UK Biobank Dataset Research Articles

A genome-wide association study implicates the pleiotropic effect of NMUR2 on asthma and COPD

Asthma and chronic obstructive pulmonary disease (COPD) are two distinct diseases that are associated with chronic inflammation. They share common features in terms of their advanced stages and genetic factors. This study aimed to identify novel genes underlying both asthma and COPD using genome-wide association study (GWAS) to differentiate between the two diseases. We performed a GWAS of asthma and COPD in 7828 Koreans from three hospitals. In addition, we investigated genetic correlations. The UK Biobank dataset was used for the replication studies. We found that rs2961757, located near neuromedin U receptor 2 (NMUR2) on chromosome 5, was genome-wide significant ({upbeta }_{mathrm{Asthma}-mathrm{COPD}} = 0.44, P-valueAsthma-COPD = 3.41 × 10−8), and significant results were replicated with the UK Biobank data ({upbeta }_{mathrm{Asthma}-mathrm{COPD}} = 0.04, P-valueAsthma-COPD = 0.0431). A positive genetic correlation was observed between asthma and COPD (39.8% in the Korean dataset and 49.8% in the UK Biobank dataset). In this study, 40–45% of the genetic effects were common to asthma and COPD. Moreover, NMUR2 increases the risk of asthma development and suppresses COPD development. This indicates that NMUR2 allows for better differentiation of both diseases, which can facilitate tailored medical therapy.

Hormonal factors moderate the associations between vascular risk factors and white matter hyperintensities.

To examine the moderation effects of hormonal factors on the associations between vascular risk factors and white matter hyperintensities in men and women, separately.White matter hyperintensities were automatically segmented and quantified in the UK Biobank dataset (N = 18,294). Generalised linear models were applied to examine (1) the main effects of vascular and hormonal factors on white matter hyperintensities, and (2) the moderation effects of hormonal factors on the relationship between vascular risk factors and white matter hyperintensities volumes.In men with testosterone levels one standard deviation higher than the mean value, smoking was associated with 27.8% higher white matter hyperintensities volumes in the whole brain. In women with a shorter post-menopause duration (one standard deviation below the mean), diabetes and higher pulse wave velocity were associated with 28.8% and 2.0% more deep white matter hyperintensities, respectively.These findings highlighted the importance of considering hormonal risk factors in the prevention and management of white matter hyperintensities.

Low Regional Homogeneity of Intrinsic Cerebellar Activity in Ankle Instability: An Externally Validated rs-fMRI Study.

Joint deafferentation after post-ankle sprain ligament healing can disrupt sensory input from the ankle and induce maladaptive neuroplasticity, especially in the cerebellum. This study aimed to determine whether the regional homogeneity of intrinsic cerebellar activity differs between patients with ankle instability and healthy controls without a history of ankle injury. The current study used a primary data set of 18 patients and 22 healthy controls and an external UK Biobank data set of 16 patients with ankle instability and 69 healthy controls for a cross-database, cross-sectional investigation. All participants underwent resting-state functional magnetic resonance imaging to calculate their regional homogeneity (ReHo) value. Between-group comparisons of the sensorimotor-related subregions of the cerebellum were first performed in the primary data set to identify low cerebellar ReHo in patients with multiple comparison corrections, and the surviving subregions were then externally validated in the UK Biobank data set. Correlation analyses between the ReHo values and clinical features were also performed. The ReHo value of cerebellar lobule VIIIb was significantly lower in the ankle instability group than in the controls (0.170 ± 0.016 vs 0.184 ± 0.019 in the primary data set, 0.157 ± 0.026 vs 0.180 ± 0.042 in the UK Biobank data set). The ReHo values of this subregion showed a significant positive correlation with the Cumberland Ankle Instability Tool scores in the ankle instability group (r = 0.553, P-corrected = 0.0348). Patients with ankle instability had lower intraregional coherence in cerebellar lobule VIIIb than that of controls, which was also positively correlated with the intensity of self-reported ankle instability.

Associations among acute and chronic musculoskeletal pain, sleep duration, and C-reactive protein (CRP): A cross-sectional study of the UK biobank dataset

Both musculoskeletal pain and sleep disturbances are major health problems worldwide. Literature suggests that the two are reciprocally related and both may be associated with changes in C-reactive protein (CRP) levels. However, the relationships among musculoskeletal pain, sleep duration, and CRP remain unclear.In this cross-sectional study, we investigated the relationship between acute and chronic musculoskeletal pain, sleep, and inflammation using the data from the initial visit of the UK Biobank. 17,642 individuals with chronic musculoskeletal pain, 11,962 individuals with acute musculoskeletal pain, and 29,604 pain-free controls were included in the analysis. In addition, we validated the findings using data from the second visit assessment of the UK Biobank.We found that 1) chronic pain was associated with higher CRP levels compared to both acute pain and the pain-free controls; 2) chronic pain was associated with a lower sleep score (a measurement of sleep patterns), compared to acute pain and the pain-free controls; and acute pain was associated with lower sleep scores compared to the controls; 3) there was a significant negative association between the sleep score and CRP; 4) CRP may partially mediate the association between chronic pain and decreased sleep score. However, the effect size of the mediation was rather small, and the pathophysiological significance remains uncertain. Further validation is needed. These findings were partly replicated in the UK Biobank second visit assessment cohort with a smaller sample size. Our findings, which are based on the large UK Biobank dataset, support the interplay between musculoskeletal pain, sleep patterns, and the potential mediating role of CRP on this reciprocal relationship.

Transfer Learning in Genome-Wide Association Studies with Knockoffs

AbstractThis paper presents and compares alternative transfer learning methods that can increase the power of conditional testing via knockoffs by leveraging prior information in external data sets collected from different populations or measuring related outcomes. The relevance of this methodology is explored in particular within the context of genome-wide association studies, where it can be helpful to address the pressing need for principled ways to suitably account for, and efficiently learn from the genetic variation associated to diverse ancestries. Finally, we apply these methods to analyze several phenotypes in the UK Biobank data set, demonstrating that transfer learning helps knockoffs discover more associations in the data collected from minority populations, potentially opening the way to the development of more accurate polygenic risk scores.

Effect of blood lipids and lipid-lowering therapies on osteoarthritis risk: A Mendelian randomization study.

We aimed to investigate the effects of blood lipids and lipid-lowering agents on osteoarthritis (OA) risk. We performed Mendelian randomization (MR) analyses to estimate the causal effect of blood low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG) levels on knee and hip OA. Single nucleotide polymorphisms (SNPs) were selected from large genome-wide association studies (GWASs) of individuals of European ancestry as genetic instruments for blood lipid levels. The associations of selected genetic instruments with knee and hip OA were estimated in a recent GWAS of the UK Biobank and arcOGEN datasets. Univariate and multivariate MR analyses were performed to detect and adjust for potential pleiotropy. Furthermore, genetic instruments in HMGCR, NPC1L1, and PCSK9 regions were used to mimic LDL-C-lowering effects of statin, ezetimibe, and evolocumab, respectively. Genetically determined LDL-C increments led to reduced risks of both knee OA (OR = 0.91 per 1-SD increment, 95% CI: 0.86-0.95, P = 6.3 × 10-5) and hip OA (OR = 0.92, 95% CI: 0.85-0.99, P = 0.027). Multivariate MR analysis proved that the effect was independent of HDL-C, TG, and body mass index. TG increment was associated with reduced risks of hip OA in the univariate MR analysis; however, this was not supported by the multivariate MR analysis. Genetically proxied LDL-C-lowering effects of statins are related to increased risks of knee OA but not hip OA. The findings suggested that LDL-C increments have independent protective effects on both knee and hip OA. LDL-C-lowering effects of statins may increase the risk of knee OA.

COVID-19 and the risk of Alzheimer's disease, amyotrophic lateral sclerosis, and multiple sclerosis.

The coronavirus disease 2019 (COVID-19) pandemic has had an unprecedented impact on the healthcare system, economy, and society. Studies have reported that COVID-19 may cause various neurologic symptoms, including cognitive impairment. We aimed to assess the causal effect of COVID-19 on neurodegenerative diseases using two-sample Mendelian randomization (MR) study. Genetic variants were obtained from genome-wide association studies (GWAS) summary-level data and meta-analyses. We used the inverse variance-weighted (IVW) method as the primary analysis to estimate causal effects. Sensitivity analyses were performed to make the conclusions more robust and reliable. We found that the COVID-19 infection phenotype was associated with a higher risk of AD and inverse associated with the risk of ALS and MS. The hospitalized COVID-19 phenotype was associated with the risk of AD and wasn't associated with ALS and MS. We also found that the severe COVID-19 (main analysis) phenotype was associated with the AD outcome from UK biobank datasets but was not associated with other outcomes. The severe COVID-19 infection phenotype, the severe COVID-19 (subtype analysis) phenotype and the hospitalization risk of COVID-19 were not associated with each outcome. This MR study suggests a potential association between genetically predicted COVID-19 and a higher risk of AD and a reduced risk of ALS and MS. Further elucidations of this association and underlying mechanisms may inform public health messages to prevent COVID-19 and AD.

ApoE Modifier Alleles for Alzheimer's Disease Discovered by Information Theory Dependency Measures: MIST Software Package.

Information theory-based measures of variable dependency (previously published) have been implemented into a software package, MIST. The design of the software and its potential uses are described, and a demonstration is presented in the discovery of modifier alleles of the ApoE gene in affecting Alzheimer's disease (AD) by analyzing the UK Biobank dataset. The modifier genes uncovered overlap strongly with genes found to be associated with AD. Others include many known to influence AD. We discuss a range of uses of the dependency calculations using MIST that can uncover additional genetic effects in similar complex datasets, like higher degrees of interaction and phenotypic pleiotropy.

RF12 | PSUN80 A Novel DXA Algorithm to Aid in the Diagnosis of Familial Partial Lipodystrophy

Abstract Familial partial lipodystrophy (FPLD) is a rare and heterogenous disease without gold-standard diagnostic criteria. The heterogeneity applies to fat distribution, symptomatology, metabolic complications, and genetic etiology. Disease heterogeneity has contributed to the lack of standardized diagnostic criteria, an obstacle to understanding both the natural history of the disease and response to therapies. We have assessed whether DXA data can aid in disease diagnosis. DXA scans quantify fat and lean mass in the whole body, arms, legs, and trunk. An increased ratio of fat percentage in the trunk to the legs (termed "trunk-leg ratio", TLR) has been proposed as an objective method to detect FPLD. Using large sets of DXA data from control subjects, along with data from FPLD subjects, we sought to improve upon TLR. We hypothesized that TLR might change as a function of overall adiposity (total body fat, TF). Females were studied first because of the high female: male ratio of disease prevalence observed in FPLD. In 2713 control females with DXA measurements within the UKBiobank (UKB), TLR increased linearly as a function of TF until a TF of ∼36% and then the TLR plateaued with higher TF. We analyzed published DXA data of FPLD subjects from the USA (n=55).1 FPLD subjects had higher TLRs than controls at each level of adiposity. Due to the non-linear relationship between total adiposity and TLR, two linear fits were generated to best discriminate controls from FPLD subjects for a given adiposity. For females with body fat <36%, the algorithm predicts FPLD if TLR >TF*0.0311+0.232. For females with body fat ≥36%, the algorithm predicts FPLD if TLR >1.353. After training on this UKB data set, we tested the algorithm on independent data sets of control females from NHANES (n=2347) and FPLD subjects (n=37, from the NIH). The algorithm had a sensitivity of 92–97% and a specificity of 99.3%. This degree of specificity is still not sufficient for implementation of rare disease screening in the general population, but could be useful in a population with increased pre-test probability of having FPLD such as non-obese individuals with Type 2 diabetes and/or hypertriglyceridemia. Because race and ethnicity are possible modifiers of body fat distribution, we examined the relationship between TF and TLR across ethnic and racial groups in NHANES. Mexican-Americans (n=659), Hispanic-other (n=81), White (n=871), Black (n=612), and other/biracial (n=124) subjects from NHANES had a similar relationship between adiposity and TLR. Therefore, the same DXA criteria can be applied across these ethnicities. In summary, the relationship between adiposity and TLR may be used to aid in the diagnosis of FPLD in females. Reference Meral R, et al. Diabetes Care. 2018;41,2255–2258. Presentation: Saturday, June 11, 2022 1:06 p.m. - 1:11 p.m., Sunday, June 12, 2022 12:30 p.m. - 2:30 p.m.

Using machine learning to identify gene interaction networks associated with breast cancer

BackgroundBreast cancer (BC) is one of the most prevalent cancers worldwide but its etiology remains unclear. Obesity is recognized as a risk factor for BC, and many obesity-related genes may be involved in its occurrence and development. Research assessing the complex genetic mechanisms of BC should not only consider the effect of a single gene on the disease, but also focus on the interaction between genes. This study sought to construct a gene interaction network to identify potential pathogenic BC genes.MethodsThe study included 953 BC patients and 963 control individuals. Chi-square analysis was used to assess the correlation between demographic characteristics and BC. The joint density-based non-parametric differential interaction network analysis and classification (JDINAC) was used to build a BC gene interaction network using single nucleotide polymorphisms (SNP). The odds ratio (OR) and 95% confidence interval (95% CI) of hub gene SNPs were evaluated using a logistic regression model. To assess reliability, the hub genes were quantified by edgeR program using BC RNA-seq data from The Cancer Genome Atlas (TCGA) and identical edges were verified by logistic regression using UK Biobank datasets. Go and KEGG enrichment analysis were used to explore the biological functions of interactive genes.ResultsBody mass index (BMI) and menopause are important risk factors for BC. After adjusting for potential confounding factors, the BC gene interaction network was identified using JDINAC. LEP, LEPR, XRCC6, and RETN were identified as hub genes and both hub genes and edges were verified. LEPR genetic polymorphisms (rs1137101 and rs4655555) were also significantly associated with BC. Enrichment analysis showed that the identified genes were mainly involved in energy regulation and fat-related signaling pathways.ConclusionWe explored the interaction network of genes derived from SNP data in BC progression. Gene interaction networks provide new insight into the underlying mechanisms of BC.

Sex-specific genetic association between psychiatric disorders and cognition, behavior and brain imaging in children and adults

Although there are pronounced sex differences for psychiatric disorders, relatively little has been published on the heterogeneity of sex-specific genetic effects for these traits until very recently for adults. Much less is known about children because most psychiatric disorders will not manifest until later in life and existing studies for children on psychiatric traits such as cognitive functions are underpowered. We used results from publicly available genome-wide association studies for six psychiatric disorders and individual-level data from the Adolescent Brain Cognitive Development (ABCD) study and the UK Biobank (UKB) study to evaluate the associations between the predicted polygenic risk scores (PRS) of these six disorders and observed cognitive functions, behavioral and brain imaging traits. We further investigated the mediation effects of the brain structure and function, which showed heterogeneity between males and females on the correlation between genetic risk of schizophrenia and fluid intelligence. There was significant heterogeneity in genetic associations between the cognitive traits and psychiatric disorders between sexes. Specifically, the PRSs of schizophrenia of boys showed stronger correlation with eight of the ten cognitive functions in the ABCD data set; whereas the PRSs of autism of females showed a stronger correlation with fluid intelligence in the UKB data set. Besides cognitive traits, we also found significant sexual heterogeneity in genetic associations between psychiatric disorders and behavior and brain imaging. These results demonstrate the underlying early etiology of psychiatric disease and reveal a shared and unique genetic basis between the disorders and cognition traits involved in brain functions between the sexes.

Derivation and utility of schizophrenia polygenic risk associated multimodal MRI frontotemporal network

Schizophrenia is a highly heritable psychiatric disorder characterized by widespread functional and structural brain abnormalities. However, previous association studies between MRI and polygenic risk were mostly ROI-based single modality analyses, rather than identifying brain-based multimodal predictive biomarkers. Based on schizophrenia polygenic risk scores (PRS) from healthy white people within the UK Biobank dataset (N = 22,459), we discovered a robust PRS-associated brain pattern with smaller gray matter volume and decreased functional activation in frontotemporal cortex, which distinguished schizophrenia from controls with >83% accuracy, and predicted cognition and symptoms across 4 independent schizophrenia cohorts. Further multi-disease comparisons demonstrated that these identified frontotemporal alterations were most severe in schizophrenia and schizo-affective patients, milder in bipolar disorder, and indistinguishable from controls in autism, depression and attention-deficit hyperactivity disorder. These findings indicate the potential of the identified PRS-associated multimodal frontotemporal network to serve as a trans-diagnostic gene intermediated brain biomarker specific to schizophrenia.

Examining the role of systemic chronic inflammation in diet and sleep relationship.

Background:It is well known that systemic chronic inflammation (SCI), which can be modulated by diet, is associated with poor sleep outcomes. However, the role of SCI in diet health and sleep quality relationship has not been well established.Methods:Here, by using the UK Biobank data set, we assessed the association between markers of SCI (leukocyte, platelet, lymphocyte, neutrophil, and basophil counts; C-reactive protein levels and neutrophil to lymphocyte ratio (NLR)), habitual intake of food groups, diet health and sleep quality in 449,084 participants. We also formally tested the possibility that SCI might mediate the relationship between diet health and sleep quality.Results:Our results revealed (i) negative associations between SCI and food groups that are abundant in healthy diets (fruit, vegetable and oily and non-oily fish) and (ii) positive associations between SCI and food groups that are abundant in unhealthy diets (processed meat). Sleep quality was also negatively associated with platelet counts, CRP levels and NLR. Crucially, however, while platelet and neutrophil counts, CRP levels and NLR fully mediated the association between diet health and sleep quality, leukocyte, lymphocyte and basophil counts partially mediated the associations between diet health and sleep quality.Conclusion:Reducing SCI via dietary interventions could be an effective primary and/or complementary strategy to increase sleep quality. Further interventional trials are warranted to (i) establish the strength of associations, preferably by using validated diet and sleep measures and (ii) examine longer term effects of anti-inflammatory diets on sleep-, diet- and inflammation-related health outcomes.

Genome-wide association study of dermatophytosis in the UK Biobank cohort.

Analyses of the hereditary propensity to dermatophytosis have revealed several proven genetic relationships. They include CARD9 deficiency, HLA-DR4 and HLA-DR8 type and genes encoding interleukin-22, defensin 2 and 4, and genetic defects in dectin-1, which increased the prevalence of dermatophytosis in families and were involved in the inheritance of susceptibility in their members. To further investigate the genetic basis of dermatophytosis, we performed a genome-wide association study (GWAS) of the UK Biobank cohort. To identify cases of dermatophytosis, we used ICD10 code B35, which covers Tinea barbae, Tinea capitis, Tinea unguium, Tinea manuum, Tinea pedis, Tinea corporis, Tinea imbricata, Tinea cruris, other dermatophytoses and dermatophytosis, unspecified. Data processing was performed on Minerva, a Linux mainframe with Centos 7.6, at the Icahn School of Medicine at Mount Sinai. We used PLINK, a whole-genome association analysis toolset, to analyse the UKB chromosome files and the UK Biobank Data Parser (ukbb parser), a python-based package that allows easy interfacing with the large UK Biobank dataset. We used LocusZoom for the Manhattan and q-q plots. Other statistical analyses were done with R and SPSS 25. Genome-wide association study (GWAS) and meta-analysis association statistics highlighted one susceptibility locus, Tubulointerstitial Nephritis Antigen (TINAG), with genome-wide significance for dermatophytosis. The top SNP was rs16885197, a missense variant within TINAG, position chr6:54308557, alleles A > G, minor allele frequency (MAF) 0.014. Multivariate logistic regression indicated that the minor G allele increased odds ratio of dermatophytosis by 7.8. Carrying two G alleles raised dermatophytosis odds ratio by a factor of 14. More research into genetic and other predisposing factors for dermatophytosis is critical because of the implications for prophylaxis and therapy. It might be possible to prevent infection and recurrence by identifying people who are vulnerable to chronic dermatophytosis. Identifying high-risk families would enable their members to be educated about the dangers of fungal diseases. New therapeutic techniques to target altered hormonal and immune response pathways might be created. TINAG is a prospective target that should be investigated, based on the findings of this article.

MorbidGCN: prediction of multimorbidity with a graph convolutional network based on integration of population phenotypes and disease network.

Exploring multimorbidity relationships among diseases is of great importance for understanding their shared mechanisms, precise diagnosis and treatment. However, the landscape of multimorbidities is still far from complete due to the complex nature of multimorbidity. Although various types of biological data, such as biomolecules and clinical symptoms, have been used to identify multimorbidities, the population phenotype information (e.g. physical activity and diet) remains less explored for multimorbidity. Here, we present a graph convolutional network (GCN) model, named MorbidGCN, for multimorbidity prediction by integrating population phenotypes and disease network. Specifically, MorbidGCN treats the multimorbidity prediction as a missing link prediction problem in the disease network, where a novel feature selection method is embedded to select important phenotypes. Benchmarking results on two large-scale multimorbidity data sets, i.e. the UK Biobank (UKB) and Human Disease Network (HuDiNe) data sets, demonstrate that MorbidGCN outperforms other competitive methods. With MorbidGCN, 9742 and 14010 novel multimorbidities are identified in the UKB and HuDiNe data sets, respectively. Moreover, we notice that the selected phenotypes that are generally differentially distributed between multimorbidity patients and single-disease patients can help interpret multimorbidities and show potential for prognosis of multimorbidities.

Predictors and determinants of albuminuria in people with prediabetes and diabetes based on smoking status: A cross-sectional study using the UK Biobank data.

SummaryBackgroundSmoking is attributed to both micro- and macrovascular complications at any stage of metabolic deregulation including prediabetes. Current global diabetes prevention programmes appear to be glucocentric, and do not fully acknowledge the ramifications of cardiorenal risk factors in smokers and ex-smokers. A more holistic approach is needed to prevent vascular complications in people with prediabetes and diabetes before and after quitting.MethodsA cross-sectional study was carried out on participants who agreed to take part in the UK Biobank dataset at the time of their first attendances between March 01, 2006, and December 31, 2010. Those who had their urinary albumin concentration (UAC) data available were included, and those who did not have this data, were excluded. A logistic regression model was fitted to explore the relationship between cardiorenal risk factors and albuminuria in people with prediabetes and diabetes, based on smoking status.FindingsA total of 502,490 participants were included in the UK Biobank dataset. Of them, 30.4% (n=152,896) had their UAC level recorded. Compared with non-smokers, the odds of albuminuria in smokers with prediabetes and diabetes were 1.21 (95% CI 1.05 – 1.39, p=0.009), and 1.26 (95% CI 1.10 – 1.44, p=0.001), respectively. The odds declined after quitting in both groups, but it was not statistically significant (p>0.05). Each unit increase in HbA1c was associated with equivalent increased odds of albuminuria in current and ex-smokers, OR 1.035 (95% CI 1.030 – 1.039, p<0.001), and 1.026 (95% CI 1.023 – 1.028, p <0.001), respectively. Compared to females, male ex-smokers were at 15% increased odds of albuminuria. In ex-smokers, each unit increase in waist circumference was associated with 1% increased risk of albuminuria. Compared with the least deprived quintiles, the odds of albuminuria in the most deprived quintiles, in current and ex-smokers were identical, OR 1.18 (95% CI 1.04–1.324, p=0.010), and 1.19 (95% CI 1.11 – 1.27, p<0.001), respectively.InterpretationMale smokers are at a higher risk of albuminuria after smoking cessation. Monitoring waist circumference in quitters may identify those who are at a higher risk of albuminuria. Combining smoking cessation intervention in smokers with prediabetes in the current diabetes prevention programmes may offset post-cessation weight gain and reduce the risk of albuminuria.FundingUniversity of Sheffield.

Interaction of background genetic risk, psychotropic medications, and primary angle closure glaucoma in the UK Biobank.

Background/AimsPsychotropic medications have been reported as a risk factor for angle closure disease. However, the interaction between background genetic risk for primary angle closure glaucoma (PACG) and susceptibility to angle closure disease among psychotropic medication users has not been investigated. Here we demonstrate the utility of a genome-wide polygenic risk score (PRS) in identifying and risk-stratifying subjects with PACG and investigate the association between PACG genetic burden and exposure to psychotropic medications on prevalent angle closure.MethodsThis analysis used the UK Biobank dataset, a prospective cohort study of 502,506 UK residents. We constructed a PACG PRS for participants using genome-wide association study summary statistics from a multiethnic meta-analysis using the Lassosum method.ResultsAmong the 441,054 participants, 959 (0.22%) were identified as PACG cases. Individuals with PACG had higher PRS compared to those without PACG (0.24±1.03 SD vs. 0.00±1.00 SD, p<0.001) and PACG prevalence increased with each decile of higher PRS. Among individuals using psychotropic medication, those with PACG had higher average PRS (0.31±1.00 SD vs. 0.00±1.00 SD, p<0.001) and were more likely to have a PRS in upper deciles of polygenic risk (p = 0.04). At each decile of PRS, psychotropic medication use was associated with increased risk of PACG. These effects were more pronounced and significant in higher deciles.ConclusionWe demonstrate the utility of a PRS for identifying individuals at higher risk of PACG. Additionally, we demonstrate an important relationship where the association between psychotropic medications use and PACG diagnosis varies across the polygenic risk spectrum.

Type 2 diabetes mellitus accelerates brain aging and cognitive decline: Complementary findings from UK Biobank and meta-analyses.

Type 2 diabetes mellitus (T2DM) is known to be associated with neurobiological and cognitive deficits; however, their extent, overlap with aging effects, and the effectiveness of existing treatments in the context of the brain are currently unknown. We characterized neurocognitive effects independently associated with T2DM and age in a large cohort of human subjects from the UK Biobank with cross-sectional neuroimaging and cognitive data. We then proceeded to evaluate the extent of overlap between the effects related to T2DM and age by applying correlation measures to the separately characterized neurocognitive changes. Our findings were complemented by meta-analyses of published reports with cognitive or neuroimaging measures for T2DM and healthy controls (HCs). We also evaluated in a cohort of T2DM-diagnosed individuals using UK Biobank how disease chronicity and metformin treatment interact with the identified neurocognitive effects. The UK Biobank dataset included cognitive and neuroimaging data (N = 20,314), including 1012 T2DM and 19,302 HCs, aged between 50 and 80 years. Duration of T2DM ranged from 0 to 31 years (mean 8.5 ± 6.1 years); 498 were treated with metformin alone, while 352 were unmedicated. Our meta-analysis evaluated 34 cognitive studies (N = 22,231) and 60 neuroimaging studies: 30 of T2DM (N = 866) and 30 of aging (N = 1088). Compared to age, sex, education, and hypertension-matched HC, T2DM was associated with marked cognitive deficits, particularly in executive functioning and processing speed. Likewise, we found that the diagnosis of T2DM was significantly associated with gray matter atrophy, primarily within the ventral striatum, cerebellum, and putamen, with reorganization of brain activity (decreased in the caudate and premotor cortex and increased in the subgenual area, orbitofrontal cortex, brainstem, and posterior cingulate cortex). The structural and functional changes associated with T2DM show marked overlap with the effects correlating with age but appear earlier, with disease duration linked to more severe neurodegeneration. Metformin treatment status was not associated with improved neurocognitive outcomes. The neurocognitive impact of T2DM suggests marked acceleration of normal brain aging. T2DM gray matter atrophy occurred approximately 26% ± 14% faster than seen with normal aging; disease duration was associated with increased neurodegeneration. Mechanistically, our results suggest a neurometabolic component to brain aging. Clinically, neuroimaging-based biomarkers may provide a valuable adjunctive measure of T2DM progression and treatment efficacy based on neurological effects. The research described in this article was funded by the W. M. Keck Foundation (to LRMP), the White House Brain Research Through Advancing Innovative Technologies (BRAIN) Initiative (NSFNCS-FR 1926781 to LRMP), and the Baszucki Brain Research Fund (to LRMP). None of the funding sources played any role in the design of the experiments, data collection, analysis, interpretation of the results, the decision to publish, or any aspect relevant to the study. DJW reports serving on data monitoring committees for Novo Nordisk. None of the authors received funding or in-kind support from pharmaceutical and/or other companies to write this article.

Genome-wide association of polygenic risk extremes for Alzheimer's disease in the UK Biobank

In just over a decade, advances in genome-wide association studies (GWAS) have offered an approach to stratify individuals based on genetic risk for disease. Using recent Alzheimer's disease (AD) GWAS results as the base data, we determined each individual's polygenic risk score (PRS) in the UK Biobank dataset. Using individuals within the extreme risk distribution, we performed a GWAS that is agnostic of AD phenotype and is instead based on known genetic risk for disease. To interpret the functions of the new risk factors, we conducted phenotype analyses, including a phenome-wide association study. We identified 246 loci surpassing the significance threshold of which 229 were not reported in the base AD GWAS. These include loci that showed suggestive levels of association in the base GWAS and loci not previously suspected to be associated with AD. Among these, there are loci, such as IL34 and KANSL1, that have since been shown to be associated with AD in recent studies. We also show highly significant genetic correlations with multiple health-related outcomes that provide insights into prodromal symptoms and comorbidities. This is the first study to utilize PRS as a phenotype-agnostic group classification in AD genetic studies. We identify potential new loci for AD and detail phenotypic analysis of these PRS extremes.

Molecular Mechanisms of ARID5B-Mediated Genetic Susceptibility to Acute Lymphoblastic Leukemia.

There is growing evidence for the inherited basis of susceptibility to childhood acute lymphoblastic leukemia (ALL). Genome-wide association studies have identified non-coding ALL risk variants at the ARID5B gene locus, but their exact functional effects and the molecular mechanism linking ARID5B to B-cell ALL leukemogenesis remain largely unknown. We performed targeted sequencing of ARID5B in germline DNA of 5008 children with ALL. Variants were evaluated for association with ALL susceptibility using 3644 patients from the UK10K cohort as non-ALL controls, under an additive model. Cis-regulatory elements in ARID5B were systematically identified using dCas9-KRAB-mediated enhancer interference system enhancer screen in ALL cells. Disruption of transcription factor binding by ARID5B variant was predicted informatically and then confirmed using chromatin immunoprecipitation and coimmunoprecipitation. ARID5B variant association with hematological traits was examined using UK Biobank dataset. All statistical tests were 2-sided. We identified 54 common variants in ARID5B statistically significantly associated with leukemia risk, all of which were noncoding. Six cis-regulatory elements at the ARID5B locus were discovered using CRISPR-based high-throughput enhancer screening. Strikingly, the top ALL risk variant (rs7090445, P = 5.57 × 10-45) is located precisely within the strongest enhancer element, which is also distally tethered to the ARID5B promoter. The variant allele disrupts the MEF2C binding motif sequence, resulting in reduced MEF2C affinity and decreased local chromosome accessibility. MEF2C influences ARID5B expression in ALL, likely via a transcription factor complex with RUNX1. Using the UK Biobank dataset (n = 349 861), we showed that rs7090445 was also associated with lymphocyte percentage and count in the general population (P = 8.6 × 10-22 and 2.1 × 10-18, respectively). Our results indicate that ALL risk variants in ARID5B function by modulating cis-regulatory elements at this locus.