The relationship between the minor allele content and Alzheimer's disease
Hongyao Chen1, Xiaoyun Lei1, Dejian Yuan2
1Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, Hunan Key Laboratory of Animal Models for Human Diseases, School of Life Sciences, Central South University, 110 Xiangya Road, Changsha, Hunan 410078, China.
Abstract:
Alzheimer's disease (AD) is a chronic neurodegenerative disease. The genetic risk factors of AD remain better understood. Using previously published dataset of common single nucleotide polymorphisms (SNPs), we studied the association between the minor allele content (MAC) in an individual and AD. We found that AD patients have higher average MAC values than matched controls. We identified a risk prediction model that could predict 2.19% of AD cases. We also identified 49 genes whose expression levels correlated with both MAC and AD. By pathway and process enrichment analyses, these genes were found in pathways or processes closely related to AD. Our study suggests that AD may be linked with too many genetic variations over a threshold. The method of correlations with both MAC and traits appears to be effective in high efficiency identification of target genes for complex traits.
Insights
Alzheimer's disease (AD) risk may increase with higher minor allele content (MAC). This study identifies potential genetic risk factors and a predictive model for AD, offering new insights into complex trait genetics.
Area of Science:
- Genetics
- Neurodegenerative Diseases
- Computational Biology
Background:
- Alzheimer's disease (AD) is a progressive neurodegenerative disorder with complex genetic underpinnings.
- Understanding the genetic risk factors for AD is crucial for developing effective diagnostic and therapeutic strategies.
Purpose of the Study:
- To investigate the association between minor allele content (MAC) and Alzheimer's disease (AD).
- To identify potential genetic risk predictors and associated genes for AD.
- To evaluate a novel method for identifying target genes in complex traits.
Main Methods:
- Analysis of a publicly available dataset of common single nucleotide polymorphisms (SNPs).
- Calculation of minor allele content (MAC) for individuals.
- Development of a risk prediction model for AD.
- Gene expression correlation analysis with MAC and AD status.
- Pathway and process enrichment analyses for identified genes.
Main Results:
- Individuals with AD exhibited significantly higher average MAC values compared to matched controls.
- A risk prediction model was developed, capable of predicting 2.19% of AD cases.
- Forty-nine genes were identified with expression levels correlated to both MAC and AD.
- Enrichment analyses revealed these genes are involved in pathways critical to AD pathogenesis.
Conclusions:
- Elevated MAC may represent a significant genetic risk factor for Alzheimer's disease.
- The correlation-based method demonstrates efficacy in identifying target genes for complex traits like AD.
- This study provides a foundation for further research into the genetic architecture of AD and potential therapeutic targets.
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