Abstract Details
|
Activity Number:
|
554
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Wednesday, August 6, 2014 : 2:00 PM to 3:50 PM
|
|
Sponsor:
|
Mental Health Statistics Section
|
|
Abstract #312587
|
|
|
Title:
|
Statistical Strategies for Psychiatric Genetic Studies with Limited Sample Sizes
|
|
Author(s):
|
Laura Lazzeroni*+
|
|
Companies:
|
Stanford University
|
|
Keywords:
|
GWAS ;
candidate gene ;
genetic association ;
phenotype ;
small sample ;
heterogeneity
|
|
Abstract:
|
Many psychiatric genetic studies rely on relatively small samples due to limitations imposed by cost, time, and the number of patients meeting relevant criteria. Small samples have low power to detect weak to moderate genetic effects when multiple-testing corrections are applied to large numbers of genetic variants. Yet, very large samples can usually be obtained only by combining data from multiple sources with different populations, phenotype measures, study protocols or entry criteria. The added heterogeneity can offset any advantage of the larger sample size. In this talk, I will discuss statistical strategies for working with complex, small-sample genetic data. I will focus on procedures that combine information across variants or phenotypes in complex data, reducing the number of tests and potentially enlarging effect sizes in order to increase power.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2014 program
|
2014 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Professional Development 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.