Increase in linkage information by stratification of pedigree data into gold-standard and standard diagnoses: application to the NIMH Alzheimer Disease Genetics Initiative Dataset.

Patients diagnosed with a standard clinical method (subject to misclassification error) are often combined with patients diagnosed with a gold-standard method (with zero or very small misclassification error) in family-based studies of complex disease. For example, non-autopsied patients (NAP) are often included along with autopsy-proven (AP) patients in family-based studies of complex diseases, such as Alzheimer's disease (AD). Theoretical and simulation studies suggest that certain misclassification errors can result in severe reduction of power in genetic linkage and association analyses and that phenotype (or diagnostic) error can produce misleading results. Morton's test for heterogeneity can identify genomic regions where error may have led to loss in power. We applied this test to pedigree data from the NIMH Alzheimer's Disease Genetics Initiative Database separated into AP and NAP pedigrees. Morton's test identified one highly significant region of heterogeneity on chromosome 2. The source of the heterogeneity was due to significant indication of linkage in the AP pedigrees at position 109 cM (p value = 6.68 x 10(-5)) with no indication in the NAP pedigrees. Furthermore, Morton's test showed no evidence for heterogeneity on chromosome 19 in early-onset pedigrees that showed highly significant evidence for linkage in other published reports. These results suggest that supplementing linkage analysis with Morton's test can be usefully applied to genetic data sets that have AP and NAP samples, or other sample mixtures that include a 'gold standard' subgroup with reduced error rate, to increase power to detect linkage in the presence of diagnostic misclassification.