Power analysis of transcriptome-wide association study: implications for practical protocol choice

Abstract

Background: Standard Genome-wide association study (GWAS) discovers genetic variants explaining phenotypic variance by directly associate them. With the availability of other omics data such as gene expression, the field is stepping into an exciting era of multi-scale omics integration. An emerging technique is transcriptome-wide association study (TWAS) that conducts association mapping by utilizing gene expression data from a separate reference dataset based on which a model predicting expression by genotype is trained. Despite its success in practice, two fundamental questions have been unaddressed yet. First, in practice, the accuracy of predicting expression by genotype is generally low, which is bounded by the expression heritability. So, the question is whether such a low accuracy may impact the power of TWAS, and what level of accuracy is sufficient. Second, since predicting expression is a critical step in TWAS, one may ask what if we have actual expression assessed by a real experiment, and whether that will improve or deteriorate power. Answering these questions will bring thorough understanding of TWAS and practical guidelines in association mapping. Results: To address the above questions, we conducted power analysis for GWAS, TWAS, and expression medicated GWAS (emGWAS). Specifically, we derived non-centrality parameters (NCPs), enabling closed-form derivation of statistical power to facilitate a thorough power analysis without relying on particular implementations. We assessed the power of the three protocols with respect to two representative scenarios: causality (genotype contributes to phenotype through expression) and pleiotropy (genotype contributes directly to both phenotype and expression). For both scenarios, we tested various properties including expression heritability. Conclusions: (1) TWAS utilizing predicted expression enjoys higher power than emGWAS that has actual expressions in the pleiotropy scenario, revealing a deep insight into TWAS models as well as a practical guideline of applying TWAS even in cases when expressions are available in a GWAS dataset. (2) TWAS is suboptimal compared to GWAS when expression heritability is too low. The superiority ordering of TWAS and GWAS disclosed a turn-point in each of the causality and pleiotropy scenarios. Analysis of published discoveries shows the selection of protocols might be questionable based on the identified turn-points.

Best practices for multi-ancestry, meta-analytic transcriptome-wide association studies: lessons from the Global Biobank Meta-analysis Initiative

“…However, optimal TWAS requires ancestry-matched training datasets of genetic and tissue-specific gene expression data, which are still lacking in non-European samples. As Cao et al points out (Cao et al, 2021), statistical power to detect GTAs in TWAS is dependent on expression heritability and the ability of the predictive expression model to recapitulate that heritable expression in the external GWAS panel. Accordingly, training expression models that perform well in all ancestry populations is necessary to ensure that discoveries made through TWAS are not restricted to European populations.…”

Identification of shared and differentiating genetic risk for autism spectrum disorder, attention deficit hyperactivity disorder and case subgroups

“…For the construction of the transcriptomic imputation models we used EpiXcan 20 , an elastic net based method, which weighs SNPs based on available epigenetic annotation information 72 . EpiXcan was recently shown to increase power to identify genes under a causality model when compared to TWAS approaches that don't integrate epigenetic information 73 . We use this model (924 samples from DLPFC) due to power considerations 20 ; in comparison, brain gene expression imputation models based on GTEx V8 74 are trained in 205 or fewer samples.…”

TIGAR-V2: Efficient TWAS Tool with Nonparametric Bayesian eQTL Weights of 49 Tissue Types from GTEx V8

“…Additionally, a recent power analysis of TWAS suggested useful threshold of expression heritability 0.04 for a causal model where gene expression is directly causal with respect to the phenotype, and a threshold of expression heritability 0.06 for a pleiotropy model where true causal SNPs of the phenotype are also true causal eQTL with respect to gene expression 74 , which allowed TWAS had higher power than single variant GWAS for a simulation cohort with sample size 2504 that was used as both training and test data. We would only suggest TWAS as a secondary analysis to standard single variant GWAS, instead of as a competing analysis.…”