Association mapping with single-feature polymorphisms.
We develop methods for exploiting "single-feature polymorphism" data, generated by hybridizing genomic DNA to oligonucleotide expression arrays. Our methods enable the use of such data, which can be regarded as very high density, but imperfect, polymorphism data, for genomewide association or linkage disequilibrium mapping. We use a simulation-based power study to conclude that our methods should have good power for organisms like Arabidopsis thaliana, in which linkage disequilibrium is extensive, the reason being that the noisiness of single-feature polymorphism data is more than compensated for by their great number. Finally, we show how power depends on the accuracy with which single-feature polymorphisms are called.
Kim
Sung
S
Department of Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089-2910, USA.
Zhao
Keyan
K
Jiang
Rong
R
Molitor
John
J
Borevitz
Justin O
JO
Nordborg
Magnus
M
Marjoram
Paul
P
D000465:Algorithms; D000483:Alleles; D017360:Arabidopsis / Q000235:genetics; D002874:Chromosome Mapping / Q000379:methods* / Q000706:statistics & numerical data; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D005819:Genetic Markers; D006239:Haplotypes; D015810:Linkage Disequilibrium; D008957:Models, Genetic; D011110:Polymorphism, Genetic
D005819:Genetic Markers