Accurate Inference of Subtle Population Structure (and Other Genetic Discontinuities) Using Principal Coordinates

Accurate inference of genetic discontinuities between populations is an essential component of intraspecific biodiversity and evolution studies, as well as associative genetics. The most widely-used methods to infer population structure are model-based, Bayesian MCMC procedures that minimize Hardy-Weinberg and linkage disequilibrium within subpopulations. These methods are useful, but suffer from large computational requirements and a dependence on modeling assumptions that may not be met in real data sets. Here we describe the development of a new approach, PCO-MC, which couples principal coordinate analysis to a clustering procedure for the inference of population structure from multilocus genotype data.PCO-MC uses data from all principal coordinate axes simultaneously to calculate a multidimensional "density landscape", from which the number of subpopulations, and the membership within subpopulations, is determined using a valley-seeking algorithm. Using extensive simulations, we show that this approach outperforms a Bayesian MCMC procedure when many loci (e.g. 100) are sampled, but that the Bayesian procedure is marginally superior with few loci (e.g. 10). When presented with sufficient data, PCO-MC accurately delineated subpopulations with population F(st) values as low as 0.03 (G'(st)>0.2), whereas the limit of resolution of the Bayesian approach was F(st) = 0.05 (G'(st)>0.35).We draw a distinction between population structure inference for describing biodiversity as opposed to Type I error control in associative genetics. We suggest that discrete assignments, like those produced by PCO-MC, are appropriate for circumscribing units of biodiversity whereas expression of population structure as a continuous variable is more useful for case-control correction in structured association studies

Unequal exchange and meiotic instability of disease-resistance genes in the Rp1 region of maize

The Rp1 region of maize was originally characterized as a complex locus which conditions resistance to the fungus Puccinia sorghi, the causal organism in the common rust disease. Some alleles of Rp1 are meiotically unstable, but the mechanism of instability is not known. We have studied the role of recombination in meiotic instability in maize lines homozygous for either Rp1-J or Rp1-G. Test cross progenies derived from a line that was homozygous for Rp1-J, but heterozygous at flanking markers, were screened for susceptible individuals. Five susceptible individuals were derived from 9772 progeny. All five had nonparental combinations of flanking markers; three had one combination of recombinant flanking markers while the other two had the opposite pair. In an identical study with Rp1-G, 20 susceptible seedlings were detected out of 5874 test cross progeny. Nineteen of these were associated with flanking marker exchange, 11 and 8 of each recombinant marker combination. Our results indicate that unequal exchange is the primary mechanism of meiotic instability of Rp1-J and Rp1-G

Random amplified polymorphic DNA analysis of the moth orchids, Phalaenopsis (Epidendroideae: Orchidaceae)

10.1007/s10681-005-4814-yEuphytica1411-211-22EUPH