scholarly journals A fast multi-locus random-SNP-effect EMMA for genome-wide association studies

2016 ◽  
Author(s):  
Yang-Jun Wen ◽  
Hanwen Zhang ◽  
Jin Zhang ◽  
Jian-Ying Feng ◽  
Bo Huang ◽  
...  
Keyword(s):  
Mixed Model ◽  
Genetic Model ◽  
Association Studies ◽  
Selection Criterion ◽  
Real Data ◽  
Mixed Linear Model ◽  
Genome Wide Association ◽  
Residual Variance ◽  
Genome Wide Association Studies ◽  
Genome Wide

AbstractAlthough the mixed linear model (MLM) such as efficient mixed model association (EMMA), has been widely used in genome-wide association studies (GWAS), relatively little is known about fast and efficient algorithms to implement multi-locus GWAS. To address this issue, we report a fast multi-locus random-SNP-effect EMMA (FASTmrEMMA). In this method, a new matrix transformation was constructed to obtain a new genetic model that includes only quantitative trait nucleotide (QTN) variation and normal residual error; letting the number of nonzero eigenvalues be one and fixing the polygenic-to-residual variance ratio was used to increase computing speed. All the putative QTNs with the ≤0.005 P-values in the first step of the new method were included in one multi-locus model for true QTN detection. Owing to the multi-locus feature, the Bonferroni correction is replaced by a less stringent selection criterion. Results from analyses of both simulated and real data showed that FASTmrEMMA is more powerful in QTN detection, model fit and robustness, has less bias in QTN effect estimation, and requires less running time than the current single- and multi-locus methodologies for GWAS, such as E-BAYES, SUPER, EMMA, CMLM and ECMLM. Therefore, FASTmrEMMA provides an alternative for multi-locus GWAS.

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 Use of the Multivariate Discriminant Analysis for Genome-Wide Association Studies in Cattle

Animals ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 1300 ◽  
Author(s):  
Elisabetta Manca ◽  
Alberto Cesarani ◽  
Giustino Gaspa ◽  
Silvia Sorbolini ◽  
Nicolò P.P. Macciotta ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Association Studies ◽  
Real Data ◽  
Genome Wide Association ◽  
Stepwise Discriminant Analysis ◽  
Genome Wide Association Studies ◽  
Multivariate Method ◽  
Genome Wide ◽  
Single Marker ◽  
Multivariate Gwas

Genome-wide association studies (GWAS) are traditionally carried out by using the single marker regression model that, if a small number of individuals is involved, often lead to very few associations. The Bayesian methods, such as BayesR, have obtained encouraging results when they are applied to the GWAS. However, these approaches, require that an a priori posterior inclusion probability threshold be fixed, thus arbitrarily affecting the obtained associations. To partially overcome these problems, a multivariate statistical algorithm was proposed. The basic idea was that animals with different phenotypic values of a specific trait share different allelic combinations for genes involved in its determinism. Three multivariate techniques were used to highlight the differences between the individuals assembled in high and low phenotype groups: the canonical discriminant analysis, the discriminant analysis and the stepwise discriminant analysis. The multivariate method was tested both on simulated and on real data. The results from the simulation study highlighted that the multivariate GWAS detected a greater number of true associated single nucleotide polymorphisms (SNPs) and Quantitative trait loci (QTLs) than the single marker model and the Bayesian approach. For example, with 3000 animals, the traditional GWAS highlighted only 29 significantly associated markers and 13 QTLs, whereas the multivariate method found 127 associated SNPs and 65 QTLs. The gap between the two approaches slowly decreased as the number of animals increased. The Bayesian method gave worse results than the other two. On average, with the real data, the multivariate GWAS found 108 associated markers for each trait under study and among them, around 63% SNPs were also found in the single marker approach. Among the top 118 associated markers, 76 SNPs harbored putative candidate genes.

scholarly journals Mixed linear model approach adapted for genome-wide association studies

2010 ◽  
Vol 42 (4) ◽  
pp. 355-360 ◽  
Author(s):  
Zhiwu Zhang ◽  
Elhan Ersoz ◽  
Chao-Qiang Lai ◽  
Rory J Todhunter ◽  
Hemant K Tiwari ◽  
...  
Keyword(s):  
Linear Model ◽  
Association Studies ◽  
Mixed Linear Model ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Model Approach

scholarly journals Mixture model-based association analysis with case-control data in genome wide association studies

2017 ◽  
Vol 16 (3) ◽  
Author(s):  
Fadhaa Ali ◽  
Jian Zhang
Keyword(s):  
Mixture Model ◽  
Multiple Testing ◽  
Hypothesis Test ◽  
Association Studies ◽  
Real Data ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Model Based ◽  
Genome Wide ◽  
The Individual

AbstractMultilocus haplotype analysis of candidate variants with genome wide association studies (GWAS) data may provide evidence of association with disease, even when the individual loci themselves do not. Unfortunately, when a large number of candidate variants are investigated, identifying risk haplotypes can be very difficult. To meet the challenge, a number of approaches have been put forward in recent years. However, most of them are not directly linked to the disease-penetrances of haplotypes and thus may not be efficient. To fill this gap, we propose a mixture model-based approach for detecting risk haplotypes. Under the mixture model, haplotypes are clustered directly according to their estimated disease penetrances. A theoretical justification of the above model is provided. Furthermore, we introduce a hypothesis test for haplotype inheritance patterns which underpin this model. The performance of the proposed approach is evaluated by simulations and real data analysis. The results show that the proposed approach outperforms an existing multiple testing method.

Sign in / Sign up

Export Citation Format

Share Document