Resources | Tanigawa Lab

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.

WhichTF is dominant in your open chromatin data?

Tue, 30 Aug 2022 00:00:00 +0000

We develop an ontology-guided approach to ranking tissue-/cell-type-specific transcription factors (TFs) from chromatin accessibility data.

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.

Genetics of 35 blood and urine biomarkers in the UK Biobank

Wed, 07 Apr 2021 00:00:00 +0000

Through UK Biobank-wide GWAS meta-analysis, we report > 10,000 GWAS associations (p < 5e-9) across 35 biomarkers and >5,700 loci. This includes > 450 large-effect (>0.1 s.d.) associations on protein-altering variants, which we highlight in a Fuji plot (gene symbols).

With FINEMAP, we identified >27,000 distinct signals in >5,000 regions across the 35 traits, of which >2,500 signals were fine-mapped to a single variant. We also trained sparse polygenic risk score (PRS) models with Lasso L1- penalized regression using the snpnet package.

To investigate the influence of the identified genetic basis on diseases, we performed single-variant PheWAS, PRS-PheWAS, and causal inference. Motivated by those results, we developed multi-PRS by combining a single-trait PRS model of disease with that of 35 biomarkers.

When we compare the predictive performance, multi-PRS showed improvements over single-trait snpnet PRS across many diseases, which we also replicated in FinnGen, suggesting both large case numbers and multi-trait modeling might complementarily contribute to improving the power.

This work was led by Nasa Sinnott-Armstrong, myself, and Dr. Manuel A. Rivas, with many contributions from colleagues. We thank UK Biobank, FinnGen, their participants, amazing collaborators, and colleagues, as well as funding.

Global Biobank Engine: enabling genotype-phenotype browsing for biobank summary statistics

Wed, 05 Dec 2018 00:00:00 +0000

We present Global Biobank Engine as a platform to visualize genome- and phenome-wide associations and perform statistical inference using those association data.