Wang WY, Barratt BJ, Clayton DG, Todd JA. Genome-wide association studies: theoretical and practical concerns. Nat Rev Genet 6: 109-118

Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research, University of Cambridge, Cambridge CB2 2XY, UK.
Nature Reviews Genetics (Impact Factor: 36.98). 03/2005; 6(2):109-18. DOI: 10.1038/nrg1522
Source: PubMed


To fully understand the allelic variation that underlies common diseases, complete genome sequencing for many individuals with and without disease is required. This is still not technically feasible. However, recently it has become possible to carry out partial surveys of the genome by genotyping large numbers of common SNPs in genome-wide association studies. Here, we outline the main factors - including models of the allelic architecture of common diseases, sample size, map density and sample-collection biases - that need to be taken into account in order to optimize the cost efficiency of identifying genuine disease-susceptibility loci.

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Available from: William Wang, Nov 30, 2014
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    • "First, examining sets of SNPs rather than each SNP independently (as done in the methods of Kraft et al. [2007] and Wang et al. [2008]) will greatly reduce multiple-testing burden. For example, in a GWAS, although the traditional single-SNP regression approach could result in millions of tests [Wang et al., 2005], grouping all typed SNPs into genes and then implementing the kernel-based approach will result in ß20,000 tests [International Human Genome Sequencing Consortium, 2004]. Second, because multiple typed markers are likely to be in linkage disequilibrium (LD) with the causal variant, joint consideration of these markers will capture the effect of a true causal variant more effectively than independent marker testing. "
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    ABSTRACT: The etiology of complex traits likely involves the effects of genetic and environmental factors, along with complicated interaction effects between them. Consequently, there has been interest in applying genetic association tests of complex traits that account for potential modification of the genetic effect in the presence of an environmental factor. One can perform such an analysis using a joint test of gene and gene-environment interaction. An optimal joint test would be one that remains powerful under a variety of models ranging from those of strong gene-environment interaction effect to those of little or no gene-environment interaction effect. To fill this demand, we have extended a kernel machine based approach for association mapping of multiple SNPs to consider joint tests of gene and gene-environment interaction. The kernel-based approach for joint testing is promising, because it incorporates linkage disequilibrium information from multiple SNPs simultaneously in analysis and permits flexible modeling of interaction effects. Using simulated data, we show that our kernel machine approach typically outperforms the traditional joint test under strong gene-environment interaction models and further outperforms the traditional main-effect association test under models of weak or no gene-environment interaction effects. We illustrate our test using genome-wide association data from the Grady Trauma Project, a cohort of highly traumatized, at-risk individuals, which has previously been investigated for interaction effects. © 2015 WILEY PERIODICALS, INC.
    Full-text · Article · Apr 2015 · Genetic Epidemiology
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    • "Based on these results, we evaluated ld-IL-2 in patients with type 1 diabetes (T1D), a condition resulting from the chronic autoimmune destruction of pancreatic beta-cells, eventually leading to insulin deficiency. There are several reasons for using IL-2 in T1D patients: (i) several susceptibility genes are directly connected to the IL-2 pathway: IL-2, CD25 (IL-2RA) [21e23], IL-2RB [24], and PTPN2 [25] [26]; (ii) some studies reported Treg deficiency in peripheral blood [9] [10], and in pancreatic lymph nodes [27] [28]; (iii) at the time of clinical diagnosis, there is significant residual beta-cell function in most patients, so that immunotherapy could curtail inflammation, promote immune tolerance, and in turn preserve beta-cell mass and function [29]. In Non Obese Diabetic (NOD) mice, a model of spontaneous autoimmune diabetes with remarkable similarities to the human disease, IL-2 prevents T1D and we showed that a short course of IL-2 at diabetes onset led to disease reversal in one third of the mice [14] [30]. "
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    ABSTRACT: Most autoimmune diseases (AID) are linked to an imbalance between autoreactive effector T cells (Teffs) and regulatory T cells (Tregs). While blocking Teffs with immunosuppression has long been the only therapeutic option, activating/expanding Tregs may achieve the same objective without the toxicity of immunosuppression. We showed that low-dose interleukin-2 (ld-IL-2) safely expands/activates Tregs in patients with AID, such HCV-induced vasculitis and Type 1 Diabetes (T1D). Here we analyzed the kinetics and dose-relationship of IL-2 effects on immune responses in T1D patients. Ld-IL-2 therapy induced a dose-dependent increase in CD4+Foxp3+ and CD8+Foxp3+ Treg numbers and proportions, the duration of which was markedly dose-dependent. Tregs expressed enhanced levels of activation markers, including CD25, GITR, CTLA-4 and basal pSTAT5, and retained a 20-fold higher sensitivity to IL-2 than Teff and NK cells. Plasma levels of regulatory cytokines were increased in a dose-dependent manner, while cytokines linked to Teff and Th17 inflammatory cells were mostly unchanged. Global transcriptome analyses showed a dose-dependent decrease in immune response signatures. At the highest dose, Teff responses against beta-cell antigens were suppressed in all 4 patients tested. These results inform of broader changes induced by ld-IL-2 beyond direct effects on Tregs, and relevant for further development of ld-IL-2 for therapy and prevention of T1D, and other autoimmune and inflammatory diseases.
    Full-text · Article · Jan 2015 · Journal of Autoimmunity
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    • "Cognitive and neuroscientific perspectives genetic contributions to psychosis remain contentious. Despite recent literature on candidate gene approaches and GWAS initially seeming to suggest small to moderate gene effects in the development of psychotic disorders (Keller and Miller, 2006; Wang et al., 2005), these cumulative approaches have proven largely inconclusive. Thus, major individual genes with small or moderate effects (examined via early linkage and candidate association studies) are unlikely to be responsible for susceptibility to – or the onset of – psychosis. "
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    ABSTRACT: Why is it that all those who have become eminent in philosophy or politics or poetry or the arts are clearly melancholics? – Aristotle Nothing in biology makes sense except in the light of evolution. – Dobzhansky (1973) Introduction Schizophrenia, a debilitating mental illness affecting roughly 1 percent of the population worldwide, is widely accepted as being highly genetically influenced (Cardno et al., 1999; Gershon et al., 1988; Kendler and Diehl, 1993). Schizophrenia is often marked by distortions of reality, disorganized thought, emotional blunting, and/or social isolation that may interfere with optimal functioning (Cornblatt et al., 2012). Schizophrenia may be associated with creativity, although research findings are mixed (e.g., Andreasen, 2011; Kyaga et al., 2013). Evidence also points to adverse effects on fertility and reproductive success among (particularly) males with schizophrenia (Svensson et al., 2007), in part accounted for by marital status (McCabe et al., 2009), suggesting potential biological and social influences. Collectively, this raises an intriguing potential evolutionary puzzle: How does schizophrenia persist in the population at a stable prevalence rate too high to be explained by simple random mutation? (Doi et al., 2009; see also Del Giudice et al., 2010). Among various hypotheses, including in the context of the emerging field of evolutionary epidemiology, schizophrenia may represent “one extreme of a sexually selected fitness factor” (Shaner et al., 2004).
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