Statistical Genetics Methods for Mixed Pathologies

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DESCRIPTION (provided by applicant): This mentored career development grant application proposes a training program to integrate Dr. Fardo's previous research in biostatistics and statistical genetics into investigations of aging disorders including highly prevalent neurodegenerative diseases. His long-term career goal is to develop an independent research program focusing on the advancement of statistical methodologies to understand the genetics underlying complex diseases, with an emphasis on diseases of brain aging. This will be achieved through acquiring knowledge in the area of brain diseases of aging through formal coursework and mentored experiences; training in the management, analysis and interpretation of associated research data; comprehensive examination of the large-scale genetic data used in aging studies and the areas where novel, cutting-edge biostatistical methodologies could improve analyses; application, refinement and development of statistical genetic approaches to the analysis of this large-scale genetic data; and the continuation of training in the responsible conduct of research. Dr. Fardo's research training will involve an enthusiastic team of cross- disciplinary researchers with a strong research track record. An overall goal of this K25 application is to capitalize on the clinical and neuropathologic data available from large, multicenter-derived databases to identify single nucleotide polymorphisms (SNPs) associated with specific neuropathologic endophenotypes. Most research investigating the genetic predisposition of age-related complex diseases has been conceptualized with a model directly comparing those with or without a disease, for example, with or without Alzheimer's disease (AD). However, this disease/non-disease approach is inefficient and not appropriate for investigating the genetic risk factors for so-called mixed pathologies which are the norm in advanced age. The proposed research program will interrogate genetic associations with neuropathologic features of a large cohort of autopsied brains. This will be done by utilizing data collected from large, NIH-funded resources. Neuropathology data and genome-wide genetic data will be merged from these resources, and genetic associations for mixed-pathology dementias and neuropathologic features will be comprehensively tested. This work will result in new methods to analyze mixed-pathology dementias and neuropathologic features as well as the exploration of associated genetic risk factors and gene-environment interactions.