Human Genetics

, Volume 130, Issue 5, pp 607–621

The efficacy of detecting variants with small effects on the Affymetrix 6.0 platform using pooled DNA

  • Charleston W. K. Chiang
  • Zofia K. Z. Gajdos
  • Joshua M. Korn
  • Johannah L. Butler
  • Rachel Hackett
  • Candace Guiducci
  • Thutrang T. Nguyen
  • Rainford Wilks
  • Terrence Forrester
  • Katherine D. Henderson
  • Loic Le Marchand
  • Brian E. Henderson
  • Christopher A. Haiman
  • Richard S. Cooper
  • Helen N. Lyon
  • Xiaofeng Zhu
  • Colin A. McKenzie
  • Mark R. Palmert
  • Joel N. Hirschhorn
Original Investigation

DOI: 10.1007/s00439-011-0974-0

Cite this article as:
Chiang, C.W.K., Gajdos, Z.K.Z., Korn, J.M. et al. Hum Genet (2011) 130: 607. doi:10.1007/s00439-011-0974-0

Abstract

Genome-wide genotyping of a cohort using pools rather than individual samples has long been proposed as a cost-saving alternative for performing genome-wide association (GWA) studies. However, successful disease gene mapping using pooled genotyping has thus far been limited to detecting common variants with large effect sizes, which tend not to exist for many complex common diseases or traits. Therefore, for DNA pooling to be a viable strategy for conducting GWA studies, it is important to determine whether commonly used genome-wide SNP array platforms such as the Affymetrix 6.0 array can reliably detect common variants of small effect sizes using pooled DNA. Taking obesity and age at menarche as examples of human complex traits, we assessed the feasibility of genome-wide genotyping of pooled DNA as a single-stage design for phenotype association. By individually genotyping the top associations identified by pooling, we obtained a 14- to 16-fold enrichment of SNPs nominally associated with the phenotype, but we likely missed the top true associations. In addition, we assessed whether genotyping pooled DNA can serve as an inexpensive screen as the second stage of a multi-stage design with a large number of samples by comparing the most cost-effective 3-stage designs with 80% power to detect common variants with genotypic relative risk of 1.1, with and without pooling. Given the current state of the specific technology we employed and the associated genotyping costs, we showed through simulation that a design involving pooling would be 1.07 times more expensive than a design without pooling. Thus, while a significant amount of information exists within the data from pooled DNA, our analysis does not support genotyping pooled DNA as a means to efficiently identify common variants contributing small effects to phenotypes of interest. While our conclusions were based on the specific technology and study design we employed, the approach presented here will be useful for evaluating the utility of other or future genome-wide genotyping platforms in pooled DNA studies.

Abbreviations

AF

Allele frequency

GRR

Genotypic relative risk

GWA

Genome-wide association

LD

Linkage disequilibrium

MEC

Multi-Ethnic Cohort

QC

Quality control

Supplementary material

439_2011_974_MOESM1_ESM.doc (28 kb)
Supplementary material (DOC 28 kb)

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Charleston W. K. Chiang
    • 1
    • 2
    • 3
  • Zofia K. Z. Gajdos
    • 1
    • 2
    • 3
  • Joshua M. Korn
    • 2
    • 4
    • 5
  • Johannah L. Butler
    • 3
  • Rachel Hackett
    • 2
  • Candace Guiducci
    • 2
  • Thutrang T. Nguyen
    • 3
  • Rainford Wilks
    • 6
  • Terrence Forrester
    • 7
  • Katherine D. Henderson
    • 8
  • Loic Le Marchand
    • 9
  • Brian E. Henderson
    • 10
  • Christopher A. Haiman
    • 10
  • Richard S. Cooper
    • 11
  • Helen N. Lyon
    • 2
    • 3
  • Xiaofeng Zhu
    • 12
  • Colin A. McKenzie
    • 7
  • Mark R. Palmert
    • 13
    • 14
  • Joel N. Hirschhorn
    • 1
    • 2
    • 3
    • 15
  1. 1.Department of GeneticsHarvard Medical SchoolBostonUSA
  2. 2.Program in Medical and Population GeneticsBroad Institute of Harvard and MITCambridgeUSA
  3. 3.Program in Genomics and Divisions of Genetics and EndocrinologyChildren’s HospitalBostonUSA
  4. 4.Center for Human Genetic ResearchMassachusetts General HospitalBostonUSA
  5. 5.Department of Molecular BiologyMassachusetts General HospitalBostonUSA
  6. 6.Epidemiology Research Unit, Tropical Medicine Research InstituteUniversity of the West IndiesKingstonJamaica
  7. 7.Tropical Metabolism Research Unit, Tropical Medicine Research InstituteUniversity of the West IndiesKingstonJamaica
  8. 8.Division of Cancer Etiology, Department of Population SciencesCity of Hope National Medical CenterDuarteUSA
  9. 9.Epidemiology Program, Cancer Research Center of HawaiiUniversity of HawaiiHonoluluUSA
  10. 10.Department of Preventive Medicine, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesUSA
  11. 11.Department of Preventive Medicine and EpidemiologyStritch School of Medicine, Loyola University ChicagoMaywoodUSA
  12. 12.Department of Biostatistics and EpidemiologyCase Western Reserve UniversityClevelandUSA
  13. 13.Division of EndocrinologyThe Hospital for Sick ChildrenTorontoCanada
  14. 14.Department of PediatricsUniversity of TorontoTorontoCanada
  15. 15.Children’s Hospital BostonBostonUSA