TY - JOUR
T1 - An evaluation of the genetic-matched pair study design using genome-wide SNP data from the European population
AU - Lu, TT
AU - Lao Grueso, Oscar
AU - Nothnagel, M
AU - Junge, O
AU - Freitag-Wolf, S
AU - Caliebe, A
AU - Balascakova, M
AU - Bertranpetit, J
AU - Bindoff, LA
AU - Comas, D
AU - Holmlund, G
AU - Kouvatsi, A
AU - Macek, M
AU - Mollet, I
AU - Nielsen, F
AU - Parson, W
AU - Palo, J
AU - Ploski, R
AU - Sajantila, A
AU - Tagliabracci, A
AU - Gether, U
AU - Werge, T
AU - Rivadeneira, Fernando
AU - Hofman, Bert
AU - Uitterlinden, André
AU - Gieger, C
AU - Wichmann, HE
AU - Ruether, A
AU - Schreiber, S
AU - Becker, C
AU - Nurnberg, P
AU - Nelson, MR
AU - Kayser, Manfred
AU - Krawczak, M
PY - 2009
Y1 - 2009
N2 - 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. European Journal of Human Genetics (2009) 17, 967-975; doi:10.1038/ejhg.2008.266; published online 21 January 2009
AB - 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. European Journal of Human Genetics (2009) 17, 967-975; doi:10.1038/ejhg.2008.266; published online 21 January 2009
U2 - 10.1038/ejhg.2008.266
DO - 10.1038/ejhg.2008.266
M3 - Article
SN - 1018-4813
VL - 17
SP - 967
EP - 975
JO - European Journal of Human Genetics
JF - European Journal of Human Genetics
IS - 7
ER -
