binary phenotypes; however, redefining the phenotype as the number of calves could enhance variability, better identify cows with consistent reproductive performance, and penalize irregular calving patterns. To address these limitations, this study aimed to: (1) propose genomic evaluations for a novel functional longevity (FL) definition via RRM; (2) compare model fit using Legendre polynomials (LP) of varying orders; (3) evaluate the implication of FL genetic parameters for selection strategies.</p><p><strong>Results: </strong>The dataset included records from 2 million Angus cows and 1.6 million genotyped animals from the American and Canadian Angus Associations. FL was defined as the cumulative number of calvings, measured annually from 2 to 10 years of age. Genetic parameters were estimated with Gibbs sampling without genomic information, and genomic breeding values (GEBV) were estimated using RRM under single-step GBLUP with LP. Models were compared via the deviance information criterion and sire GEBV rankings across ages were evaluated using Spearman correlations and percentage of individuals in common (top 1%). The best fit was with a third-order LP and homogeneous residual variance. Heritabilities ranged from 0.04 to 0.12. Genetic correlations between ages were positive and high, with minimal fluctuations. GEBV rank correlations were consistent across ages, with Spearman correlations following the genetic correlations pattern. The percentage of commonly selected individuals between adjacent years was high. However, percentage decreased with increasing age gap, reaching 53% between ages 3 and 10.</p><p><strong>Conclusions: </strong>The new functional longevity definition is feasible for genomic evaluations and has a straightforward interpretation as breeding values represent the number of additional calves when comparing animals. FL heritability estimates indicate sufficient additive genetic variance to allow moderate response to selection. Based on genetic correlations and ranking comparisons, functional longevity at 6 years of age is the optimal age to maximize genetic progress in Angus cattle.</p>","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"58 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13587375/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149003013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}