A method to incorporate prior information into score test for genetic association studies.
BACKGROUND
The interest of the scientific community in investigating the impact of rare variants on complex traits has stimulated the development of novel statistical methodologies for association studies. The fact that many of the recently proposed methods for association studies suffer from low power to identify a genetic association motivates the incorporation of prior knowledge into statistical tests.


RESULTS
In this article we propose a methodology to incorporate prior information into the region-based score test. Within our framework prior information is used to partition variants within a region into several groups, following which asymptotically independent group statistics are constructed and then combined into a global test statistic. Under the null hypothesis the distribution of our test statistic has lower degrees of freedom compared with those of the region-based score statistic. Theoretical power comparison, population genetics simulations and results from analysis of the GAW17 sequencing data set suggest that under some scenarios our method may perform as well as or outperform the score test and other competing methods.


CONCLUSIONS
An approach which uses prior information to improve the power of the region-based score test is proposed. Theoretical power comparison, population genetics simulations and the results of GAW17 data analysis showed that for some scenarios power of our method is on the level with or higher than those of the score test and other methods.
Human Genetics, Genome Institute of Singapore, 60 Biopolis Street, #02-01 Genome, Singapore 138672, Singapore. zakharovs99@gis.a-star.edu.sg.
Sergii
S
Zakharov
Garrett H K
GH
Teoh
Agus
A
Salim
Anbupalam
A
Thalamuthu
D019295:Computational Biology / Q000379:methods*; D056726:Genetic Association Studies / Q000379:methods*; D014644:Genetic Variation / Q000235:genetics*; D005828:Genetics, Population / Q000379:methods*; D008957:Models, Genetic; D010641:Phenotype