scholarly journals EigenGWAS: finding loci under selection through genome-wide association studies of eigenvectors in structured populations

Heredity ◽  
2016 ◽  
Vol 117 (1) ◽  
pp. 51-61 ◽  
Author(s):  
G-B Chen ◽  
S H Lee ◽  
Z-X Zhu ◽  
B Benyamin ◽  
M R Robinson
Keyword(s):  
Association Studies ◽  
Structured Populations ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals networkGWAS: A network-based approach for genome-wide association studies in structured populations

2021 ◽  
Author(s):  
Giulia Muzio ◽  
Leslie O'Bray ◽  
Laetitia Meng-Papaxanthos ◽  
Juliane Klatt ◽  
Karsten Borgwardt
Keyword(s):  
Genetic Markers ◽  
Complex Traits ◽  
Multiple Testing ◽  
Association Studies ◽  
Search Space ◽  
Structured Populations ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Multiple Testing Correction ◽  
Genome Wide

While the search for associations between genetic markers and complex traits has discovered tens of thousands of trait-related genetic variants, the vast majority of these only explain a tiny fraction of observed phenotypic variation. One possible strategy to detect stronger associations is to aggregate the effects of several genetic markers and to test entire genes, pathways or (sub)networks of genes for association to a phenotype. The latter, network-based genome-wide association studies, in particular suffers from a huge search space and an inherent multiple testing problem. As a consequence, current approaches are either based on greedy feature selection, thereby risking that they miss relevant associations, and/or neglect doing a multiple testing correction, which can lead to an abundance of false positive findings. To address the shortcomings of current approaches of network-based genome-wide association studies, we propose <tt>networkGWAS</tt>, a computationally efficient and statistically sound approach to gene-based genome-wide association studies based on mixed models and neighborhood aggregation. It allows for population structure correction and for well-calibrated p-values, which we obtain through a block permutation scheme. <tt>networkGWAS</tt> successfully detects known or plausible associations on simulated rare variants from H. sapiens data as well as semi-simulated and real data with common variants from A. thaliana and enables the systematic combination of gene-based genome-wide association studies with biological network information.

scholarly journals High-Performance Mixed Models Based Genome-Wide Association Analysis with omicABEL software

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 200 ◽  
Author(s):  
Diego Fabregat-Traver ◽  
Sodbo Zh. Sharapov ◽  
Caroline Hayward ◽  
Igor Rudan ◽  
Harry Campbell ◽  
...  
Keyword(s):  
High Performance ◽  
Mixed Model ◽  
Association Studies ◽  
Structured Populations ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Multiple Traits ◽  
Multiple Trait ◽  
Model Based ◽  
Genome Wide

To raise the power of genome-wide association studies (GWAS) and avoid false-positive results in structured populations, one can rely on mixed model based tests. When large samples are used, and when multiple traits are to be studied in the ’omics’ context, this approach becomes computationally challenging. Here we consider the problem of mixed-model based GWAS for arbitrary number of traits, and demonstrate that for the analysis of single-trait and multiple-trait scenarios different computational algorithms are optimal. We implement these optimal algorithms in a high-performance computing framework that uses state-of-the-art linear algebra kernels, incorporates optimizations, and avoids redundant computations,increasing throughput while reducing memory usage and energy consumption. We show that, compared to existing libraries, our algorithms and software achieve considerable speed-ups. The OmicABEL software described in this manuscript is available under the GNUGPL v. 3 license as part of the GenABEL project for statistical genomics at http: //www.genabel.org/packages/OmicABEL.

scholarly journals EigenGWAS: finding loci under selection through genome-wide association studies of eigenvectors in structured populations

2015 ◽  
Author(s):  
Guo-Bo Chen ◽  
Sang Hong Lee ◽  
Zhi-Xiang Zhu ◽  
Beben Benyamin ◽  
Matthew R Robinson
Keyword(s):  
Association Studies ◽  
Structured Populations ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Statistical Framework ◽  
Snp Data ◽  
Genome Wide ◽  
Single Marker ◽  
Marker Regression ◽  
Value Decomposition

We apply the statistical framework for genome-wide association studies (GWAS) to eigenvector decomposition (EigenGWAS), which is commonly used in population genetics to characterise the structure of genetic data. We show that loci under selection can be detected in a structured population by using eigenvectors as phenotypes in a single-marker GWAS. We find LCT to be under selection between HapMap CEU-TSI cohorts, a finding that was replicated across European countries in the POPRES samples. HERC2 was also found to be differentiated between both the CEU-TSI cohort and among POPRES samples, reflecting the likely anthropological differences in skin and hair colour between northern and southern European populations. We show that when determining the effect of a SNP on an eigenvector, three methods of single-marker regression of eigenvectors, best linear unbiased prediction of eigenvectors, and singular value decomposition of SNP data are equivalent to each other. We also demonstrate that estimated SNP effects on eigenvectors from a reference panel can be used to predict eigenvectors (the projected eigenvectors) in a target sample with high accuracy, particularly for the primary eigenvectors. Under this GWAS framework, ancestry informative markers and loci under selection can be identified, and population structure can be captured and easily interpreted. We have developed freely available software to facilitate the application of the methods (https://github.com/gc5k/GEAR/wiki/EigenGWAS).

scholarly journals A mixed-model approach for genome-wide association studies of correlated traits in structured populations

2012 ◽  
Vol 44 (9) ◽  
pp. 1066-1071 ◽  
Author(s):  
Arthur Korte ◽  
Bjarni J Vilhjálmsson ◽  
Vincent Segura ◽  
Alexander Platt ◽  
Quan Long ◽  
...  
Keyword(s):  
Mixed Model ◽  
Association Studies ◽  
Structured Populations ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Correlated Traits ◽  
Mixed Model Approach ◽  
Genome Wide ◽  
Model Approach

scholarly journals An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations

2012 ◽  
Vol 44 (7) ◽  
pp. 825-830 ◽  
Author(s):  
Vincent Segura ◽  
Bjarni J Vilhjálmsson ◽  
Alexander Platt ◽  
Arthur Korte ◽  
Ümit Seren ◽  
...  
Keyword(s):  
Mixed Model ◽  
Association Studies ◽  
Structured Populations ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Mixed Model Approach ◽  
Genome Wide ◽  
Model Approach
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