van Heerwaarden et al. 2010
Fri Jul 17 2009
Fine scale genetic structure in the wild ancestor of maize (Zea mays spp. Parviglumis)
Insufficient numbers of available markers have limited the analysis of fine scale genetic structure in continuous populations of outcrossing plant species. We used a set of 490 maize SNPs to characterize fine-scale genetic structure within- and between two dense stands of maize’s wild ancestor, teosinte (Zea mays spp. parviglumis). Our analyses confirmed that spp. parviglumis is highly outcrossing and shows little population structure over short distances. We used a refinement of recent principal component analysis (PCA) to elucidate between- and within population structure. PCA- based clustering resulted in unequivocal assignment of individuals to the two populations. We detected pronounced spatial genetic variation throughout one of the populations, resulting in the detection of at least three differentiated sub-populations. Analysis of the distribution of SNP effects suggested that this structure was not caused by selection but was probably related to small differences in topography. Correction for substructure revealed the two populations to be similar in terms of spatial autocorrelation and inbreeding levels. Our results show that significant allele frequency differences may, but need not, arise at very fine spatial scales. The present study represents one of the most detailed analysis of genetic structure to date and provides a benchmark for future studies dealing with fine scale patterns of genetic diversity in natural plant populations.
Insufficient numbers of available markers have limited the analysis of fine scale genetic structure in continuous populations of outcrossing plant species. We used a set of 490 maize SNPs to characterize fine-scale genetic structure within- and between two dense stands of maize’s wild ancestor, teosinte (Zea mays spp. parviglumis). Our analyses confirmed that spp. parviglumis is highly outcrossing and shows little population structure over short distances. We used a refinement of recent principal component analysis (PCA) to elucidate between- and within population structure. PCA- based clustering resulted in unequivocal assignment of individuals to the two populations. We detected pronounced spatial genetic variation throughout one of the populations, resulting in the detection of at least three differentiated sub-populations. Analysis of the distribution of SNP effects suggested that this structure was not caused by selection but was probably related to small differences in topography. Correction for substructure revealed the two populations to be similar in terms of spatial autocorrelation and inbreeding levels. Our results show that significant allele frequency differences may, but need not, arise at very fine spatial scales. The present study represents one of the most detailed analysis of genetic structure to date and provides a benchmark for future studies dealing with fine scale patterns of genetic diversity in natural plant populations.