title: Evaluation of replication studies, combined data analysis, and analytical methods in complex diseases.
abstract: Due to genetic heterogeneity, phenocopies, incomplete penetrance, misdiagnosis, and unknown mode of inheritance, linkage studies of most complex diseases are unlikely to provide conclusive findings with unambiguously high lod scores. Typically, several marginally significant lod scores or elevated lod scores are observed in a genome-wide screen. However, it is usually difficult to differentiate these findings from false positives (type I errors). Two approaches are commonly used to guard against false positives: replication studies in independent samples and combined data analysis. In the current paper, we evaluated these two common approaches using simulated data where data from multiple groups were available and locations of disease genes were known. We found replication studies and combined data analysis performed similarly in terms of their ability to identify true and false positive linkages. Both approaches confirmed two true linkages and did not confirm any false positive linkages. The results also indicated that it is not appropriate to apply the criteria proposed for confirming significant evidence for linkage to confirm regions with only suggestive evidence for linkage. The current results support previous findings that parametric analysis using an incorrect genetic model can still identify a true linkage.
authors.affiliation: Program in Human Genetics, University of Maryland, Baltimore, USA.
authors.forename: J
authors.initials: J
authors.lastname: Xu
authors.forename: D G
authors.initials: DG
authors.lastname: Wiesch
authors.forename: E W
authors.initials: EW
authors.lastname: Taylor
authors.forename: D A
authors.initials: DA
authors.lastname: Meyers
mesh_terms: D004777:Environment; D008040:Genetic Linkage; D005819:Genetic Markers; D005820:Genetic Testing; D016678:Genome; D006801:Humans; D008957:Models, Genetic; D020412:Multifactorial Inheritance; D010641:Phenotype; D015203:Reproducibility of Results; D018709:Statistics, Nonparametric
chemical_list: D005819:Genetic Markers