Improved genetic discovery and fine-mapping resolution through multivariate latent factor analysis of high-dimensional traits.

Feng Zhou, William J Astle, Adam S Butterworth, Jennifer L Asimit

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Article in Cell genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

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1 citing paper in PubMed.

Pleiotropic shared heritability quantifies the shared genetic variance of common diseases.Nature genetics · 2026
Article

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5 · Who and what money

Authors and funding

4 authors.

Feng ZhouMRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK.

William J AstleMRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK; NHS Blood and Transplant, Cambridge CB2 0PT, UK; NIHR Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge CB2 0BB, UK.

Adam S ButterworthNIHR Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge CB2 0BB, UK; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB2 0BB, UK; Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge CB2 0BB, UK; British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge CB2 0QQ, UK; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge CB10 1SA, UK.

Jennifer L AsimitMRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK. Electronic address: jennifer.asimit@mrc-bsu.cam.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genome-wide association studies (GWASs) of high-dimensional traits, such as blood cell or metabolic traits, often use univariate approaches, ignoring trait relationships. Biological mechanisms generating variation in high-dimensional traits can be captured parsimoniously through a GWAS of latent factors. Here, we introduce flashfmZero, a zero-correlation latent-factor-based multi-trait fine-mapping approach. In an application to 25 latent factors derived from 99 blood cell traits in the INTERVAL cohort, we show that latent factor GWASs enable the detection of signals generating sub-threshold associations with several blood cell traits. The 99% credible sets (CS99) from flashfmZero were equal to or smaller in size than those from univariate fine-mapping of blood cell traits in 87% of our comparisons. In all cases univariate latent factor CS99 contained those from flashfmZero. Our latent factor approaches can be applied to GWAS summary statistics and will enhance power for the discovery and fine-mapping of associations for many traits.

Indexed as

Chromosome MappingGenome-Wide Association StudyQuantitative Trait LociFactor Analysis, StatisticalHumansMultivariate AnalysisPhenotypePolymorphism, Single Nucleotideblood cell traitsfactor analysisfine-mappingGWASlatent factorsmulti-trait

Identifiers

PMID40220762

DOI10.1016/j.xgen.2025.100847

PMCPMC12143322

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.