Author

Siru Wang

University of CambridgeORCID 0000-0001-8630-8337Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Genetics· 2 Genetic & Genomic Medicine· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Research Focus:fine-mapping· 3 Assay:GWAS summary statistics· 3 Approach:statistical model· 3 Research Focus:GWAS / association mapping· 2 Organism:human· 2 Research Focus:benchmarking· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Andrew P. MorrisiD· 2 shared Jennifer L. AsimitiD· 2 shared Michèle RamsayiD· 2 shared Oyesola Oyewole OjewunmiiD· 2 shared Segun FatumoiD· 2 shared Tinashe ChikoworeiD· 2 shared Abram Bunya KamizaiD· 1 shared Ayesha Ahmed MotalaiD· 1 shared

Activity by Year

2024-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 34 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersGenetics

Andrew P. MorrisORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

GeneticsbioRxivPreprintNov 26, 2025

Multi-ancestry fine-mapping accounting for ancestral and environmental heterogeneity improves resolution and interpretation

Siru Wang, Oyesola Oyewole Ojewunmi, Fraser James Pirie…·Jennifer L. Asimit

The paper introduces two fine-mapping methods to account for ancestral and environmental heterogeneity in GWAS, improving resolution of causal variants across diverse populations.

DOI 10.1101/2025.11.26.690727

humanGWAS summary statistics

𝕏 PostBluesky

Genetic & Genomic MedicinemedRxivPreprintNov 9, 2025

A statistical framework to identify gene-gene interactions underlying multiple dichotomous phenotypes from genotype data

Siru Wang, Xuhui Zhu, Yu Li, Gengjie Jia

GiMat identifies gene-gene interactions across multiple dichotomous phenotypes using a statistical framework, finding four novel interaction pairs in type 2 diabetes and hypertension comorbidity.

DOI 10.1101/2025.11.09.25339828

GWAS summary statisticsstatistical model

𝕏 PostBluesky

GeneticsCommunications BiologyPeer-reviewedNov 14, 2024

Accounting for heterogeneity due to environmental sources in meta-analysis of genome-wide association studies

Siru Wang, Oyesola Oyewole Ojewunmi, Abram Bunya Kamiza…·Jennifer L. Asimit

This paper introduces env-MR-MEGA, a meta-regression model that adjusts for environmental and ancestral heterogeneity in GWAS meta-analysis, improving association power.

5 citations

humanGWAS summary statistics

𝕏 PostBluesky