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  • 1
    Online Resource
    Online Resource
    Overcoming attenuation bias in regressions using polygenic indices
    Springer Science and Business Media LLC ; 2023
    In:  Nature Communications Vol. 14, No. 1 ( 2023-07-25)
    Details
    In: Nature Communications, Springer Science and Business Media LLC, Vol. 14, No. 1 ( 2023-07-25)
    Abstract: Measurement error in polygenic indices (PGIs) attenuates the estimation of their effects in regression models. We analyze and compare two approaches addressing this attenuation bias: Obviously Related Instrumental Variables (ORIV) and the PGI Repository Correction (PGI-RC). Through simulations, we show that the PGI-RC performs slightly better than ORIV, unless the prediction sample is very small ( N   〈  1000) or when there is considerable assortative mating. Within families, ORIV is the best choice since the PGI-RC correction factor is generally not available. We verify the empirical validity of the simulations by predicting educational attainment and height in a sample of siblings from the UK Biobank. We show that applying ORIV between families increases the standardized effect of the PGI by 12% (height) and by 22% (educational attainment) compared to a meta-analysis-based PGI, yet estimates remain slightly below the PGI-RC estimates. Furthermore, within-family ORIV regression provides the tightest lower bound for the direct genetic effect, increasing the lower bound for the standardized direct genetic effect on educational attainment from 0.14 to 0.18 (+29%), and for height from 0.54 to 0.61 (+13%) compared to a meta-analysis-based PGI.
    Type of Medium: Online Resource
    ISSN: 2041-1723
    URL: Article
    DOI: 10.1038/s41467-023-40069-4
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2553671-0
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  • 2
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    Genetic predispositions moderate the effectiveness of tobacco excise taxes
    Public Library of Science (PLoS) ; 2021
    In:  PLOS ONE Vol. 16, No. 11 ( 2021-11-5), p. e0259210-
    Details
    In: PLOS ONE, Public Library of Science (PLoS), Vol. 16, No. 11 ( 2021-11-5), p. e0259210-
    Abstract: Tobacco consumption is one of the leading causes of preventable death. In this study, we analyze whether someone’s genetic predisposition to smoking moderates the response to tobacco excise taxes. Methods We interact polygenic scores for smoking behavior with state-level tobacco excise taxes in longitudinal data (1992-2016) from the US Health and Retirement Study ( N = 12,058). Results Someone’s genetic propensity to smoking moderates the effect of tobacco excise taxes on smoking behavior along the extensive margin (smoking vs. not smoking) and the intensive margin (the amount of tobacco consumed). In our analysis sample, we do not find a significant gene-environment interaction effect on smoking cessation. Conclusions When tobacco excise taxes are relatively high, those with a high genetic predisposition to smoking are less likely (i) to smoke, and (ii) to smoke heavily. While tobacco excise taxes have been effective in reducing smoking, the gene-environment interaction effects we observe in our sample suggest that policy makers could benefit from taking into account the moderating role of genes in the design of future tobacco control policies.
    Type of Medium: Online Resource
    ISSN: 1932-6203
    URL: Article
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    DOI: 10.1371/journal.pone.0259210
    DOI: 10.1371/journal.pone.0259210.g001
    DOI: 10.1371/journal.pone.0259210.g002
    DOI: 10.1371/journal.pone.0259210.g003
    DOI: 10.1371/journal.pone.0259210.t001
    DOI: 10.1371/journal.pone.0259210.t002
    DOI: 10.1371/journal.pone.0259210.t003
    DOI: 10.1371/journal.pone.0259210.t004
    DOI: 10.1371/journal.pone.0259210.s001
    Language: English
    Publisher: Public Library of Science (PLoS)
    Publication Date: 2021
    detail.hit.zdb_id: 2267670-3
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  • 3
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    Genetic insights into human cortical organization and development through genome-wide analyses of 2,347 neuroimaging phenotypes
    Springer Science and Business Media LLC ; 2023
    In:  Nature Genetics Vol. 55, No. 9 ( 2023-09), p. 1483-1493
    Details
    In: Nature Genetics, Springer Science and Business Media LLC, Vol. 55, No. 9 ( 2023-09), p. 1483-1493
    Type of Medium: Online Resource
    ISSN: 1061-4036 , 1546-1718
    URL: Article
    DOI: 10.1038/s41588-023-01475-y
    RVK:
    XA 10000
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 1494946-5
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  • 4
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    The identification of mediating effects using genome-based restricted maximum likelihood estimation
    Public Library of Science (PLoS) ; 2023
    In:  PLOS Genetics Vol. 19, No. 2 ( 2023-2-21), p. e1010638-
    Details
    In: PLOS Genetics, Public Library of Science (PLoS), Vol. 19, No. 2 ( 2023-2-21), p. e1010638-
    Abstract: Mediation analysis is commonly used to identify mechanisms and intermediate factors between causes and outcomes. Studies drawing on polygenic scores (PGSs) can readily employ traditional regression-based procedures to assess whether trait M mediates the relationship between the genetic component of outcome Y and outcome Y itself. However, this approach suffers from attenuation bias, as PGSs capture only a (small) part of the genetic variance of a given trait. To overcome this limitation, we developed MA-GREML: a method for Mediation Analysis using Genome-based Restricted Maximum Likelihood (GREML) estimation. Using MA-GREML to assess mediation between genetic factors and traits comes with two main advantages. First, we circumvent the limited predictive accuracy of PGSs that regression-based mediation approaches suffer from. Second, compared to methods employing summary statistics from genome-wide association studies, the individual-level data approach of GREML allows to directly control for confounders of the association between M and Y . In addition to typical GREML parameters (e.g., the genetic correlation), MA-GREML estimates ( i ) the effect of M on Y , ( ii ) the direct effect (i.e., the genetic variance of Y that is not mediated by M ), and ( iii ) the indirect effect (i.e., the genetic variance of Y that is mediated by M ). MA-GREML also provides standard errors of these estimates and assesses the significance of the indirect effect. We use analytical derivations and simulations to show the validity of our approach under two main assumptions, viz ., that M precedes Y and that environmental confounders of the association between M and Y are controlled for. We conclude that MA-GREML is an appropriate tool to assess the mediating role of trait M in the relationship between the genetic component of Y and outcome Y . Using data from the US Health and Retirement Study, we provide evidence that genetic effects on Body Mass Index (BMI), cognitive functioning and self-reported health in later life run partially through educational attainment. For mental health, we do not find significant evidence for an indirect effect through educational attainment. Further analyses show that the additive genetic factors of these four outcomes do partially (cognition and mental health) and fully (BMI and self-reported health) run through an earlier realization of these traits.
    Type of Medium: Online Resource
    ISSN: 1553-7404
    URL: Article
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    DOI: 10.1371/journal.pgen.1010638
    DOI: 10.1371/journal.pgen.1010638.g001
    DOI: 10.1371/journal.pgen.1010638.g002
    DOI: 10.1371/journal.pgen.1010638.t001
    DOI: 10.1371/journal.pgen.1010638.t002
    DOI: 10.1371/journal.pgen.1010638.t003
    DOI: 10.1371/journal.pgen.1010638.t004
    DOI: 10.1371/journal.pgen.1010638.s001
    DOI: 10.1371/journal.pgen.1010638.s002
    Language: English
    Publisher: Public Library of Science (PLoS)
    Publication Date: 2023
    detail.hit.zdb_id: 2186725-2
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  • 5
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    Multivariate analysis reveals shared genetic architecture of brain morphology and human behavior
    Springer Science and Business Media LLC ; 2021
    In:  Communications Biology Vol. 4, No. 1 ( 2021-10-12)
    Details
    In: Communications Biology, Springer Science and Business Media LLC, Vol. 4, No. 1 ( 2021-10-12)
    Abstract: Human variation in brain morphology and behavior are related and highly heritable. Yet, it is largely unknown to what extent specific features of brain morphology and behavior are genetically related. Here, we introduce a computationally efficient approach for multivariate genomic-relatedness-based restricted maximum likelihood (MGREML) to estimate the genetic correlation between a large number of phenotypes simultaneously. Using individual-level data ( N  = 20,190) from the UK Biobank, we provide estimates of the heritability of gray-matter volume in 74 regions of interest (ROIs) in the brain and we map genetic correlations between these ROIs and health-relevant behavioral outcomes, including intelligence. We find four genetically distinct clusters in the brain that are aligned with standard anatomical subdivision in neuroscience. Behavioral traits have distinct genetic correlations with brain morphology which suggests trait-specific relevance of ROIs. These empirical results illustrate how MGREML can be used to estimate internally consistent and high-dimensional genetic correlation matrices in large datasets.
    Type of Medium: Online Resource
    ISSN: 2399-3642
    URL: Article
    DOI: 10.1038/s42003-021-02712-y
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2919698-X
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  • 6
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    Multivariate estimation of factor structures of complex traits using SNP-based genomic relationships
    Springer Science and Business Media LLC ; 2022
    In:  BMC Bioinformatics Vol. 23, No. 1 ( 2022-12)
    Details
    In: BMC Bioinformatics, Springer Science and Business Media LLC, Vol. 23, No. 1 ( 2022-12)
    Abstract: Heritability and genetic correlation can be estimated from genome-wide single-nucleotide polymorphism (SNP) data using various methods. We recently developed multivariate genomic-relatedness-based restricted maximum likelihood (MGREML) for statistically and computationally efficient estimation of SNP-based heritability ( $$h^2_{\text{SNP}}$$ h SNP 2 ) and genetic correlation ( $$\rho _G$$ ρ G ) across many traits in large datasets. Here, we extend MGREML by allowing it to fit and perform tests on user-specified factor models, while preserving the low computational complexity. Results Using simulations, we show that MGREML yields consistent estimates and valid inferences for such factor models at low computational cost (e.g., for data on 50 traits and 20,000 individuals, a saturated model involving 50 $$h^2_{\text{SNP}}$$ h SNP 2 ’s, 1225 $$\rho _G$$ ρ G ’s, and 50 fixed effects is estimated and compared to a restricted model in less than one hour on a single notebook with two 2.7 GHz cores and 16 GB of RAM). Using repeated measures of height and body mass index from the US Health and Retirement Study, we illustrate the ability of MGREML to estimate a factor model and test whether it fits the data better than a nested model. The MGREML tool, the simulation code, and an extensive tutorial are freely available at https://github.com/devlaming/mgreml/ . Conclusion MGREML can now be used to estimate multivariate factor structures and perform inferences on such factor models at low computational cost. This new feature enables simple structural equation modeling using MGREML, allowing researchers to specify, estimate, and compare genetic factor models of their choosing using SNP data.
    Type of Medium: Online Resource
    ISSN: 1471-2105
    URL: Article
    DOI: 10.1186/s12859-022-04835-3
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2041484-5
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  • 7
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    Sex‐Specific Reproductive Factors Augment Cardiovascular Disease Risk in Women: A Mendelian Randomization Study
    Ovid Technologies (Wolters Kluwer Health) ; 2023
    In:  Journal of the American Heart Association Vol. 12, No. 5 ( 2023-03-07)
    Details
    In: Journal of the American Heart Association, Ovid Technologies (Wolters Kluwer Health), Vol. 12, No. 5 ( 2023-03-07)
    Abstract: Observational studies suggest that reproductive factors are associated with cardiovascular disease, but these are liable to influence by residual confounding. This study explores the causal relevance of reproductive factors on cardiovascular disease in women using Mendelian randomization. Methods and Results Uncorrelated ( r 2 〈 0.001), genome‐wide significant ( P 〈 5×10 −8 ) single‐nucleotide polymorphisms were extracted from sex‐specific genome‐wide association studies of age at first birth, number of live births, age at menarche, and age at menopause. Inverse‐variance weighted Mendelian randomization was used for primary analyses on outcomes of atrial fibrillation, coronary artery disease, heart failure, ischemic stroke, and stroke. Earlier genetically predicted age at first birth increased risk of coronary artery disease (odds ratio [OR] per year, 1.49 [95% CI, 1.28–1.74] , P =3.72×10 −7 ) heart failure (OR, 1.27 [95% CI, 1.06–1.53], P =0.009), and stroke (OR, 1.25 [95% CI, 1.00–1.56], P =0.048), with partial mediation through body mass index, type 2 diabetes, blood pressure, and cholesterol traits. Higher genetically predicted number of live births increased risk of atrial fibrillation (OR for 〈 2, versus 2, versus 〉 2 live births, 2.91 [95% CI, 1.16–7.29], P =0.023), heart failure (OR, 1.90 [95% CI, 1.28–2.82], P =0.001), ischemic stroke (OR, 1.86 [95% CI, 1.03–3.37], P =0.039), and stroke (OR, 2.07 [95% CI, 1.22–3.52], P =0.007). Earlier genetically predicted age at menarche increased risk of coronary artery disease (OR per year, 1.10 [95% CI, 1.06–1.14], P =1.68×10 −6 ) and heart failure (OR, 1.12 [95% CI, 1.07–1.17], P =5.06×10 −7 ); both associations were at least partly mediated by body mass index. Conclusions These results support a causal role of a number of reproductive factors on cardiovascular disease in women and identify multiple modifiable mediators amenable to clinical intervention.
    Type of Medium: Online Resource
    ISSN: 2047-9980
    URL: Article
    DOI: 10.1161/JAHA.122.027933
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2023
    detail.hit.zdb_id: 2653953-6
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  • 8
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    Association of Hypertensive Disorders of Pregnancy With Future Cardiovascular Disease
    American Medical Association (AMA) ; 2023
    In:  JAMA Network Open Vol. 6, No. 2 ( 2023-02-17), p. e230034-
    Details
    In: JAMA Network Open, American Medical Association (AMA), Vol. 6, No. 2 ( 2023-02-17), p. e230034-
    Abstract: Hypertensive disorders in pregnancy (HDPs) are major causes of maternal and fetal morbidity and are observationally associated with future maternal risk of cardiovascular disease. However, observational results may be subject to residual confounding and bias. Objective To investigate the association of HDPs with multiple cardiovascular diseases. Design, Setting, and Participants A genome-wide genetic association study using mendelian randomization (MR) was performed from February 16 to March 4, 2022. Primary analysis was conducted using inverse-variance-weighted MR. Mediation analyses were performed using a multivariable MR framework. All studies included patients predominantly of European ancestry. Female-specific summary-level data from FinnGen (sixth release). Exposures Uncorrelated ( r 2 & amp;lt;0.001) single-nucleotide variants (SNVs) were selected as instrumental variants from the FinnGen consortium summary statistics for exposures of any HDP, gestational hypertension, and preeclampsia or eclampsia. Main Outcomes and Measures Genetic association estimates for outcomes were extracted from genome-wide association studies of 122 733 cases for coronary artery disease, 34 217 cases for ischemic stroke, 47 309 cases for heart failure, and 60 620 cases for atrial fibrillation. Results Genetically predicted HDPs were associated with a higher risk of coronary artery disease (odds ratio [OR], 1.24; 95% CI, 1.08-1.43; P  = .002); this association was evident for both gestational hypertension (OR, 1.08; 95% CI, 1.00-1.17; P  = .04) and preeclampsia/eclampsia (OR, 1.06; 95% CI, 1.01-1.12; P  = .03). Genetically predicted HDPs were also associated with a higher risk of ischemic stroke (OR, 1.27; 95% CI, 1.12-1.44; P  = 2.87 × 10 −4 ). Mediation analysis revealed a partial attenuation of the effect of HDPs on coronary artery disease after adjustment for systolic blood pressure (total effect OR, 1.24; direct effect OR, 1.10; 95% CI, 1.02-1.08; P  = .02) and type 2 diabetes (total effect OR, 1.24; direct effect OR, 1.16; 95% CI, 1.04-1.29; P  = .008). No associations were noted between genetically predicted HDPs and heart failure (OR, 0.97; 95% CI, 0.76-1.23; P  = .79) or atrial fibrillation (OR, 1.11; 95% CI, 0.65-1.88; P  = .71). Conclusions and Relevance The findings of this study provide genetic evidence supporting an association between HDPs and higher risk of coronary artery disease and stroke, which is only partially mediated by cardiometabolic factors. This supports classification of HDPs as risk factors for cardiovascular disease.
    Type of Medium: Online Resource
    ISSN: 2574-3805
    URL: Article
    DOI: 10.1001/jamanetworkopen.2023.0034
    Language: English
    Publisher: American Medical Association (AMA)
    Publication Date: 2023
    detail.hit.zdb_id: 2931249-8
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  • 9
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    A comparison of robust Mendelian randomization methods using summary data
    Wiley ; 2020
    In:  Genetic Epidemiology Vol. 44, No. 4 ( 2020-06), p. 313-329
    Details
    In: Genetic Epidemiology, Wiley, Vol. 44, No. 4 ( 2020-06), p. 313-329
    Abstract: The number of Mendelian randomization (MR) 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. Since it is unlikely that all genetic variants will be valid instrumental variables, several robust methods have been proposed. We compare nine robust methods for MR based on summary data that can be implemented using standard statistical software. Methods were compared in three ways: by reviewing their theoretical properties, in an extensive simulation study, and in an empirical example. In the simulation study, the best method, judged by mean squared error was the contamination mixture method. This method had well‐controlled Type 1 error rates with up to 50% invalid instruments across a range of scenarios. Other methods performed well according to different metrics. Outlier‐robust methods had the narrowest confidence intervals in the empirical example. With isolated exceptions, all methods performed badly when over 50% of the variants were invalid instruments. Our recommendation for investigators is to perform a variety of robust methods that operate in different ways and rely on different assumptions for valid inferences to assess the reliability of MR analyses.
    Type of Medium: Online Resource
    ISSN: 0741-0395 , 1098-2272
    URL: Issue
    URL: Article
    DOI: 10.1002/gepi.v44.4
    DOI: 10.1002/gepi.22295
    Language: English
    Publisher: Wiley
    Publication Date: 2020
    detail.hit.zdb_id: 1492643-X
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  • 10
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    Safety of beta-blocker and calcium channel blocker antihypertensive drugs in pregnancy: a Mendelian randomization study
    Springer Science and Business Media LLC ; 2022
    In:  BMC Medicine Vol. 20, No. 1 ( 2022-09-06)
    Details
    In: BMC Medicine, Springer Science and Business Media LLC, Vol. 20, No. 1 ( 2022-09-06)
    Abstract: Beta-blocker (BB) and calcium channel blocker (CCB) antihypertensive drugs are commonly used in pregnancy. However, data on their relative impact on maternal and foetal outcomes are limited. We leveraged genetic variants mimicking BB and CCB antihypertensive drugs to investigate their effects on risk of pre-eclampsia, gestational diabetes and birthweight using the Mendelian randomization paradigm. Methods Genetic association estimates for systolic blood pressure (SBP) were extracted from summary data of a genome-wide association study (GWAS) on 757,601 participants. Uncorrelated single-nucleotide polymorphisms (SNPs) associated with SBP ( p   〈  5 × 10 −8 ) in BB and CCB drug target gene regions were selected as proxies for drug target perturbation. Genetic association estimates for the outcomes were extracted from GWASs on 4743 cases and 136,325 controls (women without a hypertensive disorder in pregnancy) for pre-eclampsia or eclampsia, 7676 cases and 130,424 controls (women without any pregnancy-related morbidity) for gestational diabetes, and 155,202 women (who have given birth at least once) for birthweight of the first child. All studies were in European ancestry populations. Mendelian randomization estimates were generated using the two-sample inverse-variance weighted model. Results Although not reaching the conventional threshold for statistical significance, genetically-proxied BB was associated with reduced risk of pre-eclampsia (OR per 10 mmHg SBP reduction 0.27, 95%CI 0.06–1.19, p  = 0.08) and increased risk of gestational diabetes (OR per 10 mmHg SBP reduction 2.01, 95%CI 0.91–4.42, p  = 0.08), and significantly associated with lower birthweight of first child (beta per 10 mmHg SBP reduction − 0.27, 95%CI − 0.39 to − 0.15, p  = 1.90 × 10 −5 ). Genetically-proxied CCB was associated with reduced risk of pre-eclampsia and eclampsia (OR 0.62, 95%CI 0.43–0.89, p  = 9.33 × 10 −3 ), and was not associated with gestational diabetes (OR 1.05, 95% CI 0.76–1.45, p  = 0.76) or changes in birthweight of first child (beta per 10 mmHg SBP reduction 0.02, 95%CI − 0.04–0.07, p  = 0.54). Conclusions While BB and CCB antihypertensive drugs may both be efficacious for lowering blood pressure in pregnancy, this genetic evidence suggests that BB use may lower birthweight. Conversely, CCB use may reduce risk of pre-eclampsia and eclampsia without impacting gestational diabetes risk or birthweight. These data support further study on the effects of BBs on birthweight.
    Type of Medium: Online Resource
    ISSN: 1741-7015
    URL: Article
    DOI: 10.1186/s12916-022-02483-1
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2131669-7
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