Dataset Information

A novel age-informed approach for genetic association analysis in Alzheimer's disease.

ABSTRACT:

Background

Many Alzheimer's disease (AD) genetic association studies disregard age or incorrectly account for it, hampering variant discovery.

Methods

Using simulated data, we compared the statistical power of several models: logistic regression on AD diagnosis adjusted and not adjusted for age; linear regression on a score integrating case-control status and age; and multivariate Cox regression on age-at-onset. We applied these models to real exome-wide data of 11,127 sequenced individuals (54% cases) and replicated suggestive associations in 21,631 genotype-imputed individuals (51% cases).

Results

Modeling variable AD risk across age results in 5-10% statistical power gain compared to logistic regression without age adjustment, while incorrect age adjustment leads to critical power loss. Applying our novel AD-age score and/or Cox regression, we discovered and replicated novel variants associated with AD on KIF21B, USH2A, RAB10, RIN3, and TAOK2 genes.

Conclusion

Our AD-age score provides a simple means for statistical power gain and is recommended for future AD studies.

SUBMITTER: Le Guen Y

PROVIDER: S-EPMC8017764 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Publications

A novel age-informed approach for genetic association analysis in Alzheimer's disease.

Le Guen Yann Y Belloy Michael E ME Napolioni Valerio V Eger Sarah J SJ Kennedy Gabriel G Tao Ran R He Zihuai Z Greicius Michael D MD

Alzheimer's research & therapy 20210401 1

<h4>Background</h4>Many Alzheimer's disease (AD) genetic association studies disregard age or incorrectly account for it, hampering variant discovery.<h4>Methods</h4>Using simulated data, we compared the statistical power of several models: logistic regression on AD diagnosis adjusted and not adjusted for age; linear regression on a score integrating case-control status and age; and multivariate Cox regression on age-at-onset. We applied these models to real exome-wide data of 11,127 sequenced i ...[more]

PMID: 33794991

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Project description:Objectives: To test whether genetic associations with behavioral outcomes after early childhood traumatic brain injury (TBI) are enriched for biologic pathways underpinning neurocognitive and behavioral networks. Design: Cross-sectional evaluation of the association of genetic factors with early (~ 6 months) and long-term (~ 7 years) post-TBI behavioral outcomes. We combined systems biology and genetic association testing methodologies to identify biologic pathways associated with neurocognitive and behavior outcomes after TBI. We then evaluated whether genes/single nucleotide polymorphism (SNPs) associated with these biologic pathways were more likely to demonstrate a relationship (i.e., enrichment) with short and long-term behavioral outcomes after early childhood TBI compared to genes/SNPs not associated with these biologic pathways. Setting: Outpatient research setting. Participants:140 children, ages 3-6:11 years at time of injury, admitted for a TBI or orthopedic injury (OI). Interventions: Not Applicable. Main Outcome Measures: Child behavior checklist total problems T score. Results: Systems biology methodology identified neuronal systems and neurotransmitter signaling (Glutamate receptor, dopamine, serotonin, and calcium signaling), inflammatory response, cell death, immune systems, and brain development as important biologic pathways to neurocognitive and behavioral outcomes after TBI. At 6 months post injury, the group (TBI versus OI) by polymorphism interaction was significant when the aggregate signal from the highest ranked 40% of case gene associations was compared to the control set of genes. At ~ 7 years post injury, the selected polymorphisms had a significant main effect after controlling for injury type when the aggregate signal from the highest ranked 10% of the case genes were compared to the control set of genes Conclusions: Findings demonstrate the promise of applying a genomics approach, informed by systems biology, to understanding behavioral recovery after pediatric TBI. A mixture of biologic pathways and processes are associated with behavioral recovery, specifically genes associated with cell death, inflammatory response, neurotransmitter signaling, and brain development. These results provide insights into the complex biology of TBI recovery.

Dataset Information

A novel age-informed approach for genetic association analysis in Alzheimer's disease.

Background

Methods

Results

Conclusion

Publications

A novel age-informed approach for genetic association analysis in Alzheimer's disease.

Similar Datasets

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