Literature DB >> 11754476

Efficiency of DNA pooling to estimate joint allele frequencies and measure linkage disequilibrium.

Ruth M Pfeiffer1, Joni L Rutter, Mitchell H Gail, Jeffery Struewing, Joseph L Gastwirth.   

Abstract

Pooling DNA samples can yield efficient estimates of the prevalence of genetic variants. We extend methods of analyzing pooled DNA samples to estimate the joint prevalence of variants at two or more loci. If one has a sample from the general population, one can adapt the method for joint prevalence estimation to estimate allele frequencies and D, the measure of linkage disequilibrium. The parameter D is fundamental in population genetics and in determining the power of association studies. In addition, joint allelic prevalences can be used in case-control studies to estimate the relative risks of disease from joint exposures to the genetic variants. Our methods allow for imperfect assay sensitivity and specificity. The expected savings in numbers of assays required when pooling is utilized compared to individual testing are quantified. Copyright 2002 Wiley-Liss, Inc.

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Year:  2002        PMID: 11754476     DOI: 10.1002/gepi.1046

Source DB:  PubMed          Journal:  Genet Epidemiol        ISSN: 0741-0395            Impact factor:   2.135


  10 in total

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Review 2.  Recent developments in genomewide association scans: a workshop summary and review.

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4.  Testing linkage disequilibrium from pooled DNA: a contingency table perspective.

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7.  A survey on haplotyping algorithms for tightly linked markers.

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8.  PPC: an algorithm for accurate estimation of SNP allele frequencies in small equimolar pools of DNA using data from high density microarrays.

Authors:  Jesper Brohede; Rob Dunne; James D McKay; Garry N Hannan
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9.  SNP haplotype tagging from DNA pools of two individuals.

Authors:  Josephine Hoh; Fumihiko Matsuda; Xu Peng; Daniela Markovic; Mark G Lathrop; Jurg Ott
Journal:  BMC Bioinformatics       Date:  2003-04-22       Impact factor: 3.169

10.  Allelotyping of pooled DNA with 250 K SNP microarrays.

Authors:  Stefan Wilkening; Bowang Chen; Michael Wirtenberger; Barbara Burwinkel; Asta Försti; Kari Hemminki; Federico Canzian
Journal:  BMC Genomics       Date:  2007-03-16       Impact factor: 3.969

  10 in total

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