Sparse Models | 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.

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.

Large-scale multivariate sparse regression with applications to UK Biobank

Tue, 19 Jul 2022 00:00:00 +0000

In this study led by Junyang Qian, we present a method to fit sparse multi-variate and multi-response regression model.

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.

Fast Numerical Optimization for Genome Sequencing Data in Population Biobanks

Sat, 19 Jun 2021 00:00:00 +0000

In this paper led by Ruilin Li, we describe memory-efficient implementation of snpnet (sparse-snpnet and snpnet-v2).

[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。

Survival Analysis on Rare Events Using Group-Regularized Multi-Response Cox Regression

Tue, 09 Feb 2021 00:00:00 +0000

In this paper led by Ruilin Li, we describe a new method to fit a sparse Cox Model for multiple time-to-event phenotypes from a large-scale (> 1 million features) genetic dataset.

A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank

Fri, 23 Oct 2020 00:00:00 +0000

In this project led by Junyang Qian, we developed BASIL, a novel algorithm to fit large-scale L1 penalized (Lasso) regression model using an iterative procedure, and implemented R snpnet package specially designed for genetic data.

Fast Lasso method for large-scale and ultrahigh-dimensional Cox model with applications to UK Biobank

Tue, 29 Sep 2020 00:00:00 +0000

We propose extending the BASIL/snpnet algorithm to fit the L1 penalized Cox proportional hazards model using a large-scale dataset from a genotyped cohort.