Polymerase colony (polony) technology amplifies multiple individual DNA molecules within a thin acrylamide gel attached to a microscope slide. Each DNA molecule included in the reaction produces an immobilized colony of double-stranded DNA. We genotype these polonies by performing single base extensions with dye-labeled nucleotides, and we demonstrate the accurate quantitation of two allelic variants. We also show that polony technology can determine the phase, or haplotype, of two singlenucleotide polymorphisms (SNPs) by coamplifying distally located targets on a single chromosomal fragment. We correctly determine the genotype and phase of three different pairs of SNPs. In one case, the distance between the two SNPs is 45 kb, the largest distance achieved to date without separating the chromosomes by cloning or somatic cell fusion. The results indicate that polony genotyping and haplotyping may play an important role in understanding the structure of genetic variation.T he study of genetic variation in the human population has the potential to greatly improve human health, both by predicting susceptibility to disease and guiding choice of therapy. The most common genetic variations in the human population are single-nucleotide polymorphisms (SNPs). By studying candidate genes and performing genome-wide linkage disequilibrium studies, scientists are trying to uncover the ''causative SNP,'' the SNP that alters gene function and thereby increases the risk of disease. It has been shown that deriving haplotypes increases the efficiency of linkage disequilibrium studies (1). Surprisingly, recent studies (2, 3) suggest that haplotypes will also be critical for candidate gene studies. These studies found that for some diseases, there is not one single SNP that is responsible for altering gene function, but instead, multiple SNPs interact to alter the function or expression of a protein (4). These alterations occur only when specific combinations of SNPs are present on the same chromosome, so one must determine the haplotype to find a correlation to the observed phenotype. In these cases, the focus has shifted from a causative SNP to a causative haplotype.The most common approach for determining the haplotype, or phase, of a set of SNPs is computational inference from unphased data (3,(5)(6)(7)(8). Although this methodology has greatly increased the power of both linkage and candidate gene studies, a recent study estimates the error rate to be between 19 and 48%, depending on the algorithm used (6). This error rate presents challenges for the use of this method as a research tool and makes it an unlikely candidate for use in the clinical setting. Recent findings suggest a way to improve the accuracy of haplotype inference. Daly et al. (9) and others (10, 11) have shown that SNPs tend to be inherited in larger haplotype blocks than previously thought, and that there are relatively few variants of each block. This observation has sparked a public effort to characterize all common haplotypes in the human population (12). ...