'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease?

By George Davey Smith, Shah Ebrahim. International Journal of Epidemiology, 2003

5,456 citations recorded by the source corpus.

Subfields: Genetics, Pediatrics, Perinatology and Child Health, Rheumatology

View Paper: https://www.semanticscholar.org/paper/e05d2ae8159f0ed22ea4826b19cc12a2d053a501

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. J. Bowden
2. George Davey Smith
3. Stephen Burgess
4. Gibran Hemani
5. E. Sanderson
6. S. Thompson
7. Michael Holmes
8. Alkes L Price

In Pediatrics, Perinatology and Child Health

1. George Davey Smith
2. J. Lynch
3. Stephen Burgess
4. J. Bowden
5. S. Thompson
6. Deborah Lawlor
7. Gibran Hemani
8. Dylan S. Small

In Rheumatology

1. Stephen Burgess
2. J. Bowden
3. E. Sanderson
4. George Davey Smith
5. S. Thompson
6. Dylan S. Small
7. F. Windmeijer
8. Gibran Hemani

Citations in the corpus

• Commentary: Two-sample Mendelian randomization: opportunities and challenges (2016, methodology): “Those developing MR as a method have from the start been very open about its limitations and have worked at developing methods to test and limit sources of bias.(2,3,9) It is notable, for example, that Gao et al.”
• Orienting the causal relationship between imprecisely measured traits using GWAS summary data (2017, background): “The principle, however, can be employed in extant observational data through the use of genetic variants associated with the exposure (instruments), where the inheritance of an allele serves as a random lifetime allocation of differential exposure levels [15, 16].”
• Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods (2015, background): “Much recent attention has been devoted to IV analysis in the context of Mendelian randomization, defined as the use of genetic variants as IVs [4, 5].”
• Methodological challenges in Mendelian randomization (2014, background): “unmeasured) of the exposure–outcome association, and (3)”
• Triangulation in aetiological epidemiology (2016, background): “MR studies are unlikely to be confounded by the many socioeconomic and lifestyle characteristics that plague conventional observational studies,(44) but may be confounded by population stratification.(33,34) Thus, in each of the three approaches (multivariable regression in a prospective cohort, RCT”
• A life course approach to chronic disease epidemiology. (2005, background): “levels generated by genetic polymorphisms are consistent with greater effects of lifetime cholesterol exposure than are seen with single cholesterol measures in adulthood (36).”
• Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians (2018, background): “…• Mendelian randomisation—the use of genetic variants as instrumental variables to investigate the effects of modifiable risk factors for disease 6 15 • Multiple instruments—the use of more than one genetic variant in a Mendelian randomisation analysis • Allele score—the number of alleles…”
• Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic (2016, methodology): “The horizontal dashed lines centred at each solid black dot represents the region within which we would expect to find the estimate (hollow black dot) with 95% probability, given that it is generated from equation (1).”
• Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome (2015, methodology): “Developmental canalization occurs when a genetic variant expressed during fetal development or post-natal growth stimulates compensatory processes that protect against the effect of that variant on the outcome in adulthood [1], while population stratification occurs when ancestral sub-populations wi”
• Mendelian Randomization. (2017, background): “Mendelian randomization rests on 3 assumptions: (1) the genetic variant is associated with the risk factor; (2) the genetic variant is not associated with confounders; and (3) the genetic variant influences the outcome only through the risk factor.”
• An Atlas of Genetic Correlations across Human Diseases and Traits (2015, methodology): “As a consequence of equation (1), we can estimate genetic covariance using the slope from the regression of z1jz2j on LD Score, which is computationally very fast (Online Methods).”