Genes, Environment, Health, and Disease: Facing up to Complexity

ArticleinHuman Heredity 63(2):63-6 · February 2007with10 Reads
DOI: 10.1159/000099178 · Source: PubMed
The deciphering of the human genome sequence, the DNA instruction book that makes us not only uniquely human but also unique as individuals, has provided unparalleled opportunities for identifying the relationship of genetic variation to health and disease. Yet as we move from single-gene diseases, in which a single misspelled base-pair can lead to such devastating conditions as sickle cell disease or Hutchinson-Gilford progeria syndrome, to complex diseases involving interactions among many interacting genes of small effect and many potential environmental modifiers, we need to develop tools to unravel the complexity of these interacting risk factors. Even in single-gene diseases, the frequent inter-individual variability in disease manifestations and course suggests the presence of modifiers for these presumably simple diseases, whether due to differing genetic backgrounds or differing environmental exposures, that are only beginning to be identified [1].
    • "In modern biological and medical research, gene-gene interactions, or epistatic effects, and gene-environment interactions have been studied intensively in genome-wide association studies (Evans et al., 2006; Manolio & Collins, 2007; Kooperberg & LeBlanc, 2008; Cordell, 2009 ). To deal with large and complex data sets, variable selection in regression has been under rapid development over the past two decades; see a comprehensive overview in Fan & Lv (2010) and the book Bühlmann & van de Geer (2011). "
    [Show abstract] [Hide abstract] ABSTRACT: This note aims to address and clarify several important issues in interaction selection for linear regression, especially when the input dimension $p$ is much larger than the sample size $n$. The problem has recently caught much attention in modern high dimensional data analysis. We first discuss fundamental questions including the valid definition of {\it importance} for main effects and interactions, the invariance principle, and the strong heredity condition. Then we focus on two-stage methods, which are regarded heuristic but computationally attractive for large $p$ problems. We revisit the counter example of Turlach (2004) and provide new insight on theoretical validity of two-stage methods. In the end, new strategies for interaction selection under the marginality principle are discussed.
    Full-text · Article · Dec 2014 · Sociological Review
    • "Some scientists believe that, as the result of rapid urbanization over the last few decades, genes previously protecting humans from parasitic infection may now contribute to a 'misdirected' response to environmental agents [18]. Tools to characterize and unravel interacting genetic and environmental factors are clearly required [19,20] . Understanding the contribution of environmental factors to disease susceptibility requires a more comprehensive view of exposure and biological response than has traditionally been applied. "
    [Show abstract] [Hide abstract] ABSTRACT: Complex diseases are often difficult to diagnose, treat and study due to the multi-factorial nature of the underlying etiology. Large data sets are now widely available that can be used to define novel, mechanistically distinct disease subtypes (endotypes) in a completely data-driven manner. However, significant challenges exist with regard to how to segregate individuals into suitable subtypes of the disease and understand the distinct biological mechanisms of each when the goal is to maximize the discovery potential of these data sets. A multi-step decision tree-based method is described for defining endotypes based on gene expression, clinical covariates, and disease indicators using childhood asthma as a case study. We attempted to use alternative approaches such as the Student's t-test, single data domain clustering and the Modk-prototypes algorithm, which incorporates multiple data domains into a single analysis and none performed as well as the novel multi-step decision tree method. This new method gave the best segregation of asthmatics and non-asthmatics, and it provides easy access to all genes and clinical covariates that distinguish the groups. The multi-step decision tree method described here will lead to better understanding of complex disease in general by allowing purely data-driven disease endotypes to facilitate the discovery of new mechanisms underlying these diseases. This application should be considered a complement to ongoing efforts to better define and diagnose known endotypes. When coupled with existing methods developed to determine the genetics of gene expression, these methods provide a mechanism for linking genetics and exposomics data and thereby accounting for both major determinants of disease.
    Full-text · Article · Nov 2013
    • "Commercial companies whose business plan was predicated on the discovery of the genetic bases for such common disorders saw their share values drop sharply over the opening years of the 21st century. And by 2005 Nature Biotechnology commented that 'roughly ¾ of the companies that listed during the 1999/2000 biotech bubble are still not making money'; and even in 2006 Genentech and Amgen finished the year down in share value by around Not that this approach has been without scientific criticism – for poorly defined phenotypes, poor study designs, and, perhaps most important, for failing to emphasize the low utility of the associations found for assessing the risks of developing complex diseases (effect sizes of the new loci found are modest or small) (Higgins et al., 2007; Manolio and Collins, 2007; Pearson and Manolio, 2008). Nonetheless, a new way of thinking is taking shape. "
    [Show abstract] [Hide abstract] ABSTRACT: The article is the text of a lecture given at the Faculty of the Humanities, March 2001. It argues that one implication of recent advances in the sciences of life may be that the binary opposition of the normal and the pathological is put to question. Canguilheim's distinction between vital and social norms is challenged and superseded by a Foucauldian genealogical approach to programs for the government of individuals, and the norm of life that emerged in the nineteenth and twentieth centuries are argued to be fundamentally social. Viewing genetics, biopsychiatry, and the commercialisation of drug development and biomedicine, the author argues that the logic of normalisation is loosing its hold, and being replaced by strategies for the continuous molecular management of variation, the modulation of susceptibilities, and the capitalisation of life itself.
    Full-text · Article · Oct 2010
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