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Theor Appl Genet. 2011 Jun;123(1):11-20. doi: 10.1007/s00122-011-1562-3. Epub 2011 Mar 15.

Extent and genome-wide distribution of linkage disequilibrium in commercial maize germplasm.

Author information

1
Institute of Plant Breeding, Seed Science, and Population Genetics, University of Hohenheim, 70593, Stuttgart, Germany. vaninghe@uni-hohenheim.de

Abstract

Association mapping is based on linkage disequilibrium (LD) resulting from historical recombinations and helps understanding the genetic basis of complex traits. Many factors affect LD and, therefore, it must be determined empirically in the germplasm under investigation to examine the prospects of successful genome-wide association mapping. The objectives of our study were to (1) examine the extent of LD with simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers in 1,537 commercial maize inbred lines belonging to four heterotic pools, (2) compare the LD patterns determined by these two marker types, (3) evaluate the number of SNP markers needed to perform genome-wide association analyses, and (4) investigate temporal trends of LD. Mean values of the squared correlation coefficient ([Formula: see text]) were almost identical for unlinked, linked, and adjacent SSR marker pairs. In contrast, [Formula: see text] values were lowest for the unlinked SNP loci and highest for the SNPs within amplicons. LD decay varied across the different heterotic pools and the individual chromosomes. The SSR markers employed in the present study are not adequate for association analysis, because of insufficient marker density for the germplasm evaluated. Based on the decay of LD in the various heterotic pools, we would need between 4,000 and 65,000 SNP markers to detect with a reasonable power associations with rather large quantitative trait loci (QTL). A much higher marker density is required to identify QTL with smaller effects. However, not only the total number of markers but also their distribution among and along the chromosomes are primordial for undertaking powerful association analyses.

PMID:
21404061
DOI:
10.1007/s00122-011-1562-3
[Indexed for MEDLINE]

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