Search

Abstract Background Pubertal growth patterns correlate with future health outcomes. However, the genetic mechanisms mediating growth trajectories remain largely unknown. Here, we modeled longitudinal height growth with Super-Imposition by Translation And Rotation (SITAR) growth curve analysis on ~ 56,000 trans-ancestry samples with repeated height measurements from age 5 years to adulthood. We performed genetic analysis on six phenotypes representing the magnitude, timing, and intensity of the pubertal growth spurt. To investigate the lifelong impact of genetic variants associated with pubertal growth trajectories, we performed genetic correlation analyses and phenome-wide association studies in the Penn Medicine BioBank and the UK Biobank. Results Large-scale growth modeling enables an unprecedented view of adolescent growth across contemporary and 20th-century pediatric cohorts. We identify 26 genome-wide significant loci and leverage trans-ancestry data to perform fine-mapping. Our data reveals genetic relationships between pediatric height growth and health across the life course, with different growth trajectories correlated with different outcomes. For instance, a faster tempo of pubertal growth correlates with higher bone mineral density, HOMA-IR, fasting insulin, type 2 diabetes, and lung cancer, whereas being taller at early puberty, taller across puberty, and having quicker pubertal growth were associated with higher risk for atrial fibrillation. Conclusion We report novel genetic associations with the tempo of pubertal growth and find that genetic determinants of growth are correlated with reproductive, glycemic, respiratory, and cardiac traits in adulthood. These results aid in identifying specific growth trajectories impacting lifelong health and show that there may not be a single “optimal” pubertal growth pattern.

ObjectiveThis systematic review and meta-analysis of prospective cohort studies examined the associations between egg and dietary cholesterol intake and the risk of mortality from all causes, including cardiovascular disease (CVD) and cancer.MethodsWe searched PubMed, Scopus, ISI Web of Knowledge, and Google Scholar until April 2021, as well as references to the relevant articles retrieved. Random-effects models were used to calculate summary relative risk (RR) and 95% confidence intervals (CIs) for the highest vs. lowest categories of egg and dietary cholesterol intake. Also, linear and non-linear dose–response analyses were conducted to examine the dose-response relationships.ResultsWe included 55 studies, comprising data from 2,772,486 individuals with 228,425, 71,745, and 67,211 cases of all-cause, CVD, and cancer mortality, respectively. Intake of each additional egg per day was associated with a 7% higher risk of all-cause (1.07, 95% CI: 1.02–1.12, I2 = 84.8%) and a 13% higher risk of cancer mortality (1.13, 95% CI: 1.06–1.20, I2 = 54.2%), but was not associated with CVD mortality (1.00, 95% CI: 0.92–1.09, I2 = 81.5%). Non-linear analyses showed increased risks for egg consumption of more than 1.5 and 0.5 eggs/day, respectively. Each 100 mg/day increment in dietary cholesterol intake was associated with a 6% higher risk of all-cause mortality (1.06, 95% CI: 1.03–1.08, I2 = 34.5%) and a 6% higher risk of cancer mortality (1.06, 95% CI: 1.05–1.07, I2 = 0%), but was not associated with CVD mortality (1.04, 95% CI: 0.99–1.10, I2 = 85.9%). Non-linear analyses demonstrated elevated risks of CVD and cancer mortality for intakes more than 450 and 250 mg/day, respectively.Conclusions and RelevanceHigh-dietary intake of eggs and cholesterol was associated with all-cause and cancer mortality. Little evidence for elevated risks was seen for intakes below 0.5 egg/day or 250 mg/day of dietary cholesterol. Our findings should be considered with caution because of small risk estimates and moderate between-study heterogeneity.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=252564, PROSPERO, identifier: CRD42021252564.

Abstract Background We characterised the phenotypic consequence of genetic variation at the PCSK9 locus and compared findings with recent trials of pharmacological inhibitors of PCSK9. Methods Published and individual participant level data (300,000+ participants) were combined to construct a weighted PCSK9 gene-centric score (GS). Seventeen randomized placebo controlled PCSK9 inhibitor trials were included, providing data on 79,578 participants. Results were scaled to a one mmol/L lower LDL-C concentration. Results The PCSK9 GS (comprising 4 SNPs) associations with plasma lipid and apolipoprotein levels were consistent in direction with treatment effects. The GS odds ratio (OR) for myocardial infarction (MI) was 0.53 (95% CI 0.42; 0.68), compared to a PCSK9 inhibitor effect of 0.90 (95% CI 0.86; 0.93). For ischemic stroke ORs were 0.84 (95% CI 0.57; 1.22) for the GS, compared to 0.85 (95% CI 0.78; 0.93) in the drug trials. ORs with type 2 diabetes mellitus (T2DM) were 1.29 (95% CI 1.11; 1.50) for the GS, as compared to 1.00 (95% CI 0.96; 1.04) for incident T2DM in PCSK9 inhibitor trials. No genetic associations were observed for cancer, heart failure, atrial fibrillation, chronic obstructive pulmonary disease, or Alzheimer’s disease – outcomes for which large-scale trial data were unavailable. Conclusions Genetic variation at the PCSK9 locus recapitulates the effects of therapeutic inhibition of PCSK9 on major blood lipid fractions and MI. While indicating an increased risk of T2DM, no other possible safety concerns were shown; although precision was moderate.

Red blood cell (RBC) traits are routinely measured in clinical practice as important markers of health. Deviations from the physiological ranges are usually a sign of disease, although variation between healthy individuals also occurs, at least partly due to genetic factors. Recent large scale genetic studies identified loci associated with one or more of these traits; further characterization of known loci and identification of new loci is necessary to better understand their role in health and disease and to identify potential molecular mechanisms. We performed meta-analysis of Metabochip association results for six RBC traits-hemoglobin concentration (Hb), hematocrit (Hct), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), mean corpuscular volume (MCV) and red blood cell count (RCC)-in 11 093 Europeans from seven studies of the UCL-LSHTM-Edinburgh-Bristol (UCLEB) Consortium. We identified 394 non-overlapping SNPs in five loci at genome-wide significance: 6p22.1-6p21.33 (with HFE among others), 6q23.2 (with HBS1L among others), 6q23.3 (contains no genes), 9q34.3 (only ABO gene) and 22q13.1 (with TMPRSS6 among others), replicating previous findings of association with RBC traits at these loci and extending them by imputation to 1000 Genomes. We further characterized associations between ABO SNPs and three traits: hemoglobin, hematocrit and red blood cell count, replicating them in an independent cohort. Conditional analyses indicated the independent association of each of these traits with ABO SNPs and a role for blood group O in mediating the association. The 15 most significant RBC-associated ABO SNPs were also associated with five cardiometabolic traits, with discordance in the direction of effect between groups of traits, suggesting that ABO may act through more than one mechanism to influence cardiometabolic risk.

Reference panels from the 1000 Genomes (1000G) Project Consortium provide near complete coverage of common and low-frequency genetic variation with minor allele frequency ≥0.5% across European ancestry populations. Within the European Network for Genetic and Genomic Epidemiology (ENGAGE) Consortium, we have undertaken the first large-scale meta-analysis of genome-wide association studies (GWAS), supplemented by 1000G imputation, for four quantitative glycaemic and obesity-related traits, in up to 87,048 individuals of European ancestry. We identified two loci for body mass index (BMI) at genome-wide significance, and two for fasting glucose (FG), none of which has been previously reported in larger meta-analysis efforts to combine GWAS of European ancestry. Through conditional analysis, we also detected multiple distinct signals of association mapping to established loci for waist-hip ratio adjusted for BMI (RSPO3) and FG (GCK and G6PC2). The index variant for one association signal at the G6PC2 locus is a low-frequency coding allele, H177Y, which has recently been demonstrated to have a functional role in glucose regulation. Fine-mapping analyses revealed that the non-coding variants most likely to drive association signals at established and novel loci were enriched for overlap with enhancer elements, which for FG mapped to promoter and transcription factor binding sites in pancreatic islets, in particular. Our study demonstrates that 1000G imputation and genetic fine-mapping of common and low-frequency variant association signals at GWAS loci, integrated with genomic annotation in relevant tissues, can provide insight into the functional and regulatory mechanisms through which their effects on glycaemic and obesity-related traits are mediated.

The objective of the study was to assess whether gestational and early infancy exposure to low dose vitamin D from a mandatory margarine fortification programme in Denmark influenced the risk of developing type 1 diabetes (T1D) before age of 15 years. The study population included all individuals born in Denmark from 1983 to 1988 and consisted of 331,623 individuals. The 1st of June 1985, which was the date of issue of the new ministerial order cancelling mandatory fortification of margarine with vitamin D in Denmark, served as a reference point separating the studied population into various exposure groups. We further modelled birth cohort effects in children developing T1D as a linear spline, and compared the slopes between the birth cohorts with various prenatal and infancy exposures to vitamin D fortification. In total, 886 (0.26%) individuals developed T1D before the age of 15 years. The beta coefficients (95% CI), or slopes, for linear birth cohort effect in log Hazard Ratio (HR) per one month of birth in individuals born during the periods of gestational exposure, wash-out, and non-exposure were: 0.010 (-0.002/0.021), -0.010 (-0.035/0.018), and 0.008 (- 0.017/0.032), respectively. The beta coefficients (95% CI) for individuals born during the periods of first postnatal year exposure, wash-out, and non-exposure were: 0.007 (-0.016/0.030), 0.006 (-0.004/0.016), and 0.007 (-0.002/0.016), respectively. In conclusion, we found no evidence to support that exposure to low dose vitamin D from the Danish mandatory margarine fortification regimen during gestational and first postnatal year of life changed the risk of developing T1D before the age of 15 years.

Substantial advances have been made in identifying common genetic variants influencing cardiometabolic traits and disease outcomes through genome wide association studies. Nevertheless, gaps in knowledge remain and new questions have arisen regarding the population relevance, mechanisms, and applications for healthcare. Using a new high-resolution custom single nucleotide polymorphism (SNP) array (Metabochip) incorporating dense coverage of genomic regions linked to cardiometabolic disease, the University College-London School-Edinburgh-Bristol (UCLEB) consortium of highly-phenotyped population-based prospective studies, aims to: (1) fine map functionally relevant SNPs; (2) precisely estimate individual absolute and population attributable risks based on individual SNPs and their combination; (3) investigate mechanisms leading to altered risk factor profiles and CVD events; and (4) use Mendelian randomisation to undertake studies of the causal role in CVD of a range of cardiovascular biomarkers to inform public health policy and help develop new preventative therapies.

Abstract Multidimensional host and viral factors determine the clinical course of COVID-19. While the virology of the disease is well studied, investigating host-related factors, including genome, transcriptome, metabolome, and exposome, can provide valuable insights into the underlying pathophysiology. We conducted integrative omics analyses to explore their intricate interplay in COVID-19. We used data from the UK Biobank (UKB), and employed single-omics, pairwise-omics, and multi-omics models to illustrate the effects of different omics layers. The dataset included COVID-19 phenotypic data as well as genome, imputed-transcriptome, metabolome and exposome data. We examined the main, interaction effects and correlations between omics layers underlying COVID-19. Single-omics analyses showed that the transcriptome (derived from the coronary artery tissue) and exposome captured 3–4% of the variation in COVID-19 susceptibility, while the genome and metabolome contributed 2–2.5% of the phenotypic variation. In the omics-exposome model, where individual omics layers were simultaneously fitted with exposome data, the contributions of genome and metabolome were diminished and considered negligible, whereas the effects of the transcriptome showed minimal change. Through mediation analysis, the findings revealed that exposomic factors mediated about 60% of the genome and metabolome’s effects, while having a relatively minor impact on the transcriptome, mediating only 7% of its effects. In conclusion, our integrative-omics analyses shed light on the contribution of omics layers to the variance of COVID-19.

Abstract Background Shapley values have been used extensively in machine learning, not only to explain black box machine learning models, but among other tasks, also to conduct model debugging, sensitivity and fairness analyses and to select important features for robust modelling and for further follow-up analyses. Shapley values satisfy certain axioms that promote fairness in distributing contributions of features toward prediction or reducing error, after accounting for non-linear relationships and interactions when complex machine learning models are employed. Recently, feature selection methods using predictive Shapley values and p-values have been introduced, including powershap. Methods We present a novel feature selection method, LLpowershap, that takes forward these recent advances by employing loss-based Shapley values to identify informative features with minimal noise among the selected sets of features. We also enhance the calculation of p-values and power to identify informative features and to estimate number of iterations of model development and testing. Results Our simulation results show that LLpowershap not only identifies higher number of informative features but outputs fewer noise features compared to other state-of-the-art feature selection methods. Benchmarking results on four real-world datasets demonstrate higher or comparable predictive performance of LLpowershap compared to other Shapley based wrapper methods, or filter methods. LLpowershap is also ranked the best in mean ranking among the seven feature selection methods tested on the benchmark datasets. Conclusion Our results demonstrate that LLpowershap is a viable wrapper feature selection method that can be used for feature selection in large biomedical datasets and other settings.

Abstract Background Ovarian cancer (OC) is commonly diagnosed among older women who have comorbidities. This hypothesis‐free phenome‐wide association study (PheWAS) aimed to identify comorbidities associated with OC, as well as traits that share a genetic architecture with OC. Methods We used data from 181,203 white British female UK Biobank participants and analysed OC and OC subtype‐specific genetic risk scores (OC‐GRS) for an association with 889 diseases and 43 other traits. We conducted PheWAS and colocalization analyses for individual variants to identify evidence for shared genetic architecture. Results The OC‐GRS was associated with 10 diseases, and the clear cell OC‐GRS was associated with five diseases at the FDR threshold (p = 5.6 × 10−4). Mendelian randomizaiton analysis (MR) provided robust evidence for the association of OC with higher risk of “secondary malignant neoplasm of digestive systems” (OR 1.64, 95% CI 1.33, 2.02), “ascites” (1.48, 95% CI 1.17, 1.86), “chronic airway obstruction” (1.17, 95% CI 1.07, 1.29), and “abnormal findings on examination of the lung” (1.51, 95% CI 1.22, 1.87). Analyses of lung spirometry measures provided further support for compromised respiratory function. PheWAS on individual OC variants identified five genetic variants associated with other diseases, and seven variants associated with biomarkers (all, p ≤ 4.5 × 10−8). Colocalization analysis identified rs4449583 (from TERT locus) as the shared causal variant for OC and seborrheic keratosis. Conclusions OC is associated with digestive and respiratory comorbidities. Several variants affecting OC risk were associated with other diseases and biomarkers, with this study identifying a novel genetic locus shared between OC and skin conditions.

Analysis of data from the UK Biobank considering multiple hormone-sensitive cancers as a single disease suggests substantial SNP-based heritability, identifying 55 variants with genome-wide significance.

Abstract We assigned 329,908 UK Biobank participants into six subgroups based on a self-organizing map of 51 biochemical measures (blinded for clinical outcomes). The subgroup with the most favorable metabolic traits was chosen as the reference. Hazard ratios (HR) for incident disease were modeled by Cox regression. Enrichment ratios (ER) of incident multi-morbidity versus randomly expected co-occurrence were evaluated by permutation tests; ER is like HR but captures co-occurrence rather than event frequency. The subgroup with high urinary excretion without kidney stress (HR = 1.24) and the subgroup with the highest apolipoprotein B and blood pressure (HR = 1.52) were associated with ischemic heart disease (IHD). The subgroup with kidney stress, high adiposity and inflammation was associated with IHD (HR = 2.11), cancer (HR = 1.29), dementia (HR = 1.70) and mortality (HR = 2.12). The subgroup with high liver enzymes and triglycerides was at risk of diabetes (HR = 15.6). Multimorbidity was enriched in metabolically favorable subgroups (3.4 ≤ ER ≤ 4.0) despite lower disease burden overall; the relative risk of co-occurring disease was higher in the absence of obvious metabolic dysfunction. These results provide synergistic insight into metabolic health and its associations with cardiovascular disease in a large population sample.

Abstract We present a simple and efficient hypothesis-free machine learning pipeline for risk factor discovery that accounts for non-linearity and interaction in large biomedical databases with minimal variable pre-processing. In this study, mortality models were built using gradient boosting decision trees (GBDT) and important predictors were identified using a Shapley values-based feature attribution method, SHAP values. Cox models controlled for false discovery rate were used for confounder adjustment, interpretability, and further validation. The pipeline was tested using information from 502,506 UK Biobank participants, aged 37–73 years at recruitment and followed over seven years for mortality registrations. From the 11,639 predictors included in GBDT, 193 potential risk factors had SHAP values ≥ 0.05, passed the correlation test, and were selected for further modelling. Of the total variable importance summed up, 60% was directly health related, and baseline characteristics, sociodemographics, and lifestyle factors each contributed about 10%. Cox models adjusted for baseline characteristics, showed evidence for an association with mortality for 166 out of the 193 predictors. These included mostly well-known risk factors (e.g., age, sex, ethnicity, education, material deprivation, smoking, physical activity, self-rated health, BMI, and many disease outcomes). For 19 predictors we saw evidence for an association in the unadjusted but not adjusted analyses, suggesting bias by confounding. Our GBDT-SHAP pipeline was able to identify relevant predictors ‘hidden’ within thousands of variables, providing an efficient and pragmatic solution for the first stage of hypothesis free risk factor identification.

Abstract Context Multiple observational studies have reported an inverse relationship between 25-hydroxyvitamin D concentrations (25(OH)D) and type 2 diabetes (T2D). However, the results of short- and long-term interventional trials concerning the relationship between 25(OH)D and T2D risk have been inconsistent. Objectives and methods To evaluate the causal role of reduced blood 25(OH)D in T2D, here we have performed a bidirectional Mendelian randomization study using 59,890 individuals (5,862 T2D cases and 54,028 controls) from European and Asian Indian ancestries. We used six known SNPs, including three T2D SNPs and three vitamin D pathway SNPs, as a genetic instrument to evaluate the causality and direction of the association between T2D and circulating 25(OH)D concentration. Results Results of the combined meta-analysis of eight participating studies showed that a composite score of three T2D SNPs would significantly increase T2D risk by an odds ratio (OR) of 1.24, p = 1.82 × 10–32; Z score 11.86, which, however, had no significant association with 25(OH)D status (Beta -0.02nmol/L ± SE 0.01nmol/L; p = 0.83; Z score -0.21). Likewise, the genetically instrumented composite score of 25(OH)D lowering alleles significantly decreased 25(OH)D concentrations (-2.1nmol/L ± SE 0.1nmol/L, p = 7.92 × 10–78; Z score -18.68) but was not associated with increased risk for T2D (OR 1.00, p = 0.12; Z score 1.54). However, using 25(OH)D synthesis SNP (DHCR7; rs12785878) as an individual genetic instrument, a per allele reduction of 25(OH)D concentration (-4.2nmol/L ± SE 0.3nmol/L) was predicted to increase T2D risk by 5%, p = 0.004; Z score 2.84. This effect, however, was not seen in other 25(OH)D SNPs (GC rs2282679, CYP2R1 rs12794714) when used as an individual instrument. Conclusion Our new data on this bidirectional Mendelian randomization study suggests that genetically instrumented T2D risk does not cause changes in 25(OH)D levels. However, genetically regulated 25(OH)D deficiency due to vitamin D synthesis gene (DHCR7) may influence the risk of T2D.

Metabolic syndrome is a group of heritable metabolic traits that are highly associated with type 2 diabetes (T2DM). Classical interventions to T2DM include individual self-management of environmental risk factors, such as improving diet quality, increasing physical activity, and reducing smoking and alcohol consumption, which decreases the risk of developing metabolic syndrome. However, it is poorly understood how the phenotypes of diabetes-related metabolic traits change with respect to lifestyle modifications at the individual level. In the analysis, we used 12 diabetes-related metabolic traits and eight lifestyle covariates from the UK Biobank comprising 288,837 white British participants genotyped for 1,133,273 genome-wide single nucleotide polymorphisms. We found 16 GxE interactions. Modulation of genetic effects by physical activity was seen for four traits (glucose, HbA1c, C-reactive protein, systolic blood pressure) and by alcohol and smoking for three (BMI, glucose, waist–hip ratio and BMI and diastolic and systolic blood pressure, respectively). We also found a number of significant phenotypic modulations by the lifestyle covariates, which were not attributed to the genetic effects in the model. Overall, modulation in the metabolic risk in response to the level of lifestyle covariates was clearly observed, and its direction and magnitude were varied depending on individual differences. We also showed that the metabolic risk inferred by our model was notably higher in T2DM prospective cases than controls. Our findings highlight the importance of individual genetic differences in the prevention and management of diabetes and suggest that the one-size-fits-all approach may not benefit all.

Substantial genetic correlations have been reported across psychiatric disorders and numerous cross-disorder genetic variants have been detected. To identify the genetic variants underlying general psychopathology in childhood, we performed a genome-wide association study using a total psychiatric problem score. We analyzed 6,844,199 common SNPs in 38,418 school-aged children from 20 population-based cohorts participating in the EAGLE consortium. The SNP heritability of total psychiatric problems was 5.4% (SE = 0.01) and two loci reached genome-wide significance: rs10767094 and rs202005905. We also observed an association of SBF2, a gene associated with neuroticism in previous GWAS, with total psychiatric problems. The genetic effects underlying the total score were shared with common psychiatric disorders only (attention-deficit/hyperactivity disorder, anxiety, depression, insomnia) (rG > 0.49), but not with autism or the less common adult disorders (schizophrenia, bipolar disorder, or eating disorders) (rG < 0.01). Importantly, the total psychiatric problem score also showed at least a moderate genetic correlation with intelligence, educational attainment, wellbeing, smoking, and body fat (rG > 0.29). The results suggest that many common genetic variants are associated with childhood psychiatric symptoms and related phenotypes in general instead of with specific symptoms. Further research is needed to establish causality and pleiotropic mechanisms between related traits.

Gestational duration depends on both maternal and fetal genetic influences. Here, the authors perform a fetal genome-wide association meta-analysis and find that a locus on 2q13 is associated with pregnancy duration and further show that the lead SNP rs7594852 changes the binding properties of transcriptional repressor HIC1.

Complex traits are often influenced by genetic and non-genetic factors (such as environmental exposures), which are themselves interconnected. Here, the authors develop a method for disentangling genotype–covariate correlation and interaction, and investigate their effects on estimating statistical genetic parameters.

The genetic background of childhood body mass index (BMI), and the extent to which the well-known associations of childhood BMI with adult diseases are explained by shared genetic factors, are largely unknown. We performed a genome-wide association study meta-analysis of BMI in 61,111 children aged between 2 and 10 years. Twenty-five independent loci reached genome-wide significance in the combined discovery and replication analyses. Two of these, located near NEDD4L and SLC45A3, have not previously been reported in relation to either childhood or adult BMI. Positive genetic correlations of childhood BMI with birth weight and adult BMI, waist-to-hip ratio, diastolic blood pressure and type 2 diabetes were detected (Rg ranging from 0.11 to 0.76, P-values

Background: The three main alleles of the APOE gene (ε4, ε3 and ε2) carry differential risks for conditions including Alzheimer's disease (AD) and cardiovascular disease. Due to their clinical significance, we explored disease associations of the APOE genotypes using a hypothesis-free, data-driven, phenome-wide association study (PheWAS) approach. Methods: We used data from the UK Biobank to screen for associations between APOE genotypes and over 950 disease outcomes using genotype ε3ε3 as a reference. Data was restricted to 337,484 white British participants (aged 37–73 years). Findings: After correction for multiple testing, PheWAS analyses identified associations with 37 outcomes, representing 18 distinct diseases. As expected, ε3ε4 and ε4ε4 genotypes associated with increased odds of AD (p ≤ 7.6 × 10−46), hypercholesterolaemia (p ≤ 7.1 × 10−17) and ischaemic heart disease (p ≤ 2.3 × 10−4), while ε2ε3 provided protection for the latter two conditions (p ≤ 3.7 × 10−10) compared to ε3ε3. In contrast, ε4-associated disease protection was seen against obesity, chronic airway obstruction, type 2 diabetes, gallbladder disease, and liver disease (all p ≤ 5.2 × 10−4) while ε2ε2 homozygosity increased risks of peripheral vascular disease, thromboembolism, arterial aneurysm, peptic ulcer, cervical disorders, and hallux valgus (all p ≤ 6.1 × 10−4). Sensitivity analyses using brain neuroimaging, blood biochemistry, anthropometric, and spirometric biomarkers supported the PheWAS findings on APOE associations with respective disease outcomes. Interpretation: PheWAS confirms strong associations between APOE and AD, hypercholesterolaemia, and ischaemic heart disease, and suggests potential ε4-associated disease protection and harmful effects of the ε2ε2 genotype, for several conditions. Funding: National Health and Medical Research Council of Australia.

Background: Smoking is one of the greatest threats to public health worldwide. We integrated phenome-wide association study (PheWAS) and Mendelian randomization (MR) approaches to explore causal effects of genetically predicted smoking intensity across the human disease spectrum. Methods: We conducted PheWAS case-control analyses in 152,483 ever smokers of White-British ancestry, aged 39–73 years. Disease diagnoses were based on hospital inpatient and mortality registrations. Smoking intensity was instrumented by four genetic variants, and disease risks estimated for one cigarette per day heavier intakes. Associations passing the FDR threshold (p

Background Both genetic and nongenetic factors can predispose individuals to cardiovascular risk. Finding ways to alter these predispositions is important for cardiovascular disease prevention. Methods and Results We used a novel whole‐genome approach to estimate the genetic and nongenetic effects on—and hence their predispositions to—cardiovascular risk and determined whether they vary with respect to lifestyle factors such as physical activity, smoking, alcohol consumption, and dietary intake. We performed analyses on the ARIC (Atherosclerosis Risk in Communities) Study (N=6896–7180) and validated findings using the UKBB (UK Biobank, N=14 076–34 538). Lifestyle modulation was evident for many cardiovascular traits such as body mass index and resting heart rate. For example, alcohol consumption modulated both genetic and nongenetic effects on body mass index, whereas smoking modulated nongenetic effects on heart rate, pulse pressure, and white blood cell count. We also stratified individuals according to estimated genetic and nongenetic effects that are modulated by lifestyle factors and showed distinct phenotype–lifestyle relationships across the stratified groups. Finally, we showed that neglecting lifestyle modulations of cardiovascular traits would on average reduce single nucleotide polymorphism heritability estimates of these traits by a small yet significant amount, primarily owing to the overestimation of residual variance. Conclusions Lifestyle changes are relevant to cardiovascular disease prevention. Individual differences in the genetic and nongenetic effects that are modulated by lifestyle factors, as shown by the stratified group analyses, implies a need for personalized lifestyle interventions. In addition, single nucleotide polymorphism–based heritability of cardiovascular traits without accounting for lifestyle modulations could be underestimated.

Few studies have examined the contribution of life-course factors in explaining familial aggregation of chronic lung conditions. Using data from the 1958 British Birth Cohort, a life-course approach was used to examine whether, and how, exposure to risk factors through one's life explained the association between parental respiratory disease history and symptomatic airflow obstruction (AO). Cohort participants (n=6212) were characterised in terms of parental respiratory disease history and symptomatic AO at 45 years. Life-course factors (e.g. smoking, asthma and early-life factors) were operationalised as life period-specific and cumulative measures. Logistic regression and path analytic models predicting symptomatic AO adjusted for parental respiratory disease history were used to test different life-course models (critical period, accumulation- and chain-of-risks models). While some life-course factors (e.g. childhood passive smoking and occupational exposure) were individually associated with parental respiratory disease history and symptomatic AO, asthma (OR 6.44, 95% CI 5.01–8.27) and persistent smoking in adulthood (OR 5.42, 95% CI 4.19–7.01) had greater impact on the association between parental respiratory disease history and symptomatic AO. A critical period model provided a better model fit compared with an accumulation-of-risk model and explained 57% of the effect of parental respiratory disease history on symptomatic AO. Adulthood asthma and smoking status explained around half of the effect of parental respiratory disease history on chronic obstructive pulmonary disease. Beyond smoking history, the combination of parental respiratory disease history and adulthood asthma may provide an opportunity for early diagnosis and intervention.

Abstract Background Obesity and depression are both highly prevalent public health disorders and evidence on their relationship is inconsistent. This study examined whether depressive symptoms are associated with current obesity, and further, whether obesity in turn is associated with an increased odds of depressive symptoms five years later after accounting for potential lifestyle confounders and depressive symptoms at baseline. Methods Data were obtained from the 1958 British birth cohort (N = 9217 for cross-sectional and 7340 for prospective analysis). Clinical Interview Schedule-Revised and Mental Health Inventory-5 were used for screening depressive symptoms at ages 45 and 50 years, respectively. General and central obesity were defined using measurements of body mass index (BMI) and waist circumference (WC) at 45 years, respectively. Results There was a cross-sectional association between depressive symptoms and obesity: participants with ≥2 depressive symptoms had 31% (95%CI 11% to 55%) higher odds of general and 26% higher odds of central obesity (95%CI 8% to 47%). In prospective analyses, both general and central obesity were associated with higher odds of depressive symptoms five years later among women but not in men (Pinteraction

Abstract Background Whilst observational studies establish that lower plasma 25-hydroxyvitamin D (25-OHD) levels are associated with higher risk of colorectal cancer (CRC), establishing causality has proven challenging. Since vitamin D is modifiable, these observations have substantial clinical and public health implications. Indeed, many health agencies already recommend supplemental vitamin D. Here, we explore causality in a large Mendelian randomisation (MR) study using an improved genetic instrument for circulating 25-OHD. Methods We developed a weighted genetic score for circulating 25-OHD using six genetic variants that we recently reported to be associated with circulating 25-OHD in a large genome-wide association study (GWAS) meta-analysis. Using this score as instrumental variable in MR analyses, we sought to determine whether circulating 25-OHD is causally linked with CRC risk. We conducted MR analysis using individual-level data from 10,725 CRC cases and 30,794 controls (Scotland, UK Biobank and Croatia). We then applied estimates from meta-analysis of 11 GWAS of CRC risk (18,967 cases; 48,168 controls) in a summary statistics MR approach. Results The new genetic score for 25-OHD was strongly associated with measured plasma 25-OHD levels in 2821 healthy Scottish controls (P = 1.47 × 10− 11), improving upon previous genetic instruments (F-statistic 46.0 vs. 13.0). However, individual-level MR revealed no association between 25-OHD score and CRC risk (OR 1.03/unit log-transformed circulating 25-OHD, 95% CI 0.51–2.07, P = 0.93). Similarly, we found no evidence for a causal relationship between 25-OHD and CRC risk using summary statistics MR analysis (OR 0.91, 95% CI 0.69–1.19, P = 0.48). Conclusions Despite the scale of this study and employing an improved score capturing more of the genetic contribution to circulating 25-OHD, we found no evidence for a causal relationship between circulating 25-OHD and CRC risk. Although the magnitude of effect for vitamin D suggested by observational studies can confidently be excluded, smaller effects sizes and non-linear relationships remain plausible. Circulating vitamin D may be a CRC biomarker, but a causal effect on CRC risk remains unproven.

Abstract Coffee’s long-term effect on cognitive function remains unclear with studies suggesting both benefits and adverse effects. We used Mendelian randomization to investigate the causal relationship between habitual coffee consumption and cognitive function in mid- to later life. This included up to 415,530 participants and 300,760 coffee drinkers from 10 meta-analysed European ancestry cohorts. In each cohort, composite cognitive scores that capture global cognition and memory were computed using available tests. A genetic score derived using CYP1A1/2 (rs2472297) and AHR (rs6968865) was chosen as a proxy for habitual coffee consumption. Null associations were observed when examining the associations of the genetic score with global and memory cognition (β = −0.0007, 95% C.I. −0.009 to 0.008, P = 0.87; β = −0.001, 95% C.I. −0.005 to 0.002, P = 0.51, respectively), with high consistency between studies (Pheterogeneity > 0.4 for both). Domain specific analyses using available cognitive measures in the UK Biobank also did not support effects by habitual coffee intake for reaction time, pairs matching, reasoning or prospective memory (P ≥ 0.05 for all). Despite the power to detect very small effects, our meta-analysis provided no evidence for causal long-term effects of habitual coffee consumption on global cognition or memory.

Vitamin D deficiency is associated with multiple human pathologic conditions. In a genome-wide association study of 79,366 individuals, Jiang et al. replicate four and identify two new genetic loci for serum levels of 25-hydroxyvitamin D and find evidence for a shared genetic basis with autoimmune diseases.

Abstract Observational studies on smoking and risk of hay fever and asthma have shown inconsistent results. However, observational studies may be biased by confounding and reverse causation. Mendelian randomization uses genetic variants as markers of exposures to examine causal effects. We examined the causal effect of smoking on hay fever and asthma by using the smoking-associated single nucleotide polymorphism (SNP) rs16969968/rs1051730. We included 231,020 participants from 22 population-based studies. Observational analyses showed that current vs never smokers had lower risk of hay fever (odds ratio (OR) = 0·68, 95% confidence interval (CI): 0·61, 0·76; P

Summary: 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

BackgroundIron is integral to many physiological processes, and variations in its levels, even within the normal range, can have implications for health. The objective of this study was to explore the broad clinical effects of varying iron status.Methods and findingsGenome-wide association study (GWAS) summary data obtained from 48,972 European individuals (55% female) across 19 cohorts in the Genetics of Iron Status Consortium were used to identify 3 genetic variants (rs1800562 and rs1799945 in the hemochromatosis gene [HFE] and rs855791 in the transmembrane protease serine 6 gene [TMPRSS6]) that associate with increased serum iron, ferritin, and transferrin saturation and decreased transferrin levels, thus serving as instruments for systemic iron status. Phenome-wide association study (PheWAS) of these instruments was performed on 424,439 European individuals (54% female) in the UK Biobank who were aged 40-69 years when recruited from 2006 to 2010, with their genetic data linked to Hospital Episode Statistics (HES) from April, 1995 to March, 2016. Two-sample summary data mendelian randomization (MR) analysis was performed to investigate the effect of varying iron status on outcomes across the human phenome. MR-PheWAS analysis for the 3 iron status genetic instruments was performed separately and then pooled by meta-analysis. Correction was made for testing of multiple correlated phenotypes using a 5% false discovery rate (FDR) threshold. Heterogeneity between MR estimates for different instruments was used to indicate possible bias due to effects of the genetic variants through pathways unrelated to iron status. There were 904 distinct phenotypes included in the MR-PheWAS analyses. After correcting for multiple testing, the 3 genetic instruments for systemic iron status demonstrated consistent evidence of a causal effect of higher iron status on decreasing risk of traits related to anemia (iron deficiency anemia: odds ratio [OR] scaled to a standard deviation [SD] increase in genetically determined serum iron levels 0.72, 95% confidence interval [CI] 0.64-0.81, P = 4 × 10-8) and hypercholesterolemia (hypercholesterolemia: OR 0.88, 95% CI 0.83-0.93, P = 2 × 10-5) and increasing risk of traits related to infection of the skin and related structures (cellulitis and abscess of the leg: OR 1.25, 95% CI 1.10-1.42, P = 6 × 10-4). The main limitations of this study relate to possible bias from pleiotropic effects of the considered genetic variants and misclassification of diagnoses in the HES data. Furthermore, this work only investigated participants with European ancestry, and the findings may not be applicable to other ethnic groups.ConclusionsOur findings offer novel, to our knowledge, insight into previously unreported effects of iron status, highlighting a potential protective effect of higher iron status on hypercholesterolemia and a detrimental role on risk of skin and skin structure infections. Given the modifiable and variable nature of iron status, these findings warrant further investigation.

Studies of the relationship between serum 25-hydroxyvitamin D (25(OH)D) and changes in measures of adiposity have shown inconsistent results, and interaction with genetic predisposition to obesity has rarely been examined. We examined whether 25(OH)D was associated with subsequent annual changes in body weight (ΔBW) or waist circumference (ΔWC), and whether the associations were modified by genetic predisposition to a high BMI, WC or waist-hip ratio adjusted for BMI (WHRBMI). The study was based on 10,898 individuals from the Danish Inter99, the 1958 British Birth Cohort and the Northern Finland Birth Cohort 1966. We combined 42 adiposity-associated Single Nucleotide Polymorphisms (SNPs) into four scores indicating genetic predisposition to BMI, WC and WHRBMI, or all three traits combined. Linear regression was used to examine the association between serum 25(OH)D and ΔBW or ΔWC, SNP-score × 25(OH)D interactions were examined, and results from the individual cohorts were meta-analyzed. In the meta-analyses, we found no evidence of an association between 25(OH)D and ΔBW (-9.4 gram/y per 10 nmol/L higher 25(OH)D [95% CI: -23.0, +4.3; P = 0.18]) or ΔWC (-0.06 mm/y per 10 nmol/L higher 25(OH)D [95% CI: -0.17, +0.06; P = 0.33]). Furthermore, we found no statistically significant interactions between the four SNP-scores and 25(OH)D in relation to ΔBW or ΔWC. Thus, in view of the narrow CIs, our results suggest that an association between 25(OH)D and changes in measures of adiposity is absent or marginal. Similarly, the study provided evidence that there is either no or very limited dependence on genetic predisposition to adiposity.

We previously used a single nucleotide polymorphism (SNP) in the CHRNA5-A3-B4 gene cluster associated with heaviness of smoking within smokers to confirm the causal effect of smoking in reducing body mass index (BMI) in a Mendelian randomisation analysis. While seeking to extend these findings in a larger sample we found that this SNP is associated with 0.74% lower body mass index (BMI) per minor allele in current smokers (95% CI -0.97 to -0.51, P = 2.00 × 10(-10)), but also unexpectedly found that it was associated with 0.35% higher BMI in never smokers (95% CI +0.18 to +0.52, P = 6.38 × 10(-5)). An interaction test confirmed that these estimates differed from each other (P = 4.95 × 10(-13)). This difference in effects suggests the variant influences BMI both via pathways unrelated to smoking, and via the weight-reducing effects of smoking. It would therefore be essentially undetectable in an unstratified genome-wide association study of BMI, given the opposite association with BMI in never and current smokers. This demonstrates that novel associations may be obscured by hidden population sub-structure. Stratification on well-characterized environmental factors known to impact on health outcomes may therefore reveal novel genetic associations.