Using eQTL weights to improve power for genome-wide association studies: a genetic study of childhood asthma

Li, Lin; Kabesch, Michael; Bouzigon, Emmanuelle; Demenais, Florence; Farrall, Martin; Moffatt, Miriam F.; Lin, Xihong; Liang, Liming

doi:10.3389/fgene.2013.00103

Cited by 59 publications

(68 citation statements)

References 48 publications

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“…Our simulation results confirmed that the proposed weighting procedure dramatically improved the statistical power of GWA studies while controlling FWER at the nominal level, when gene expression is in the middle of the etiological pathway. Under this mechanism, our methods demonstrated greater power gain compared to that of Li et al [14]. It also provides ways to draw valuable information from massive data to assist functional interpretations of GWA signals.…”

Section: Discussionmentioning

confidence: 83%

“…In Table 1, we recorded the fraction of times that a SNP was declared significant at

α = 1 \frac{0.05}{10}

for the four approaches: the conventional GWA analysis, the weight adjusted approach with (

W_{M P}^{T}

) and without ( W MP ) trimming and Li’s approach [14]. The overall FWERs from the 10,000 simulations were estimated by counting the percent of times when any significant results were reported.…”

Section: Simulationmentioning

confidence: 99%

“…Li et al [14] adapted the p value weighting idea and proposed using gene expression information to formally derive weights and to apply the derived weights to p adjust the p values from a GWA study. They specified weights

w_{i} = \sqrt{- {log}_{10} p_{eQTL}}

where p eQTL is the p value for association between a SNP marker and gene expression (these SNP markers sometimes were referred to as eQTLs).…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we propose approaches that utilize both (SNP, gene expression) as well as (gene expression, phenotype) associations and are expected to achieve greater power gains than that of Li et al [14] under some situations. A study-wise threshold for the weight adjusted p values can then be used to determine genome-wide significance.…”

Section: Introductionmentioning

confidence: 99%

“…We also compared the performance of our proposed weighting approaches to that of Li et al [14] in the simulation analysis.…”

Section: Introductionmentioning

confidence: 99%

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

Baechler

Ortmann

et al. 2014

Hum Hered

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Background/Aims: Genome-wide association (GWA) studies have reported susceptible regions in the human genome for many common diseases and traits; however, these loci only explain a minority of trait heritability. To boost the power of a GWA study, substantial research endeavors have been focused on integrating other available genomic information in the analysis. Advances in high through-put technologies have generated a wealth of genomic data and made combining SNP and gene expression data become feasible. Results: In this paper, we propose a novel procedure to incorporate gene expression information into GWA analysis. This procedure utilizes weights constructed by gene expression measurements to adjust p values from a GWA analysis. Results from simulation analyses indicate that the proposed procedures may achieve substantial power gains, while controlling family-wise type I error rates at the nominal level. To demonstrate the implementation of our proposed approach, we apply the weight adjustment procedure to a GWA study on serum interferon-regulated chemokine levels in systemic lupus erythematosus patients. The study results can provide valuable insights for the functional interpretation of GWA signals. Availability: The R source code for implementing the proposed weighting procedure is available at http://www.biostat.umn.edu/∼yho/research.html.

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Section: Discussionmentioning

confidence: 83%

“…In Table 1, we recorded the fraction of times that a SNP was declared significant at

α = 1 \frac{0.05}{10}

for the four approaches: the conventional GWA analysis, the weight adjusted approach with (

W_{M P}^{T}

Section: Simulationmentioning

confidence: 99%

w_{i} = \sqrt{- {log}_{10} p_{eQTL}}

where p eQTL is the p value for association between a SNP marker and gene expression (these SNP markers sometimes were referred to as eQTLs).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…We also compared the performance of our proposed weighting approaches to that of Li et al [14] in the simulation analysis.…”

Section: Introductionmentioning

confidence: 99%

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

Baechler

Ortmann

et al. 2014

Hum Hered

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Leveraging cell type specific regulatory regions to detect SNPs associated with tissue factor pathway inhibitor plasma levels

et al. 2017

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Tissue factor pathway inhibitor (TFPI) regulates the formation of intravascular blood clots, which manifest clinically as ischemic heart disease, ischemic stroke, and venous thromboembolism (VTE). TFPI plasma levels are heritable, but the genetics underlying TFPI plasma level variability are poorly understood. Herein we report the first genome-wide association scan (GWAS) of TFPI plasma levels, conducted in 251 individuals from five extended French-Canadian Families ascertained on VTE. To improve discovery, we also applied a hypothesis-driven (HD) GWAS approach that prioritized single nucleotide polymorphisms (SNPs) in (1) hemostasis pathway genes, and (2) vascular endothelial cell (EC) regulatory regions, which are among the highest expressers of TFPI. Our GWAS identified 131 SNPs with suggestive evidence of association (P-value < 5 × 10 ), but no SNPs reached the genome-wide threshold for statistical significance. Hemostasis pathway genes were not enriched for TFPI plasma level associated SNPs (global hypothesis test P-value = 0.147), but EC regulatory regions contained more TFPI plasma level associated SNPs than expected by chance (global hypothesis test P-value = 0.046). We therefore stratified our genome-wide SNPs, prioritizing those in EC regulatory regions via stratified false discovery rate (sFDR) control, and reranked the SNPs by q-value. The minimum q-value was 0.27, and the top-ranked SNPs did not show association evidence in the MARTHA replication sample of 1,033 unrelated VTE cases. Although this study did not result in new loci for TFPI, our work lays out a strategy to utilize epigenomic data in prioritization schemes for future GWAS studies.

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A fast and powerful eQTL weighted method to detect genes associated with complex trait using GWAS summary data

Zhang

Xie²,

Gonzales

et al. 2020

Genetic Epidemiology

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Although genomewide association studies (GWASs) have identified many genetic variants underlying complex traits, a large fraction of heritability still remains unexplained. Integrative analysis that incorporates additional information, such as expression quantitativetrait locus (eQTL) data into sequencing studies (denoted as transcriptomewide association study [TWAS]), can aid the discovery of trait‐associated genetic variants. However, general TWAS methods only incorporate one eQTL‐derived weight (e.g., cis‐effect), and thus can suffer a substantial loss of power when the single estimated cis‐effect is not predictive for the effect size of a genetic variant or when there are estimation errors in the estimated cis‐effect, or if the data are not consistent with the model assumption. In this study, we propose an omnibus test (OT) which utilizes a Cauchy association test to integrate association evidence demonstrated by three different traditional tests (burden test, quadratic test, and adaptive test) using GWAS summary data with multiple eQTL‐derived weights. The p value of the proposed test can be calculated analytically, and thus it is fast and efficient. We applied our proposed test to two schizophrenia (SCZ) GWAS summary data sets and two lipids trait (HDL) GWAS summary data sets. Compared with the three traditional tests, our proposed OT can identify more trait‐associated genes.

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Using eQTL weights to improve power for genome-wide association studies: a genetic study of childhood asthma

Cited by 59 publications

References 48 publications

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

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

Leveraging cell type specific regulatory regions to detect SNPs associated with tissue factor pathway inhibitor plasma levels

A fast and powerful eQTL weighted method to detect genes associated with complex trait using GWAS summary data

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