rare mendelian diseases versus common multi-factorial diseases

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e.g., cystic fibrosis is one of the most common life-shortening childhood-onset inherited diseases in the United States, affecting 1 in 3900 births; one of every 31 individuals is a carrier of the recessive disease allele - PowerPoint PPT Presentation


  • rare Mendelian diseases versus common multi-factorial diseases

  • 1980: DNA markers are the key to identifying Mendelian disease genes

  • 1989: successful cloning of CFTR gene responsible for cystic fibrosis

  • Online Mendelian Inheritance in Man currently lists 2284 phenotypes whose molecular basis is known

  • 1990-6: birth and death of sib pair analysis for linkage based studies of common multi-factorial diseasesRisch N, Merikangas K. 1996. The future of genetic studies of complex human diseases. Science 273: 1516-1517

    linkage analysis had been successfully used to find genes for Mendelian diseases; in 1990, Risch popularized a method (sib pairs) to find genes for complex multi-factorial diseases; that method failed and they wanted to propose a different method that would be more powerful

    association studies were to be performed on functional polymorphisms for as many candidate genes as technically feasible, the entire genome if need be, regardless of how impractical that was; at least the number of patients would no longer be a limiting factor

  • past, present, and (near) future genetic studies of human diseases

  • population bottleneck, subsequent recombination, linkage disequilibrium

  • we need not test all the functional polymorphisms, just enough markers to be within linkage disequilibrium

  • common-disease-common-variant versus common-disease-rare-variant CDCV hypothesis: a few common allelic variants account for most of the genetic variance in disease susceptibilityReich DE, Lander ES. 2001. On the allelic spectrum of human disease. Trends Genet 17: 502-510

    CDRV hypothesis: a large number of rare allelic variants account for the genetic variance in disease susceptibilityTerwilliger JD, Weiss KM. 1998. Linkage disequilibrium mapping of complex disease: fantasy or reality? Curr Opin Biotechnol 9: 578-594

    for complex reasons having to do with human population history, linkage disequilibrium would only work in diseases where the CDCV hypothesis is valid; the best justification for the HapMap was that one common variant has more public health impact than many rare variants, so it makes sense to find these first

  • multiple rare alleles contribute to low plasma HDL cholesterol levels

  • International HapMap Consortiumhttp://www.hapmap.org/thehapmap.html.en

  • 7 tag SNPs capture all the common variation in a locus on chromosome 2

  • Wellcome Trust Case Control genome wide association studies

  • genome wide scan in seven diseases; y-axis represents statistical significance using -log10 of a p-value

    the chromosomes are shown in alternating colors; significant SNPs with p-value

  • a doubling in relative risk for a disease is not as bad as it sounds

  • but gene therapy remains elusive 19 years after the cystic fibrosis geneJesse Gelsinger (June 18, 1981 to September 17, 1999) was the first person identified as having died in a clinical trial for gene therapy. He was only 18 years old. Gelsinger suffered from ornithine transcarbamylase OTC deficiency, a disease of the liver whose victims are unable to metabolize ammonia, a byproduct of protein breakdown.Gelsinger was injected with adenoviruses containing the corrected gene in the hope that it would manufacture the much needed enzyme. He died four days later, having suffered a massive immune response, triggered by the viral vector used to transport the gene into his cells. This led to multiple organ failure and brain death. Food and Drug Administration investigators concluded that scientists involved in the trial, including lead researcher Dr. James M. Wilson (University of Pennsylvania), broke several rules of conduct: (a) Inclusion of Gelsinger as a substitute for another volunteer who had dropped out, despite his having high ammonia levels that should have led to his exclusion from the trial, (2) Failure by the university to report that 2 other patients had experienced serious side effects from the therapy, (3) Failure to mention the deaths of monkeys given a similar treatment, as should be been done for the informed consent.The university paid the parents an undisclosed amount.

  • http://www.genetic-future.com/2008/03/why-do-genome-wide-scans-fail.html

    Alleles with small effect sizes: To separate true signals from noise, researchers have to set an exceptionally high threshold that a marker needs to exceed before it is acceptable as a likely disease-causing candidate. By increasing the numbers of samples in their disease and control groups, researchers will steadily dial down the statistical noise until even disease genes with small effects stand out above the crowd. However, the logistical challenge of collecting a large number of carefully-ascertained patients will always be a serious obstacle.

    Rare variants: Our catalogue of human genetic variation, i.e. the HapMap, is largely restricted to common variants, since rare variants are much harder to identify. The instrumentation has restrictions on how many different SNPs one can analyze with a single chip. Everyone agrees that some non-trivial fraction of the genetic risk of common diseases will be the result of rare variants, especially as the latest results in a variety of diseases failed to provide unambiguous support for the CDCV hypothesis. The problem is not so much the costs of sequencing itself, as that is plummeting due to massive investment in rapid sequencing technologies, but rather the interpretation of the resultant data.

    Population differences: Markers that are associated with disease in one population can never be assumed to show the same associations in other human groups. This will be especially true for rare variants. The more difficult challenge will be in collecting large numbers of ancestry-homogeneous samples of validated disease patients and healthy controls.


    Epistatic interactions: Most genetic approaches assume that genetic risk is additive, in other words, that the presence of two risk factors in an individual will increase risk by the sum of the two factors. There is however no reason to expect that this will always be the case. Epistatic interactions, in which combined risk is greater (or less) than the sum of the risk from individual genes, are difficult to identify by genome scans and even harder to untangle.

    Copy number variation: CNVs are now known to account for a substantial fraction of human genetic variation, and have been shown to play an important role in gene expression variation and in human evolution. It seems highly likely that CNVs will be responsible for a non-trivial proportion of disease risk, but only time will tell.

    Epigenetic inheritance: Although epigenetic inheritance does occur, the degree to which it is influencing human physical variation and disease risk is essentially unknown. It needs to be established that epigenetically inherited variations actually contribute to a non-trivial fraction of human disease risk before we can include them in our systematic scans.

    Disease heterogeneity: Lumping patients with fundamentally different conditions into a single patient cohort is a recipe for failure, even if there are strong genetic risk factors for each of the separate conditions, since each will be drowned out by noise from the other. The geneticists cannot fix this problem. It will take a combined effort of clinicians and biomedical researchers to stratify the disease into useful subcategories.



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