‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease?
George Davey Smith,Shah Ebrahim +1 more
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TLDR
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.read more
Citations
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Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression
TL;DR: An adaption of Egger regression can detect some violations of the standard instrumental variable assumptions, and provide an effect estimate which is not subject to these violations, and provides a sensitivity analysis for the robustness of the findings from a Mendelian randomization investigation.
Journal ArticleDOI
An atlas of genetic correlations across human diseases and traits.
Brendan Bulik-Sullivan,Brendan Bulik-Sullivan,Hilary K. Finucane,Verneri Anttila,Verneri Anttila,Alexander Gusev,Felix R. Day,Po-Ru Loh,Po-Ru Loh,Laramie E. Duncan,Laramie E. Duncan,John R. B. Perry,Nick Patterson,Elise B. Robinson,Elise B. Robinson,Mark J. Daly,Mark J. Daly,Alkes L. Price,Alkes L. Price,Benjamin M. Neale,Benjamin M. Neale +20 more
TL;DR: This work introduces a technique—cross-trait LD Score regression—for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap, and uses this method to estimate 276 genetic correlations among 24 traits.
Journal ArticleDOI
Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator.
TL;DR: A novel weighted median estimator for combining data on multiple genetic variants into a single causal estimate is presented, which is consistent even when up to 50% of the information comes from invalid instrumental variables.
Journal ArticleDOI
Genome-wide association studies for complex traits: consensus, uncertainty and challenges
Mark I. McCarthy,Mark I. McCarthy,Gonçalo R. Abecasis,Lon R. Cardon,Lon R. Cardon,David Goldstein,Julian Little,John P. A. Ioannidis,John P. A. Ioannidis,Joel N. Hirschhorn,Joel N. Hirschhorn,Joel N. Hirschhorn +11 more
TL;DR: This Review highlights the knowledge gained, defines areas of emerging consensus, and describes the challenges that remain as researchers seek to obtain more complete descriptions of the susceptibility architecture of biomedical traits of interest and to translate the information gathered into improvements in clinical management.
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10 Years of GWAS Discovery: Biology, Function, and Translation
Peter M. Visscher,Naomi R. Wray,Qian Zhang,Pamela Sklar,Mark I. McCarthy,Matthew A. Brown,Jian Yang +6 more
TL;DR: The remarkable range of discoveriesGWASs has facilitated in population and complex-trait genetics, the biology of diseases, and translation toward new therapeutics are reviewed.
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