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Crohn's disease (CD) is a complex and heterogeneous condition with no perfect preclinical model or cure. To address this, we explore adult stem cell-derived organoids that retain their tissue identity and disease-driving traits. We prospectively create a biobank of CD patient-derived organoid cultures (PDOs) from colonic biopsies of 53 subjects across all clinical subtypes and healthy subjects. Gene expression analyses enabled benchmarking of PDOs as tools for modeling the colonic epithelium in active disease and identified two major molecular subtypes: immune-deficient infectious CD (IDICD) and stress and senescence-induced fibrostenotic CD (S2FCD). Each subtype shows internal consistency in the transcriptome, genome, and phenome. The spectrum of morphometric, phenotypic, and functional changes within the "living biobank" reveals distinct differences between the molecular subtypes. Drug screens reverse subtype-specific phenotypes, suggesting phenotyped-genotyped CD PDOs can bridge basic biology and patient trials by enabling preclinical phase "0" human trials for personalized therapeutics.

Polygenic scores (PGS) have emerged as the tool of choice for genomic prediction in a wide range of fields. We show that PGS performance varies broadly across contexts and biobanks. Contexts such as age, sex and income can impact PGS accuracy with similar magnitudes as genetic ancestry. Here we introduce an approach (CalPred) that models all contexts jointly to produce prediction intervals that vary across contexts to achieve calibration (include the trait with 90% probability), whereas existing methods are miscalibrated. In analyses of 72 traits across large and diverse biobanks (All of Us and UK Biobank), we find that prediction intervals required adjustment by up to 80% for quantitative traits. For disease traits, PGS-based predictions were miscalibrated across socioeconomic contexts such as annual household income levels, further highlighting the need of accounting for context information in PGS-based prediction across diverse populations.

Polygenic scores (PGSs) summarize the combined effect of common risk variants and are associated with breast cancer risk in patients without identifiable monogenic risk factors. One of the most well-validated PGSs in breast cancer to date is PGS313, which was developed from a Northern European biobank but has shown attenuated performance in non-European ancestries. We further investigate the generalizability of the PGS313 for American women of European (EA), African (AFR), Asian (EAA), and Latinx (HL) ancestry within one institution with a singular electronic health record (EHR) system, genotyping platform, and quality control process. We found that the PGS313 achieved overlapping areas under the receiver operator characteristic (ROC) curve (AUCs) in females of HL (AUC = 0.68, 95% confidence interval [CI] = 0.65-0.71) and EA ancestry (AUC = 0.70, 95% CI = 0.69-0.71) but lower AUCs for the AFR and EAA populations (AFR: AUC = 0.61, 95% CI = 0.56-0.65; EAA: AUC = 0.64, 95% CI = 0.60-0.680). While PGS313 is associated with hormone-receptor-positive (HR+) disease in EA Americans (odds ratio [OR] = 1.42, 95% CI = 1.16-1.64), this association is lost in African, Latinx, and Asian Americans. In summary, we found that PGS313 was significantly associated with breast cancer but with attenuated accuracy in women of AFR and EAA descent within a singular health system in Los Angeles. Our work further highlights the need for additional validation in diverse cohorts prior to the clinical implementation of PGSs.

INTRODUCTION: While it is well recognised that aging is a heterogeneous process, our understanding of the determinants of biological aging and its heterogeneity remains unclear. The San Diego Nathan Shock Center (SD-NSC) Clinical Cohort aims to establish a resource of biospecimens and extensive donor clinical data such as physical, cognitive and sensory function to support other studies that aim to explore the heterogeneity of normal human aging and its biological underpinnings. METHODS AND ANALYSIS: The SD-NSC Clinical Cohort is composed of 80 individuals across the adult human lifespan. Strict inclusion and exclusion criteria are implemented to minimise extrinsic factors that may impede the study of normal aging. Across three visits, participants undergo extensive phenotyping for collection of physical performance, body composition, cognitive function, sensory ability, mental health and haematological data. During these visits, we also collected biospecimens including plasma, platelets, peripheral blood mononuclear cells and fibroblasts for banking and future studies on aging. ETHICS AND DISSEMINATION: Ethics approval from the UC San Diego School of Medicine Institutional Review Board (IRB #201 141 SHOCK Center Clinical Cohort, PI: Molina) was obtained on 11 November 2020. Written informed consent is obtained from all participants after objectives and procedures of the study have been fully explained. Congruent with the goal of establishing a core resource, biological samples and clinical data are made available to the research community through the SD-NSC.

INTRODUCTION: We estimated the ages when associations between Alzheimers disease (AD) genes and brain volumes begin among middle-aged and older adults. METHODS: Among 45,616 dementia-free participants aged 45-80, linear regressions tested whether genetic risk score for AD (AD-GRS) had age-dependent associations with 38 regional brain magnetic resonance imaging volumes. Models were adjusted for sex, assessment center, genetic ancestry, and intracranial volume. RESULTS: AD-GRS modified the estimated effect of age (per decade) on the amygdala (-0.41 mm3 [-0.42, -0.40]); hippocampus (-0.45 mm3 [-0.45, -0.44]), nucleus accumbens (-0.55 mm3 [-0.56, -0.54]), thalamus (-0.38 mm3 [-0.39, -0.37]), and medial orbitofrontal cortex (-0.23 mm3 [-0.24, -0.22]). Trends began by age 45 for the nucleus accumbens and thalamus, 48 for the hippocampus, 51 for the amygdala, and 53 for the medial orbitofrontal cortex. An AD-GRS excluding apolipoprotein E (APOE) was additionally associated with entorhinal and middle temporal cortices. DISCUSSION: APOE and other genes that increase AD risk predict lower hippocampal and other brain volumes by middle age.

Individuals with Down syndrome (DS) have a partial or complete trisomy of chromosome 21, resulting in an increased risk for early-onset Alzheimer's disease (AD)-type dementia by early midlife. Despite ongoing clinical trials to treat late-onset AD, individuals with DS are often excluded. Furthermore, timely diagnosis or management is often not available. Of the genetic causes of AD, people with DS represent the largest cohort. Currently, there is a knowledge gap regarding the underlying neurobiological mechanisms of DS-related AD (DS-AD), partly due to limited access to well-characterized brain tissue and biomaterials for research. To address this challenge, we created an international consortium of brain banks focused on collecting and disseminating brain tissue from persons with DS throughout their lifespan, named the Down Syndrome Biobank Consortium (DSBC) consisting of 11 biobanking sites located in Europe, India, and the USA. This perspective describes the DSBC harmonized protocols and tissue dissemination goals.

Nearly two hundred common-variant depression risk loci have been identified by genome-wide association studies (GWAS). However, the impact of rare coding variants on depression remains poorly understood. Here, we present whole-exome sequencing analyses of depression with seven different definitions based on survey, questionnaire, and electronic health records in 320,356 UK Biobank participants. We showed that the burden of rare damaging coding variants in loss-of-function intolerant genes is significantly associated with risk of depression with various definitions. We compared the rare and common genetic architecture across depression definitions by genetic correlation and showed different genetic relationships between definitions across common and rare variants. In addition, we demonstrated that the effects of rare damaging coding variant burden and polygenic risk score on depression risk are additive. The gene set burden analyses revealed overlapping rare genetic variant components with developmental disorder, autism, and schizophrenia. Our study provides insights into the contribution of rare coding variants, separately and in conjunction with common variants, on depression with various definitions and their genetic relationships with neurodevelopmental disorders.

Background: Iran Premature Coronary Artery Disease (IPAD) is one of the first and largest studies of its kind in Asia that investigates different aspects of premature coronary artery disease (PCAD) in different ethnic groups in multiple cities. In this paper, we aim to describe the IPAD biobank establishment and present some preliminary results of the IPAD study. Methods: This case-control study was conducted on patients with documented angiography from different ethnicities in more than ten cities of Iran (males aged 60 years and below and females aged 70 years and below). Patients with coronary artery stenosis of more than 75% in at least one vessel (or left-main stenosis of more than 50%) were defined as the case group and patients with normal coronary angiography were considered as the control group. In addition to completing questionnaires and performing physical measurements, samples of serum, buffy coat, plasma, whole blood, saliva, urine, and feces were stored in the freezer at -80 °C. Results: The mean age of patients was 51.1±8.2, of which 43% were women. There were 1221 people in the control group and 1702 in the case group. Our enrollment is completed and data entry and transferring biosamples from different cities is still ongoing. About 30000 biosamples of different ethnicities are saved in the IPAD biobank. Conclusion: This study aims to develop a high-quality biobank and facilitate research on different aspects of PCAD, especially gene-environment interaction regarding ethnicity. [ABSTRACT FROM AUTHOR]

BACKGROUND: The value, speed of completion and robustness of the evidence generated by TB treatment trials could be improved by implementing standards for best practice.METHODS: A global panel of experts participated in a Delphi process, using a 7-point Likert scale to score and revise draft standards until consensus was reached.RESULTS: Eleven standards were defined: Standard 1, high quality data on TB regimens are essential to inform clinical and programmatic management; Standard 2, the research questions addressed by TB trials should be relevant to affected communities, who should be included in all trial stages; Standard 3, trials should make every effort to be as inclusive as possible; Standard 4, the most efficient trial designs should be considered to improve the evidence base as quickly and cost effectively as possible, without compromising quality; Standard 5, trial governance should be in line with accepted good clinical practice; Standard 6, trials should investigate and report strategies that promote optimal engagement in care; Standard 7, where possible, TB trials should include pharmacokinetic and pharmacodynamic components; Standard 8, outcomes should include frequency of disease recurrence and post-treatment sequelae; Standard 9, TB trials should aim to harmonise key outcomes and data structures across studies; Standard 10, TB trials should include biobanking; Standard 11, treatment trials should invest in capacity strengthening of local trial and TB programme staff.CONCLUSION: These standards should improve the efficiency and effectiveness of evidence generation, as well as the translation of research into policy and practice.

Biobanks that collect deep phenotypic and genomic data across many individuals have emerged as a key resource in human genetics. However, phenotypes in biobanks are often missing across many individuals, limiting their utility. We propose AutoComplete, a deep learning-based imputation method to impute or fill-in missing phenotypes in population-scale biobank datasets. When applied to collections of phenotypes measured across ~300,000 individuals from the UK Biobank, AutoComplete substantially improved imputation accuracy over existing methods. On three traits with notable amounts of missingness, we show that AutoComplete yields imputed phenotypes that are genetically similar to the originally observed phenotypes while increasing the effective sample size by about twofold on average. Further, genome-wide association analyses on the resulting imputed phenotypes led to a substantial increase in the number of associated loci. Our results demonstrate the utility of deep learning-based phenotype imputation to increase power for genetic discoveries in existing biobank datasets.

Biobanks often contain several phenotypes relevant to diseases such as major depressive disorder (MDD), with partly distinct genetic architectures. Researchers face complex tradeoffs between shallow (large sample size, low specificity/sensitivity) and deep (small sample size, high specificity/sensitivity) phenotypes, and the optimal choices are often unclear. Here we propose to integrate these phenotypes to combine the benefits of each. We use phenotype imputation to integrate information across hundreds of MDD-relevant phenotypes, which significantly increases genome-wide association study (GWAS) power and polygenic risk score (PRS) prediction accuracy of the deepest available MDD phenotype in UK Biobank, LifetimeMDD. We demonstrate that imputation preserves specificity in its genetic architecture using a novel PRS-based pleiotropy metric. We further find that integration via summary statistics also enhances GWAS power and PRS predictions, but can introduce nonspecific genetic effects depending on input. Our work provides a simple and scalable approach to improve genetic studies in large biobanks by integrating shallow and deep phenotypes.

Surgical resection represents the standard of care for people with newly diagnosed diffuse gliomas, and the neuropathological and molecular profile of the resected tissue guides clinical management and forms the basis for research. The Response Assessment in Neuro-Oncology (RANO) consortium is an international, multidisciplinary effort that aims to standardise research practice in neuro-oncology. These recommendations represent a multidisciplinary consensus from the four RANO groups: RANO resect, RANO recurrent glioblastoma, RANO radiotherapy, and RANO/PET for a standardised workflow to achieve a representative tumour evaluation in a disease characterised by intratumoural heterogeneity, including recommendations on which tumour regions should be surgically sampled, how to define those regions on the basis of preoperative imaging, and the optimal sample volume. Practical recommendations for tissue sampling are given for people with low-grade and high-grade gliomas, as well as for people with newly diagnosed and recurrent disease. Sampling of liquid biopsies is also addressed. A standardised workflow for subsequent handling of the resected tissue is proposed to avoid information loss due to decreasing tissue quality or insufficient clinical information. The recommendations offer a framework for prospective biobanking studies.

Epidemiological studies have identified an inverse association between cancer and dementia. Underlying methodological biases have been postulated, yet no studies have systematically investigated the potential for each source of bias within a single dataset. We used the UK Biobank to compare estimates for the cancer-dementia association using different analytical specifications designed to sequentially address multiple sources of bias, including competing risk of death, selective survival, confounding bias, and diagnostic bias. We included 140,959 UK Biobank participants aged ≥ 55 without dementia before enrollment and with linked primary care data. We used cancer registry data to identify cancer cases prevalent before UK Biobank enrollment and incident cancer diagnosed after enrollment. We used Cox models to evaluate associations of prevalent and incident cancer with all-cause dementia, Alzheimers disease (AD), and vascular dementia. We used time-varying models to evaluate diagnostic bias. Over a median follow-up of 12.3 years, 3,310 dementia cases were diagnosed. All-site incident cancer was positively associated with all-cause dementia incidence (hazard ratio [HR] = 1.14, 95% CI: 1.02-1.29), but prevalent cancer was not (HR = 1.04, 95% CI: 0.92-1.17). Results were similar for vascular dementia. AD was not associated with prevalent or incident cancer. Dementia diagnosis was substantially elevated in the first year after cancer diagnosis (HR = 1.83, 95% CI: 1.42-2.36), after which the association attenuated to null, suggesting diagnostic bias. Following a cancer diagnosis, health care utilization or cognitive consequences of diagnosis or treatment may increase chance of receiving a dementia diagnosis, creating potential diagnostic bias in electronic health records-based studies.

BACKGROUND: The veterinary care of cats and dogs is increasingly embracing innovations first applied to human health, including an increased emphasis on preventative care and precision medicine. Large scale human population biobanks have advanced research in these areas; however, few have been established in veterinary medicine. The MARS PETCARE BIOBANK™ (MPB) is a prospective study that aims to build a longitudinal bank of biological samples, with paired medical and lifestyle data, from 20,000 initially healthy cats and dogs (10,000 / species), recruited through veterinary hospitals over a ten-year period. Here, we describe the MPB protocol and discuss its potential as a platform to increase understanding of why and how diseases develop and how to advance personalised veterinary healthcare. METHODS: At regular intervals, extensive diet, health and lifestyle information, electronic medical records, clinicopathology and activity data are collected, genotypes, whole genome sequences and faecal metagenomes analysed, and blood, plasma, serum, and faecal samples stored for future research. DISCUSSION: Proposed areas for research include the early detection and progression of age-related disease, risk factors for common conditions, the influence of the microbiome on health and disease and, through genome wide association studies, the identification of candidate loci for disease associated genetic variants. Genomic data will be open access and research proposals for access to data and samples will be considered. Over the coming years, the MPB will provide the longitudinal data and systematically collected biological samples required to generate important insights into companion animal health, identifying biomarkers of disease, supporting earlier identification of risk, and enabling individually tailored interventions to manage disease.

Our knowledge of non-linear genetic effects on complex traits remains limited, in part, due to the modest power to detect such effects. While kernel-based tests offer a versatile approach to test for non-linear relationships between sets of genetic variants and traits, current approaches cannot be applied to Biobank-scale datasets containing hundreds of thousands of individuals. We propose, FastKAST, a kernel-based approach that can test for non-linear effects of a set of variants on a quantitative trait. FastKAST provides calibrated hypothesis tests while enabling analysis of Biobank-scale datasets with hundreds of thousands of unrelated individuals from a homogeneous population. We apply FastKAST to 53 quantitative traits measured across ≈ 300 K unrelated white British individuals in the UK Biobank to detect sets of variants with non-linear effects at genome-wide significance.

New onset refractory status epilepticus (NORSE), including its subtype with a preceding febrile illness known as febrile infection-related epilepsy syndrome (FIRES), is one of the most severe forms of status epilepticus. The exact causes of NORSE are currently unknown, and there is so far no disease-specific therapy. Identifying the underlying pathophysiology and discovering specific biomarkers, whether immunologic, infectious, genetic, or other, may help physicians in the management of patients with NORSE. A broad spectrum of biomarkers has been proposed for status epilepticus patients, some of which were evaluated for patients with NORSE. Nonetheless, none has been validated, due to significant variabilities in study cohorts, collected biospecimens, applied analytical methods, and defined outcome endpoints, and to small sample sizes. The NORSE Institute established an open NORSE/FIRES biorepository for health-related data and biological samples allowing the collection of biospecimens worldwide, promoting multicenter research and sharing of data and specimens. Here, we suggest standard operating procedures for biospecimen collection and biobanking in this rare condition. We also propose criteria for the appropriate use of previously collected biospecimens. We predict that the widespread use of standardized procedures will reduce heterogeneity, facilitate the future identification of validated biomarkers for NORSE, and provide a better understanding of the pathophysiology and best clinical management for these patients.

There is growing need to distinguish between sex and gender. While sex is assigned at birth, gender is socially constructed and may not correspond to one's assigned sex. However, in most research studies, sex or gender is assessed in isolation or the terms are used interchangeably, which has implications for research accuracy and inclusivity. We used data from the UK Biobank to quantify the prevalence of disagreement between chromosomal and self-reported sex and identify potential reasons for discordance. Among approximately 200 individuals with sex discordance, 71% of discordances were potentially explained by the presence of intersex traits or transgender identity. The findings indicate that when describing sex- and/or gender-specific differences in health, researchers may be limited in their ability to draw conclusions regarding specific sex and/or gender health information.

Admixture estimation plays a crucial role in ancestry inference and genome-wide association studies (GWASs). Computer programs such as ADMIXTURE and STRUCTURE are commonly employed to estimate the admixture proportions of sample individuals. However, these programs can be overwhelmed by the computational burdens imposed by the 105 to 106 samples and millions of markers commonly found in modern biobanks. An attractive strategy is to run these programs on a set of ancestry-informative SNP markers (AIMs) that exhibit substantially different frequencies across populations. Unfortunately, existing methods for identifying AIMs require knowing ancestry labels for a subset of the sample. This supervised learning approach creates a chicken and the egg scenario. In this paper, we present an unsupervised, scalable framework that seamlessly carries out AIM selection and likelihood-based estimation of admixture proportions. Our simulated and real data examples show that this approach is scalable to modern biobank datasets. OpenADMIXTURE, our Julia implementation of the method, is open source and available for free.

Background South Asian individuals have increased cardiovascular disease and mortality risks. Reliance on creatinine- rather than cystatin C-based estimated glomerular filtration rate (eGFRcys) may underestimate the cardiovascular disease risk associated with chronic kidney disease. Methods and Results Among 7738 South Asian UK BioBank participants without prevalent heart failure (HF) or atherosclerotic cardiovascular disease, we investigated associations of 4 eGFRcys and creatinine-based estimated glomerular filtration rate categories (

Background: Formalin-fixed, paraffin-embedded (FFPE) tissues are a valuable resource for clinical and basic science research. Paraffin blocks and the resulting unstained sections (USS) are often stored for years before being used. Previous studies have evaluated the effects of time, temperature, humidity, and inert gases on preservation of USS; however, no study has examined all four variables together. Methods: In the current work, we prospectively and blindly assessed time points from 0 to 24 months, room versus refrigerated temperature, and presence of a desiccant and/or nitrogen atmosphere on a variety of benign and malignant tissues from North America and Africa. End points included immunohistochemistry (IHC), in situ hybridization (ISH), extracted RNA and DNA quantity and quality, and messenger RNA performance in a novel, multiplexed digital gene expression profiling assay of both housekeeping and tumor-specific genes. Results: We found that using current methods of antigen retrieval, staining, and extraction, the end points of IHC, ISH, RNA, and DNA were well preserved under the various conditions tested, with implications that pre-embedding factors contribute to variability in subsequent tissue integrity. We also document that spectrophotometric estimations of nucleic acid concentrations were in general estimated to be higher than with fluorimetric methods, which may be pertinent to end assay development. We further describe a new multiplex assay, the PlexSet digital gene expression assay, suitable for evaluating RNA quality in FFPE tissues. Conclusion: Altogether, these results may provide helpful guidance with regard to approaches for long-term storage conditions for USS.

BackgroundHigh alcohol intake is associated with increased mortality. We aimed to identify factors affecting mortality in people drinking extreme amounts of alcohol.MethodsWe obtained information from the UK Biobank on approximately 500,000 participants aged 40-70 years at baseline assessment in 2006-2010. Habitual alcohol intake, lifestyle and physiological data, laboratory test results, and hospital diagnoses and death certificate data (to June 2020) for 5136 men (2.20% of male participants) and 1504 women (0.60%) who reported consuming ≥80 or ≥50 g/day, respectively, were used in survival analysis.ResultsMortality hazard ratios for these excessive drinkers, compared to all other participants, were 2.02 (95% CI 1.89-2.17) for all causes, 1.89 (1.69-2.12) for any cancer, 1.87 (1.61-2.17) for any circulatory disease, and 9.40 (7.00-12.64) for any liver disease. Liver disease diagnosis or abnormal liver function tests predicted not only deaths attributed to liver disease but also those from cancers or circulatory diseases. Mortality among excessive drinkers was also associated with quantitative alcohol intake; diagnosed alcohol dependence, harmful use, or withdrawal syndrome; and current smoking at assessment.ConclusionsPeople with chronic excessive alcohol intake experience decreased average survival, but there is substantial variation in their mortality, with liver abnormality and alcohol dependence or other alcohol use disorders associated with a worse prognosis. Clinically, patients with these risk factors and high alcohol intake should be considered for early or intensive management. Research can usefully focus on the factors predisposing to dependence or liver abnormality.

BackgroundChronic kidney disease has been linked to worse cognition. However, this association may be dependent on the marker of kidney function used, and studies assessing modification by genetics are lacking. This study examined associations between multiple measures of kidney function and assessed effect modification by a polygenic score for general cognitive function.MethodsIn this cross-sectional study of up to 341,208 European ancestry participants from the UK Biobank study, we examined associations between albuminuria and estimated glomerular filtration rate based on creatinine (eGFRcre) or cystatin C (eGFRcys) with cognitive performance on tests of verbal-numeric reasoning, reaction time and visual memory. Adjustment for confounding factors was performed using multivariate regression and propensity-score matching. Interaction between kidney function markers and a polygenic risk score for general cognitive function was also assessed.ResultsAlbuminuria was associated with worse performance on tasks of verbal-numeric reasoning (β(points) = -0.09, p

Individual-level studies with adjustment for important COVID-19 risk factors suggest positive associations of long-term air pollution exposure (particulate matter and nitrogen dioxide) with COVID-19 infection, hospitalisations and mortality. The evidence, however, remains limited and mechanisms unclear. We aimed to investigate these associations within UK Biobank, and to examine the role of underlying chronic disease as a potential mechanism. UK Biobank COVID-19 positive laboratory test results were ascertained via Public Health England and general practitioner record linkage, COVID-19 hospitalisations via Hospital Episode Statistics, and COVID-19 mortality via Office for National Statistics mortality records from March-December 2020. We used annual average outdoor air pollution modelled at 2010 residential addresses of UK Biobank participants who resided in England (n = 424,721). We obtained important COVID-19 risk factors from baseline UK Biobank questionnaire responses (2006-2010) and general practitioner record linkage. We used logistic regression models to assess associations of air pollution with COVID-19 outcomes, adjusted for relevant confounders, and conducted sensitivity analyses. We found positive associations of fine particulate matter (PM2.5) and nitrogen dioxide (NO2) with COVID-19 positive test result after adjustment for confounders and COVID-19 risk factors, with odds ratios of 1.05 (95% confidence intervals (CI) = 1.02, 1.08), and 1.05 (95% CI = 1.01, 1.08), respectively. PM 2.5 and NO 2 were positively associated with COVID-19 hospitalisations and deaths in minimally adjusted models, but not in fully adjusted models. No associations for PM10 were found. In analyses with additional adjustment for pre-existing chronic disease, effect estimates were not substantially attenuated, indicating that underlying chronic disease may not fully explain associations. We found some evidence that long-term exposure to PM2.5 and NO2 was associated with a COVID-19 positive test result in UK Biobank, though not with COVID-19 hospitalisations or deaths.

BackgroundThe burden of valvular heart disease (VHD) has increased significantly among ageing populations, yet remains poorly understood in the present-day context of percutaneous and surgical interventions.ObjectiveTo define the incidence, clinical correlates and associated mortality of VHD in the UK Biobank cohort.MethodsWe interrogated data collected in the UK Biobank between 1 January 2000 and 30 June 2020. VHD incidence was determined using International Classification of Disease-10 codes for aortic stenosis (AS), aortic regurgitation (AR), mitral stenosis, mitral regurgitation (MR) and mitral valve prolapse. We calculated HRs for incident VHD and all-cause mortality. Clinical correlates of VHD included demographics, coronary artery disease, heart failure and atrial fibrillation. Surgical and percutaneous interventions for mitral and aortic VHD were considered time-dependent variables.ResultsAmong 486 187 participants, the incidence of any VHD was 16 per 10 000 person-years, with highest rates for MR (8.2), AS (7.2) and AR (5.0). Age, heart failure, coronary artery disease and atrial fibrillation were significantly associated with all types of VHD. In our adjusted model, aortic and mitral VHD had an increased risk of all-cause death compared with no VHD (HR 1.62, 95% CI 1.44 to 1.82, p

IntroductionPancreatic cancer (PC) screening recommendations have been based on studies performed solely at high-volume academic centers. To make PC screening more widely available, community-based efforts are essential. We implemented a prospective PC screening study in the community of Fairfield County, CT, and report our early safety and efficacy results.MethodsEligible individuals were enrolled into an investigator-initiated study and underwent a baseline and 3 annual magnetic resonance imagings/magnetic resonance cholangiopancreatographies (MRIs/MRCPs) with gadolinium, biannual blood donations for biobanking, and assessments for anxiety and depression. All MRIs were presented at a multidisciplinary board to determine whether further investigation was warranted.ResultsSeventy-five individuals have been enrolled and 201 MRIs performed over a 2.6-year average length of follow-up. Abnormal pancreatic findings (predominantly small cysts) were detected in 58.7% of the participants. Among these, 6.7% underwent endoscopic ultrasound, with 1 case complicated by postprocedural pancreatitis. One surgical resection was performed on a 4.7-cm intraductal papillary mucinous neoplasm with a focus on low-grade pancreatic intraepithelial neoplasia. One incidental finding of fibrosing mediastinitis was detected. Anxiety and depression scores decreased over the course of this study from 21.4% to 5.4% and 10.7% to 3.6%, respectively.DiscussionThis preliminary report supports the feasibility of performing MRI/magnetic resonance cholangiopancreatographies-based PC screening as part of a clinical trial in a community setting. A longer follow-up is needed to better assess safety and efficacy. To the best of our knowledge, this is the first report from a community-based PC screening effort ( clinicaltrials.gov ID: NCT03250078).

ObjectiveTo determine if methotrexate or folic acid prescription was associated with differential risk for COVID-19 diagnosis or mortality.DesignCase-control analysis.SettingThe population-based UK Biobank (UKBB) cohort.ParticipantsData from 380 380 UKBB participants with general practice prescription data for 2019-2021. Updated medical information was retrieved on 13 December 2021.Primary and secondary outcome measuresThe outcomes of COVID-19 diagnosis and COVID-19-related mortality were analysed by multivariable logistic regression. Exposures evaluated were prescription of folic acid and/or methotrexate. Criteria for COVID-19 diagnosis were (1) a positive SARS-CoV-2 test or (2) ICD-10 code for confirmed COVID-19 (U07.1) or probable COVID-19 (U07.2) in hospital records, or death records. By these criteria, 26 003 individuals were identified with COVID-19 of whom 820 were known to have died from COVID-19. Logistic regression statistical models were adjusted for age sex, ethnicity, Townsend deprivation index, body mass index, smoking status, presence of rheumatoid arthritis, sickle cell disease, use of anticonvulsants, statins and iron supplements.ResultsCompared with people prescribed neither folic acid nor methotrexate, people prescribed folic acid supplementation had increased risk of diagnosis of COVID-19 (OR 1.51 (1.42-1.61)). The prescription of methotrexate with or without folic acid was not associated with COVID-19 diagnosis (p≥0.18). People prescribed folic acid supplementation had positive association with death after a diagnosis of COVID-19 (OR 2.64 (2.15-3.24)) in a fully adjusted model. The prescription of methotrexate in combination with folic acid was not associated with an increased risk for COVID-19-related death (1.07 (0.57-1.98)).ConclusionsWe report an association of increased risk for COVID-19 diagnosis and COVID-19-related death in people prescribed folic acid supplementation. Our results also suggest that methotrexate might attenuate these associations.

HIV cure research requires interrogating latent HIV reservoirs in deep tissues, which necessitates autopsies to avoid risks to participants. An HIV autopsy biobank would facilitate this research, but such research raises ethical issues and requires participant engagement. This study explores the willingness to participate in HIV cure research at the end of life. Participants include Canadians with HIV [people with HIV (PWHIV)] aged 55 years or older. Following a mixed-method study design, all participants completed a phone or online survey, and a subset of participants participated in in-depth phone or videoconference interviews. We produced descriptive statistics of quantitative data and a thematic analysis of qualitative data. Barriers and facilitators were categorized under domains of the Theoretical Domains Framework. From April 2020 to August 2021, 37 participants completed the survey (mean age = 69.9 years old; mean duration of HIV infection = 28.5 years), including 15 interviewed participants. About three quarters of participants indicated being willing to participate in hypothetical medical studies toward the end of life (n = 30; 81.1%), in HIV biobanking (n = 30; 81.1%), and in a research autopsy (n = 28; 75.7%) to advance HIV cure research, mainly for altruistic benefits. The main perceived risks had to do with physical pain and confidentiality. Barriers and facilitators were distributed across five domains: social/professional role and identity, environmental context and resources, social influences, beliefs about consequences, and capabilities. Participants wanted more information about study objectives and procedures, possible accommodations with their last will, and rationale for studies or financial interests funding studies. Our results indicate that older PWHIV would be willing to participate in HIV cure research toward the end of life, HIV biobanking, and research autopsy. However, a dialogue should be initiated to inform participants thoroughly about HIV cure studies, address concerns, and accommodate their needs and preferences. Additional work is required, likely through increased community engagement, to address educational needs.

Diabetes-related complications reflect longstanding damage to small and large vessels throughout the body. In addition to the duration of diabetes and poor glycemic control, genetic factors are important contributors to the variability in the development of vascular complications. Early heritability studies found strong familial clustering of both macrovascular and microvascular complications. However, they were limited by small sample sizes and large phenotypic heterogeneity, leading to less accurate estimates. We take advantage of two independent studies-UK Biobank and the Action to Control Cardiovascular Risk in Diabetes trial-to survey the single nucleotide polymorphism heritability for diabetes microvascular (diabetic kidney disease and diabetic retinopathy) and macrovascular (cardiovascular events) complications. Heritability for diabetic kidney disease was estimated at 29%. The heritability estimate for microalbuminuria ranged from 24 to 60% and was 41% for macroalbuminuria. Heritability estimates of diabetic retinopathy ranged from 6 to 33%, depending on the phenotype definition. More severe diabetes retinopathy possessed higher genetic contributions. We show, for the first time, that rare variants account for much of the heritability of diabetic retinopathy. This study suggests that a large portion of the genetic risk of diabetes complications is yet to be discovered and emphasizes the need for additional genetic studies of diabetes complications.

ObjectiveThis study aimed to compare causes of death in the most prevalent neuropathologically diagnosed dementias.MethodsWe analyzed causes of death in a community-based cohort of participants aged 50 or older, submitted to full-body autopsy and a comprehensive neuropathologic examination of the brain. Individuals with Alzheimer disease (AD), vascular dementia (VaD), mixed dementia (AD+VaD), or dementia with Lewy bodies (DLBs) were compared with individuals with no dementia.ResultsIn a sample of 920 individuals, 456 had no dementia, 147 had AD, 120 had VaD, 53 had DLB, and 37 had AD+VaD. Pneumonia as the cause of death was more frequent in the AD (P=0.023), AD+VaD (P=0.046), and DLB (P=0.043) groups. In addition, VaD (P=0.041) and AD+VaD (P=0.028) groups had a higher frequency of atherosclerosis as detected by full-body autopsy.ConclusionOur findings highlight the importance of preventive measures regarding atherosclerosis and pneumonia in patients with dementia. Moreover, because of cognitive impairment, these patients may not fully account for symptoms to make early detection and diagnosis possible. These results confirm findings from previous studies that were based on clinical data, with added accuracy provided by neuropathologic diagnosis and full-body autopsy reports.

Inferring the structure of human populations from genetic variation data is a key task in population and medical genomic studies. Although a number of methods for population structure inference have been proposed, current methods are impractical to run on biobank-scale genomic datasets containing millions of individuals and genetic variants. We introduce SCOPE, a method for population structure inference that is orders of magnitude faster than existing methods while achieving comparable accuracy. SCOPE infers population structure in about a day on a dataset containing one million individuals and variants as well as on the UK Biobank dataset containing 488,363 individuals and 569,346 variants. Furthermore, SCOPE can leverage allele frequencies from previous studies to improve the interpretability of population structure estimates.

Rural populations continue to experience persistent cancer disparities compared with urban populations particularly in cancers that can be prevented or detected early through screening and vaccination. Although the National Cancer Institute and the larger cancer research community have identified rural community partnerships as the foundation for reducing the disparities, we have identified limited application of community-based participatory research in cancer prevention and control research. Guided by the Community-Based Participatory Research Conceptual Model and our collective experience, we provide a framework for a community-cancer center partnership that focuses on promoting health equity. In this commentary, we articulate that the partnership process must foster capacity for communities and cancer centers, strive for rural representation in clinical trials and biobanking, build a pipeline for dissemination and implementation research, and create a bidirectional flow of knowledge between communities and academic institutions. Authentic partnerships with rural communities should be the ultimate goal of cancer centers, and the process described in this commentary can serve as an initial platform to build capacity and continue to strive toward that goal.

Biobanks linked to massive, longitudinal electronic health record (EHR) data make numerous new genetic research questions feasible. One among these is the study of biomarker trajectories. For example, high blood pressure measurements over visits strongly predict stroke onset, and consistently high fasting glucose and Hb1Ac levels define diabetes. Recent research reveals that not only the mean level of biomarker trajectories but also their fluctuations, or within-subject (WS) variability, are risk factors for many diseases. Glycemic variation, for instance, is recently considered an important clinical metric in diabetes management. It is crucial to identify the genetic factors that shift the mean or alter the WS variability of a biomarker trajectory. Compared to traditional cross-sectional studies, trajectory analysis utilizes more data points and captures a complete picture of the impact of time-varying factors, including medication history and lifestyle. Currently, there are no efficient tools for genome-wide association studies (GWASs) of biomarker trajectories at the biobank scale, even for just mean effects. We propose TrajGWAS, a linear mixed effect model-based method for testing genetic effects that shift the mean or alter the WS variability of a biomarker trajectory. It is scalable to biobank data with 100,000 to 1,000,000 individuals and many longitudinal measurements and robust to distributional assumptions. Simulation studies corroborate that TrajGWAS controls the type I error rate and is powerful. Analysis of eleven biomarkers measured longitudinally and extracted from UK Biobank primary care data for more than 150,000 participants with 1,800,000 observations reveals loci that significantly alter the mean or WS variability.

Semiparametric joint models of longitudinal and competing risk data are computationally costly, and their current implementations do not scale well to massive biobank data. This paper identifies and addresses some key computational barriers in a semiparametric joint model for longitudinal and competing risk survival data. By developing and implementing customized linear scan algorithms, we reduce the computational complexities from O(n 2) or O(n 3) to O(n) in various steps including numerical integration, risk set calculation, and standard error estimation, where n is the number of subjects. Using both simulated and real-world biobank data, we demonstrate that these linear scan algorithms can speed up the existing methods by a factor of up to hundreds of thousands when n > 104, often reducing the runtime from days to minutes. We have developed an R package, FastJM, based on the proposed algorithms for joint modeling of longitudinal and competing risk time-to-event data and made it publicly available on the Comprehensive R Archive Network (CRAN).

BackgroundLarge medical centers in urban areas, like Los Angeles, care for a diverse patient population and offer the potential to study the interplay between genetic ancestry and social determinants of health. Here, we explore the implications of genetic ancestry within the University of California, Los Angeles (UCLA) ATLAS Community Health Initiative-an ancestrally diverse biobank of genomic data linked with de-identified electronic health records (EHRs) of UCLA Health patients (N=36,736).MethodsWe quantify the extensive continental and subcontinental genetic diversity within the ATLAS data through principal component analysis, identity-by-descent, and genetic admixture. We assess the relationship between genetically inferred ancestry (GIA) and >1500 EHR-derived phenotypes (phecodes). Finally, we demonstrate the utility of genetic data linked with EHR to perform ancestry-specific and multi-ancestry genome and phenome-wide scans across a broad set of disease phenotypes.ResultsWe identify 5 continental-scale GIA clusters including European American (EA), African American (AA), Hispanic Latino American (HL), South Asian American (SAA) and East Asian American (EAA) individuals and 7 subcontinental GIA clusters within the EAA GIA corresponding to Chinese American, Vietnamese American, and Japanese American individuals. Although we broadly find that self-identified race/ethnicity (SIRE) is highly correlated with GIA, we still observe marked differences between the two, emphasizing that the populations defined by these two criteria are not analogous. We find a total of 259 significant associations between continental GIA and phecodes even after accounting for individuals' SIRE, demonstrating that for some phenotypes, GIA provides information not already captured by SIRE. GWAS identifies significant associations for liver disease in the 22q13.31 locus across the HL and EAA GIA groups (HL p-value=2.32×10-16, EAA p-value=6.73×10-11). A subsequent PheWAS at the top SNP reveals significant associations with neurologic and neoplastic phenotypes specifically within the HL GIA group.ConclusionsOverall, our results explore the interplay between SIRE and GIA within a disease context and underscore the utility of studying the genomes of diverse individuals through biobank-scale genotyping linked with EHR-based phenotyping.

BackgroundPrecision health in adolescents relies on the successful collection of data and biospecimens from an adequately sized sample of cases and comparison group(s), often healthy controls, to answer the research question. This research report describes the recruitment strategy, enrollment rates, and approach utilized in a successful biobehavioral research study. The study was designed to examine key health indicators in adolescents (13-17 years of age) with juvenile idiopathic arthritis (JIA) compared to a control group of healthy adolescents. The purpose of this analysis is to establish best practices and identify strategies to overcome barriers to recruitment of older adolescents, an age group that tends to be underrepresented in research studies.MethodsA retrospective secondary analysis of data from a parent study about JIA with high consent rates was employed to explore factors affecting enrollment into the biobehavioral study.ResultsOf the 113 subjects who were recruited to the study, 74 met the eligibility criteria and reviewed the consent form. The consented group (n=40) represents 54% of those who were eligible upon initial screening. The rate of project enrollment was 2.7 participants per month. The pediatric rheumatologists referred 85% of the JIA group, and the study's principal investigator, a nurse scientist, referred 95% of the control group. Typical recruitment strategies, such as posting on social media, distributing flyers, and cold-calling potential participants from the clinic schedule were ineffective for both cases and controls. Barriers to enrollment included scheduling and fear of venipuncture. There were no demographic characteristics that significantly explained enrollment, differentiating between those who agreed to participate compared to those who refused. Successful strategies for enrollment of adolescents into this biobehavioral research study included scheduling study visits on weekends and school holidays; an informed consent and assent process that addressed adolescent fears of venipuncture; including a JIA patient on the study team; and utilizing existing relationships to maximize enrollment efforts.ConclusionsEffective recruitment and enrollment practices were relationship-specific and patient-centered. Researchers should utilize best practices to ensure that precision health for adolescents is advanced.

An important goal of clinical genomics is to be able to estimate the risk of adverse disease outcomes. Between 5% and 10% of individuals with ulcerative colitis (UC) require colectomy within 5 years of diagnosis, but polygenic risk scores (PRSs) utilizing findings from genome-wide association studies (GWASs) are unable to provide meaningful prediction of this adverse status. By contrast, in Crohn disease, gene expression profiling of GWAS-significant genes does provide some stratification of risk of progression to complicated disease in the form of a transcriptional risk score (TRS). Here, we demonstrate that a measured TRS based on bulk rectal gene expression in the PROTECT inception cohort study has a positive predictive value approaching 50% for colectomy. Single-cell profiling demonstrates that the genes are active in multiple diverse cell types from both the epithelial and immune compartments. Expression quantitative trait locus (QTL) analysis identifies genes with differential effects at baseline and week 52 follow-up, but for the most part, differential expression associated with colectomy risk is independent of local genetic regulation. Nevertheless, a predicted polygenic transcriptional risk score (PPTRS) derived by summation of transcriptome-wide association study (TWAS) effects identifies UC-affected individuals at 5-fold elevated risk of colectomy with data from the UK Biobank population cohort studies, independently replicated in an NIDDK-IBDGC dataset. Prediction of gene expression from relatively small transcriptome datasets can thus be used in conjunction with TWASs for stratification of risk of disease complications.

Plasma lipids are known heritable risk factors for cardiovascular disease, but increasing evidence also supports shared genetics with diseases of other organ systems. We devised a comprehensive three-phase framework to identify new lipid-associated genes and study the relationships among lipids, genotypes, gene expression and hundreds of complex human diseases from the Electronic Medical Records and Genomics (347 traits) and the UK Biobank (549 traits). Aside from 67 new lipid-associated genes with strong replication, we found evidence for pleiotropic SNPs/genes between lipids and diseases across the phenome. These include discordant pleiotropy in the HLA region between lipids and multiple sclerosis and putative causal paths between triglycerides and gout, among several others. Our findings give insights into the genetic basis of the relationship between plasma lipids and diseases on a phenome-wide scale and can provide context for future prevention and treatment strategies.

In the brain, both HIV-1 and methamphetamine (meth) use result in increases in oxidative and nitrosative stress. This redox stress is thought to contribute to the pathogenesis of HIV-associated neurocognitive disorder (HAND) and further worsening cognitive activity in the setting of drug abuse. One consequence of such redox stress is aberrant protein S-nitrosylation, derived from nitric oxide, which may disrupt normal protein activity. Here, we report an improved, mass spectrometry-based technique to assess S-nitrosylated protein in human postmortem brains using selective enrichment of S-nitrosocysteine residues with an organomercury resin. The data show increasing S-nitrosylation of tricarboxylic acid (TCA) enzymes in the setting of HAND and HAND/meth use compared with HIV+ control brains without CNS pathology. The consequence is systematic inhibition of multiple TCA cycle enzymes, resulting in energy collapse that can contribute to the neuronal and synaptic damage observed in HAND and meth use.

The proportion of variation in complex traits that can be attributed to non-additive genetic effects has been a topic of intense debate. The availability of biobank-scale datasets of genotype and trait data from unrelated individuals opens up the possibility of obtaining precise estimates of the contribution of non-additive genetic effects. We present an efficient method to estimate the variation in a complex trait that can be attributed to additive (additive heritability) and dominance deviation (dominance heritability) effects across all genotyped SNPs in a large collection of unrelated individuals. Over a wide range of genetic architectures, our method yields unbiased estimates of additive and dominance heritability. We applied our method, in turn, to array genotypes as well as imputed genotypes (at common SNPs with minor allele frequency [MAF] > 1%) and 50 quantitative traits measured in 291,273 unrelated white British individuals in the UK Biobank. Averaged across these 50 traits, we find that additive heritability on array SNPs is 21.86% while dominance heritability is 0.13% (about 0.48% of the additive heritability) with qualitatively similar results for imputed genotypes. We find no statistically significant evidence for dominance heritability (p

The rat oxycodone and cocaine biobanks contain samples that vary by genotypes (by using genetically diverse genotyped HS rats), phenotypes (by measuring addiction-like behaviors in an advanced SA model), timepoints (samples are collected longitudinally before, during, and after SA, and terminally at three different timepoints in the addiction cycle: intoxication, withdrawal, and abstinence or without exposure to drugs through age-matched naive rats), samples collected (organs, cells, biofluids, feces), preservation (paraformaldehyde-fixed, snap-frozen, or cryopreserved) and application (proteomics, transcriptomics, microbiomics, metabolomics, epigenetics, anatomy, circuitry analysis, biomarker discovery, etc.Substance use disorders (SUDs) are pervasive in our society and have substantial personal and socioeconomical costs. A critical hurdle in identifying biomarkers and novel targets for medication development is the lack of resources for obtaining biological samples with a detailed behavioral characterization of SUD. Moreover, it is nearly impossible to find longitudinal samples. As part of two ongoing large-scale behavioral genetic studies in heterogeneous stock (HS) rats, we have created two preclinical biobanks using well-validated long access (LgA) models of intravenous cocaine and oxycodone self-administration (SA) and comprehensive characterization of addiction-related behaviors. The genetic diversity in HS rats mimics diversity in the human population and includes individuals that are vulnerable or resilient to compulsive-like responding for cocaine or oxycodone. Longitudinal samples are collected throughout the experiment, before exposure to the drug, during intoxication, acute withdrawal, and protracted abstinence, and include naive, age-matched controls. Samples include, but are not limited to, blood plasma, feces and urine, whole brains, brain slices and punches, kidney, liver, spleen, ovary, testis, and adrenal glands. Three preservation methods (fixed in formaldehyde, snap-frozen, or cryopreserved) are used to facilitate diverse downstream applications such as proteomics, metabolomics, transcriptomics, epigenomics, microbiomics, neuroanatomy, biomarker discovery, and other cellular and molecular approaches. To date, >20,000 samples have been collected from over 1000 unique animals and made available free of charge to non-profit institutions through https://www.cocainebiobank.org/ and https://www.oxycodonebiobank.org/.

Liver cancer is the second most lethal malignancy worldwide. Cell lines and murine models are the most common tools for modeling human liver carcinogenesis. Most recently, organoids with a three-dimensional structure derived from primary tissues or cells have been applied to liver cancer research. Organoids can be generated from induced pluripotent stem cells, embryonic or adult, healthy or diseased tissues. In particular, liver organoids have been widely employed in mechanistic studies aimed at delineating the molecular pathways responsible for hepatocarcinogenesis. The introduction of clustered regularly interspaced palindromic repeats (CRISPR)-associated protein 9 (Cas9) and microengineered miniorganoid technologies into liver organoids for cancer study has significantly accelerated these investigations. Translational advances have been made by utilizing liver tumor organoids for anticancer drug screening, biobanking, omics profiling, and biomarker discovery. This review summarizes the latest advances and the remaining challenges in the use of organoid models for the study of liver cancer.

PurposeErythropoietic protoporphyria (EPP), characterized by painful cutaneous photosensitivity, results from pathogenic variants in ferrochelatase (FECH). For 96% of patients, EPP results from coinheriting a rare pathogenic variant in trans of a common hypomorphic variant c.315-48T>C (minor allele frequency 0.05). The estimated prevalence of EPP derived from the number of diagnosed individuals in Europe is 0.00092%, but this may be conservative due to underdiagnosis. No study has estimated EPP prevalence using large genetic data sets.MethodsDisease-associated FECH variants were identified in the UK Biobank, a data set of 500,953 individuals including 49,960 exome sequences. EPP prevalence was then estimated. The association of FECH variants with EPP-related traits was assessed.ResultsAnalysis of pathogenic FECH variants in the UK Biobank provides evidence that EPP prevalence is 0.0059% (95% confidence interval [CI]: 0.0042-0.0076%), 1.7-3.0 times more common than previously thought in the UK. In homozygotes for the common c.315-48T>C FECH variant, there was a novel decrement in both erythrocyte mean corpuscular volume (MCV) and hemoglobin.ConclusionThe prevalence of EPP has been underestimated secondary to underdiagnosis. The common c.315-48T>C allele is associated with both MCV and hemoglobin, an association that could be important both for those with and without EPP.

The ectodysplasin receptor (EDAR) is a tumor necrosis factor receptor (TNF) superfamily member. A substitution in an exon of EDAR at position 370 (EDARV370A) creates a gain of function mutant present at high frequencies in Asian and Indigenous American populations but absent in others. Its frequency is intermediate in populations of Mexican ancestry. EDAR regulates the development of ectodermal tissues, including mammary ducts. Obesity and type 2 diabetes mellitus are prevalent in people with Indigenous and Latino ancestry. Latino patients also have altered prevalence and presentation of breast cancer. It is unknown whether EDARV370A might connect these phenomena. The goals of this study were to determine 1) whether EDARV370A is associated with metabolic phenotypes and 2) if there is altered breast anatomy in women carrying EDARV370A. Participants were from two Latino cohorts, the Arizona Insulin Resistance (AIR) registry and Sangre por Salud (SPS) biobank. The frequency of EDARV370A was 47% in the Latino cohorts. In the AIR registry, carriers of EDARV370A (GG homozygous) had significantly (p < 0.05) higher plasma triglycerides, VLDL, ALT, 2-hour post-challenge glucose, and a higher prevalence of prediabetes/diabetes. In a subset of the AIR registry, serum levels of ectodysplasin A2 (EDA-A2) also were associated with HbA1c and prediabetes (p < 0.05). For the SPS biobank, participants that were carriers of EDARV370A had lower breast density and higher HbA1c (both p < 0.05). The significant associations with measures of glycemia remained when the cohorts were combined. We conclude that EDARV370A is associated with characteristics of the metabolic syndrome and breast density in Latinos.

Pharmaceutical drugs targeting dyslipidemia and cardiovascular disease (CVD) may increase the risk of fatty liver disease and other metabolic disorders. To identify potential novel CVD drug targets without these adverse effects, we perform genome-wide analyses of participants in the HUNT Study in Norway (n = 69,479) to search for protein-altering variants with beneficial impact on quantitative blood traits related to cardiovascular disease, but without detrimental impact on liver function. We identify 76 (11 previously unreported) presumed causal protein-altering variants associated with one or more CVD- or liver-related blood traits. Nine of the variants are predicted to result in loss-of-function of the protein. This includes ZNF529:p.K405X, which is associated with decreased low-density-lipoprotein (LDL) cholesterol (P = 1.3 × 10-8) without being associated with liver enzymes or non-fasting blood glucose. Silencing of ZNF529 in human hepatoma cells results in upregulation of LDL receptor and increased LDL uptake in the cells. This suggests that inhibition of ZNF529 or its gene product should be prioritized as a novel candidate drug target for treating dyslipidemia and associated CVD.

BackgroundPrivacy-related concerns can prevent equitable participation in health research by US Indigenous communities. However, studies focused on these communities' views regarding health data privacy, including systematic reviews, are lacking.MethodsWe conducted a systematic literature review analyzing empirical, US-based studies involving American Indian/Alaska Native (AI/AN) and Native Hawaiian or other Pacific Islander (NHPI) perspectives on health data privacy, which we define as the practice of maintaining the security and confidentiality of an individual's personal health records and/or biological samples (including data derived from biological specimens, such as personal genetic information), as well as the secure and approved use of those data.ResultsTwenty-one studies involving 3234 AI/AN and NHPI participants were eligible for review. The results of this review suggest that concerns about the privacy of health data are both prevalent and complex in AI/AN and NHPI communities. Many respondents raised concerns about the potential for misuse of their health data, including discrimination or stigma, confidentiality breaches, and undesirable or unknown uses of biological specimens.ConclusionsParticipants cited a variety of individual and community-level concerns about the privacy of their health data, and indicated that these deter their willingness to participate in health research. Future investigations should explore in more depth which health data privacy concerns are most salient to specific AI/AN and NHPI communities, and identify the practices that will make the collection and use of health data more trustworthy and transparent for participants.

Introduction: Transdiagnostic dimensional phenotypes are essential to investigate the relationship between continuous symptom dimensions and pathological changes. This is a fundamental challenge to post‐mortem work, as assessments of phenotypic concepts need to rely on existing records. Methods: We adapted well‐validated methodologies to compute National Institute of Mental Health Research Domain Criteria (RDoC) scores using natural language processing (NLP) from electronic health records (EHRs) obtained from post‐mortem brain donors and tested whether cognitive domain scores were associated with Alzheimer's disease neuropathological measures. Results: Our results confirm an association of EHR‐derived cognitive scores with neuropathological findings. Notably, higher neuropathological load, particularly neuritic plaques, was associated with higher cognitive burden scores in the frontal (ß = 0.38, P = 0.0004), parietal (ß = 0.35, P = 0.0008), temporal (ß = 0.37, P = 0.0004) and occipital (ß = 0.37, P = 0.0003) lobes. Discussion: This proof‐of‐concept study supports the validity of NLP‐based methodologies to obtain quantitative measures of RDoC clinical domains from post‐mortem EHR. The associations may accelerate post‐mortem brain research beyond classical case–control designs. [ABSTRACT FROM AUTHOR]

BackgroundIntrahepatic cholangiocarcinoma (ICC) arises from cholangiocytes in the intrahepatic bile duct and is the second most common type of liver cancer. Cholangiocytes express both oestrogen receptor-α and -β, and oestrogens positively modulate cholangiocyte proliferation. Studies in women and men have reported higher circulating oestradiol is associated with increased ICC risk, further supporting a hormonal aetiology. However, no observational studies have examined the associations between exogenous hormone use and reproductive factors, as proxies of endogenous hormone levels, and risk of ICC.MethodsWe harmonised data from 1,107,498 women who enroled in 12 North American-based cohort studies (in the Liver Cancer Pooling Project, LCPP) and the UK Biobank between 1980-1998 and 2006-2010, respectively. Cox proportional hazards regression models were used to generate hazard ratios (HR) and 95% confidence internals (CI). Then, meta-analytic techniques were used to combine the estimates from the LCPP (n = 180 cases) and the UK Biobank (n = 57 cases).ResultsHysterectomy was associated with a doubling of ICC risk (HR = 1.98, 95% CI: 1.27-3.09), compared to women aged 50-54 at natural menopause. Long-term oral contraceptive use (9+ years) was associated with a 62% increased ICC risk (HR = 1.62, 95% CI: 1.03-2.55). There was no association between ICC risk and other exogenous hormone use or reproductive factors.ConclusionsThis study suggests that hysterectomy and long-term oral contraceptive use may be associated with an increased ICC risk.

Logistic regression is the primary analysis tool for binary traits in genome-wide association studies (GWAS). Multinomial regression extends logistic regression to multiple categories. However, many phenotypes more naturally take ordered, discrete values. Examples include (a) subtypes defined from multiple sources of clinical information and (b) derived phenotypes generated by specific phenotyping algorithms for electronic health records (EHR). GWAS of ordinal traits have been problematic. Dichotomizing can lead to a range of arbitrary cutoff values, generating inconsistent, hard to interpret results. Using multinomial regression ignores trait value hierarchy and potentially loses power. Treating ordinal data as quantitative can lead to misleading inference. To address these issues, we analyze ordinal traits with an ordered, multinomial model. This approach increases power and leads to more interpretable results. We derive efficient algorithms for computing test statistics, making ordinal trait GWAS computationally practical for Biobank scale data. Our method is available as a Julia package OrdinalGWAS.jl. Application to a COPDGene study confirms previously found signals based on binary case-control status, but with more significance. Additionally, we demonstrate the capability of our package to run on UK Biobank data by analyzing hypertension as an ordinal trait.

Introduction: Informed consent for research biospecimen donations is traditionally obtained through a face-to-face interaction with research staff and by signing an Institutional Review Board (IRB)-approved printed form. Electronic signatures (eSign) are routinely used in the electronic medical record (EMR) for the consenting of clinical services after patients review printed documentation. Our goal was to develop an electronic self-consenting workflow that mimicked clinical services. Specifically, we tested a research consent process for the biobanking of remnant clinical samples that relies solely on clinical resources in a busy outpatient practice. Materials and Methods: The Biorepositories Core Resource (BCR) unit initiated a new enterprise-wide biobanking infrastructure for consenting patients, termed Biospecimen Use for Research-Related Investigations and Translational Objectives (BURRITO). BURRITO is modeled after an established clinical process called Terms and Conditions of Service (TACOS). The TACOS requires patients to annually review printed documentation and self-consent electronically for clinical services. BURRITO also requires patients to review printed documentation and self-consent with eSign to opt-in for remnant biospecimen banking, but patients must complete this process only once. We captured eSign for consents directly into the EMR without research staff. Results: Patients reviewed the IRB-approved documents and self-consented during their cardiology clinic visit. At checkout, their participation preferences were electronically documented by clinic staff. During a 6-month period, 123 patients agreed to donate. After a review of process, a second 3-month period identified 202 patients agreeing to donate. BURRITO did not require face-to-face interactions with research staff, used a "no-paper" eSign for consent, and created discrete fields in the clinical EMR of the patient's preference. Conclusions: BURRITO electronically documents informed consent using an EMR functionality and the least amount of clinical and research resources. Our results show promise for developing institutionally adopted processes, which could leverage existing clinical workflows for universal research consenting and scalability.