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Statistical Analysis of Genetic Data

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Statistics Applied to Clinical Trials

Abstract

In 1860, the benchmark experiments of the monk Gregor Mendel led him to propose the existence of genes. The results of Mendel’s pea data were astoundingly close to those predicted by his theory. When we recently looked into Mendel’s pea data and performed a chi-square test, we had to conclude the the chi-square value was too small not to reject the null-hypothesis, this would mean that Mendel’s reported data were so close to what he expected that we could only conclude that he had somewhat fudged the data.

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6. References

  1. Cornelisse CJ, Cornells RS, Devilee P. Genes responsible for familial breast cancer. Pathol Res Pract 1996 Jul;192(7):684–693.

    Article  Google Scholar 

  2. Wijnen JT, Vasen HF, Khan PM, Zwinderman AH, van der Klift H, Mulder A, Tops C, Moller P, Fodde R. Clinical findings with implications for genetic testing in families with clustering of colorectal cancer. N Engl J Med 1998 Aug 20;339(8):511–518.

    Article  Google Scholar 

  3. Jordan B (Ed.). DNA Microarrays: gene expression applications. Berlin: Springer-Verlag, 2001.

    Google Scholar 

  4. Claverie JM. Computional methods for the identification of differential and coordinated gene expression. Hum Mol Genet 2001; 8(10): 1821–1832.

    Article  Google Scholar 

  5. McLachlan G. Mixture.model clustering of microarray expression data. Aus Biometrics and New Zealand Stat Association Joint Conference, 2001, Christchurch, New Zealand.

    Google Scholar 

  6. Alizadeh et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 2000, 403: 503–511.

    Article  Google Scholar 

  7. Eisen M et al. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA, 1998, 95: 14863–14867.

    Article  Google Scholar 

  8. Tavazoie et al. Sustematic determination of genetic network architecture. Nat Genet 1999, 22: 281–285.

    Article  Google Scholar 

  9. Tamayo et al. Interpreting patterns of gene-expression with self-organizing maps. Proc Natl Acad Sci USA, 1999, 96: 2907–2912.

    Article  Google Scholar 

  10. Tibshirani et al. Clustering methods for the analysis of DNA microarray data. Tech. rep. Stanford University, Dept of Statistics, Stanford.

    Google Scholar 

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© 2002 Springer Science+Business Media Dordrecht

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Cleophas, T.J., Zwinderman, A.H., Cleophas, T.F. (2002). Statistical Analysis of Genetic Data. In: Statistics Applied to Clinical Trials. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0337-7_16

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  • DOI: https://doi.org/10.1007/978-94-010-0337-7_16

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-1-4020-0570-1

  • Online ISBN: 978-94-010-0337-7

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