Genomic prediction of bull fertility in US Jersey dairy cattle

Service sire has a major effect on reproductive success in dairy cattle. Recent studies have reported accurate predictions for Holstein bull fertility using genomic data. The objective of this study was to assess the feasibility of genomic prediction of sire conception rate (SCR) in US Jersey cattle using alternative predictive models. Data set consisted of 1.5k Jersey bulls with SCR records and 95k SNP covering the entire genome. The analyses included the use of linear and Gaussian kernel-based models fitting either all the SNP or subsets of markers with presumed functional roles, such as SNP significantly associated with SCR or SNP located within or close to annotated genes. Model predictive ability was evaluated using 5-fold cross-validation with 10 replicates. The entire SNP set exhibited predictive correlations around 0.30. Interestingly, either SNP marginally associated with SCR or genic SNP achieved higher predictive abilities than their counterparts using random sets of SNP. Among alternative SNP subsets, Gaussian kernel models fitting significant SNP achieved the best performance with increases in predictive correlation up to 7% compared with the standard whole-genome approach. Notably, the use of a multi-breed reference population including the entire US Holstein SCR data set (11.5k bulls) allowed us to achieve predictive correlations up to 0.315, gaining 8% in accuracy compared with the standard model fitting a pure Jersey reference set. Overall, our findings indicate that genomic prediction of Jersey bull fertility is feasible. The use of Gaussian kernels fitting markers with relevant roles and the inclusion of Holstein records in the training set seem to be promising alternatives to the standard whole-genome approach. These results have the potential to help the dairy industry improve US Jersey sire fertility through accurate genome-guided decisions.EEA RafaelaFil: Rezende, Fernanda M. University of Florida. Department of Animal Sciences; Estados Unidos. Universidade Federal de Uberlândia. Faculdade de Medicina Veterinária; BrasilFil: Nani, Juan Pablo. University of Florida. Department of Animal Sciences; Estados Unidos. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rafaela; ArgentinaFil: Peñagaricano, Francisco. University of Florida. Department of Animal Sciences; Estados Unidos. University of Florida. University of Florida Genetics Institute; Estados Unido

Predicting male fertility in dairy cattle using markers with large effect and functional annotation data

Background: Fertility is among the most important economic traits in dairy cattle. Genomic prediction for cow fertility has received much attention in the last decade, while bull fertility has been largely overlooked. The goal of this study was to assess genomic prediction of dairy bull fertility using markers with large effect and functional annotation data. Sire conception rate (SCR) was used as a measure of service sire fertility. Dataset consisted of 11.5 k U.S. Holstein bulls with SCR records and about 300 k single nucleotide polymorphism (SNP) markers. The analyses included the use of both single-kernel and multi-kernel predictive models fitting either all SNPs, markers with large effect, or markers with presumed functional roles, such as non-synonymous, synonymous, or non-coding regulatory variants. Results: The entire set of SNPs yielded predictive correlations of 0.340. Five markers located on chromosomes BTA8, BTA9, BTA13, BTA17, and BTA27 showed marked dominance effects. Interestingly, the inclusion of these five major markers as fixed effects in the predictive models increased predictive correlations to 0.403, representing an increase in accuracy of about 19% compared with the standard model. Single-kernel models fitting functional SNP classes outperformed their counterparts using random sets of SNPs, suggesting that the predictive power of these functional variants is driven in part by their biological roles. Multi-kernel models fitting all the functional SNP classes together with the five major markers exhibited predictive correlations around 0.405. Conclusions: The inclusion of markers with large effect markedly improved the prediction of dairy sire fertility. Functional variants exhibited higher predictive ability than random variants, but did not outperform the standard whole-genome approach. This research is the foundation for the development of novel strategies that could help the dairy industry make accurate genome-guided selection decisions on service sire fertility.EEA RafaelaFil: Nani, Juan Pablo. University of Florida. Department of Animal Sciences; Estados Unidos. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rafaela; ArgentinaFil: Rezende, Fernanda M. University of Florida. Department of Animal Sciences; Estados Unidos. Universidade Federal de Uberlândia. Faculdade de Medicina Veterinária; BrasilFil: Peñagaricano, Francisco. University of Florida. Department of Animal Sciences; Estados Unidos. University of Florida. University of Florida Genetics Institute; Estados Unido

Structural Equation Modeling and Whole-Genome Scans Uncover Chromosome Regions and Enriched Pathways for Carcass and Meat Quality in Beef

Structural equation models involving latent variables are useful tools for formulating hypothesized models defined by theoretical variables and causal links between these variables. The objectives of this study were: (1) to identify latent variables underlying carcass and meat quality traits and (2) to perform whole-genome scans for these latent variables in order to identify genomic regions and individual genes with both direct and indirect effects. A total of 726 steers from an Angus-Brahman multibreed population with records for 22 phenotypes were used. A total of 480 animals were genotyped with the GGP Bovine F-250. The single-step genomic best linear unbiased prediction method was used to estimate the amount of genetic variance explained for each latent variable by chromosome regions of 20 adjacent SNP-windows across the genome. Three types of genetic effects were considered: (1) direct effects on a single latent phenotype; (2) direct effects on two latent phenotypes simultaneously; and (3) indirect effects. The final structural model included carcass quality as an independent latent variable and meat quality as a dependent latent variable. Carcass quality was defined by quality grade, fat over the ribeye and marbling, while the meat quality was described by juiciness, tenderness and connective tissue, all of them measured through a taste panel. From 571 associated genomic regions (643 genes), each one explaining at least 0.05% of the additive variance, 159 regions (179 genes) were associated with carcass quality, 106 regions (114 genes) were associated with both carcass and meat quality, 242 regions (266 genes) were associated with meat quality, and 64 regions (84 genes) were associated with carcass quality, having an indirect effect on meat quality. Three biological mechanisms emerged from these findings: postmortem proteolysis of structural proteins and cellular compartmentalization, cellular proliferation and differentiation of adipocytes, and fat deposition

RNA-Seq analysis uncovers transcriptomic variations between morphologically similar in vivo- and in vitro-derived bovine blastocysts

<p>Abstract</p> <p>Background</p> <p>A valuable tool for both research and industry, in vitro fertilization (IVF) has applications range from gamete selection and preservation of traits to cloning. Although IVF has achieved worldwide use, with approximately 339,685 bovine embryos transferred in 2010 alone, there are still continuing difficulties with efficiency. It is rare to have more than 40% of fertilized in vitro cattle oocytes reach blastocyst stage by day 8 of culture, and pregnancy rates are reported as less than 45% for in vitro produced embryos. To investigate potential influences in-vitro fertilization (IVF) has on embryonic development, this study compares in vivo- and in vitro-derived bovine blastocysts at a similar stage and quality grade (expanded, excellent quality) to determine the degree of transcriptomic variation beyond morphology using RNA-Seq.</p> <p>Results</p> <p>A total of 26,906,451 and 38,184,547 fragments were sequenced for in vitro and in vivo embryo pools, respectively. We detected expression for a total of 17,634 genes, with 793 genes showing differential expression between the two embryo populations with false discovery rate (FDR) < 0.05. There were also 395 novel transcribed units found, of which 45 were differentially expressed (FDR < 0.05). In addition, 4,800 genes showed evidence of alternative splicing, with 873 genes displaying differential alternative splicing between the two pools (FDR < 0.05). Using GO enrichment analysis, multiple biological pathways were found to be significantly enriched for differentially expressed genes (FDR < 0.01), including cholesterol and sterol synthesis, system development, and cell differentiation.</p> <p>Conclusions</p> <p>Thus, our results support that IVF may influence at the transcriptomic level and that morphology is limited in full characterization of bovine preimplantation embryos.</p

The Resilient Dairy Genome Project - a general overview of methods and objectives related to feed efficiency and methane emissions.

The Resilient Dairy Genome Project (RDGP) is an international large-scale applied research project that aims to generate genomic tools to breed more resilient dairy cows. In this context, improving feed efficiency and reducing greenhouse gases from dairy is a high priority. The inclusion of traits related to feed efficiency (e.g., dry matter intake [DMI]) or greenhouse gases (e.g., methane emissions [CH4]) relies on available genotypes as well as high quality phenotypes. Currently, 7 countries, i.e., Australia [AUS], Canada [CAN], Denmark [DNK], Germany [DEU], Spain [ESP], Switzerland [CHE], and United States of America [USA] contribute with genotypes and phenotypes including DMI and CH4. However, combining data is challenging due to differences in recording protocols, measurement technology, genotyping, and animal management across sources. In this study, we provide an overview of how the RDGP partners address these issues to advance international collaboration to generate genomic tools for resilient dairy. Specifically, we describe the current state of the RDGP database, data collection protocols in each country, and the strategies used for managing the shared data. As of February 2022, the database contains 1,289,593 DMI records from 12,687 cows and 17,403 CH4 records from 3,093 cows and continues to grow as countries upload new data over the coming years. No strong genomic differentiation between the populations was identified in this study, which may be beneficial for eventual across-country genomic predictions. Moreover, our results reinforce the need to account for the heterogeneity in the DMI and CH4 phenotypes in genomic analysis

MeSH-informed enrichment analysis and MeSH-guided semantic similarity among functional terms and gene products in chicken

Biomedical vocabularies and ontologies aid in recapitulating biological knowledge. The annotation of gene products is mainly accelerated by Gene Ontology (GO) and more recently by Medical Subject Headings (MeSH). Here we report a suite of MeSH packages for chicken in Bioconductor and illustrate some features of different MeSH-based analyses, including MeSH-informed enrichment analysis and MeSH-guided semantic similarity among terms and gene products, using two lists of chicken genes available in public repositories. The two published data sets that were employed represent (i) differentially expressed genes and (ii) candidate genes under selective sweep or epistatic selection. The comparison of MeSH with GO overrepresentation analyses suggested not only that MeSH supports the findings obtained from GO analysis but also that MeSH is able to further enrich the representation of biological knowledge and often provide more interpretable results. Based on the hierarchical structures of MeSH and GO, we computed semantic similarities among vocabularies as well as semantic similarities among selected genes. These yielded the similarity levels between significant functional terms, and the annotation of each gene yielded the measures of gene similarity. Our findings show the benefits of using MeSH as an alternative choice of annotation in order to draw biological inferences from a list of genes of interest. We argue that the use of MeSH in conjunction with GO will be instrumental in facilitating the understanding of the genetic basis of complex traits

Transcriptomic Analysis of the Mouse Mammary Gland Reveals New Insights for the Role of Serotonin in Lactation.

Serotonin regulates numerous processes in the mammary gland. Our objective was to discover novel genes, pathways and functions which serotonin modulates during lactation. The rate limiting enzyme in the synthesis of non-neuronal serotonin is tryptophan-hydroxylase (TPH1). Therefore, we used TPH1 deficient dams (KO; serotonin deficient, n = 4) and compared them to wild-type (WT; n = 4) and rescue (RC; KO + 100 mg/kg 5-hydroxytryptophan injected daily, n = 4) dams. Mammary tissues were collected on day 10 of lactation. Total RNA extraction, amplification, library preparation and sequencing were performed following the Illumina mRNA-Seq. Overall, 97 and 204 genes (false discovery rate, FDR ≤ 0.01) exhibited a minimum of a 2-fold expression difference between WT vs. KO and WT vs. RC dams, respectively. Most differentially expressed genes were related to calcium homeostasis, apoptosis regulation, cell cycle, cell differentiation and proliferation, and the immune response. Additionally, gene set enrichment analysis using Gene Ontology and Medical Subject Headings databases revealed the alteration of several biological processes (FDR ≤ 0.01) including fat cell differentiation and lipid metabolism, regulation of extracellular signal-related kinase and mitogen-activated kinase cascades, insulin resistance, nuclear transport, membrane potential regulation, and calcium release from the endoplasmic reticulum into the cytosol. The majority of the biological processes and pathways altered in the KO dams are central for mammary gland homeostasis. Increasing peripheral serotonin in the RC dams affects specific pathways that favor lactation. Our data confirms the importance of serotonin during lactation in the mammary gland