Analysis of multiple phenotypes in genome-wide genetic mapping studies

Chen Suo*, Timothea Toulopoulou, Elvira Bramon, Muriel Walshe, Marco Picchioni, Robin Murray, Jurg Ott

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


Background: Complex traits may be defined by a range of different criteria. It would result in a loss of information to perform analyses simply on the basis of a final clinical dichotomized affected / unaffected variable.

Results: We assess the performance of four alternative approaches for the analysis of multiple phenotypes in genetic association studies. We describe the four methods in detail and discuss their relative theoretical merits and disadvantages. Using simulation we demonstrate that PCA provides the greatest power when applied to both correlated phenotypes and with large numbers of phenotypes. The multivariate approach had low type I error only with independent phenotypes or small numbers of phenotypes. In this study, our application of the four methods to schizophrenia data provides converging evidence of the relative performance of the methods.

Conclusions: Via power analysis of simulated data and testing of experimental data, we conclude that PCA, creating one variable based on a linear combination of all the traits, performs optimally. We propose that our comparison will provide insight into the properties of the methods and help researchers to choose appropriate strategy in future experimental studies.

Original languageEnglish
Article number151
Number of pages7
JournalBMC Bioinformatics
Publication statusPublished - 2 May 2013


  • Multiple phenotypes
  • Statistical method
  • Genetic mapping
  • LOCI


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