Population-based Biobanks Research Articles

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.

The Genetic Spectrum of Maturity-Onset Diabetes of the Young (MODY) in Qatar, a Population-Based Study.

Maturity-onset diabetes of the young (MODY) is a rare monogenic form of diabetes mellitus. In this study, we estimated the prevalence and genetic spectrum of MODY in the Middle Eastern population of Qatar using whole-genome sequencing (WGS) of 14,364 subjects from the population-based Qatar biobank (QBB) cohort. We focused our investigations on 14 previously identified genes ascribed to the cause of MODY and two potentially novel MODY-causing genes, RFX6 and NKX6-1. Genetic variations within the 16 MODY-related genes were assessed for their pathogenicity to identify disease-causing mutations. Analysis of QBB phenotype data revealed 72 subjects (0.5%) with type 1 diabetes, 2915 subjects (20.3%) with type 2 diabetes and 11,377 (79.2%) without diabetes. We identified 22 mutations in 67 subjects that were previously reported in the Human Genetic Mutation Database (HGMD) as disease-causing (DM) or likely disease causing (DM?) for MODY. We also identified 28 potentially novel MODY-causing mutations, predicted to be among the top 1% most deleterious mutations in the human genome, which showed complete (100%) disease penetrance in 34 subjects. Overall, we estimated that MODY accounts for around 2.2-3.4% of diabetes patients in Qatar. This is the first population-based study to determine the genetic spectrum and estimate the prevalence of MODY in the Middle East. Further research to characterize the newly identified mutations is warranted.

The WID-CIN test identifies women with, and at risk of, cervical intraepithelial neoplasia grade 3 and invasive cervical cancer

BackgroundCervical screening is transitioning from primary cytology to primary human papillomavirus (HPV) testing. HPV testing is highly sensitive but there is currently no high-specificity triage method for colposcopy referral to detect cervical intraepithelial neoplasia grade 3 or above (CIN3+) in women positive for high-risk (hr) HPV subtypes. An objective, automatable test that could accurately perform triage, independently of sample heterogeneity and age, is urgently required.MethodsWe analyzed DNA methylation at ~850,000 CpG sites across the genome in a total of 1254 cervical liquid-based cytology (LBC) samples from cases of screen-detected histologically verified CIN1-3+ (98% hrHPV-positive) and population-based control women free from any cervical disease (100% hrHPV-positive). Samples were provided by a state-of-the-art population-based cohort biobank and consisted of (i) a discovery set of 170 CIN3+ cases and 202 hrHPV-positive/cytology-negative controls; (ii) a diagnostic validation set of 87 CIN3+, 90 CIN2, 166 CIN1, and 111 hrHPV-positive/cytology-negative controls; and (iii) a predictive validation set of 428 cytology-negative samples (418 hrHPV-positive) of which 210 were diagnosed with CIN3+ in the upcoming 1–4 years and 218 remained disease-free.ResultsWe developed the WID-CIN (Women’s cancer risk IDentification-Cervical Intraepithelial Neoplasia) test, a DNA methylation signature consisting of 5000 CpG sites. The receiver operating characteristic area under the curve (AUC) in the independent diagnostic validation set was 0.92 (95% CI 0.88–0.96). At 75% specificity (≤CIN1), the overall sensitivity to detect CIN3+ is 89.7% (83.3–96.1) in all and 92.7% (85.9–99.6) and 65.6% (49.2–82.1) in women aged ≥30 and <30. In hrHPV-positive/cytology-negative samples in the predictive validation set, the WID-CIN detected 54.8% (48.0–61.5) cases developing 1–4 years after sample donation in all ages or 56.9% (47.6–66.2) and 53.5% (43.7–63.2) in ≥30 and <30-year-old women, at a specificity of 75%.ConclusionsThe WID-CIN test identifies the vast majority of hrHPV-positive women with current CIN3+ lesions. In the absence of cytologic abnormalities, a positive WID-CIN test result is likely to indicate a significantly increased risk of developing CIN3+ in the near future.

Janus serumbank – jakten på biomarkører for kreft

Janus is a population-based prospective cancer biobank established in 1973. The biobank consists of residual blood serum samples from more than 300 000 men and women, who participated in different health surveys in Norway or as Red Cross blood donors during the period 1972–2004. It was established on the initiative of Professor of Pathology, Olav Torgersen (1907–78), referring to the longstanding maxim that ‘prevention is better than cure’. The purpose of the biobank was to build up a resource for studies of cancer aetiology and early detection of cancer, by measuring biochemical and immunological changes several years before the patient’s diagnosis. The Janus cohort, with comprehensive registry-based follow-up, enables longitudinal assessment of the preclinical stage in cancer patients or the latent period before the tumour has been established, and is ideal for the search of novel biomarkers of cancer. The Cancer Registry of Norway took over the cohort in 2004. In 2019 the biobank was moved into new semi-automated storage facilities and all samples were barcoded. The biobank is annually linked to the Cancer Registry for updates on new cancer cases and by the end of 2020 the number of incident cancer cases in Janus was 107 366. A continuous work in Janus has been on quality assurance of the biospecimens by investigating sample quality parameters like the effect of different pre-processing of the samples as well as storage time and temperature. We have investigated the stability of various hormones, proteins, metabolites, electrolytes and RNAs. This work has contributed to important knowledge in establishing Good Biobank Practice in Norway. In recent years we have also shown that the trace amounts of DNA in Janus is of sufficient quality for genotyping and methylation studies. Today Janus is used in a large number of national and international studies and is an active part in several international cancer consortia. The scientific output from the biobank contains a substantial proportion of high impact papers that have contributed to increased knowledge on cancer biomarkers for use in cancer control. Many of the projects have focused on investigating the association between infections and cancer, environmental exposures and cancer and early detection biomarkers. In recent years we have identified RNAs as early detection and potentially screening biomarkers of cancer. We have developed and optimized an RNA sequencing method for samples with low input RNA and produced RNA profiles of pre-clinical samples from 1631 cancer patients and 673 cancer-free controls. The sequencing data is combined with detailed cancer information from the Cancer Registry of Norway and information on environmental exposures from health surveys, in advanced biocomputational analysis. Results published on the healthy control group shows that RNA expression levels are significantly affected by age and smoking. For lung cancer the results showed dynamic changes in differentially expressed circulating RNAs specific to histology and stage. In the future we aim to utilize Janus in omics analyses and produce large scale datasets that can be shared and used in many research projects.

Lessons learned during the process of reporting individual genomic results to participants of a population-based biobank.

The return of individual genomic results (ROR) to research participants is still in its early phase, and insight on how individuals respond to ROR is scarce. Studies contributing to the evidence base for best practices are crucial before these can be established. Here, we describe a ROR procedure conducted at a population-based biobank, followed by surveying the responses of almost 3000 participants to a range of results, and discuss lessons learned from the process, with the aim of facilitating large-scale expansion. Overall, participants perceived the information that they received with counseling as valuable, even when the reporting of high risks initially caused worry. The face-to-face delivery of results limited the number of participants who received results. Although the participants highly valued this type of communication, additional means of communication need to be considered to improve the feasibility of large-scale ROR. The feedback collected sheds light on the value judgements of the participants and on potential responses to the receipt of genetic risk information. Biobanks in other countries are planning or conducting similar projects, and the sharing of lessons learned may provide valuable insight and aid such endeavors.

Genetics of irritable bowel syndrome: shifting gear via biobank-scale studies.

The pathophysiology of irritable bowel syndrome (IBS) is multifactorial and probably involves genetic predisposition and the effect of environmental factors. Unlike other gastrointestinal diseases with a heritable component, genetic research in IBS has been scarce and mostly characterized by small underpowered studies, leading to inconclusive results. The availability of genomic and health-related data from large international cohorts and population-based biobanks offers unprecedented opportunities for long-awaited, well-powered genetic studies in IBS. This Review focuses on the latest advances that provide compelling evidence for the importance of genes involved in the digestion of carbohydrates, ion channel function, neurotransmitters and their receptors, neuronal pathways and the control of gut motility. These discoveries have generated novel information that might be further refined for the identification of predisposed individuals and selection of management strategies for patients. This Review presents a conceptual framework, the advantages and potential limitations of modern genetic research in IBS, and a summary of available evidence.

Incorporating family disease history and controlling case-control imbalance for population-based genetic association studies.

ABSTRACTMotivationIn the genome-wide association analysis of population-based biobanks, most diseases have low prevalence, which results in low detection power. One approach to tackle the problem is using family disease history, yet existing methods are unable to address type I error inflation induced by increased correlation of phenotypes among closely related samples, as well as unbalanced phenotypic distribution.ResultsWe propose a new method for genetic association test with family disease history, mixed-model-based Test with Adjusted Phenotype and Empirical saddlepoint approximation, which controls for increased phenotype correlation by adopting a two-variance-component mixed model, accounts for case–control imbalance by using empirical saddlepoint approximation, and is flexible to incorporate any existing adjusted phenotypes, such as phenotypes from the LT-FH method. We show through simulation studies and analysis of UK Biobank data of white British samples and the Korean Genome and Epidemiology Study of Korean samples that the proposed method is robust and yields better calibration compared to existing methods while gaining power for detection of variant–phenotype associations.Availability and implementationThe summary statistics and code generated in this study are available at https://github.com/styvon/TAPE.Supplementary information Supplementary data are available at Bioinformatics online.

Mendelian Randomization Analysis Identifies Blood Tyrosine Levels as a Biomarker of Non-Alcoholic Fatty Liver Disease.

Non-alcoholic fatty liver disease (NAFLD) is a complex disease associated with premature mortality. Its diagnosis is challenging, and the identification of biomarkers causally influenced by NAFLD may be clinically useful. We aimed at identifying blood metabolites causally impacted by NAFLD using two-sample Mendelian randomization (MR) with validation in a population-based biobank. Our instrument for genetically predicted NAFLD included all independent genetic variants from a recent genome-wide association study. The outcomes included 123 blood metabolites from 24,925 individuals. After correction for multiple testing, a positive effect of NAFLD on plasma tyrosine levels but not on other metabolites was identified. This association was consistent across MR methods and was robust to outliers and pleiotropy. In observational analyses performed in the Estonian Biobank (10,809 individuals including 359 patients with NAFLD), after multivariable adjustment, tyrosine levels were positively associated with the presence of NAFLD (odds ratio per 1 SD increment = 1.23 [95% confidence interval = 1.12–1.36], p = 2.19 × 10−5). In a small proof-of-concept study on bariatric surgery patients, blood tyrosine levels were higher in patients with NAFLD than without. This study revealed a potentially causal effect of NAFLD on blood tyrosine levels, suggesting it may represent a new biomarker of NAFLD.

Spectrum and frequency of CHEK2 variants in breast cancer affected and general population in the Baltic states region, initial results and literature review

BackgroundWhile BRCA1/2 gene mutational spectrum and clinical features are widely studied, there is limited data on breast cancer-predisposing non-BRCA pathogenic/likely pathogenic variants (PV/LPVs) in the Baltic states region. According to previous studies, CHEK2 is the most frequent moderate-risk breast cancer predisposition gene. The study aimed to analyse the frequency and mutational spectrum of CHEK2 PV/LPVs in the Baltic states region and perform a literature review on the subject. MethodsThe study includes two cohorts - population-based Estonian biobank (EstBB) (N-152 349) and breast cancer affected cases from Latvia (N-105). In the cohort from Latvia, CHEK2, BRCA1, BRCA2, PALB2 testing with next-generation sequencing (NGS) was carried out in selected breast cancer cases. In the EstBB, the full SNP genotyped dataset Global Screening Array (GSA) (N-152 349) was used to screen CHEK2 PV/LPVs and variants c.319+2T > A (p.(?)), c.444+1G>A (p.(?)), c.433C > T (p.Arg145Trp), c.283C > T (p.Arg95*) in CHEK2 are reported from this dataset. In addition, a subset of the EstBB (N-4776) underwent whole-genome sequencing (WGS, N-2420) and whole-exome sequencing (WES, N-2356) and founder variants c.470T > C (p.Ile157Thr), c.444+1G>A (p.(?)), c.1100delC (p.Thr367Metfs*15) in CHEK2 were reported from this dataset. Moreover, a literature overview was performed on April 1, 2021, using the PubMed search of keywords ‘CHEK2’, ‘breast cancer’, ‘Estonia’, ‘Lithuania’, ‘Latvia’, ‘Poland’, ‘Belarus’ and ‘Russia’. ResultsIn the breast cancer affected cohort from Latvia 6 CHEK2 variants, classified as PV/LPVs, were observed (6/105; 5.7%), including recurrent ones c.470T > C (p.Ile157Thr) (1.9%) and del5395(ex9-10del; (p.Met304Leufs*16)) (1.9%), as well as single ones – c.1100delC (p.Thr367Metfs*15) (1%) and c.444+1G>A (p.(?)) (1%). From EstBB NGS data (N-4776) CHEK2 variant c.470T > C (p.Ile157Thr) was detected in 8.6% of cases, c.1100delC (p.Thr367Metfs*15) in 0.6% and c.444+1G>A (p.(?)) in 0.2% of cases. In the EstBB full cohort of SNP array data (N-152 349) CHEK2 variant c.444+1G>A (p.(?)) was detected in 0.02% of cases, c.319+2T > A (p.(?)) in 0.09% of cases, c.433C > T (p.Arg145Trp) in 0.02% of cases and c.283C > T (p.Arg95*) in <0.001% of cases. For the literature review altogether, 49 PubMed articles were found, 23 of which were relevant, representing CHEK2 PV/LPVs in the population of interest. Ten publications are from Poland, eight from Russia, three from Latvia and two from Belarus. ConclusionsThis study is the first combined report on complete CHEK2 PV/LPVs screening in selected breast cancer affected cases in Latvia and large-scale population screening in Estonia, providing insight into the CHEK2 mutational spectrum in the Baltic states region. The initial results are in line with other studies that CHEK2 PV/LPVs frequency is around 5–6% of selected breast cancer cases. Here we report three CHEK2 PV/LPV - c.319+2T > A (p.(?)), c.433C > T (p.Arg145Trp), c.283C > T (p.Arg95*), that are novel for the Baltic states region. This is also the first report on c.1100delC (p.Thr367Metfs*15) and c.444+1G>A (p.(?)) from the Baltic states. High population frequency of c.470T > C (p. Ile157Thr) (8.6%) continues to question the variant's pathogenicity in particular populations. Other findings are concordant with previous reports from Latvia and neighbouring populations.

It is not a big deal: a qualitative study of clinical biobank donation experience and motives

BackgroundThe success of biobanking is directly linked to the willingness of people to donate their biological materials for research and storage. Ethical issues related to patient consent are an essential component of the current biobanking agenda. The majority of data available are focused on population-based biobanks in USA, Canada and Western Europe. The donation decision process and its ethical applications in clinical populations and populations in countries with other cultural contexts are very limited. This study aimed to evaluate the decision-making experience of the clinical biobank donors, as well as psychological and social motivators and deterrents of this decision and associated ethical risks.MethodsSemi-structured interviews were conducted in two medical institutions, in St Petersburg (Russia), in 2016–2017, among 13 donors of a clinical biobank (pregnant women, cardiac patients, and patients with multiple sclerosis) and three donation organisers—medical specialists involved in recruiting donors for a clinical biobank. Analysis of interview data was based on qualitative content analysis.ResultsDonors of a clinical biobank express beliefs in the absence of risks associated with the donation. The primary motivators for donating to the biobank were: prosocial, indirect reciprocity (response to or anticipation of an act in kind by a third party), intrinsic motivation (to enhance their self-esteem and satisfying their curiosity about the donation process), and comparability with personal values. A high level of trust in biomedical research and the particular physician can contribute to a favourable decision. The overall decision-making process regarding the biobank donation could be described as quick and not based on a careful reading of informed consent documents. The integration of biobank donation decision-making in the process of medical care might prompt patient to donate to biobank without proper consideration. The specific type of therapeutic misconception—the presence of unrealistic hope that donation could provide a direct benefit for a third person in need was discovered.ConclusionsPatients recruited to a clinical biobank in Russia have virtually no concerns as to the storage of their biomaterials. The donation decision is mainly motivated by prosocial attitudes and other factors that are similar to the motivating factors of blood donation. The fact of going through inpatient treatment and poor differentiation between donation for other people's benefit and for research purposes can make the process of obtaining consent more ethically problematic.

Development and Validation of a Prediction Model for Unemployment and Disability Benefits: Results from the Longitudinal Population-Based Lifelines Cohort Study and Biobank

Development and Validation of a Prediction Model for Unemployment and Disability Benefits: Results from the Longitudinal Population-Based Lifelines Cohort Study and Biobank

Motives for withdrawal of participation in biobanking and participants\u2019 willingness to allow linkages of their data

Data repositories, like research biobanks, seek to optimise the number of responding participants while simultaneously attempting to increase the amount of data donated per participant. Such efforts aim to increase the repository’s value for its uses in medical research to contribute to improve health care, especially when data linkage is permitted by participants. We investigated individuals’ motives for participating in such projects and potential reasons for their withdrawal from participation in a population-based biobank. In addition, we analysed how these motives were related to various characteristics of the participants and their willingness to permit data linkage to their personal data for research. These questions were explored using a sample of participants in the Dutch Lifelines biobank (n = 2615). Our results indicated that motives for participation and withdrawal were premised on benefits or harm to society and to the individuals themselves. Although general values and trust both played key roles in participation, potential withdrawal and willingness to permit data linkage, they were differentially associated with motives for participation and withdrawal. These findings support and nuance previous findings by highlighting the distinctiveness and complexity of decision making regarding participation in or withdrawal from data donation. We suggest some new directions for improving recruitment, retention and safeguarding strategies in biobanking. In addition, our data provide initial evidence regarding how factors may relate with the probability that individuals will agree to data linkages, when controlling for their unique effects. Future research should further investigate how perceptions of harm and benefits may influence decision making on withdrawal of participation.

Idéfix: identifying accidental sample mix-ups in biobanks using polygenic scores.

MotivationIdentifying sample mix-ups in biobanks is essential to allow the repurposing of genetic data for clinical pharmacogenetics. Pharmacogenetic advice based on the genetic information of another individual is potentially harmful. Existing methods for identifying mix-ups are limited to datasets in which additional omics data (e.g. gene expression) is available. Cohorts lacking such data can only use sex, which can reveal only half of the mix-ups. Here, we describe Idéfix, a method for the identification of accidental sample mix-ups in biobanks using polygenic scores.ResultsIn the Lifelines population-based biobank, we calculated polygenic scores (PGSs) for 25 traits for 32 786 participants. We then applied Idéfix to compare the actual phenotypes to PGSs, and to use the relative discordance that is expected for mix-ups, compared to correct samples. In a simulation, using induced mix-ups, Idéfix reaches an AUC of 0.90 using 25 polygenic scores and sex. This is a substantial improvement over using only sex, which has an AUC of 0.75. Subsequent simulations present Idéfix’s potential in varying datasets with more powerful PGSs. This suggests its performance will likely improve when more highly powered GWASs for commonly measured traits will become available. Idéfix can be used to identify a set of high-quality participants for whom it is very unlikely that they reflect sample mix-ups, and for these participants we can use genetic data for clinical purposes, such as pharmacogenetic profiles. For instance, in Lifelines, we can select 34.4% of participants, reducing the sample mix-up rate from 0.15% to 0.01%.Availabilityand implementation Idéfix is freely available at https://github.com/molgenis/systemsgenetics/wiki/Idefix. The individual-level data that support the findings were obtained from the Lifelines biobank under project application number ov16_0365. Data is made available upon reasonable request submitted to the LifeLines Research office (research@lifelines.nl, https://www.lifelines.nl/researcher/how-to-apply/apply-here).Supplementary information Supplementary data are available at Bioinformatics online.

A survey of functional dyspepsia in 361,360 individuals: Phenotypic and genetic cross-disease analyses.

Functional dyspepsia (FD) is a common gastrointestinal condition of poorly understood pathophysiology. While symptoms' overlap with other conditions may indicate common pathogenetic mechanisms, genetic predisposition is suspected but has not been adequately investigated. Using healthcare, questionnaire, and genetic data from three large population-based biobanks (UK Biobank, EGCUT, and MGI), we surveyed FD comorbidities, heritability, and genetic correlations across a wide spectrum of conditions and traits in 10,078 cases and 351,282 non-FD controls of European ancestry. In UK Biobank, 281 diagnoses were detected at increased prevalence in FD, based on healthcare records. Among these, gastrointestinal conditions (OR=4.0, p<1.0×10-300 ), anxiety disorders (OR=2.3, p<1.4×10-27 ), ischemic heart disease (OR=2.2, p<2.3×10-76 ), and infectious and parasitic diseases (OR=2.1, p=1.5×10-73 ) showed strongest association with FD. Similar results were obtained in an analysis of self-reported conditions and use of medications from questionnaire data. Based on a genome-wide association meta-analysis of genotypes across all cohorts, FD heritability was estimated close to 5% ( =0.047, p=0.014). Genetic correlations indicate FD predisposition is shared with several other diseases and traits (rg >0.344), mostly overlapping with those also enriched in FD patients. Suggestive (p<5.0×10-6 ) association with FD risk was detected for 13loci, with 2showing nominal replication (p<0.05) in an independent cohort of 192 FD patients. FD has a weak heritable component that shows commonalities with multiple conditions across a wide spectrum of pathophysiological domains. This new knowledge contributes to a better understanding of FD etiology and may have implications for improving its treatment.

O-116 Genetic association analyses identify links between pelvic prolapse (PP) and connective tissue biology, cardiovascular and reproductive health

Abstract Study question Can genome-wide association analysis unravel the biological underpinnings of PP and facilitate personalized risk assessment via genetic risk scores construction? Summary answer We unravel novel links with urogenital development and vascular health in PP and present polygenic risk score as a tool to stratify PP risk. What is known already Prolapse is characterized by a descent of the pelvic organs into the vaginal cavity. PP affects around 40% of women after menopause and is the main indication for major gynecological surgery, having an important health, social and economic burden. Although the etiology and biological mechanisms underlying PP remain poorly understood, prior studies suggest genetic factors might play a role. Recently, a genome-wide association study (GWAS) identified seven genome-wide significant loci, located in or near genes involved in connective tissue metabolism and estrogen exposure in the etiology of PP. Study design, size, duration We conducted a three-stage case-control genome-wide association study. Firstly, in the discovery phase, we meta-analyzed Icelandic, UK Biobank and the FinnGen R3 datasets, comprising a total of 20118 cases and 427426 controls of European ancestry. For replication we used an independent dataset from Estonian Biobank (7968 cases and 118895 controls). Finally, we conducted a joint meta-analysis, containing 28086 cases and 546321 controls, which is the largest GWAS of PP to date. Participants/materials, setting, methods We performed functional annotation on genetic variants unraveled by GWAS and integrated these with expression quantitative trait loci and chromatin interaction data. In addition, we looked at enrichment of association signal on gene-set, tissue and cell type level and analyzed associations with other phenotypes both on genetic and phenotypic level. Colocalisation analyses were conducted to help pinpoint causal genes. We further constructed polygenic risk scores to explore options for personalized risk assessment and prevention. Main results and the role of chance In the discovery phase, we identified 18 genetic loci and 20 genetic variants significantly associated with POP (p &amp;lt; 5 × 10−8) and 75% of the variants show nominal significance association (p &amp;lt; 0.05) in the replication. Notably, the joint meta-analyses detected 20 genetic loci significantly associated with POP, from which 13 loci were novel. Novel genetic variants are located in or near genes involved in gestational duration and preterm birth (rs2687728 p = 2.19x10-9, EEFSEC), cardiovascular health and pregnancy success (rs1247943 p = 5.83x10-18, KLF13), endometriosis (rs12325192 p = 3.72x10-18, CRISPLD2), urogenital tract development (rs7126322, p = 4.35x10-15, WT1 and rs42400, p = 4.8x10-10, ADAMTS16) and regulation of the oxytocin receptor (rs2267372, p = 4.49x10-13, MAFF). Further analyses demonstrated that POP GWAS signals colocalise with several eQTLS (including EEFSEC, MAFF, KLF13, etc.), providing further evidence for mapping associated genes. Tissue and cell enrichment analyses underlined the role of the urogenital system, muscle cells, myocytes and adipocytes (p &amp;lt; 0.00001, FDR&amp;lt;0.05). Furthermore, genetic correlation analyses supported a shared genetic background with gastrointestinal disorders, joint and musculoskeletal disorders and cardiovascular disease. Polygenic risk scores analyses included a total of 125551 people in the target dataset, with 5379 prevalent patients and 2517 incident patients. Analyzing the best GRS as a quintile showed association with incident disease (Harrell c-statistic= 0.603, SD = 0.006). Limitations, reasons for caution This GWAS meta-analyses focused on European ancestry populations, which challenges the generalizability of GWAS findings to non-European populations. Moreover, this study included women with PP from population-based biobanks identified using the ICD-10 code N81, which limits analyses considering different disease stages and severity. Wider implications of the findings Our study provides genetic evidence to improve the current understanding of PP pathogenesis and serves as basis for further functional studies. Moreover, we provide a genetic tool for personalized risk stratification, which could help prevent PP development and improve the quality of a vast quantity of women. Trial registration number not applicable

BIGwas: Single-command quality control and association testing for multi-cohort and biobank-scale GWAS/PheWAS data.

BackgroundGenome-wide association studies (GWAS) and phenome-wide association studies (PheWAS) involving 1 million GWAS samples from dozens of population-based biobanks present a considerable computational challenge and are carried out by large scientific groups under great expenditure of time and personnel. Automating these processes requires highly efficient and scalable methods and software, but so far there is no workflow solution to easily process 1 million GWAS samples.ResultsHere we present BIGwas, a portable, fully automated quality control and association testing pipeline for large-scale binary and quantitative trait GWAS data provided by biobank resources. By using Nextflow workflow and Singularity software container technology, BIGwas performs resource-efficient and reproducible analyses on a local computer or any high-performance compute (HPC) system with just 1 command, with no need to manually install a software execution environment or various software packages. For a single-command GWAS analysis with 974,818 individuals and 92 million genetic markers, BIGwas takes ∼16 days on a small HPC system with only 7 compute nodes to perform a complete GWAS QC and association analysis protocol. Our dynamic parallelization approach enables shorter runtimes for large HPCs.ConclusionsResearchers without extensive bioinformatics knowledge and with few computer resources can use BIGwas to perform multi-cohort GWAS with 1 million GWAS samples and, if desired, use it to build their own (genome-wide) PheWAS resource. BIGwas is freely available for download from http://github.com/ikmb/gwas-qc and http://github.com/ikmb/gwas-assoc.

Polygenic Risk Scores to Identify CVD Risk and Tailor Therapy: Hope or Hype?

The purpose of this review is to understand the conceptual basis and implications of polygenic risk scores (PRS) in assessing risk of future coronary artery disease (CAD). Genetic information from the USA and beyond has been pooled together to create population-based biobanks, composed of millions of genotyped individuals, which have helped further our understanding of the relationship between single nucleotide polymorphisms (SNPs) and CAD. Contemporary PRS composed of millions of SNPs now serve as the gold standard and have been evaluated in several cohort studies to predict risk of CAD and potentially help guide pharmacotherapy. The development of PRS has enhanced our understanding of the relationship between genes and disease, thereby facilitating CAD risk prediction. While certain constraints currently limit their utility in clinical practice, further refinement of this tool will enable clinicians to more fully understand genetic risk and improve preventive care.

The landscape of biobanks in Poland\u2014characteristics of Polish biobanking units at the beginning of BBMRI.pl organization

BackgroundBiobanking is an area of scientific activity that is growing in strength and importance. The variety of collections combining biological samples and medical scientific information makes biobanking an indispensable tool in the development of modern medicine. In 2016, Poland, a country with one of the largest populations in Europe, joined the Biobanking and BioMolecular resources Research Infrastructure-European Research Infrastructure Consortium (BBMRI-ERIC) to facilitate access to quality-defined human disease-relevant biological resources. This push led to the development of the Polish Biobanking Network. The purpose of this paper is to present the current state of biobanks in Poland in the context of their location, nature and resources.MethodsTo obtain information about and overall characteristics of Polish entities dealing with biobanking biological material, the dedicated Information Survey was designed. The survey was prepared in an electronic form and consisted of 53 questions—both open and closed, single and multiple choice—with some questions depending on each other. Sixty-five Polish biobanks/biorepositories participated in the survey.ResultsPolish biobanks are mostly affiliated with research entities (universities—42% and research institutes—30%). The data collected indicate that a considerable number of Polish biobanks are specialized (33 units), in contrast to population-based biobanks (8 units). These biobanks are mostly focused on collecting samples from oncological (23 biobanks) and rare diseases (12 biobanks). In general, great diversity was found in the material collected. Scientists working in Polish biobanks are very open to scientific cooperation (declared by 60% of units) and sharing their collections with the international scientific environment. In terms of quality issues, most biobanks declared that their quality management system was in the process of implementation (45%) or had already been implemented (23%).ConclusionsAlthough biobanking in Poland is still in its infancy, the results of this study seem promising and may be valuable to the wider biobanking research community. The distribution of biobanks throughout the Polish territory, their connection with scientific and clinical units, and their involvement in research on rare diseases may contribute to an increase in the number of multicenter studies.

Genetic contributions to bipolar disorder: current status and future directions.

Bipolar disorder (BD) is a highly heritable mental disorder and is estimated to affect about 50 million people worldwide. Our understanding of the genetic etiology of BD has greatly increased in recent years with advances in technology and methodology as well as the adoption of international consortiums and large population-based biobanks. It is clear that BD is also highly heterogeneous and polygenic and shows substantial genetic overlap with other psychiatric disorders. Genetic studies of BD suggest that the number of associated loci is expected to substantially increase in larger future studies and with it, improved genetic prediction of the disorder. Still, a number of challenges remain to fully characterize the genetic architecture of BD. First among these is the need to incorporate ancestrally-diverse samples to move research away from a Eurocentric bias that has the potential to exacerbate health disparities already seen in BD. Furthermore, incorporation of population biobanks, registry data, and electronic health records will be required to increase the sample size necessary for continued genetic discovery, while increased deep phenotyping is necessary to elucidate subtypes within BD. Lastly, the role of rare variation in BD remains to be determined. Meeting these challenges will enable improved identification of causal variants for the disorder and also allow for equitable future clinical applications of both genetic risk prediction and therapeutic interventions.

Population-based biobank for analyzing the frequencies of clinically relevant DNA markers in the Russian population: bioinformatic aspects

One of the tasks of population-based biobanks is to determine the frequencies of clinically relevant genetic polymorphisms in the population. The population of Russia is very heterogeneous both ethnically and genetically. Therefore, the frequencies of genetic markers are in demand not in one sample, but in a series of samples reflecting the heterogeneity of the gene pool of different peoples and regions.Aim. To divide the population of Russia and neighboring countries into population groups meeting certain conditions, as well as having a representative sample in existing data and biobanks.Material and methods. We developed a method for combining populations into larger groups with maintaining intragroup homogeneity based on the principal components analysis with K-means clustering, followed by refinement of clustering for higher homogeneity and a more equal distribution of group sizes using FST distances. The technology has been adjusted using the example of the Biobank of Northern Eurasia. Therefore, the material was the genome-wide data on 4.5 million genetic markers for 1,883 samples representing 247 populations of Russia and neighboring countries from this biobank. The developed approach, the resulting set of populations and related map can be applied for other collections of biomaterials from Russian populations.Results. Application of this approach made it possible to divide the entire population of Russia and neighboring countries into 29 ethnogeographic groups, characterized by relative genetic homogeneity. This set of populations is recommended as a baseline for population screenings to identify the frequency of any genetic markers among the population of Russia. A map has been constructed showing the division of population into 29 ethnogeographic areas.Conclusion. On the basis of a reliable genome-wide data, the zoning of gene pool of the Russian population was carried out. We identified ethnogeographic groups with intergroup contrasting allele frequencies, but at the same time with relatively homogeneous intragroup parameters. The resulting map and register of groups can be used in population genetic, medical genetic and pharmacogenetic studies.