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

National Human Genome Research Institute, NIH, Bethesda, MD 20892-2152, USA.
Human Heredity (Impact Factor: 1.47). 02/2007; 63(2):63-6. 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].

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    • "With the advent of relatively inexpensive molecular methods for genotyping, genome-wide association studies (GWAS) have been carried out with notable success. Although the primary interest in GWAS is to identify single-nucleotide polymorphisms (SNPs) that are directly associated with a disease, there is growing evidence supporting the occurrence of epistasis and its contribution to risk for complex disease [Evans et al., 2006; Manolio and Collins, 2007]. Consequently, there is much interest in searching for interactions between two or more SNPs [Cordell, 2009]. "
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    ABSTRACT: Epistasis could be an important source of risk for disease. How interacting loci might be discovered is an open question for genome-wide association studies (GWAS). Most researchers limit their statistical analyses to testing individual pairwise interactions (i.e., marginal tests for association). A more effective means of identifying important predictors is to fit models that include many predictors simultaneously (i.e., higher-dimensional models). We explore a procedure called screen and clean (SC) for identifying liability loci, including interactions, by using the lasso procedure, which is a model selection tool for high-dimensional regression. We approach the problem by using a varying dictionary consisting of terms to include in the model. In the first step the lasso dictionary includes only main effects. The most promising single-nucleotide polymorphisms (SNPs) are identified using a screening procedure. Next the lasso dictionary is adjusted to include these main effects and the corresponding interaction terms. Again, promising terms are identified using lasso screening. Then significant terms are identified through the cleaning process. Implementation of SC for GWAS requires algorithms to explore the complex model space induced by the many SNPs genotyped and their interactions. We propose and explore a set of algorithms and find that SC successfully controls Type I error while yielding good power to identify risk loci and their interactions. When the method is applied to data obtained from the Wellcome Trust Case Control Consortium study of Type 1 Diabetes it uncovers evidence supporting interaction within the HLA class II region as well as within Chromosome 12q24.
    Genetic Epidemiology 04/2010; 34(3):275-85. DOI:10.1002/gepi.20459 · 2.60 Impact Factor
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    • "Such interactions may shed light on potential disease associated pathways, and they may identify people that are at extreme high risks [e.g. Manolio and Collins, 2007]. "
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    ABSTRACT: In this paper we investigate the power to identify gene x gene interactions in genome-wide association studies. In our analysis we focus on two-stage analyses: analyses in which we only test for interactions between single nucleotide polymorphisms that show some marginal effect. We give two algorithms to compute significance levels for such an analyses. One involves a Bonferoni correction on the number of interactions that are actually tested, and one is a resampling procedure similar to the one proposed by [Lin (2006) Am. J. Hum. Genet. 78:505-509]. We also give an algorithm to carry out approximate power calculations for studies that plan to use a two-stage analysis. We find that for most plausible interaction effects a two-stage analysis can dramatically increase the power to identify interactions compared to a single-stage analysis based on simulation studies using known genetic models and data from existing genome-wide association studies.
    Genetic Epidemiology 04/2008; 32(3):255-63. DOI:10.1002/gepi.20300 · 2.60 Impact Factor
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    ABSTRACT: This article presents the findings of a qualitative study of multiracial individuals' understanding of identity, race and human genetic variation. The debate regarding the correlation between race, genetics and disease has expanded, but limited empirical data has been collected regarding the lay public's perspective. Participants in this study explore their identity and its relationships to their health care interactions. Participants also share their views on race-based therapeutics, health disparities and the connections between race, ancestry and genetics. Their voices highlight the limitations of racial categories in describing differences within our increasingly diverse communities. The genomic era will be a pivotal period in challenging current understandings and uses of racial categories in health.
    Social Forces 01/2008; 86(2):795-820. DOI:10.1093/sf/86.2.795 · 1.29 Impact Factor
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