Convolutional neural network model to predict causal risk factors that share complex regulatory features

Lee Taeyeop; Sung Min Kyung; Lee Seulkee; Yang Woojin; Oh Jaeho; Kim Jeong Yeon; Hwang Seongwon; Ban Hyo-Jeong; Choi Jung Kyoon

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Convolutional neural network model to predict causal risk factors that share complex regulatory features

机译：卷积神经网络模型预测共享复杂监管特征的因果危险因素

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Major progress in disease genetics has been made through genome-wide association studies (GWASs). One of the key tasks for post-GWAS analyses is to identify causal noncoding variants with regulatory function. Here, on the basis of >2000 functional features, we developed a convolutional neural network framework for combinatorial, nonlinear modeling of complex patterns shared by risk variants scattered among multiple associated loci. When applied for major psychiatric disorders and autoimmune diseases, neural and immune features, respectively, exhibited high explanatory power while reflecting the pathophysiology of the relevant disease. The predicted causal variants were concentrated in active regulatory regions of relevant cell types and tended to be in physical contact with transcription factors while residing in evolutionarily conserved regions and resulting in expression changes of genes related to the given disease. We demonstrate some examples of novel candidate causal variants and associated genes. Our method is expected to contribute to the identification and functional interpretation of potential causal noncoding variants in post-GWAS analyses.

机译：通过基因组 - 宽协会研究（GWASS）进行了疾病遗传学的主要进展。后GWAS分析的关键任务之一是识别具有监管函数的因果非编码变体。在这里，在> 2000功能特征的基础上，我们开发了一种卷积神经网络框架，用于组合，非线性建模的复杂模式，这些复杂模式由散射多个相关基因座之间的风险变量共享。当申请重大精神疾病和自身免疫疾病时，神经和免疫特征分别在反映相关疾病的病理生理学的同时表现出高的解释力。将预测的因果变体浓缩，在相关细胞类型的活性调节区域中，倾向于与转录因子的物理接触，同时居住在进化的保守区域中，导致与给定疾病相关的基因的表达变化。我们证明了一些新型候选因果变体和相关基因的例子。我们的方法有助于识别和功能解释后GWAS分析中的潜在因果性非编码变体。

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《Nucleic Acids Research》 |2019年第22期|共11页
作者
Lee Taeyeop; Sung Min Kyung; Lee Seulkee; Yang Woojin; Oh Jaeho; Kim Jeong Yeon; Hwang Seongwon; Ban Hyo-Jeong; Choi Jung Kyoon;
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作者单位

Korea Adv Inst Sci &

Technol Grad Sch Med Sci &

Engn Daejeon 34141 South Korea;

Korea Adv Inst Sci &

Technol Dept Bio &

Brain Engn Daejeon 34141 South Korea;

Korea Adv Inst Sci &

Technol Grad Sch Med Sci &

Engn Daejeon 34141 South Korea;

Korea Adv Inst Sci &

Technol Dept Bio &

Brain Engn Daejeon 34141 South Korea;

Korea Adv Inst Sci &

Technol Dept Bio &

Brain Engn Daejeon 34141 South Korea;

Korea Adv Inst Sci &

Technol Dept Bio &

Brain Engn Daejeon 34141 South Korea;

Eidgenoss Tech Hsch ETH Zurich Seminar Stat CH-8092 Zurich Switzerland;

Korea Inst Oriental Med Future Med Div Daejeon 34054 South Korea;

Korea Adv Inst Sci &

Technol Dept Bio &

Brain Engn Daejeon 34141 South Korea;

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正文语种 eng
中图分类生物化学;
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1. Convolutional neural network model to predict causal risk factors that share complex regulatory features [J] . Lee Taeyeop, Sung Min Kyung, Lee Seulkee, Nucleic Acids Research . 2019,第22期

机译：卷积神经网络模型预测共享复杂监管特征的因果危险因素
2. Predicting death by suicide using administrative health care system data: Can recurrent neural network, one-dimensional convolutional neural network, and gradient boosted trees models improve prediction performance? [J] . Sanderson Michael, Bulloch Andrew G. M., Wang JianLi, Journal of affective disorders . 2020,第期

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5. Applying Neural Network Models to Predict Recurrent Maltreatment in Child Welfare Cases with Static and Dynamic Risk Factors [D] . Jolley, Jennifer Marie. 2012

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6. Convolutional neural network model to predict causal risk factors that share complex regulatory features [O] . Taeyeop Lee, Min Kyung Sung, Seulkee Lee, 2019

机译：卷积神经网络模型可预测具有复杂监管特征的因果风险因素
7. Convolutional neural network model to predict causal risk factors that share complex regulatory features [O] . Taeyeop Lee, Min Kyung Sung, Seulkee Lee, 2019

机译：卷积神经网络模型预测共享复杂监管特征的因果危险因素

Convolutional neural network model to predict causal risk factors that share complex regulatory features

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