kTWAS: Integrating kernel-machine with transcriptome-wide association studies improves statistical power and reveals novel genes

Cao, Chen; Kwok, Devin; Edie, Shannon; Li, Qing; Ding, Bowei; Kossinna, Pathum; Campbell, Simone; Wu, Jingjing; Greenberg, Matthew; Long, Quan

doi:10.1101/2020.06.29.177121

Cited by 5 publications

(20 citation statements)

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“…Protocols (3) and (4) separate feature selection and aggregation into different models. Protocol (3), referred to as ElasticNet + Kernel, uses the ElasticNet model from PrediXcan 6 for feature selection and SKAT 24 for feature aggregation as implemented in our previous method kTWAS 9 . Protocol (4), referred to as Marginal + Kernel, uses marginal genotype-expression effects for feature selection and SKAT for aggregation, as described above in our novel method mkTWAS.…”

Section: Resultsmentioning

confidence: 99%

“…Although TWAS is most often conducted on summary statistics (i.e., meta-analysis) rather than subject-level genotypes 1,3,7,22 , our previous results show that the relative power between protocols utilizing summary statistics is consistent with the relative power of their counterparts utilizing subject-level genotype data 9 . We therefore chose to analyze subject-level data in order to simplify the comparison between GReX-based and disentangled TWAS protocols.…”

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confidence: 76%

“…This work extends the findings of two recent publications which replace GReX-based feature aggregation in TWAS with kernel-based methods. In our recent paper 9 , we developed a protocol called kTWAS (kernel-TWAS) using the ElasticNet model from PrediXcan to select variants and the well-known Sequence Kernel Association Test (SKAT) to associate variants to phenotype 21 . We demonstrated that kTWAS outperforms TWAS in real data and simulations under different genetic architectures 9 .…”

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confidence: 99%

“…In our recent paper 9 , we developed a protocol called kTWAS (kernel-TWAS) using the ElasticNet model from PrediXcan to select variants and the well-known Sequence Kernel Association Test (SKAT) to associate variants to phenotype 21 . We demonstrated that kTWAS outperforms TWAS in real data and simulations under different genetic architectures 9 . Independently, another group at Emory University has released VC-TWAS (Variance Component TWAS) 10 , which utilizes a Bayesian linear model adapted from Tigar 8 to select features and an equivalent kernel test for aggregation.…”

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confidence: 99%

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Disentangling genetic feature selection and aggregation in transcriptome-wide association studies

Cao

Kwok

et al. 2020

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The success of transcriptome-wide association studies (TWAS) has led to substantial research towards improving its core component of genetically regulated expression (GReX). GReX links expression information with phenotype by serving as both the outcome of genotype-based expression models and the predictor for downstream association testing. In this work, we demonstrate that current linear models of GReX inadvertently combine two separable steps of machine learning - feature selection and aggregation - which can be independently replaced to improve overall power. We show that the monolithic approach of GReX limits the adaptability of TWAS methodology and practice, especially given low expression heritability.

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Section: Resultsmentioning

confidence: 99%

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confidence: 76%

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confidence: 99%

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confidence: 99%

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Disentangling genetic feature selection and aggregation in transcriptome-wide association studies

Cao

Kwok

et al. 2020

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“…Finally, in our comparison we only used linear models for GWAS and TWAS, and, in a totally different direction, kernel-based nonparametric and semiparametric methods were not considered. As a future work, we may explore the more robust analysis based on kernel methods for both GWAS [48,49] and TWAS [50]. (19) and (20), the MLEs of E " and H " are given by…”

Section: Conclusion and Discussionmentioning

confidence: 99%

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

Ding

Cao

et al. 2020

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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.

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rvTWAS: identifying gene-trait association using sequences by utilizing transcriptome-directed feature selection

Zhang

2023

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Towards the identification of genetic basis of complex traits, transcriptome-wide association study (TWAS) is successful in integrating transcriptome data. However, TWAS is only applicable for common variants, excluding rare variants in exome or whole genome sequences. This is partly because of the inherent limitation of TWAS protocols that rely on predicting gene expressions. Briefly, a typical TWAS protocol has two steps: it trains an expression prediction model in a reference dataset containing gene expressions and genotype, and then applies this prediction model to a genotype-phenotype dataset to impute the unobserved expression (that is called GReX) to be associated to the phenotype. In this procedure, rare variants are not used due to its low power in predicting expressions. Our previous research has revealed the insight into TWAS: the two steps are essentially genetic feature selection and aggregations that do not have to involve predictions. Based on this insight disentangling TWAS, the inability of using rare variants to predict expression traits is no longer an obstacle. Herein, we developed rare variant TWAS, or rvTWAS, that first uses a Bayesian model to conduct expression-directed feature selection and then use a kernel machine to carry out feature aggregation, forming a model leveraging expressions for association mapping including rare variants. We demonstrated the performance of rvTWAS by thorough simulations and real data analysis in three psychiatric disorders, namely schizophrenia, bipolar disorder, and autism spectrum disorder. rvTWAS will open a door for sequence-based association mappings integrating gene expressions.

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kTWAS: Integrating kernel-machine with transcriptome-wide association studies improves statistical power and reveals novel genes

Cited by 5 publications

References 73 publications

Disentangling genetic feature selection and aggregation in transcriptome-wide association studies

Disentangling genetic feature selection and aggregation in transcriptome-wide association studies

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

rvTWAS: identifying gene-trait association using sequences by utilizing transcriptome-directed feature selection

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