Wei Jiang, PhD
Research & Publications
Biography
Research Summary
Dr Wei Jiang’s research interests lie in the fields of Bioinformatics and Biostatistics. His current research focus on using data from genome-wide association studies (GWAS) to explore replicable genetic risk factors, to quantify genetic contribution, and to predict genetic risks for human diseases. The paper for designing replication studies of GWAS received the Best Paper Award in APBC2016. He also developed methods covering other topics in genomics data analysis, such as region-based association mapping, epistasis detection, data integration. Those genomic analysis methods were published in Nature Communications, AJHG, PLoS Genetics, Briefings in Bioinformatics, Bioinformatics etc.
Extensive Research Description
The human body is a complex system, which makes it extremely challenged to model how molecular components regulate the traits. With the development of sequencing technologies, data of molecular signals inside the human body are released and accumulating. The ultimate goal of his research is to develop a multi-omics data analysis framework to explore molecular regulatory mechanisms of complex traits, so that we can precisely control our health outcomes from different molecular signals.
His current research focus on developing statistical or computational methods for analyzing data from genome-wide association studies (GWAS). Genome contains the most fundamental information distinguishing each person, and GWAS directly investigate the relationship of our genomes to complex traits. He developed a series of methods for exploring replicable genetic factors, quantifying genetic contribution, and predicting genetic risks for complex traits based on GWAS data.
Coauthors
Research Interests
Computational Biology; Genomics; Genome-Wide Association Study; Biostatistics
Public Health Interests
Bioinformatics; Genetics, Genomics, Epigenetics
Research Image
Heritability estimates with LDER and LDSC among 221 quantitative phenotypes and 593 dichotomous phenotypes in the UKBB
Details are provided in Song, S., Jiang, W., Zhang, Y., Hou, L. and Zhao, H., 2022. Leveraging LD eigenvalue regression to improve the estimation of SNP heritability and confounding inflation. The American Journal of Human Genetics, 109(5), pp.802-811.
Selected Publications
- Tuning parameters for polygenic risk score methods using GWAS summary statistics from training dataJiang W, Chen L, Girgenti M, Zhao H. Tuning parameters for polygenic risk score methods using GWAS summary statistics from training data. Nature Communications 2024, 15: 24. PMID: 38169469, PMCID: PMC10762162, DOI: 10.1038/s41467-023-44009-0.
- Prioritized candidate causal haplotype blocks in plant genome-wide association studiesWu X, Jiang W, Fragoso C, Huang J, Zhou G, Zhao H, Dellaporta S. Prioritized candidate causal haplotype blocks in plant genome-wide association studies. PLOS Genetics 2022, 18: e1010437. PMID: 36251695, PMCID: PMC9612827, DOI: 10.1371/journal.pgen.1010437.
- Leveraging LD eigenvalue regression to improve the estimation of SNP heritability and confounding inflationSong S, Jiang W, Zhang Y, Hou L, Zhao H. Leveraging LD eigenvalue regression to improve the estimation of SNP heritability and confounding inflation. American Journal Of Human Genetics 2022, 109: 802-811. PMID: 35421325, PMCID: PMC9118121, DOI: 10.1016/j.ajhg.2022.03.013.
- High-dimensional asymptotic behavior of inference based on GWAS summary statisticsJiang J, Jiang W, Paul D, Zhang Y, Zhao H. High-dimensional asymptotic behavior of inference based on GWAS summary statistics. Statistica Sinica 2023, 33, 1555-1576. doi:10.5705/ss.202021.0060
- Controlling the joint local false discovery rate is more powerful than meta-analysis methods in joint analysis of summary statistics from multiple genome-wide association studies.Jiang W, Yu W. Controlling the joint local false discovery rate is more powerful than meta-analysis methods in joint analysis of summary statistics from multiple genome-wide association studies. Bioinformatics 2016, 33: 500-507. PMID: 28011772, DOI: 10.1093/bioinformatics/btw690.
- What is the probability of replicating a statistically significant association in genome-wide association studies?Jiang W, Xue JH, Yu W. What is the probability of replicating a statistically significant association in genome-wide association studies? Briefings In Bioinformatics 2016, 18: 928-939. PMID: 27687799, DOI: 10.1093/bib/bbw091.