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DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning

Janine Arloth, Gökcen Eraslan, Till F. M. Andlauer, Jade Martins, Stella Iurato, Brigitte Kühnel, Melanie Waldenberger, Josef Frank, Ralf Gold, Bernhard Hemmer, Felix Luessi, Sandra Nischwitz (+17 others)
2020 PLoS Computational Biology  
We here describe "DeepWAS", a new approach that integrates these regulatory effect predictions of single variants into a multivariate GWAS setting.  ...  A specific class of deep learning-based methods allows for the prediction of regulatory effects per variant on several cell type-specific chromatin features.  ...  We would like to thank Richa Batra, Linda Krause, Christoph Orgis, Karolina Worf, Matthias Heinig and Martin Preusse for useful discussions on the approach.  ... 
doi:10.1371/journal.pcbi.1007616 pmid:32012148 pmcid:PMC7043350 fatcat:eyjwxczrarhwjij3kuygaev25y

DeepWAS: Directly integrating regulatory information into GWAS using deep learning supports master regulator MEF2C as risk factor for major depressive disorder [article]

Gökcen Eraslan, Janine Arloth, Jade Martins, Stella Iurato, Darina Czamara, Elisabeth B. Binder, Fabian J. Theis, Nikola S. Mueller
2016 bioRxiv   pre-print
The recently developed deep learning-based method DeepSEA uses DNA sequences to predict regulatory effects for up to 1000 functional units, namely regulatory elements and chromatin features in specific  ...  Conclusions: DeepWAS is a novel concept with the power to directly identify individual regulatory SNPs from genotypes.  ...  Ethics declaraion All studies were approved by the local ethics committee and all individuals gave written informed consent. All experimental methods comply with the Helsinki Declaration.  ... 
doi:10.1101/069096 fatcat:d4x2x4wfs5c33moigcqw7frcjq

Integrated Analysis of Whole Genome and Epigenome Data Using Machine Learning Technology: Toward the Establishment of Precision Oncology

Ken Asada, Syuzo Kaneko, Ken Takasawa, Hidenori Machino, Satoshi Takahashi, Norio Shinkai, Ryo Shimoyama, Masaaki Komatsu, Ryuji Hamamoto
2021 Frontiers in Oncology  
learning (ML) technologies, are being actively used to make more efficient and accurate predictions.  ...  However, the current precision oncology is dominated by a method called targeted-gene panel (TGP), which uses next-generation sequencing (NGS) to analyze a limited number of specific cancer-related genes  ...  AUTHOR CONTRIBUTIONS KA and RH contributed to the study concept, design, and are guarantor of integrity of the entire study.  ... 
doi:10.3389/fonc.2021.666937 pmid:34055633 pmcid:PMC8149908 fatcat:qxjeqxbxpvbcblv3ysrt4lp5pm

Identifying interpretable gene-biomarker associations with functionally informed kernel-based tests in 190,000 exomes [article]

Remo Monti, Pia Rautenstrauch, Mahsa L Ghanbari, Alva Rani James, Uwe Ohler, Stefan Konigorski, Christoph Lippert
2021 biorxiv/medrxiv   pre-print
We introduce local collapsing by amino acid position for missense variants and use this approach to identify potential novel gain of function variants in PIEZO1, and interpret a position-specific association  ...  In addition to performing gene-based variant collapsing tests, we design and apply variant-category-specific kernel-based tests that integrate quantitative functional variant effect predictions for missense  ...  Deepwas: Multivariate genotype-phenotype associations by directly integrating regulatory 595 information using deep learning. PLoS computational biology 16, e1007616 (2020). 596 [66] Li, X. et al.  ... 
doi:10.1101/2021.05.27.444972 fatcat:ibrysk5sfvhndkrnh7y5wxtnw4