Abstract Details
Activity Number:
|
476
|
Type:
|
Topic Contributed
|
Date/Time:
|
Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
|
Sponsor:
|
Biometrics Section
|
Abstract - #309119 |
Title:
|
Statistical Methods and Applications in Next-Generation Sequencing Data
|
Author(s):
|
Yun Li*+ and Song Yan
|
Companies:
|
The University of North Carolina and The University of North Carolina
|
Keywords:
|
next generation sequencing ;
rare variant association testing ;
unbalanced design ;
case control study
|
Abstract:
|
A Powerful Method to Control Size of Unbalanced Sequencing Designs in Rare Variant Association Testing
Although next generation sequencing technologies have been transforming complex disease genetics, deep whole genome sequencing of large samples remains cost prohibitive. When studying binary outcome and under the reasonable assumption that the rare causal alleles are enriched among affected individuals, it is cost efficient to adopt unbalanced designs where more, if not solely, affected cases are sequenced than unaffected controls. However, such unbalanced designs lead to inflated type I error in aggregated rare variant association testing if not adjusted properly. A few methods have been proposed to correct the bias but tend to be overly conservative and underpowered. Here we propose a new adjustment method that controls type I error properly and is more powerful than existing approaches.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2013 program
|
2013 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.