Author

Daniel Howrigan

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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Genomics· 2 Genetics· 1

Signature Assays, Organisms & Methods

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Research Focus:GWAS / association mapping· 3 Approach:statistical model· 3 Organism:human· 2 Research Focus:variant calling· 2 Assay:whole-exome sequencing· 2 Assay:whole-genome sequencing· 2 Research Focus:benchmarking· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Adam Kiezun· 1 shared Ami Levy Moonshine· 1 shared Andrew Hill· 1 shared Aniket ShettyiD· 1 shared Anne O'Donnell-Luria· 1 shared Barbara Rakitsch· 1 shared Benjamin M. Neale· 1 shared Beryl Cummings· 1 shared

Activity by Year

2016-2016 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 14 mutual co-author links

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1 shared paperGenomics

Adam Kiezun

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Author profile →

3 papers

Recent·Most cited

A contribution of novel CNVs to schizophrenia from a genome-wide study of 41,321 subjects

Christian Marshall, Daniel Howrigan, Daniele Merico, Bhooma Thiruvahindrapuram, Wenting Wu, Douglas Greer, Daniel Antaki, Aniket Shetty, Peter Holmans, Dalila Pinto, Madhusudan Gujral, William Brandler, Dheeraj Malholtra, Zhouzhi Wang, Karin Fuentes Fajarado, , Rita Ca

This study identifies eight genome-wide significant CNV loci associated with schizophrenia using a large cohort of 41,321 subjects.

Nature Genetics · Nov 21, 2016Peer-reviewedGenetics

Analysis of protein-coding genetic variation in 60,706 humans

Exome Aggregation Consortium, Monkol Lek, Konrad Karczewski, Eric Minikel, Kaitlin Samocha, Eric Banks, Timothy Fennell, Anne O'Donnell-Luria, James Ware, Andrew Hill, Beryl Cummings, Taru Tukiainen, Daniel Birnbaum, Jack Kosmicki, Laramie Duncan, , Douglas

This paper analyzes protein-coding genetic variation in 60,706 humans to catalog diversity, assess pathogenicity, and identify genes under strong selection.

Nature · Aug 17, 2016Peer-reviewedGenomics

Bootstrat: Population Informed Bootstrapping for Rare Variant Tests

Hailiang Huang, Gina M. Peloso, Daniel Howrigan, Barbara Rakitsch, Carl Johann Simon-Gabriel, Jacqueline I. Goldstein, Mark J. Daly, Karsten Borgwardt, Benjamin M. Neale

Bootstrat corrects population stratification in rare variant tests by using genetic distance to create phenotype probabilities, maintaining statistical power while controlling type I error.

bioRxiv · Aug 11, 2016 · PreprintGenomics