Dissecting genetic complexity of Alzheimer's disease and cognitive function


bioinformatics, computational genetics, genomics, cognitive function, Alzheimer's disease, endophenotypes, quantitative traits, genome-wide association analysis


Alzheimer's disease (AD) is the most frequent neurodegenerative disorder and the primary cause of dementia in the elderly. Four genes are known to increase susceptibility to AD, explaining 20% of the patients. The genes identified to date include 3 rare genes with a major effect on the risk of AD (amyloid precursor protein gene and the presenilin 1 and 2 genes) and a common gene (apolipoprotein E) conveying a 2-fold increase in risk. Through the identification of these genes, our understanding of the pathways involved in AD has increased substantially. Most likely other genes are involved, which have been difficult to identify because these are either very rare mutations with an substantial effect on the disease risk or common mutations with a very modest effect (relative risk<1.5). The identification of these genes requires an approach beyond standard methods and designs. We propose to use new methods, models and software to study early pathology (cognitive function) as an endophenotype for AD.

The data will be derived from 3 ongoing studies at the Erasmus MC, Rotterdam the Netherlands: (1) Rotterdam Study, a population-based cohort study of 11,000 persons aged 55 and over, who have been followed for more than 15 years and in which 800 patients with AD have been identified, (2) A study population of 180 AD patients from a genetically isolated population from the Southwest of the Netherlands that was conducted as part of the Genetic Research in Isolated Populations (GRIP) program, (3) The Erasmus Rucphen Family (ERF) study, a family based study of 3000 relatives from the GRIP region, who have been characterised with an extensive cognitive test battery for early AD pathology. For all of these populations, genome-wide data with 250.000 to 500.000 single nucleotide polymorphisms allowing association analysis have been generated or are to be generated in the near future. These studies provide a unique tool for localisation and identification of new genes involved in dementia and cognitive functioning.

However, these abundant data and complex mechanism of genetic control are imposing a major challenge to existing data analysis techniques. To analyse effectively the data, obtained in these projects, new methods, models and software are to be developed. The aim of this project is to develop these methods, apply these in the studies of AD and cognitive function, and make the software available for third parties through a website.

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