Abstract
For most complex trait association studies using next-generation sequencing, in addition to the primary phenotype of interest, many clinically important secondary traits are also available, which can be analyzed to map susceptibility genes. Owing to high sequencing costs, most studies use selected samples, and the sampling mechanisms of these studies can be complicated. When the primary and secondary traits are correlated, analyses of secondary phenotypes can cause spurious associations in selected samples and existing methods are inadequate to adjust for them. To address this problem, a likelihood-based method, MULTI-TRAIT-ASSOCIATION (MTA) was developed. MTA is flexible and can be applied to any study with known sampling mechanisms. It also allows efficient inferences of genetic parameters. To investigate the power of MTA and different study designs, extensive simulations were performed under rigorous population genetic and phenotypic models. It is demonstrated that there are great benefits for analyzing secondary phenotypes in selected samples. In particular, using case–control samples and samples with extreme primary phenotypes can be more powerful than analyzing random samples of equivalent size. One major challenge for sequence-based association studies is that most data sets are not of sufficient size to be adequately powered. By applying MTA, data sets ascertained under distinct mechanisms or targeted at different primary traits can be jointly analyzed to map common phenotypes and greatly increase power. The combined analysis can be performed using freely available data sets from public repositories, for example, dbGaP. In conclusion, MTA will have an important role in dissecting the etiology of complex traits.
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Acknowledgements
This research is supported by National Institutes of Health Grants 1RC4MD005964 and 1RC2HL102926 (SML). DJL is partially supported by a training fellowship from the Keck Center Pharmacoinformatics Training Program of the Gulf Coast Consortia (NIH Grant no. 5 R90 DK071505-04). We thank Drs Jonathan Cohen (JC) and Helen Hobbs for providing us with data from the Dallas Heart Study on the ANGTPL-family genes, which was supported by National Institutes of Health Grant RL1HL092550 (JC). Computation for this research was supported in part by the Shared University Grid at Rice funded by NSF under Grant EIA-0216467, and a partnership between Rice University, Sun Microsystems and Sigma Solutions Inc.
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Liu, D., Leal, S. A flexible likelihood framework for detecting associations with secondary phenotypes in genetic studies using selected samples: application to sequence data. Eur J Hum Genet 20, 449–456 (2012). https://doi.org/10.1038/ejhg.2011.211
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DOI: https://doi.org/10.1038/ejhg.2011.211
