Empirical Bayes scan statistics for detecting clusters of disease risk variants in genetic studies

摘要: Summary Recent developments of high-throughput genomic technologies offer an unprecedented detailed view the genetic variation in various human populations, and promise to lead significant progress understanding basis complex diseases. Despite this tremendous advance data generation, it remains very challenging analyze interpret these due their sparse high-dimensional nature. Here, we propose novel applications new empirical Bayes scan statistics identify regions significantly enriched with disease risk variants. We show that proposed methodology can be substantially more powerful than existing methods especially so presence many non-disease variants, situations when there is a mixture protective Furthermore, approach has greater flexibility accommodate covariates such as functional prediction scores additional biomarkers. As proof-of-concept apply whole-exome sequencing study for autism spectrum disorders several promising candidate genes.