The UK Biobank resource with deep phenotyping and genomic data

Assessing the Relationship Between Gut Microbiota and Bone Mineral Density

Background

Recent study demonstrates the comprehensive effects of gut microbiota on complex diseases or traits. However, limited effort has been conducted to explore the potential relationships between gut microbiota and BMD.

Methods

We performed a polygenetic risk scoring (PRS) analysis to systematically explore the relationships between gut microbiota and body BMD. Significant SNP sets associated with gut microbiota were derived from previous genome-wide association study (GWAS). In total, 2,294 to 5,065 individuals with BMD values of different sites and their genotype data were obtained from UK Biobank cohort. The gut microbiota PRS of each individual was computed from the SNP genotype data for each study subject of UK Biobank by PLINK software. Using computed PRS as the instrumental variables of gut microbiota, Pearson correlation analysis of individual PRS values and BMD values was finally conducted to test the potential association between gut microbiota and target trait.

Results

In total, 31 BMD traits were selected as outcome to assess their relationships with gut microbiota. After adjusted for age, sex, body mass index, and the first 5 principal components (PCs) as the covariates using linear regression model, pelvis BMD (P = 0.0437) showed suggestive association signal with gut microbiota after multiple testing correction.

Conclusion

Our study findings support the weak relevance of gut microbiota with the development of BMD.

Variable prediction accuracy of polygenic scores within an ancestry group

Fields as diverse as human genetics and sociology are increasingly using polygenic scores based on genome-wide association studies (GWAS) for phenotypic prediction. However, recent work has shown that polygenic scores have limited portability across groups of different genetic ancestries, restricting the contexts in which they can be used reliably and potentially creating serious inequities in future clinical applications. Using the UK Biobank data, we demonstrate that even within a single ancestry group (i.e., when there are negligible differences in linkage disequilibrium or in causal alleles frequencies), the prediction accuracy of polygenic scores can depend on characteristics such as the socio-economic status, age or sex of the individuals in which the GWAS and the prediction were conducted, as well as on the GWAS design. Our findings highlight both the complexities of interpreting polygenic scores and underappreciated obstacles to their broad use.

Polygenic risk for coronary heart disease acts through atherosclerosis in type 2 diabetes

Background

Type 2 diabetes increases the risk of coronary heart disease (CHD), yet the mechanisms involved remain poorly described. Polygenic risk scores (PRS) provide an opportunity to understand risk factors since they reflect etiologic pathways from the entire genome. We therefore tested whether a PRS for CHD influenced risk of CHD in individuals with type 2 diabetes and which risk factors were associated with this PRS.

Methods

We tested the association of a CHD PRS with CHD and its traditional clinical risk factors amongst individuals with type 2 diabetes in UK Biobank (N = 21,102). We next tested the association of the CHD PRS with atherosclerotic burden in a cohort of 352 genome-wide genotyped participants with type 2 diabetes who had undergone coronary angiograms.

Results

In the UK Biobank we found that the CHD PRS was strongly associated with CHD amongst individuals with type 2 diabetes (OR per standard deviation increase = 1.50; p = 1.5 × 10^− 59). But this CHD PRS was, at best, only weakly associated with traditional clinical risk factors, such as hypertension, hyperlipidemia, glycemic control, obesity and smoking. Conversely, in the angiographic cohort, the CHD PRS was strongly associated with multivessel stenosis (OR = 1.65; p = 4.9 × 10^− 4) and increased number of major stenotic lesions (OR = 1.35; p = 9.4 × 10^− 3).

Conclusions

Polygenic predisposition to CHD is strongly associated with atherosclerotic burden in individuals with type 2 diabetes and this effect is largely independent of traditional clinical risk factors. This suggests that genetic risk for CHD acts through atherosclerosis with little effect on most traditional risk factors, providing the opportunity to explore new biological pathways.

Individual-patient prediction of meningioma malignancy and survival using the Surveillance, Epidemiology, and End Results database

Meningiomas are known to have relatively lower aggressiveness and better outcomes than other central nervous system (CNS) tumors. However, there is considerable overlap between clinical and radiological features characterizing benign, atypical, and malignant tumors. In this study, we developed methods and a practical app designed to assist with the diagnosis and prognosis of meningiomas. Statistical learning models were trained and validated on 62,844 patients from the Surveillance, Epidemiology, and End Results database. We used balanced logistic regression-random forest ensemble classifiers and proportional hazards models to learn multivariate patterns of association between malignancy, survival, and a series of basic clinical variables-such as tumor size, location, and surgical procedure. We demonstrate that our models are capable of predicting meaningful individual-specific clinical outcome variables and show good generalizability across 16 SEER registries. A free smartphone and web application is provided for readers to access and test the predictive models (www.meningioma.app). Future model improvements and prospective replication will be necessary to demonstrate true clinical utility. Rather than being used in isolation, we expect that the proposed models will be integrated into larger and more comprehensive models that integrate imaging and molecular biomarkers. Whether for meningiomas or other tumors of the CNS, the power of these methods to make individual-patient predictions could lead to improved diagnosis, patient counseling, and outcomes.

Psoriasis and Genetics

Psoriasis is a common inflammatory skin disease caused by the interplay between multiple genetic and environmental risk factors. This review summarises recent progress in elucidating the genetic basis of psoriasis, particularly through large genome-wide association studies. We illustrate the power of genetic analyses for disease stratification. Psoriasis can be stratified by phenotype (common plaque versus rare pustular variants), or by outcome (prognosis, comorbidities, response to treatment); recent progress has been made in delineating the genetic contribution in each of these areas. We also highlight how genetic data can directly inform the development of effective psoriasis treatments.

Variable prediction accuracy of polygenic scores within an ancestry group

Fields as diverse as human genetics and sociology are increasingly using polygenic scores based on genome-wide association studies (GWAS) for phenotypic prediction. However, recent work has shown that polygenic scores have limited portability across groups of different genetic ancestries, restricting the contexts in which they can be used reliably and potentially creating serious inequities in future clinical applications. Using the UK Biobank data, we demonstrate that even within a single ancestry group (i.e., when there are negligible differences in linkage disequilibrium or in causal alleles frequencies), the prediction accuracy of polygenic scores can depend on characteristics such as the socio-economic status, age or sex of the individuals in which the GWAS and the prediction were conducted, as well as on the GWAS design. Our findings highlight both the complexities of interpreting polygenic scores and underappreciated obstacles to their broad use.

Genomic influences on self-reported childhood maltreatment

Childhood maltreatment is highly prevalent and serves as a risk factor for mental and physical disorders. Self-reported childhood maltreatment appears heritable, but the specific genetic influences on this phenotype are largely unknown. The aims of this study were to (1) identify genetic variation associated with self-reported childhood maltreatment, (2) estimate SNP-based heritability (h2snp), (3) assess predictive value of polygenic risk scores (PRS) for childhood maltreatment, and (4) quantify genetic overlap of childhood maltreatment with mental and physical health-related phenotypes, and condition the top hits from our analyses when such overlap is present. Genome-wide association analysis for childhood maltreatment was undertaken, using a discovery sample from the UK Biobank (UKBB) (n = 124,000) and a replication sample from the Psychiatric Genomics Consortium-posttraumatic stress disorder group (PGC-PTSD) (n = 26,290). h2snp for childhood maltreatment and genetic correlations with mental/physical health traits were calculated using linkage disequilibrium score regression. PRS was calculated using PRSice and mtCOJO was used to perform conditional analysis. Two genome-wide significant loci associated with childhood maltreatment (rs142346759, p = 4.35 × 10-8, FOXP1; rs10262462, p = 3.24 × 10-8, FOXP2) were identified in the discovery dataset but were not replicated in PGC-PTSD. h2snp for childhood maltreatment was ~6% and the PRS derived from the UKBB was significantly predictive of childhood maltreatment in PGC-PTSD (r2 = 0.0025; p = 1.8 × 10-15). The most significant genetic correlation of childhood maltreatment was with depressive symptoms (rg = 0.70, p = 4.65 × 10-40), although we show evidence that our top hits may be specific to childhood maltreatment. This is the first large-scale genetic study to identify specific variants associated with self-reported childhood maltreatment. Speculatively, FOXP genes might influence externalizing traits and so be relevant to childhood maltreatment. Alternatively, these variants may be associated with a greater likelihood of reporting maltreatment. A clearer understanding of the genetic relationships of childhood maltreatment, including particular abuse subtypes, with a range of phenotypes, may ultimately be useful in in developing targeted treatment and prevention strategies.

Personality, lifestyle and job satisfaction: causal association between neuroticism and job satisfaction using Mendelian randomisation in the UK biobank cohort

Job-related stress has been associated with poor health outcomes but little is known about the causal nature of these findings. We employed Mendelian randomisation (MR) approach to investigate the causal effect of neuroticism, education, and physical activity on job satisfaction. Trait-specific genetic risk score (GRS) based on recent genome wide association studies were used as instrumental variables (IV) using the UK Biobank cohort (N = 315,536). Both single variable and multivariable MR analyses were used to determine the effect of each trait on job satisfaction. We observed a clear evidence of a causal association between neuroticism and job satisfaction. In single variable MR, one standard deviation (1 SD) higher genetically determined neuroticism score (4.07 units) was associated with -0.31 units lower job satisfaction (95% confidence interval (CI): -0.38 to -0.24; P = 9.5 × 10^-20). The causal associations remained significant after performing sensitivity analyses by excluding invalid genetic variants from GRS_Neuroticism (β(95%CI): -0.28(-0.35 to -0.21); P = 3.4 x 10^-15). Education (0.02; -0.08 to 0.12; 0.67) and physical activity (0.08; -0.34 to 0.50; 0.70) did not show any evidence for causal association with job satisfaction. When genetic instruments for neuroticism, education and physical activity were included together, the association of neuroticism score with job satisfaction was reduced by only -0.01 units, suggesting an independent inverse causal association between neuroticism score (P = 2.7 x 10^-17) and job satisfaction. Our findings show an independent causal association between neuroticism score and job satisfaction. Physically active lifestyle may help to increase job satisfaction despite presence of high neuroticism scores. Our study highlights the importance of considering the confounding effect of negative personality traits for studies on job satisfaction.

Multitrait analysis of glaucoma identifies new risk loci and enables polygenic prediction of disease susceptibility and progression

Glaucoma, a disease characterized by progressive optic nerve degeneration, can be prevented through timely diagnosis and treatment. We characterize optic nerve photographs of 67,040 UK Biobank participants and use a multitrait genetic model to identify risk loci for glaucoma. A glaucoma polygenic risk score (PRS) enables effective risk stratification in unselected glaucoma cases and modifies penetrance of the MYOC variant encoding p.Gln368Ter, the most common glaucoma-associated myocilin variant. In the unselected glaucoma population, individuals in the top PRS decile reach an absolute risk for glaucoma 10 years earlier than the bottom decile and are at 15-fold increased risk of developing advanced glaucoma (top 10% versus remaining 90%, odds ratio = 4.20). The PRS predicts glaucoma progression in prospectively monitored, early manifest glaucoma cases (P = 0.004) and surgical intervention in advanced disease (P = 3.6 × 10^-6). This glaucoma PRS will facilitate the development of a personalized approach for earlier treatment of high-risk individuals, with less intensive monitoring and treatment being possible for lower-risk groups.

Penetrance of Parkinson's Disease in LRRK2 p.G2019S Carriers Is Modified by a Polygenic Risk Score

BACKGROUND

Although the leucine-rich repeat kinase 2 p.G2019S mutation has been demonstrated to be a strong risk factor for PD, factors that contribute to penetrance among carriers, other than aging, have not been well identified.

OBJECTIVES

To evaluate whether a cumulative genetic risk identified in the recent genome-wide study is associated with penetrance of PD among p.G2019S mutation carriers.

METHODS

We included p.G2019S heterozygote carriers with European ancestry in three genetic cohorts in which the mutation carriers with and without PD were selectively recruited. We also included the carriers from two data sets: one from a case-control setting without selection of mutation carriers and the other from a population sampling. Associations between polygenic risk score constructed from 89 variants reported recently and PD were tested and meta-analyzed. We also explored the interaction of age and PRS.

RESULTS

After excluding eight homozygotes, 833 p.G2019S heterozygote carriers (439 PD and 394 unaffected) were analyzed. Polygenic risk score was associated with a higher penetrance of PD (odds ratio: 1.34; 95% confidence interval: [1.09, 1.64] per +1 standard deviation; P = 0.005). In addition, associations with polygenic risk score and penetrance were stronger in the younger participants (main effect: odds ratio 1.28 [1.04, 1.58] per +1 standard deviation; P = 0.022; interaction effect: odds ratio 0.78 [0.64, 0.94] per +1 standard deviation and + 10 years of age; P = 0.008).

CONCLUSIONS

Our results suggest that there is a genetic contribution for penetrance of PD among p.G2019S carriers. These results have important etiological consequences and potential impact on the selection of subjects for clinical trials. © 2020 International Parkinson and Movement Disorder Society.

Eighty-eight variants highlight the role of T cell regulation and airway remodeling in asthma pathogenesis

Asthma is one of the most common chronic diseases affecting both children and adults. We report a genome-wide association meta-analysis of 69,189 cases and 702,199 controls from Iceland and UK biobank. We find 88 asthma risk variants at 56 loci, 19 previously unreported, and evaluate their effect on other asthma and allergic phenotypes. Of special interest are two low frequency variants associated with protection against asthma; a missense variant in TNFRSF8 and 3‘ UTR variant in TGFBR1. Functional studies show that the TNFRSF8 variant reduces TNFRSF8 expression both on cell surface and in soluble form, acting as loss of function. eQTL analysis suggests that the TGFBR1 variant acts through gain of function and together with an intronic variant in a downstream gene, SMAD3, points to defective TGFβR1 signaling as one of the biological perturbations increasing asthma risk. Our results increase the number of asthma variants and implicate genes with known role in T cell regulation, inflammation and airway remodeling in asthma pathogenesis.

Asthma is a common allergic airway disease with significant inter-individual heterogeneity. Here, Olafsdottir et al. report a genome-wide meta-analysis of two large population-based cohorts to identify sequence variants that associate with asthma risk and perform follow-up functional analyses on a protective loss-of-function variant in TNFRSF8.

Genetic studies of urinary metabolites illuminate mechanisms of detoxification and excretion in humans

The kidneys integrate information from continuous systemic processes related to the absorption, distribution, metabolism and excretion (ADME) of metabolites. To identify underlying molecular mechanisms, we performed genome-wide association studies of the urinary concentrations of 1,172 metabolites among 1,627 patients with reduced kidney function. The 240 unique metabolite-locus associations (metabolite quantitative trait loci, mQTLs) that were identified and replicated highlight novel candidate substrates for transport proteins. The identified genes are enriched in ADME-relevant tissues and cell types, and they reveal novel candidates for biotransformation and detoxification reactions. Fine mapping of mQTLs and integration with single-cell gene expression permitted the prioritization of causal genes, functional variants and target cell types. The combination of mQTLs with genetic and health information from 450,000 UK Biobank participants illuminated metabolic mediators, and hence, novel urinary biomarkers of disease risk. This comprehensive resource of genetic targets and their substrates is informative for ADME processes in humans and is relevant to basic science, clinical medicine and pharmaceutical research.

Genetic Interleukin 6 Signaling Deficiency Attenuates Cardiovascular Risk in Clonal Hematopoiesis

BACKGROUND

Clonal hematopoiesis of indeterminate potential (CHIP) refers to clonal expansion of hematopoietic stem cells attributable to acquired leukemic mutations in genes such as DNMT3A or TET2. In humans, CHIP associates with prevalent myocardial infarction. In mice, CHIP accelerates atherosclerosis and increases IL-6/IL-1β expression, raising the hypothesis that IL-6 pathway antagonism in CHIP carriers would decrease cardiovascular disease (CVD) risk.

METHODS

We analyzed exome sequences from 35 416 individuals in the UK Biobank without prevalent CVD, to identify participants with DNMT3A or TET2 CHIP. We used the IL6R p.Asp358Ala coding mutation as a genetic proxy for IL-6 inhibition. We tested the association of CHIP status with incident CVD events (myocardial infarction, coronary revascularization, stroke, or death), and whether it was modified by IL6R p.Asp358Ala.

RESULTS

We identified 1079 (3.0%) individuals with CHIP, including 432 (1.2%) with large clones (allele fraction >10%). During 6.9-year median follow-up, CHIP associated with increased incident CVD event risk (hazard ratio, 1.27 [95% CI, 1.04-1.56], P=0.019), with greater risk from large CHIP clones (hazard ratio, 1.59 [95% CI, 1.21-2.09], P<0.001). IL6R p.Asp358Ala attenuated CVD event risk among participants with large CHIP clones (hazard ratio, 0.46 [95% CI, 0.29-0.73], P<0.001) but not in individuals without CHIP (hazard ratio, 0.95 [95% CI, 0.89-1.01], P=0.08; Pinteraction=0.003). In 9951 independent participants, the association of CHIP status with myocardial infarction similarly varied by IL6R p.Asp358Ala (Pinteraction=0.036).

CONCLUSIONS

CHIP is associated with increased risk of incident CVD. Among carriers of large CHIP clones, genetically reduced IL-6 signaling abrogated this risk.

Phenome-based approach identifies RIC1-linked Mendelian syndrome through zebrafish models, biobank associations and clinical studies

Discovery of genotype-phenotype relationships remains a major challenge in clinical medicine. Here, we combined three sources of phenotypic data to uncover a novel mechanism for rare and common diseases resulting from collagen secretion deficits. Using zebrafish genetic screen, we identified the ric1 gene to be essential for skeletal biology. Using a gene-based phenome-wide association study (PheWAS) in the EHR-linked BioVU biobank, we show that reduced genetically determined expression of RIC1 is associated with musculoskeletal and dental conditions. Whole exome sequencing (WES) identified individuals homozygous-by-descent for a rare variant in RIC1, and, through a guided clinical re-evaluation, they were discovered to share signs with the BioVU-associated phenome. We named this novel Mendelian syndrome CATIFA (Cleft lip, cAtaract, Tooth abnormality, Intellectual disability, Facial dysmorphism, ADHD), and revealed further disease mechanisms. This gene-based PheWAS-guided approach can accelerate the discovery of clinically relevant disease phenome and associated biological mechanisms.

Associations between Serum Levels of Cholesterol and Survival to Age 90 in Postmenopausal Women

OBJECTIVES

Although elevated lipid levels predict increased risk of coronary heart disease and death in middle-aged women and men, evidence is mixed if lipid levels measured in later life predict survival to very old ages. We examined lipid levels and survival to age 90 with or without intact mobility in a large cohort of older women.

DESIGN

Prospective cohort.

SETTING

Laboratory collection at a Women's Health Initiative (WHI) center and longitudinal follow-up via mail.

PARTICIPANTS

Women aged 68 to 81 years at baseline.

MEASUREMENTS

Serum high-density lipoprotein (HDL) and low-density lipoprotein (LDL) cholesterol were collected at baseline. Participant survival status and self-reported mobility was compared across lipid levels.

RESULTS

HDL and LDL levels were not associated with survival to age 90 after adjustment for cardiovascular risk factors (HDL: quartile (Q) 2: odds ratio [OR] = 1.14 [95% confidence interval [CI] = .94-1.38]; Q3 OR = 1.08 [95% CI = .88-1.33]; Q4 OR = 1.09 [95% CI = .88-1.35]; LDL: Q2 OR = 1.07 [95% CI = .88-1.31]; Q3 OR = 1.27 [95% CI = 1.04-1.55]; Q4 OR = 1.07 [95% CI = .88-1.31]). Similarly, no associations were observed between HDL and LDL levels and survival to age 90 with mobility disability. High HDL was not associated with survival to age 90 with intact mobility after adjustment for other cardiovascular risk factors. Compared with the lowest LDL quartile, the three upper LDL quartiles were associated with greater odds of survival to age 90 with intact mobility (LDL: Q2 OR = 1.31 [95% CI = .99-1.74]; Q3 OR = 1.43 [95% CI = 1.07-1.92]; Q4 OR = 1.35 [95% CI = 1.01-1.80]; P = .05).

CONCLUSION

Neither higher HDL nor lower LDL levels predicted survival to age 90, but higher LDL predicted healthy survival. These findings suggest the need for reevaluation of healthy LDL levels in older women. J Am Geriatr Soc 68:288-296, 2020.

Axes of a revolution: challenges and promises of big data in healthcare

Health data are increasingly being generated at a massive scale, at various levels of phenotyping and from different types of resources. Concurrent with recent technological advances in both data-generation infrastructure and data-analysis methodologies, there have been many claims that these events will revolutionize healthcare, but such claims are still a matter of debate. Addressing the potential and challenges of big data in healthcare requires an understanding of the characteristics of the data. Here we characterize various properties of medical data, which we refer to as 'axes' of data, describe the considerations and tradeoffs taken when such data are generated, and the types of analyses that may achieve the tasks at hand. We then broadly describe the potential and challenges of using big data in healthcare resources, aiming to contribute to the ongoing discussion of the potential of big data resources to advance the understanding of health and disease.

Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure

Heart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies.

Immune-mediated genetic pathways resulting in pulmonary function impairment increase lung cancer susceptibility

Impaired lung function is often caused by cigarette smoking, making it challenging to disentangle its role in lung cancer susceptibility. Investigation of the shared genetic basis of these phenotypes in the UK Biobank and International Lung Cancer Consortium (29,266 cases, 56,450 controls) shows that lung cancer is genetically correlated with reduced forced expiratory volume in one second (FEV1: rg = 0.098, p = 2.3 × 10-8) and the ratio of FEV1 to forced vital capacity (FEV1/FVC: rg = 0.137, p = 2.0 × 10-12). Mendelian randomization analyses demonstrate that reduced FEV1 increases squamous cell carcinoma risk (odds ratio (OR) = 1.51, 95% confidence intervals: 1.21-1.88), while reduced FEV1/FVC increases the risk of adenocarcinoma (OR = 1.17, 1.01-1.35) and lung cancer in never smokers (OR = 1.56, 1.05-2.30). These findings support a causal role of pulmonary impairment in lung cancer etiology. Integrative analyses reveal that pulmonary function instruments, including 73 novel variants, influence lung tissue gene expression and implicate immune-related pathways in mediating the observed effects on lung carcinogenesis.

Using Mendelian randomization to evaluate the causal relationship between serum C-reactive protein levels and age-related macular degeneration

Serum C-reactive protein (CRP), an important inflammatory marker, has been associated with age-related macular degeneration (AMD) in observational studies; however, the findings are inconsistent. It remains unclear whether the association between circulating CRP levels and AMD is causal. We used two-sample Mendelian randomization (MR) to evaluate the potential causal relationship between serum CRP levels and AMD risk. We derived genetic instruments for serum CRP levels in 418,642 participants of European ancestry from UK Biobank, and then conducted a genome-wide association study for 12,711 advanced AMD cases and 14,590 controls of European descent from the International AMD Genomics Consortium. Genetic variants which predicted elevated serum CRP levels were associated with advanced AMD (odds ratio [OR] for per standard deviation increase in serum CRP levels: 1.31, 95% confidence interval [CI]: 1.19-1.44, P = 5.2 × 10^-8). The OR for the increase in advanced AMD risk when moving from low (< 3 mg/L) to high (> 3 mg/L) CRP levels is 1.29 (95% CI: 1.17-1.41). Our results were unchanged in sensitivity analyses using MR models which make different modelling assumptions. Our findings were broadly similar across the different forms of AMD (intermediate AMD, choroidal neovascularization, and geographic atrophy). We used multivariable MR to adjust for the effects of other potential AMD risk factors including smoking, body mass index, blood pressure and cholesterol; this did not alter our findings. Our study provides strong genetic evidence that higher circulating CRP levels lead to increases in risk for all forms of AMD. These findings highlight the potential utility for using circulating CRP as a biomarker in future trials aimed at modulating AMD risk via systemic therapies.

UK Biobank Whole-Exome Sequence Binary Phenome Analysis with Robust Region-Based Rare-Variant Test

In biobank data analysis, most binary phenotypes have unbalanced case-control ratios, and this can cause inflation of type I error rates. Recently, a saddle point approximation (SPA) based single-variant test has been developed to provide an accurate and scalable method to test for associations of such phenotypes. For gene- or region-based multiple-variant tests, a few methods exist that can adjust for unbalanced case-control ratios; however, these methods are either less accurate when case-control ratios are extremely unbalanced or not scalable for large data analyses. To address these problems, we propose SKAT- and SKAT-O- type region-based tests; in these tests, the single-variant score statistic is calibrated based on SPA and efficient resampling (ER). Through simulation studies, we show that the proposed method provides well-calibrated p values. In contrast, when the case-control ratio is 1:99, the unadjusted approach has greatly inflated type I error rates (90 times that of exome-wide sequencing α = 2.5 × 10-6). Additionally, the proposed method has similar computation time to the unadjusted approaches and is scalable for large sample data. In our application, the UK Biobank whole-exome sequence data analysis of 45,596 unrelated European samples and 791 PheCode phenotypes identified 10 rare-variant associations with p value < 10-7, including the associations between JAK2 and myeloproliferative disease, HOXB13 and cancer of prostate, and F11 and congenital coagulation defects. All analysis summary results are publicly available through a web-based visual server, and this availability can help facilitate the identification of the genetic basis of complex diseases.

Clinical implications of recent advances in primary open-angle glaucoma genetics

Over the last decade, genetic studies, including genome-wide association studies (GWAS), have accelerated the discovery of genes and genomic regions contributing to primary open-angle glaucoma (POAG), a leading cause of irreversible vision loss. Here, we review the findings of genetic studies of POAG published in English prior to September 2019. In total, 74 genomic regions have been associated at a genome-wide level of significance with POAG susceptibility. Recent POAG GWAS provide not only insight into global and ethnic-specific genetic risk factors for POAG susceptibility across populations of diverse ancestry, but also important functional insights underlying biological mechanisms of glaucoma pathogenesis. In this review, we also summarize the genetic overlap between POAG, glaucoma endophenotypes, such as intraocular pressure and vertical cup-disc ratio (VCDR), and other eye disorders. We also discuss approaches recently developed to increase power for POAG locus discovery and to predict POAG risk. Finally, we discuss the recent development of POAG gene-based therapies and future strategies to treat glaucoma effectively. Understanding the genetic architecture of POAG is essential for an earlier diagnosis of this common eye disorder, predictive testing of at-risk patients, and design of gene-based targeted medical therapies none of which are currently available.

Frequency of ClinVar Pathogenic Variants in Chronic Kidney Disease Patients Surveyed for Return of Research Results at a Cleveland Public Hospital

Return of results is not common in research settings as standards are not yet in place for what to return, how to return, and to whom. As a pioneer of large-scale of return of research results, the Precision Medicine Initiative Cohort now known of All of Us plans to return pharmacogenomic results and variants of clinical significance to its participants starting late 2019. To better understand the local landscape of possibilities regarding return of research results, we assessed the frequency of pathogenic variants and APOL1 renal risk variants in a small diverse cohort of chronic kidney disease patients (CKD) ascertained from a public hospital in Cleveland, Ohio genotyped on the Illumina Infinium MegaEX. Of the 23,720 ClinVar-designated variants directly assayed by the MegaEX, 8,355 (35%) had at least one alternate allele in the 130 participants genotyped. Of these, 18 ClinVar variants deemed pathogenic by multiple submitters with no conflicts in interpretation were distributed across 27 participants. The majority of these pathogenic ClinVar variants (14/18) were associated with autosomal recessive disorders. Of note were four African American carriers of TTR rs76992529 associated with amyloidogenic transthyretin amyloidosis, otherwise known as familial transthyretin amyloidosis (FTA). FTA, an autosomal dominant disorder with variable penetrance, is more common among African-descent populations compared with European-descent populations. Also common in this CKD population were APOL1 renal risk alleles G1 (rs73885319) and G2 (rs71785313) with 60% of the study population carrying at least one renal risk allele. Both pathogenic ClinVar variants and APOL1 renal risk alleles were distributed among participants who wanted actionable genetic results returned, wanted genetic results returned regardless of actionability, and wanted no results returned. Results from this local genetic study highlight challenges in which variants to report, how to interpret them, and the participant's potential for follow-up, only some of the challenges in return of research results likely facing larger studies such as All of Us.

A brief history of human disease genetics

A primary goal of human genetics is to identify DNA sequence variants that influence biomedical traits, particularly those related to the onset and progression of human disease. Over the past 25 years, progress in realizing this objective has been transformed by advances in technology, foundational genomic resources and analytical tools, and by access to vast amounts of genotype and phenotype data. Genetic discoveries have substantially improved our understanding of the mechanisms responsible for many rare and common diseases and driven development of novel preventative and therapeutic strategies. Medical innovation will increasingly focus on delivering care tailored to individual patterns of genetic predisposition.

Causal Inference for Genetically Determined Levels of High-Density Lipoprotein Cholesterol and Risk of Infectious Disease

OBJECTIVE

HDL (high-density lipoprotein) cholesterol (HDL-C) and LDL (low-density lipoprotein) cholesterol (LDL-C) are inversely associated with infectious hospitalizations. Whether these represent causal relationships is unknown. Approach and Results: Adults of 40 to 69 years of age were recruited from across the United Kingdom between 2006 and 2010 and followed until March 31, 2016, as part of the UK Biobank. We determined HDL-C, LDL-C, and triglyceride polygenic scores for UK Biobank participants of British white ancestry (n=407 558). We examined the association of lipid levels and polygenic scores with infectious hospitalizations, antibiotic usage, and 28-day sepsis survival using Cox proportional hazards or logistic regression models. Measured levels of HDL-C and LDL-C were inversely associated with risk of infectious hospitalizations, while triglycerides displayed a positive association. A 1-mmol/L increase in genetically determined levels of HDL-C associated with a hazard ratio for infectious disease of 0.84 ([95% CI, 0.75-0.95]; P=0.004). Mendelian randomization using genetic variants associated with HDL-C as an instrumental variable was consistent with a causal relationship between elevated HDL-C and reduced risk of infectious hospitalizations (inverse weighted variance method, P=0.001). Furthermore, of 3222 participants who experienced an index episode of sepsis, there was a significant inverse association between continuous HDL-C polygenic score and 28-day mortality (adjusted hazard ratio, 0.37 [95% CI, 0.14-0.96] per 1 mmol/L increase; P=0.04). LDL-C and triglyceride polygenic scores were not significantly associated with hospitalization for infection, antibiotic use, or sepsis mortality.

CONCLUSIONS

Our results provide causal inference for an inverse relationship between HDL-C, but not LDL-C or triglycerides, and risk of an infectious hospitalization.

Leveraging Population Genomics for Individualized Correction of the Hallmarks of Alpha-1 Antitrypsin Deficiency

Deep medicine is rapidly moving towards a high-definition approach for therapeutic management of the patient as an individual given the rapid progress of genome sequencing technologies and machine learning algorithms. While considered a monogenic disease, alpha-1 antitrypsin (AAT) deficiency (AATD) patients present with complex and variable phenotypes we refer to as the "hallmarks of AATD" that involve distinct molecular mechanisms in the liver, plasma and lung tissues, likely due to both coding and non-coding variation as well as genetic and environmental modifiers in different individuals. Herein, we briefly review the current therapeutic strategies for the management of AATD. To embrace genetic diversity in the management of AATD, we provide an overview of the disease phenotypes of AATD patients harboring different AAT variants. Linking genotypic diversity to phenotypic diversity illustrates the potential for sequence-specific regions of AAT protein fold design to play very different roles during nascent synthesis in the liver and/or function in post-liver plasma and lung environments. We illustrate how to manage diversity with recently developed machine learning (ML) approaches that bridge sequence-to-function-to-structure knowledge gaps based on the principle of spatial covariance (SCV). SCV relationships provide a deep understanding of the genotype to phenotype transformation initiated by AAT variation in the population to address the role of genetic and environmental modifiers in the individual. Embracing the complexity of AATD in the population is critical for risk management and therapeutic intervention to generate a high definition medicine approach for the patient.

Brain Imaging Genomics: Integrated Analysis and Machine Learning

Brain imaging genomics is an emerging data science field, where integrated analysis of brain imaging and genomics data, often combined with other biomarker, clinical and environmental data, is performed to gain new insights into the phenotypic, genetic and molecular characteristics of the brain as well as their impact on normal and disordered brain function and behavior. It has enormous potential to contribute significantly to biomedical discoveries in brain science. Given the increasingly important role of statistical and machine learning in biomedicine and rapidly growing literature in brain imaging genomics, we provide an up-to-date and comprehensive review of statistical and machine learning methods for brain imaging genomics, as well as a practical discussion on method selection for various biomedical applications.

From genome-wide association studies to rational drug target prioritisation in inflammatory arthritis

Early identification of genetically validated drug targets can increase the chances of successful late-stage drug development. 81 high-quality genome-wide association studies (GWAS) in diseases related to inflammatory arthritis have been curated into the GWAS catalogue; however, translation of genetic findings from GWAS into rational drug target discovery has been poor. No human genetic findings have completely driven drug development for inflammatory arthritis; however, genetic associations have partly driven the development of abatacept (CTLA-4-Ig) in rheumatoid arthritis and secukinumab (anti-IL-23R) in ankylosing spondylitis. Roadblocks to progress exist, including little knowledge of the genetic architecture and regulatory mechanisms underlying associations, and the need to identify gene regulatory networks and assess target tractability. New opportunities are arising that could maximise the informativeness of GWAS for drug target validation. Genetic variants can be linked to core genes by using functional genomics and then to peripheral genes interconnected to core genes using network information. Moreover, identification of crosstalk between biological pathways might highlight key points for therapeutic intervention.

Population genetic simulation study of power in association testing across genetic architectures and study designs

While it is well established that genetics can be a major contributor to population variation of complex traits, the relative contributions of rare and common variants to phenotypic variation remains a matter of considerable debate. Here, we simulate genetic and phenotypic data across different case/control panel sampling strategies, sequencing methods, and genetic architecture models based on evolutionary forces to determine the statistical performance of rare variant association tests (RVATs) widely in use. We find that the highest statistical power of RVATs is achieved by sampling case/control individuals from the extremes of an underlying quantitative trait distribution. We also demonstrate that the use of genotyping arrays, in conjunction with imputation from a whole-genome sequenced (WGS) reference panel, recovers the vast majority (90%) of the power that could be achieved by sequencing the case/control panel using current tools. Finally, we show that for dichotomous traits, the statistical performance of RVATs decreases as rare variants become more important in the trait architecture. Our results extend previous work to show that RVATs are insufficiently powered to make generalizable conclusions about the role of rare variants in dichotomous complex traits.

Significant shared heritability underlies suicide attempt and clinically predicted probability of attempting suicide

Suicide accounts for nearly 800,000 deaths per year worldwide with rates of both deaths and attempts rising. Family studies have estimated substantial heritability of suicidal behavior; however, collecting the sample sizes necessary for successful genetic studies has remained a challenge. We utilized two different approaches in independent datasets to characterize the contribution of common genetic variation to suicide attempt. The first is a patient reported suicide attempt phenotype asked as part of an online mental health survey taken by a subset of participants (n = 157,366) in the UK Biobank. After quality control, we leveraged a genotyped set of unrelated, white British ancestry participants including 2433 cases and 334,766 controls that included those that did not participate in the survey or were not explicitly asked about attempting suicide. The second leveraged electronic health record (EHR) data from the Vanderbilt University Medical Center (VUMC, 2.8 million patients, 3250 cases) and machine learning to derive probabilities of attempting suicide in 24,546 genotyped patients. We identified significant and comparable heritability estimates of suicide attempt from both the patient reported phenotype in the UK Biobank (h²_SNP = 0.035, p = 7.12 × 10^-4) and the clinically predicted phenotype from VUMC (h²_SNP = 0.046, p = 1.51 × 10^-2). A significant genetic overlap was demonstrated between the two measures of suicide attempt in these independent samples through polygenic risk score analysis (t = 4.02, p = 5.75 × 10^-5) and genetic correlation (rg = 1.073, SE = 0.36, p = 0.003). Finally, we show significant but incomplete genetic correlation of suicide attempt with insomnia (rg = 0.34-0.81) as well as several psychiatric disorders (rg = 0.26-0.79). This work demonstrates the contribution of common genetic variation to suicide attempt. It points to a genetic underpinning to clinically predicted risk of attempting suicide that is similar to the genetic profile from a patient reported outcome. Lastly, it presents an approach for using EHR data and clinical prediction to generate quantitative measures from binary phenotypes that can improve power for genetic studies.

Genome-wide association study identifies locus at chromosome 2q32.1 associated with syncope and collapse

AIMS

Syncope is a common condition associated with frequent hospitalization or visits to the emergency department. Family aggregation and twin studies have shown that syncope has a heritable component. We investigated whether common genetic variants predispose to syncope and collapse.

METHODS AND RESULTS

We used genome-wide association data on syncope on 408 961 individuals with European ancestry from the UK Biobank study. In a replication study, we used the Integrative Psychiatric Research Consortium (iPSYCH) cohort (n = 86 189), to investigate the risk of incident syncope stratified by genotype carrier status. We report on a genome-wide significant locus located on chromosome 2q32.1 [odds ratio = 1.13, 95% confidence interval (CI) 1.10-1.17, P = 5.8 × 10-15], with lead single nucleotide polymorphism rs12465214 in proximity to the gene zinc finger protein 804a (ZNF804A). This association was also shown in the iPSYCH cohort, where homozygous carriers of the C allele conferred an increased hazard ratio (1.30, 95% CI 1.15-1.46, P = 1.68 × 10-5) of incident syncope. Quantitative polymerase chain reaction analysis showed ZNF804A to be expressed most abundantly in brain tissue.

CONCLUSION

We identified a genome-wide significant locus (rs12465214) associated with syncope and collapse. The association was replicated in an independent cohort. This is the first genome-wide association study to associate a locus with syncope and collapse.

Genetic contributions to two special factors of neuroticism are associated with affluence, higher intelligence, better health, and longer life

Higher scores on the personality trait of neuroticism, the tendency to experience negative emotions, are associated with worse mental and physical health. Studies examining links between neuroticism and health typically operationalize neuroticism by summing the items from a neuroticism scale. However, neuroticism is made up of multiple heterogeneous facets, each contributing to the effect of neuroticism as a whole. A recent study showed that a 12-item neuroticism scale described one broad trait of general neuroticism and two special factors, one characterizing the extent to which people worry and feel vulnerable, and the other characterizing the extent to which people are anxious and tense. This study also found that, although individuals who were higher on general neuroticism lived shorter lives, individuals whose neuroticism was characterized by worry and vulnerability lived longer lives. Here, we examine the genetic contributions to the two special factors of neuroticism-anxiety/tension and worry/vulnerability-and how they contrast with that of general neuroticism. First, we show that, whereas the polygenic load for neuroticism is associated with the genetic risk of coronary artery disease, lower intelligence, lower socioeconomic status (SES), and poorer self-rated health, the genetic variants associated with high levels of anxiety/tension, and high levels of worry/vulnerability are associated with genetic variants linked to higher SES, higher intelligence, better self-rated health, and longer life. Second, we identify genetic variants that are uniquely associated with these protective aspects of neuroticism. Finally, we show that different neurological pathways are linked to each of these neuroticism phenotypes.

qgg: an R package for large-scale quantitative genetic analyses

Summary

Here, we present the R package qgg, which provides an environment for large-scale genetic analyses of quantitative traits and diseases. The qgg package provides an infrastructure for efficient processing of large-scale genetic data and functions for estimating genetic parameters, and performing single and multiple marker association analyses and genomic-based predictions of phenotypes.

Availability and implementation

The qgg package is freely available. For the latest updates, user guides and example scripts, consult the main page http://psoerensen.github.io/qgg. The current release is available from CRAN (https://CRAN.R-project.org/package=qgg) for all major operating systems.

Supplementary information

Supplementary data are available at Bioinformatics online.

Machine learning applications in drug development

Due to the huge amount of biological and medical data available today, along with well-established machine learning algorithms, the design of largely automated drug development pipelines can now be envisioned. These pipelines may guide, or speed up, drug discovery; provide a better understanding of diseases and associated biological phenomena; help planning preclinical wet-lab experiments, and even future clinical trials. This automation of the drug development process might be key to the current issue of low productivity rate that pharmaceutical companies currently face. In this survey, we will particularly focus on two classes of methods: sequential learning and recommender systems, which are active biomedical fields of research.

Smoking, blood cells and myeloproliferative neoplasms: meta-analysis and Mendelian randomization of 2·3 million people

Meta-analyses and Mendelian randomization (MR) may clarify the associations of smoking, blood cells and myeloproliferative neoplasms (MPN). We investigated the association of smoking with blood cells in the Danish General Suburban Population Study (GESUS, n = 11 083), by meta-analyses (including GESUS) of 92 studies (n = 531 741) and MR of smoking variant CHRNA3 (rs1051730[A]) in UK Biobank, and with MPN in a meta-analysis of six studies (n (total/cases):1 425 529/2187), totalling 2 307 745 participants. In the meta-analysis the random-effects standardized mean difference (SMD) in current smokers versus non-smokers was 0·82 (0·75-0·89, P = 2·0 * 10^-108 ) for leukocytes, 0·09 (-0·02 to 0·21, P = 0·12) for erythrocytes, 0·53 (0·42-0·64, P = 8·0 * 10^-22 ) for haematocrit, 0·42 (0·34-0·51, P = 7·1 * 10^-21 ) for haemoglobin, 0·19 (0·08-0·31, P = 1·2 * 10^-3 ) for mean corpuscular haemoglobin (MCH), 0·29 (0·19-0·39, P = 1·6 * 10^-8 ) for mean corpuscular volume (MCV), and 0·04 (-0·04 to 0·13, P = 0·34) for platelets with trends for ever/ex-/current smokers, light/heavy smokers and female/male smokers. Analyses presented high heterogeneity but low publication bias. Per allele in CHRNA3, cigarettes per day in current smokers was associated with increased blood cell counts (leukocytes, neutrophils), MCH, red cell distribution width (RDW) and MCV. The pooled fixed-effects odds ratio for MPN was 1·44 [95% confidence interval (CI): 1·33-1·56; P = 1·8 * 10^-19 ; I²  = 0%] in current smokers, 1·29 (1·15-1·44; P = 8·0 * 10^-6 ; I²  = 0%) in ex-smokers, 1·49 (1·26-1·77; P = 4·4 * 10^-6 ; I²  = 0%) in light smokers and 2·04 (1·74-2·39, P = 2·3 * 10^-18 ; I²  = 51%) in heavy smokers compared with non-smokers. Smoking is observationally and genetically associated with increased leukocyte counts and red blood cell indices (MCH, MCV, RDW) and observationally with risk of MPN in current and ex-smokers versus non/never-smokers.

Association of Premature Natural and Surgical Menopause With Incident Cardiovascular Disease

IMPORTANCE

Recent guidelines endorse using history of menopause before age 40 years to refine atherosclerotic cardiovascular disease risk assessments among middle-aged women. Robust data on cardiovascular disease risk in this population are lacking.

OBJECTIVE

To examine the development of cardiovascular diseases and cardiovascular risk factors in women with natural and surgical menopause before age 40 years.

DESIGN, SETTING, AND PARTICIPANTS

Cohort study (UK Biobank), with adult residents of the United Kingdom recruited between 2006 and 2010. Of women who were 40 to 69 years old and postmenopausal at study enrollment, 144 260 were eligible for inclusion. Follow-up occurred through August 2016.

EXPOSURES

Natural premature menopause (menopause before age 40 without oophorectomy) and surgical premature menopause (bilateral oophorectomy before age 40). Postmenopausal women without premature menopause served as the reference group.

MAIN OUTCOMES AND MEASURES

The primary outcome was a composite of incident coronary artery disease, heart failure, aortic stenosis, mitral regurgitation, atrial fibrillation, ischemic stroke, peripheral artery disease, and venous thromboembolism. Secondary outcomes included individual components of the primary outcome, incident hypertension, hyperlipidemia, and type 2 diabetes.

RESULTS

Of 144 260 postmenopausal women included (mean [SD] age at enrollment, 59.9 [5.4] years), 4904 (3.4%) had natural premature menopause and 644 (0.4%) had surgical premature menopause. Participants were followed up for a median of 7 years (interquartile range, 6.3-7.7). The primary outcome occurred in 5415 women (3.9%) with no premature menopause (incidence, 5.70/1000 woman-years), 292 women (6.0%) with natural premature menopause (incidence, 8.78/1000 woman-years) (difference vs no premature menopause, +3.08/1000 woman-years [95% CI, 2.06-4.10]; P < .001), and 49 women (7.6%) with surgical premature menopause (incidence, 11.27/1000 woman-years) (difference vs no premature menopause, +5.57/1000 woman-years [95% CI, 2.41-8.73]; P < .001). For the primary outcome, natural and surgical premature menopause were associated with hazard ratios of 1.36 (95% CI, 1.19-1.56; P < .001) and 1.87 (95% CI, 1.36-2.58; P < .001), respectively, after adjustment for conventional cardiovascular disease risk factors and use of menopausal hormone therapy.

CONCLUSIONS AND RELEVANCE

Natural and surgical premature menopause (before age 40 years) were associated with a small but statistically significant increased risk for a composite of cardiovascular diseases among postmenopausal women. Further research is needed to understand the mechanisms underlying these associations.

Identifying cross-disease components of genetic risk across hospital data in the UK Biobank

Genetic risk factors frequently affect multiple common human diseases, providing insight into shared pathophysiological pathways and opportunities for therapeutic development. However, systematic identification of genetic profiles of disease risk is limited by the availability of both comprehensive clinical data on population-scale cohorts and the lack of suitable statistical methodology that can handle the scale of and differential power inherent in multi-phenotype data. Here, we develop a disease-agnostic approach to cluster genetic risk profiles for 3,025 genome-wide independent loci across 19,155 disease classification codes from 320,644 participants in the UK Biobank, representing a large and heterogeneous population. We identify 339 distinct disease association profiles and use multiple approaches to link clusters to underlying biological pathways. We show how clusters can decompose the variance and covariance in risk for disease, thereby identifying underlying biological processes and their impact. We demonstrate the use of clusters in defining disease relationships and their potential in informing therapeutic strategies.

Genomic risk score offers predictive performance comparable to clinical risk factors for ischaemic stroke

Recent genome-wide association studies in stroke have enabled the generation of genomic risk scores (GRS) but their predictive power has been modest compared to established stroke risk factors. Here, using a meta-scoring approach, we develop a metaGRS for ischaemic stroke (IS) and analyse this score in the UK Biobank (n = 395,393; 3075 IS events by age 75). The metaGRS hazard ratio for IS (1.26, 95% CI 1.22-1.31 per metaGRS standard deviation) doubles that of a previous GRS, identifying a subset of individuals at monogenic levels of risk: the top 0.25% of metaGRS have three-fold risk of IS. The metaGRS is similarly or more predictive compared to several risk factors, such as family history, blood pressure, body mass index, and smoking. We estimate the reductions needed in modifiable risk factors for individuals with different levels of genomic risk and suggest that, for individuals with high metaGRS, achieving risk factor levels recommended by current guidelines may be insufficient to mitigate risk.

Recapitulation of previously reported associations for type 2 diabetes and metabolic traits in the 126K East Asians

Over the last decade, genome-wide association studies (GWASs) have provided an unprecedented amount of genetic variations that are associated with various phenotypes. However, previous GWAS were mostly conducted in European populations, and these biased results for non-Europeans may result in a significant reduction in risk prediction for non-Europeans. An issue with the early GWAS was the winner’s curse problem, which led to misleading results when constructing the polygenic risk scores (PRS). Therefore, more non-European population-based studies are needed to validate reported variants and improve genetic risk assessment across diverse populations. In this study, we validated 422 variants independently associated with glycemic indexes, liver enzymes, and type 2 diabetes in 125,872 samples from a Korean population, and further validated the results by assessing publicly available summary statistics from European GWAS (n = 898,130). Among the 422 independently associated variants, 284, 320, and 361 variants were replicated in Koreans, Europeans, and either one of the two populations. In addition, the effect sizes for Koreans and Europeans were moderately correlated (r = 0.33–0.68). However, 61 variants were not replicated in both Koreans and Europeans. Our findings provide valuable information on effect sizes and statistical significance, which is essential to improve the assessment of disease risk using PRS analysis.

Genetic correlations of psychiatric traits with body composition and glycemic traits are sex- and age-dependent

Body composition is often altered in psychiatric disorders. Using genome-wide common genetic variation data, we calculate sex-specific genetic correlations amongst body fat %, fat mass, fat-free mass, physical activity, glycemic traits and 17 psychiatric traits (up to N = 217,568). Two patterns emerge: (1) anorexia nervosa, schizophrenia, obsessive-compulsive disorder, and education years are negatively genetically correlated with body fat % and fat-free mass, whereas (2) attention-deficit/hyperactivity disorder (ADHD), alcohol dependence, insomnia, and heavy smoking are positively correlated. Anorexia nervosa shows a stronger genetic correlation with body fat % in females, whereas education years is more strongly correlated with fat mass in males. Education years and ADHD show genetic overlap with childhood obesity. Mendelian randomization identifies schizophrenia, anorexia nervosa, and higher education as causal for decreased fat mass, with higher body fat % possibly being a causal risk factor for ADHD and heavy smoking. These results suggest new possibilities for targeted preventive strategies.

Genome-wide analysis identifies molecular systems and 149 genetic loci associated with income

Socioeconomic position (SEP) is a multi-dimensional construct reflecting (and influencing) multiple socio-cultural, physical, and environmental factors. In a sample of 286,301 participants from UK Biobank, we identify 30 (29 previously unreported) independent-loci associated with income. Using a method to meta-analyze data from genetically-correlated traits, we identify an additional 120 income-associated loci. These loci show clear evidence of functionality, with transcriptional differences identified across multiple cortical tissues, and links to GABAergic and serotonergic neurotransmission. By combining our genome wide association study on income with data from eQTL studies and chromatin interactions, 24 genes are prioritized for follow up, 18 of which were previously associated with intelligence. We identify intelligence as one of the likely causal, partly-heritable phenotypes that might bridge the gap between molecular genetic inheritance and phenotypic consequence in terms of income differences. These results indicate that, in modern era Great Britain, genetic effects contribute towards some of the observed socioeconomic inequalities.

Insights into malaria susceptibility using genome-wide data on 17,000 individuals from Africa, Asia and Oceania

The human genetic factors that affect resistance to infectious disease are poorly understood. Here we report a genome-wide association study in 17,000 severe malaria cases and population controls from 11 countries, informed by sequencing of family trios and by direct typing of candidate loci in an additional 15,000 samples. We identify five replicable associations with genome-wide levels of evidence including a newly implicated variant on chromosome 6. Jointly, these variants account for around one-tenth of the heritability of severe malaria, which we estimate as ~23% using genome-wide genotypes. We interrogate available functional data and discover an erythroid-specific transcription start site underlying the known association in ATP2B4, but are unable to identify a likely causal mechanism at the chromosome 6 locus.  Previously reported HLA associations do not replicate in these samples. This large dataset will provide a foundation for further research on thegenetic determinants of malaria resistance in diverse populations.

Genome-wide analysis identifies molecular systems and 149 genetic loci associated with income

Socioeconomic position (SEP) is a multi-dimensional construct reflecting (and influencing) multiple socio-cultural, physical, and environmental factors. In a sample of 286,301 participants from UK Biobank, we identify 30 (29 previously unreported) independent-loci associated with income. Using a method to meta-analyze data from genetically-correlated traits, we identify an additional 120 income-associated loci. These loci show clear evidence of functionality, with transcriptional differences identified across multiple cortical tissues, and links to GABAergic and serotonergic neurotransmission. By combining our genome wide association study on income with data from eQTL studies and chromatin interactions, 24 genes are prioritized for follow up, 18 of which were previously associated with intelligence. We identify intelligence as one of the likely causal, partly-heritable phenotypes that might bridge the gap between molecular genetic inheritance and phenotypic consequence in terms of income differences. These results indicate that, in modern era Great Britain, genetic effects contribute towards some of the observed socioeconomic inequalities.

Omics Potential in Herbicide-Resistant Weed Management

The rapid development of omics technologies has drastically altered the way biologists conduct research. Basic plant biology and genomics have incorporated these technologies, while some challenges remain for use in applied biology. Weed science, on the whole, is still learning how to integrate omics technologies into the discipline; however, omics techniques are more frequently being implemented in new and creative ways to address basic questions in weed biology as well as the more practical questions of improving weed management. This has been especially true in the subdiscipline of herbicide resistance where important questions are the evolution and genetic basis of herbicide resistance. This review examines the advantages, challenges, potential solutions, and outlook for omics technologies in the discipline of weed science, with examples of how omics technologies will impact herbicide resistance studies and ultimately improve management of herbicide-resistant populations.

Coffee Consumption and Kidney Function: A Mendelian Randomization Study

RATIONALE & OBJECTIVE

Chronic kidney disease (CKD) is a leading cause of morbidity and mortality worldwide, with limited strategies for prevention and treatment. Coffee is a complex mixture of chemicals, and consumption has been associated with mostly beneficial health outcomes. This work aimed to determine the impact of coffee consumption on kidney function.

STUDY DESIGN

Genome-wide association study (GWAS) and Mendelian randomization.

SETTING & PARTICIPANTS

UK Biobank baseline data were used for a coffee consumption GWAS and included 227,666 participants. CKDGen Consortium data were used for kidney outcomes and included 133,814 participants (12,385 cases of CKD) of mostly European ancestry across various countries.

EXPOSURE

Coffee consumption.

OUTCOMES

Estimated glomerular filtration rate (eGFR), CKD GFR categories 3 to 5 (G3-G5; eGFR<60mL/min/1.73m²), and albuminuria.

ANALYTICAL APPROACH

GWAS to identify single-nucleotide polymorphisms (SNPs) associated with coffee consumption in UK Biobank and use of those SNPs in Mendelian randomization analyses of coffee consumption and kidney outcomes in CKDGen.

RESULTS

2,126 SNPs were associated with coffee consumption (P<5×10^-8), 25 of which were independent and available in CKDGen. Drinking an extra cup of coffee per day conferred a protective effect against CKD G3-G5 (OR, 0.84; 95% CI, 0.72-0.98; P=0.03) and albuminuria (OR, 0.81; 95% CI, 0.67-0.97; P=0.02). An extra cup was also associated with higher eGFR (β=0.022; P=1.6×10^-6) after removal of 3 SNPs responsible for significant heterogeneity (Cochran Q P = 3.5×10^-15).

LIMITATIONS

Assays used to measure creatinine and albumin varied between studies that contributed data and a sex-specific definition was used for albuminuria rather than KDIGO guideline recommendations.

CONCLUSIONS

This study provides evidence of a beneficial effect of coffee on kidney function. Given widespread coffee consumption and limited interventions to prevent CKD incidence and progression, this could have significant implications for global public health in view of the increasing burden of CKD worldwide.

Genome-wide Associations Reveal Human-Mouse Genetic Convergence and Modifiers of Myogenesis, CPNE1 and STC2

Muscle bulk in adult healthy humans is highly variable even after height, age, and sex are accounted for. Low muscle mass, due to fewer and/or smaller constituent muscle fibers, would exacerbate the impact of muscle loss occurring in aging or disease. Genetic variability substantially influences muscle mass differences, but causative genes remain largely unknown. In a genome-wide association study (GWAS) on appendicular lean mass (ALM) in a population of 85,750 middle-aged (aged 38-49 years) individuals from the UK Biobank (UKB), we found 182 loci associated with ALM (p < 5 × 10-8). We replicated associations for 78% of these loci (p < 5 × 10-8) with ALM in a population of 181,862 elderly (aged 60-74 years) individuals from UKB. We also conducted a GWAS on hindlimb skeletal muscle mass of 1,867 mice from an advanced intercross between two inbred strains (LG/J and SM/J); this GWAS identified 23 quantitative trait loci. Thirty-eight positional candidates distributed across five loci overlapped between the two species. In vitro studies of positional candidates confirmed CPNE1 and STC2 as modifiers of myogenesis. Collectively, these findings shed light on the genetics of muscle mass variability in humans and identify targets for the development of interventions for treatment of muscle loss. The overlapping results between humans and the mouse model GWAS point to shared genetic mechanisms across species.

A Fast and Accurate Method for Genome-wide Scale Phenome-wide G × E Analysis and Its Application to UK Biobank

The etiology of most complex diseases involves genetic variants, environmental factors, and gene-environment interaction (G × E) effects. Compared with marginal genetic association studies, G × E analysis requires more samples and detailed measure of environmental exposures, and this limits the possible discoveries. Large-scale population-based biobanks with detailed phenotypic and environmental information, such as UK-Biobank, can be ideal resources for identifying G × E effects. However, due to the large computation cost and the presence of case-control imbalance, existing methods often fail. Here we propose a scalable and accurate method, SPAGE (SaddlePoint Approximation implementation of G × E analysis), that is applicable for genome-wide scale phenome-wide G × E studies. SPAGE fits a genotype-independent logistic model only once across the genome-wide analysis in order to reduce computation cost, and SPAGE uses a saddlepoint approximation (SPA) to calibrate the test statistics for analysis of phenotypes with unbalanced case-control ratios. Simulation studies show that SPAGE is 33-79 times faster than the Wald test and 72-439 times faster than the Firth's test, and SPAGE can control type I error rates at the genome-wide significance level even when case-control ratios are extremely unbalanced. Through the analysis of UK-Biobank data of 344,341 white British European-ancestry samples, we show that SPAGE can efficiently analyze large samples while controlling for unbalanced case-control ratios.