The authors have no conflict of interest.
Nonreplication in Genetic Studies of Complex Diseases—Lessons Learned From Studies of Osteoporosis and Tentative Remedies†
Article first published online: 29 NOV 2004
Copyright © 2005 ASBMR
Journal of Bone and Mineral Research
Volume 20, Issue 3, pages 365–376, March 2005
How to Cite
Shen, H., Liu, Y., Liu, P., Recker, R. R. and Deng, H.-W. (2005), Nonreplication in Genetic Studies of Complex Diseases—Lessons Learned From Studies of Osteoporosis and Tentative Remedies. J Bone Miner Res, 20: 365–376. doi: 10.1359/JBMR.041129
- Issue published online: 4 DEC 2009
- Article first published online: 29 NOV 2004
- Manuscript Accepted: 15 OCT 2004
- Manuscript Revised: 29 AUG 2004
- Manuscript Received: 16 JUN 2004
- statistical power;
- study design
Inconsistent results have accumulated in genetic studies of complex diseases/traits over the past decade. Using osteoporosis as an example, we address major potential factors for the nonreplication results and propose some potential remedies.
Over the past decade, numerous linkage and association studies have been performed to search for genes predisposing to complex human diseases. However, relatively little success has been achieved, and inconsistent results have accumulated. We argue that those nonreplication results are not unexpected, given the complicated nature of complex diseases and a number of confounding factors. In this article, based on our experience in genetic studies of osteoporosis, we discuss major potential factors for the inconsistent results and propose some potential remedies. We believe that one of the main reasons for this lack of reproducibility is overinterpretation of nominally significant results from studies with insufficient statistical power. We indicate that the power of a study is not only influenced by the sample size, but also by genetic heterogeneity, the extent and degree of linkage disequilibrium (LD) between the markers tested and the causal variants, and the allele frequency differences between them. We also discuss the effects of other confounding factors, including population stratification, phenotype difference, genotype and phenotype quality control, multiple testing, and genuine biological differences. In addition, we note that with low statistical power, even a “replicated” finding is still likely to be a false positive. We believe that with rigorous control of study design and interpretation of different outcomes, inconsistency will be largely reduced, and the chances of successfully revealing genetic components of complex diseases will be greatly improved.