Multivariate analysis reveals shared genetic architecture of brain morphology and human behavior.

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

Maternal Hypertension Increases Risk of Preeclampsia and Low Fetal Birthweight:Genetic Evidence From a Mendelian Randomization Study

BACKGROUND: Maternal cardiovascular risk factors have been associated with adverse maternal and fetal outcomes. Given the difficulty in establishing causal relationships using epidemiological data, we applied Mendelian randomization to explore the role of cardiovascular risk factors on risk of developing pre-eclampsia or eclampsia, and low fetal birthweight. METHODS: Uncorrelated single nucleotide polymorphisms associated systolic blood pressure, body mass index, type 2 diabetes mellitus, low-density lipoprotein with cholesterol, smoking, urinary albumin-to-creatinine ratio and estimated glomerular filtration rate at genome-wide significance in studies of 298,957 to 1,201,909 European ancestry participants were selected as instrumental variables. A two-sample Mendelian randomization study was performed with primary outcome of pre-eclampsia or eclampsia (PET). Risk factors associated with PET were further investigated for their association with low birthweight. RESULTS: Higher genetically-predicted systolic blood pressure was associated increased risk of PET [odds ratio (OR) per 1-SD systolic blood pressure increase 1.90 (95% confidence interval (CI)1.45-2.49;p=3.23x10(-6) and reduced birthweight (OR=0.83; 95%CI=0.79-0.86;p=3.96x10(-18)), and this was not mediated by PET. Body mass index and type 2 diabetes were also associated with PET (respectively, OR per 1-SD body mass index increase=1.67 95%CI=1.44-1.94,;p=7.45x10(-12); and OR per logOR increase type 2 diabetes=1.11 95%CI=1.04-1.19p;=1.19x10(-3)), but not with reduced birthweight. CONCLUSIONS: Our results provide evidence for causal effects of systolic blood pressure, body mass index and type 2 diabetes on PET, and identify that systolic blood pressure is associated with reduced birthweight independently of PET. The results provide insight into the pathophysiological basis of PET and identify hypertension as a potentially modifiable risk factor amenable to therapeutic intervention

Overcoming attenuation bias in regressions using polygenic indices

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 &lt; 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.</p

The identification of mediating effects using genome-based restricted maximum likelihood estimation.

Funder: NIHR Cambridge Biomedical Research CentreMediation 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

Sex‐Specific Reproductive Factors Augment Cardiovascular Disease Risk in Women: A Mendelian Randomization Study

Background 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 (r22 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

Investigating the potential impact of PCSK9-inhibitors on mood disorders using eQTL-based Mendelian randomization

Prescription of PCSK9-inhibitors has increased in recent years but not much is known about its off-target effects. PCSK9-expression is evident in non-hepatic tissues, notably the brain, and genetic variation in the PCSK9 locus has recently been shown to be associated with mood disorder-related traits. We investigated whether PCSK9 inhibition, proxied by a genetic reduction in expression of PCSK9 mRNA, might have a causal adverse effect on mood disorder-related traits. We used genetic variants in the PCSK9 locus associated with reduced PCSK9 expression (eQTLs) in the European population from GTEx v8 and examined the effect on PCSK9 protein levels and three mood disorder-related traits (major depressive disorder, mood instability, and neuroticism), using summary statistics from the largest European ancestry genome-wide association studies. We conducted summary-based Mendelian randomization analyses to estimate the causal effects, and attempted replication using data from eQTLGen, Brain-eMETA, and the CAGE consortium. We found that genetically reduced PCSK9 gene-expression levels were significantly associated with reduced PCSK9 protein levels but not with increased risk of mood disorder-related traits. Further investigation of nearby genes demonstrated that reduced USP24 gene-expression levels was significantly associated with increased risk of mood instability (p-value range = 5.2x10-5–0.03), and neuroticism score (p-value range = 2.9x10-5–0.02), but not with PCSK9 protein levels. Our results suggest that genetic variation in this region acts on mood disorders through a PCSK9-independent pathway, and therefore PCSK9-inhibitors are unlikely to have an adverse impact on mood disorder-related traits

Additional file 1 of Safety of beta-blocker and calcium channel blocker antihypertensive drugs in pregnancy: a Mendelian randomization study

Additional file 1: Table S1. Instrumental variables for systolic blood pressure. Table S2. Mendelian randomization results. Table S3. Colocalization results. Table S4. Mendelian randomization results in analyses considering females only. Table S5. Colocalization results in analyses considering females only. Table S6. Mendelian randomization results in analyses considering diastolic blood pressure in females only. Table S7. Colocalization results in analyses considering diastolic blood pressure in females only. Table S8. Colocalization results considering alternative priors. Table S9. MR-Egger results. Table S10. Weighted median results. Table S11. MR-PRESSO results. Table S12. Instrumental variables selected at the ADRB1 locus. Table S13. Instrumental variables selected at the CACNA1C locus. Table S14. Instrumental variables selected at the CACNA1D locus. Table S15. Instrumental variables selected at the CACNB2 locus. Table S16. Instrumental variables selected at the CACNB3 locus. Table S17. Instrumental variables selected at all calcium channel blocker gene regions

Overcoming attenuation bias in regressions using polygenic indices

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

Multivariate estimation of factor structures of complex traits using SNP-based genomic relationships

Abstract Background 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