MS Defense of Long Tran | Department of Mathematics

MS Defense of Long Tran

Event Information
Event Location: 
via Zoom
Event Date: 
Wednesday, June 29, 2022 - 10:00am

Professor Helen Wang invites you to attend the Master's Project Defense of Long Hai Tran

WHEN: Wednesday, June 29, 2022, at 10:00am via Zoom

"Improved analyses of GWAS summary statistics with a novel gene-based association test"


Genome-wide association studies (GWASs) have been extraordinarily successful in identifying complex traits associated genetic variants, even if these genetic variants could only explain low to modest heritability of the complex traits. Missing heritability may be due to the single mark analysis method which is commonly used in GWAS. Single mark analysis ignores linkage disequilibrium among genetic variants and requires larger sample sizes. Thus, it is often restricted to detecting genetic variants with weak or medium effect sizes. Gene-based methods aggregating the effects of genetic variants in a gene can increase statistical power. The existing gene-based methods may suffer significant power loss due to their reliance on prior assumptions about causal genetic variants such as number, effect sizes, and directions of the effects, which are typically unknown. The aggregated Cauchy association test (ACAT) is a general, powerful, and computationally efficient p value combination method. By combining different tests, the ACAT method can reduce the reliance on prior assumptions of genetic architecture for complex traits, and hence increase the power in detecting genetic association. In this study, we propose an omnibus test by using ACAT to combine the burden test, the minor allele frequency (MAF) weighted sum test, and the optimally weighted score test which contains 6 different other tests as its special cases. Using a dimension-reduction method based on singular value decomposition technique, our method is computationally efficient across a wide range of different genetic architectures. Extensive simulation studies demonstrate that our proposed method is not only valid but also more robust and powerful than comparison methods. A real analysis based on Schizophrenia summary dataset from the Psychiatric Genomic Consortium (PGC) indicates that the proposed method identified more unique Schizophrenia associated genes than other methods.

Cookies and coffee will be served in GAB 472 following this event.

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