Searching for the causal effects of body mass index in over 300 000 participants in UK Biobank, using Mendelian randomization

Prediction of atrial fibrillation and stroke using machine learning models in UK Biobank

Objective

Atrial fibrillation (AF) is the most common cardiac arrythmia, and it is associated with increased risk for ischemic stroke, which is underestimated, as AF can be asymptomatic. The aim of this study was to develop optimal ML models for prediction of AF in the population, and secondly for ischemic stroke in AF patients.

Methods

To develop ML models for prediction of 1) AF in the general population and 2) ischemic stroke in patients with AF we constructed XGBoost, LightGBM, Random Forest, Deep Neural Network, Support Vector Machine and Lasso penalised logistic regression models using UK-Biobank's extensive real-world clinical data, questionnaires, as well as biochemical and genetic data, and their predictive performances were compared. Ranking and contribution of the different features was assessed by SHapley Additive exPlanations (SHAP) analysis. The clinical tool CHA2DS2-VASc for prediction of ischemic stroke among AF patients, was used for comparison to the best performing ML model.

Findings

The best performing model for AF prediction was LightGBM, with an area-under-the-roc-curve (AUROC) of 0.729 (95% confidence intervals (CI): 0.719, 0.738). The best performing model for ischemic stroke prediction in AF patients was XGBoost with AUROC of 0.631 (95% CI: 0.604, 0.657). The improved AUROC in the XGBoost model compared to CHA2DS2-VASc was statistically significant based on DeLong's test (p-value = 2.20E-06). In addition, the SHAP analysis showed that several peripheral blood biomarkers (e.g. creatinine, glycated haemoglobin, monocytes) were associated with ischemic stroke, which are not considered by CHA2DS2-VASc.

Implications

The best performing ML models presented have the potential for clinical use, but further validation in independent studies is required. Our results endorse the incorporation of some routinely measured blood biomarkers for ischemic stroke prediction in AF patients.

Using the global randomization test as a Mendelian randomization falsification test for the exclusion restriction assumption

Mendelian randomization may give biased causal estimates if the instrument affects the outcome not solely via the exposure of interest (violating the exclusion restriction assumption). We demonstrate use of a global randomization test as a falsification test for the exclusion restriction assumption. Using simulations, we explored the statistical power of the randomization test to detect an association between a genetic instrument and a covariate set due to (a) selection bias or (b) horizontal pleiotropy, compared to three approaches examining associations with individual covariates: (i) Bonferroni correction for the number of covariates, (ii) correction for the effective number of independent covariates, and (iii) an r2 permutation-based approach. We conducted proof-of-principle analyses in UK Biobank, using CRP as the exposure and coronary heart disease (CHD) as the outcome. In simulations, power of the randomization test was higher than the other approaches for detecting selection bias when the correlation between the covariates was low (r2 < 0.1), and at least as powerful as the other approaches across all simulated horizontal pleiotropy scenarios. In our applied example, we found strong evidence of selection bias using all approaches (e.g., global randomization test p < 0.002). We identified 51 of the 58 CRP genetic variants as horizontally pleiotropic, and estimated effects of CRP on CHD attenuated somewhat to the null when excluding these from the genetic risk score (OR = 0.96 [95% CI: 0.92, 1.00] versus 0.97 [95% CI: 0.90, 1.05] per 1-unit higher log CRP levels). The global randomization test can be a useful addition to the MR researcher's toolkit.

Dissecting the mediating and moderating effects of depression on the associations between traits and coronary artery disease: A two-step Mendelian randomization and phenome-wide interaction study

Background

Depression is often present concurrently with coronary artery disease (CAD), a disease with which it shares many risk factors. However, the manner in which depression mediates and moderates the association between traits (including biomarkers, anthropometric indicators, lifestyle behaviors, etc.) and CAD is largely unknown.

Methods

In our causal mediation analyses using two-step Mendelian randomization (MR), univariable MR was first used to investigate the causal effects of 108 traits on liability to depression and CAD. The traits with significant causal effects on both depression and CAD, but not causally modulated by depression, were selected for the second-step analyses. Multivariable MR was used to estimate the direct effects (independent of liability to depression) of these traits on CAD, and the indirect effects (mediated via liability to depression) were calculated. To investigate the moderating effect of depression on the association between 364 traits and CAD, a cross-sectional phenome-wide interaction study (PheWIS) was conducted in a study population from UK Biobank (UKBB) (N=275,257). Additionally, if the relationship between traits and CAD was moderated by both phenotypic and genetically predicted depression at a suggestive level of significance (Pinteraction≤0.05) in the PheWIS, the results were further verified by a cohort study using Cox proportional hazards regression.

Results

Univariable MR indicated that 10 of 108 traits under investigation were significantly associated with both depression and CAD, which showed a similar direct effect compared to the total effect for most traits. However, the traits "drive faster than speed limit" and "past tobacco smoking" were both exceptions, with the proportions mediated by depression at 24.6% and 7.2%, respectively. In the moderation analyses, suggestive evidence of several traits was found for moderating effects of phenotypic depression or susceptibility to depression, as estimated by polygenic risk score, including chest pain when hurrying, reason of smoking quitting and weight change. Consistent results were observed in survival analyses and Cox regression.

Conclusion

The independent role of traits in CAD pathogenesis regardless of depression was highlighted in our mediation analyses, and the moderating effects of depression observed in our study may be helpful for CAD risk stratification and optimized allocation of scarce medical resources.

Obesity as a causal risk factor for depression: Systematic review and meta-analysis of Mendelian Randomization studies and implications for population mental health

BACKGROUND/OBJECTIVES

Obesity has been associated with elevated risk of depression. If this association is causal, the increasing obesity prevalence might lead to worsening population mental health, but the strength of the causal effect has not been systematically evaluated.

SUBJECTS/METHODS

The current study provides a systematic review and meta-analysis of studies examining associations between body mass index and depression using Mendelian randomization with multiple genetic variants as instruments for body mass index. We used this estimate to calculate the expected changes in prevalence of population psychological distress from the 1990s-2010s, which were compared with the empirically observed trends in psychological distress in the Health Survey for England (HSE) and U.S. National Health Interview Surveys (NHIS).

RESULTS

Meta-analysis of 8 Mendelian randomization studies indicated an OR = 1.33 higher depression risk associated with obesity (95% confidence interval 1.19, 1.48). Between 15% and 20% of the participants of HSE and NHIS reported at least moderate psychological distress. The increase of obesity prevalence from the 1990s-2010s in HSE and NHIS would have led to a 0.6 percentage-point increase in population psychological distress.

CONCLUSIONS

Mendelian randomization studies suggest that obesity is a causal risk factor for elevated risk of depression. The increasing obesity rates may have modestly increased the prevalence of depressive symptoms in the general population. Mendelian randomization relies on methodological assumptions that may not always hold, so other quasi-experimental methods are needed to confirm the current conclusions.

Association of Plant-Based Diet Indices and Abdominal Obesity with Mental Disorders among Older Chinese Adults

We aimed to explore the correlation between plant-based diet indices and abdominal obesity with depression and anxiety among older Chinese adults. This study used a cross-sectional design using data from the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS). We used a simplified food frequency questionnaire to evaluate the overall plant-based diet index (PDI), the healthful plant-based diet index (hPDI), and the unhealthful plant-based diet index (uPDI) separately, based on the potential health effects of the foods. Waist circumference (WC) was used to define abdominal obesity. The 10-item Center for Epidemiologic Studies Depression Scale (CES-D-10) and the 7-item Generalized Anxiety Disorder Scale (GAD-7) were applied to estimate depression symptoms and anxiety symptoms, respectively. Multi-adjusted binary logistic regression models were conducted to explore the effects of the three plant-based diet indices, abdominal obesity status, and their interaction on depression and anxiety. We enrolled a total of 11,623 participants aged 83.21 ± 10.98 years, of which 3140 (27.0%) participants had depression and 1361 (11.7%) had anxiety. The trend in the prevalence of depression/anxiety across increasing quartiles of the plant-based diet indices was statistically significant after controlling for potential confounders (p-trend < 0.05). Abdominal obesity was related to a lower prevalence of depression (OR = 0.86, 95% CI: 0.77-0.95) and anxiety (OR = 0.79, 95% CI: 0.69-0.90) compared with non-abdominal obesity. The protective effects of the PDI and hPDI against depression (OR = 0.52, 95% CI: 0.41-0.64; OR = 0.59, 95% CI: 0.48-0.73, respectively) and anxiety (OR = 0.75, 95% CI: 0.57-1.00; OR = 0.52, 95% CI: 0.39-0.70, respectively) were more pronounced in non-abdominally obese participants. The harmful effects of the uPDI against depression (OR = 1.78, 95% CI: 1.42-2.23) and anxiety (OR = 1.56, 95% CI: 1.16-2.10) were more pronounced in non-abdominally obese participants. In addition, a significant interaction between the plant-based diet indices and abdominal obesity was observed in terms of causing the prevalence of depression and anxiety. Consuming more of a healthful plant-based diet and less of an animal-based diet is related to a lower prevalence of depression and anxiety. A healthful plant-based diet plays a vital role in non-abdominally obese individuals.

Using language models and ontology topology to perform semantic mapping of traits between biomedical datasets

MOTIVATION

Human traits are typically represented in both the biomedical literature and large population studies as descriptive text strings. Whilst a number of ontologies exist, none of these perfectly represent the entire human phenome and exposome. Mapping trait names across large datasets is therefore time-consuming and challenging. Recent developments in language modelling have created new methods for semantic representation of words and phrases, and these methods offer new opportunities to map human trait names in the form of words and short phrases, both to ontologies and to each other. Here, we present a comparison between a range of established and more recent language modelling approaches for the task of mapping trait names from UK Biobank to the Experimental Factor Ontology (EFO), and also explore how they compare to each other in direct trait-to-trait mapping.

RESULTS

In our analyses of 1191 traits from UK Biobank with manual EFO mappings, the BioSentVec model performed best at predicting these, matching 40.3% of the manual mappings correctly. The BlueBERT-EFO model (finetuned on EFO) performed nearly as well (38.8% of traits matching the manual mapping). In contrast, Levenshtein edit distance only mapped 22% of traits correctly. Pairwise mapping of traits to each other demonstrated that many of the models can accurately group similar traits based on their semantic similarity.

AVAILABILITY AND IMPLEMENTATION

Our code is available at https://github.com/MRCIEU/vectology.

Using genetic association data to guide drug discovery and development: Review of methods and applications

Evidence on the validity of drug targets from randomized trials is reliable but typically expensive and slow to obtain. In contrast, evidence from conventional observational epidemiological studies is less reliable because of the potential for bias from confounding and reverse causation. Mendelian randomization is a quasi-experimental approach analogous to a randomized trial that exploits naturally occurring randomization in the transmission of genetic variants. In Mendelian randomization, genetic variants that can be regarded as proxies for an intervention on the proposed drug target are leveraged as instrumental variables to investigate potential effects on biomarkers and disease outcomes in large-scale observational datasets. This approach can be implemented rapidly for a range of drug targets to provide evidence on their effects and thus inform on their priority for further investigation. In this review, we present statistical methods and their applications to showcase the diverse opportunities for applying Mendelian randomization in guiding clinical development efforts, thus enabling interventions to target the right mechanism in the right population group at the right time. These methods can inform investigators on the mechanisms underlying drug effects, their related biomarkers, implications for the timing of interventions, and the population subgroups that stand to gain the most benefit. Most methods can be implemented with publicly available data on summarized genetic associations with traits and diseases, meaning that the only major limitations to their usage are the availability of appropriately powered studies for the exposure and outcome and the existence of a suitable genetic proxy for the proposed intervention.

Genomics and phenomics of body mass index reveals a complex disease network

Elevated body mass index (BMI) is heritable and associated with many health conditions that impact morbidity and mortality. The study of the genetic association of BMI across a broad range of common disease conditions offers the opportunity to extend current knowledge regarding the breadth and depth of adiposity-related diseases. We identify 906 (364 novel) and 41 (6 novel) genome-wide significant loci for BMI among participants of European (N~1.1 million) and African (N~100,000) ancestry, respectively. Using a BMI genetic risk score including 2446 variants, 316 diagnoses are associated in the Million Veteran Program, with 96.5% showing increased risk. A co-morbidity network analysis reveals seven disease communities containing multiple interconnected diseases associated with BMI as well as extensive connections across communities. Mendelian randomization analysis confirms numerous phenotypes across a breadth of organ systems, including conditions of the circulatory (heart failure, ischemic heart disease, atrial fibrillation), genitourinary (chronic renal failure), respiratory (respiratory failure, asthma), musculoskeletal and dermatologic systems that are deeply interconnected within and across the disease communities. This work shows that the complex genetic architecture of BMI associates with a broad range of major health conditions, supporting the need for comprehensive approaches to prevent and treat obesity.

The association of measures of body shape and adiposity with incidence of cardiometabolic disease from an ageing perspective

While obesity increases the risk of developing cardiometabolic diseases (CMDs), these associations seem to attenuate with increasing age, albeit studied poorly. The present study aimed to investigate the associations between adiposity and CMDs in sex-specific groups of chronological age and leukocyte telomere length (LTL) as a measure of biological age. We investigated the associations between BMI, a body shape index, waist-to-hip ratio (adjusted for BMI) and total body fat, and incident coronary artery disease (CAD), type 2 diabetes (T2D) and ischemic stroke (IS) in 413,017 European-ancestry participants of the UK Biobank without CMD at baseline. We assessed the change in the associations between adiposity and CMD over strata of increasing chronological age or decreasing LTL. Participants (56% women) had a median (IQR) age of 57.0 (50.0-63.0) years. The median follow-up time was 12 years. People with higher BMI had a higher risk of incident CAD (HR 1.14 (95% confidence interval [CI] 1.13, 1.16)), T2D (HR 1.70 (95% CI 1.68, 1.72)) and IS (HR 1.09 (95% CI 1.06, 1.12)). In groups based on chronological age and LTL, adiposity measures were associated with higher risk of CAD and T2D in both men and women, but these associations attenuated with increasing chronological age (P_interactions < 0.001), but not with decreasing LTL (P_interaction men = 0.85; P_interaction women = 0.27). Increased (abdominal) adiposity was associated with higher risk of incident CMDs, which attenuated with increasing chronological age but not with decreasing LTL. Future research may validate these findings using different measures of biological age.

Identifying potential causal effects of age at menopause: a Mendelian randomization phenome-wide association study

Age at natural menopause (ANM) is associated with a range of health-related traits, including bone health, female reproductive cancers, and cardiometabolic health. Our objective was to conduct a Mendelian randomization phenome-wide association study (MR-pheWAS) of ANM. We conducted a hypothesis-free analysis of the genetic risk score (GRS) for ANM with 18,961 health-related traits among 181,279 women in UK Biobank. We also stratified the GRS according to the involvement of SNPs in DNA damage response. We sought to replicate our findings in independent cohorts. We conducted a negative control MR-pheWAS among men. Among women, we identified potential effects of ANM on 221 traits (1.17% of all traits) at a false discovery rate (P value ≤ 5.83 × 10^-4), and 91 (0.48%) potential effects when using Bonferroni threshold (P value ≤ 2.64 × 10^-6). Our findings included 55 traits directly related to ANM (e.g. hormone replacement therapy, gynaecological conditions and menstrual conditions), and liver function, kidney function, lung function, blood-cell composition, breast cancer and bone and cardiometabolic health. Replication analyses confirmed that younger ANM was associated with HbA1c (adjusted mean difference 0.003 mmol/mol; 95% CI 0.001, 0.006 per year decrease in ANM), breast cancer (adjusted OR 0.96; 95% CI 0.95, 0.98), and bone-mineral density (adjusted mean difference - 0.05; 95% CI - 0.07, - 0.03 for lumbar spine). In men, 30 traits were associated with the GRS at a false discovery rate (P value ≤ 5.49 × 10^-6), and 11 potential effects when using Bonferroni threshold (P value ≤ 2.75 × 10^-6). In conclusion, our results suggest that younger ANM has potential causal effects on a range of health-related traits.

Exploring the clinical and genetic associations of adult weight trajectories using electronic health records in a racially diverse biobank: a phenome-wide and polygenic risk study

BACKGROUND

Weight trajectories might reflect individual health status. In this study, we aimed to examine the clinical and genetic associations of adult weight trajectories using electronic health records (EHRs) in the BioMe Biobank.

METHODS

We constructed four weight trajectories based on a-priori definitions of weight changes (5% or 10%) using annual weight in EHRs (stable weight, weight gain, weight loss, and weight cycle); the final weight dataset included 21 487 participants with 162 783 annual weight measures. To confirm accurate assignment of weight trajectories, we manually reviewed weight trajectory plots for 100 random individuals. We then did a hypothesis-free phenome-wide association study (PheWAS) to identify diseases associated with each weight trajectory. Next, we estimated the single-nucleotide polymorphism-based heritability (hSNP2) of weight trajectories using GCTA-GREML, and we did a hypothesis-driven analysis of anorexia nervosa and depression polygenic risk scores (PRS) on these weight trajectories, given both diseases are associated with weight changes. We extended our analyses to the UK Biobank to replicate findings from a patient population to a generally healthy population.

FINDINGS

We found high concordance between manually assigned weight trajectories and those assigned by the algorithm (accuracy ≥98%). Stable weight was consistently associated with lower disease risks among those passing Bonferroni-corrected p value in our PheWAS (p≤4·4 × 10-5). Additionally, we identified an association between depression and weight cycle (odds ratio [OR] 1·42, 95% CI 1·31-1·55, p≤7·7 × 10-16). The adult weight trajectories were heritable (using 5% weight change as the cutoff: hSNP2 of 2·1%, 95% CI 0·9-3·3, for stable weight; 4·1%, 1·4-6·8, for weight gain; 5·5%, 2·8-8·2, for weight loss; and 4·7%, 2·3-7·1%, for weight cycle). Anorexia nervosa PRS was positively associated with weight loss trajectory among individuals without eating disorder diagnoses (OR1SD 1·16, 95% CI 1·07-1·26, per 1 SD higher PRS, p=0·011), and the association was not attenuated by obesity PRS. No association was found between depression PRS and weight trajectories after permutation tests. All main findings were replicated in the UK Biobank (p<0·05).

INTERPRETATION

Our findings suggest the importance of considering weight from a longitudinal aspect for its association with health and highlight a crucial role of weight management during disease development and progression.

FUNDING

Klarman Family Foundation, US National Institute of Mental Health (NIMH).

Mental Health Symptom Reduction Using Digital Therapeutics Care Informed by Genomic SNPs and Gut Microbiome Signatures

Neuropsychiatric diseases and obesity are major components of morbidity and health care costs, with genetic, lifestyle, and gut microbiome factors linked to their etiology. Dietary and weight-loss interventions can help improve mental health, but there is conflicting evidence regarding their efficacy; and moreover, there is substantial interindividual heterogeneity that needs to be understood. We aimed to identify genetic and gut microbiome factors that explain interindividual differences in mental health improvement after a dietary and lifestyle intervention for weight loss. We recruited 369 individuals participating in Digbi Health’s personalized digital therapeutics care program and evaluated the association of 23 genetic scores, the abundance of 178 gut microbial genera, and 42 bacterial pathways with mental health. We studied the presence/absence of anxiety or depression, or sleep problems at baseline and improvement on anxiety, depression, and insomnia after losing at least 2% body weight. Participants lost on average 5.4% body weight and >95% reported improving mental health symptom intensity. There were statistically significant correlations between: (a) genetic scores with anxiety or depression at baseline, gut microbial functions with sleep problems at baseline, and (b) genetic scores and gut microbial taxa and functions with anxiety, depression, and insomnia improvement. Our results are concordant with previous findings, including the association between anxiety or depression at baseline with genetic scores for alcohol use disorder and major depressive disorder. As well, our results uncovered new associations in line with previous epidemiological literature. As evident from previous literature, we also observed associations of gut microbial signatures with mental health including short-chain fatty acids and bacterial neurotoxic metabolites specifically with depression. Our results also show that microbiome and genetic factors explain self-reported mental health status and improvement better than demographic variables independently. The genetic and microbiome factors identified in this study provide the basis for designing and personalizing dietary interventions to improve mental health.

Exploring and mitigating potential bias when genetic instrumental variables are associated with multiple non-exposure traits in Mendelian randomization

With the increasing size and number of genome-wide association studies, individual single nucleotide polymorphisms are increasingly found to associate with multiple traits. Many different mechanisms could result in proposed genetic IVs for an exposure of interest being associated with multiple non-exposure traits, some of which could bias MR results. We describe and illustrate, through causal diagrams, a range of scenarios that could result in proposed IVs being related to non-exposure traits in MR studies. These associations could occur due to five scenarios: (i) confounding, (ii) vertical pleiotropy, (iii) horizontal pleiotropy, (iv) reverse causation and (v) selection bias. For each of these scenarios we outline steps that could be taken to explore the underlying mechanism and mitigate any resulting bias in the MR estimation. We recommend MR studies explore possible IV-non-exposure associations across a wider range of traits than is usually the case. We highlight the pros and cons of relying on sensitivity analyses without considering particular pleiotropic paths versus systematically exploring and controlling for potential pleiotropic or other biasing paths via known traits. We apply our recommendations to an illustrative example of the effect of maternal insomnia on offspring birthweight in UK Biobank.

A multi-population phenome-wide association study of genetically-predicted height in the Million Veteran Program

BACKGROUND

Height has been associated with many clinical traits but whether such associations are causal versus secondary to confounding remains unclear in many cases. To systematically examine this question, we performed a Mendelian Randomization-Phenome-wide association study (MR-PheWAS) using clinical and genetic data from a national healthcare system biobank.

METHODS AND FINDINGS

Analyses were performed using data from the US Veterans Affairs (VA) Million Veteran Program in non-Hispanic White (EA, n = 222,300) and non-Hispanic Black (AA, n = 58,151) adults in the US. We estimated height genetic risk based on 3290 height-associated variants from a recent European-ancestry genome-wide meta-analysis. We compared associations of measured and genetically-predicted height with phenome-wide traits derived from the VA electronic health record, adjusting for age, sex, and genetic principal components. We found 345 clinical traits associated with measured height in EA and an additional 17 in AA. Of these, 127 were associated with genetically-predicted height at phenome-wide significance in EA and 2 in AA. These associations were largely independent from body mass index. We confirmed several previously described MR associations between height and cardiovascular disease traits such as hypertension, hyperlipidemia, coronary heart disease (CHD), and atrial fibrillation, and further uncovered MR associations with venous circulatory disorders and peripheral neuropathy in the presence and absence of diabetes. As a number of traits associated with genetically-predicted height frequently co-occur with CHD, we evaluated effect modification by CHD status of genetically-predicted height associations with risk factors for and complications of CHD. We found modification of effects of MR associations by CHD status for atrial fibrillation/flutter but not for hypertension, hyperlipidemia, or venous circulatory disorders.

CONCLUSIONS

We conclude that height may be an unrecognized but biologically plausible risk factor for several common conditions in adults. However, more studies are needed to reliably exclude horizontal pleiotropy as a driving force behind at least some of the MR associations observed in this study.

A hypothesis-driven study to comprehensively investigate the association between genetic polymorphisms in EPHX2 gene and cardiovascular diseases: Findings from the UK Biobank

BACKGROUND

Epoxyeicosatrienoic acids (EETs) are protective factors against cardiovascular diseases (CVDs) because of their vasodilatory, cholesterol-lowering, and anti-inflammatory effects. Soluble epoxide hydrolase (sEH), encoded by the EPHX2 gene, degrades EETs into less biologically active metabolites. EPHX2 is highly polymorphic, and genetic polymorphisms in EPHX2 have been linked to various types of CVDs, such as coronary heart disease, essential hypertension, and atrial fibrillation recurrence.

METHODS

Based on a priori hypothesis that EPHX2 genetic polymorphisms play an important role in the pathogenesis of CVDs, we comprehensively investigated the associations between 210 genetic polymorphisms in the EPHX2 gene and an array of 118 diseases in the circulatory system using a large sample from the UK Biobank (N = 307,516). The diseases in electronic health records were mapped to the phecode system, which was more representative of independent phenotypes. Survival analyses were employed to examine the effects of EPHX2 variants on CVD incidence, and a phenome-wide association study was conducted to study the impact of EPHX2 polymorphisms on 62 traits, including blood pressure, blood lipid levels, and inflammatory indicators.

RESULTS

A novel association between the intronic variant rs116932590 and the phenotype "aneurysm and dissection of heart" was identified. In addition, the rs149467044 and rs200286838 variants showed nominal evidence of association with arterial aneurysm and cerebrovascular disease, respectively. Furthermore, the variant rs751141, which was linked with a lower hydrolase activity of sEH, was significantly associated with metabolic traits, including blood levels of triglycerides, creatinine, and urate.

CONCLUSIONS

Multiple novel associations observed in the present study highlight the important role of EPHX2 genetic variation in the pathogenesis of CVDs.

First genome-wide association study of 99 body measures derived from 3-dimensional body scans

Body height, body mass index, hip and waist circumference are important risk factors or outcome variables in clinical and epidemiological research with complex underlying genetics. However, these classical anthropometric traits represent only a very limited view on the human body and other traits with potentially higher functional specificity are not yet studied to a larger extent. Participants of LIFE-Adult were assessed by three-dimensional body scanner VITUS XXL determining 99 high-quality anthropometric traits in parallel. Genotyping was performed by Axiom Genome-Wide CEU 1 Array Plate microarray technology and imputation was done using 1000 Genomes phase 3 reference panel. Combined phenotype and genetic information are available for a total of 7,562 participants. Largest heritabilities were estimated for height traits (maximum heritability with h² = 44% for neck height) and 61 traits achieved values larger than 20%. By genome-wide analyses, we identified 16 loci associated with at least one of the 99 traits. Ten of these loci were not described for association with classical anthropometric traits so far. The strongest novel association was observed for 7p14.3 (rs11979006, P = 2.12 × 10⁻⁹) for the trait Back Width with ZNRF2 as the most plausible candidate gene. Loci established for association with classical anthropometric traits were subjected to anthropometric phenome-wide association analysis. From the reported 709 loci, 211 are co-associated with body scanner traits (enrichment: OR = 1.96, P = 1.08 × 10⁻⁶¹). We conclude that genetics of 3D laser-based anthropometry is promising to identify novel loci and to improve the functional understanding of established ones.

Association of Genetic Variants Linked to Late-Onset Alzheimer Disease With Cognitive Test Performance by Midlife

IMPORTANCE

Identifying the youngest age when Alzheimer disease (AD) influences cognition and the earliest affected cognitive domains will improve understanding of the natural history of AD and approaches to early diagnosis.

OBJECTIVE

To evaluate the age at which cognitive differences between individuals with higher compared with lower genetic risk of AD are first apparent and which cognitive assessments show the earliest difference.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study used data from UK Biobank participants of European genetic ancestry, aged 40 years or older, who contributed genotypic and cognitive test data from January 1, 2006, to December 31, 2015. Data analysis was performed from March 10, 2020, to January 4, 2022.

EXPOSURE

The AD genetic risk score (GRS), which is a weighted sum of 23 single-nucleotide variations.

MAIN OUTCOMES AND MEASURES

Seven cognitive tests were administered via touchscreen at in-person visits or online. Cognitive domains assessed included fluid intelligence, episodic memory, processing speed, executive functioning, and prospective memory. Multiple cognitive measures were derived from some tests, yielding 32 separate measures. Interactions between age and AD-GRS for each of the 32 cognitive measures were tested with linear regression using a Bonferroni-corrected P value threshold. For cognitive measures with significant evidence of age by AD-GRS interaction, the youngest age of interaction was assessed with new regression models, with nonlinear specification of age terms. Models with youngest age of interaction from 40 to 70 years, in 1-year increments, were compared, and the best-fitting model for each cognitive measure was chosen. Results across cognitive measures were compared to determine which cognitive indicators showed earliest AD-related change.

RESULTS

A total of 405 050 participants (mean [SD] age, 57.1 [7.9] years; 54.1% female) were included. Sample sizes differed across cognitive tests (from 12 455 to 404 682 participants). The AD-GRS significantly modified the association with age on 13 measures derived from the pairs matching (range in difference in mean cognition per decade increase in age for 1-SD higher AD-GRS, 2.5%-11.5%), symbol digit substitution (range in difference in mean cognition per decade increase in age for 1-SD higher AD-GRS, 2.0%-5.8%), and numeric memory tests (difference in mean cognition per decade increase in age for 1-SD higher AD-GRS, 8.8%) (P = 1.56 × 10-3). Best-fitting models suggested that cognitive scores of individuals with a high vs low AD-GRS began to diverge by 56 years of age for all 13 measures and by 47 years of age for 9 measures.

CONCLUSIONS AND RELEVANCE

In this cross-sectional study, by early midlife, subtle differences in memory and attention were detectable among individuals with higher genetic risk of AD.

Whole Exome Sequencing in Healthy Individuals of Extreme Constitution Types Reveals Differential Disease Risk: A Novel Approach towards Predictive Medicine

Precision medicine aims to move from traditional reactive medicine to a system where risk groups can be identified before the disease occurs. However, phenotypic heterogeneity amongst the diseased and healthy poses a major challenge for identification markers for risk stratification and early actionable interventions. In Ayurveda, individuals are phenotypically stratified into seven constitution types based on multisystem phenotypes termed “Prakriti”. It enables the prediction of health and disease trajectories and the selection of health interventions. We hypothesize that exome sequencing in healthy individuals of phenotypically homogeneous Prakriti types might enable the identification of functional variations associated with the constitution types. Exomes of 144 healthy Prakriti stratified individuals and controls from two genetically homogeneous cohorts (north and western India) revealed differential risk for diseases/traits like metabolic disorders, liver diseases, and body and hematological measurements amongst healthy individuals. These SNPs differ significantly from the Indo-European background control as well. Amongst these we highlight novel SNPs rs304447 (IFIT5) and rs941590 (SERPINA10) that could explain differential trajectories for immune response, bleeding or thrombosis. Our method demonstrates the requirement of a relatively smaller sample size for a well powered study. This study highlights the potential of integrating a unique phenotyping approach for the identification of predictive markers and the at-risk population amongst the healthy.

Robust Mendelian randomization in the presence of residual population stratification, batch effects and horizontal pleiotropy

Mendelian Randomization (MR) studies are threatened by population stratification, batch effects, and horizontal pleiotropy. Although a variety of methods have been proposed to mitigate those problems, residual biases may still remain, leading to highly statistically significant false positives in large databases. Here we describe a suite of sensitivity analysis tools that enables investigators to quantify the robustness of their findings against such validity threats. Specifically, we propose the routine reporting of sensitivity statistics that reveal the minimal strength of violations necessary to explain away the MR results. We further provide intuitive displays of the robustness of the MR estimate to any degree of violation, and formal bounds on the worst-case bias caused by violations multiple times stronger than observed variables. We demonstrate how these tools can aid researchers in distinguishing robust from fragile findings by examining the effect of body mass index on diastolic blood pressure and Townsend deprivation index.

Robust Mendelian randomization in the presence of residual population stratification, batch effects and horizontal pleiotropy

Mendelian Randomization (MR) studies are threatened by population stratification, batch effects, and horizontal pleiotropy. Although a variety of methods have been proposed to mitigate those problems, residual biases may still remain, leading to highly statistically significant false positives in large databases. Here we describe a suite of sensitivity analysis tools that enables investigators to quantify the robustness of their findings against such validity threats. Specifically, we propose the routine reporting of sensitivity statistics that reveal the minimal strength of violations necessary to explain away the MR results. We further provide intuitive displays of the robustness of the MR estimate to any degree of violation, and formal bounds on the worst-case bias caused by violations multiple times stronger than observed variables. We demonstrate how these tools can aid researchers in distinguishing robust from fragile findings by examining the effect of body mass index on diastolic blood pressure and Townsend deprivation index.

Body mass index and childhood symptoms of depression, anxiety, and attention-deficit hyperactivity disorder: A within-family Mendelian randomization study

Background

Higher BMI in childhood is associated with emotional and behavioural problems, but these associations may not be causal. Results of previous genetic studies imply causal effects but may reflect influence of demography and the family environment.

Methods

This study used data on 40,949 8-year-old children and their parents from the Norwegian Mother, Father and Child Cohort Study (MoBa) and Medical Birth Registry of Norway (MBRN). We investigated the impact of BMI on symptoms of depression, anxiety, and attention-deficit hyperactivity disorder (ADHD) at age 8. We applied within-family Mendelian randomization, which accounts for familial effects by controlling for parental genotype.

Results

Within-family Mendelian randomization estimates using genetic variants associated with BMI in adults suggested that a child's own BMI increased their depressive symptoms (per 5 kg/m² increase in BMI, beta = 0.26 S.D., CI = -0.01,0.52, p=0.06) and ADHD symptoms (beta = 0.38 S.D., CI = 0.09,0.63, p=0.009). These estimates also suggested maternal BMI, or related factors, may independently affect a child's depressive symptoms (per 5 kg/m² increase in maternal BMI, beta = 0.11 S.D., CI:0.02,0.09, p=0.01). However, within-family Mendelian randomization using genetic variants associated with retrospectively-reported childhood body size did not support an impact of BMI on these outcomes. There was little evidence from any estimate that the parents' BMI affected the child's ADHD symptoms, or that the child's or parents' BMI affected the child's anxiety symptoms.

Conclusions

We found inconsistent evidence that a child's BMI affected their depressive and ADHD symptoms, and little evidence that a child's BMI affected their anxiety symptoms. There was limited evidence of an influence of parents' BMI. Genetic studies in samples of unrelated individuals, or using genetic variants associated with adult BMI, may have overestimated the causal effects of a child's own BMI.

Funding

This research was funded by the Health Foundation. It is part of the HARVEST collaboration, supported by the Research Council of Norway. Individual co-author funding: the European Research Council, the South-Eastern Norway Regional Health Authority, the Research Council of Norway, Helse Vest, the Novo Nordisk Foundation, the University of Bergen, the South-Eastern Norway Regional Health Authority, the Trond Mohn Foundation, the Western Norway Regional Health Authority, the Norwegian Diabetes Association, the UK Medical Research Council. The Medical Research Council (MRC) and the University of Bristol support the MRC Integrative Epidemiology Unit.

The relationship between modified Nordic diet and resting metabolic rate among overweight and obese women in Tehran, Iran: A cross-sectional study

PURPOSE

Obesity as a worldwide phenomenon is a multifactorial condition. Healthy diets have effect on obesity-related factors like resting metabolic rate (RMR). In the present study, we investigate the association between adherence to modified Nordic diet and RMR among overweight and obese participants.

METHODS

We enrolled 404 overweight and obese (BMI ≥ 25 kg/m² ) women aged 18-48 years in this cross-sectional study. For each participant, anthropometrics measurements, biochemical tests and blood pressure were evaluated. RMR was measured by indirect calorimetry. RMR/kg was also measured. Modified Nordic diet score was measured using a validated 147-item food frequency questionnaire (FFQ).

RESULTS

Among all participants, the mean and standard deviation (SD) for age and body mass index (BMI) were 36.67 years (SD = 9.10) and 31.26 kg/m2 (SD = 4.29), respectively. There was a significant association between RMR/kg status and age, body mass index (BMI), RMR (P < .001), respiratory quotient (RQ), fat percentage (P = .01), systolic blood pressure (SBP) (P = .03) and diastolic blood pressure (DBP) (P = .04), after adjustment for age, BMI, energy intake and physical activity. Participants with the highest adherence to modified Nordic diet had lower odds of hypometabolic status after adjusting for confounders and it was significant (odds ratio (OR) = 3.15, 95% CI = 0.97-10.15, P = .05).

CONCLUSIONS

The present results indicate that adherence to modified Nordic diet is associated with lower odds of hypometabolic status in overweight and obese women. However, more studies are needed to confirm our findings.

Association Study of Genetic Variants in Calcium Signaling-Related Genes With Cardiovascular Diseases

Background: Calcium ions (Ca2+) play an essential role in excitation-contraction coupling in the heart. The association between cardiovascular diseases (CVDs) and genetic polymorphisms in key regulators of Ca2+ homeostasis is well established but still inadequately understood. Methods: The associations of 11,274 genetic variants located in nine calcium signaling-related genes with 118 diseases of the circulatory system were explored using a large sample from the United Kingdom Biobank (N = 308,366). The clinical outcomes in electronic health records were mapped to the phecode system. Survival analyses were employed to study the role of variants in CVDs incidence and mortality. Phenome-wide association studies (PheWAS) were performed to investigate the effect of variants on cardiovascular risk factors. Results: The reported association between rs1801253 in β1-adrenergic receptor (ADRB1) and hypertension was successfully replicated, and we additionally found the blood pressure-lowering G allele of this variant was associated with a delayed onset of hypertension and a decreased level of apolipoprotein A. The association of rs4484922 in calsequestrin 2 (CASQ2) with atrial fibrillation/flutter was identified, and this variant also displayed nominal evidence of association with QRS duration and carotid intima-medial thickness. Moreover, our results indicated suggestive associations of rs79613429 in ryanodine receptor 2 (RYR2) with precordial pain. Conclusion: Multiple novel associations established in our study highlight genetic testing as a useful method for CVDs diagnosis and prevention.

Two genetic analyses to elucidate causality between body mass index and personality

BACKGROUND/OBJECTIVES

Many personality traits correlate with BMI, but the existence and direction of causal links between them are unclear. If personality influences BMI, knowing this causal direction could inform weight management strategies. Knowing that BMI instead influences personality would contribute to a better understanding of the mechanisms of personality development and the possible psychological effects of weight change. We tested the existence and direction of causal links between BMI and personality.

SUBJECTS/METHODS

We employed two genetically informed methods. In Mendelian randomization, allele scores were calculated to summarize genetic propensity for the personality traits neuroticism, worry, and depressive affect and used to predict BMI in an independent sample (N = 3 541). Similarly, an allele score for BMI was used to predict eating-specific and domain-general phenotypic personality scores (PPSs; aggregate scores of personality traits weighted by BMI). In a direction of causation (DoC) analysis, twin data from five countries (N = 5424) were used to assess the fit of four alternative models: PPSs influencing BMI, BMI influencing PPSs, reciprocal causation, and no causation.

RESULTS

In Mendelian randomization, the allele score for BMI predicted domain-general (β = 0.05; 95% CI: 0.02, 0.08; P = 0.003) and eating-specific PPS (β = 0.06; 95% CI: 0.03, 0.09; P < 0.001). The allele score for worry also predicted BMI (β = -0.05; 95% CI: -0.08, -0.02; P < 0.001), while those for neuroticism and depressive affect did not (P ≥ 0.459). In DoC, BMI similarly predicted domain-general (β = 0.21; 95% CI:, 0.18, 0.24; P < 0.001) and eating-specific personality traits (β = 0.19; 95% CI:, 0.16, 0.22; P < 0.001), suggesting causality from BMI to personality traits. In exploratory analyses, links between BMI and domain-general personality traits appeared reciprocal for higher-weight individuals (BMI > ~25).

CONCLUSIONS

Although both genetic analyses suggested an influence of BMI on personality traits, it is not yet known if weight management interventions could influence personality. Personality traits may influence BMI in turn, but effects in this direction appeared weaker.

COVID-19 susceptibility variants associate with blood clots, thrombophlebitis and circulatory diseases

Epidemiological studies suggest that individuals with comorbid conditions including diabetes, chronic lung, inflammatory and vascular disease, are at higher risk of adverse COVID-19 outcomes. Genome-wide association studies have identified several loci associated with increased susceptibility and severity for COVID-19. However, it is not clear whether these associations are genetically determined or not. We used a Phenome-Wide Association (PheWAS) approach to investigate the role of genetically determined COVID-19 susceptibility on disease related outcomes. PheWAS analyses were performed in order to identify traits and diseases related to COVID-19 susceptibility and severity, evaluated through a predictive COVID-19 risk score. We utilised phenotypic data in up to 400,000 individuals from the UK Biobank, including Hospital Episode Statistics and General Practice data. We identified a spectrum of associations between both genetically determined COVID-19 susceptibility and severity with a number of traits. COVID-19 risk was associated with increased risk for phlebitis and thrombophlebitis (OR = 1.11, p = 5.36e-08). We also identified significant signals between COVID-19 susceptibility with blood clots in the leg (OR = 1.1, p = 1.66e-16) and with increased risk for blood clots in the lung (OR = 1.12, p = 1.45 e-10). Our study identifies significant association of genetically determined COVID-19 with increased blood clot events in leg and lungs. The reported associations between both COVID-19 susceptibility and severity and other diseases adds to the identification and stratification of individuals at increased risk, adverse outcomes and long-term effects.

Impact of sex hormone-binding globulin on the human phenome

Background: Sex hormone-binding globulin (SHBG) is a circulating glycoprotein and a regulator of sex hormone levels, which has been shown to influence various traits and diseases. The molecular nature of SHBG makes it a feasible target for preventative or therapeutic interventions. A systematic study of its effects across the human phenome may uncover novel associations. Methods: We used a Mendelian randomization phenome-wide association study (MR-pheWAS) approach to systematically appraise the potential functions of SHBG while reducing potential biases such as confounding and reverse causation common to the literature. We searched for potential causal effects of SHBG in UK Biobank (N = 334 977) and followed-up our top findings using two-sample MR analyses to evaluate whether estimates may be biased due to horizontal pleiotropy. Results: Results of the MR-pheWAS across over 21 000 outcome phenotypes identified 12 phenotypes associated with genetically elevated SHBG after Bonferroni correction for multiple testing. Follow-up analysis using two-sample MR indicated the associations of increased natural log SHBG with higher impedance of the arms and whole body, lower pulse rate, lower bone density, higher odds of hip replacement, lower odds of high cholesterol or cholesterol medication use and higher odds of gallbladder removal. Conclusions: Our systematic MR-pheWAS of SHBG, which was comprehensive to the range of phenotypes available in UK Biobank, suggested that higher circulating SHBG affects the body impedance, bone density and cholesterol levels, among others. These phenotypes should be prioritized in future studies aiming to investigate the biological effects of SHBG or develop targets for therapeutic intervention.

Metabolomic profiles discriminating anxiety from depression

OBJECTIVE

Depression has been associated with metabolomic alterations. Depressive and anxiety disorders are often comorbid diagnoses and are suggested to share etiology. We investigated whether differential metabolomic alterations are present between anxiety and depressive disorders and which clinical characteristics of these disorders are related to metabolomic alterations.

METHODS

Data were from the Netherlands Study of Depression and Anxiety (NESDA), including individuals with current comorbid anxiety and depressive disorders (N = 531), only a current depression (N = 304), only a current anxiety disorder (N = 548), remitted depressive and/or anxiety disorders (N = 897), and healthy controls (N = 634). Forty metabolites from a proton nuclear magnetic resonance lipid-based metabolomics panel were analyzed. First, we examined differences in metabolites between disorder groups and healthy controls. Next, we assessed whether depression or anxiety clinical characteristics (severity and symptom duration) were associated with metabolites.

RESULTS

As compared to healthy controls, seven metabolomic alterations were found in the group with only depression, reflecting an inflammatory (glycoprotein acetyls; Cohen's d = 0.12, p = 0.002) and atherogenic-lipoprotein-related (e.g., apolipoprotein B: Cohen's d = 0.08, p = 0.03, and VLDL cholesterol: Cohen's d = 0.08, p = 0.04) profile. The comorbid group showed an attenuated but similar pattern of deviations. No metabolomic alterations were found in the group with only anxiety disorders. The majority of metabolites associated with depression diagnosis were also associated with depression severity; no associations were found with anxiety severity or disease duration.

CONCLUSION

While substantial clinical overlap exists between depressive and anxiety disorders, this study suggests that altered inflammatory and atherogenic-lipoprotein-related metabolomic profiles are uniquely associated with depression rather than anxiety disorders.

Observational or Genetically Predicted Higher Vegetable Intake and Kidney Function Impairment: An Integrated Population-Scale Cross-Sectional Analysis and Mendelian Randomization Study

BACKGROUND

Further exploration of the possible effects of vegetable intake on kidney function is warranted.

OBJECTIVE

We aimed to study the causality of the association between vegetable intake and kidney function by implementing Mendelian randomization (MR) analysis.

METHODS

This study comprised a cross-sectional dietary investigation using UK Biobank data and MR analysis. For the cross-sectional investigation, 432,732 participants aged 40-69 y from the UK Biobank cohort were included. Self-reported vegetable intake was the exposure, and the outcomes were the estimated glomerular filtration rate (eGFR) and chronic kidney disease (CKD). Next, we included 337,138 participants of white British ancestry in the UK Biobank, and a genome-wide association study (GWAS) was performed to generate a genetic instrument. For MR, we first performed polygenic score (PGS)-based 1-sample MR. In addition, 2-sample MR was performed with CKDGen GWAS for kidney function traits, and the inverse variance weighted method was the main MR method.

RESULTS

Higher vegetable intake was cross-sectionally associated with a higher eGFR (per heaped tablespoon increase; β: 0.154; 95% CI: 0.144, 0.165) and lower odds of CKD (OR: 0.975; 95% CI: 0.968, 0.982). A PGS for vegetable intake was significantly associated with a higher eGFR [per ordinal category increase (0, 1-3, 4-6, ≥7 tablespoons per day); β: 4.435; 95% CI: 2.337, 6.533], but the association with CKD remained nonsignificant (OR: 0.468; 95% CI: 0.143, 1.535). In the 2-sample MR, the causal estimates indicated that a higher genetically predicted vegetable intake was associated with a higher eGFR (percent change; β: 3.071; 95% CI: 0.602, 0.560) but nonsignificantly associated with the risk of CKD (OR: 0.560; 95% CI: 0.289, 1.083) in the European ancestry data from the CKDGen.

CONCLUSIONS

This study suggests that higher vegetable intake may have a causal effect on higher eGFRs in the European population.

The relationship between body mass index and the risk of development of Dupuytren's disease: a Mendelian randomization study

We performed Mendelian randomization analyses of body mass index and waist-hip ratio adjusted for body mass index in Dupuytren's disease using summary statistics from genome-wide association study meta-analyses. We found that adiposity is causally protective against Dupuytren's disease, with the inverse-variance weighted Mendelian randomization analysis estimating that a 1 standard deviation increase in body mass index (equivalent to 4.8 kg/m²) leads to 28% (95% confidence interval: 18-37%) lower relative odds of developing Dupuytren's disease, and a 1 standard deviation increase in waist-hip ratio adjusted for body mass index (equivalent to a waist-hip ratio of 0.09) leads to 26% (95% confidence interval: 6-42%) lower relative odds of developing Dupuytren's disease. We conclude from this study that regardless of the well-established negative health effects of obesity, the raised body mass index is associated with a lower risk of Dupuytren's disease and may be causally protective for the development of the disease.

Phenome-wide investigation of the causal associations between childhood BMI and adult trait outcomes: a two-sample Mendelian randomization study

Background

Childhood obesity is reported to be associated with the risk of many diseases in adulthood. However, observational studies cannot fully account for confounding factors. We aimed to systematically assess the causal associations between childhood body mass index (BMI) and various adult traits/diseases using two-sample Mendelian randomization (MR).

Methods

After data filtering, 263 adult traits genetically correlated with childhood BMI (P < 0.05) were subjected to MR analyses. Inverse-variance weighted, MR-Egger, weighted median, and weighted mode methods were used to estimate the causal effects. Multivariable MR analysis was performed to test whether the effects of childhood BMI on adult traits are independent from adult BMI.

Results

We identified potential causal effects of childhood obesity on 60 adult traits (27 disease-related traits, 27 lifestyle factors, and 6 other traits). Higher childhood BMI was associated with a reduced overall health rating (β = − 0.10, 95% CI − 0.13 to − 0.07, P = 6.26 × 10⁻¹¹). Specifically, higher childhood BMI was associated with increased odds of coronary artery disease (OR = 1.09, 95% CI 1.06 to 1.11, P = 4.28 × 10⁻¹¹), essential hypertension (OR = 1.12, 95% CI 1.08 to 1.16, P = 1.27 × 10⁻¹¹), type 2 diabetes (OR = 1.36, 95% CI 1.30 to 1.43, P = 1.57 × 10⁻³⁴), and arthrosis (OR = 1.09, 95% CI 1.06 to 1.12, P = 8.80 × 10⁻⁹). However, after accounting for adult BMI, the detrimental effects of childhood BMI on disease-related traits were no longer present (P > 0.05). For dietary habits, different from conventional understanding, we found that higher childhood BMI was associated with low calorie density food intake. However, this association might be specific to the UK Biobank population.

Conclusions

In summary, we provided a phenome-wide view of the effects of childhood BMI on adult traits. Multivariable MR analysis suggested that the associations between childhood BMI and increased risks of diseases in adulthood are likely attributed to individuals remaining obese in later life. Therefore, ensuring that childhood obesity does not persist into later life might be useful for reducing the detrimental effects of childhood obesity on adult diseases.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-021-00865-3.

Genetically predicted education attainment in relation to somatic and mental health

A deeper understanding of the causal links from education level to health outcomes may shed a light for disease prevention. In the present Mendelian randomization study, we found that genetically higher education level was associated with lower risk of major mental disorders and most somatic diseases, independent of intelligence. Higher education level adjusted for intelligence was associated with lower risk of suicide attempts, insomnia, major depressive disorder, heart failure, stroke, coronary artery disease, lung cancer, breast cancer, type 2 diabetes and rheumatoid arthritis but with higher risk of obsessive-compulsive disorder, anorexia nervosa, anxiety, bipolar disorder and prostate cancer. Higher education level was associated with reduced obesity and smoking, which mediated quite an extent of the associations between education level and health outcomes. These findings emphasize the importance of education to reduce the burden of common diseases.

An Academic Clinician's Road Map to Hypertension Genomics: Recent Advances and Future Directions MMXX

At the dawn of the new decade, it is judicious to reflect on the boom of knowledge about polygenic risk for essential hypertension supplied by the wealth of genome-wide association studies. Hypertension continues to account for significant cardiovascular morbidity and mortality, with increasing prevalence anticipated. Here, we overview recent advances in the use of big data to understand polygenic hypertension, as well as opportunities for future innovation to translate this windfall of knowledge into clinical benefit.

Short or Long Sleep Duration and CKD: A Mendelian Randomization Study

BACKGROUND

Studies have found sleeping behaviors, such as sleep duration, to be associated with kidney function and cardiovascular disease risk. However, whether short or long sleep duration is a causative factor for kidney function impairment has been rarely studied.

METHODS

We studied data from participants aged 40-69 years in the UK Biobank prospective cohort, including 25,605 self-reporting short-duration sleep (<6 hours per 24 hours), 404,550 reporting intermediate-duration sleep (6-8 hours), and 35,659 reporting long-duration sleep (≥9 hours) in the clinical analysis. Using logistic regression analysis, we investigated the observational association between the sleep duration group and prevalent CKD stages 3-5, analyzed by logistic regression analysis. We performed Mendelian randomization (MR) analysis involving 321,260 White British individuals using genetic instruments (genetic variants linked with short- or long-duration sleep behavior as instrumental variables). We performed genetic risk score analysis as a one-sample MR and extended the finding with a two-sample MR analysis with CKD outcome information from the independent CKDGen Consortium genome-wide association study meta-analysis.

RESULTS

Short or long sleep duration clinically associated with higher prevalence of CKD compared with intermediate duration. The genetic risk score for short (but not long) sleep was significantly related to CKD (per unit reflecting a two-fold increase in the odds of the phenotype; adjusted odds ratio, 1.80; 95% confidence interval, 1.25 to 2.60). Two-sample MR analysis demonstrated causal effects of short sleep duration on CKD by the inverse variance weighted method, supported by causal estimates from MR-Egger regression.

CONCLUSIONS

These findings support an adverse effect of a short sleep duration on kidney function. Clinicians may encourage patients to avoid short-duration sleeping behavior to reduce CKD risk.

Genetic mapping of etiologic brain cell types for obesity

The underlying cell types mediating predisposition to obesity remain largely obscure. Here, we integrated recently published single-cell RNA-sequencing (scRNA-seq) data from 727 peripheral and nervous system cell types spanning 17 mouse organs with body mass index (BMI) genome-wide association study (GWAS) data from >457,000 individuals. Developing a novel strategy for integrating scRNA-seq data with GWAS data, we identified 26, exclusively neuronal, cell types from the hypothalamus, subthalamus, midbrain, hippocampus, thalamus, cortex, pons, medulla, pallidum that were significantly enriched for BMI heritability (p<1.6×10⁻⁴). Using genes harboring coding mutations associated with obesity, we replicated midbrain cell types from the anterior pretectal nucleus and periaqueductal gray (p<1.2×10⁻⁴). Together, our results suggest that brain nuclei regulating integration of sensory stimuli, learning and memory are likely to play a key role in obesity and provide testable hypotheses for mechanistic follow-up studies.

Polymorphic Inversions Underlie the Shared Genetic Susceptibility of Obesity-Related Diseases

The burden of several common diseases including obesity, diabetes, hypertension, asthma, and depression is increasing in most world populations. However, the mechanisms underlying the numerous epidemiological and genetic correlations among these disorders remain largely unknown. We investigated whether common polymorphic inversions underlie the shared genetic influence of these disorders. We performed an inversion association analysis including 21 inversions and 25 obesity-related traits on a total of 408,898 Europeans and validated the results in 67,299 independent individuals. Seven inversions were associated with multiple diseases while inversions at 8p23.1, 16p11.2, and 11q13.2 were strongly associated with the co-occurrence of obesity with other common diseases. Transcriptome analysis across numerous tissues revealed strong candidate genes for obesity-related traits. Analyses in human pancreatic islets indicated the potential mechanism of inversions in the susceptibility of diabetes by disrupting the cis-regulatory effect of SNPs from their target genes. Our data underscore the role of inversions as major genetic contributors to the joint susceptibility to common complex diseases.

The Use Of Genetic Correlation And Mendelian Randomization Studies To Increase Our Understanding of Relationships Between Complex Traits

PURPOSE OF THE REVIEW

Increasing access to large-scale genetic datasets in population-based studies allows for genetic association studies as a means to examine previously known and novel relationships among complex traits. In this review, we discuss two widely used approaches to leverage genetic data to study the links between traits: Genome-wide genetic correlation and Mendelian Randomization (MR) studies.

RECENT FINDINGS

Both genetic correlation and MR studies have provided important novel insights. However, although they are less sensitive to many sources of bias present in traditional, observational epidemiology, they still rely on assumptions that in practice might be difficult to assess. To overcome this, development of novel methods less sensitive to these assumptions is an active area of research.

SUMMARY

We believe that as population-based genetic datasets grow larger and novel methods allowing for weaker forms of current assumptions become available, genetic correlation and MR studies will become an integral part of genetic epidemiology studies.

A cross-disorder PRS-pheWAS of 5 major psychiatric disorders in UK Biobank

Psychiatric disorders are highly heritable and associated with a wide variety of social adversity and physical health problems. Using genetic liability (rather than phenotypic measures of disease) as a proxy for psychiatric disease risk can be a useful alternative for research questions that would traditionally require large cohort studies with long-term follow up. Here we conducted a hypothesis-free phenome-wide association study in about 330,000 participants from the UK Biobank to examine associations of polygenic risk scores (PRS) for five psychiatric disorders (major depression (MDD), bipolar disorder (BP), schizophrenia (SCZ), attention-deficit/ hyperactivity disorder (ADHD) and autism spectrum disorder (ASD)) with 23,004 outcomes in UK Biobank, using the open-source PHESANT software package. There was evidence after multiple testing (p<2.55x10^-06) for associations of PRSs with 294 outcomes, most of them attributed to associations of PRS_MDD (n = 167) and PRS_SCZ (n = 157) with mental health factors. Among others, we found strong evidence of association of higher PRS_ADHD with 1.1 months younger age at first sexual intercourse [95% confidence interval [CI]: -1.25,-0.92] and a history of physical maltreatment; PRS_ASD with 0.01% lower erythrocyte distribution width [95%CI: -0.013,-0.007]; PRS_SCZ with 0.95 lower odds of playing computer games [95%CI:0.95,0.96]; PRS_MDD with a 0.12 points higher neuroticism score [95%CI:0.111,0.135] and PRS_BP with 1.03 higher odds of having a university degree [95%CI:1.02,1.03]. We were able to show that genetic liabilities for five major psychiatric disorders associate with long-term aspects of adult life, including socio-demographic factors, mental and physical health. This is evident even in individuals from the general population who do not necessarily present with a psychiatric disorder diagnosis.

Psychiatric disorders are associated with a wide range of adverse health, social and economic problems. Our study investigated the association of genetic risk for five common psychiatric disorders with socio- demographics, lifestyle and health of about 330,000 participants in the UK Biobank using a systematic, hypothesis-free approach. We found that genetic risk for attention deficit/hyperactivity disorder (ADHD) and bipolar disorder were most strongly associated with lifestyle factors, such as time of first sexual intercourse and educational attainment. Genetic risks for autism spectrum disorder and schizophrenia were associated with altered blood cell counts and decreased risk of playing computer games, respectively. Increased genetic risk for depression was associated with other mental health outcomes such as neuroticism and irritability. In general, our results suggest that genetic risk for psychiatric disorders associates with a range of health and lifestyle traits that were measured in adulthood, in individuals from the general population who do not necessarily present with a psychiatric disorder diagnosis. However, it is important to note that these associations are not necessary causal but can also represent genetic correlation or be influenced by other factors, such as socio-economic factors and selection into the cohort. The findings should inform future research using causally informative designs.

Detecting Genotype-Population Interaction Effects by Ancestry Principal Components

Heterogeneity in the phenotypic mean and variance across populations is often observed for complex traits. One way to understand heterogeneous phenotypes lies in uncovering heterogeneity in genetic effects. Previous studies on genetic heterogeneity across populations were typically based on discrete groups in populations stratified by different countries or cohorts, which ignored the difference of population characteristics for the individuals within each group and resulted in loss of information. Here, we introduce a novel concept of genotype-by-population (G × P) interaction where population is defined by the first and second ancestry principal components (PCs), which are less likely to be confounded with country/cohort-specific factors. We applied a reaction norm model fitting each of 70 complex traits with significant SNP-heritability and the PCs as covariates to examine G × P interactions across diverse populations including white British and other white Europeans from the UK Biobank (N = 22,229). Our results demonstrated a significant population genetic heterogeneity for behavioral traits such as age at first sexual intercourse and academic qualification. Our approach may shed light on the latent genetic architecture of complex traits that underlies the modulation of genetic effects across different populations.

Identifying potential causal effects of age at menarche: a Mendelian randomization phenome-wide association study

BACKGROUND

Age at menarche has been associated with various health outcomes. We aimed to identify potential causal effects of age at menarche on health-related traits in a hypothesis-free manner.

METHODS

We conducted a Mendelian randomization phenome-wide association study (MR-pheWAS) of age at menarche with 17,893 health-related traits in UK Biobank (n = 181,318) using PHESANT. The exposure of interest was the genetic risk score for age at menarche. We conducted a second MR-pheWAS after excluding SNPs associated with BMI from the genetic risk score, to examine whether results might be due to the genetic overlap between age at menarche and BMI. We followed up a subset of health-related traits to investigate MR assumptions and seek replication in independent study populations.

RESULTS

Of the 17,893 tests performed in our MR-pheWAS, we identified 619 associations with the genetic risk score for age at menarche at a 5% false discovery rate threshold, of which 295 were below a Bonferroni-corrected P value threshold. These included potential effects of younger age at menarche on lower lung function, higher heel bone-mineral density, greater burden of psychosocial/mental health problems, younger age at first birth, higher risk of childhood sexual abuse, poorer cardiometabolic health, and lower physical activity. After exclusion of variants associated with BMI, the genetic risk score for age at menarche was related to 37 traits at a 5% false discovery rate, of which 29 were below a Bonferroni-corrected P value threshold. We attempted to replicate findings for bone-mineral density, lung function, neuroticism, and childhood sexual abuse using 5 independent cohorts/consortia. While estimates for lung function, higher bone-mineral density, neuroticism, and childhood sexual abuse in replication cohorts were consistent with UK Biobank estimates, confidence intervals were wide and often included the null.

CONCLUSIONS

The genetic risk score for age at menarche was related to a broad range of health-related traits. Follow-up analyses indicated imprecise evidence of an effect of younger age at menarche on greater bone-mineral density, lower lung function, higher neuroticism score, and greater risk of childhood sexual abuse in the smaller replication samples available; hence, these findings need further exploration when larger independent samples become available.

A Comprehensive Genome-Wide and Phenome-Wide Examination of BMI and Obesity in a Northern Nevadan Cohort

The aggregation of Electronic Health Records (EHR) and personalized genetics leads to powerful discoveries relevant to population health. Here we perform genome-wide association studies (GWAS) and accompanying phenome-wide association studies (PheWAS) to validate phenotype-genotype associations of BMI, and to a greater extent, severe Class 2 obesity, using comprehensive diagnostic and clinical data from the EHR database of our cohort. Three GWASs of 500,000 variants on the Illumina platform of 6,645 Healthy Nevada participants identified several published and novel variants that affect BMI and obesity. Each GWAS was followed with two independent PheWASs to examine associations between extensive phenotypes (incidence of diagnoses, condition, or disease), significant SNPs, BMI, and incidence of extreme obesity. The first GWAS examines associations with BMI in a cohort with no type 2 diabetics, focusing exclusively on BMI. The second GWAS examines associations with BMI in a cohort that includes type 2 diabetics. In the second GWAS, type 2 diabetes is a comorbidity, and thus becomes a covariate in the statistical model. The intersection of significant variants of these two studies is surprising. The third GWAS is a case vs. control study, with cases defined as extremely obese (Class 2 or 3 obesity), and controls defined as participants with BMI between 18.5 and 25. This last GWAS identifies strong associations with extreme obesity, including established variants in the FTO and NEGR1 genes, as well as loci not yet linked to obesity. The PheWASs validate published associations between BMI and extreme obesity and incidence of specific diagnoses and conditions, yet also highlight novel links. This study emphasizes the importance of our extensive longitudinal EHR database to validate known associations and identify putative novel links with BMI and obesity.

A robust and efficient method for Mendelian randomization with hundreds of genetic variants

Mendelian randomization (MR) is an epidemiological technique that uses genetic variants to distinguish correlation from causation in observational data. The reliability of a MR investigation depends on the validity of the genetic variants as instrumental variables (IVs). We develop the contamination mixture method, a method for MR with two modalities. First, it identifies groups of genetic variants with similar causal estimates, which may represent distinct mechanisms by which the risk factor influences the outcome. Second, it performs MR robustly and efficiently in the presence of invalid IVs. Compared to other robust methods, it has the lowest mean squared error across a range of realistic scenarios. The method identifies 11 variants associated with increased high-density lipoprotein-cholesterol, decreased triglyceride levels, and decreased coronary heart disease risk that have the same directions of associations with various blood cell traits, suggesting a shared mechanism linking lipids and coronary heart disease risk mediated via platelet aggregation.

Causal relationships between obesity and the leading causes of death in women and men

Obesity traits are causally implicated with risk of cardiometabolic diseases. It remains unclear whether there are similar causal effects of obesity traits on other non-communicable diseases. Also, it is largely unexplored whether there are any sex-specific differences in the causal effects of obesity traits on cardiometabolic diseases and other leading causes of death. We constructed sex-specific genetic risk scores (GRS) for three obesity traits; body mass index (BMI), waist-hip ratio (WHR), and WHR adjusted for BMI, including 565, 324, and 337 genetic variants, respectively. These GRSs were then used as instrumental variables to assess associations between the obesity traits and leading causes of mortality in the UK Biobank using Mendelian randomization. We also investigated associations with potential mediators, including smoking, glycemic and blood pressure traits. Sex-differences were subsequently assessed by Cochran's Q-test (Phet). A Mendelian randomization analysis of 228,466 women and 195,041 men showed that obesity causes coronary artery disease, stroke (particularly ischemic), chronic obstructive pulmonary disease, lung cancer, type 2 and 1 diabetes mellitus, non-alcoholic fatty liver disease, chronic liver disease, and acute and chronic renal failure. Higher BMI led to higher risk of type 2 diabetes in women than in men (Phet = 1.4×10-5). Waist-hip-ratio led to a higher risk of chronic obstructive pulmonary disease (Phet = 3.7×10-6) and higher risk of chronic renal failure (Phet = 1.0×10-4) in men than women. Obesity traits have an etiological role in the majority of the leading global causes of death. Sex differences exist in the effects of obesity traits on risk of type 2 diabetes, chronic obstructive pulmonary disease, and renal failure, which may have downstream implications for public health.

Performing different kinds of physical exercise differentially attenuates the genetic effects on obesity measures: Evidence from 18,424 Taiwan Biobank participants

Obesity is a worldwide health problem that is closely linked to many metabolic disorders. Regular physical exercise has been found to attenuate the genetic predisposition to obesity. However, it remains unknown what kinds of exercise can modify the genetic risk of obesity. This study included 18,424 unrelated Han Chinese adults aged 30–70 years who participated in the Taiwan Biobank (TWB). A total of 5 obesity measures were investigated here, including body mass index (BMI), body fat percentage (BFP), waist circumference (WC), hip circumference (HC), and waist-to-hip ratio (WHR). Because there have been no large genome-wide association studies on obesity for Han Chinese, we used the TWB internal weights to construct genetic risk scores (GRSs) for each obesity measure, and then test the significance of GRS-by-exercise interactions. The significance level throughout this work was set at 0.05/550 = 9.1x10^-5 because a total of 550 tests were performed. Performing regular exercise was found to attenuate the genetic effects on 4 obesity measures, including BMI, BFP, WC, and HC. Among the 18 kinds of self-reported regular exercise, 6 mitigated the genetic effects on at least one obesity measure. Regular jogging blunted the genetic effects on BMI, BFP, and HC. Mountain climbing, walking, exercise walking, international standard dancing, and a longer practice of yoga also attenuated the genetic effects on BMI. Exercises such as cycling, stretching exercise, swimming, dance dance revolution, and qigong were not found to modify the genetic effects on any obesity measure. Across all 5 obesity measures, regular jogging consistently presented the most significant interactions with GRSs. Our findings show that the genetic effects on obesity measures can be decreased to various extents by performing different kinds of exercise. The benefits of regular physical exercise are more impactful in subjects who are more predisposed to obesity.

The complex interplay of genetics and lifestyle makes obesity a challenging issue. Previous studies have found performing regular physical exercise could blunt the genetic effects on body mass index (BMI). However, BMI does not take into account lean body mass or identify central obesity. Moreover, it remains unclear what kinds of exercise could more effectively attenuate the genetic effects on obesity measures. With a sample of 18,424 unrelated Han Chinese adults, we comprehensively investigated gene-exercise interactions on 5 obesity measures: BMI, body fat percentage, waist circumference, hip circumference, and waist-to-hip ratio. Moreover, we tested whether the genetic effects on obesity measures could be modified by any of 18 kinds of self-reported regular exercise. Because no large genome-wide association studies on obesity have been done for Han Chinese, we constructed genetic risk scores with internal weights for analyses. Among these exercises, regular jogging consistently presented the strongest evidence to mitigate the genetic effects on all 5 obesity measures. Moreover, mountain climbing, walking, exercise walking, international standard dancing, and a longer practice of yoga attenuated the genetic effects on BMI. The benefits of regularly performing these 6 kinds of exercise are more impactful in subjects who are more predisposed to obesity.

A data-driven approach for studying the role of body mass in multiple diseases: a phenome-wide registry-based case-control study in the UK Biobank

BACKGROUND

Mendelian randomisation allows for the testing of causal effects in situations where clinical trials are challenging to do. In this hypothesis-free, data-driven phenome-wide association study (PheWAS), we sought to assess possible associations of high body-mass index (BMI) with multiple disease outcomes.

METHODS

For this registry-based case-control PheWAS, we used genome-wide data available from the UK Biobank to construct a genetic risk score of 76 variants related to BMI. Eligible UK Biobank participants were aged 37-73 years during recruitment, were white British, were unrelated to each other, and had available genetic information. Disease outcomes from these participants were mapped to a phenotype code (phecode). Participants with a phecode of interest were recoded as cases, whereas participants without a phecode of interest or any codes under a parent phecode were classified as controls. We did a PheWAS to analyse possible associations between the BMI genetic risk score and a range of disease outcomes. Disease associations passing stringent correction for multiple testing (Bonferroni corrected threshold p<5·4 × 10^-5, false discovery rate corrected p<0·0074) were assessed for causal association with use of inverse-variance weighted mendelian randomisation. We did sensitivity analyses to assess pleiotropy and stability of estimation with use of weighted median, weighted mode, Egger regression, and mendelian randomisation pleiotropy residual sum and outlier methods.

FINDINGS

Our study population comprised 337 536 UK Biobank participants, and analyses were done for 925 unique phecodes from 17 different disease categories. After Bonferroni correction, PheWAS identified that BMI genetic risk score was associated with hospital-diagnosed obesity and 58 other outcomes; 30 distinct disease associations were supported by the mendelian randomisation analyses. 30 distinct disease associations were supported by the mendelian randomisation analyses. In inverse-variance weighted mendelian randomisation, genetically determined BMI was associated with endocrine disorders (odds ratio per one SD or 4·1 kg/m² higher BMI 2·72, 95% CI 2·33-3·29 for type 2 diabetes; 2·11, 1·62-2·76 for type 1 diabetes; and 1·46, 1·25-1·70 for hypothyroidism), circulatory diseases (1·96, 1·53-2·51 for phlebitis and thrombophlebitis; 1·89, 1·39-2·57 for cardiomegaly; 1·68, 1·35-2·09 for congestive heart failure; 1·55, 1·37-1·76 for hypertension; 1·31, 1·13-1·52 for ischaemic heart disease; and 1·25, 1·14-1·37 for cardiac dysrhythmias), and inflammatory or dermatological conditions (2·00, 1·72-2·23 for superficial cellulitis and abscess; 3·37, 2·17-5·25 for chronic ulcers of leg and foot; 4·99, 2·54-9·82 for gangrene; and 2·24, 1·53-3·28 for atopy). Mendelian randomisation analyses provided further support for a causal effect of BMI on renal failure, osteoarthrosis, neurological (insomnia and peripheral nerve disorders) and respiratory diseases (asthma and chronic bronchitis), structural problems (hernias and knee derangement), and chemotherapy treatment. Mendelian randomisation with Egger regression produced consistently wider CIs compared with those of other methods. 26 of 72 distinct diseases detected under false discovery rate correction produced consistent estimates across at least four mendelian randomisation methods, and consistent evidence across all five approaches was obtained for 14 diseases.

INTERPRETATION

Our data-driven approach identified a range of diseases as possibly affected by high BMI. This population-level screening approximated the accumulated consequences of high BMI, whereas the true effects might be more complex and vary by life stage. Our results highlight the importance of obesity prevention and effective management of obesity-related comorbidities.

FUNDING

National Health and Medical Research Council of Australia.

Assessing for interaction between APOE ε4, sex, and lifestyle on cognitive abilities

OBJECTIVE

To test for interactions between APOE ε4 genotype and lifestyle factors on worse cognitive abilities in UK Biobank.

METHODS

Using UK Biobank cohort data, we tested for interactions between APOE ε4 allele presence, lifestyle factors of alcohol intake, smoking, total physical activity and obesity, and sex, on cognitive tests of reasoning, information processing speed, and executive function (n range = 70,988-324,725 depending on the test). We statistically adjusted for potential confounders of age, sex, deprivation, cardiometabolic conditions, and educational attainment.

RESULTS

There were significant associations between APOE ε4 and worse cognitive abilities, independent of potential confounders, and between lifestyle risk factors and worse cognitive abilities; however, there were no interactions at multiple correction-adjusted p < 0.05, against our hypotheses.

CONCLUSIONS

Our results do not provide support for the idea that ε4 genotype increases vulnerability to the negative effects of lifestyle risk factors on cognitive ability, but rather support a primarily outright association between APOE ε4 genotype and worse cognitive ability.