Identifying associations among genomic, proteomic and imaging biomarkers via adaptive sparse multi-view canonical correlation analysis

被引：39

作者：

Du, Lei ^{[1
]}

Zhang, Jin ^{[1
]}

Liu, Fang ^{[1
]}

Wang, Huiai ^{[1
]}

Guo, Lei ^{[1
]}

Han, Junwei ^{[1
]}

机构：

[1] Northwestern Polytech Univ, Sch Automat, Xian 710072, Peoples R China

来源：

MEDICAL IMAGE ANALYSIS | 2021年 / 70卷

基金：

美国国家卫生研究院; 中国博士后科学基金; 中国国家自然科学基金; 加拿大健康研究院;

关键词：

Imaging genetics; Multi-omics associations; Sparse canonical correlation analysis; Multi-way bi-multivariate associations; ALZHEIMERS-DISEASE; PLASMA BIOMARKERS; APOLIPOPROTEIN-E; GENETICS; RISK;

D O I：

10.1016/j.media.2021.102003

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

To uncover the genetic underpinnings of brain disorders, brain imaging genomics usually jointly analyzes genetic variations and imaging measurements. Meanwhile, other biomarkers such as proteomic expressions can also carry valuable complementary information. Therefore, it is necessary yet challenging to investigate the underlying relationships among genetic variations, proteomic expressions, and neuroimaging measurements, which stands a chance of gaining new insights into the pathogenesis of brain disorders. Given multiple types of biomarkers, using sparse multi-view canonical correlation analysis (SMCCA) and its variants to identify the multi-way associations is straightforward. However, due to the gradient domination issue caused by the naive fusion of multiple SCCA objectives, SMCCA is suboptimal. In this paper, we proposed two adaptive SMCCA (AdaSMCCA) methods, i.e. the robustness-aware AdaSMCCA and the uncertainty-aware AdaSMCCA, to analyze the complicated associations among genetic, proteomic, and neuroimaging biomarkers. We also imposed a data-driven feature grouping penalty to the genetic data with aim to uncover the joint inheritance of neighboring genetic variations. An efficient optimization algorithm, which is guaranteed to converge, was provided. Using two state-of-the-art SMCCA as benchmarks, we evaluated robustness-aware AdaSMCCA and uncertainty-aware AdaSMCCA on both synthetic data and real neuroimaging, proteomics, and genetic data. Both proposed methods obtained higher associations and cleaner canonical weight profiles than comparison methods, indicating their promising capability for association identification and feature selection. In addition, the subsequent analysis showed that the identified biomarkers were related to Alzheimer's disease, demonstrating the power of our methods in identifying multi-way bi-multivariate associations among multiple heterogeneous biomarkers. (c) 2021 Elsevier B.V. All rights reserved.

引用

页数：12

共 37 条

[1] Multimodal Data Analysis of Alzheimer's Disease Based on Clustering Evolutionary Random Forest [J].

Bi, Xia-an ;

Hu, Xi ;

Wu, Hao ;

Wang, Yang .

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2020, 24 (10) :2973-2983

[2] Morbigenous brain region and gene detection with a genetically evolved random neural network cluster approach in late mild cognitive impairment [J].

Bi, Xia-an ;

Liu, Yingchao ;

Xie, Yiming ;

Hu, Xi ;

Jiang, Qinghua .

BIOINFORMATICS, 2020, 36 (08) :2561-2568

[3]

Chen M., 2013, ARXIV PREPRINT ARXIV

[4] Chromogranin A induces a neurotoxic phenotype in brain microglial cells [J].

Ciesielski-Treska, J ;

Ulrich, G ;

Taupenot, L ;

Chasserot-Golaz, S ;

Corti, A ;

Aunis, D ;

Bader, MF .

JOURNAL OF BIOLOGICAL CHEMISTRY, 1998, 273 (23) :14339-14346

[5] Strongly reduced volumes of putamen and thalamus in Alzheimers disease: an MRI study [J].

de Jong, L. W. ;

van der Hiele, K. ;

Veer, I. M. ;

Houwing, J. J. ;

Westendorp, R. G. J. ;

Bollen, E. L. E. M. ;

de Bruin, P. W. ;

Middelkoop, H. A. M. ;

van Buchem, M. A. ;

van der Grond, J. .

BRAIN, 2008, 131 :3277-3285

[6] RAGE mediates amyloid-β peptide transport across the blood-brain barrier and accumulation in brain [J].

Deane, R ;

Yan, SD ;

Submamaryan, RK ;

LaRue, B ;

Jovanovic, S ;

Hogg, E ;

Welch, D ;

Manness, L ;

Lin, C ;

Yu, J ;

Zhu, H ;

Ghiso, J ;

Frangione, B ;

Stern, A ;

Schmidt, AM ;

Armstrong, DL ;

Arnold, B ;

Liliensiek, B ;

Nawroth, P ;

Hofman, F ;

Kindy, M ;

Stern, D ;

Zlokovic, B .

NATURE MEDICINE, 2003, 9 (07) :907-913

[7] Associating Multi-Modal Brain Imaging Phenotypes and Genetic Risk Factors via a Dirty Multi-Task Learning Method [J].

Du, Lei ;

Liu, Fang ;

Liu, Kefei ;

Yao, Xiaohui ;

Risacher, Shannon L. ;

Han, Junwei ;

Saykin, Andrew J. ;

Shen, Li .

IEEE TRANSACTIONS ON MEDICAL IMAGING, 2020, 39 (11) :3416-3428

[8] Identifying diagnosis-specific genotype-phenotype associations via joint multitask sparse canonical correlation analysis and classification [J].

Du, Lei ;

Liu, Fang ;

Liu, Kefei ;

Yao, Xiaohui ;

Risacher, Shannon L. ;

Han, Junwei ;

Guo, Lei ;

Saykin, Andrew J. ;

Shen, Li .

BIOINFORMATICS, 2020, 36 :371-379

[9] Detecting genetic associations with brain imaging phenotypes in Alzheimer's disease via a novel structured SCCA approach [J].

Du, Lei ;

Liu, Kefei ;

Yao, Xiaohui ;

Risacher, Shannon L. ;

Han, Junwei ;

Saykin, Andrew J. ;

Guo, Lei ;

Shen, Li .

MEDICAL IMAGE ANALYSIS, 2020, 61

[10] Multi-Task Sparse Canonical Correlation Analysis with Application to Multi-Modal Brain Imaging Genetics [J].

Du, Lei ;

Liu, Kefei ;

Yao, Xiaohui ;

Risacher, Shannon L. ;

Han, Junwei ;

Saykin, Andrew J. ;

Guo, Lei ;

Shen, Li .

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2021, 18 (01) :227-239

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