scholarly journals Genome-Wide Association Studies for Growth Curves in Meat Rabbits Through the Single-Step Nonlinear Mixed Model

2021 ◽  
Vol 12 ◽  
Author(s):  
Yonglan Liao ◽  
Zhicheng Wang ◽  
Leonardo S. Glória ◽  
Kai Zhang ◽  
Cuixia Zhang ◽  
...  
Keyword(s):  
Mixed Model ◽  
Growth Traits ◽  
Association Studies ◽  
Growth Curves ◽  
Single Step ◽  
The Body ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Nonlinear Mixed Model ◽  
Genome Wide

Growth is a complex trait with moderate to high heritability in livestock and must be described by the longitudinal data measured over multiple time points. Therefore, the used phenotype in genome-wide association studies (GWAS) of growth traits could be either the measures at the preselected time point or the fitted parameters of whole growth trajectory. A promising alternative approach was recently proposed that combined the fitting of growth curves and estimation of single-nucleotide polymorphism (SNP) effects into single-step nonlinear mixed model (NMM). In this study, we collected the body weights at 35, 42, 49, 56, 63, 70, and 84 days of age for 401 animals in a crossbred population of meat rabbits and compared five fitting models of growth curves (Logistic, Gompertz, Brody, Von Bertalanffy, and Richards). The logistic model was preferably selected and subjected to GWAS using the approach of single-step NMM, which was based on 87,704 genome-wide SNPs. A total of 45 significant SNPs distributed on five chromosomes were found to simultaneously affect the two growth parameters of mature weight (A) and maturity rate (K). However, no SNP was found to be independently associated with either A or K. Seven positional genes, including KCNIP4, GBA3, PPARGC1A, LDB2, SHISA3, GNA13, and FGF10, were suggested to be candidates affecting growth performances in meat rabbits. To the best of our knowledge, this is the first report of GWAS based on single-step NMM for longitudinal traits in rabbits, which also revealed the genetic architecture of growth traits that are helpful in implementing genome selection.

Genome-wide association studies for growth traits in buffaloes using the single step genomic BLUP

2019 ◽  
Vol 61 (1) ◽  
pp. 113-115 ◽  
Author(s):  
Francisco Ribeiro de Araujo Neto ◽  
Daniel Jordan de Abreu Santos ◽  
Gerardo Alves Fernandes Júnior ◽  
Rusbel Raul Aspilcueta-Borquis ◽  
André Vieira do Nascimento ◽  
...  
Keyword(s):  
Growth Traits ◽  
Association Studies ◽  
Single Step ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals GWAS-Flow: A GPU accelerated framework for efficient permutation based genome-wide association studies

2019 ◽  
Author(s):  
Jan A. Freudenthal ◽  
Markus J. Ankenbrand ◽  
Dominik G. Grimm ◽  
Arthur Korte
Keyword(s):  
Complex Traits ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Large Datasets ◽  
Genome Wide Association ◽  
Small Data ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Non Gaussian

AbstractMotivationGenome-wide association studies (GWAS) are one of the most commonly used methods to detect associations between complex traits and genomic polymorphisms. As both genotyping and phenotyping of large populations has become easier, typical modern GWAS have to cope with massive amounts of data. Thus, the computational demand for these analyses grew remarkably during the last decades. This is especially true, if one wants to implement permutation-based significance thresholds, instead of using the naïve Bonferroni threshold. Permutation-based methods have the advantage to provide an adjusted multiple hypothesis correction threshold that takes the underlying phenotypic distribution into account and will thus remove the need to find the correct transformation for non Gaussian phenotypes. To enable efficient analyses of large datasets and the possibility to compute permutation-based significance thresholds, we used the machine learning framework TensorFlow to develop a linear mixed model (GWAS-Flow) that can make use of the available CPU or GPU infrastructure to decrease the time of the analyses especially for large datasets.ResultsWe were able to show that our application GWAS-Flow outperforms custom GWAS scripts in terms of speed without loosing accuracy. Apart from p-values, GWAS-Flow also computes summary statistics, such as the effect size and its standard error for each individual marker. The CPU-based version is the default choice for small data, while the GPU-based version of GWAS-Flow is especially suited for the analyses of big data.AvailabilityGWAS-Flow is freely available on GitHub (https://github.com/Joyvalley/GWAS_Flow) and is released under the terms of the MIT-License.

scholarly journals Genome-Wide Association Studies Reveal Susceptibility Loci for Digital Dermatitis in Holstein Cattle

Animals ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 2009
Author(s):  
Ellen Lai ◽  
Alexa L. Danner ◽  
Thomas R. Famula ◽  
Anita M. Oberbauer
Keyword(s):  
Predictive Value ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Bos Taurus ◽  
Association Studies ◽  
Bayesian Regression ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Digital Dermatitis ◽  
Genome Wide

Digital dermatitis (DD) causes lameness in dairy cattle. To detect the quantitative trait loci (QTL) associated with DD, genome-wide association studies (GWAS) were performed using high-density single nucleotide polymorphism (SNP) genotypes and binary case/control, quantitative (average number of FW per hoof trimming record) and recurrent (cases with ≥2 DD episodes vs. controls) phenotypes from cows across four dairies (controls n = 129 vs. FW n = 85). Linear mixed model (LMM) and random forest (RF) approaches identified the top SNPs, which were used as predictors in Bayesian regression models to assess the SNP predictive value. The LMM and RF analyses identified QTL regions containing candidate genes on Bos taurus autosome (BTA) 2 for the binary and recurrent phenotypes and BTA7 and 20 for the quantitative phenotype that related to epidermal integrity, immune function, and wound healing. Although larger sample sizes are necessary to reaffirm these small effect loci amidst a strong environmental effect, the sample cohort used in this study was sufficient for estimating SNP effects with a high predictive value.

scholarly journals Genome-wide association studies for feedlot and growth traits in cattle1

2011 ◽  
Vol 89 (6) ◽  
pp. 1684-1697 ◽  
Author(s):  
S. Bolormaa ◽  
B. J. Hayes ◽  
K. Savin ◽  
R. Hawken ◽  
W. Barendse ◽  
...  
Keyword(s):  
Growth Traits ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals Variable Selection in Heterogeneous Datasets: A Truncated-rank Sparse Linear Mixed Model with Applications to Genome-wide Association Studies

2017 ◽  
Author(s):  
Haohan Wang ◽  
Bryon Aragam ◽  
Eric P. Xing
Keyword(s):  
Population Structure ◽  
Variable Selection ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Genome Wide Association ◽  
Low Rank ◽  
Genome Wide Association Studies ◽  
Unified Framework ◽  
Genome Wide

AbstractA fundamental and important challenge in modern datasets of ever increasing dimensionality is variable selection, which has taken on renewed interest recently due to the growth of biological and medical datasets with complex, non-i.i.d. structures. Naïvely applying classical variable selection methods such as the Lasso to such datasets may lead to a large number of false discoveries. Motivated by genome-wide association studies in genetics, we study the problem of variable selection for datasets arising from multiple subpopulations, when this underlying population structure is unknown to the researcher. We propose a unified framework for sparse variable selection that adaptively corrects for population structure via a low-rank linear mixed model. Most importantly, the proposed method does not require prior knowledge of sample structure in the data and adaptively selects a covariance structure of the correct complexity. Through extensive experiments, we illustrate the effectiveness of this framework over existing methods. Further, we test our method on three different genomic datasets from plants, mice, and human, and discuss the knowledge we discover with our method.

scholarly journals Genome-Wide Association Study of Body Fat Distribution identifies Novel Adiposity Loci and Sex-Specific Genetic Effects

2017 ◽  
Author(s):  
Mathias Rask-Andersen ◽  
Torgny Karlsson ◽  
Weronica E Ek ◽  
Åsa Johansson
Keyword(s):  
Body Fat ◽  
Genome Wide Association Study ◽  
Metabolic Diseases ◽  
Association Studies ◽  
Fat Distribution ◽  
Body Fat Distribution ◽  
The Body ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

Body mass and body fat composition are of clinical interest due to their links to cardiovascular- and metabolic diseases. Fat stored in the trunk has been suggested as more pathogenic compared to fat stored in other compartments of the body. In this study, we performed genome-wide association studies (GWAS) for the proportion of body fat distributed to the arms, legs and trunk estimated from segmental bio-electrical impedance analysis (sBIA) for 362,499 individuals from the UK Biobank. A total of 97 loci, were identified to be associated with body fat distribution, 40 of which have not previously been associated with an anthropometric trait. A high degree of sex-heterogeneity was observed and associations were primarily observed in females, particularly for distribution of fat to the legs or trunk. Our findings also implicate that body fat distribution in females involves mesenchyme derived tissues and cell types, female endocrine tissues a well as several enzymatically active members of the ADAMTS family of metalloproteinases, which are involved in extracellular matrix maintenance and remodeling.

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