A Powerful Framework for Integrating eQTL and GWAS Summary Data

Xu, Zhiyuan; Wu, Chong; Wei, Peng; Pan, Wei

doi:10.1534/genetics.117.300270

Cited by 71 publications

(85 citation statements)

References 43 publications

(44 reference statements)

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“…As discussed in Xu et al. (), in spite of the connections of TWAS with two‐stage least squares and Mendelian randomization (MR), due to the adopted strong assumptions that are likely to be violated in practice, cautions should be taken to avoid extrapolating any discovered GWAS associations to causal effects mediated through gene expression. Hence, we simply regard TWAS as a special case of weighted association testing.…”

Section: Discussionmentioning

confidence: 99%

“…Note that, with GWAS individual‐level data, TWAS can be interpreted as testing for association between an imputed gene expression and the GWAS trait; however, with GWAS summary data,

W^{'} Z

may be regarded as an imputed z ‐ score for the gene, but not imputed expression level. It turns out that TWAS is equivalent to the weighted Sum test (Pan, ; Xu et al., ). Because the Sum test implicitly assumes that all variants have an equal effect size and the same effect direction, the Sum test and thus TWAS, as discussed in the previous studies (Pan, ; Pan et al., ; Wu et al., ), may lose statistical power if the true association effects are sparse (i.e., with many 0s) or the effect directions are different.…”

Section: Methodsmentioning

confidence: 99%

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Integrating eQTL data with GWAS summary statistics in pathway‐based analysis with application to schizophrenia

Pan

2018

Genetic Epidemiology

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Many genetic variants affect complex traits through gene expression, which can be exploited to boost statistical power and enhance interpretation in genome-wide association studies (GWASs) as demonstrated by the transcriptome-wide association study (TWAS) approach. Furthermore, due to polygenic inheritance, a complex trait is often affected by multiple genes with similar functions as annotated in gene pathways. Here, we extend TWAS from gene-based analysis to pathway-based analysis: we integrate public pathway collections, expression quantitative trait locus (eQTL) data and GWAS summary association statistics (or GWAS individual-level data) to identify gene pathways associated with complex traits. The basic idea is to weight the SNPs of the genes in a pathway based on their estimated cis-effects on gene expression, then adaptively test for association of the pathway with a GWAS trait by effectively aggregating possibly weak association signals across the genes in the pathway. The P values can be calculated analytically and thus fast. We applied our proposed test with the KEGG and GO pathways to two schizophrenia (SCZ) GWAS summary association data sets, denoted by SCZ1 and SCZ2 with about 20,000 and 150,000 subjects, respectively. Most of the significant pathways identified by analyzing the SCZ1 data were reproduced by the SCZ2 data. Importantly, we identified 15 novel pathways associated with SCZ, such as GABA receptor complex (GO:1902710), which could not be uncovered by the standard single SNP-based analysis or gene-based TWAS. The newly identified pathways may help us gain insights into the biological mechanism underlying SCZ. Our results showcase the power of incorporating gene expression information and gene functional annotations into pathway-based association testing for GWAS.

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Section: Discussionmentioning

confidence: 99%

“…Note that, with GWAS individual‐level data, TWAS can be interpreted as testing for association between an imputed gene expression and the GWAS trait; however, with GWAS summary data,

W^{'} Z

Section: Methodsmentioning

confidence: 99%

Integrating eQTL data with GWAS summary statistics in pathway‐based analysis with application to schizophrenia

Pan

2018

Genetic Epidemiology

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“…Xu et al (2017) showed that, more generally, any more powerful tests, such as an adaptive aSPU test, can be applied. The aSPU test is based on choosing one of the SPU tests, SPU( γ ) with positive integers γ ≥ 1 in a candidate set, e.g.…”

Section: Methodsmentioning

confidence: 99%

Imaging-wide association study: Integrating imaging endophenotypes in GWAS

Pan

2017

NeuroImage

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A new and powerful approach, called imaging-wide association study (IWAS), is proposed to integrate imaging endophenotypes with GWAS to boost statistical power and enhance biological interpretation for GWAS discoveries. IWAS extends the promising transcriptome-wide association study (TWAS) from using gene expression endophenotypes to using imaging and other endophenotypes with a much wider range of possible applications. As illustration, we use gray-matter volumes of several brain regions of interest (ROIs) drawn from the ADNI-1 structural MRI data as imaging endophenotypes, which are then applied to the individual-level GWAS data of ADNI-GO/2 and a large meta-analyzed GWAS summary statistics dataset (based on about 74000 individuals), uncovering some novel genes significantly associated with Alzheimer’s disease (AD). We also compare the performance of IWAS with TWAS, showing much larger numbers of significant AD-associated genes discovered by IWAS, presumably due to the stronger link between brain atrophy and AD than that between gene expression of normal individuals and the risk for AD. The proposed IWAS is general and can be applied to other imaging endophenotypes, and GWAS individual-level or summary association data.

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“…We consider an arbitrary gene and a set of

k

SNPs in some region around the gene:

X = (X_{1}, \dots, X_{k})'

. We have the corresponding score vector:

U = (U_{1}, \dots, U_{k})'

with

U_{j} = X_{j}^{true'} false(Y - {\overset{μ}{true}}^{0} false)

, where

{\hat{μ}}^{0}

is the estimated mean of

Y

under the null hypothesis:

H_{0} : β = false(β_{1}, \dots, β_{k} false)' = 0.

Given the weights

\overset{W}{true}

, we denote the weighted score statistics as:

U^{*} = \hat{W} U = ({\overset{w}{true}}_{1} U_{1}, \dots, {\overset{w}{true}}_{k} U_{k})' .

Thus, we can construct the following TWAS test statistic, where we aggregate weighted score statistics across a gene:

T_{T W A S} = \sum_{j = 1}^{k} U_{j}^{*} .

We now consider the extension of TWAS to other SPU tests (Xu et al, ). Xu et al make the observation that TWAS is equivalent to a weighted Sum test of multiple SNPs in a generalized linear model (2).…”

Section: Methodsmentioning

confidence: 99%

“…The TWAS and PrediXcan test statistics can be thought of as weighted sum tests. This framework can be extended to other sums of powered score (SPU) tests (Su et al, ; Xu, Wu, Wei, & Pan, ), and to other mediating phenotypes in addition to gene expression (Xu, Wu, & Pan, ). It has been convincingly shown that PrediXcan and TWAS increase the power of GWAS analyses for certain complex traits, and help inform the biological mechanism by which SNPs influence the expression of complex traits.…”

Section: Introductionmentioning

confidence: 99%

Integrating germline and somatic genetics to identify genes associated with lung cancer

Pattee

Zhan

Xiao

et al. 2019

Genetic Epidemiology

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Genome‐wide association studies (GWAS) have successfully identified many genetic variants associated with complex traits. However, GWAS experience power issues, resulting in the failure to detect certain associated variants. Additionally, GWAS are often unable to parse the biological mechanisms of driving associations. An existing gene‐based association test framework, Transcriptome‐Wide Association Studies (TWAS), leverages expression quantitative trait loci data to increase the power of association tests and illuminate the biological mechanisms by which genetic variants modulate complex traits. We extend the TWAS methodology to incorporate somatic information from tumors. By integrating germline and somatic data we are able to leverage information from the nuanced somatic landscape of tumors. Thus we can augment the power of TWAS‐type tests to detect germline genetic variants associated with cancer phenotypes. We use somatic and germline data on lung adenocarcinomas from The Cancer Genome Atlas in conjunction with a meta‐analyzed lung cancer GWAS to identify novel genes associated with lung cancer.

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A Powerful Framework for Integrating eQTL and GWAS Summary Data

Cited by 71 publications

References 43 publications

Integrating eQTL data with GWAS summary statistics in pathway‐based analysis with application to schizophrenia

Integrating eQTL data with GWAS summary statistics in pathway‐based analysis with application to schizophrenia

Imaging-wide association study: Integrating imaging endophenotypes in GWAS

Integrating germline and somatic genetics to identify genes associated with lung cancer

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