Selecting cases from nuclear families for case-control association analysis.
We examine the efficiency of a number of schemes to select cases from nuclear families for case-control association analysis using the Genetic Analysis Workshop 14 simulated dataset. We show that with this simulated dataset comparing all affected siblings with unrelated controls is considerably more powerful than all of the other approaches considered. We find that the test statistic is increased by almost 3-fold compared to the next best sampling schemes of selecting all affected sibs only from families with affected parents (AF aff), one affected sib with most evidence of allele-sharing from each family (SF), and all affected sibs from families with evidence for linkage (AF L). We consider accounting for biological relatedness of samples in the association analysis to maintain the correct type I error. We also discuss the relative efficiencies of increasing the ratio of unrelated cases to controls, methods to confirm associations and issues to consider when applying our conclusions to other complex disease datasets.
Moore
Rachael M
RM
Research and Development Genetics, AstraZeneca, Alderley Park, Macclesfield, SK10 4TG, UK. rachael.moore@astrazeneca.com
Pinel
Tracy
T
Zhao
Jing Hua
JH
March
Ruth
R
Jawaid
Ansar
A
D016022:Case-Control Studies; D056426:Genetic Loci / Q000235:genetics; D055106:Genome-Wide Association Study; D006801:Humans; D015810:Linkage Disequilibrium / Q000235:genetics; D009679:Nuclear Family; D010554:Personality Disorders / Q000235:genetics; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D015203:Reproducibility of Results