Interpretation of simultaneous linkage and family-based association tests in genome screens.
Linkage and association analyses have played important roles in identifying susceptibility genes for complex diseases. Linkage tests and family-based tests of association are often applied in the same data to help fine-map disease loci or validate results. This paradigm increases efficiency by making maximal use of family data sets. However, it is not intuitively clear under what conditions association and linkage tests performed in the same data set may be correlated. Understanding this relationship is important for interpreting the combined results of both tests. We used computer simulations and theoretical statements to estimate the correlation between linkage statistics (affected sib pair maximum LOD scores) and family-based association statistics (pedigree disequilibrium test (PDT) and association in the pressure of linkage (APL)) under various hypotheses. Different types of pedigrees were studied: nuclear families with affected sib pairs, extended pedigrees and incomplete pedigrees. Both simulation and theoretical results showed that when there is no linkage or no association, the linkage and association tests are not correlated. When there is linkage and association in the data, the two tests have a positive correlation. We concluded that when linkage and association tests are applied in the same data, the type I error rate of neither test will be affected and that power can be increased by applying tests conditionally.
Chung
Ren-Hua
RH
Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27710, USA.
Hauser
Elizabeth R
ER
Martin
Eden R
ER
D000483:Alleles; D002874:Chromosome Mapping; D003198:Computer Simulation; D005260:Female; D005819:Genetic Markers; D015894:Genome, Human; D005838:Genotype; D006801:Humans; D015810:Linkage Disequilibrium / Q000235:genetics*; D008297:Male; D008957:Models, Genetic; D009679:Nuclear Family; D010375:Pedigree; D015995:Prevalence; D035781:Siblings; D016008:Statistical Distributions
D005819:Genetic Markers