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Genetic epidemiology
About: Genetic epidemiology is a research topic. Over the lifetime, 1614 publications have been published within this topic receiving 68523 citations.
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TL;DR: Mendelian randomization provides new opportunities to test causality and demonstrates how investment in the human genome project may contribute to understanding and preventing the adverse effects on human health of modifiable exposures.
Abstract: Associations between modifiable exposures and disease seen in observational epidemiology are sometimes confounded and thus misleading, despite our best efforts to improve the design and analysis of studies. Mendelian randomization-the random assortment of genes from parents to offspring that occurs during gamete formation and conception-provides one method for assessing the causal nature of some environmental exposures. The association between a disease and a polymorphism that mimics the biological link between a proposed exposure and disease is not generally susceptible to the reverse causation or confounding that may distort interpretations of conventional observational studies. Several examples where the phenotypic effects of polymorphisms are well documented provide encouraging evidence of the explanatory power of Mendelian randomization and are described. The limitations of the approach include confounding by polymorphisms in linkage disequilibrium with the polymorphism under study, that polymorphisms may have several phenotypic effects associated with disease, the lack of suitable polymorphisms for studying modifiable exposures of interest, and canalization-the buffering of the effects of genetic variation during development. Nevertheless, Mendelian randomization provides new opportunities to test causality and demonstrates how investment in the human genome project may contribute to understanding and preventing the adverse effects on human health of modifiable exposures.
3,646 citations
TL;DR: A meta-analysis of relevant data from primary studies of the genetic epidemiology of major depression suggested that familial aggregation was due to additive genetic effects, with a minimal contribution of environmental effects common to siblings and substantial individual-specific environmental effects/measurement error.
Abstract: OBJECTIVE: The authors conducted a meta-analysis of relevant data from primary studies of the genetic epidemiology of major depression.METHOD: The authors searched MEDLINE and the reference lists of previous review articles to identify relevant primary studies. On the basis of a review of family, adoption, and twin studies that met specific inclusion criteria, the authors derived quantitative summary statistics. RESULTS: Five family studies met the inclusion criteria. The odds ratios for proband (subjects with major depression or comparison subjects) versus first-degree relative status (affected or unaffected with major depression) were homogeneous across the five studies (Mantel-Haenszel odds ratio=2.84, 95% CI=2.31–3.49). No adoption study met the inclusion criteria, but the results of two of the three reports were consistent with genetic influences on liability to major depression. Five twin studies met the inclusion criteria, and their statistical summation suggested that familial aggregation was due ...
2,958 citations
TL;DR: It is concluded that a systematic meta-analytic approach may assist in estimating population-wide effects of genetic risk factors in human disease.
Abstract: The rapid growth of human genetics creates countless opportunities for studies of disease association. Given the number of potentially identifiable genetic markers and the multitude of clinical outcomes to which these may be linked, the testing and validation of statistical hypotheses in genetic epidemiology is a task of unprecedented scale. Meta-analysis provides a quantitative approach for combining the results of various studies on the same topic, and for estimating and explaining their diversity. Here, we have evaluated by meta-analysis 370 studies addressing 36 genetic associations for various outcomes of disease. We show that significant between-study heterogeneity (diversity) is frequent, and that the results of the first study correlate only modestly with subsequent research on the same association. The first study often suggests a stronger genetic effect than is found by subsequent studies. Both bias and genuine population diversity might explain why early association studies tend to overestimate the disease protection or predisposition conferred by a genetic polymorphism. We conclude that a systematic meta-analytic approach may assist in estimating population-wide effects of genetic risk factors in human disease.
1,900 citations
01 Jan 2009
TL;DR: Merikangas et al. as discussed by the authors showed that most common genetic risks, at least when studied individually, are modest in magnitude, with relative risks in the range of 1.3 or less.
Abstract: THE SUCCESSFUL STATISTICAL identification and independent replication of numerous genetic markers in association studies have confirmed the utility of the genome-wide approach for the detection of genetic markers for complex disorders. However, recent genome-wide association studies have also indicated that most common genetic risks, at least when studied individually, are modest in magnitude, with relative risks in the range of 1.3 or less. This suggests that complex disorders result from the combination of numerous individual genetic and environmental contributors, with the potential for interactions among them. However, there is a lack of consensus regarding whether gene gene or gene environment interactions should be examined at the stage of gene detection or only after a gene effect has been clearly identified and replicated. Author Affiliations are listed at the end of this article. Corresponding Author: Kathleen Ries Merikangas, PhD, Genetic Epidemiology Research Branch, Intramural Research Program, National Institute of Mental Health, 35 Convent Dr, MSC#3720, Bethesda, MD 20892-3720 (kathleen.merikangas@nih.gov). Context Substantial resources are being devoted to identify candidate genes for complex mental and behavioral disorders through inclusion of environmental exposures following the report of an interaction between the serotonin transporter linked polymorphic region (5-HTTLPR) and stressful life events on an increased risk of major depression.
1,484 citations
Harvard University1, Broad Institute2, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico3, McMaster University4, McGill University5, University of Leicester6, University of Lübeck7, University of Pennsylvania8, Vanderbilt University9, University of Missouri–Kansas City10, University of Münster11, University of Verona12, Queen's University Belfast13, University of Washington14, Boston University15, University of Helsinki16, National Institute for Health and Welfare17, Lund University18, University of Cambridge19, Vita-Salute San Raffaele University20, University of Ferrara21, University of Turin22, Hebrew University of Jerusalem23, University of Girona24, University of Milan25, University of Leeds26, University of Regensburg27, Ludwig Maximilian University of Munich28, University of Kiel29, Wellcome Trust Sanger Institute30, University of Paris31, MedStar Washington Hospital Center32, deCODE genetics33, University of Iceland34
TL;DR: SNPs at nine loci were reproducibly associated with myocardial infarction, but tests of common and rare CNVs failed to identify additional associations with my Cardiovascular Infarction risk.
Abstract: We conducted a genome-wide association study testing single nucleotide polymorphisms (SNPs) and copy number variants (CNVs) for association with early-onset myocardial infarction in 2,967 cases and 3,075 controls We carried out replication in an independent sample with an effective sample size of up to 19,492 SNPs at nine loci reached genome-wide significance: three are newly identified (21q22 near MRPS6-SLC5A3-KCNE2, 6p24 in PHACTR1 and 2q33 in WDR12) and six replicated prior observations1, 2, 3, 4 (9p21, 1p13 near CELSR2-PSRC1-SORT1, 10q11 near CXCL12, 1q41 in MIA3, 19p13 near LDLR and 1p32 near PCSK9) We tested 554 common copy number polymorphisms (>1% allele frequency) and none met the pre-specified threshold for replication (P < 10-3) We identified 8,065 rare CNVs but did not detect a greater CNV burden in cases compared to controls, in genes compared to the genome as a whole, or at any individual locus SNPs at nine loci were reproducibly associated with myocardial infarction, but tests of common and rare CNVs failed to identify additional associations with myocardial infarction risk
1,092 citations