Functional modeling of noncoding risk variants based on convolutional neural networks = 컨볼루션 신경망 기반의 비코딩 질환위험 변이들의 기능적 모델링

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One of the key tasks for post-GWAS analyses is to identify causal noncoding variants with regulatory function. On the basis of >2,000 functional features, I developed a convolutional neural network framework for combinatorial, nonlinear modeling of complex patterns shared by risk variants scattered across multiple associated loci. I evaluated the performance and validity of this method in various ways in the context of multiple complex diseases, especially psychiatric disorders. A main advantage of the method is its applicability for prioritization of rare variants. Although trained with independent data, the model made positive predictions for candidate rare variants derived from multiplex autism families, including those mapped to CHD5 and FRRS1L. CHD5 knockout mice reportedly show abnormal social behavior. Behavioral experiments conducted in collaboration with Prof. Jin-Hee Han’s laboratory showed that FRRS1L, a component of the AMPA receptor, was specifically involved in recognition of social novelty. In conclusion, I propose a novel approach to discover biological patterns shared by disease-causing regulatory variants based on their regulatory function.
Advisors
Choi, Jung Kyoonresearcher최정균researcher
Description
한국과학기술원 :바이오및뇌공학과,
Publisher
한국과학기술원
Issue Date
2018
Identifier
325007
Language
eng
Description

학위논문(박사) - 한국과학기술원 : 바이오및뇌공학과, 2018.2,[iv, 73 p. :]

Keywords

GWAS▼acomplex disease▼anon-coding variant▼aconvolutional neural network▼arare variant; 전장 유전체 연관성 분석▼a복합 질환▼a비코딩 유전변이▼a컨볼루션 신경망▼a희귀 돌연변이

URI
http://hdl.handle.net/10203/264709
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=734297&flag=dissertation
Appears in Collection
BiS-Theses_Ph.D.(박사논문)
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