scholarly journals Efficient multivariate analysis algorithms for longitudinal genome-wide association studies

2019 ◽  
Vol 35 (23) ◽  
pp. 4879-4885 ◽  
Author(s):  
Chao Ning ◽  
Dan Wang ◽  
Lei Zhou ◽  
Julong Wei ◽  
Yuanxin Liu ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Software Package ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Genome Wide Association ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Computational Speed

Abstract Motivation Current dynamic phenotyping system introduces time as an extra dimension to genome-wide association studies (GWAS), which helps to explore the mechanism of dynamical genetic control for complex longitudinal traits. However, existing methods for longitudinal GWAS either ignore the covariance among observations of different time points or encounter computational efficiency issues. Results We herein developed efficient genome-wide multivariate association algorithms for longitudinal data. In contrast to existing univariate linear mixed model analyses, the proposed method has improved statistic power for association detection and computational speed. In addition, the new method can analyze unbalanced longitudinal data with thousands of individuals and more than ten thousand records within a few hours. The corresponding time for balanced longitudinal data is just a few minutes. Availability and implementation A software package to implement the efficient algorithm named GMA (https://github.com/chaoning/GMA) is available freely for interested users in relevant fields. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Efficient Multivariate Analysis Algorithms for Longitudinal Genome-wide Association Studies

2018 ◽  
Author(s):  
Chao Ning ◽  
Dan Wang ◽  
Lei Zhou ◽  
Julong Wei ◽  
Yuanxin Liu ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Software Package ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Association Studies ◽  
New Method ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Computational Speed

AbstractMotivationCurrent dynamic phenotyping system introduces time as an extra dimension to genome-wide association studies (GWAS), which helps to explore the mechanism of dynamical genetic control for complex longitudinal traits. However, existing methods for longitudinal GWAS either ignore the covariance among observations of different time points or encounter computational efficiency issues.ResultsWe herein developed efficient genome-wide multivariate association algorithms (GMA) for longitudinal data. In contrast to existing univariate linear mixed model analyses, the proposed new method has improved statistic power for association detection and computational speed. In addition, the new method can analyze unbalanced longitudinal data with thousands of individuals and more than ten thousand records within a few hours. The corresponding time for balanced longitudinal data is just a few minutes.Availability and ImplementationWe wrote a software package to implement the efficient algorithm named GMA (https://github.com/chaoning/GMA), which is available freely for interested users in relevant fields.

Fast linear mixed model computations for genome-wide association studies with longitudinal data

2012 ◽  
Vol 32 (1) ◽  
pp. 165-180 ◽  
Author(s):  
Karolina Sikorska ◽  
Fernando Rivadeneira ◽  
Patrick J.F. Groenen ◽  
Albert Hofman ◽  
André G. Uitterlinden ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals GWASpro: a high-performance genome-wide association analysis server

2018 ◽  
Vol 35 (14) ◽  
pp. 2512-2514 ◽  
Author(s):  
Bongsong Kim ◽  
Xinbin Dai ◽  
Wenchao Zhang ◽  
Zhaohong Zhuang ◽  
Darlene L Sanchez ◽  
...  
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Learning Curves ◽  
Experimental Designs ◽  
Genome Wide Association ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Genome Wide

Abstract Summary We present GWASpro, a high-performance web server for the analyses of large-scale genome-wide association studies (GWAS). GWASpro was developed to provide data analyses for large-scale molecular genetic data, coupled with complex replicated experimental designs such as found in plant science investigations and to overcome the steep learning curves of existing GWAS software tools. GWASpro supports building complex design matrices, by which complex experimental designs that may include replications, treatments, locations and times, can be accounted for in the linear mixed model. GWASpro is optimized to handle GWAS data that may consist of up to 10 million markers and 10 000 samples from replicable lines or hybrids. GWASpro provides an interface that significantly reduces the learning curve for new GWAS investigators. Availability and implementation GWASpro is freely available at https://bioinfo.noble.org/GWASPRO. Supplementary information Supplementary data are available at Bioinformatics online.

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 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.

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