A Monte Carlo procedure for two-stage tests with correlated data

✍ Scribed by E. R. Martin; N. L. Kaplan

• Publisher: John Wiley and Sons
• Year: 2000
• Tongue: English
• Weight: 57 KB
• Volume: 18
• Category: Article
• ISSN: 0741-0395
• DOI: 10.1002/(sici)1098-2272(200001)18:1<48::aid-gepi4>3.0.co;2-s

✦ Synopsis

One strategy for mapping disease loci using marker-disease associations is to test for association with case-control samples and follow up a positive result with a family-based test. Using a family-based test in the second stage can help provide protection against false-positive results that can result from use of inappropriate controls and provides assurance that association identified in the first stage is occurring between linked loci. It is crucial for this two-stage strategy that the first stage be as powerful as possible to detect association since only positive results are tested in the second stage. In certain situations, the power of the first-stage test can be increased by combining the case-control and family data. However, this introduces correlation between the first-and second-stage tests, and treating them as independent tests causes a bias. Here we propose a Monte Carlo method that accounts for the correlation and provides the correct significance level for the second-stage test. We also discuss the use of a two-stage procedure when doing a genome scan for the data presented in the Genetic Analysis Workshop 9 study.