BACKGROUND: Markers for individual genotyping can be selected using quantitative genotyping of pooled DNA. This strategy saves time and money. METHODS: To determine the efficacy of this approach, we investigated the bivariate distribution of association test statistics from pooled and individual genotypes. We used a sample of approximately 1,000 samples with individual and pooled genotyping on 40,000 SNPs. RESULTS AND CONCLUSIONS: We found that the distribution of the joint test statistics can be modelled as a mixture of two bivariate normal distributions. One distribution has a correlation of zero, and is probably due to SNPs whose pooled genotyping was unsuccessful. The other distribution has a correlation of approximately 0.65 in our data. This latter distribution is probably accounted for by SNPs whose pooled genotyping accurately predicts the underlying allele frequency. Approximately 87% of the data belongs to this distribution. We also derived a method to investigate the effect of both the correlation and selection cut-off on the relative power of pooling studies. We demonstrate that pooled genotyping has good power to detect SNPs that are truly associated with disease-causing variants for SNPs showing good correlation between pooled and individual genotyping. Therefore, this approach is a cost effective tool for association studies.
BACKGROUND: Markers for individual genotyping can be selected using quantitative genotyping of pooled DNA. This strategy saves time and money. METHODS: To determine the efficacy of this approach, we investigated the bivariate distribution of association test statistics from pooled and individual genotypes. We used a sample of approximately 1,000 samples with individual and pooled genotyping on 40,000 SNPs. RESULTS AND CONCLUSIONS: We found that the distribution of the joint test statistics can be modelled as a mixture of two bivariate normal distributions. One distribution has a correlation of zero, and is probably due to SNPs whose pooled genotyping was unsuccessful. The other distribution has a correlation of approximately 0.65 in our data. This latter distribution is probably accounted for by SNPs whose pooled genotyping accurately predicts the underlying allele frequency. Approximately 87% of the data belongs to this distribution. We also derived a method to investigate the effect of both the correlation and selection cut-off on the relative power of pooling studies. We demonstrate that pooled genotyping has good power to detect SNPs that are truly associated with disease-causing variants for SNPs showing good correlation between pooled and individual genotyping. Therefore, this approach is a cost effective tool for association studies.
Authors: John V Pearson; Matthew J Huentelman; Rebecca F Halperin; Waibhav D Tembe; Stacey Melquist; Nils Homer; Marcel Brun; Szabolcs Szelinger; Keith D Coon; Victoria L Zismann; Jennifer A Webster; Thomas Beach; Sigrid B Sando; Jan O Aasly; Reinhard Heun; Frank Jessen; Heike Kolsch; Magdalini Tsolaki; Makrina Daniilidou; Eric M Reiman; Andreas Papassotiropoulos; Michael L Hutton; Dietrich A Stephan; David W Craig Journal: Am J Hum Genet Date: 2006-12-06 Impact factor: 11.025
Authors: Laura Jean Bierut; Pamela A F Madden; Naomi Breslau; Eric O Johnson; Dorothy Hatsukami; Ovide F Pomerleau; Gary E Swan; Joni Rutter; Sarah Bertelsen; Louis Fox; Douglas Fugman; Alison M Goate; Anthony L Hinrichs; Karel Konvicka; Nicholas G Martin; Grant W Montgomery; Nancy L Saccone; Scott F Saccone; Jen C Wang; Gary A Chase; John P Rice; Dennis G Ballinger Journal: Hum Mol Genet Date: 2006-12-07 Impact factor: 6.150
Authors: Qing-Rong Liu; Tomas Drgon; Catherine Johnson; Donna Walther; Judith Hess; George R Uhl Journal: Am J Med Genet B Neuropsychiatr Genet Date: 2006-12-05 Impact factor: 3.568
Authors: S Shifman; A Bhomra; S Smiley; N R Wray; M R James; N G Martin; J M Hettema; S S An; M C Neale; E J C G van den Oord; K S Kendler; X Chen; D I Boomsma; C M Middeldorp; J J Hottenga; P E Slagboom; J Flint Journal: Mol Psychiatry Date: 2007-07-31 Impact factor: 15.992
Authors: A E Baum; N Akula; M Cabanero; I Cardona; W Corona; B Klemens; T G Schulze; S Cichon; M Rietschel; M M Nöthen; A Georgi; J Schumacher; M Schwarz; R Abou Jamra; S Höfels; P Propping; J Satagopan; S D Detera-Wadleigh; J Hardy; F J McMahon Journal: Mol Psychiatry Date: 2007-05-08 Impact factor: 15.992
Authors: George Kirov; Ivan Nikolov; Lyudmila Georgieva; Valentina Moskvina; Michael J Owen; Michael C O'Donovan Journal: BMC Genomics Date: 2006-02-15 Impact factor: 3.969
Authors: Tomas Drgon; Ping-Wu Zhang; Catherine Johnson; Donna Walther; Judith Hess; Michelle Nino; George R Uhl Journal: PLoS One Date: 2010-01-21 Impact factor: 3.240