Phenomics Platform
The mission of the Phenomics Platform is to enable primary and systems biology analyses of human metabolic health and disease based on existing and newly generated human data and biological samples. Through multi-disciplinary collaborations with epidemiological researchers, that bridges genomics and phenomics, the Platform supports research that deepens our insight into metabolic health and abnormalities in a population level setting.
The general aims of the Phenomics Platform are to:
- Upgrade and further expand an already internationally competitive resource of carefully phenotyped samples from large population-based cohorts and other selected human samples.
- Make cohort data, analyses and samples accessible to scientists and their collaborators in order to facilitate translational studies and increase publication impact.
- Facilitate collaboration within CBMR and through CBMR PIs with national and international researchers.
We offer:
• Restricted access to data and samples for > 250,000 individuals from several Danish cohorts*.
• Support for data analysis of human genomics and transcriptomics data.
• Support for data analysis of human microbiome data. (Coming soon).
• Contribution to metabolic research analysis for scientific publications.
• Guidelines for performing quality control, imputation, and post-imputation quality control of chip-genotype data.
• Imputation of genotype data.
• In-house-lab Illumina chip-based genotyping, QC and imputation.
• In-house-lab DNA extraction from buffy-coat/faecal and saliva samples.
• In-house-lab RNA extraction from tissue samples.
• Statistical advice for research projects at CBMR.
• Clinical facilities for selected human physiological studies (for instance recruit-by-genotype studies).
(*) IMPORTANT NOTE: The Phenomics Platform does NOT own any of the cohorts it has access to. Specific approval procedures may adhere depending of the nature of the project.
The platform provides restricted access to >250,000 biological samples obtained from cohorts representing the general Danish population, families and patient groups with selected metabolic diseases as well as samples from the isolated Greenlandic Inuit population.
These cohorts provide detailed prospective information including baseline characteristics and periodic changes of lifestyle and anthropometrics. Genetic, epigenetic, transcriptomics, serum proteomics, serum metabolomics, and intestinal and saliva microbiomics data are available for many samples.
This multitude of pertinent individual data is linked to the Danish health, disease, and death registries. Furthermore, the platform involves a clinical facility for investigations of selected participants.
A first-generation web-based tool for search into human phenotype data in cohorts is coming in Q3 2021.
Selected examples of the cohorts we are allowed to use after project-specific approvals are:
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Inter99: Danish population-based randomized non-pharmacological intervention study for the prevention of ischemic heart disease. 6,784 individuals attended the baseline health examination. Follow-up examinations were conducted after 5 years. The Inter99 cohort has been extensively phenotyped, including anthropometrics, basal fasting biochemistry, an oral glucose tolerance test as well as electrocardiograms. Genome-wide genotyping, exome chip genotyping are available. Exome sequencing is available for a subset of individuals.
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Health cohorts (Health 2006 + 2008 + 2020): Danish general population studies with a focus on estimation of physical activity. Fasting biochemistry measurements and genome-wide and exome chip genotyping are available for these cohorts.
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DanFunD (The Danish study of Functional Disorders): A population-based epidemiological study of general health and fitness of ~9,500 individuals aged 18-69 years with a focus of functional disorders. Fasting biochemistry measurements and genome-wide genotyping are available as well as 16S gut microbiome data for a subset. The cohort has recently been re-investigated after 5 years.
Array-based genotyping and imputation
In-house facilities include methods and equipment for DNA and RNA extraction from various types of samples and tissues, and for chip-based genotyping. The genotyping facility consists of an Illumina iScan System with a standard throughput of 576 samples per week and a maximum throughput of 1,152 samples per week. Pipelines for quality control and analysis of the genotype data are in place.
Whole genome and exome sequencing and data analysis
Nucleotide sequencing is performed with the Single-Cell Omics platform or with external collaborators. Basic processing and analysis of next generation sequencing data is performed in-house using current best practices, from QC to read processing, mapping, genotype calling and variant annotation. We are currently working on implementing this process as an open-source pipeline.
Biobank: Access to and support in relation to storage of human biological samples
The platform houses a physical biobank with biological samples. The biobank consists of blood, serum, plasma, urine, fecal and other samples from participants stored in approximately sixty -80° freezers. The biobank is stored in collaboration with The Danish National Biobank located at Statens Serum Institut.
Analytical tools for genomics analyses
While the Phenomics platform generally strives to use established tools wherever possible, it is occasionally necessary to supplement with in-house developed software. Where relevant, such tools are available through github in the hopes that they may prove useful. Please bear in mind that they are distributed under open-source licenses without any warranty; without even the implied warranty of merchantability or fitness for particular purpose. Please see the specific licenses for more details. Use at your own risk..
Phenotool: Organize sample information for GWAS analyses
Read column-based sample information and perform simple sorting, filtering and transformations on phenotype values. Outputs sample information in formats appropriate for popular GWAS tools including Snptest, RVtest and Plink.
Reads a Variant Call Format (VCF) file with population/sample genotype information and outputs a genetic risk score based on predefined weights. Several different algorithms are available including a generic aggregate score and several specific, published algorithms. Predefined weights are included based on the previously published algorithms, but the user can override these if desired.
Surendran P et al. Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals. Nature Genetics. 2020; 52:1314-1332.
Ahluwalia TS et al. FUT2-ABO epistasis increases the risk of early childhood asthma and Streptococcus pneumoniae respiratory illnesses. Nature Communications. 2020; 16:6398.
Vogelezang S et al. Novel loci for childhood body mass index and shared heritability with adult cardiometabolic traits. PLoS Genetics. 2020; 16:e1008718.
Yaghootkar H et al. Genetic Studies of Leptin Concentrations Implicate Leptin in the Regulation of Early Adiposity. Diabetes. 2020; 69:2806-2818.
Vuckovic D et al. The Polygenic and Monogenic Basis of Blood Traits and Diseases. Cell. 2020; 182:1214-1231.
Williams K et al. Skeletal muscle enhancer interactions identify genes controlling whole-body metabolism. Nature Communications. 2020; 11:2695.
If your research group would like access the Phenomics Platform’s cohort data, or would to collaborate some other way, please send an email to CBMR-PhenomicsInfo@sund.ku.dk describing the project you are interested to do with us.
We will advise you and send you a synopsis form to fill in.
After your synopsis has been evaluated, we will get back to you with the next steps.
The Phenomics Platform has established expertise and Standard Operating Procedures (SOP) for obtaining samples for optimal downstream processing, and establishing procedures that follow guidelines from ethical committees and data protection agencies.
Teaching and workshops in genomic and meta-genomic data handling and analysis is possible. The platform brings together researchers across CBMR in a collaborative framework with national and international research groups.