Project Summary/Abstract We have developed a novel and powerful statistical approach to identify genetic variants associated with age-at-onset (AAO) or time-to-event traits. Here, we propose to identify genetic modifiers of AAO of Alzheimer's disease (AD) through genome-wide association testing in two large data sets representing different ancestries, followed by replication studies in independent data sets. The proposed studies improve upon previous work in three areas. First and foremost, we introduce a novel statistical approach that evaluates variation in AAO of AD as a censored trait. By analyzing AAO as a quantitative censored trait, we also improve the power of our study relative to case-control studies as the quantitative trait is more informative. We will maximize that information by reducing the genetic and phenotypic variation caused by known sources (ex., APOE genotype and population structure). Secondly, we improve upon previous work by incorporating data sets representing diverse ancestries and adequately adjusting for both population structure and relatedness within the data. The more-distant relationships between populations involve more recombination between variants, resulting in smaller shared haplotypes and therefore more precise estimates of the location of AAO modifiers. Furthermore, many of the known AD risk loci are ancestry-informative, varying significantly in frequency across human populations. As the power to detect association increases with allele frequency, we improve the power of our study by studying diverse populations. Lastly, we will be able to more precisely locate AAO modifiers by incorporating denser marker data through imputation. Imputation is a cost-effective strategy for obtaining sequence-level genotype data from microarray data. Recent advances in imputation methods and the establishment of large and diverse sequence-based reference panels have made it possible to accurately impute variants with frequencies as low as 0.1%, ensuring that we capture much of the polymorphic variation within these data sets. By identifying genetic modifiers of AAO, we will provide further insight into the biology of AD and nominate additional therapeutic targets.