Literature DB >> 19156175

An evaluation of the genetic-matched pair study design using genome-wide SNP data from the European population.

Timothy Tehua Lu1, Oscar Lao, Michael Nothnagel, Olaf Junge, Sandra Freitag-Wolf, Amke Caliebe, Miroslava Balascakova, Jaume Bertranpetit, Laurence Albert Bindoff, David Comas, Gunilla Holmlund, Anastasia Kouvatsi, Milan Macek, Isabelle Mollet, Finn Nielsen, Walther Parson, Jukka Palo, Rafal Ploski, Antti Sajantila, Adriano Tagliabracci, Ulrik Gether, Thomas Werge, Fernando Rivadeneira, Albert Hofman, André Gerardus Uitterlinden, Christian Gieger, Heinz-Erich Wichmann, Andreas Ruether, Stefan Schreiber, Christian Becker, Peter Nürnberg, Matthew Roberts Nelson, Manfred Kayser, Michael Krawczak.   

Abstract

Genetic matching potentially provides a means to alleviate the effects of incomplete Mendelian randomization in population-based gene-disease association studies. We therefore evaluated the genetic-matched pair study design on the basis of genome-wide SNP data (309,790 markers; Affymetrix GeneChip Human Mapping 500K Array) from 2457 individuals, sampled at 23 different recruitment sites across Europe. Using pair-wise identity-by-state (IBS) as a matching criterion, we tried to derive a subset of markers that would allow identification of the best overall matching (BOM) partner for a given individual, based on the IBS status for the subset alone. However, our results suggest that, by following this approach, the prediction accuracy is only notably improved by the first 20 markers selected, and increases proportionally to the marker number thereafter. Furthermore, in a considerable proportion of cases (76.0%), the BOM of a given individual, based on the complete marker set, came from a different recruitment site than the individual itself. A second marker set, specifically selected for ancestry sensitivity using singular value decomposition, performed even more poorly and was no more capable of predicting the BOM than randomly chosen subsets. This leads us to conclude that, at least in Europe, the utility of the genetic-matched pair study design depends critically on the availability of comprehensive genotype information for both cases and controls.

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Year:  2009        PMID: 19156175      PMCID: PMC2986489          DOI: 10.1038/ejhg.2008.266

Source DB:  PubMed          Journal:  Eur J Hum Genet        ISSN: 1018-4813            Impact factor:   4.246


  25 in total

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Authors:  N E Morton; W Zhang; P Taillon-Miller; S Ennis; P Y Kwok; A Collins
Journal:  Proc Natl Acad Sci U S A       Date:  2001-04-17       Impact factor: 11.205

2.  Inference of population structure using multilocus genotype data.

Authors:  J K Pritchard; M Stephens; P Donnelly
Journal:  Genetics       Date:  2000-06       Impact factor: 4.562

3.  Genetic signatures of strong recent positive selection at the lactase gene.

Authors:  Todd Bersaglieri; Pardis C Sabeti; Nick Patterson; Trisha Vanderploeg; Steve F Schaffner; Jared A Drake; Matthew Rhodes; David E Reich; Joel N Hirschhorn
Journal:  Am J Hum Genet       Date:  2004-04-26       Impact factor: 11.025

Review 4.  Genome-wide association studies: theoretical and practical concerns.

Authors:  William Y S Wang; Bryan J Barratt; David G Clayton; John A Todd
Journal:  Nat Rev Genet       Date:  2005-02       Impact factor: 53.242

Review 5.  Genetic approaches to studying common diseases and complex traits.

Authors:  Joel N Hirschhorn
Journal:  Pediatr Res       Date:  2005-04-06       Impact factor: 3.756

Review 6.  What can mendelian randomisation tell us about modifiable behavioural and environmental exposures?

Authors:  George Davey Smith; Shah Ebrahim
Journal:  BMJ       Date:  2005-05-07

Review 7.  KORA-gen--resource for population genetics, controls and a broad spectrum of disease phenotypes.

Authors:  H-E Wichmann; C Gieger; T Illig
Journal:  Gesundheitswesen       Date:  2005-08

8.  European population substructure: clustering of northern and southern populations.

Authors:  Michael F Seldin; Russell Shigeta; Pablo Villoslada; Carlo Selmi; Jaakko Tuomilehto; Gabriel Silva; John W Belmont; Lars Klareskog; Peter K Gregersen
Journal:  PLoS Genet       Date:  2006-07-25       Impact factor: 5.917

9.  A map of recent positive selection in the human genome.

Authors:  Benjamin F Voight; Sridhar Kudaravalli; Xiaoquan Wen; Jonathan K Pritchard
Journal:  PLoS Biol       Date:  2006-03-07       Impact factor: 8.029

10.  Efficacy assessment of SNP sets for genome-wide disease association studies.

Authors:  Andreas Wollstein; Alexander Herrmann; Michael Wittig; Michael Nothnagel; Andre Franke; Peter Nürnberg; Stefan Schreiber; Michael Krawczak; Jochen Hampe
Journal:  Nucleic Acids Res       Date:  2007-08-28       Impact factor: 16.971

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  3 in total

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Journal:  Eur J Hum Genet       Date:  2015-08-19       Impact factor: 4.246

2.  Rare and low frequency variant stratification in the UK population: description and impact on association tests.

Authors:  Marie-Claude Babron; Marie de Tayrac; Douglas N Rutledge; Eleftheria Zeggini; Emmanuelle Génin
Journal:  PLoS One       Date:  2012-10-05       Impact factor: 3.240

3.  GAGA: a new algorithm for genomic inference of geographic ancestry reveals fine level population substructure in Europeans.

Authors:  Oscar Lao; Fan Liu; Andreas Wollstein; Manfred Kayser
Journal:  PLoS Comput Biol       Date:  2014-02-20       Impact factor: 4.475

  3 in total

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