Polygenic Scores | Tanigawa Lab

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

Thu, 13 Nov 2025 00:00:00 +0000

Accurate genetic prediction of complex traits has the potential to substantially reduce the disease burden, but the limited transferability of polygenic score (PGS) across genetic ancestry groups remains a major challenge.

Here we propose to integrate three major approaches to address the PGS transferability. We develop PRISM and apply it to 7352 fine-mapped variants from MVP, 414 ENCODE annotations, and 406,659 individuals from the UK Biobank.

Using PRISM, we evaluated the effects of ancestry and tissue-specific integration of genomic annotations. We also compared our approach with existing methods and investigated which genomic annotation would be the most informative.

We found that tissue-matched genomic annotations (from ENCODE) are most effective for enhancing PGS transferability, even though the tissue-agnostic strategy can leverage a ~18-fold larger number of genomic annotation datasets.

Similarly, the use of genetic ancestry-specific fine-mapped variants is most effective, despite the power difference. Our approach offers a pragmatic solution for working with data with uneven coverage across contexts.

Our benchmarking analysis shows PRISM benefits from three different strategies for enhancing PGS transferability.

We showed, through feature importance analysis, that having one reference genomic annotation is not sufficient, highlighting the advantage of systematic integration with our approach.

We confirm that the genetic variants selected from our approach are supported by various genomic annotations compared to other variants in LD.

I am grateful to Lucy and the team for making this work possible.

A polygenic score method boosted by non-additive models

Wed, 29 May 2024 00:00:00 +0000

We developed GenoBoost, a polygenic score modeling approach, incorporating both additive and non-additive genetic dominance effects.

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 …

Yosuke Tanigawa, Ph.D.

• Mar 24, 2022 • 1 min read

Power of inclusion: Enhancing polygenic prediction with admixed individuals

Thu, 26 Oct 2023 00:00:00 +0000

Polygenic score (PGS), a statistical approach to estimating genetic predisposition on traits, attracted substantial research interest. The current PGS models show limited transferability across populations, and there are a number of great new methods to address this challenge. We propose inclusive polygenic score (iPGS), a PGS training strategy to capture ancestry-shared genetic effects by analyzing individuals across the continuum of genetic ancestry. We work directly on the individual-level data without relying on GWAS results and LD references.

We tested our approach across 33 simulation configurations and 60 quantitative traits in UK Biobank. We see increased power by including ancestry-diverse individuals compared to our baseline model trained only on white British individuals.

We observe improvements in performance for all population groups. The average improvement across the 60 traits was 60.8% for African, 11.6% for South Asian, 7.3% for non-British White, 4.8% for White British, and 17.8% for other diverse individuals.

To consider ancestry-dependent genetic effects on top of ancestry-shared effects, we developed iPGS+refit. We used a heterogeneity test in GWAS meta-analysis and identified genetic variants with heterogeneous associations, such as the ACKR1 locus for neutrophil count.

Our iPGS+refit starts with one ancestry-shared component (iPGS) and adds ancestry-dependent effects using a relatively small number of genetic loci, facilitating better interpretation. We used hematological traits to show improved predictive performance.

We compared our model with PRS-CSx, a commonly-used multi-ancestry PGS method from summary statistics from multiple population groups and ancestry-matched reference panels. In our analysis, our iPGS/iPGS+refit models showed competitive or improved performance.

We thank UK Biobank, its participants, amazing collaborators and colleagues, as well as funding.

You can browse and download our iPGS models at our iPGS browser. Taking advantage of the sparsity of our PGS models, it offers direct integration with HaploReg and GREAT.

Integration of rare expression outlier-associated variants improves polygenic risk prediction

Thu, 02 Jun 2022 00:00:00 +0000

Polygenic risk score (PRS), an approach to estimate genetic liability to complex traits by aggregating the effects across multiple genetic variants, has attracted increasing research interest.

Significant Sparse Polygenic Risk Scores across 813 traits in UK Biobank

Thu, 24 Mar 2022 00:00:00 +0000

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 performance.

[Review in Japanese] 複数の表現型を用いた人類遺伝統計学の大規模情報解析 (Large-scale human genetic statistical inference with multiple phenotypes)

Fri, 23 Apr 2021 00:00:00 +0000

[invited review written in Japanese]. This is an invited review written in Japanese, published by the Japanese Society of Bioinformatics (JSBi).

日本語総説の執筆の機会をいただき、ゲノムワイド相関解析（GWAS）、ポリジェニック・リスク・スコア（polygenic risk score）、高次元データセットでの正則化つきの回帰モデル（penalized regression、Lasso 回帰など）に関する人類統計遺伝学の解析手法について執筆しました。

Polygenic risk modeling with latent trait-related genetic components

Mon, 08 Feb 2021 00:00:00 +0000

Polygenic risk score (PRS) has been proposed for disease risk prediction with potential clinical relevance for some traits, but its personalized interpretation is generally difficult, especially when there exist disease subtypes driven by different genetic components.

Sex-specific genetic effects across biomarkers

Tue, 01 Sep 2020 00:00:00 +0000

In this study led by Emily Flynn, we discovered a surprising sex-specificity in the genetics of testosterone.