5 research outputs found

    A simple DNA stretching method for fluorescence imaging of single DNA molecules

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    Stretching or aligning DNA molecules onto a surface by means of molecular combing techniques is one of the critical steps in single DNA molecule analysis. However, many of the current studies have focused on λ-DNA, or other large DNA molecules. There are very few studies on stretching methodologies for DNA molecules generated via PCR (typically smaller than 20 kb). Here we describe a simple method of stretching DNA molecules up to 18 kb in size on a modified glass surface. The very low background fluorescence allows efficient detection of single fluorescent dye labels incorporated into the stretched DNA molecules

    Rapid DNA mapping by fluorescent single molecule detection

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    DNA mapping is an important analytical tool in genomic sequencing, medical diagnostics and pathogen identification. Here we report an optical DNA mapping strategy based on direct imaging of individual DNA molecules and localization of multiple sequence motifs on the molecules. Individual genomic DNA molecules were labeled with fluorescent dyes at specific sequence motifs by the action of nicking endonuclease followed by the incorporation of dye terminators with DNA polymerase. The labeled DNA molecules were then stretched into linear form on a modified glass surface and imaged using total internal reflection fluorescence (TIRF) microscopy. By determining the positions of the fluorescent labels with respect to the DNA backbone, the distribution of the sequence motif recognized by the nicking endonuclease can be established with good accuracy, in a manner similar to reading a barcode. With this approach, we constructed a specific sequence motif map of lambda-DNA. We further demonstrated the capability of this approach to rapidly type a human adenovirus and several strains of human rhinovirus

    Population Structure, Admixture, and Aging-Related Phenotypes in African American Adults: The Cardiovascular Health Study

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    U.S. populations are genetically admixed, but surprisingly little empirical data exists documenting the impact of such heterogeneity on type I and type II error in genetic-association studies of unrelated individuals. By applying several complementary analytical techniques, we characterize genetic background heterogeneity among 810 self-identified African American subjects sampled as part of a multisite cohort study of cardiovascular disease in older adults. On the basis of the typing of 24 ancestry-informative biallelic single-nucleotide–polymorphism markers, there was evidence of substantial population substructure and admixture. We used an allele-sharing–based clustering algorithm to infer evidence for four genetically distinct subpopulations. Using multivariable regression models, we demonstrate the complex interplay of genetic and socioeconomic factors on quantitative phenotypes related to cardiovascular disease and aging. Blood glucose level correlated with individual African ancestry, whereas body mass index was associated more strongly with genetic similarity. Blood pressure, HDL cholesterol level, C-reactive protein level, and carotid wall thickness were not associated with genetic background. Blood pressure and HDL cholesterol level varied by geographic site, whereas C-reactive protein level differed by occupation. Both ancestry and genetic similarity predicted the number and quality of years lived during follow-up, but socioeconomic factors largely accounted for these associations. When the 24 genetic markers were tested individually, there were an excess number of marker-trait associations, most of which were attenuated by adjustment for genetic ancestry. We conclude that the genetic demography underlying older individuals who self identify as African American is complex, and that controlling for both genetic admixture and socioeconomic characteristics will be required in assessing genetic associations with chronic-disease–related traits in African Americans. Complementary methods that identify discrete subgroups on the basis of genetic similarity may help to further characterize the complex biodemographic structure of human populations
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