I study how genetics, genomics, and environmental factors together drive the development of human diseases. I specialize in statistical methodology developments, data analysis, and theoretical investigations, and my research typically utilizes large-scale datasets from biobanks and epidemiological studies that include molecular genetic and genomic data, along with human behavioral and lifestyle factors.<\/p>\n
One area of my research is polygenic scores (PGS), measures meant to summarize a person’s genetic predisposition for a trait and\/or a disease such as cancers and childhood developmental disorders. While PGS are becoming more and more predictive of disease risks, there is much work to be done in the interpretations and applications of PGS. In particular, I developed novel statistical methods to jointly model gene-environment correlations and interactions using PGS in case-control studies, with data applications in the UK Biobank (Wang et al., AJE, 2024); I also developed methods in estimating risk parameters of PGS in family-based studies to understand genetic direct, indirect, and gene-environment interactions between genotype-phenotype associations, with data applications in the SPARK Consortium for Autism (Wang et al., 2026).<\/p>\n
Another aspect of my research is in integrative omics using individual-level and summary statistics to understand the disease mechanisms and improve disease risk predictions. I developed novel methods using machine learning algorithms that improve the statistical power of large-scale association studies by incorporating the between-data correlations using summary statistics in multiple omic and spatially-resolved omic datasets for cancers (Wang et al., Bioinformatics, 2020; Wang et al., 2023). I also investigated DNA methylation biomarkers associated with pancreatic cancer through epigenome-wide association studies (Wang et al., Epigenetics, 2022). We recently identified potential pre-diagnostic plasma proteomic markers for 7 solid cancers within a multi-center, prospective cohort study in the Atherosclerosis Risk in Communities study (Wang et al., 2026).<\/p>\n","protected":false},"featured_media":1736400,"template":"","meta":{"_acf_changed":false,"inline_featured_image":false,"_links_to":"","_links_to_target":""},"otheraff":[],"phd-degree":[],"primary":[2360],"research-discipline":[2492,2495,2539,2489,2531],"research-opportunity":[],"training-grant":[],"class_list":["post-1736363","faculty-listing","type-faculty-listing","status-publish","has-post-thumbnail","hentry","primary-genome-sciences","research-discipline-bioinformatics-and-genomics","research-discipline-computational-biology","research-discipline-data-science-in-medicine","research-discipline-genetics","research-discipline-statistics"],"acf":[],"yoast_head":"\n