FBAT-approaches for region-based analysis, using haplotype information, meta analysis approaches of Alzheimer's disease studies and developmentof Polygenic risk score models for Alzheimer's disease

Using the haplotype-algorithm for FBAT, we will develop a general testing framework that will allow for the implementation of region-based association tests, e.g. SKAT, burden, multi-variate and, using a permutation approach, the calculation of exact p-values. All association test statistics will be computed based on the exact genetic variance/ covariance matrix. Permutation/ simulation-based p-values are obtained using our new haplotype-algorithm. We will also develop higher criticism approaches for the region-based association analysis of the AD samples. Recent developments in theoretical statistics have shown that higher criticism approaches are, by far, the most powerful statistical techniques to detect association signals in very spare data, for instance rare variant WGS data. In the framework that we developed for the previous funding cycle, the higher criticism approaches will be implemented based on the exact genetic exact variance/ covariance matrix. Permutation/ simulation-based p-values will be obtained using our haplotype-algorithm.

We will also develop meta-analysis approaches to combine locus-specific and region-based association findings for AD across studies.

Using reported association findings in the literature, we will develop polygenic risk score models/ integrated risk models that are based on the methodology of marker assisted selection, and evaluate their performance in terms of prediction of AD in simulation studies.

We will evaluate all of our approaches by application to the requested data set (NG00067).

We will develop haplotype-based approaches for the region-based analysis of WGS data in family-based designs for Alzheimer's Disease. We will develop meta-analysis approaches to aggregate locus specific and region-based associations with Alzheimer's Disease across studies. Furthermore, we will develop polygenic risk score/ integrated risk model approaches that model age-at-onset and non-affection status as the primary phenotype for AD, thereby achieving better performances than standard approaches.