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Webinar: Biostatistics and Bioinformatics Seminar – ‘Novel TWAS Techniques for Studying Complex Human Diseases’

December 1, 12:00 pm to 1:00 pm

Presented by: Michael Epstein, PhD, Professor of Human Genetics, Emory University School of Medicine

About the presentation: Transcriptome-wide association studies (TWAS) have been widely used to integrate transcriptomic and genetic data to study complex human diseases. Within a test dataset lacking transcriptomic data, traditional TWAS analysis involves two steps. The first step imputes gene expression in the dataset using a weighted sum that aggregates SNPs with their corresponding eQTL effects on reference transcriptome. The second step then assesses the relationship between this imputed expression and outcome using a regression framework. In this talk, I propose novel techniques for both steps that increase robustness of TWAS while also enabling application to more general settings. The majority of the talk will be spent on a novel variance-component method for relating imputed gene expression to phenotype that relaxes assumptions made by traditional TWAS to improve performance. The method is applicable to both continuous and dichotomous phenotypes, as well as individual-level and summary-level GWAS data. We apply the method to both individual-level (N=~3.4K) and summary-level (N=~54K) GWAS data to study Alzheimer’s dementia and identify several risk genes missed by traditional TWAS tools. Time permitting, I will then discuss a new method for improved estimation of eQTL effects on reference transcriptome when multiple reference panels of a given tissue exist. To leverage these multiple panels for improved prediction, we use an Ensemble Machine Learning technique of stacked regression to form optimal linear combinations of prediction models trained from multiple reference panels of the same tissue. Using simulated and real data, we show that the use of stacked regression can improve prediction accuracy and power of TWAS relative to standard procedures and demonstrate its use in Alzheimer’s dementia GWAS. This talk represents joint work with Dr. Jingjing Yang.

To join the seminar via phone, dial: 929-205-6099

To join the seminar via Zoom, visit: https://pshealth.zoom.us/j/94860982030

The meeting ID is 948 6098 2030 and the passcode is 320367

Details

Date:
December 1
Time:
12:00 pm to 1:00 pm
Event Category:
Website:
https://pshealth.zoom.us/j/94860982030

Organizer

Department of Public Health Sciences
Email:
pennstatepublichealth@phs.psu.edu
View Organizer Website

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