A novel classification framework for genome-wide association study of whole brain MRI images using deep learning.

IF 3.8 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS PLoS Computational Biology Pub Date : 2024-10-15 eCollection Date: 2024-10-01 DOI:10.1371/journal.pcbi.1012527

Shaojun Yu, Junjie Wu, Yumeng Shao, Deqiang Qiu, Zhaohui S Qin

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Abstract

Genome-wide association studies (GWASs) have been widely applied in the neuroimaging field to discover genetic variants associated with brain-related traits. So far, almost all GWASs conducted in neuroimaging genetics are performed on univariate quantitative features summarized from brain images. On the other hand, powerful deep learning technologies have dramatically improved our ability to classify images. In this study, we proposed and implemented a novel machine learning strategy for systematically identifying genetic variants that lead to detectable nuances on Magnetic Resonance Images (MRI). For a specific single nucleotide polymorphism (SNP), if MRI images labeled by genotypes of this SNP can be reliably distinguished using machine learning, we then hypothesized that this SNP is likely to be associated with brain anatomy or function which is manifested in MRI brain images. We applied this strategy to a catalog of MRI image and genotype data collected by the Alzheimer's Disease Neuroimaging Initiative (ADNI) consortium. From the results, we identified novel variants that show strong association to brain phenotypes.

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利用深度学习对全脑磁共振成像图像进行全基因组关联研究的新型分类框架。

全基因组关联研究（GWAS）已被广泛应用于神经影像领域，以发现与脑相关特征相关的基因变异。迄今为止，几乎所有在神经影像遗传学领域开展的全基因组关联研究都是基于从大脑图像中总结出的单变量定量特征进行的。另一方面，强大的深度学习技术极大地提高了我们对图像进行分类的能力。在这项研究中，我们提出并实施了一种新颖的机器学习策略，用于系统识别导致磁共振成像（MRI）上可检测到细微差别的遗传变异。对于特定的单核苷酸多态性（SNP），如果用机器学习方法能可靠地区分由该 SNP 基因型标记的 MRI 图像，那么我们就假设该 SNP 很可能与 MRI 脑图像中显示的大脑解剖或功能有关。我们将这一策略应用于阿尔茨海默病神经成像倡议（ADNI）联盟收集的核磁共振成像图像和基因型数据目录。从结果中，我们发现了与大脑表型密切相关的新型变异。

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来源期刊

PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY

CiteScore

7.10

自引率

4.70%

发文量

820

审稿时长

2.5 months

期刊介绍： PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.