Shi-Heng Wang, Ph.D. | Lab of Quantitative Genetics and Epidemiology

This lab focuses on (1) the paternal age effect on the offspring’s psychiatric risk; (2) genetics of human complex traits; (3) the genetic architecture of psychiatric disorders; (4) health outcomes research and clinical medicine.

Evidence for an association between advanced paternal age and risk of psychiatric disorders in offspring is accumulating, but if it is a causal effect, i.e., delaying fatherhood produce more de novo mutations and hence increase the occurrence of more severe biological sequelae in offspring, is still under debate. An informative public health debate on such matters needs a solid empirical foundation. We have applied family studies with thousands families from laborious field work to support an independent role of paternal age per se in the increased psychiatric risk in offspring. We have resolved the whole-genome sequence from multiplex schizophrenia families to quantify the magnitude of risk conferred by paternal-age-related de novo mutations in the male germline. Beyond traditional family studies, we undertake a nationwide population-scale family study to assess the magnitude of offspring’s psychiatric risk conferred by paternal age effect, to clarify if there is a causal paternal age effect, and to clarify the public significance of delayed paternity.

Most existed genetic studies have been from European population, and lead to better genetic risk prediction than among non-European population. Our research contributed to genetic research in Taiwan to mitigate the health disparities across populations. We are interested in studying pleiotropy and polygenicity, investigating heritability of human complex traits, and exploring gene–environment interaction.

We aim to identify loci influencing psychiatric risk and explore genetic correlation between major psychiatric disorders, which help to inform psychiatric nosology beyond descriptive syndromes, identify potential biological mechanism, and provide new models for prevention and treatment.

Medical treatment or health intervention should be tailored to an individual’s characteristics to optimize benefits. We have integrated the use of spontaneous reporting systems and insurance claims databases to study drug safety; we found that males have a higher risk of metformin-associated myocardial infarction than females, which suggests that sex-drug interactions are a key issue in diabetes treatment plan development. In addition, we performed population-based cohort study to assess the effect of depression on diabetes complications and suicide, and found that such adverse effect was more prominent among young adults than among middle-aged and older adults, suggesting that intervention plan should targeted the high-risk populations.