Human Genetics

, Volume 133, Issue 3, pp 265–279

On individual genome-wide association studies and their meta-analysis

Original Investigation

Abstract

Individual genome-wide association (GWA) studies and their meta-analyses represent two approaches for identifying genetic loci associated with complex diseases/traits. Inconsistent findings and non-replicability between individual GWA studies and meta-analyses are commonly observed, hence posing the critical question as to how to interpret their respective results properly. In this study, we performed a series of simulation studies to investigate and compare the statistical properties of the two approaches. Our results show that (1) as expected, meta-analysis of larger sample size is more powerful than individual GWA studies under the ideal setting of population homogeneity among individual studies; (2) under the realistic setting of heterogeneity among individual studies, detection of heterogeneity is usually difficult and meta-analysis (even with the random-effects model) may introduce elevated false positive and/or negative rates; (3) despite relatively small sample size, well-designed individual GWA study has the capacity to identify novel loci for complex traits; (4) replicability between meta-analysis and independent individual studies or between independent meta-analyses is limited, and thus inconsistent findings are not unexpected.

Supplementary material

439_2013_1366_MOESM1_ESM.doc (1 mb)
Supplementary material 1 (DOC 1074 kb)

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Center of System Biomedical SciencesUniversity of Shanghai for Science and TechnologyShanghaiPeople’s Republic of China
  2. 2.Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, School of Public Health and Tropical MedicineTulane UniversityNew OrleansUSA
  3. 3.Department of Basic Medical ScienceUniversity of Missouri-Kansas CityKansas CityUSA

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