Events Calendar

21 Sep
Biostatistics Seminar: Robust Mendelian randomization by leveraging genetic interactions and variance QTL
Event Type

Lectures, Symposia, Etc.

Topic

Research

Target Audience

Undergraduate Students, Faculty, Graduate Students, Postdocs

University Unit
Department of Biostatistics
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Biostatistics Seminar: Robust Mendelian randomization by leveraging genetic interactions and variance QTL

This is a past event.

Title: Robust Mendelian randomization by leveraging genetic interactions and variance QTL

Presenter: Zhonghua Liu, PhD

Abstract:

Mendelian randomization (MR) uses genetic variants as instrument variables (IV) to identify and estimate causal effects in the presence of potential unmeasured confounding. However, potential violations of core IV assumptions threaten the validity of MR in biomedical studies. In this talk, I will introduce two methods to address such violations. First, in the multiple IV framework, we proposed to use genetic interactions to remove the bias due to violations of the IV independence and exclusion restriction assumptions; and estimation can be easily carried out using existing off-the-shelf software. Second, in the single IV framework, we proposed a mixed-scale robust identification strategy by leveraging outcome variance quantitative trait loci under the following two homogeneity assumptions: (i) homogeneous causal effect on the additive scale; and (ii) homogeneous confounding bias on the odds ratio scale. We further proposed an efficient one-step update estimator based on a preliminary consistent three-stage estimator. The proposed methods are illustrated using UK Biobank data.

Thursday, September 21 at 3:30 p.m. to 4:30 p.m.

130 De Soto Street, Pittsburgh PA, 15261, Public Health, A115

Biostatistics Seminar: Robust Mendelian randomization by leveraging genetic interactions and variance QTL

Title: Robust Mendelian randomization by leveraging genetic interactions and variance QTL

Presenter: Zhonghua Liu, PhD

Abstract:

Mendelian randomization (MR) uses genetic variants as instrument variables (IV) to identify and estimate causal effects in the presence of potential unmeasured confounding. However, potential violations of core IV assumptions threaten the validity of MR in biomedical studies. In this talk, I will introduce two methods to address such violations. First, in the multiple IV framework, we proposed to use genetic interactions to remove the bias due to violations of the IV independence and exclusion restriction assumptions; and estimation can be easily carried out using existing off-the-shelf software. Second, in the single IV framework, we proposed a mixed-scale robust identification strategy by leveraging outcome variance quantitative trait loci under the following two homogeneity assumptions: (i) homogeneous causal effect on the additive scale; and (ii) homogeneous confounding bias on the odds ratio scale. We further proposed an efficient one-step update estimator based on a preliminary consistent three-stage estimator. The proposed methods are illustrated using UK Biobank data.

Thursday, September 21 at 3:30 p.m. to 4:30 p.m.

130 De Soto Street, Pittsburgh PA, 15261, Public Health, A115

Topic

Research

University Unit
Department of Biostatistics

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