Literature DB >> 17418690

The application of the HapMap to diabetic nephropathy and other causes of chronic renal failure.

Sudha K Iyengar1, Sharon G Adler.   

Abstract

The human nuclear genome consists of approximately 3 billion nucleotides. Human beings are 99% similar in DNA sequence to each other, but natural genetic variation in approximately 1% of the DNA sequence is responsible for interindividual differences, including determining who will develop disease and who will remain healthy. The pace and timing of disease initiation also is regulated by exposure to individual-level environmental factors and other random causes. Therefore, an examination of the DNA sequences of individuals with and without diabetic nephropathy, or, more broadly, chronic renal failure, can predict which sequence differences vary with disease (or health). The technology is not yet economical enough to analyze large numbers of individuals down to each nucleotide, but standardized dense genotyping sets for interrogating 1 marker for every 5,000, 10,000, or 15,000 nucleotides now are affordable even in large samples. The swiftness with which disease-gene associations can be mined has improved radically as a result of the availability of discovery human genetic variation data from large-scale public and private initiatives, such as those provided by the International Haplotype Map Consortium and Perlegen Sciences, Inc. (Mountain View, CA). These projects have captured many of the common genetic variants (>1%) in the genome. This information has been buttressed with improvements in large-scale genotyping technologies and statistical methods for data analysis. In summary, the renal community is now poised for discovery of genes for chronic renal failure using these resources.

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Year:  2007        PMID: 17418690     DOI: 10.1016/j.semnephrol.2007.01.003

Source DB:  PubMed          Journal:  Semin Nephrol        ISSN: 0270-9295            Impact factor:   5.299


  1 in total

Review 1.  Defining human diabetic nephropathy on the molecular level: integration of transcriptomic profiles with biological knowledge.

Authors:  Sebastian Martini; Felix Eichinger; Viji Nair; Matthias Kretzler
Journal:  Rev Endocr Metab Disord       Date:  2008-12       Impact factor: 6.514

  1 in total

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