METAINTER: meta-analysis of multiple regression models in genome-wide association studies

Vaitsiakhovich, Tatsiana; Drichel, Dmitriy; Herold, Christine; Lacour, André; Becker, Tim

doi:10.1093/bioinformatics/btu629

Cited by 7 publications

(5 citation statements)

References 26 publications

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“…NMA is perhaps the most popular method for capturing flexibility in docking (Mashiach et al, 2010; May and Zacharias, 2005; Moal and Bates, 2010; Oliwa and Shen, 2015; Shen, 2013; Venkatraman and Ritchie, 2012). Although the metrics of docking evaluations and the dataset vary between studies, the flexible component RMSDs between the native complex conformation and the best predictions typically improve less than 0.5 Å, and sometimes the conformation of the docked monomer is even further from the bound than the unbound (Mashiach et al, 2010; May and Zacharias, 2008).…”

Section: Introductionmentioning

confidence: 99%

Pushing the Backbone in Protein-Protein Docking

Kuroda

Gray

2016

Structure

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Conformational changes of proteins that occur upon binding typically confound computational docking algorithms. In this study, we test computational methods to capture protein backbone conformational change related to binding. To address how well existing algorithms can sample bound-like backbones, we query seven techniques including Monte Carlo-based sampling, molecular dynamics and normal mode analysis. All methods tested rarely sample near-bound-states from the unbound-conformation. Nevertheless, the direction of the predicted motions overlap with the actual conformational change. We next forced the backbone from the unbound toward the bound conformation to create a family of docking energy landscapes. 70% of docking targets succeed when the unbound-backbones is pushed to within 0.6 Å of the bound. Current methods can capture an average of 22% of unbound-bound transitions through conformer-selection methods and another 57% through induced-fit methodologies, delineating a stubborn gap (21%) in backbone motion not covered by any current approach.

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Section: Introductionmentioning

confidence: 99%

Pushing the Backbone in Protein-Protein Docking

Kuroda

Gray

2016

Structure

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“…Recently, two kinds of approaches for utilising univariate summary statistics in multivariate testing have been proposed: 1) one SNP against multiple traits (Stephens, 2013;Vuckovic et al, 2015;Zhu et al, 2015) and 2) multiple SNPs against one trait (Vaitsiakhovich et al, 2015;Feng et al, 2014;Yang et al, 2012). We propose a new framework, metaCCA, that unifies both of the existing approaches by allowing canonical correlation analysis (CCA) of multiple SNPs against multiple traits based on univariate summary statistics and publicly available databases.…”

Section: Introductionmentioning

confidence: 99%

metaCCA: Summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis

Cichońska

Rousu

Marttinen

et al. 2015

Preprint

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Motivation: A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests. Results: We introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness. Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent

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“…To conduct meta-analysis METAINTER software was employed assuming a fixed effects model. Gamma approximated negative sum of log transformed interaction statistics from each of the two sets were considered as the test statistic for each variant pair and was tested with a weighted Chi-square statistic with four degrees of freedom 44 . Odds ratio and associated 95% confidence intervals were calculated with unconditional logistic regression with independence assumption among each component of SNP pairs.…”

Section: Methodsmentioning

confidence: 99%

Genome-wide interaction and pathway-based identification of key regulators in multiple myeloma

et al. 2019

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Inherited genetic susceptibility to multiple myeloma has been investigated in a number of studies. Although 23 individual risk loci have been identified, much of the genetic heritability remains unknown. Here we carried out genome-wide interaction analyses on two European cohorts accounting for 3,999 cases and 7,266 controls and characterized genetic susceptibility to multiple myeloma with subsequent meta-analysis that discovered 16 unique interacting loci. These risk loci along with previously known variants explain 17% of the heritability in liability scale. The genes associated with the interacting loci were found to be enriched in transforming growth factor beta signaling and circadian rhythm regulation pathways suggesting immunoglobulin trait modulation, T H 17 cell differentiation and bone morphogenesis as mechanistic links between the predisposition markers and intrinsic multiple myeloma biology. Further tissue/cell-type enrichment analysis associated the discovered genes with hemic-immune system tissue types and immune-related cell types indicating overall involvement in immune response.

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METAINTER: meta-analysis of multiple regression models in genome-wide association studies

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 7 publications

References 26 publications

Pushing the Backbone in Protein-Protein Docking

Pushing the Backbone in Protein-Protein Docking

metaCCA: Summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis

Genome-wide interaction and pathway-based identification of key regulators in multiple myeloma

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