Jiacheng Miao is a Ph.D. student in the Biomedical Data Science program and member of Qiongshi Lu’s lab, where he is honing his skills in cutting-edge research and the invaluable art of effective collaboration.
Originally from Nanchang, China, Jiacheng was drawn to statistics from his love for math and science: “For me, math is rigorous, and science is intriguing. I saw statistics as the perfect bridge to connect the rigor of mathematics with the intrigue of science,” he says.
His works use statistical genetics to explain if, how, and why the influence of genetic variation on human complex traits and diseases differs by environments and contexts.
Can you give us an overview of your research interests and projects?
My research interests are in two directions: 1) developing generic statistical methods and efficient computational tools for understanding biological questions, especially those related to large-scale human genetics data, and 2) unifying and formulating different statistical methods in a rigorous and intuitive framework.
A specific area I am working on is GxE (gene-environment interactions, when the phenotype caused by a genetic locus is different depending on the environment the organism is exposed to). GxE in humans has always been a mystery. In model organisms, GxE is ubiquitous. In humans, however, researchers rarely find strong [evidence for] GxE . After extensive GWAS (genome-wide association studies) over the years, one widely accepted understanding in the human genetics arena is that human complex traits are “polygenic”, meaning they are driven by many (very, very, very many) genetic variants. Each of these variants plays only a tiny role.
With this in mind, we have developed the concept of “polygenic GxE” in our recent paper. The idea is that while genetic influences on complex human traits vary from environment to environment, these differences are determined by many genetic variants. Moreover, the effect of each variant is only slightly different in different environments. We also propose a method to elucidate why certain genetic variants show different effects in different environments while others remain consistent.
The other line of my research is multi-ancestry genetic risk prediction. Currently, most participants in genetic studies are of European ancestry. As a result, existing genetic risk prediction models, such as those that predict the likelihood of developing type II diabetes based on genetic data, are more effective in Europeans but are known to have significantly reduced accuracy in other populations. Unless this issue is addressed, precision medicine based on genetic information has the potential to exacerbate racial disparities in health care and outcomes. To address this, we introduced a statistical framework that combines Bayesian inference with ensemble learning to improve the accuracy of genetic risk prediction for non-European populations. Our method, coupled with the ever-increasing GWAS sample sizes, especially in non-European populations, offers hope for broad and equitable applications of genomic precision medicine around the globe.
What has been the most gratifying aspect of graduate school for you?
The freedom to work on research that you are passionate about and the opportunity to interact with experts in different fields. For example, my co-advisor, Dr. Lauren Schmitz, specializes in economics. Working with her has been an enlightening experience. Through our collaboration, I have been exposed to innovative concepts, methods, and perspectives in economics that I would not have been exposed to otherwise. I have also been exposed to concepts in sociology through my experience in a social genomics group led by Dr. Jason Fletcher. Taking classes and talking to people from statistics, computer science, molecular genetics, population genetics, and even art have all played an important role in my journey through graduate school.
What about challenges?
The biggest challenge is getting things done. We may have a plethora of excellent research ideas, but the key is to successfully implement those ideas using the data.
What advice would you have for a young person interested in graduate school or research?
Choose research that you are passionate about and talk to people in different fields.
Jiacheng emphasizes that he, like many statisticians, is very open and enthusiastic about collaboration. “We thrive in interdisciplinary environments and appreciate the opportunity to apply our skills to different challenges,” he explains. “If you are ever faced with a statistical dilemma or need insights into data analysis, please don’t hesitate to reach out.”