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

Complex expression of natural killer receptor genes in single natural killer cells

Human natural killer (NK) cells express several inhibitory and non-inhibitory NK receptors per cell. Understanding the expression patterns of these receptor genes in individual cells is important to understanding their function. Using a single-cell reverse transcription–polymerase chain reaction (RT-PCR) method, we analysed the expression of nine NK receptor genes in 38 resting CD56(+) NK cells from peripheral blood of normal donors. We observed highly diverse patterns of receptor expression in these cells. No NK receptor is expressed universally in every CD56(+) NK cell. The expressed receptor types per cell varied from two to eight. We specifically analysed the distribution of inhibitory (DL) and non-inhibitory (DS) killer immunoglobulin-like receptors (KIR). The frequency of individual receptor expression varied from 26% for 2DS2 to 68% for both 2DL1 and 2DL4. A comparison of the coexpression of DL and DS receptors showed a significant association in the expression of 2DL2 and 2DS2 (χ(2)=16·6; P<0·001) genes but no association between 2DL1 and 2DS1 or between 3DL1 and 3DS1 genes. Coexpression analysis of the 2DL1 and 2DL2 genes in 2DL4(+) and 2DL4(−) cells showed a strong association in 2DL4(+) but not in 2DL4(−) cells, suggesting a differential effect of the 2DL4 gene on the expression of 2DL1 and 2DL2 genes. Single-cell RT-PCR is a powerful tool to study multiple receptor gene expression ex vivo in individual NK cells and provides information about the expression pattern of KIR receptors that may suggest mechanisms of gene expression responsible for generation of the KIR repertoire