Genetics Selection Evolution最新文献

{"title":"Unravelling the genetic variability of host resilience to endo- and ectoparasites in Nellore commercial herds.","authors":"Gabriela Canabrava Gouveia, Virgínia Mara Pereira Ribeiro, Marina Rufino Salinas Fortes, Fernanda Santos Silva Raidan, Antonio Reverter, Laercio Ribeiro Porto-Neto, Mariana Mamedes de Moraes, Daniel Resende Gonçalves, Marcos Vinicius Gualberto Barbosa da Silva, Fabio Luiz Buranelo Toral","doi":"10.1186/s12711-023-00844-9","DOIUrl":"10.1186/s12711-023-00844-9","url":null,"abstract":"<p><strong>Background: </strong>Host resilience (HR) to parasites can affect the performance of animals. Therefore, the aim of this study was to present a detailed investigation of the genetic mechanisms of HR to ticks (TICK), gastrointestinal nematodes (GIN), and Eimeria spp. (EIM) in Nellore cattle that were raised under natural infestation and a prophylactic parasite control strategy. In our study, HR was defined as the slope coefficient of body weight (BW) when TICK, GIN, and EIM burdens were used as environmental gradients in random regression models. In total, 1712 animals were evaluated at five measurement events (ME) at an average age of 331, 385, 443, 498, and 555 days, which generated 7307 body weight (BW) records. Of the 1712 animals, 1075 genotyped animals were used in genome-wide association studies to identify genomic regions associated with HR.</p><p><strong>Results: </strong>Posterior means of the heritability estimates for BW ranged from 0.09 to 0.54 across parasites and ME. The single nucleotide polymorphism (SNP)-derived heritability for BW at each ME ranged from a low (0.09 at ME.331) to a moderate value (0.23 at ME.555). Those estimates show that genetic progress can be achieved for BW through selection. Both genetic and genomic associations between BW and HR to TICK, GIN, and EIM confirmed that parasite infestation impacted the performance of animals. Selection for BW under an environment with a controlled parasite burden is an alternative to improve both, BW and HR. There was no impact of age of measurement on the estimates of genetic variance for HR. Five quantitative trait loci (QTL) were associated with HR to EIM but none with HR to TICK and to GIN. These QTL contain genes that were previously shown to be associated with the production of antibody modulators and chemokines that are released in the intestinal epithelium.</p><p><strong>Conclusions: </strong>Selection for BW under natural infestation and controlled parasite burden, via prophylactic parasite control, contributes to the identification of animals that are resilient to nematodes and Eimeria ssp. Although we verified that sufficient genetic variation existed for HR, we did not find any genes associated with mechanisms that could justify the expression of HR to TICK and GIN.</p>","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10664541/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138292468","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: Sequence-based GWAS meta-analyses for beef production traits","authors":"Marie-Pierre Sanchez, Thierry Tribout, Naveen K. Kadri, Praveen K. Chitneedi, Steffen Maak, Chris Hozé, Mekki Boussaha, Pascal Croiseau, Romain Philippe, Mirjam Spengeler, Christa Kühn, Yining Wang, Changxi Li, Graham Plastow, Hubert Pausch, Didier Boichard","doi":"10.1186/s12711-023-00852-9","DOIUrl":"https://doi.org/10.1186/s12711-023-00852-9","url":null,"abstract":"<br/><p><b>Correction: Genetics Selection Evolution (2023) 55:70 </b><b>https://doi.org/10.1186/s12711-023-00848-5</b></p><br/><p>Following publication of the original article [1], it has been reported that the incorrect copyright holder was used. The correct copyright holder is: © His Majesty the King in Right of Canada as represented by the Minister of Agriculture and Agri-Food Canada. The original article [1] has been corrected.</p><ol data-track-component=\"outbound reference\"><li data-counter=\"1.\"><p>Sanchez MP, Tribout T, Kadri NK, Chitneedi PK, Maak S, Hozé C, Boussaha M, Croiseau P, Philippe R, Spengeler M, Kühn C, Wang Y, Li C, Plastow G, Pausch H, Boichard D. Sequence-based GWAS meta-analyses for beef production traits. Genet Sel Evol. 2023;55:70. https://doi.org/10.1186/s12711-023-00848-5.</p><p>Article CAS PubMed PubMed Central Google Scholar </p></li></ol><p>Download references<svg aria-hidden=\"true\" focusable=\"false\" height=\"16\" role=\"img\" width=\"16\"><use xlink:href=\"#icon-eds-i-download-medium\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"></use></svg></p><span>Author notes</span><ol><li><p>Christa Kühn</p><p>Present address: Friedrich-Loefer-Institut (FLI), Insel Riems, 17493, Greifswald, Germany</p></li></ol><h3>Authors and Affiliations</h3><ol><li><p>Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France</p><p>Marie-Pierre Sanchez, Thierry Tribout, Chris Hozé, Mekki Boussaha, Pascal Croiseau &amp; Didier Boichard</p></li><li><p>Animal Genomics, ETH Zurich, 8092, Zurich, Switzerland</p><p>Naveen K. Kadri &amp; Hubert Pausch</p></li><li><p>Research Institute for Farm Animal Biology (FBN), 18196, Dummerstorf, Germany</p><p>Praveen K. Chitneedi, Steffen Maak &amp; Christa Kühn</p></li><li><p>Eliance, 75595, Paris, France</p><p>Chris Hozé</p></li><li><p>INRAE, USC1061 GAMAA, Université de Limoges, 87060, Limoges, France</p><p>Romain Philippe</p></li><li><p>QualitasAG, 6300, Zug, Switzerland</p><p>Mirjam Spengeler</p></li><li><p>Agricultural and Environmental Faculty, University Rostock, 18059, Rostock, Germany</p><p>Christa Kühn</p></li><li><p>Lacombe Research and Development Centre, Agriculture and Agri-Food Canada, Lacombe, AB, T4L 1W1, Canada</p><p>Yining Wang &amp; Changxi Li</p></li><li><p>Department of Agricultural, Food and Nutritional Science, Livestock Gentec, University of Alberta, Edmonton, AB, T6G 2HI, Canada</p><p>Changxi Li &amp; Graham Plastow</p></li></ol><span>Authors</span><ol><li><span>Marie-Pierre Sanchez</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Thierry Tribout</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Naveen K. Kadri</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Praveen K. Chitneedi</span>View author publicatio","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91398621","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":"Exploring the potential of incremental feature selection to improve genomic prediction accuracy","authors":"Felix Heinrich, Thomas Martin Lange, Magdalena Kircher, Faisal Ramzan, Armin Otto Schmitt, Mehmet Gültas","doi":"10.1186/s12711-023-00853-8","DOIUrl":"https://doi.org/10.1186/s12711-023-00853-8","url":null,"abstract":"The ever-increasing availability of high-density genomic markers in the form of single nucleotide polymorphisms (SNPs) enables genomic prediction, i.e. the inference of phenotypes based solely on genomic data, in the field of animal and plant breeding, where it has become an important tool. However, given the limited number of individuals, the abundance of variables (SNPs) can reduce the accuracy of prediction models due to overfitting or irrelevant SNPs. Feature selection can help to reduce the number of irrelevant SNPs and increase the model performance. In this study, we investigated an incremental feature selection approach based on ranking the SNPs according to the results of a genome-wide association study that we combined with random forest as a prediction model, and we applied it on several animal and plant datasets. Applying our approach to different datasets yielded a wide range of outcomes, i.e. from a substantial increase in prediction accuracy in a few cases to minor improvements when only a fraction of the available SNPs were used. Compared with models using all available SNPs, our approach was able to achieve comparable performances with a considerably reduced number of SNPs in several cases. Our approach showcased state-of-the-art efficiency and performance while having a faster computation time. The results of our study suggest that our incremental feature selection approach has the potential to improve prediction accuracy substantially. However, this gain seems to depend on the genomic data used. Even for datasets where the number of markers is smaller than the number of individuals, feature selection may still increase the performance of the genomic prediction. Our approach is implemented in R and is available at https://github.com/FelixHeinrich/GP_with_IFS/ .","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71524388","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":"Disentangling the dynamics of energy allocation to develop a proxy for robustness of fattening pigs.","authors":"Guillaume Lenoir, Loïc Flatres-Grall, Rafael Muñoz-Tamayo, Ingrid David, Nicolas C Friggens","doi":"10.1186/s12711-023-00851-w","DOIUrl":"10.1186/s12711-023-00851-w","url":null,"abstract":"<p><strong>Background: </strong>There is a growing need to improve robustness of fattening pigs, but this trait is difficult to phenotype. Our first objective was to develop a proxy for robustness of fattening pigs by modelling the longitudinal energy allocation coefficient to growth, with the resulting environmental variance of this allocation coefficient considered as a proxy for robustness. The second objective was to estimate its genetic parameters and correlations with traits under selection and with phenotypes that are routinely collected. In total, 5848 pigs from a Pietrain NN paternal line were tested at the AXIOM boar testing station (Azay-sur-Indre, France) from 2015 to 2022. This farm is equipped with an automatic feeding system that records individual weight and feed intake at each visit. We used a dynamic linear regression model to characterize the evolution of the allocation coefficient between the available cumulative net energy, which was estimated from feed intake, and cumulative weight gain during the fattening period. Longitudinal energy allocation coefficients were analysed using a two-step approach to estimate both the genetic variance of the coefficients and the genetic variance in their residual variance, which will be referred to as the log-transformed squared residual (LSR).</p><p><strong>Results: </strong>The LSR trait, which could be interpreted as an indicator of the response of the animal to perturbations/stress, showed a low heritability (0.05 ± 0.01), a high favourable genetic correlation with average daily growth (- 0.71 ± 0.06), and unfavourable genetic correlations with feed conversion ratio (- 0.76 ± 0.06) and residual feed intake (- 0.83 ± 0.06). Segmentation of the population in four classes using estimated breeding values for LSR showed that animals with the lowest estimated breeding values were those with the worst values for phenotypic proxies of robustness, which were assessed using records routinely collected on farm.</p><p><strong>Conclusions: </strong>Results of this study show that selection for robustness, based on estimated breeding values for environmental variance of the allocation coefficients to growth, can be considered in breeding programs for fattening pigs.</p>","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10629156/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71489227","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":"Across-country genetic and genomic analyses of foot score traits in American and Australian Angus cattle.","authors":"Amanda B Alvarenga,&nbsp;Kelli J Retallick,&nbsp;Andre Garcia,&nbsp;Stephen P Miller,&nbsp;Andrew Byrne,&nbsp;Hinayah R Oliveira,&nbsp;Luiz F Brito","doi":"10.1186/s12711-023-00850-x","DOIUrl":"10.1186/s12711-023-00850-x","url":null,"abstract":"<p><strong>Background: </strong>Hoof structure and health are essential for the welfare and productivity of beef cattle. Therefore, we assessed the genetic and genomic background of foot score traits in American (US) and Australian (AU) Angus cattle and investigated the feasibility of performing genomic evaluations combining data for foot score traits recorded in US and AU Angus cattle. The traits evaluated were foot angle (FA) and claw set (CS). In total, 109,294 and ~ 1.12 million animals had phenotypic and genomic information, respectively. Four sets of analyses were performed: (1) genomic connectedness between US and AU Angus cattle populations and population structure, (2) estimation of genetic parameters, (3) single-step genomic prediction of breeding values, and (4) single-step genome-wide association studies for FA and CS.</p><p><strong>Results: </strong>There was no clear genetic differentiation between US and AU Angus populations. Similar heritability estimates (FA: 0.22-0.24 and CS: 0.22-0.27) and moderate-to-high genetic correlations between US and AU foot scores (FA: 0.61 and CS: 0.76) were obtained. A joint-genomic prediction using data from both populations outperformed within-country genomic evaluations. A genomic prediction model considering US and AU datasets as a single population performed similarly to the scenario accounting for genotype-by-environment interactions (i.e., multiple-trait model considering US and AU records as different traits), even though the genetic correlations between countries were lower than 0.80. Common significant genomic regions were observed between US and AU for FA and CS. Significant single nucleotide polymorphisms were identified on the Bos taurus (BTA) chromosomes BTA1, BTA5, BTA11, BTA13, BTA19, BTA20, and BTA23. The candidate genes identified were primarily from growth factor gene families, including FGF12 and GDF5, which were previously associated with bone structure and repair.</p><p><strong>Conclusions: </strong>This study presents comprehensive population structure and genetic and genomic analyses of foot scores in US and AU Angus cattle populations, which are essential for optimizing the implementation of genomic selection for improved foot scores in Angus cattle breeding programs. We have also identified candidate genes associated with foot scores in the largest Angus cattle populations in the world and made recommendations for genomic evaluations for improved foot score traits in the US and AU.</p>","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10621155/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71429288","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":"Approaching autozygosity in a small pedigree of Gochu Asturcelta pigs.","authors":"Katherine D Arias, Juan Pablo Gutiérrez, Iván Fernández, Isabel Álvarez, Félix Goyache","doi":"10.1186/s12711-023-00846-7","DOIUrl":"10.1186/s12711-023-00846-7","url":null,"abstract":"<p><strong>Background: </strong>In spite of the availability of single nucleotide polymorphism (SNP) array data, differentiation between observed homozygosity and that caused by mating between relatives (autozygosity) introduces major difficulties. Homozygosity estimators show large variation due to different causes, namely, Mendelian sampling, population structure, and differences among chromosomes. Therefore, the ascertainment of how inbreeding is reflected in the genome is still an issue. The aim of this research was to study the usefulness of genomic information for the assessment of genetic diversity in the highly endangered Gochu Asturcelta pig breed. Pedigree depth varied from 0 (founders) to 4 equivalent discrete generations (t). Four homozygosity parameters (runs of homozygosity, F_ROH; heterozygosity-rich regions, F_HRR; Li and Horvitz's, F_LH; and Yang and colleague's F_YAN) were computed for each individual, adjusted for the variability in the base population (BP; six individuals) and further jackknifed over autosomes. Individual increases in homozygosity (depending on t) and increases in pairwise homozygosity (i.e., increase in the parents' mean) were computed for each individual in the pedigree, and effective population size (N_e) was computed for five subpopulations (cohorts). Genealogical parameters (individual inbreeding, individual increase in inbreeding, and N_e) were used for comparisons.</p><p><strong>Results: </strong>The mean F was 0.120 ± 0.074 and the mean BP-adjusted homozygosity ranged from 0.099 ± 0.081 (F_LH) to 0.152 ± 0.075 (F_YAN). After jackknifing, the mean values were slightly lower. The increase in pairwise homozygosity tended to be twofold higher than the corresponding individual increase in homozygosity values. When compared with genealogical estimates, estimates of N_e obtained using F_YAN tended to have low root-mean-squared errors. However, N_e estimates based on increases in pairwise homozygosity using both F_ROH and F_HRR estimates of genomic inbreeding had lower root-mean-squared errors.</p><p><strong>Conclusions: </strong>Parameters characterizing homozygosity may not accurately depict losses of variability in small populations in which breeding policy prohibits matings between close relatives. After BP adjustment, the performance of F_ROH and F_HRR was highly consistent. Assuming that an increase in homozygosity depends only on pedigree depth can lead to underestimating it in populations with shallow pedigrees. An increase in pairwise homozygosity computed from either F_ROH or F_HRR is a promising approach for characterizing autozygosity.</p>","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10601182/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50163843","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":"Combined single-step evaluation of functional longevity of dairy cows including correlated traits.","authors":"Laure-Hélène Maugan, Roberta Rostellato, Thierry Tribout, Sophie Mattalia, Vincent Ducrocq","doi":"10.1186/s12711-023-00839-6","DOIUrl":"10.1186/s12711-023-00839-6","url":null,"abstract":"<p><strong>Background: </strong>For years, multiple trait genetic evaluations have been used to increase the accuracy of estimated breeding values (EBV) using information from correlated traits. In France, accurate approximations of multiple trait evaluations were implemented for traits that are described by different models by combining the results of univariate best linear unbiased prediction (BLUP) evaluations. Functional longevity (FL) is the trait that has most benefited from this approach. Currently, with many single-step (SS) evaluations, only univariate FL evaluations can be run. The aim of this study was to implement a \"combined\" SS (CSS) evaluation that extends the \"combined\" BLUP evaluation to obtain more accurate genomic (G) EBV for FL when information from five correlated traits (somatic cell score, clinical mastitis, conception rate for heifers and cows, and udder depth) is added.</p><p><strong>Results: </strong>GEBV obtained from univariate SS (USS) evaluations and from a CSS evaluation were compared. The correlations between these GEBV showed the benefits of including information from correlated traits. Indeed, a CSS evaluation run without any performances on FL showed that the indirect information from correlated traits to evaluate FL was substantial. USS and CSS evaluations that mimic SS evaluations with data available in 2016 were compared. For each evaluation separately, the GEBV were sorted and then split into 10 consecutive groups (deciles). Survival curves were calculated for each group, based on the observed productive life of these cows as known in 2021. Regardless of their genotyping status, the worst group of heifers based on their GEBV in 2016 was well identified in the CSS evaluation and they had a substantially shorter herd life, while those in the best heifer group had a longer herd life. The gaps between groups were more important for the genotyped than the ungenotyped heifers, which indicates better prediction of future survival.</p><p><strong>Conclusions: </strong>A CSS evaluation is an efficient tool to improve FL. It allows a proper combination of information on functional traits that influence culling. In contrast, because of the strong selection intensity on young bulls for functional traits, the benefit of such a \"combined\" evaluation of functional traits is more modest for these males.</p>","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10601146/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50163844","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":"Comparative analyses of dynamic transcriptome profiles highlight key response genes and dominant isoforms for muscle development and growth in chicken.","authors":"Zhang Wang, Weihua Tian, Dandan Wang, Yulong Guo, Zhimin Cheng, Yanyan Zhang, Xinyan Li, Yihao Zhi, Donghua Li, Zhuanjian Li, Ruirui Jiang, Guoxi Li, Yadong Tian, Xiangtao Kang, Hong Li, Ian C Dunn, Xiaojun Liu","doi":"10.1186/s12711-023-00849-4","DOIUrl":"10.1186/s12711-023-00849-4","url":null,"abstract":"<p><strong>Background: </strong>Modern breeding strategies have resulted in significant differences in muscle mass between indigenous chicken and specialized broiler. However, the molecular regulatory mechanisms that underlie these differences remain elusive. The aim of this study was to identify key genes and regulatory mechanisms underlying differences in breast muscle development between indigenous chicken and specialized broiler.</p><p><strong>Results: </strong>Two time-series RNA-sequencing profiles of breast muscles were generated from commercial Arbor Acres (AA) broiler (fast-growing) and Chinese indigenous Lushi blue-shelled-egg (LS) chicken (slow-growing) at embryonic days 10, 14, and 18, and post-hatching day 1 and weeks 1, 3, and 5. Principal component analysis of the transcriptome profiles showed that the top four principal components accounted for more than 80% of the total variance in each breed. The developmental axes between the AA and LS chicken overlapped at the embryonic stages but gradually separated at the adult stages. Integrative investigation of differentially-expressed transcripts contained in the top four principal components identified 44 genes that formed a molecular network associated with differences in breast muscle mass between the two breeds. In addition, alternative splicing analysis revealed that genes with multiple isoforms always had one dominant transcript that exhibited a significantly higher expression level than the others. Among the 44 genes, the TNFRSF6B gene, a mediator of signal transduction pathways and cell proliferation, harbored two alternative splicing isoforms, TNFRSF6B-X1 and TNFRSF6B-X2. TNFRSF6B-X1 was the dominant isoform in both breeds before the age of one week. A switching event of the dominant isoform occurred at one week of age, resulting in TNFRSF6B-X2 being the dominant isoform in AA broiler, whereas TNFRSF6B-X1 remained the dominant isoform in LS chicken. Gain-of-function assays demonstrated that both isoforms promoted the proliferation of chicken primary myoblasts, but only TNFRSF6B-X2 augmented the differentiation and intracellular protein content of chicken primary myoblasts.</p><p><strong>Conclusions: </strong>For the first time, we identified several key genes and dominant isoforms that may be responsible for differences in muscle mass between slow-growing indigenous chicken and fast-growing commercial broiler. These findings provide new insights into the regulatory mechanisms underlying breast muscle development in chicken.</p>","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10591418/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49694152","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 prediction based on selective linkage disequilibrium pruning of low-coverage whole-genome sequence variants in a pure Duroc population.","authors":"Di Zhu, Yiqiang Zhao, Ran Zhang, Hanyu Wu, Gengyuan Cai, Zhenfang Wu, Yuzhe Wang, Xiaoxiang Hu","doi":"10.1186/s12711-023-00843-w","DOIUrl":"10.1186/s12711-023-00843-w","url":null,"abstract":"<p><strong>Background: </strong>Although the accumulation of whole-genome sequencing (WGS) data has accelerated the identification of mutations underlying complex traits, its impact on the accuracy of genomic predictions is limited. Reliable genotyping data and pre-selected beneficial loci can be used to improve prediction accuracy. Previously, we reported a low-coverage sequencing genotyping method that yielded 11.3 million highly accurate single-nucleotide polymorphisms (SNPs) in pigs. Here, we introduce a method termed selective linkage disequilibrium pruning (SLDP), which refines the set of SNPs that show a large gain during prediction of complex traits using whole-genome SNP data.</p><p><strong>Results: </strong>We used the SLDP method to identify and select markers among millions of SNPs based on genome-wide association study (GWAS) prior information. We evaluated the performance of SLDP with respect to three real traits and six simulated traits with varying genetic architectures using two representative models (genomic best linear unbiased prediction and BayesR) on samples from 3579 Duroc boars. SLDP was determined by testing 180 combinations of two core parameters (GWAS P-value thresholds and linkage disequilibrium r²). The parameters for each trait were optimized in the training population by five fold cross-validation and then tested in the validation population. Similar to previous GWAS prior-based methods, the performance of SLDP was mainly affected by the genetic architecture of the traits analyzed. Specifically, SLDP performed better for traits controlled by major quantitative trait loci (QTL) or a small number of quantitative trait nucleotides (QTN). Compared with two commercial SNP chips, genotyping-by-sequencing data, and an unselected whole-genome SNP panel, the SLDP strategy led to significant improvements in prediction accuracy, which ranged from 0.84 to 3.22% for real traits controlled by major or moderate QTL and from 1.23 to 11.47% for simulated traits controlled by a small number of QTN.</p><p><strong>Conclusions: </strong>The SLDP marker selection method can be incorporated into mainstream prediction models to yield accuracy improvements for traits with a relatively simple genetic architecture, however, it has no significant advantage for traits not controlled by major QTL. The main factors that affect its performance are the genetic architecture of traits and the reliability of GWAS prior information. Our findings can facilitate the application of WGS-based genomic selection.</p>","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10583454/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49685287","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":"Multi-breed genomic evaluation for tropical beef cattle when no pedigree information is available.","authors":"Ben J Hayes, James Copley, Elsie Dodd, Elizabeth M Ross, Shannon Speight, Geoffry Fordyce","doi":"10.1186/s12711-023-00847-6","DOIUrl":"10.1186/s12711-023-00847-6","url":null,"abstract":"<p><strong>Background: </strong>It has been challenging to implement genomic selection in multi-breed tropical beef cattle populations. If commercial (often crossbred) animals could be used in the reference population for these genomic evaluations, this could allow for very large reference populations. In tropical beef systems, such animals often have no pedigree information. Here we investigate potential models for such data, using marker heterozygosity (to model heterosis) and breed composition derived from genetic markers, as covariates in the model. Models treated breed effects as either fixed or random, and included genomic best linear unbiased prediction (GBLUP) and BayesR. A tropically-adapted beef cattle dataset of 29,391 purebred, crossbred and composite commercial animals was used to evaluate the models.</p><p><strong>Results: </strong>Treating breed effects as random, in an approach analogous to genetic groups allowed partitioning of the genetic variance into within-breed and across breed-components (even with a large number of breeds), and estimation of within-breed and across-breed genomic estimated breeding values (GEBV). We demonstrate that moderately-accurate (0.30-0.43) GEBV can be calculated using these models. Treating breed effects as random gave more accurate GEBV than treating breed as fixed. A simple GBLUP model where no breed effects were fitted gave the same accuracy (and correlations of GEBV very close to 1) as a model where GEBV for within-breed and the GEBV for (random) across-breed effects were included. When GEBV were predicted for herds with no data in the reference population, BayesR resulted in the highest accuracy, with 3% accuracy improvement averaged across traits, especially when the validation population was less related to the reference population. Estimates of heterosis from our models were in line with previous estimates from beef cattle. A method for estimating the number of effective breed comparisons for each breed combination accumulated across contemporary groups is presented.</p><p><strong>Conclusions: </strong>When no pedigree is available, breed composition and heterosis for inclusion in multi-breed genomic evaluation can be estimated from genotypes. When GEBV were predicted for herds with no data in the reference population, BayesR resulted in the highest accuracy.</p>","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10578004/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41241102","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}