ALBERT

All Library Books, journals and Electronic Records Telegrafenberg

feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • BioMed Central  (2)
  • 2005-2009  (2)
  • 2000-2004
  • 1
    Publication Date: 2008-07-05
    Description: Background In human case-control association studies, one of the chi-square tests typically carried out is based on a 2 × 3 table of genotypes (homogeneity of three genotype frequencies in case and control individuals). We formulate the two degrees of freedom associated with a given genotype distribution in terms of two biologically relevant parameters, (1) the probability F that an individual's two alleles are identical by descent (IBD) and (2) the frequency p of one of the alleles. Results Imposing the restriction, F ≥ 0, makes some of the genotype frequencies invalid thereby reducing noise. We propose a new statistical association test, the FP test, by focusing on allele frequency differences between case and control individuals while allowing for suitable IBD probabilities. Power calculations show that (1) the practice of generally carrying out two association tests (allele and genotype test) has an increased type I error and (2) our test is more powerful than conventional genotype and allele tests under recessive trait inheritance, and at least as powerful as these conventional tests under dominant inheritance. Conclusion For dominant and recessive modes of inheritance, any apparent power gain by an allele test when carried out in conjunction with a genotype test tends to be purchased entirely by an increased rate of false positive results due to omission of a multiple testing correction. As an alternative to these two standard association tests, our FP test represents a convenient and more powerful alternative.
    Electronic ISSN: 1471-2156
    Topics: Biology
    Published by BioMed Central
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 2
    Publication Date: 2009-01-01
    Description: Background In addition to single-locus (main) effects of disease variants, there is a growing consensus that gene-gene and gene-environment interactions may play important roles in disease etiology. However, for the very large numbers of genetic markers currently in use, it has proven difficult to develop suitable and efficient approaches for detecting effects other than main effects due to single variants. Results We developed a method for jointly detecting disease-causing single-locus effects and gene-gene interactions. Our method is based on finding differences of genotype pattern frequencies between case and control individuals. Those single-nucleotide polymorphism markers with largest single-locus association test statistics are included in a pattern. For a logistic regression model comprising three disease variants exerting main and epistatic interaction effects, we demonstrate that our method is vastly superior to the traditional approach of looking for single-locus effects. In addition, our method is suitable for estimating the number of disease variants in a dataset. We successfully apply our approach to data on Parkinson Disease and heroin addiction. Conclusion Our approach is suitable and powerful for detecting disease susceptibility variants with potentially small main effects and strong interaction effects. It can be applied to large numbers of genetic markers.
    Electronic ISSN: 1471-2105
    Topics: Biology , Computer Science
    Published by BioMed Central
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...