scholarly journals 300 ASAS-EAAP Talk: Towards an increasing number and complexity of breeding goal traits in dairy cattle

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 30-31
Author(s):  
Nina Krattenmacher ◽  
Anita Seidel ◽  
Georg Thaller
Keyword(s):  
Dairy Cattle ◽  
Feed Intake ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Genetic Correlations ◽  
Methane Emissions ◽  
Repeated Measurements ◽  
Phenotypic Data ◽  
Breeding Goal ◽  
Lactation Stage

Abstract Worldwide, dairy cattle breeding companies and farmers face several challenges, including concerns about climatic impact of milk production, increasing scarcity of natural resources and feed, and concerns about animal welfare and health. The recording of accurate and comprehensive phenotypic data for these new issues is important for both management and breeding. Technological developments play a key role in this context. An increasing spectrum of traits with relevance to the breeding goal has become available (e.g. behavioral traits from sensor-derived activity patterns, milk metabolites reflecting the metabolic status, direct or indirect measurements of methane emissions). The biological background and genetic architecture of many of the evolving novel traits as well as their relationship with other traits of interest is not yet well understood, which hinders appropriate implementation in breeding programs. Especially for traits that are difficult or expensive to measure, such as feed intake or methane emissions, phenotypes are scarce. Interdisciplinary research and across-country data pooling can be enormously helpful to ensure a fast progress. Hence, the development of universal guidelines for recording is a crucial step, also with regard to a successful application of genomic selection, which enables the improvement of difficult-to-measure traits by transferring genomic knowledge from estimates within comparatively small reference populations to the population level. Furthermore, some traits (e.g. feed intake) show a lactation-stage specific genetic architecture. This highlights the importance of repeated measurements as well as knowledge on genetic correlations among all relevant traits across days in milk, the latter being an important prerequisite for designing balanced breeding strategies. With more traits, especially more complex traits, increasing data sources and volumes, setting up reasonable breeding goals becomes much more sophisticated and often requires innovative approaches.

scholarly journals Mitigation of greenhouse gases in dairy cattle via genetic selection: 2. Incorporating methane emissions into the breeding goal

2020 ◽  
Vol 103 (8) ◽  
pp. 7210-7221 ◽  
Author(s):  
O. González-Recio ◽  
J. López-Paredes ◽  
L. Ouatahar ◽  
N. Charfeddine ◽  
E. Ugarte ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Greenhouse Gases ◽  
Genetic Selection ◽  
Methane Emissions ◽  
Breeding Goal

scholarly journals Localizing components of shared transethnic genetic architecture of complex traits from GWAS summary data

2019 ◽  
Author(s):  
Huwenbo Shi ◽  
Kathryn S. Burch ◽  
Ruth Johnson ◽  
Malika K. Freund ◽  
Gleb Kichaev ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Empirical Bayes ◽  
Genetic Correlations ◽  
Causal Snps ◽  
European Ancestry ◽  
Risk Scores ◽  
Common Snps ◽  
Genome Wide ◽  
Summary Data

AbstractDespite strong transethnic genetic correlations reported in the literature for many complex traits, the non-transferability of polygenic risk scores across populations suggests the presence of population-specific components of genetic architecture. We propose an approach that models GWAS summary data for one trait in two populations to estimate genome-wide proportions of population-specific/shared causal SNPs. In simulations across various genetic architectures, we show that our approach yields approximately unbiased estimates with in-sample LD and slight upward-bias with out-of-sample LD. We analyze 9 complex traits in individuals of East Asian and European ancestry, restricting to common SNPs (MAF > 5%), and find that most common causal SNPs are shared by both populations. Using the genome-wide estimates as priors in an empirical Bayes framework, we perform fine-mapping and observe that high-posterior SNPs (for both the population-specific and shared causal configurations) have highly correlated effects in East Asians and Europeans. In population-specific GWAS risk regions, we observe a 2.8x enrichment of shared high-posterior SNPs, suggesting that population-specific GWAS risk regions harbor shared causal SNPs that are undetected in the other GWAS due to differences in LD, allele frequencies, and/or sample size. Finally, we report enrichments of shared high-posterior SNPs in 53 tissue-specific functional categories and find evidence that SNP-heritability enrichments are driven largely by many low-effect common SNPs.

scholarly journals Neglect of lactation stage leads to naive assessment of residual feed intake in dairy cattle

2017 ◽  
Vol 100 (11) ◽  
pp. 9076-9084 ◽  
Author(s):  
B. Li ◽  
B. Berglund ◽  
W.F. Fikse ◽  
J. Lassen ◽  
M.H. Lidauer ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Feed Intake ◽  
Residual Feed Intake ◽  
Lactation Stage

scholarly journals New residual feed intake criterion for longitudinal data

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Ingrid David ◽  
Van-Hung Huynh Tran ◽  
Hélène Gilbert
Keyword(s):  
Regression Model ◽  
Feed Intake ◽  
Genetic Correlations ◽  
Residual Feed Intake ◽  
Repeated Measurements ◽  
Regression Coefficients ◽  
Production Traits ◽  
Breeding Values ◽  
Time Points ◽  
Heritability Estimates

Abstract Background Residual feed intake (RFI) is one measure of feed efficiency, which is usually obtained by multiple regression of feed intake (FI) on measures of production, body weight gain and tissue composition. If phenotypic regression is used, the resulting RFI is generally not genetically independent of production traits, whereas if RFI is computed using genetic regression coefficients, RFI and production traits are independent at the genetic level. The corresponding regression coefficients can be easily derived from the result of a multiple trait model that includes FI and production traits. However, this approach is difficult to apply in the case of multiple repeated measurements of FI and production traits. To overcome this difficulty, we used a structured antedependence approach to account for the longitudinality of the data with a phenotypic regression model or with different genetic and environmental regression coefficients [multi- structured antedependence model (SAD) regression model]. Results After demonstrating the properties of RFI obtained by the multi-SAD regression model, we applied the two models to FI and production traits that were recorded for 2435 French Large White pigs over a 10-week period. Heritability estimates were moderate with both models. With the multi-SAD regression model, heritability estimates were quite stable over time, ranging from 0.14 ± 0.04 to 0.16 ± 0.05, while heritability estimates showed a U-shaped profile with the phenotypic regression model (ranging from 0.19 ± 0.06 to 0.28 ± 0.06). Estimates of genetic correlations between RFI at different time points followed the same pattern for the two models but higher estimates were obtained with the phenotypic regression model. Estimates of breeding values that can be used for selection were obtained by eigen-decomposition of the genetic covariance matrix. Correlations between these estimated breeding values obtained with the two models ranged from 0.66 to 0.83. Conclusions The multi-SAD model is preferred for the genetic analysis of longitudinal RFI because, compared to the phenotypic regression model, it provides RFI that are genetically independent of production traits at all time points. Furthermore, it can be applied even when production records are missing at certain time points.

scholarly journals Genomic SEM Provides Insights into the Multivariate Genetic Architecture of Complex Traits

2018 ◽  
Author(s):  
Andrew D. Grotzinger ◽  
Mijke Rhemtulla ◽  
Ronald de Vlaming ◽  
Stuart J. Ritchie ◽  
Travis T. Mallard ◽  
...  
Keyword(s):  
Complex Traits ◽  
Structural Equation ◽  
Genetic Architecture ◽  
Genetic Correlations ◽  
Covariance Structure ◽  
Equation Modeling ◽  
Joint Analysis ◽  
Summary Statistics ◽  
Individual Traits ◽  
Polygenic Scores

AbstractMethods for using GWAS to estimate genetic correlations between pairwise combinations of traits have produced “atlases” of genetic architecture. Genetic atlases reveal pervasive pleiotropy, and genome-wide significant loci are often shared across different phenotypes. We introduce genomic structural equation modeling (Genomic SEM), a multivariate method for analyzing the joint genetic architectures of complex traits. Using formal methods for modeling covariance structure, Genomic SEM synthesizes genetic correlations and SNP-heritabilities inferred from GWAS summary statistics of individual traits from samples with varying and unknown degrees of overlap. Genomic SEM can be used to identify variants with effects on general dimensions of cross-trait liability, boost power for discovery, and calculate more predictive polygenic scores. Finally, Genomic SEM can be used to identify loci that cause divergence between traits, aiding the search for what uniquely differentiates highly correlated phenotypes. We demonstrate several applications of Genomic SEM, including a joint analysis of GWAS summary statistics from five genetically correlated psychiatric traits. We identify 27 independent SNPs not previously identified in the univariate GWASs, 5 of which have been reported in other published GWASs of the included traits. Polygenic scores derived from Genomic SEM consistently outperform polygenic scores derived from GWASs of the individual traits. Genomic SEM is flexible, open ended, and allows for continuous innovations in how multivariate genetic architecture is modeled.

scholarly journals Estimating genetic nurture with summary statistics of multigenerational genome-wide association studies

2021 ◽  
Vol 118 (25) ◽  
pp. e2023184118
Author(s):  
Yuchang Wu ◽  
Xiaoyuan Zhong ◽  
Yunong Lin ◽  
Zijie Zhao ◽  
Jiawen Chen ◽  
...  
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Genetic Correlations ◽  
Genetic Effects ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Phenotypic Data ◽  
Individual Level ◽  
Indirect Genetic Effects ◽  
Genome Wide

Marginal effect estimates in genome-wide association studies (GWAS) are mixtures of direct and indirect genetic effects. Existing methods to dissect these effects require family-based, individual-level genetic, and phenotypic data with large samples, which is difficult to obtain in practice. Here, we propose a statistical framework to estimate direct and indirect genetic effects using summary statistics from GWAS conducted on own and offspring phenotypes. Applied to birth weight, our method showed nearly identical results with those obtained using individual-level data. We also decomposed direct and indirect genetic effects of educational attainment (EA), which showed distinct patterns of genetic correlations with 45 complex traits. The known genetic correlations between EA and higher height, lower body mass index, less-active smoking behavior, and better health outcomes were mostly explained by the indirect genetic component of EA. In contrast, the consistently identified genetic correlation of autism spectrum disorder (ASD) with higher EA resides in the direct genetic component. A polygenic transmission disequilibrium test showed a significant overtransmission of the direct component of EA from healthy parents to ASD probands. Taken together, we demonstrate that traditional GWAS approaches, in conjunction with offspring phenotypic data collection in existing cohorts, could greatly benefit studies on genetic nurture and shed important light on the interpretation of genetic associations for human complex traits.

scholarly journals Sexual differences in genetic architecture in UK Biobank

2020 ◽  
Author(s):  
Elena Bernabeu ◽  
Oriol Canela-Xandri ◽  
Konrad Rawlik ◽  
Andrea Talenti ◽  
James Prendergast ◽  
...  
Keyword(s):  
Sexual Dimorphism ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Mammalian Species ◽  
Genetic Correlations ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Uk Biobank

ABSTRACTSex is arguably the most important differentiating characteristic in most mammalian species, separating populations into different groups, with varying behaviors, morphologies, and physiologies based on their complement of sex chromosomes. In humans, despite males and females sharing nearly identical genomes, there are differences between the sexes in complex traits and in the risk of a wide array of diseases. Gene by sex interactions (GxS) are thought to account for some of this sexual dimorphism. However, the extent and basis of these interactions are poorly understood.Here we provide insights into both the scope and mechanism of GxS across the genome of circa 450,000 individuals of European ancestry and 530 complex traits in the UK Biobank. We found small yet widespread differences in genetic architecture across traits through the calculation of sex-specific heritability, genetic correlations, and sex-stratified genome-wide association studies (GWAS). We also found that, in some cases, sex-agnostic GWAS efforts might be missing loci of interest, and looked into possible improvements in the prediction of high-level phenotypes. Finally, we studied the potential functional role of the dimorphism observed through sex-biased eQTL and gene-level analyses.This study marks a broad examination of the genetics of sexual dimorphism. Our findings parallel previous reports, suggesting the presence of sexual genetic heterogeneity across complex traits of generally modest magnitude. Our results suggest the need to consider sex-stratified analyses for future studies in order to shed light into possible sex-specific molecular mechanisms.

scholarly journals 179 Breeding for enhancing feed efficiency in dairy cattle

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 183-184
Author(s):  
Flavio Schenkel ◽  
Luiz Brito ◽  
Hinayah Oliveira ◽  
Tatiane Chud ◽  
David Seymour ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Feed Efficiency ◽  
Selection Index ◽  
Genetic Correlations ◽  
Genetic Selection ◽  
Methane Emissions ◽  
Genome Project ◽  
Nutrient Loss ◽  
Breeding Value ◽  
Selection For

Abstract Genetically selecting for improved feed efficiency has been recognized by the dairy cattle industry as an important economic and environmental goal. Improved feed efficiency has the potential to significantly reduce costs, improving dairy farmers’ profitability and, at the same time, minimize environmental impact, for example by reducing nutrient loss in manure and methane emissions. Feed efficiency is recognized as a complex trait that may be define in different ways, but it generally describes units of product output per unit of feed required. An overview of genetic selection for improved feed efficiency and international initiatives to implement genomic selection for feed efficiency in dairy cattle is presented. In general, studies have indicated that feed efficiency, defined and assessed in alternative ways, is moderately heritable and genetic selection could be successfully implemented. Various initiatives around the world have worked collaboratively to carried out research and create reference datasets for joint genomic evaluations. An example is the large international Efficient Dairy Genome Project (EDGP) led by Canada. The EDGP database was developed in 2017 to allow data sharing among the international collaborators. Currently, the database contains genotypes and records on feed intake of 5,289 cows and on methane emissions of 1,337 cows from eight research herds in six countries (Australia, Canada, Denmark, Switzerland, United Kingdom and United States). Genetic parameters (heritability and genetic correlations) were estimated for dry matter intake, metabolic body weight and energy corrected milk at two time-periods: a) 5–60 DIM and b) 60–150 DIM. These parameters provide a basis for development of breeding value estimation procedures and subsequent selection index for feed efficiency, which will incorporate genomic information.

scholarly journals Estimating genetic nurture with summary statistics of multi-generational genome-wide association studies

2020 ◽  
Author(s):  
Yuchang Wu ◽  
Xiaoyuan Zhong ◽  
Yunong Lin ◽  
Zijie Zhao ◽  
Jiawen Chen ◽  
...  
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Genetic Correlations ◽  
Genetic Effects ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Phenotypic Data ◽  
Individual Level ◽  
Indirect Genetic Effects ◽  
Genome Wide

AbstractMarginal effect estimates in genome-wide association studies (GWAS) are mixtures of direct and indirect genetic effects. Existing methods to dissect these effects require family-based, individual-level genetic and phenotypic data with large samples, which is difficult to obtain in practice. Here, we propose a novel statistical framework to estimate direct and indirect genetic effects using summary statistics from GWAS conducted on own and offspring phenotypes. Applied to birth weight, our method showed nearly identical results with those obtained using individual-level data. We also decomposed direct and indirect genetic effects of educational attainment (EA), which showed distinct patterns of genetic correlations with 45 complex traits. The known genetic correlations between EA and higher height, lower BMI, less active smoking behavior, and better health outcomes were mostly explained by the indirect genetic component of EA. In contrast, the consistently identified genetic correlation of autism spectrum disorder (ASD) with higher EA resides in the direct genetic component. Polygenic transmission disequilibrium test showed a significant over-transmission of the direct component of EA from healthy parents to ASD probands. Taken together, we demonstrate that traditional GWAS approaches, in conjunction with offspring phenotypic data collection in existing cohorts, could greatly benefit studies on genetic nurture and shed important light on the interpretation of genetic associations for human complex traits.

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