Title: MR-LDP: a two-sample Mendelian randomization for GWAS summary statistics accountinglinkage disequilibrium and horizontal pleiotropy
Time:Friday, January 3, 2020 10:30~11:30 am
Abstract:The proliferation of genome-wide association studies (GWAS) has prompted the use of two-sample Mendelian randomization (MR) with genetic variants as instrumental variables (IV) for drawing reliable causal relationships between health risk factors and disease outcomes. However, the unique features of GWAS demand MR methods account for both linkage disequilibrium (LD) and ubiquitously existing horizontal pleiotropy among complex traits, which is a phenomenon that a variant affects the outcome other than exclusively through the exposure. Therefore, statistical methods that fail to consider LD and horizontal pleiotropy can lead to biased estimates and false-positive causal relationships. To overcome these limitations, we propose a probabilistic model for MR analysis to identify causal effect between risk factors and disease outcomes by using GWAS summary statistics in the presence of LD, as well as properly accounts for horizontal Pleiotropy among genetic variants (MR-LDP). MR-LDP utilizes a computationally efficient parameter-expanded variational Bayes expectation-maximization (PX-VBEM) algorithm, calibrating the evidence lower bound (ELBO) for a likelihood ratio test. We further conducted comprehensive simulation studies to demonstrate the advantages of MR-LDP over existing methods in terms of both type-I error control and point estimates. Moreover, we used two real exposure-outcome pairs (CAD-CAD and BMI-BMI; CAD for coronary artery disease and BMI for body mass index) to validate results from MR-LDP in comparison with alternative methods, particularly showing that our method is more efficient using all instrumental variants in LD. By further applying MR-LDP to lipid traits and BMI as risk factors on complex diseases, we identified multiple pairs of significant causal relationships, including protective effect of high-density lipoprotein cholesterol (HDL-C) on peripheral vascular disease (PVD), and positive causal effect of body mass index (BMI) on hemorrhoids.
Reporter Introduction:Qing received her Ph.D. in Statistics from the Shanghai University of Finance and Economics. She is now a research fellow at Duke-NUS Medical School. Her current research interests are on the functional regression model, interaction detection, Empirical Bayes, Variational inference and Bayesian variable selection.
Title: Cross-Complementary Pairs (CCP) for Optimal Training in Spatial Modulation
Time:9:00-10:00 am on December 25, 2019
Abstract:Golay complementary pair (GCP) is a celebrated sequence pair whose aperiodic autocorrelations sum to zero for all the non-zero time-shifts. Despite numerous applications of GCPs in engineering, it is noted that the transmission of a GCP requires two separate and non-interfering channels. In this talk, I will introduce a new class of sequence pairs, called “cross-complementary pairs (CCPs)”, which may be transmitted in two non-orthogonal channels and hence proper CCP design should be conducted to minimize the cross-interference of the two constituent sequences. I will present the properties and systematic constructions of perfect CCPs, followed by their applications for optimal training sequence design in spatial modulation (SM) systems under frequency-selective channels.
Reporter Introduction:ZHe received his M.S. degree in the Department of Electronic Engineering from Tsinghua University and B.S. degree in the School of Electronics and Information Engineering from Huazhong University of Science and Technology (HUST), in 2007 and 2004, respectively. .