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Zhang J, Ma Z, Yang Y, Guo L, Du L. Modeling genotype-protein interaction and correlation for Alzheimer's disease: a multi-omics imaging genetics study. Brief Bioinform 2024; 25:bbae038. [PMID: 38348747 PMCID: PMC10939371 DOI: 10.1093/bib/bbae038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/23/2023] [Accepted: 01/14/2024] [Indexed: 02/15/2024] Open
Abstract
Integrating and analyzing multiple omics data sets, including genomics, proteomics and radiomics, can significantly advance researchers' comprehensive understanding of Alzheimer's disease (AD). However, current methodologies primarily focus on the main effects of genetic variation and protein, overlooking non-additive effects such as genotype-protein interaction (GPI) and correlation patterns in brain imaging genetics studies. Importantly, these non-additive effects could contribute to intermediate imaging phenotypes, finally leading to disease occurrence. In general, the interaction between genetic variations and proteins, and their correlations are two distinct biological effects, and thus disentangling the two effects for heritable imaging phenotypes is of great interest and need. Unfortunately, this issue has been largely unexploited. In this paper, to fill this gap, we propose $\textbf{M}$ulti-$\textbf{T}$ask $\textbf{G}$enotype-$\textbf{P}$rotein $\textbf{I}$nteraction and $\textbf{C}$orrelation disentangling method ($\textbf{MT-GPIC}$) to identify GPI and extract correlation patterns between them. To ensure stability and interpretability, we use novel and off-the-shelf penalties to identify meaningful genetic risk factors, as well as exploit the interconnectedness of different brain regions. Additionally, since computing GPI poses a high computational burden, we develop a fast optimization strategy for solving MT-GPIC, which is guaranteed to converge. Experimental results on the Alzheimer's Disease Neuroimaging Initiative data set show that MT-GPIC achieves higher correlation coefficients and classification accuracy than state-of-the-art methods. Moreover, our approach could effectively identify interpretable phenotype-related GPI and correlation patterns in high-dimensional omics data sets. These findings not only enhance the diagnostic accuracy but also contribute valuable insights into the underlying pathogenic mechanisms of AD.
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Affiliation(s)
- Jin Zhang
- Department of Intelligent Science and Technology, Northwestern Polytechnical University School of Automation, 127 Youyi Road, 710072 Shaanxi, China
| | - Zikang Ma
- Department of Intelligent Science and Technology, Northwestern Polytechnical University School of Automation, 127 Youyi Road, 710072 Shaanxi, China
| | - Yan Yang
- Department of Intelligent Science and Technology, Northwestern Polytechnical University School of Automation, 127 Youyi Road, 710072 Shaanxi, China
| | - Lei Guo
- Department of Intelligent Science and Technology, Northwestern Polytechnical University School of Automation, 127 Youyi Road, 710072 Shaanxi, China
| | - Lei Du
- Department of Intelligent Science and Technology, Northwestern Polytechnical University School of Automation, 127 Youyi Road, 710072 Shaanxi, China
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Baker E, Leonenko G, Schmidt KM, Hill M, Myers AJ, Shoai M, de Rojas I, Tesi N, Holstege H, van der Flier WM, Pijnenburg YAL, Ruiz A, Hardy J, van der Lee S, Escott-Price V. What does heritability of Alzheimer's disease represent? PLoS One 2023; 18:e0281440. [PMID: 37115753 PMCID: PMC10146480 DOI: 10.1371/journal.pone.0281440] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 01/24/2023] [Indexed: 04/29/2023] Open
Abstract
INTRODUCTION Both late-onset Alzheimer's disease (AD) and ageing have a strong genetic component. In each case, many associated variants have been discovered, but how much missing heritability remains to be discovered is debated. Variability in the estimation of SNP-based heritability could explain the differences in reported heritability. METHODS We compute heritability in five large independent cohorts (N = 7,396, 1,566, 803, 12,528 and 3,963) to determine whether a consensus for the AD heritability estimate can be reached. These cohorts vary by sample size, age of cases and controls and phenotype definition. We compute heritability a) for all SNPs, b) excluding APOE region, c) excluding both APOE and genome-wide association study hit regions, and d) SNPs overlapping a microglia gene-set. RESULTS SNP-based heritability of late onset Alzheimer's disease is between 38 and 66% when age and genetic disease architecture are correctly accounted for. The heritability estimates decrease by 12% [SD = 8%] on average when the APOE region is excluded and an additional 1% [SD = 3%] when genome-wide significant regions were removed. A microglia gene-set explains 69-84% of our estimates of SNP-based heritability using only 3% of total SNPs in all cohorts. CONCLUSION The heritability of neurodegenerative disorders cannot be represented as a single number, because it is dependent on the ages of cases and controls. Genome-wide association studies pick up a large proportion of total AD heritability when age and genetic architecture are correctly accounted for. Around 13% of SNP-based heritability can be explained by known genetic loci and the remaining heritability likely resides around microglial related genes.
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Affiliation(s)
- Emily Baker
- Division of Neuroscience and Mental Health, School of Medicine, Cardiff University, Cardiff, United Kingdom
- Dementia Research Institute, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Ganna Leonenko
- Division of Neuroscience and Mental Health, School of Medicine, Cardiff University, Cardiff, United Kingdom
- Dementia Research Institute, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | | | - Matthew Hill
- Dementia Research Institute, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Amanda J Myers
- Department of Cell Biology, Miller School of Medicine, University of Miami, Coral Gables, FL, United States of America
| | - Maryam Shoai
- Institute of Neurology, University College London, London, United Kingdom
| | - Itziar de Rojas
- Research Center and Memory Clinic, ACE Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Niccoló Tesi
- Genomics of Neurodegenerative Diseases and Aging, Human Genetics, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
| | - Henne Holstege
- Genomics of Neurodegenerative Diseases and Aging, Human Genetics, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Wiesje M van der Flier
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Yolande A L Pijnenburg
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Agustin Ruiz
- Research Center and Memory Clinic, ACE Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - John Hardy
- Institute of Neurology, University College London, London, United Kingdom
| | - Sven van der Lee
- Genomics of Neurodegenerative Diseases and Aging, Human Genetics, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Valentina Escott-Price
- Division of Neuroscience and Mental Health, School of Medicine, Cardiff University, Cardiff, United Kingdom
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