Population Biobanks Research Articles

Proteogenomic analysis in population biobanks identifies new therapeutic targets across heart failure subtypes

Abstract Background Heart failure (HF) represents a multifactorial clinical syndrome with differential response to existing therapies across its subtypes. Proteogenomic analysis of genetic variants associated with changes in circulating protein level and risk of heart failure subtypes provides an opportunity for a systematic identification of proteins with subtype-specific causal effects as novel therapeutic targets. Purpose To identify proteins as potential therapeutic targets for specific heart failure subtypes by leveraging proteomics and genomics data in population biobanks. Methods We evaluated the causal effects of 2,773 proteins on 5 HF-related outcomes, including overall HF, non-ischaemic HF (ni-HF), ni-HF with reduced / preserved ejection fraction (ni-HFrEF / ni-HFpEF), and dilated cardiomyopathy (DCM). For each protein-phenotype pair, we performed a two-sample Mendelian randomisation (MR) analysis using protein-specific genetic association estimates derived from protein quantitative trait locus (pQTL) studies in 114,145 individuals from deCODE, UK Biobank, Fenland study, and SCALLOP consortium and genome-wide association studies (GWAS) of HF outcomes in 145,795 individuals by HERMES Consortium. We further prioritised candidate protein targets based on replication of results across datasets, model parameters, assays, and colocalization of causal genetic variants. Finally, we profiled the causal effects of identified proteins on 32 related cardiac traits and risk factors to explore potential mechanisms of actions and on-target side effects. Results We identified 493 proteins associated with at least one HF outcome at a false discovery rate (FDR) < 1% in the primary MR analysis. We further prioritised 44 proteins with concordant effect direction across datasets, model parameters, and assays or colocalization in at least one dataset. All 44 identified proteins showed cross association with at least one of 35 tested traits other than HF or cardiomyopathies at FDR <1%, including coronary artery disease (18 proteins), body mass index (11 proteins), type 2 diabetes (6 proteins), systolic / diastolic blood pressure (13 proteins), and chronic kidney disease (9 proteins). Conclusion We characterised the causal associations of 2,773 circulating proteins across HF-related outcomes and prioritised 44 proteins by integrating proteomics and genomics data in population biobanks. These findings extend our understanding of the molecular mechanism underlying different forms of HF and may inform the development of new therapeutic strategies.Study design

Efficient storage and regression computation for population-scale genome sequencing studies.

In the era of big data in human genetics, large-scale biobanks aggregating genetic data from diverse populations have emerged as important for advancing our understanding of human health and disease. However, the computational and storage demands of whole genome sequencing (WGS) studies pose significant challenges, especially for researchers from underfunded institutions or developing countries, creating a disparity in research capabilities. We introduce new approaches that significantly enhance computational efficiency and reduce data storage requirements for WGS studies. By developing algorithms for compressed storage of genetic data, focusing particularly on optimising the representation of rare variants, and designing regression methods tailored for the scale and complexity of WGS data, we significantly lower computational and storage costs. We integrate our approach into PLINK 2.0. The implementation demonstrates considerable reductions in storage space and computational time without compromising analytical accuracy, as evidenced by the application to the AllofUs project data. We optimized the runtime of an exome-wide association analysis involving 19.4 million variants and the body mass index phenotype of 125,077 individuals, reducing it from 695.35 minutes (approximately 11.5 hours) on a single machine to just 1.57 minutes using 30 GB of memory and 50 threads (or 8.67 minutes with 4 threads). Additionally, we extended this approach to support multi-phenotype analyses. We anticipate that our approach will enable researchers across the globe to unlock the potential of population biobanks, accelerating the pace of discoveries that can improve our understanding of human health and disease.

Novel insights into the pleiotropic health effects of growth differentiation factor 11 gained from genome-wide association studies in population biobanks

BackgroundGrowth differentiation factor 11 (GDF11) is a member of the transforming growth factor-β (TGF-β) superfamily that has gained considerable attention over the last decade for its observed ability to reverse age-related deterioration of multiple tissues, including the heart. Yet as many researchers have struggled to confirm the cardioprotective and anti-aging effects of GDF11, the topic has grown increasingly controversial, and the field has reached an impasse. We postulated that a clearer understanding of GDF11 could be gained by investigating its health effects at the population level.Methods and resultsWe employed a comprehensive strategy to interrogate results from genome-wide association studies in population Biobanks. Interestingly, phenome-wide association studies (PheWAS) of GDF11 tissue-specific cis-eQTLs revealed associations with asthma, immune function, lung function, and thyroid phenotypes. Furthermore, PheWAS of GDF11 genetic variants confirmed these results, revealing similar associations with asthma, immune function, lung function, and thyroid health. To complement these findings, we mined results from transcriptome-wide association studies, which uncovered associations between predicted tissue-specific GDF11 expression and the same health effects identified from PheWAS analyses.ConclusionsIn this study, we report novel relationships between GDF11 and disease, namely asthma and hypothyroidism, in contrast to its formerly assumed role as a rejuvenating factor in basic aging and cardiovascular health. We propose that these associations are mediated through the involvement of GDF11 in inflammatory signaling pathways. Taken together, these findings provide new insights into the health effects of GDF11 at the population level and warrant future studies investigating the role of GDF11 in these specific health conditions.

Harnessing Artificial Intelligence in Multimodal Omics Data Integration: Paving the Path for the Next Frontier in Precision Medicine.

The integration of multiomics data with detailed phenotypic insights from electronic health records marks a paradigm shift in biomedical research, offering unparalleled holistic views into health and disease pathways. This review delineates the current landscape of multimodal omics data integration, emphasizing its transformative potential in generating a comprehensive understanding of complex biological systems. We explore robust methodologies for data integration, ranging from concatenation-based to transformation-based and network-based strategies, designed to harness the intricate nuances of diverse data types. Our discussion extends from incorporating large-scale population biobanks to dissecting high-dimensional omics layers at the single-cell level. The review underscores the emerging role of large language models in artificial intelligence, anticipating their influence as a near-future pivot in data integration approaches. Highlighting both achievements and hurdles, we advocate for a concerted effort toward sophisticated integration models, fortifying the foundation for groundbreaking discoveries in precision medicine.

Synthetic surrogates improve power for genome-wide association studies of partially missing phenotypes in population biobanks.

Within population biobanks, incomplete measurement of certain traits limits the power for genetic discovery. Machine learning is increasingly used to impute the missing values from the available data. However, performing genome-wide association studies (GWAS) on imputed traits can introduce spurious associations, identifying genetic variants that are not associated with the original trait. Here we introduce a new method, synthetic surrogate (SynSurr) analysis, which makes GWAS on imputed phenotypes robust to imputation errors. Rather than replacing missing values, SynSurr jointly analyzes the original and imputed traits. We show that SynSurr estimates the same genetic effect as standard GWAS and improves power in proportion to the quality of the imputations. SynSurr requires a commonly made missing-at-random assumption but relaxes the requirements of existing imputation methods by not requiring correct model specification. We present extensive simulations and ablation analyses to validate SynSurr and apply it to empower the GWAS of dual-energy X-ray absorptiometry traits within the UK Biobank.

Artificial Intelligence Needs Data: Challenges Accessing Italian Databases to Train AI

Population biobanks are an increasingly important infrastructure to support research and will be a much-needed resource in the delivery of personalised medicine. Artificial intelligence (AI) systems can process and cross-link very large amounts of data quickly and be used not only for improving research power but also for helping with complex diagnosis and prediction of diseases based on health profiles. AI, therefore, potentially has a critical role to play in personalised medicine, and biobanks can provide a lot of the necessary baseline data related to healthy populations that will enable the development of AI tools. To develop these tools, access to personal data, and in particular, sensitive data, is required. Such data could be accessed from biobanks. Biobanks are a valuable resource for research but accessing and using the data contained within such biobanks raise a host of legal, ethical, and social issues (ELSI). This includes the appropriate consent to manage the collection, storage, use, and sharing of samples and data, and appropriate governance models that provide oversight of secondary use of samples and data. Biobanks have developed new consent models and governance tools to enable access that address some of these ELSI-related issues. In this paper, we consider whether such governance frameworks can enable access to biobank data to develop AI. As Italy has one of the most restrictive regulatory frameworks on the use of genetic data in Europe, we examine the regulatory framework in Italy. We also look at the proposed changes under the European Health Data Space (EHDS). We conclude by arguing that currently, regulatory frameworks are misaligned and unless addressed, accessing data within Italian biobanks to train AI will be severely limited.

TWINGEN: protocol for an observational clinical biobank recall and biomarker cohort study to identify Finnish individuals with high risk of Alzheimer’s disease

IntroductionA better understanding of the earliest stages of Alzheimer’s disease (AD) could expedite the development or administration of treatments. Large population biobanks hold the promise to identify individuals at an...

Latin American Challenges and Recommendations for Poly Adenosine Diphosphate Ribose Polymerase Inhibitor Treatment in Metastatic Castration Resistant Prostate Cancer: An Expert Overview.

In Latin America, prostate cancer is the third most common cancer overall and the most common in men, with the highest mortality rate of all cancers. In 2022, there were approximately 22,985 new prostate cancer cases and 61,056 deaths from prostate cancer in the region. Patients with metastatic disease that is resistant to cure by castration now have multiple therapeutic options, including poly-ADP ribose polymerase inhibitors. These treatment advances present new challenges, such as developing monitoring protocols for early detection of disease progression to castration resistance. The Americas Health Foundation organized a 3-day meeting with 8 regional oncologists and pathologists to create a paper on metastatic castration-resistant prostate cancer diagnosis and therapy, including the new poly-ADP ribose polymerase inhibitors. The panel examined metastatic castration-resistant prostate cancer in Latin America and recommended ways to improve patient care using published literature and their expertise. Gene mutations play an important role in prostate cancer development. Precision medicine innovations highlight the importance of genotyping DNA variants and tumor biomarkers for targeted treatment. Access to appropriate genetic testing is difficult, medications are available but expensive, and there is a lack of infrastructure and regulatory frameworks that prevent patients from benefiting from innovative therapies. The panel recommends developing a population database and biobank and creating tumor tissue collection, processing, and storage facilities. Multi-stakeholder collaboration is needed to integrate the information gathered, train staff, select target populations, improve patient accessibility, and reduce the cost burden of drugs, genetic counselors, and cancer geneticists in Latin America. Collaboration is essential among healthcare professionals, policymakers, patient advocacy groups, pharmaceutical companies, and international organizations to address these challenges and needs in Latin America.

Genogeographic technologies of a population biobank as a tool for assessing selection effects (using the example of pharmacogenetic biomarkers of cardiovascular diseases)

Significant differences between the gene pools of Russian peoples require the development of ethno- regional adapted pharmacogenetic tests and the identification of priority regions for their implementation.Aim. To develop a genogeographic technology to identify selection effects using the example of biomarkers that are significant for pharmacotherapy of patients with cardiovascular diseases (CVD), using a population biobank and the Pharmacogenetics of Populations of Russia and Adjacent Countries database.Material and methods. Deoxyribonucleic acid (DNA) samples from the Biobank of Northern Eurasia from 20 metapopulations of the indigenous population of the European Russia were studied using two following data sets: 24 pharmacogenetic markers of CVDs (3170 samples); 1 276 191 polymorphic DNA markers of the autosomal genome (1293 samples). For each data set, estimates of interpopulation variability in the gene pool are provided — the difference between these estimates characterizes the selection pressure on each of the 24 CVD biomarkers. A genogeographic atlas has been created, the maps of which demonstrate the selection pressure on each biomarker according to the degree of deviation from the selective- neutral variability of the gene pool.Results. Twenty-four CVD biomarkers are divided into three following classes: those close to selective- neutral variability, those subject to stabilizing and differentiating selection. For each of the 24 CVD biomarkers, genogeographic maps were created that reveal selection effects in each of the 20 metapopulations. Most maps have identified populations that are under differential selection pressure and therefore a priority for the implementation of ethno- regionally adapted pharmacogenetic protocols.Conclusion. Pharmacogenetic markers and populations under differential selection require the development of ethno- regionally adapted pharmacogenetic tests. The created cartographic atlas of selection can serve as the basis for pharmacogenetic studies carried out using genogeographic methods.

Pervasive biases in GWAS using family history of Alzheimer’s disease as proxy phenotypes

AbstractBackgroundUsing family health history as proxy phenotype has become a common approach in genome‐wide association studies (GWAS) of Alzheimer’s disease (AD). This approach leverages large samples in population biobanks where most mid‐aged participants do not have any AD diagnosis. However, methodological issues in GWAS‐by‐proxy (GWAX) and quality of its association results have not been carefully investigated.MethodWe performed GWAX on parental AD history in UK Biobank (N = 48,031 proxy cases and 315,286 controls). We then applied GWAS‐by‐subtraction to obtain the non‐AD component underlying AD GWAX. We computed genetic correlations of AD case‐control GWAS, AD GWAX, and the non‐AD component underlying GWAX with a suite of complex traits. We also explored the impact of various quality control procedures and additional covariates in AD GWAX analysis.ResultAD GWAS and GWAX showed drastically different genetic correlations with several other complex traits especially cognitive abilities (Figure 1). AD GWAX had a surprising genetic correlation with higher education (rg = 0.167, p = 1.7e‐11) as opposed to the negative AD‐education correlation observed for GWAS (rg = ‐0.133, p = 2.4e‐5). The positive genetic correlation between AD and cognition is explained by the non‐AD component in AD GWAX (rg = 0.260 with education, p = 5.2e‐11). The non‐AD component in GWAX also showed correlations with lower risks of health outcomes such as coronary artery disease (rg = ‐0.136, p = 2.8e‐3) and diabetes (rg = ‐0.206, p = 2.8e‐3), indicating substantial survival bias in samples reporting parental AD history. Adjusting for parental age in GWAX substantially reduced but did not fully remove the genetic correlation of AD and higher education (rg = 0.013, p = 0.67), hinting at additional unaccounted factors in the current GWAX practice. We also provide evidence that genetic correlation between family health history awareness and education (rg = 0.285, p = 1.4e‐10) leads to these biases in GWAX associations.ConclusionGWAX based on parental health history introduces substantial and systematic biases in AD genetic associations due to the non‐representativeness of the biobank health history survey. Naively combining GWAX with regular case‐control GWAS will lead to misleading results both at the single variant level and in genome‐wide analyses such as genetic correlation estimation and polygenic risk score applications.

Statistical methods for gene-environment interaction analysis.

Most human complex phenotypes result from multiple genetic and environmental factors and their interactions. Understanding the mechanisms by which genetic and environmental factors interact offers valuable insights into the genetic architecture of complex traits and holds great potential for advancing precision medicine. The emergence of large population biobanks has led to the development of numerous statistical methods aiming at identifying gene-environment interactions (G × E). In this review, we present state-of-the-art statistical methodologies for G × E analysis. We will survey a spectrum of approaches for single-variant G × E mapping, followed by various techniques for polygenic G × E analysis. We conclude this review with a discussion on the future directions and challenges in G × E research.

The MARS PETCARE BIOBANK protocol: establishing a longitudinal study of health and disease in dogs and cats

BackgroundThe 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.MethodsAt 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.DiscussionProposed 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.

UPO Biobank: The Challenge of Integrating Biobanking into the Academic Environment to Support Translational Research.

Biobanks are driving motors of precision and personalized medicine by providing high-quality biological material/data through the standardization and harmonization of their collection, preservation, and distribution. UPO Biobank was established in 2020 as an institutional, disease, and population biobank within the University of Piemonte Orientale (UPO) for the promotion and support of high-quality, multidisciplinary studies. UPO Biobank collaborates with UPO researchers, sustaining academic translational research, and supports the Novara Cohort Study, a longitudinal cohort study involving the population in the Novara area that will collect data and biological specimens that will be available for epidemiological, public health, and biological studies on aging. UPO Biobank has been developed by implementing the quality standards for the field and the ethical and legal issues and normative about privacy protection, data collection, and sharing. As a member of the "Biobanking and Biomolecular Resources Research Infrastructure" (BBMRI) network, UPO Biobank aims to expand its activity worldwide and launch cooperation with new national and international partners and researchers. The objective of this manuscript is to report an institutional and operational experience through the description of the technical and procedural solutions and ethical and scientific implications associated with the establishment of this university research biobank.

Variants in ATRIP are associated with breast cancer susceptibility in the Polish population and UK Biobank

Several breast cancer susceptibility genes have been discovered, but more are likely to exist. To identify additional breast cancer susceptibility genes, we used the founder population of Poland and performed whole-exome sequencing on 510 women with familial breast cancer and 308 control subjects. We identified a rare mutation in ATRIP (GenBank: NM_130384.3: c.1152_1155del [p.Gly385Ter]) in two women with breast cancer. At the validation phase, we found this variant in 42/16,085 unselected Polish breast cancer-affected individuals and in 11/9,285 control subjects (OR= 2.14, 95% CI= 1.13-4.28, p= 0.02). By analyzing the sequence data of the UK Biobank study participants (450,000 individuals), we identified ATRIP loss-of-function variants among 13/15,643 breast cancer-affected individuals versus 40/157,943 control subjects (OR= 3.28, 95% CI= 1.76-6.14, p<0.001). Immunohistochemistry and functional studies showed the ATRIP c.1152_1155del variant allele is weakly expressed compared to the wild-type allele, and truncated ATRIP fails to perform its normal function to prevent replicative stress. We showed that tumors of women with breast cancer who have a germline ATRIP mutation have loss of heterozygosity at the site of ATRIP mutation and genomic homologous recombination deficiency. ATRIP is a critical partner of ATR that binds to RPA coating single-stranded DNA at sites of stalled DNA replication forks. Proper activation of ATR-ATRIP elicits a DNA damage checkpoint crucial in regulating cellular responses to DNA replication stress. Based on our observations, we conclude ATRIP is a breast cancer susceptibility gene candidate linking DNA replication stress to breast cancer.

Genome-wide screen of otosclerosis in population biobanks: 27 loci and shared associations with skeletal structure

Otosclerosis is one of the most common causes of conductive hearing loss, affecting 0.3% of the population. It typically presents in adulthood and half of the patients have a positive family history. The pathophysiology of otosclerosis is poorly understood. A previous genome-wide association study (GWAS) identified a single association locus in an intronic region of RELN. Here, we report a meta-analysis of GWAS studies of otosclerosis in three population-based biobanks comprising 3504 cases and 861,198 controls. We identify 23 novel risk loci (p < 5 × 10−8) and report an association in RELN and three previously reported candidate gene or linkage regions (TGFB1, MEPE, and OTSC7). We demonstrate developmental stage-dependent immunostaining patterns of MEPE and RUNX2 in mouse otic capsules. In most association loci, the nearest protein-coding genes are implicated in bone remodelling, mineralization or severe skeletal disorders. We highlight multiple genes involved in transforming growth factor beta signalling for follow-up studies.

Assessing the genetic burden of familial hypercholesterolemia in a large middle eastern biobank

BackgroundThe genetic architecture underlying Familial Hypercholesterolemia (FH) in Middle Eastern Arabs is yet to be fully described, and approaches to assess this from population-wide biobanks are important for public health planning and personalized medicine.MethodsWe evaluate the pilot phase cohort (n = 6,140 adults) of the Qatar Biobank (QBB) for FH using the Dutch Lipid Clinic Network (DLCN) criteria, followed by an in-depth characterization of all genetic alleles in known dominant (LDLR, APOB, and PCSK9) and recessive (LDLRAP1, ABCG5, ABCG8, and LIPA) FH-causing genes derived from whole-genome sequencing (WGS). We also investigate the utility of a globally established 12-SNP polygenic risk score to predict FH individuals in this cohort with Arab ancestry.ResultsUsing DLCN criteria, we identify eight (0.1%) ‘definite’, 41 (0.7%) ‘probable’ and 334 (5.4%) ‘possible’ FH individuals, estimating a prevalence of ‘definite or probable’ FH in the Qatari cohort of ~ 1:125. We identify ten previously known pathogenic single-nucleotide variants (SNVs) and 14 putatively novel SNVs, as well as one novel copy number variant in PCSK9. Further, despite the modest sample size, we identify one homozygote for a known pathogenic variant (ABCG8, p. Gly574Arg, global MAF = 4.49E-05) associated with Sitosterolemia 2. Finally, calculation of polygenic risk scores found that individuals with ‘definite or probable’ FH have a significantly higher LDL-C SNP score than ‘unlikely’ individuals (p = 0.0003), demonstrating its utility in Arab populations.ConclusionWe design and implement a standardized approach to phenotyping a population biobank for FH risk followed by systematically identifying known variants and assessing putative novel variants contributing to FH burden in Qatar. Our results motivate similar studies in population-level biobanks – especially those with globally under-represented ancestries – and highlight the importance of genetic screening programs for early detection and management of individuals with high FH risk in health systems.

Population biobank as a basis for determining spatial variation of clinically relevant pharmacogenetic biomarkers of cardiovascular diseases

The introduction of pharmacogenetic tests among the Russian population faces a fundamental limitation — pronounced genetic differences between populations. The genetic geography of pharmacogenetic markers of deoxyribonucleic acid (DNA) helps to remove these limitations.Aim. To reveal the spatial variation of the gene pools of the indigenous European Russian population in terms of DNA markers that are significant for the pharmacotherapy of cardiovascular diseases (CVDs) using the population biobank collections.Material and methods. A total of 3170 samples from 61 populations of the Biobank of Northern Eurasia, which represents the gene pools of the indigenous Eastern Europe population, were studied using two pharmacogenetic DNA marker arrays as follows: 60 most significant markers and 24 markers associated with CVDs. Using the multivariate statistics and genetic geography, a comparison of gene pool variation was made.Results. A cartographic atlas has been created that includes maps of the distribution among the Eastern Europe population of 24 pharmacogenetic CVD markers. These cartographic models allow various specialists to analyze patterns in the distribution of pharmacogenetic markers. General patterns are supplemented by regional studies in the North Caucasus, the Cisurals and the Russian Plain, which identify population groups with similar pharmacogenetic status. For each region, a comparison of gene pool variation for two arrays of above-mentioned DNA markers was made.Conclusion. The created atlas is the basis for the development of pharmacogenetic studies conducted by genetic geography methods using a single panel of markers and representative samples provided by population biobanks. The reliability of the results is ensured by a detailed genealogical and population annotation of each biobank sample and representative samples from the populations.

Ethical issues raised by new genomic technologies: the case study of newborn genome screening.

Over the last two decades, the ability to sequence a person's genetic code has improved exponentially, while the cost of doing so has plummeted. As genome sequencing is used more widely, diagnoses are being found for people with previously unexplained rare disease, and this has raised hopes that such analysis might usefully be employed to detect and mitigate diseases as early as possible in the life course. However, research with adults by initiatives such as population biobanks should shake our confidence in our ability to make clear health predictions from a genetic code - in many cases, we are learning that the links between genomic variants and disease are far less strong than we once thought. The UK Newborn Genomes Programme aspires to sequence up to 200,000 babies at birth, and analyse their genomic data aiming to identify 'actionable genetic conditions which may affect their health in early years. This aims to ensure timely diagnosis, access to treatment pathways, and enable better outcomes and quality of life for babies and their families' (Genomics England, 2021). This is a laudable aim, but the path from obtaining genome sequences to enabling better outcomes will not be straightforward and illustrates many of the ethical challenges raised by the use of new genomic technologies. We focus particularly on the challenge of determining 'results' from the analysis of a genetic code, against a backdrop of promotional public discourses which tend to amplify best case scenarios from genome sequencing while minimising its potential to generate uncertainty.

Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes

Genome-wide association studies have successfully discovered thousands of common variants associated with human diseases and traits, but the landscape of rare variations in human disease has not been explored at scale. Exome-sequencing studies of population biobanks provide an opportunity to systematically evaluate the impact of rare coding variations across a wide range of phenotypes to discover genes and allelic series relevant to human health and disease. Here, we present results from systematic association analyses of 4,529 phenotypes using single-variant and gene tests of 394,841 individuals in the UK Biobank with exome-sequence data. We find that the discovery of genetic associations is tightly linked to frequency and is correlated with metrics of deleteriousness and natural selection. We highlight biological findings elucidated by these data and release the dataset as a public resource alongside the Genebass browser for rapidly exploring rare-variant association results.

Enhanced rare disease mapping for phenome-wide genetic association in the UK Biobank

BackgroundRare diseases collectively affect up to 10% of the population, but often lack effective treatment, and typically little is known about their pathophysiology. Major challenges include suboptimal phenotype mapping and limited statistical power. Population biobanks, such as the UK Biobank, recruit many individuals who can be affected by rare diseases; however, investigation into their utility for rare disease research remains limited. We hypothesized the UK Biobank can be used as a unique population assay for rare diseases in the general population.MethodsWe constructed a consensus mapping between ICD-10 codes and ORPHA codes for rare diseases, then identified individuals with each rare condition in the UK Biobank, and investigated their age at recruitment, sex bias, and comorbidity distributions. Using exome sequencing data from 167,246 individuals of European ancestry, we performed genetic association controlling for case/control imbalance (SAIGE) to identify potential rare pathogenic variants for each disease.ResultsUsing our mapping approach, we identified and characterized 420 rare diseases affecting 23,575 individuals in the UK Biobank. Significant genetic associations included JAK2 V617F for immune thrombocytopenic purpura (p = 1.24 × 10−13) and a novel CALR loss of function variant for essential thrombocythemia (p = 1.59 × 10−13). We constructed an interactive resource highlighting demographic information (http://www-personal.umich.edu/~mattpat/rareDiseases.html) and demonstrate transferability by applying our mapping to a medical claims database.ConclusionsEnhanced disease mapping and increased power from population biobanks can elucidate the demographics and genetic associations for rare diseases.