A statistical framework for cross-tissue transcriptome-wide association analysis

Hu, Yiming; Li, Mo; Lu, Qiongshi; Weng, Haoyi; Wang, Jiawei; Zekavat, Seyedeh M.; Yu, Zhaolong; Li, Boyang; Gu, Jianlei; Muchnik, Sydney; Shi, Yu; Kunkle, Brian W.; Mukherjee, Shubhabrata; Natarajan, Pradeep; Naj, Adam C.; Kuzma, Amanda B.; Zhao, Yi; Crane, Paul K.; Liu, Hui; Zhao, Hongyu

doi:10.1038/s41588-019-0345-7

Cited by 306 publications

(318 citation statements)

References 99 publications

(96 reference statements)

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“…Then we test the association between the imputed gene expression and the GWAS trait. If there is an association, then, under suitable modeling assumptions (Hu et al, 2019;Mancuso et al, 2019;Wainberg et al, 2019;Xu, Wu, Wei, & Pan, 2017), it is claimed that the gene is (putatively) causal to the trait: some causal SNPs affect the trait through the mediating effects of the gene's expression.…”

Section: Introductionmentioning

confidence: 99%

Some statistical consideration in transcriptome‐wide association studies

Xue

Pan

Initiative³

2019

Genetic Epidemiology

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The methodology of transcriptome‐wide association studies (TWAS) has become popular in integrating a reference expression quantitative trait (eQTL) data set with an independent main GWAS data set to identify (putatively) causal genes, shedding mechanistic insights to biological pathways from genetic variants to a GWAS trait mediated by gene expression. Statistically TWAS is a (two‐sample) 2‐stage least squares (2SLS) method in the framework of instrumental variables analysis for causal inference: in Stage 1 it uses the reference eQTL data to impute a genes expression for the main GWAS data, then in Stage 2 it tests for association between the imputed gene expression and the GWAS trait; if an association is detected in Stage 2, a (putatively) causal relationship between the gene and the GWAS trait is claimed. If a nonlinear model or a generalized linear model (GLM) is fitted in Stage 2 (e.g., for a binary GWAS trait), it is known that using only imputed gene expression, as in standard TWAS, in general does not lead to a consistent (i.e., asymptotically unbiased) estimate for the causal effect; accordingly, a variation of 2SLS, called two‐stage residual inclusion (2SRI), has been proposed to yield better estimates (e.g., being consistent under suitable conditions). Our main goal is to investigate whether it is necessary or even better to apply 2SRI, instead of the standard 2SLS. In addition, due to the use of imputed gene expression (i.e., with measurement errors), it is known that in general some correction to the standard error estimate of the causal effect estimate has to be applied, while in the standard TWAS no correction is applied. Is this an issue? We also compare one‐sample 2SLS with two‐sample 2SLS (i.e., the standard TWAS). We used the Alzheimer's Disease Neuroimaging Initiative (ADNI) data and simulated data mimicking the ADNI data to address the above questions. At the end, we conclude that, in practice with the large sample sizes and small effect sizes of genetic variants, the standard TWAS performs well and is recommended.

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Section: Introductionmentioning

confidence: 99%

Some statistical consideration in transcriptome‐wide association studies

Xue

Pan

Initiative³

2019

Genetic Epidemiology

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“…One potential approach to better dissect the genetic basis of ASD is to fine-map candidate genes affected by common SNPs and then investigate how they interact with genes harboring rare pathogenic variants implicated in WES studies. Transcriptome-wide association study (TWAS) is an analytical strategy that integrates expression quantitative trait loci (eQTL) annotations with GWAS data to identify disease genes [11][12][13] . Through advanced predictive modeling for gene expression traits, TWAS effectively combines association evidence across many eQTL in diverse tissues and has identified risk genes for numerous complex diseases 14 .…”

Section: Introductionmentioning

confidence: 99%

Transcriptome-wide transmission disequilibrium analysis identifies novel risk genes for autism spectrum disorder

Huang

Shin

et al. 2019

Preprint

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Recent advances in consortium-scale genome-wide association studies (GWAS) have highlighted the involvement of common genetic variants in autism spectrum disorder (ASD), but our understanding of their etiologic roles, especially the interplay with rare variants, is incomplete. In this work, we introduce an analytical framework to quantify the transmission disequilibrium of genetically regulated gene expression from parents to offsprings. We applied this framework to conduct a transcriptome-wide association study (TWAS) on 7,805 ASD proband-parent trios, and replicated our findings using 35,740 independent samples. We identified 31 associations at the transcriptome-wide significance level. In particular, we identified POU3F2 (p=2.1e-7), a transcription factor (TF) mainly expressed in developmental brain. TF targets regulated by POU3F2 showed a 2.1-fold enrichment for known ASD genes (p=4.6e-5) and a 2.7-fold enrichment for loss-of-function de novo mutations in ASD probands (p=7.1e-5). These results provide a clear example of the connection between ASD genes affected by very rare mutations and an unlinked key regulator affected by common genetic variations. Results Transmission disequilibrium of polygenic risk, gene expression, and SNP allelesWe applied multiple analytical approaches to dissect common SNPs' contributions to ASD risk at different scales. First, we performed polygenic transmission disequilibrium test (pTDT) 9 to examine the transmission disequilibrium of ASD polygenic risk in probands. ASD polygenic risk scores (PRS) were constructed using case-control samples from the iPSYCH cohort (N=35,740; Methods). We confirmed a highly significant over-transmission of ASD PRS from parents to probands in multiple datasets (p=1.4e-25 in the meta-analysis), including the SPARK cohort which has not been previously analyzed (p=1.0e-11; Supplementary Figure 1). No significant overtransmission was identified in 3,245 healthy siblings (p=0.88).

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“…We applied PUMAS to the stage-1 summary statistics from the 2013 study conducted by the International Genomics of Alzheimer's Project (IGAP; N=54,162) to optimize PRS models for AD. These PRSs were then evaluated on 7,050 independent samples 22 from the Alzheimer's Disease Genetics Consortium (ADGC) and 355,583 samples in the UK Biobank with a family history-based proxy phenotype for AD (Methods). 23 Our summary statistics-based analyses showed highly consistent results compared with external validations (Figure 3; Supplementary Table 2).…”

Section: Pumas Effectively Fine-tunes Prs Models Based On Genetic Arcmentioning

confidence: 99%

Fine-tuning Polygenic Risk Scores with GWAS Summary Statistics

Zhao

et al. 2019

Preprint

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Polygenic risk scores (PRSs) have wide applications in human genetics research. Notably, most PRS models include tuning parameters which improve predictive performance when properly selected. However, existing model-tuning methods require validation data that is independent with both training and testing samples. These data rarely exist in practice, creating a significant gap between PRS methodology and applications. Here, we introduce PUMAS, a novel method to finetune PRS models using summary statistics from genome-wide association studies (GWASs). Through extensive simulations, external validations, and analysis of 65 GWAS traits, we demonstrate that PUMAS can perform a variety of model-tuning procedures (e.g. cross-validation) using GWAS summary statistics and can effectively benchmark and optimize PRS models under diverse genetic architecture. Applied to 211 neuroimaging traits and Alzheimer's disease, we show that fine-tuned PRSs will improve statistical power in association analysis. We believe our method resolves a fundamental problem without a current solution and will greatly benefit genetic prediction applications.

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A statistical framework for cross-tissue transcriptome-wide association analysis

Cited by 306 publications

References 99 publications

Some statistical consideration in transcriptome‐wide association studies

Some statistical consideration in transcriptome‐wide association studies

Transcriptome-wide transmission disequilibrium analysis identifies novel risk genes for autism spectrum disorder

Fine-tuning Polygenic Risk Scores with GWAS Summary Statistics

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