LabCompass

Nianjun Liu

Biochemistry, Genetics and Molecular Biology · Indiana University

OpenAlex ORCID
Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and AnimalsGenetic and phenotypic traits in livestockGene expression and cancer classificationPharmacogenetics and Drug Metabolism

Publications

161

Citations

3,725

Est. group size

—

Recurring co-author estimate

Active years

26

Publishing since 2001

Research summary
AI-generated

Nianjun Liu works at the intersection of statistics and genetics, developing methods to analyze how genetic variation relates to disease and health traits. Much of the work involves statistical tools for genetic association studies, Mendelian randomization (a technique using genetic variants to study cause-and-effect in disease), and confidence-interval methods, applied to conditions such as cardiovascular disease, cancer, asthma, and drug response. Recent projects also extend to clinical prediction models and population health topics like post-COVID cognitive effects.

Statistical genetics and genetic association testingMendelian randomization methodsPharmacogenetics (genetics of drug response)Biostatistical methods (confidence intervals, omics integration)Clinical prediction and epidemiology

Publication activity has been uneven year to year but is broadly sustained, with several high-output years recently (around 6 papers per year over the last five years).

Generated by claude-opus-4-8 from public bibliographic data · Jul 11, 2026

Publication cadence
Publications per year over the last 10 years — averaging 6.2/year recently
2017: 7 publications17182019: 2 publications192020: 3 publications202021: 10 publications10212022: 3 publications222023: 9 publications232024: 2 publications242025: 9 publications252026: 8 publications26
Recent publications
Publishes in
  • medRxiv×5
  • Figshare×3
  • GeroScience×2
  • JMIRx Med×2
  • Genetic Epidemiology×2

This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.

Last updated Jul 11, 2026.

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