Implementation of paediatric precision oncology into clinical practice: The Individualized Therapies for Children with cancer program 'iTHER'.

iTHER is a Dutch prospective national precision oncology program aiming to define tumour molecular profiles in children and adolescents with primary very high-risk, relapsed, or refractory paediatric tumours. Between April 2017 and April 2021, 302 samples from 253 patients were included. Comprehensive molecular profiling including low-coverage whole genome sequencing (lcWGS), whole exome sequencing (WES), RNA sequencing (RNA-seq), Affymetrix, and/or 850k methylation profiling was successfully performed for 226 samples with at least 20% tumour content. Germline pathogenic variants were identified in 16% of patients (35/219), of which 22 variants were judged causative for a cancer predisposition syndrome. At least one somatic alteration was detected in 204 (90.3%), and 185 (81.9%) were considered druggable, with clinical priority very high (6.1%), high (21.3%), moderate (26.0%), intermediate (36.1%), and borderline (10.5%) priority. iTHER led to revision or refinement of diagnosis in 8 patients (3.5%). Temporal heterogeneity was observed in paired samples of 15 patients, indicating the value of sequential analyses. Of 137 patients with follow-up beyond twelve months, 21 molecularly matched treatments were applied in 19 patients (13.9%), with clinical benefit in few. Most relevant barriers to not applying targeted therapies included poor performance status, as well as limited access to drugs within clinical trial. iTHER demonstrates the feasibility of comprehensive molecular profiling across all ages, tumour types and stages in paediatric cancers, informing of diagnostic, prognostic, and targetable alterations as well as reportable germline variants. Therefore, WES and RNA-seq is nowadays standard clinical care at the Princess Máxima Center for all children with cancer, including patients at primary diagnosis. Improved access to innovative treatments within biology-driven combination trials is required to ultimately improve survival.

Prevalence of (Epi)genetic Predisposing Factors in a 5-Year Unselected National Wilms Tumor Cohort: A Comprehensive Clinical and Genomic Characterization.

PURPOSE: Wilms tumor (WT) is associated with (epi)genetic predisposing factors affecting a growing number of WT predisposing genes and loci, including those causing Beckwith-Wiedemann spectrum (BWSp) or WT1-related syndromes. To guide genetic counseling and testing, we need insight into the prevalence of WT predisposing (epi)genetic factors. PATIENTS AND METHODS: All children diagnosed with WT in the Netherlands between 2015 and 2020 were referred to a clinical geneticist. Phenotypic data, disease characteristics, and diagnostic test results were collected. If no genetic predisposition was identified by targeted diagnostic testing, germline (trio-)whole-exome sequencing and BWSp testing on normal kidney-derived DNA were offered. RESULTS: A total of 126 cases were analyzed of 128 identified patients. (Epi)genetic predisposing factors were present in 42 of 126 patients (33.3%) on the basis of a molecular diagnosis in blood-derived DNA (n = 26), normal kidney-derived DNA (n = 12), or solely a clinical diagnosis of BWSp (n = 4). Constitutional, heterozygous DIS3L2 variants were identified as a recurrent predisposing factor in five patients (4%), with a second somatic hit in 4 of 5 tumors. Twenty patients (16%) were diagnosed with BWSp while four additional patients without BWSp features harbored chromosome 11p15 methylation defects in normal kidney tissue. Remaining findings included WT1-related syndromes (n = 10), Fanconi anemia (n = 1), neurofibromatosis type 1 (n = 1), and a pathogenic REST variant (n = 1). In addition, (likely) pathogenic variants in adult-onset cancer predisposition genes (BRCA2, PMS2, CHEK2, and MUTYH) were identified in 5 of 56 (8.9%) patients with available whole-exome sequencing data. Several candidate WT predisposition genes were identified, which require further validation. CONCLUSION: (Epi)genetic WT predisposing factors, including mosaic aberrations and recurrent heterozygous DIS3L2 variants, were present in at least 33.3% of patients with WT. On the basis of these results, we encourage standard genetic testing after counseling by a clinical geneticist.

Posttranscriptional Regulation of the Human LDL Receptor by the U2-Spliceosome.

BACKGROUND: The LDLR (low-density lipoprotein receptor) in the liver is the major determinant of LDL-cholesterol levels in human plasma. The discovery of genes that regulate the activity of LDLR helps to identify pathomechanisms of hypercholesterolemia and novel therapeutic targets against atherosclerotic cardiovascular disease. METHODS: We performed a genome-wide RNA interference screen for genes limiting the uptake of fluorescent LDL into Huh-7 hepatocarcinoma cells. Top hit genes were validated by in vitro experiments as well as analyses of data sets on gene expression and variants in human populations. RESULTS: The knockdown of 54 genes significantly inhibited LDL uptake. Fifteen of them encode for components or interactors of the U2-spliceosome. Knocking down any one of 11 out of 15 genes resulted in the selective retention of intron 3 of LDLR. The translated LDLR fragment lacks 88% of the full length LDLR and is detectable neither in nontransfected cells nor in human plasma. The hepatic expression of the intron 3 retention transcript is increased in nonalcoholic fatty liver disease as well as after bariatric surgery. Its expression in blood cells correlates with LDL-cholesterol and age. Single nucleotide polymorphisms and 3 rare variants of one spliceosome gene, RBM25, are associated with LDL-cholesterol in the population and familial hypercholesterolemia, respectively. Compared with overexpression of wild-type RBM25, overexpression of the 3 rare RBM25 mutants in Huh-7 cells led to lower LDL uptake. CONCLUSIONS: We identified a novel mechanism of posttranscriptional regulation of LDLR activity in humans and associations of genetic variants of RBM25 with LDL-cholesterol levels.

Minimal residual disease (MRD) detection in acute lymphoblastic leukaemia based on fusion genes and genomic deletions: towards MRD for all.

Minimal residual disease (MRD) diagnostics are implemented in most clinical protocols for patients with acute lymphoblastic leukaemia (ALL) and are mostly performed using rearranged immunoglobulin (IG) and/or T-cell receptor (TR) gene rearrangements as molecular polymerase chain reaction targets. Unfortunately, in 5-10% of patients no or no sensitive IG/TR targets are available, and patients therefore cannot be stratified appropriately. In the present study, we used fusion genes and genomic deletions as alternative MRD targets in these patients, which retrospectively revealed appropriate MDR stratification in 79% of patients with no (sensitive) IG/TR target, and a different risk group stratification in more than half of the cases.

Optical genome mapping identifies a germline retrotransposon insertion in SMARCB1 in two siblings with atypical teratoid rhabdoid tumors.

In a subset of pediatric cancers, a germline cancer predisposition is highly suspected based on clinical and pathological findings, but genetic evidence is lacking, which hampers genetic counseling and predictive testing in the families involved. We describe a family with two siblings born from healthy parents who were both neonatally diagnosed with atypical teratoid rhabdoid tumor (ATRT). This rare and aggressive pediatric tumor is associated with biallelic inactivation of SMARCB1, and in 30% of the cases, a predisposing germline mutation is involved. Whereas the tumors of both siblings showed loss of expression of SMARCB1 and acquired homozygosity of the locus, whole exome and whole genome sequencing failed to identify germline or somatic SMARCB1 pathogenic mutations. We therefore hypothesized that the insertion of a pathogenic repeat-rich structure might hamper its detection, and we performed optical genome mapping (OGM) as an alternative strategy to identify structural variation in this locus. Using this approach, an insertion of ~2.8 kb within intron 2 of SMARCB1 was detected. Long-range PCR covering this region remained unsuccessful, but PacBio HiFi genome sequencing identified this insertion to be a SINE-VNTR-Alu, subfamily E (SVA-E) retrotransposon element, which was present in a mosaic state in the mother. This SVA-E insertion disrupts correct splicing of the gene, resulting in loss of a functional allele. This case demonstrates the power of OGM and long-read sequencing to identify genomic variations in high-risk cancer-predisposing genes that are refractory to detection with standard techniques, thereby completing the clinical and molecular diagnosis of such complex cases and greatly improving counseling and surveillance of the families involved. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

Feasibility of predicting allele specific expression from DNA sequencing using machine learning.

Allele specific expression (ASE) concerns divergent expression quantity of alternative alleles and is measured by RNA sequencing. Multiple studies show that ASE plays a role in hereditary diseases by modulating penetrance or phenotype severity. However, genome diagnostics is based on DNA sequencing and therefore neglects gene expression regulation such as ASE. To take advantage of ASE in absence of RNA sequencing, it must be predicted using only DNA variation. We have constructed ASE models from BIOS (n = 3432) and GTEx (n = 369) that predict ASE using DNA features. These models are highly reproducible and comprise many different feature types, highlighting the complex regulation that underlies ASE. We applied the BIOS-trained model to population variants in three genes in which ASE plays a clinically relevant role: BRCA2, RET and NF1. This resulted in predicted ASE effects for 27 variants, of which 10 were known pathogenic variants. We demonstrated that ASE can be predicted from DNA features using machine learning. Future efforts may improve sensitivity and translate these models into a new type of genome diagnostic tool that prioritizes candidate pathogenic variants or regulators thereof for follow-up validation by RNA sequencing. All used code and machine learning models are available at GitHub and Zenodo.

A pipeline-friendly software tool for genome diagnostics to prioritize genes by matching patient symptoms to literature.

Despite an explosive growth of next-generation sequencing data, genome diagnostics only provides a molecular diagnosis to a minority of patients. Software tools that prioritize genes based on patient symptoms using known gene-disease associations may complement variant filtering and interpretation to increase chances of success. However, many of these tools cannot be used in practice because they are embedded within variant prioritization algorithms, or exist as remote services that cannot be relied upon or are unacceptable because of legal/ethical barriers. In addition, many tools are not designed for command-line usage, closed-source, abandoned, or unavailable. We present Variant Interpretation using Biomedical literature Evidence (VIBE), a tool to prioritize disease genes based on Human Phenotype Ontology codes. VIBE is a locally installed executable that ensures operational availability and is built upon DisGeNET-RDF, a comprehensive knowledge platform containing gene-disease associations mostly from literature and variant-disease associations mostly from curated source databases. VIBE's command-line interface and output are designed for easy incorporation into bioinformatic pipelines that annotate and prioritize variants for further clinical interpretation. We evaluate VIBE in a benchmark based on 305 patient cases alongside seven other tools. Our results demonstrate that VIBE offers consistent performance with few cases missed, but we also find high complementarity among all tested tools. VIBE is a powerful, free, open source and locally installable solution for prioritizing genes based on patient symptoms. Project source code, documentation, benchmark and executables are available at https://github.com/molgenis/vibe.

A common variant in CCDC93 protects against myocardial infarction and cardiovascular mortality by regulating endosomal trafficking of low-density lipoprotein receptor.

AIMS: Genome-wide association studies have previously identified INSIG2 as a candidate gene for plasma low-density lipoprotein cholesterol (LDL-c). However, we suspect a role for CCDC93 in the same locus because of its involvement in the recycling of the LDL-receptor (LDLR). METHODS AND RESULTS: Characterization of the INSIG2 locus was followed by studies in over 107 000 individuals from the general population, the Copenhagen General Population Study and the Copenhagen City Heart Study, for associations of genetic variants with plasma lipids levels, with risk of myocardial infarction (MI) and with cardiovascular mortality. CCDC93 was furthermore studied in cells and mice. The lead variant of the INSIG2 locus (rs10490626) is not associated with changes in the expression of nearby genes but is a part of a genetic block, which excludes INSIG2. This block includes a coding variant in CCDC93 p.Pro228Leu, which is in strong linkage disequilibrium with rs10490626 (r2 > 0.96). In the general population, separately and combined, CCDC93 p.Pro228Leu is dose-dependently associated with lower LDL-c (P-trend 2.5 × 10-6 to 8.0 × 10-9), with lower risk of MI (P-trend 0.04-0.002) and lower risk of cardiovascular mortality (P-trend 0.005-0.004). These results were validated for LDL-c, risk of both coronary artery disease and MI in meta-analyses including from 194 000 to >700 000 participants. The variant is shown to increase CCDC93 protein stability, while overexpression of human CCDC93 decreases plasma LDL-c in mice. Conversely, CCDC93 ablation reduces LDL uptake as a result of reduced LDLR levels at the cell membrane. CONCLUSION: This study provides evidence that a common variant in CCDC93, encoding a protein involved in recycling of the LDLR, is associated with lower LDL-c levels, lower risk of MI and cardiovascular mortality.

NIPTeR: an R package for fast and accurate trisomy prediction in non-invasive prenatal testing.

BACKGROUND: Various algorithms have been developed to predict fetal trisomies using cell-free DNA in non-invasive prenatal testing (NIPT). As basis for prediction, a control group of non-trisomy samples is needed. Prediction accuracy is dependent on the characteristics of this group and can be improved by reducing variability between samples and by ensuring the control group is representative for the sample analyzed. RESULTS: NIPTeR is an open-source R Package that enables fast NIPT analysis and simple but flexible workflow creation, including variation reduction, trisomy prediction algorithms and quality control. This broad range of functions allows users to account for variability in NIPT data, calculate control group statistics and predict the presence of trisomies. CONCLUSION: NIPTeR supports laboratories processing next-generation sequencing data for NIPT in assessing data quality and determining whether a fetal trisomy is present. NIPTeR is available under the GNU LGPL v3 license and can be freely downloaded from https://github.com/molgenis/NIPTeR or CRAN.

Author Correction: Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps.

In the version of the article published, the surname of author Aaron Isaacs is misspelled as Issacs.

Genetics, Lifestyle, and Low-Density Lipoprotein Cholesterol in Young and Apparently Healthy Women.

BACKGROUND: Atherosclerosis starts in childhood but low-density lipoprotein cholesterol (LDL-C), a causal risk factor, is mostly studied and dealt with when clinical events have occurred. Women are usually affected later in life than men and are underdiagnosed, undertreated, and understudied in cardiovascular trials and research. This study aims at a better understanding of lifestyle and genetic factors that affect LDL-C in young women. METHODS: We randomly selected for every year of age 8 women with LDL-C ≤1st percentile (≤50 mg/dL) and 8 women with LDL-C ≥99th percentile (≥186 mg/dL) from 28 000 female participants aged between 25 to 40 years of a population-based cohort study. The resulting groups include 119 and 121 women, respectively, of an average 33 years of age. A gene-sequencing panel was used to assess established monogenic and polygenic origins of these phenotypes. Information on lifestyle was extracted from questionnaires. A healthy lifestyle score was allocated based on a recently developed algorithm. RESULTS: Of the women with LDL-C ≤1st percentile, 19 (15.7%) carried mutations that are causing monogenic hypocholesterolemia and 60 (49.6%) were genetically predisposed to low LDL-C on the basis of an extremely low weighted genetic risk score. In comparison with control groups, a healthier lifestyle was not associated with low LDL-C in women without genetic predispositions. Among women with LDL-C ≥99th percentile, 20 women (16.8%) carried mutations that cause familial hypercholesterolemia, whereas 25 (21%) were predisposed to high LDL-C on the basis of a high-weighted genetic risk score. The women in whom no genetic origin for hypercholesterolemia could be identified were found to exhibit a significantly unfavorable lifestyle in comparison with controls. CONCLUSIONS: This study highlights the need for early assessment of the cardiovascular risk profile in apparently healthy young women to identify those with LDL-C ≥99th percentile for their age: first, because, in this study, 17% of the cases were molecularly diagnosed with familial hypercholesterolemia, which needs further attention; second, because our data indicate that an unfavorable lifestyle is significantly associated with severe hypercholesterolemia in genetically unaffected women, which may also need further attention.

Identification of context-dependent expression quantitative trait loci in whole blood.

Genetic risk factors often localize to noncoding regions of the genome with unknown effects on disease etiology. Expression quantitative trait loci (eQTLs) help to explain the regulatory mechanisms underlying these genetic associations. Knowledge of the context that determines the nature and strength of eQTLs may help identify cell types relevant to pathophysiology and the regulatory networks underlying disease. Here we generated peripheral blood RNA-seq data from 2,116 unrelated individuals and systematically identified context-dependent eQTLs using a hypothesis-free strategy that does not require previous knowledge of the identity of the modifiers. Of the 23,060 significant cis-regulated genes (false discovery rate (FDR) ≤ 0.05), 2,743 (12%) showed context-dependent eQTL effects. The majority of these effects were influenced by cell type composition. A set of 145 cis-eQTLs depended on type I interferon signaling. Others were modulated by specific transcription factors binding to the eQTL SNPs.

Disease variants alter transcription factor levels and methylation of their binding sites.

Most disease-associated genetic variants are noncoding, making it challenging to design experiments to understand their functional consequences. Identification of expression quantitative trait loci (eQTLs) has been a powerful approach to infer the downstream effects of disease-associated variants, but most of these variants remain unexplained. The analysis of DNA methylation, a key component of the epigenome, offers highly complementary data on the regulatory potential of genomic regions. Here we show that disease-associated variants have widespread effects on DNA methylation in trans that likely reflect differential occupancy of trans binding sites by cis-regulated transcription factors. Using multiple omics data sets from 3,841 Dutch individuals, we identified 1,907 established trait-associated SNPs that affect the methylation levels of 10,141 different CpG sites in trans (false discovery rate (FDR) < 0.05). These included SNPs that affect both the expression of a nearby transcription factor (such as NFKB1, CTCF and NKX2-3) and methylation of its respective binding site across the genome. Trans methylation QTLs effectively expose the downstream effects of disease-associated variants.

Meta-GWAS and Meta-Analysis of Exome Array Studies Do Not Reveal Genetic Determinants of Serum Hepcidin.

Serum hepcidin concentration is regulated by iron status, inflammation, erythropoiesis and numerous other factors, but underlying processes are incompletely understood. We studied the association of common and rare single nucleotide variants (SNVs) with serum hepcidin in one Italian study and two large Dutch population-based studies. We genotyped common SNVs with genome-wide association study (GWAS) arrays and subsequently performed imputation using the 1000 Genomes reference panel. Cohort-specific GWAS were performed for log-transformed serum hepcidin, adjusted for age and gender, and results were combined in a fixed-effects meta-analysis (total N 6,096). Six top SNVs (p<5x10-6) were genotyped in 3,821 additional samples, but associations were not replicated. Furthermore, we meta-analyzed cohort-specific exome array association results of rare SNVs with serum hepcidin that were available for two of the three cohorts (total N 3,226), but no exome-wide significant signal (p<1.4x10-6) was identified. Gene-based meta-analyses revealed 19 genes that showed significant association with hepcidin. Our results suggest the absence of common SNVs and rare exonic SNVs explaining a large proportion of phenotypic variation in serum hepcidin. We recommend extension of our study once additional substantial cohorts with hepcidin measurements, GWAS and/or exome array data become available in order to increase power to identify variants that explain a smaller proportion of hepcidin variation. In addition, we encourage follow-up of the potentially interesting genes that resulted from the gene-based analysis of low-frequency and rare variants.

A high-quality human reference panel reveals the complexity and distribution of genomic structural variants.

Structural variation (SV) represents a major source of differences between individual human genomes and has been linked to disease phenotypes. However, the majority of studies provide neither a global view of the full spectrum of these variants nor integrate them into reference panels of genetic variation. Here, we analyse whole genome sequencing data of 769 individuals from 250 Dutch families, and provide a haplotype-resolved map of 1.9 million genome variants across 9 different variant classes, including novel forms of complex indels, and retrotransposition-mediated insertions of mobile elements and processed RNAs. A large proportion are previously under reported variants sized between 21 and 100 bp. We detect 4 megabases of novel sequence, encoding 11 new transcripts. Finally, we show 191 known, trait-associated SNPs to be in strong linkage disequilibrium with SVs and demonstrate that our panel facilitates accurate imputation of SVs in unrelated individuals.

Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps.

Large-scale whole-genome sequence data sets offer novel opportunities to identify genetic variation underlying human traits. Here we apply genotype imputation based on whole-genome sequence data from the UK10K and 1000 Genomes Project into 35,981 study participants of European ancestry, followed by association analysis with 20 quantitative cardiometabolic and hematological traits. We describe 17 new associations, including 6 rare (minor allele frequency (MAF) < 1%) or low-frequency (1% < MAF < 5%) variants with platelet count (PLT), red blood cell indices (MCH and MCV) and HDL cholesterol. Applying fine-mapping analysis to 233 known and new loci associated with the 20 traits, we resolve the associations of 59 loci to credible sets of 20 or fewer variants and describe trait enrichments within regions of predicted regulatory function. These findings improve understanding of the allelic architecture of risk factors for cardiometabolic and hematological diseases and provide additional functional insights with the identification of potentially novel biological targets.

CoNVaDING: Single Exon Variation Detection in Targeted NGS Data.

We have developed a tool for detecting single exon copy-number variations (CNVs) in targeted next-generation sequencing data: CoNVaDING (Copy Number Variation Detection In Next-generation sequencing Gene panels). CoNVaDING includes a stringent quality control (QC) metric, that excludes or flags low-quality exons. Since this QC shows exactly which exons can be reliably analyzed and which exons are in need of an alternative analysis method, CoNVaDING is not only useful for CNV detection in a research setting, but also in clinical diagnostics. During the validation phase, CoNVaDING detected all known CNVs in high-quality targets in 320 samples analyzed, giving 100% sensitivity and 99.998% specificity for 308,574 exons. CoNVaDING outperforms existing tools by exhibiting a higher sensitivity and specificity and by precisely identifying low-quality samples and regions.

Molgenis-impute: imputation pipeline in a box.

BACKGROUND: Genotype imputation is an important procedure in current genomic analysis such as genome-wide association studies, meta-analyses and fine mapping. Although high quality tools are available that perform the steps of this process, considerable effort and expertise is required to set up and run a best practice imputation pipeline, particularly for larger genotype datasets, where imputation has to scale out in parallel on computer clusters. RESULTS: Here we present MOLGENIS-impute, an 'imputation in a box' solution that seamlessly and transparently automates the set up and running of all the steps of the imputation process. These steps include genome build liftover (liftovering), genotype phasing with SHAPEIT2, quality control, sample and chromosomal chunking/merging, and imputation with IMPUTE2. MOLGENIS-impute builds on MOLGENIS-compute, a simple pipeline management platform for submission and monitoring of bioinformatics tasks in High Performance Computing (HPC) environments like local/cloud servers, clusters and grids. All the required tools, data and scripts are downloaded and installed in a single step. Researchers with diverse backgrounds and expertise have tested MOLGENIS-impute on different locations and imputed over 30,000 samples so far using the 1,000 Genomes Project and new Genome of the Netherlands data as the imputation reference. The tests have been performed on PBS/SGE clusters, cloud VMs and in a grid HPC environment. CONCLUSIONS: MOLGENIS-impute gives priority to the ease of setting up, configuring and running an imputation. It has minimal dependencies and wraps the pipeline in a simple command line interface, without sacrificing flexibility to adapt or limiting the options of underlying imputation tools. It does not require knowledge of a workflow system or programming, and is targeted at researchers who just want to apply best practices in imputation via simple commands. It is built on the MOLGENIS compute workflow framework to enable customization with additional computational steps or it can be included in other bioinformatics pipelines. It is available as open source from: https://github.com/molgenis/molgenis-imputation.

Genome of The Netherlands population-specific imputations identify an ABCA6 variant associated with cholesterol levels.

Variants associated with blood lipid levels may be population-specific. To identify low-frequency variants associated with this phenotype, population-specific reference panels may be used. Here we impute nine large Dutch biobanks (~35,000 samples) with the population-specific reference panel created by the Genome of The Netherlands Project and perform association testing with blood lipid levels. We report the discovery of five novel associations at four loci (P value <6.61 × 10(-4)), including a rare missense variant in ABCA6 (rs77542162, p.Cys1359Arg, frequency 0.034), which is predicted to be deleterious. The frequency of this ABCA6 variant is 3.65-fold increased in the Dutch and its effect (ßLDL-C=0.135, ßTC=0.140) is estimated to be very similar to those observed for single variants in well-known lipid genes, such as LDLR.

Cohort Profile: LifeLines, a three-generation cohort study and biobank.

The LifeLines Cohort Study is a large population-based cohort study and biobank that was established as a resource for research on complex interactions between environmental, phenotypic and genomic factors in the development of chronic diseases and healthy ageing. Between 2006 and 2013, inhabitants of the northern part of The Netherlands and their families were invited to participate, thereby contributing to a three-generation design. Participants visited one of the LifeLines research sites for a physical examination, including lung function, ECG and cognition tests, and completed extensive questionnaires. Baseline data were collected for 167 729 participants, aged from 6 months to 93 years. Follow-up visits are scheduled every 5 years, and in between participants receive follow-up questionnaires. Linkage is being established with medical registries and environmental data. LifeLines contains information on biochemistry, medical history, psychosocial characteristics, lifestyle and more. Genomic data are available including genome-wide genetic data of 15 638 participants. Fasting blood and 24-h urine samples are processed on the day of collection and stored at -80 °C in a fully automated storage facility. The aim of LifeLines is to be a resource for the national and international scientific community. Requests for data and biomaterials can be submitted to the LifeLines Research Office [LLscience@umcg.nl].