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A PARTIALLY FUNCTIONAL LINEAR REGRESSION FRAMEWORK FOR INTEGRATING GENETIC, IMAGING, AND CLINICAL DATA.

Ting Li1, Yang Yu2, J S Marron2

  • 1School of Statistics and Management, Shanghai University of Finance and Economics.

The Annals of Applied Statistics
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Summary

This study introduces a new statistical framework to analyze genetic and brain imaging data for Alzheimer's disease (AD). Findings reveal complex genetic influences and hippocampal effects on cognitive decline, aiding future AD research.

Keywords:
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Area of Science:

  • Neuroscience
  • Genetics
  • Biostatistics

Background:

  • Alzheimer's Disease Neuroimaging Initiative (ADNI) provides rich genetic, imaging, and clinical data.
  • Understanding the interplay between genetic factors and brain structure is crucial for Alzheimer's disease (AD) research.

Purpose of the Study:

  • To develop a statistical framework for joint analysis of genetic and neuroimaging data in AD.
  • To identify key genetic and imaging features influencing cognitive decline in AD patients.
  • To explore shared and distinct heritability patterns across cognitive scores.

Main Methods:

  • Proposed a partially functional linear regression (PFLR) framework.
  • Employed a joint model selection and estimation procedure using reproducing kernel Hilbert space and L1 penalty.
  • Applied the method to ADNI data, analyzing genetic polymorphisms and baseline hippocampus surface effects on 13 cognitive scores.

Main Results:

  • Identified heterogeneous effects of hippocampal and genetic data on cognitive scores.
  • Observed negative associations between hippocampal volume and cognitive deficits.
  • Confirmed polygenic effects for all 13 cognitive scores, with APOE4 explaining only a small portion.
  • Found shared genetic etiology but greater heterogeneity within disease classifications.

Conclusions:

  • The PFLR framework effectively maps GIC-related pathways for AD.
  • Genetic and hippocampal factors significantly influence cognitive trajectories in AD.
  • Results provide insights into the complex genetic architecture and functional mechanisms underlying AD progression.