Application of the STAAR Framework in Detecting Rare Variant Associations with Alzheimer’s Disease.

Abstract Number:

1340 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Dongyu Wang (1), Nancy Heard-Costa (2), Andy Rampersaud (3), Eden Martin (4), Adam Naj (5), Bilcag Akgun (6), Brian Kunkle (7), Gina Peloso (1), Anita DeStefano (8), Xihao Li (9), Seung Hoan Choi (8)

Institutions:

(1) N/A, N/A, (2) Department of Medicine, Boston University School of Medicine;NHLBI Framingham Heart Study, N/A, (3) Research Computing Services, Information Services & Technology, Boston University, N/A, (4) University of Miami-Miami Institute of Human Genomics, N/A, (5) Department of Biostatistics, Epidemiology, and Informatics, Department of Pathology and Laboratory, N/A, (6) John P Hussman Institute for Human Genomics, N/A, (7) John P Hussman Institute for Human Genomics; John T Macdonald Department of Human Genetics, N/A, (8) Department of Biostatistics, Boston University School of Public Health, N/A, (9) University of North Carolina at Chapel Hill, N/A

Co-Author(s):

Nancy Heard-Costa  
Department of Medicine, Boston University School of Medicine;NHLBI Framingham Heart Study
Andy Rampersaud  
Research Computing Services, Information Services & Technology, Boston University
Eden Martin  
University of Miami-Miami Institute of Human Genomics
Adam Naj  
Department of Biostatistics, Epidemiology, and Informatics, Department of Pathology and Laboratory
Bilcag Akgun  
John P Hussman Institute for Human Genomics
Brian Kunkle  
John P Hussman Institute for Human Genomics; John T Macdonald Department of Human Genetics
Gina Peloso  
N/A
Anita DeStefano  
Department of Biostatistics, Boston University School of Public Health
Xihao Li  
University of North Carolina at Chapel Hill
Seung Hoan Choi  
Department of Biostatistics, Boston University School of Public Health

First Author:

Dongyu Wang  
N/A

Presenting Author:

Dongyu Wang  
N/A

Abstract Text:

Introduction: Rare genetic variation is considered as a potential source of heritability in individuals with sporadic Alzheimer's Disease (AD). The STAAR framework leverages functional annotations of genetic variants and combines association statistics from analytic methods including burden, SKAT, and ACAT-V into a single measure of significance. Method: Using the R4 whole-genome sequencing data from Alzheimer's Disease Sequencing project (ADSP), we comprehensively examined the association between rare genetic variation and AD in 23,455 individuals with diverse ancestries via the STAAR framework. Results: We identified several genes such as PTK7, OPRD1, and ZNF200 that were significantly associated with AD. Our analysis revealed limitations within the framework that could lead to spurious associations for these genes. To enhance the robustness of the results, we proposed several methodological refinements. After implementing the modifications, the associations for these genes were no longer statistically significant. Conclusion: We identified issues that could lead to false positive results in the STAAR framework and made modifications to obtain more statistically robust results.

Keywords:

Rare varaint analysis|STAAR framework|Alzheimer's disease| | |

Sponsors:

Section on Statistics in Genomics and Genetics

Tracks:

Miscellaneous

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