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Influence of Genetic Relationships in Age-Specific Cognitive Patterns in the Long-Life Family Study

Abstract Number:

2177 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Salil Koner (1), Sheng Luo (1), Konstantin Arbeev (1), Igor Akushevich (1), Anatoliy Yashnin (1), Dhrubajyoti Ghosh (2)

Institutions:

(1) Duke University, N/A, (2) Washington University in St. Louis, N/A

Co-Author(s):

Sheng Luo  
Duke University
Konstantin Arbeev  
Duke University
Igor Akushevich  
Duke University
Anatoliy Yashnin  
Duke University
Dhrubajyoti Ghosh  
Washington University in St. Louis

First Author:

Salil Koner  
Duke University

Presenting Author:

Salil Koner  
Duke University

Abstract Text:

The Long Life Family Study (LLFS) enrolled 5,089 individuals from 593 two-generation families, selected from the top 1% of the Family Longevity Selection Score. LLFS families, on average, exhibited superior aging outcomes, though with notable variation among pedigrees. The heritability of key healthy aging indicators, both short and long-term, underscores a genetic influence on protection against aging. This project introduces a robust methodology to analyze longitudinal changes in cognitive function, accounting for genetic relations, and other correlated biomarkers. We adopt a nonparametric hierarchical functional model to address the familial structure inherent in LLFS. Departing from conventional approaches that consider the time from baseline as the longitudinal indicator, this model utilizes age as the natural temporal variable, offering advantages in handling limited observations and facilitating the integration of diverse study data. This innovative approach enhances the understanding of cognitive aspects related to exceptional longevity within the LLFS cohort by pooling the shared information from the subjects in a family, even under less than three data per subject.

Keywords:

Hierarchical functional model|Long-life family study|Functional Principal Component analysis|Generalized additive model| |

Sponsors:

Biometrics Section

Tracks:

Longitudinal/Correlated Data

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