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      Is genetic liability to ADHD and ASD causally linked to educational attainment?

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          Abstract

          Background

          The association patterns of attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) with educational attainment (EA) are complex; children with ADHD and ASD are at risk of poor academic outcomes, and parental EA has been associated with risk of ADHD/ASD in the offspring. Little is known on the causal links between ADHD, ASD, EA and the potential contribution of cognitive ability.

          Methods

          Using the latest genome-wide association studies (GWAS) summary data on ADHD, ASD and EA, we applied two-sample Mendelian randomization (MR) to assess the effects of genetic liability to ADHD and ASD on EA. Reverse direction analyses were additionally performed. Multivariable MR was performed to estimate any effects independent of cognitive ability.

          Results

          Genetic liability to ADHD had a negative effect on EA, independently of cognitive ability ( MVMRIVW: -1.7 months of education per doubling of genetic liability to ADHD; 95% CI: -2.8 to -0.7), whereas genetic liability to ASD a positive effect ( MVMRIVW: 30 days per doubling of the genetic liability to ASD; 95% CI: 2 to 53). Reverse direction analyses suggested that genetic liability to higher EA had an effect on lower risk of ADHD, independently of cognitive ability ( MVMRIVW OR: 0.33 per SD increase; 95% CI: 0.26 to 0.43) and increased risk of ASD ( MRIVW OR: 1.51 per SD increase; 95% CI: 1.29 to 1.77), which was partly explained by cognitive ability ( MVMRIVW OR per SD increase: 1.24; 95%CI: 0.96 to 1.60).

          Conclusions

          Genetic liability to ADHD and ASD is likely to affect educational attainment, independently of underlying cognitive ability.

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          Most cited references85

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          Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

          Background: The number of Mendelian randomization analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. However, some genetic variants may not be valid instrumental variables, in particular due to them having more than one proximal phenotypic correlate (pleiotropy). Methods: We view Mendelian randomization with multiple instruments as a meta-analysis, and show that bias caused by pleiotropy can be regarded as analogous to small study bias. Causal estimates using each instrument can be displayed visually by a funnel plot to assess potential asymmetry. Egger regression, a tool to detect small study bias in meta-analysis, can be adapted to test for bias from pleiotropy, and the slope coefficient from Egger regression provides an estimate of the causal effect. Under the assumption that the association of each genetic variant with the exposure is independent of the pleiotropic effect of the variant (not via the exposure), Egger’s test gives a valid test of the null causal hypothesis and a consistent causal effect estimate even when all the genetic variants are invalid instrumental variables. Results: We illustrate the use of this approach by re-analysing two published Mendelian randomization studies of the causal effect of height on lung function, and the causal effect of blood pressure on coronary artery disease risk. The conservative nature of this approach is illustrated with these examples. Conclusions: An adaption of Egger regression (which we call MR-Egger) can detect some violations of the standard instrumental variable assumptions, and provide an effect estimate which is not subject to these violations. The approach provides a sensitivity analysis for the robustness of the findings from a Mendelian randomization investigation.
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            Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator

            ABSTRACT Developments in genome‐wide association studies and the increasing availability of summary genetic association data have made application of Mendelian randomization relatively straightforward. However, obtaining reliable results from a Mendelian randomization investigation remains problematic, as the conventional inverse‐variance weighted method only gives consistent estimates if all of the genetic variants in the analysis are valid instrumental variables. We present a novel weighted median estimator for combining data on multiple genetic variants into a single causal estimate. This estimator is consistent even when up to 50% of the information comes from invalid instrumental variables. In a simulation analysis, it is shown to have better finite‐sample Type 1 error rates than the inverse‐variance weighted method, and is complementary to the recently proposed MR‐Egger (Mendelian randomization‐Egger) regression method. In analyses of the causal effects of low‐density lipoprotein cholesterol and high‐density lipoprotein cholesterol on coronary artery disease risk, the inverse‐variance weighted method suggests a causal effect of both lipid fractions, whereas the weighted median and MR‐Egger regression methods suggest a null effect of high‐density lipoprotein cholesterol that corresponds with the experimental evidence. Both median‐based and MR‐Egger regression methods should be considered as sensitivity analyses for Mendelian randomization investigations with multiple genetic variants.
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              The MR-Base platform supports systematic causal inference across the human phenome

              Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base (http://www.mrbase.org): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies.
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                Author and article information

                Journal
                Int J Epidemiol
                Int J Epidemiol
                ije
                International Journal of Epidemiology
                Oxford University Press
                0300-5771
                1464-3685
                December 2021
                07 June 2021
                07 June 2021
                : 50
                : 6
                : 2011-2023
                Affiliations
                [1 ] Centre of Academic Mental Health, Bristol Medical School, University of Bristol , Bristol, UK
                [2 ] Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University , Cardiff, UK
                [3 ] Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol , Bristol, UK
                [4 ] Population Health Sciences, Bristol Medical School, University of Bristol , Bristol, UK
                [5 ] K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology , Trondheim, Norway
                Author notes
                Corresponding author. Centre for Academic Mental Health, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK. E-mail: christina.dardani@ 123456bristol.ac.uk
                [†]

                Equal contributions.

                Author information
                https://orcid.org/0000-0002-2019-7897
                https://orcid.org/0000-0002-5300-3331
                https://orcid.org/0000-0001-5188-5775
                https://orcid.org/0000-0002-2460-0508
                Article
                dyab107
                10.1093/ije/dyab107
                8743131
                34999873
                2f19a7d6-b938-423c-837f-3c0d4e16ddce
                © The Author(s) 2021. Published by Oxford University Press on behalf of the International Epidemiological Association.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 17 August 2021
                : 07 April 2021
                : 09 May 2021
                Page count
                Pages: 13
                Funding
                Funded by: The Medical Research Council (MRC);
                Funded by: University of Bristol support the MRC Integrative Epidemiology Unit;
                Award ID: MC_UU_00011/1
                Award ID: MC_UU_00011/3
                Award ID: MC_UU_00011/5
                Funded by: Social Research Council (ESRC);
                Award ID: ES/N000757/1
                Funded by: Norwegian Research Council, DOI 10.13039/501100005416;
                Award ID: 295989
                Funded by: Wellcome Trust, DOI 10.13039/100010269;
                Award ID: 108902/B/15/Z
                Award ID: 204895/Z/16/Z
                Funded by: Career Development Award from the UK Medical Research Council;
                Award ID: MR/M020894/1
                Funded by: Health Foundation’s Efficiency Research Programme;
                Award ID: 807293
                Funded by: The Health Foundation, DOI 10.13039/501100000724;
                Categories
                Mendelian Randomization
                AcademicSubjects/MED00860

                Public health
                adhd,asd,education,cognitive ability,mendelian randomization,multivariable
                Public health
                adhd, asd, education, cognitive ability, mendelian randomization, multivariable

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