scispace - formally typeset
J

Jie Zheng

Researcher at University of Bristol

Publications -  127
Citations -  9389

Jie Zheng is an academic researcher from University of Bristol. The author has contributed to research in topics: Mendelian randomization & Medicine. The author has an hindex of 22, co-authored 76 publications receiving 4852 citations. Previous affiliations of Jie Zheng include Medical Research Council.

Papers
More filters
Journal ArticleDOI

Proteome-wide Mendelian randomization in global biobank meta-analysis reveals multi-ancestry drug targets for common diseases

TL;DR: In this article , a multi-ancestry proteome-wide Mendelian randomization (MR) analysis based on cross-population data from the Global Biobank Meta-analysis Initiative (GBMI) is presented.
Journal ArticleDOI

HAPRAP: a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics

TL;DR: An empirical iterative method, HAPlotype Regional Association analysis Program (HAPRAP), that enables fine mapping using summary statistics and haplotype information from an individual-level reference panel and is demonstrated to be less sensitive to poor LD estimates.
Journal ArticleDOI

Collapsed Methylation Quantitative Trait Loci analysis for Low Frequency and Rare variants

TL;DR: The potential of this novel approach to mQTL analysis by analysing the combined effect of multiple low frequency or rare variants to identify regions of low frequency and rare variants associated with DNA methylation levels is demonstrated.
Journal ArticleDOI

Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts

Ying Wang, +125 more
- 01 Jan 2023 - 
TL;DR: In this article , the authors used data from Global Biobank Meta-analysis Initiative (GBMI) to explore methodological considerations and polygenic risk scores (PRSs) performance in 9 different biobanks for 14 disease endpoints.
Posted ContentDOI

MR-TRYX: A Mendelian randomization framework that exploits horizontal pleiotropy to infer novel causal pathways

TL;DR: A multi-trait pleiotropy model of the heterogeneity in the exposure-outcome analysis due to pathways through candidate traits and adjustment for pleiotropic pathways reduced the heterogeneity across the analyses.