DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning
By
Janine Knauer-Arloth,
Gokcen Eraslan,
Till FM Andlauer,
Jade Martins,
Stella Iurato,
Brigitte Kühnel,
Melanie Waldenberger,
Josef Frank,
Ralf Gold,
Bernhard Hemmer,
Felix Luessi,
Sandra Nischwitz,
Friedemann Paul,
Heinz Wiendl,
Christian Gieger,
Stefanie Heilmann-Heimbach,
Tim Kacprowski,
Matthias Laudes,
Thomas Meitinger,
Annette Peters,
Rajesh Rawal,
Konstantin Strauch,
Susanne Lucae,
Bertram Mueller-Myhsok,
Marcella Rietschel,
Fabian J Theis,
Elisabeth B. Binder,
Nikola S. Mueller
Posted 11 Aug 2016
bioRxiv DOI: 10.1101/069096
(published DOI: 10.1371/journal.pcbi.1007616)
Genome-wide association studies (GWAS) identify genetic variants associated with quantitative traits or disease. Thus, GWAS never directly link variants to regulatory mechanisms, which, in turn, are typically inferred during post-hoc analyses. In parallel, a recent deep learning-based method allows for prediction of regulatory effects per variant on currently up to 1,000 cell type-specific chromatin features. We here describe "DeepWAS", a new approach that directly integrates predictions of these regulatory effects of single variants into a multivariate GWAS setting. As a result, single variants associated with a trait or disease are, by design, coupled to their impact on a chromatin feature in a cell type. Up to 40,000 regulatory single-nucleotide polymorphisms (SNPs) were associated with multiple sclerosis (MS, 4,888 cases and 10,395 controls), major depressive disorder (MDD, 1,475 cases and 2,144 controls), and height (5,974 individuals) to each identify 43-61 regulatory SNPs, called deepSNPs, which are shown to reach at least nominal significance in large GWAS. MS- and height-specific deepSNPs resided in active chromatin and introns, whereas MDD-specific deepSNPs located mostly to intragenic regions and repressive chromatin states. We found deepSNPs to be enriched in public or cohort-matched expression and methylation quantitative trait loci and demonstrate the potential of the DeepWAS method to directly generate testable functional hypotheses based on genotype data alone. DeepWAS is an innovative GWAS approach with the power to identify individual SNPs in non-coding regions with gene regulatory capacity with a joint contribution to disease risk. DeepWAS is available at https://github.com/cellmapslab/DeepWAS.
Download data
- Downloaded 4,495 times
- Download rankings, all-time:
- Site-wide: 4,039
- In systems biology: 52
- Year to date:
- Site-wide: 78,797
- Since beginning of last month:
- Site-wide: 203,431
Altmetric data
Downloads over time
Distribution of downloads per paper, site-wide
PanLingua
News
- 27 Nov 2020: The website and API now include results pulled from medRxiv as well as bioRxiv.
- 18 Dec 2019: We're pleased to announce PanLingua, a new tool that enables you to search for machine-translated bioRxiv preprints using more than 100 different languages.
- 21 May 2019: PLOS Biology has published a community page about Rxivist.org and its design.
- 10 May 2019: The paper analyzing the Rxivist dataset has been published at eLife.
- 1 Mar 2019: We now have summary statistics about bioRxiv downloads and submissions.
- 8 Feb 2019: Data from Altmetric is now available on the Rxivist details page for every preprint. Look for the "donut" under the download metrics.
- 30 Jan 2019: preLights has featured the Rxivist preprint and written about our findings.
- 22 Jan 2019: Nature just published an article about Rxivist and our data.
- 13 Jan 2019: The Rxivist preprint is live!