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Related Concept Videos

Human Genetics01:28

Human Genetics

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Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
The complex relationship between genetics and psychology is observable through common biological components such...
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Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
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Associating brain imaging phenotypes and genetic risk factors via a hypergraph based netNMF method.

Junli Zhuang1, Jinping Tian2, Xiaoxing Xiong3

  • 1Department of Vascular Surgery, Renmin Hospital of Wuhan University, Wuhan, China.

Frontiers in Aging Neuroscience
|March 20, 2023
PubMed
Summary

This study introduces a novel hypergraph-based algorithm (HG-netNMF) for early Alzheimer's disease (AD) and mild cognitive impairment (MCI) diagnosis. The method integrates brain imaging and gene data, achieving high diagnostic accuracy for both conditions.

Keywords:
Alzheimer’s diseasebiomarkershypergraph learningmild cognitive impairmentnon-negative matrix factorization

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Area of Science:

  • Neuroscience
  • Bioinformatics
  • Medical Imaging

Background:

  • Alzheimer's disease (AD) and mild cognitive impairment (MCI) are neurodegenerative conditions lacking effective treatments.
  • Early diagnosis of AD and MCI is crucial for clinical management and intervention.
  • Current diagnostic methods may benefit from integrating diverse data types.

Purpose of the Study:

  • To propose a novel hypergraph-based netNMF (HG-netNMF) algorithm for early AD and MCI diagnosis.
  • To integrate structural magnetic resonance imaging (sMRI) and gene expression data.
  • To identify risk regions of interest (ROIs) and key genes associated with AD and MCI.

Main Methods:

  • Developed and applied the HG-netNMF algorithm for multimodal data integration.
  • Constructed brain structure connection networks and protein-interaction networks (PPIs).
  • Performed bioinformatics analyses to identify risk ROIs and key genes.

Main Results:

  • The HG-netNMF algorithm successfully integrated sMRI and gene expression data.
  • Identified specific ROIs and key genes implicated in AD and MCI.
  • Achieved high diagnostic performance with AUC values of 0.8 for AD and 0.797 for MCI.

Conclusions:

  • The proposed HG-netNMF algorithm demonstrates significant potential for early AD and MCI diagnosis.
  • Integrating neuroimaging and genetic data provides a powerful approach for understanding disease mechanisms.
  • The identified risk ROIs and key genes can inform the development of diagnostic biomarkers.