Using Gene Expression to Improve the Power of Genome-Wide Association Analysis

Ho, Yen Yi; Baechler, Emily C.; Ortmann, Ward; Behrens, Timothy W.; Graham, Robert; Bhangale, Tushar; Pan, Wei

doi:10.1159/000362837

Cited by 8 publications

(9 citation statements)

References 43 publications

(54 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In other words, PrediXcan and TWAS put a higher weight on an SNP (eSNP) that is more strongly associated with the gene's expression level, in agreement with empirical evidence that eSNPs are more likely to be associated with complex traits and diseases (Nicolae et al 2010). This new formulation also points out the connection to existing weighted association analysis (Roeder et al 2006;Ho et al 2014). More importantly, since the same association test in PrediXcan and TWAS suffers from power loss under some common situations, we develop an alternative and more powerful association test with broader applications.…”

supporting

confidence: 77%

A Powerful Framework for Integrating eQTL and GWAS Summary Data

Wei

et al. 2017

Genetics

Self Cite

View full text Add to dashboard Cite

Two new gene-based association analysis methods, called and for GWAS individual-level and summary data, respectively, were recently proposed to integrate GWAS with eQTL data, alleviating two common problems in GWAS by boosting statistical power and facilitating biological interpretation of GWAS discoveries. Based on a novel reformulation of PrediXcan and TWAS, we propose a more powerful gene-based association test to integrate single set or multiple sets of eQTL data with GWAS individual-level data or summary statistics. The proposed test was applied to several GWAS datasets, including two lipid summary association datasets based on [Formula: see text] and [Formula: see text] samples, respectively, and uncovered more known or novel trait-associated genes, showcasing much improved performance of our proposed method. The software implementing the proposed method is freely available as an R package.

show abstract

supporting

confidence: 77%

A Powerful Framework for Integrating eQTL and GWAS Summary Data

Wei

et al. 2017

Genetics

Self Cite

View full text Add to dashboard Cite

show abstract

“…This weight-adjustment approach is expected to boost the power of association analysis by incorporating additional genomic information while keeping the FWER controlled at a nominal level. Both simulation studies and experimental findings reported in Li et al [7] and Ho et al [8] support the expected gain in power through the weight-adjustment procedure.…”

Section: Discussionsupporting

confidence: 67%

“…It is also modifiable for when SNP , gene expression data and gene expression, phenotype data are from two different sets of cohorts, instead of paired gene expression and GWAS data from the same cohort. However, paired gene expression and GWAS data from the same cohort might be preferable, as the data will have increased power to detect the causative relationship (SNP→E→P) but not the reactive relationship (SNP→P→E) based on the simulation study described in [8]. …”

Section: Discussionmentioning

confidence: 99%

“…In this paper, our contribution is to provide a formal mechanism to construct weights using available information about rare variants and gene expression. Studies by Li et al [7] and Ho et al [8] demonstrate that by incorporating additional genomic information, the weight-adjustment procedure can increase statistical power drastically compared to the traditional genetic association analysis.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Powerful association test combining rare variant and gene expression using family data from Genetic Analysis Workshop 19

Guan

O’Connell

et al. 2016

BMC Proc

Self Cite

View full text Add to dashboard Cite

BackgroundGenetic association studies aim to test for disease or trait association with genetic variants, either throughout the human genome or in regions of interest. However, for most diseases and traits, the combined effects of associated genetic variants explain only a small proportion of the genetic variation. This “missing heritability” may be a result of the small effects of common variants considered in the genetic association studies. Rare variants may also play an important role in understanding the missing heritability of complex traits.MethodWe propose a novel weight-adjustment approach to combine gene expression into rare variant analysis. Results from previous simulation studies suggested that incorporating gene expression information can lead to substantial gain in statistical power.ResultsUsing the family data set provided through the Genetic Analysis Workshop 19, we identified susceptible genes associated with blood pressure regulation.ConclusionsThese findings provide valuable information for further functional studies for blood pressure control and mechanism.

show abstract

“…Simulation studies have suggested that incorporating together such rich data can lead to substantial gain in statistical power. This integrative approach was applied successfully to find susceptibility variants in genes associated with blood pressure regulation [78,102]. Furthermore, a number of studies have successfully used similar methods to integrate GWAS data with biological networks data (protein-protein interaction and coexpression networks) to predict causal genes at associated GWAS loci for various disorders [103][104][105][106].…”

Section: Alternate Methodsmentioning

confidence: 99%

Benefits and Challenges of Rare Genetic Variation in Alzheimer’s Disease

2019

View full text Add to dashboard Cite

Purpose of Review It is well established that sporadic Alzheimer's disease (AD) is polygenic with common and rare genetic variation alongside environmental factors contributing to disease. Here, we review our current understanding of the genetic architecture of disease, paying specific attention to rare susceptibility variants, and explore some of the limitations in rare variant detection and analysis. Recent Findings Rare variation has been shown to robustly associate with disease. These include potentially damaging and loss of function mutations that are easily modelled in silico, in vitro and in vivo, and represent potentially druggable targets. A number of risk genes, including TREM2, SORL1 and ABCA7 show multiple independent associations suggesting that they may influence disease via multiple mechanisms. With transcriptional regulation, inflammatory response and modification of protein production suggested to be of primary importance. Summary We are at the beginning of our journey of rare variant detection in AD. Whole exome sequencing has been the predominant technology of choice. While fruitful, this has introduced a number of challenges with regard to data integration. Ultimately the future of disease-associated rare variant identification lies in whole genome sequencing projects that will allow the testing of the full range of genomic variation.

show abstract

Using Gene Expression to Improve the Power of Genome-Wide Association Analysis

Cited by 8 publications

References 43 publications

A Powerful Framework for Integrating eQTL and GWAS Summary Data

A Powerful Framework for Integrating eQTL and GWAS Summary Data

Powerful association test combining rare variant and gene expression using family data from Genetic Analysis Workshop 19

Benefits and Challenges of Rare Genetic Variation in Alzheimer’s Disease

Contact Info

Product

Resources

About