PROJECT SUMMARY/ABSTRACT
Late-onset Alzheimer’s disease (LOAD) affects a large portion of the human population and is highly heritable,
though due to the difficulty of acquiring well-phentoyped data, genome-wide association studies (GWASs) of
LOAD have had limited success in identifying associated genes. Additional statistical power would likely produce
many discoveries related to the biology of LOAD, as it has for other complex phenotypes.
This research plan proposes alternate data sources and new methods to increase the statistical power in
genetic studies of LOAD. First, because LOAD is diagnosed late in life, large, cross-sectional studies cannot
easily classify individuals as cases or controls. This limitation can be somewhat attenuated using pedigree
information, as is done in the existing method, GWAX. Dr. Turley will extend GWAX to account for case-status,
age, and other characteristics of both parents. These results will be meta-analyzed with available case-control-
based results using Multi-Trait Analysis of GWAS (MTAG), leading to substantial gains in power and reduced
risk of bias due to misclassification of cases. Second, LOAD and educational attainment (EA) have a genetic
correlation of -0.3, suggesting that they may be associated with both common and unique biological pathways.
Dr. Turley will seek to better understand LOAD by classifying and analyzing SNPs that are either jointly or
uniquely associated with LOAD using Bayes-MTAG, an extension of MTAG that he is developing. Third, a lack
of non-European GWAS cohorts have resulted in polygenic scores that perform poorly in those populations. Dr.
Turley will develop Multi-Ancestry Meta-Analysis (MAMA), a trans-ethnic meta-analysis extension of MTAG that
accounts for differences in linkage disequilibrium and genetic architecture across ancestries, to improve
prediction of LOAD in non-European populations. The methods developed in each of these aims will increase
statistical power, identifying novel loci, elucidating biological pathways, and improving polygenic prediction.
Under the guidance his mentor, Dr. Benjamin Neale, his co-mentor, Dr. Xihong Lin, and a team of other
advisers, Dr. Turley will pursue a rigorous program of training to accomplish the aims of this proposal and to
develop into an independent researcher. The domains of this training include (i) epidemiology and genetics of
aging, (ii) statistical and population genetics, (iii) large-scale data analysis and tools, and (iv) professional
development. Development in these domains will be accomplished through coursework, attendance at
conferences and workshops, experience leading teams and mentoring others, and regular feedback from his
committee. Most importantly, the plan includes a detailed timeline, but which Dr. Turley and his mentoring team
can monitor and evaluate progress. Overall, the training environment for the candidate is excellent, the mentors
and advisors are world-class, the proposed studies address a crucial and timely unmet need, and the additional
skills developed during this award will undoubtedly provide a strong foundation for the candidate to establish
independent leadership in Alzheimer’s disease and statistical genetics.