Work
If interested, please refer to Qiuran’s CV or contact me for more details.
Causal Mediation Analysis with Mendelian Randomization and Summary Data
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We used structural equation to construct the relationship between mediator, exposure, and outcome effect based on the causal diagram. A three-step procedure was designed for conducting mediation analysis with integrated multiple GWAS using joint rerandomization and rao-blackwellization to eliminate the winner’s curse. (working on)
Mendelian Randomization with Summary Data
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We empirically demonstrated the winner’s curse caused by LD clumping. We also empirically demonstrated rerandomization and rao-blackwellization can reduce bias for thirteen popular Mendelian Randomization estimators. (preparing manuscript)
Benchmark of different isoform quantification methods + FastQTL
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We compared performance of rsem, kallisto, cufflinks, salmon + FastQTL on simulated dataset and we empirically demonstrated rsem has the worst performance in terms of power and false discovery rate. See slides.
GMS training framework and WMMLP
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We constructed the weighted multiplicative MLP (WMMLP) in PyTorch based on Taylor expansion of M estimators and used neural networks to solve M-estimation problem under the bootstrap and cross validation context. See final summer research report.
