Predictive modeling of disease

Thu, 11 Jan 2024 00:00:00 +0000

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.

Systematic integration of genomic annotations into predictive models
Biologically interpretable sparse models for genetic prediction of disease
Flexible predictive modeling applicable across distinct contexts

Representative studies

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

UK Biobank Polygenic Scores Resources +1 more

Significant Sparse Polygenic Risk Scores across 813 traits in UK Biobank

We performed a systematic assessment of the predictive performance of PRS models across >1,500 traits in UK Biobank and report 813 PRS models with significant predictive …