Refining Disease Association Signals Using Cross-Population Contrasts
Author Information
Author(s): Nancy L Saccone, Scott F Saccone, Alison M Goate, Richard A Grucza, Anthony L Hinrichs, John P Rice, Laura J Bierut
Primary Institution: Washington University
Hypothesis
Important underlying biological mechanisms are shared across human populations, and we can filter correlated variants by testing for heterogeneity of genetic effects in different population samples.
Conclusion
Filtering out SNPs that demonstrate cross-population heterogeneity enriches for variants more likely to be important and causative.
Supporting Evidence
- The study used a case-control design with a total of 1087 participants.
- Three SNPs showed significant cross-population heterogeneity.
- The method helps prioritize SNPs for follow-up studies.
Takeaway
The study looks at how different genetic variants are related to cocaine dependence in different populations, helping to find the most important ones.
Methodology
A systematic method for testing association across diverse population samples using a logistic regression framework.
Limitations
Power to filter out SNPs is reduced due to differences in allele frequency between populations.
Participant Demographics
504 European-American and 583 African-American participants.
Statistical Information
P-Value
p<0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website