Welcome to the UCLA Tanigawa Lab

Why does the same disease affect individuals so differently? Some people progress faster, develop different complications, or respond differently to treatment. Precise understanding of the causal biology of disease heterogeneity leads to better prevention, management, and treatment.

Our group develops statistical models and computational tools to dissect interindividual differences in disease. We leverage our expertise in analyzing population-scale genomic data, predictive modeling, and therapeutic target discovery.

📣 Recruiting

We are recruiting at all levels. Please check the Join Us page for more information.

Meet the team

Yosuke Tanigawa, Ph.D.

Assistant Professor

Mapping the biological basis of heterogeneity in disease.

Disease heterogeneity dissection Biomedical data science Statistical genetics

Nabil Mohammed

PhD Student

Developing methods to uncover genetics of complex traits.

Statistical genetics Functional genomics Drug discovery

Ananya Rai

Graduate Student

Gene-by-sex interactions Disease heterogeneity Genome-Wide Association Studies

Jihye Lee

Graduate Student

Graduate student researching disease heterogeneity and genetic feature discovery using machine learning.

Deep Learning Genetic Associations Disease Heterogeneity

Ashley Do

Undergraduate Student

Undergraduate student interested in applying statistical methods to better understand and quantify pleiotropy.

Gene Latent Features Polygenic Risk Scores Personalized Medicine

Keira Hundhausen

Undergraduate Student

Undergraduate bioengineering student interested in using large-scale genomic and phenotypic data to predict aging.

Genome-Wide Association Studies Bioinformatics Protein Folding

Team

Meet the Team

Learn current lab members, alumni, and their backgrounds and research interests.

Meet the Team

Research Areas

Statistical genetics, computational genomics, precision medicine

We study the underlying biology of disease heterogeneity and develop statistical and computational methods to improve prevention, disease management, and treatment.

Active

Disease heterogeneity dissection

Diagnosis for the same disease affects individuals differently. However, we don’t know how best to represent biologically meaningful axes of variation within a disease, beyond the standard case vs. control comparison or proxy measure of severity. We combine human genetics, causal inference, and modern AI to address this challenge.

Representation learning Causal inference Biobanks

Explore disease heterogeneity

Active

Predictive modeling of disease

Accurate prediction of disease risk using genomics enables earlier identification of individuals at elevated risk and facilitates more effective prevention strategies. However, existing genetic approaches show limited generalizability across cohorts and lack biological interpretation. We develop robust and biologically interpretable models using high-dimensional data.

Statistical genetics Polygenic score Genetic epidemiology

Explore predictive modeling

Active

Therapeutic target discovery

Low success rates in clinical trials remain a major challenge in drug development. We leverage our expertise and bring human genetic evidence to therapeutic target discovery and validation. We envision nominating therapeutic targets for disease subtypes based on their underlying biology.

Experiments by nature Rare variant associations Translational genomics

Explore target discovery

Learn more about our research

Select Publications

Alzheimers Polygenic Scores Disease Heterogeneity

Dissecting Alzheimer's disease heterogeneity by cross-trait polygenic prediction

Led by Bill Li, this work introduces cross-trait polygenic prediction as a strategy for using phenome-wide PGS libraries in deeply phenotyped cohorts to reveal multiple polygenic …

William Li

• May 15, 2026 • 1 min read

Polygenic Scores Sparse Models Epigenomics +1 more

PRISM: ancestry-aware integration of tissue-specific genomic annotations enhances the transferability of polygenic scores

In this study, led by Xiaohe (Lucy) Tian, we showed that ancestry-aware integration of tissue-specific genomic annotations enhances the transferability of polygenic scores (PGS).

Lucy Tian

• Nov 13, 2025 • 1 min read

Polygenic Scores Resources Sparse Models +1 more

Power of inclusion: Enhancing polygenic prediction with admixed individuals

We developed a polygenic score training approach that allows direct inclusion of admixed individuals without the need for local ancestry inference and showed ancestry-diverse …

Yosuke Tanigawa, Ph.D.

• Oct 26, 2023 • 1 min read

Resources UK Biobank Phenotyping

Genetics of 35 blood and urine biomarkers in the UK Biobank

We analyzed the genetic basis of 35 blood and urine biomarkers in UK Biobank. We revealed that genetic effects on biomarkers inform the genetic basis of disease.

Yosuke Tanigawa, Ph.D.

• Apr 7, 2021 • 1 min read

Rare Variants UK Biobank Glaucoma

Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma

We identified an allelic series of rare protein-altering variants in ANGPTL7 that lower intraocular pressure and protect against glaucoma, highlighting ANGPTL7 as a therapeutic …

Yosuke Tanigawa, Ph.D.

• Sep 1, 2020 • 1 min read

Publications

List of Publications

Browse the full list of publications and preprints.

See all publications

Teaching and training

Prof. Tanigawa teaches at UCLA. The lab provides training in statistical genetics, computational genomics, and clear scientific communication through coursework, research mentoring, and collaboration.