DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning
DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning
复制标题
DeepWAS：使用深度学习直接整合监管信息来实现多变量基因型-表型关联
DOI：
10.1101/069096
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Mueller
中科院分区：
文献类型：
--
作者：
Eraslan;Arloth;Martins;Iurato;Czamara;Binder;Mueller
全基因组关联研究（GWAS）识别与性状或疾病相关的遗传变异。GWAS从未直接将变异与调控机制联系起来。相反，变异体的功能注释通常是通过事后分析来推断的。一类特定的基于深度学习的方法允许预测每个变体对几种细胞类型特异性染色质特征的调节作用。我们在这里描述“DeepWAS”，这是一种新的方法，它将这些单一变体的调节效应预测整合到多变量GWAS设置中。因此，与性状或疾病相关的单个变体直接与它们对细胞类型中的染色质特征的影响相关联。多达61个被称为dSNPs的调节性SNP与多发性硬化症（MS，4，888例和10，395例对照），重度抑郁症（MDD，1，475例和2，144例对照）和身高（5，974例）相关。这些变异主要是非编码的，在经典GWAS中至少达到了名义上的显著性。对于91%的全基因组显著的MS特异性dSNP，DeepWAS的预测准确度高于经典GWAS模型。DSNP在公共或群体匹配的表达和甲基化数量性状基因座中富集，我们证明了DeepWAS仅基于基因型数据生成可测试功能假设的潜力。DeepWAS可在https://github.com/cellmapslab/DeepWAS上获得。
Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases. GWAS never directly link variants to regulatory mechanisms. Instead, the functional annotation of variants is typically inferred bypost hocanalyses. 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 here describe “DeepWAS”, a new approach that integrates these regulatory effect predictions of single variants into a multivariate GWAS setting. Thereby, single variants associated with a trait or disease are directly coupled to their impact on a chromatin feature in a cell type. Up to 61 regulatory SNPs, called dSNPs, 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). These variants were mainly non-coding and reached at least nominal significance in classical GWAS. The prediction accuracy was higher for DeepWAS than for classical GWAS models for 91% of the genome-wide significant, MS-specific dSNPs. DSNPs were enriched in public or cohort-matched expression and methylation quantitative trait loci and we demonstrated the potential of DeepWAS to generate testable functional hypotheses based on genotype data alone. DeepWAS is available at https://github.com/cellmapslab/DeepWAS.
登录
查看更多内容
DOI：
10.5493/wjem.v4.i3.27
发表时间：
2014-08-20
期刊：
World journal of experimental medicine
影响因子：
--
作者：
Hoglund, Rune A;Maghazachi, Azzam A
通讯作者：
Maghazachi, Azzam A
影响因子：
56.9
作者：
Patsopoulos, Nikolas A.;Baranzini, Sergio E.;De Jager, Philip L.
通讯作者：
De Jager, Philip L.
影响因子：
16.2
作者：
Arloth J;Bogdan R;Weber P;Frishman G;Menke A;Wagner KV;Balsevich G;Schmidt MV;Karbalai N;Czamara D;Altmann A;Trümbach D;Wurst W;Mehta D;Uhr M;Klengel T;Erhardt A;Carey CE;Conley ED;Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium (PGC);Ruepp A;Müller-Myhsok B;Hariri AR;Binder EB;Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium PGC
通讯作者：
Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium PGC
影响因子：
5.8
作者：
Balkhi MY;Wittmann G;Xiong F;Junghans RP
通讯作者：
Junghans RP
全基因组关联、复制和神经影像学研究表明 HOMER1 与重度抑郁症的病因有关
影响因子：
6.6
作者：
M. Rietschel;M. Mattheisen;J. Frank;J. Treutlein;A. Meyer;S. Cichon
通讯作者：
S. Cichon