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Powerful and Adaptive Testing for Multi-trait and Multi-SNP Associations with GWAS and Sequencing Data
Junghi Kim1, Yiwei Zhang1, Wei Pan
1Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota 55455.
Genetics
|April 15, 2016
Summary
This study introduces an adaptive genetic association test for multiple traits, improving power for complex diseases like Alzheimer's. The new method identified novel gene associations with brain network atrophy, including AMOTL1, not found by traditional single-SNP analyses.
Area of Science:
- Genetics
- Neuroscience
- Biostatistics
Background:
- Complex diseases like Alzheimer's are linked to disrupted brain networks, necessitating genetic association studies across multiple brain regions.
- Traditional single-SNP association tests may lack power for complex genetic architectures involving multiple SNPs and traits.
- Existing multivariate methods can be less powerful than univariate tests if not carefully applied.
Purpose of the Study:
- To develop a highly adaptive SNP set-based association test for multiple traits to enhance statistical power.
- To identify genetic variants associated with gray matter atrophy in the default mode network (DMN) in Alzheimer's Disease Neuroimaging Initiative (ADNI) data.
- To compare the performance of the proposed adaptive test against existing methods using simulated and real data.
Main Methods:
- Developed an adaptive SNP set-based association test that assigns weights to SNPs and traits based on their likelihood of association.
- Applied the test to structural MRI data from the ADNI cohort, analyzing gray matter atrophy in the DMN.
- Compared the proposed method with single-SNP analyses and other existing tests on simulated and real genetic data.
Main Results:
- The adaptive test identified significant associations between genes AMOTL1 (chromosome 11) and APOE (chromosome 19) and the DMN in Alzheimer's disease patients.
- Gene AMOTL1, associated with cognitive impairment, was detected by the new method but missed by single-SNP analyses.
- The proposed test demonstrated superior or comparable power across various scenarios compared to existing methods.
Conclusions:
- The novel adaptive SNP set-based test offers a powerful approach for identifying genetic associations with multiple traits, particularly in complex diseases.
- The findings highlight potential novel genetic contributors to Alzheimer's disease pathology, such as AMOTL1, within brain networks.
- The method is versatile, applicable to rare variants, and extendable to pathway analyses for broader genetic discoveries.
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