Author

Hannele Mattsson

Finnish Institute for Health and Welfare

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· 1 Genetics· 1

Signature Assays, Organisms & Methods

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Research Focus:GWAS / association mapping· 2 Assay:GWAS summary statistics· 2 Organism:human· 2 Approach:statistical model· 2 Approach:Bayesian inference· 1 Research Focus:database / resource· 1 Assay:electronic health records· 1 Research Focus:fine-mapping· 1

Frequent Co-Authors

Elina Kilpeläinen· 2 shared Joseph MaranvilleiD· 2 shared Mary Pat ReeveiD· 2 shared Sally John· 2 shared Aarno V. PalotieiD· 1 shared Aaron Day-Williams· 1 shared Abdelrahman G. ElnahasiD· 1 shared Adriana Huertas‐Vázquez· 1 shared

Activity by Year

2017-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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2 shared papersGenetic & Genomic Medicine

Elina Kilpeläinen

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2 papers

Recent·Most cited

FinnGen: Unique genetic insights from combining isolated population and national health register data

Mitja Kurki, Juha M Karjalainen, Priit Palta, Timo Petteri Sipila, Kati Kristiansson, Kati M. Donner, Mary Pat Reeve, Hannele M. Laivuori, Mervi Aavikko, Mari Anneli Kaunisto, Anu Loukola, Elisa Lahtela, Hannele Mattsson, Päivi Laiho, Pietro Della Briotta Parolo, , Marianna Niemi

Finnish-enriched low-frequency variants, often coding, underlie many GWAS signals across 1,932 register endpoints in 218,792 Finns; a 15-disease meta-analysis with Estonian and UK biobanks adds 30 unreported associations.

medRxiv · Mar 3, 2022 · PreprintGenetic & Genomic Medicine✦ In-depth digest

Phenome-wide association studies (PheWAS) across large "real-world data" population cohorts support drug target validation

Dorothée Diogo, Chao Tian, Christopher S. Franklin, Mervi Alanne-Kinnunen, Michael March, Chris C. A. Spencer, Ciara Vangjeli, Michael E. Weale, Hannele Mattsson, Elina Kilpeläinen, Patrick M. Abou‐Sleiman, Dermot F. Reilly, Joshua J. McElwee, Joseph Maranville, Arnaub K. Chatterjee, , Heiko Runz

The paper uses PheWAS across large cohorts to identify genetic associations with drug targets and predict drug safety and efficacy.

bioRxiv · Nov 13, 2017 · PreprintGenetics