Author

Walter Nelson

Hamilton Health Sciences

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Genetic & Genomic Medicine· 3

Signature Assays, Organisms & Methods

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Research Focus:GWAS / association mapping· 3 Organism:human· 3 Approach:statistical model· 3 Research Focus:benchmarking· 1 Research Focus:fine-mapping· 1 Assay:GWAS summary statistics· 1 Assay:proteomics· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Guillaume ParéiD· 3 shared Michael ChongiD· 3 shared Nicolas PerrotiD· 3 shared Conor S. JudgeiD· 2 shared Jeremy PetchiD· 2 shared Marie PigeyreiD· 2 shared Mohammad Daud KhaniD· 2 shared Albert TenesaiD· 1 shared

Activity by Year

2021-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 39 mutual co-author links

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3 shared papersGenetic & Genomic Medicine

Guillaume ParéORCID

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3 papers

Recent·Most cited

Proteome-wide Mendelian randomization study implicates inflammaging biomarkers in retinal vasculature, cardiometabolic diseases and longevity

Ana Villaplana-Velasco, Nicolas Perrot, Yu Hang, Michael Chong, Emmanuelle Trucco, Walter Nelson, Jeremy Petch, Hertzel C. Gerstein, Raina Parminder, Salim Yusuf, Miguel O. Bernabéu, Albert Tenesa, Konrad Rawlik, Guillaume Paré, Alex S. F. Doney, , Marie Pigeyre

medRxiv · Jul 13, 2024 · PreprintGenetic & Genomic Medicine

GWAS and ExWAS of blood Mitochondrial DNA copy number identifies 73 loci and highlights a potential causal role in dementia

Michael Chong, Pedrum Mohammadi‐Shemirani, Nicolas Perrot, Walter Nelson, Robert W. Morton, Sukrit Narula, Ricky Lali, Irfan I. Khan, Mohammad Daud Khan, Conor S. Judge, Tafadzwa Machipisa, Nathan Cawte, Martin James O'Donnell, Marie Pigeyre, Loubna Akhabir, Guillaume Paré

This study identifies 73 genetic loci associated with blood mitochondrial DNA copy number and suggests a causal link between low mtDNA-CN and dementia risk.

eLife · Jan 12, 2022Peer-reviewedGenetic & Genomic Medicine

A versatile, fast and unbiased method for estimation of gene-by-environment interaction effects on biobank-scale datasets

Mohammad Daud Khan, Matteo Di Scipio, Conor S. Judge, Nicolas Perrot, Michael Chong, Shihong Mao, Shuang Di, Walter Nelson, Jeremy Petch, Guillaume Paré

This paper introduces MonsterLM, a method for estimating gene-by-environment interactions on large datasets, finding significant interaction variance and improved polygenic score prediction.

medRxiv · Apr 24, 2021 · PreprintGenetic & Genomic Medicine