50: Joint FDR Control Under Multiple Conditions

Music City Center 

Integrating information across correlated conditions can improve statistical power by utilizing shared underlying mechanisms. Here, we are concerned with the problem of identifying which variables, among a large number of them, respond to two different conditions. Rather than treating it as two separate multiple comparisons problems, we propose to jointly estimate three proportions: the proportion of variables responding to each of the two conditions and the proportion responding to both conditions, a scenario not uncommon in biological sciences. By utilizing the shared information, our method achieves higher statistical power. The advantage of our method will be illustrated using two examples: (1) identifying genes whose expression levels in the brain are altered by radiation exposure but restored by a treatment designed to mitigate the harm caused by radiation therapy, and (2) detecting DNA variants associated with a psychometric disorder using information from a related disorder.

statistical power

false discovery rate

gene expression analysis

high-dimension 

Section on Statistics in Genomics and Genetics