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. 2011 Jun 6;2(7):519–522. doi: 10.1007/s13238-011-1059-5

Multiple phenotypes in genome-wide genetic mapping studies

Jurg Ott 1,✉, Jing Wang 1
PMCID: PMC4875235  PMID: 21647556

Abstract

For many psychiatric and other traits, diagnoses are based on a number of different criteria or phenotypes. Rather than carrying out genetic analyses on the final diagnosis, it has been suggested that relevant phenotypes should be analyzed directly. We provide an overview of statistical methods for the joint analysis of multiple phenotypes in case-control association studies.

Keywords: multiple phenotypes, genetic mapping, case-control, statistical method

References

  1. Allison D.B., Thiel B., St Jean P., Elston R.C., Infante M.C., Schork N.J. Multiple phenotype modeling in genemapping studies of quantitative traits: power advantages. Am J Hum Genet. 1998;63:1190–1201. doi: 10.1086/302038. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Benjamini Y. Discovering the false discovery rate. J R Stat Soc, B. 2010;72:405–416. doi: 10.1111/j.1467-9868.2010.00746.x. [DOI] [Google Scholar]
  3. Benjamini Y., Drai D., Elmer G., Kafkafi N., Golani I. Controlling the false discovery rate in behavior genetics research. Behav Brain Res. 2001;125:279–284. doi: 10.1016/S0166-4328(01)00297-2. [DOI] [PubMed] [Google Scholar]
  4. Bookman E.B., Taylor R.E., Adams-Campbell L., Kittles R.A. DRD4 promoter SNPs and gender effects on Extraversion in African Americans. Mol Psychiatry. 2002;7:786–789. doi: 10.1038/sj.mp.4001075. [DOI] [PubMed] [Google Scholar]
  5. Braff D.L., Freedman R., Schork N.J., Gottesman I.I. Deconstructing schizophrenia: an overview of the use of endophenotypes in order to understand a complex disorder. Schizophr Bull. 2007;33:21–32. doi: 10.1093/schbul/sbl049. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Cheverud J.M. A simple correction for multiple comparisons in interval mapping genome scans. Heredity. 2001;87:52–58. doi: 10.1046/j.1365-2540.2001.00901.x. [DOI] [PubMed] [Google Scholar]
  7. DeVellis R.F. Scale development: theory and applications. 2nd ed. Thousand Oaks, Calif.: Sage Publications, Inc.; 2003. p. 171. [Google Scholar]
  8. Dubay C., Vincent M., Samani N.J., Hilbert P., Kaiser M.A., Beressi J.P., Kotelevtsev Y., Beckmann J.S., Soubrier F., Sassard J., et al. Genetic determinants of diastolic and pulse pressure map to different loci in Lyon hypertensive rats. Nat Genet. 1993;3:354–357. doi: 10.1038/ng0493-354. [DOI] [PubMed] [Google Scholar]
  9. Hoh J., Ott J. Scan statistics to scan markers for susceptibility genes. Proc Natl Acad Sci U S A. 2000;97:9615–9617. doi: 10.1073/pnas.170179197. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Hoh J., Wille A., Ott J. Trimming, weighting, and grouping SNPs in human case-control association studies. Genome Res. 2001;11:2115–2119. doi: 10.1101/gr.204001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Holmkvist J., Banasik K., Andersen G., Unoki H., Jensen T.S., Pisinger C., Borch-Johnsen K., Sandbaek A., Lauritzen T., Brunak S., et al. The type 2 diabetes associated minor allele of rs2237895 KCNQ1 associates with reduced insulin release following an oral glucose load. PLoS One. 2009;4:e5872. doi: 10.1371/journal.pone.0005872. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Klei L., Luca D., Devlin B., Roeder K. Pleiotropy and principal components of heritability combine to increase power for association analysis. Genet Epidemiol. 2008;32:9–19. doi: 10.1002/gepi.20257. [DOI] [PubMed] [Google Scholar]
  13. Lander E., Kruglyak L. Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat Genet. 1995;11:241–247. doi: 10.1038/ng1195-241. [DOI] [PubMed] [Google Scholar]
  14. Manly B.F.J. Randomization, bootstrap, and Monte Carlo methods in biology. 3rd ed. Boca Raton, FL: Chapman & Hall/CRC; 2007. p. 455. [Google Scholar]
  15. Nyholt D.R. A simple correction for multiple testing for singlenucleotide polymorphisms in linkage disequilibrium with each other. Am J Hum Genet. 2004;74:765–769. doi: 10.1086/383251. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Ott J. Association of genetic loci: Replication or not, that is the question. Neurology. 2004;63:955–958. doi: 10.1212/WNL.63.6.955. [DOI] [PubMed] [Google Scholar]
  17. Ott J., Rabinowitz D. A principal-components approach based on heritability for combining phenotype information. Hum Hered. 1999;49:106–111. doi: 10.1159/000022854. [DOI] [PubMed] [Google Scholar]
  18. Patterson N., Price A.L., Reich D. Population structure and eigenanalysis. PLoS Genet. 2006;2:e190. doi: 10.1371/journal.pgen.0020190. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Pe’er I., Yelensky R., Altshuler D., Daly M.J. Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genet Epidemiol. 2008;32:381–385. doi: 10.1002/gepi.20303. [DOI] [PubMed] [Google Scholar]
  20. Price A.L., Patterson N.J., Plenge R.M., Weinblatt M.E., Shadick N.A., Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38:904–909. doi: 10.1038/ng1847. [DOI] [PubMed] [Google Scholar]
  21. Zhang Q., Ott J. Multiple Comparisons/Testing Issues. In: Lin S., Zhao H., editors. Handbook on Analyzing Human Genetic Data: Computational Approaches and Software. Berlin: Springer; 2009. pp. 277–287. [Google Scholar]

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