1. Epidemiology and Global Health
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Multivariable two-sample Mendelian randomization estimates of the effects of intelligence and education on health

  1. Neil Martin Davies  Is a corresponding author
  2. W David Hill
  3. Emma L Anderson
  4. Eleanor Sanderson
  5. Ian J Deary
  6. George Davey Smith
  1. University of Bristol, United Kingdom
  2. University of Edinburgh, United Kingdom
Research Article
Cite this article as: eLife 2019;8:e43990 doi: 10.7554/eLife.43990
4 figures, 1 table, 1 data set and 2 additional files

Figures

Possible explanations for associations of SNPs and intelligence, education and outcomes later in life.

(A) Vertical pleiotropy: SNPs associated with intelligence and educational attainment could be vertically pleiotropic. This could occur if all of the effect of the SNPs on the outcomes is mediated via their effects on intelligence, the effect of intelligence on educational attainment, and the effect of educational attainment on the outcome. Confounders omitted from this figure for clarity. (B) Horizontal pleiotropy: The SNPs could be associated with the outcome via intelligence and educational attainment because of horizontal pleiotropy. This would occur if the SNPs had effects on the outcome via intelligence or education that were not mediated via the other trait. (C) Confounding pleiotropy: The SNPs could be associated with the outcomes, intelligence and educational attainment because of the effect of the SNPs on intelligence, and hence education, but that education had no direct effect on the outcome (i.e. the effects of the SNPs were entirely mediated via intelligence. This would occur if the SNPs had effects on the outcome via intelligence or education that were not mediated via the other trait (or vice versa). Multivariable Mendelian randomization does not overcome bias due to other pleiotropic effects by pathways other than intelligence or education.

https://doi.org/10.7554/eLife.43990.003
Multivariable Mendelian randomization estimates the direct effect of intelligence and education on the outcomes.

The direct effect (red arrow) excludes any effect of either intelligence (or education) that is mediated via education (intelligence) on the outcome. It requires genetic variation that explains a sufficient proportion of the variation in intelligence and education conditional on the other trait (Davey Smith and Hemani, 2014). It uses a set of SNPs that associate with intelligence and/or education at p<5×10−08.

https://doi.org/10.7554/eLife.43990.004
The bidirectional effects of SD differences in years of education and intelligence in UK Biobank.

The error bars indicate 95% confidence intervals around the estimated phenotype-SNP associations. Two sample multivariable Mendelian randomization using results from Hill et al. (2019), Okbay et al. (2016) and UK Biobank data. These results suggest that intelligence increases length of schooling and that higher education leads to higher intelligence. Pleiotropy robust methods, such as MR-Egger suggested little bias in the IVW estimates of the effect of intelligence on education, but that the IVW results may substantially overestimate the effect of education on intelligence. Estimates adjusted for month of birth, year of birth, sex, interaction of sex and year of birth and the first 40 principal components. For panel A, Q-stat = 368.7 (p<0.0001), I2gx=34.7%. For panel B, Q = 183.3 (p<0.0001), I2gx=12.5%.

https://doi.org/10.7554/eLife.43990.006
The direct effects of SD changes in years of education and intelligence on later outcomes in UK Biobank.

The error bars indicate 95% confidence intervals around the estimated effects. Estimated using two sample multivariable Mendelian randomization. Higher intelligence had direct effects on higher household income and alcohol consumption and less moderate and vigorous physical activity. Higher education had direct effects on decreased smoking, BMI, and sedentary behaviour and increased household income and rates of vigorous physical activity. These are estimates of the direct effects of intelligence (education) that are not mediated via education (intelligence).

https://doi.org/10.7554/eLife.43990.007

Tables

Table 1
Characteristics of 138,670 participants of the UK Biobank used to estimate the association of the SNPs and the outcomes.

The sample is more educated and healthier than the UK population. The sample was restricted to participants included in the two-sample analysis used for our primary results.

https://doi.org/10.7554/eLife.43990.005
CovariatesMean/percentStandard deviation/ count
Male138,67045.5%63,111
Year of birth138,67019518.08
Age at assessment centre visit138,67057.38.08
Exposures
Intelligence*137,3966.202.10
Educational attainment137,35414.555.16
Outcomes
Hypertension134,75124.9%33,494
Diabetes137,8834.3%5988
Stroke138,4171.5%2090
Heart attack138,4172.4%3260
Depression137,73334.6%47,624
Cancer138,07813.4%18,482
Mortality138,6702.0%2783
Ever smoker138,04445.2%62,365
Smoker138,0449.3%12,831
Income over £18 k117,75076.9%90,537
Income over £31 k117,75051.0%60,077
Income over £52 k117,75024.9%29,358
Income over £100 k117,7505.0%5871
Grip strength (kg)*138,4327.8%11.04
Height (cm)*138,374168.749.27
BMI (kg/m2)*138,24127.364.72
Diastolic blood pressure (mmHg)*132,95582.3110.16
Systolic blood pressure (mmHg)*132,954138.2418.64
Alcohol consumption (one low, five high)*138,5503.151.48
Hours of television viewing per day*133,7142.881.62
Vigorous physical activity (days/week)*131,5291.821.95
Moderate physical activity (days/week)*131,5613.602.33
  1. Notes: * Intelligence used in the single sample analysis reported in Supplementary Table 4. Both intelligence and educational attainment were normalised mean zero, standard deviation one for these analyses.

Data availability

All code used to produce the results in this study will be uploaded to GitHub (https://github.com/nmdavies/ukbiobank-intell-vs-ea; copy archived at https://github.com/elifesciences-publications/ukbiobank-intell-vs-ea). All data used in this study will be archived with the UK Biobank. Researchers who obtain the relevant permissions from the UK Biobank data access committee will be able to access the dataset (http://biobank.ndph.ox.ac.uk/crystal/dset.cgi?id=1762).

The following data sets were generated
  1. 1
    UK Biobank
    1. Martin Davies Neil
    2. David Hill W
    3. L Anderson Emma
    4. Sanderson Eleanor
    5. J Deary Ian
    6. Davey Smith George
    (2018)
    Data from: Multivariable two-sample Mendelian randomization estimates of the effects of intelligence and education on health.

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