Author

Nicholas Eriksson

insitro, inc. (United States)Primary ORCID profile (0000-0003-2031-2872 · 15 papers)Unclaimed ORCID profile · follow to get notified of new preprints

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Nicholas ErikssoniD15 papers · 23andMe (United States)

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Research Focus:GWAS / association mapping· 1 Assay:GWAS summary statistics· 1 Organism:human· 1 Approach:statistical model· 1

Frequent Co-Authors

Colm T. O’DushlaineiD· 1 shared David AmariD· 1 shared Hari SomineniiD· 1 shared Kaiwen XuiD· 1 shared Rounak DeyiD· 1 shared Sumit MukherjeeiD· 1 shared Thomas W. SoareiD· 1 shared Zachary Ryan McCawiD· 1 shared

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2024-2024 · peak 1/yr

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8 top collaborators · 28 mutual co-author links

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1 shared paperGenetics

Colm T. O’DushlaineORCID

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1 paper

Recent·Most cited

Causal considerations can determine the utility of machine learning assisted GWAS

Sumit Mukherjee, Zachary Ryan McCaw, David Amar, Rounak Dey, Thomas W. Soare, Kaiwen Xu, Hari Somineni, Nicholas Eriksson, Colm T. O’Dushlaine

This study shows that machine learning-derived phenotypes for GWAS can lead to false discoveries depending on their causal relationship with the target phenotype and environment.

bioRxiv · Dec 16, 2024 · PreprintGenetics