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The transcriptome-wide association studies (TWAS) is a pioneering approach utilizing gene expression data to identify genetic basis of complex diseases. Its core component is called 鈥済enetically regulated expression (GReX)鈥�. GReX links gene expression information with phenotype by serving as both the outcome of genotype-based expression models and the predictor for downstream association testing. Although it is popular and has been used in many high-profile projects, its mathematical nature and interpretation haven鈥檛 been rigorously verified. As such, we have first conducted power analysis using NCP-based closed forms (Cao et al, PLoS Genet 2021), based on which we realized that the common interpretation of TWAS that looks biologically sensible is actually mathematically questionable. Following this insight, by real data analysis and simulations, we demonstrated 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 (Cao et al, Genetics 2021). Based on this new interpretation, we have developed novel protocols disentangling feature selections and aggregations, leading to improved power and novel biological discoveries (Cao et al, BiB 2021; Genetics 2021). To promote this new understanding, we moved forward to develop two statistical tools utilizing gene expressions in identifying genetic basis of gene-gene interactions (Kossinna et al, in preparation) and low-effect genetic variants (Li et al, in review), respectively. Looking forward, our mathematical characterization of TWAS opens a door for a new way to integrate gene expressions in genetic studies towards the realization of precision medicine.

Quan Long is an Associate Professor at University of Calgary, hosted by the Dept. of Biochemistry and Molecular Biology. He has a joint appointment in the Dept. of Medical Genetics and an adjunct appointment in the Dept. of Math and Stats. He is a member of Alberta Children鈥檚 Hospital Research Institute and Hotchkiss Brain Institute. Currently he is leading a research group to develop computational and statistical tools, focusing on genomic problems with high-dimensional features and low sample sizes. He is also interested in theoretical problems in machine learning.

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