Display options
Cite
Vujkovic M, Aplenc R, Alonzo TA, et al. Comparing Analytic Methods for Longitudinal GWAS and a Case-Study Evaluating Chemotherapy Course Length in Pediatric AML. A Report from the Children's Oncology Group. Front Genet. 2016;7:139doi: 10.3389/fgene.2016.00139.
Vujkovic, M., Aplenc, R., Alonzo, T. A., Gamis, A. S., & Li, Y. (2016). Comparing Analytic Methods for Longitudinal GWAS and a Case-Study Evaluating Chemotherapy Course Length in Pediatric AML. A Report from the Children's Oncology Group. Frontiers in genetics, 7139. https://doi.org/10.3389/fgene.2016.00139
Vujkovic, Marijana, et al. "Comparing Analytic Methods for Longitudinal GWAS and a Case-Study Evaluating Chemotherapy Course Length in Pediatric AML. A Report from the Children's Oncology Group." Frontiers in genetics vol. 7 (2016): 139. doi: https://doi.org/10.3389/fgene.2016.00139
Vujkovic M, Aplenc R, Alonzo TA, Gamis AS, Li Y. Comparing Analytic Methods for Longitudinal GWAS and a Case-Study Evaluating Chemotherapy Course Length in Pediatric AML. A Report from the Children's Oncology Group. Front Genet. 2016 Aug 05;7:139. doi: 10.3389/fgene.2016.00139. eCollection 2016. PMID: 27547214; PMCID: PMC4974249.
Copy Download .nbib Format: NLM
Share it on

Front Genet. 2016 Aug 05;7:139. doi: 10.3389/fgene.2016.00139. eCollection 2016.

Comparing Analytic Methods for Longitudinal GWAS and a Case-Study Evaluating Chemotherapy Course Length in Pediatric AML. A Report from the Children's Oncology Group.

Frontiers in genetics

Marijana Vujkovic, Richard Aplenc, Todd A Alonzo, Alan S Gamis, Yimei Li

Affiliations

  1. Division of Oncology, Children's Hospital of Philadelphia Philadelphia, PA, USA.
  2. Department of Preventive Medicine, Keck School of Medicine, University of Southern California Los Angeles, CA, USA.
  3. Division of Hematology, Oncology Bone Marrow Transplantation, Children's Mercy Hospitals and Clinics Kansas City, MO, USA.

PMID: 27547214 PMCID: PMC4974249 DOI: 10.3389/fgene.2016.00139

Abstract

Regression analysis is commonly used in genome-wide association studies (GWAS) to test genotype-phenotype associations but restricts the phenotype to a single observation for each individual. There is an increasing need for analytic methods for longitudinally collected phenotype data. Several methods have been proposed to perform longitudinal GWAS for family-based studies but few methods are described for unrelated populations. We compared the performance of three statistical approaches for longitudinal GWAS in unrelated subjectes: (1) principal component-based generalized estimating equations (PC-GEE); (2) principal component-based linear mixed effects model (PC-LMEM); (3) kinship coefficient matrix-based linear mixed effects model (KIN-LMEM), in a study of single-nucleotide polymorphisms (SNPs) on the duration of 4 courses of chemotherapy in 624 unrelated children with de novo acute myeloid leukemia (AML) genotyped on the Illumina 2.5 M OmniQuad from the COG studies AAML0531 and AAML1031. In this study we observed an exaggerated type I error with PC-GEE in SNPs with minor allele frequencies < 0.05, wheras KIN-LMEM produces more than expected type II errors. PC-MEM showed balanced type I and type II errors for the observed vs. expected P-values in comparison to competing approaches. In general, a strong concordance was observed between the P-values with the different approaches, in particular among P < 0.01 where the between-method AUCs exceed 99%. PC-LMEM accounts for genetic relatedness and correlations among repeated phenotype measures, shows minimal genome-wide inflation of type I errors, and yields high power. We therefore recommend PC-LMEM as a robust analytic approach for GWAS of longitudinal data in unrelated populations.

Keywords: generalized estimating equations; genome wide association analysis; linear mixed effects model; longitudinal analysis; unrelated population

References

  1. PLoS Genet. 2006 Dec;2(12):e190 - PubMed
  2. BMC Proc. 2014 Jun 17;8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S78 - PubMed
  3. BMC Proc. 2014 Jun 17;8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S79 - PubMed
  4. Biol Blood Marrow Transplant. 2012 May;18(5):722-30 - PubMed
  5. BMC Proc. 2009 Dec 15;3 Suppl 7:S119 - PubMed
  6. Nucleic Acids Res. 2014 Jan;42(Database issue):D1001-6 - PubMed
  7. BMC Proc. 2014 Jun 17;8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S85 - PubMed
  8. BMC Proc. 2014 Jun 17;8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S84 - PubMed
  9. Eur J Hum Genet. 2015 Oct;23 (10 ):1384-91 - PubMed
  10. Genet Epidemiol. 2014 Sep;38 Suppl 1:S68-73 - PubMed
  11. Clin Cancer Res. 2012 Mar 15;18(6):1547-54 - PubMed
  12. Genetics. 2007 Sep;177(1):577-85 - PubMed
  13. BMC Proc. 2009 Dec 15;3 Suppl 7:S113 - PubMed
  14. BMC Proc. 2009 Dec 15;3 Suppl 7:S112 - PubMed
  15. Nat Genet. 2010 Apr;42(4):355-60 - PubMed
  16. Genet Epidemiol. 2012 Jul;36(5):463-71 - PubMed
  17. Genet Epidemiol. 2009;33 Suppl 1:S93-8 - PubMed
  18. BMC Proc. 2014 Jun 17;8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S81 - PubMed
  19. BMC Proc. 2014 Jun 17;8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S82 - PubMed
  20. Nat Rev Genet. 2006 Oct;7(10):771-80 - PubMed
  21. Am J Hum Genet. 2007 Sep;81(3):559-75 - PubMed
  22. J Clin Oncol. 2014 Sep 20;32(27):3021-32 - PubMed
  23. Am J Hum Genet. 2004 Apr;74(4):765-9 - PubMed
  24. BMC Proc. 2014 Jun 17;8(Suppl 1):S83 - PubMed
  25. Am J Hum Genet. 2011 Jan 7;88(1):76-82 - PubMed
  26. Cancer Res. 2005 Mar 15;65(6):2482-7 - PubMed
  27. BMC Proc. 2014 Jun 17;8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S80 - PubMed
  28. BMC Proc. 2009 Dec 15;3 Suppl 7:S117 - PubMed
  29. BMC Proc. 2009 Dec 15;3 Suppl 7:S118 - PubMed
  30. BMC Proc. 2009 Dec 15;3 Suppl 7:S115 - PubMed
  31. BMC Proc. 2009 Dec 15;3 Suppl 7:S116 - PubMed
  32. Nat Genet. 2010 Apr;42(4):348-54 - PubMed

Publication Types

Grant support