Kaixiong Ye
University of Georgia, GA·Genetics
Nearly every human disease arises from a mix of inherited genetic variation, environmental exposures such as diet or smoking, and the interplay between the two. Large studies have found many disease-linked genetic variants, and separate studies have found many disease-linked environmental factors, but the interactions between them remain poorly mapped.
- Fields
Genetics
Public Health & Epidemiology
Mathematics & Statistics
- Topics
gene-environment interaction
human evolution
ancient DNA
- Grants
- 1 known grant, through May 2027
What they work on
- Develop and apply statistical tests to detect genetic and polygenic responses to the Agricultural Revolution using ancient DNA, identifying adaptive variants whose mismatch with modern lifestyles may underlie today's common diseases.
- Build an efficient computational pipeline and run genome-wide by phenome-wide interaction analyses with selected high-quality environmental exposures in UK Biobank, releasing all summary statistics as a public database.
- Create the first bioinformatics tool for phenome-wide interaction study, enabling unbiased searches for phenotypic effects of a target genetic variant that a given environmental exposure can modify.
- Test whether Mendelian randomization, which uses genetic variants as natural experiments, combined with phenome-wide association study can prioritize causal exposure–outcome pairs and reduce the multiple-testing burden in interaction studies.
- Test with simulated and empirical data whether explicitly modeling gene–environment interactions improves the portability of polygenic risk scores across ancestries, and whether score–environment interaction analysis reveals exposures that attenuate genetic risk.
More about the research
The obstacles are practical: interactions require very large populations, high-quality measures of exposure, and efficient software. This project develops statistical methods, computational tools and public data resources to characterize these gene–environment interactions, both in the human past and in present-day health.
Why it matters
Most common diseases reflect genes acting in particular environments, yet clinical genetics rarely accounts for that. Tools, methods and a public interaction database from this work could help identify modifiable exposures, such as aspects of diet or lifestyle, that reduce inherited disease risk, and could make polygenic risk prediction more accurate for people of non-European ancestry, a current weakness in precision medicine.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Dabeeru Rao
Washington University in St Louis, MO·Genetics
Identify new genetic loci for cardiometabolic traits through genome-wide interaction studies (GWIS) with lifestyle factors in about 1.267 million European, African, Hispanic and Asian ancestry participants.
Jeffrey Rade
University of Massachusetts Chan Medical School, MA·Medicine & Clinical Research
Determine whether urinary TXB2-MGFR, a kidney-corrected urine marker of thromboxane production, predicts hospitalization, death and disease severity in 3,252 heart failure patients from the EMPEROR trials, and how its 12-week change tracks treatment response.
David E Reich
Harvard University, MA·Genetics
Assemble an ancient DNA time series of 34,000 West Eurasians, enough to measure natural selection coefficients as small as about 0.25%, and release it as a public resource before publication.
The school
University of Georgia: 27 labs on PI Pages, $628M a year on research, 598 research doctorates a year.
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