- updated and augmented results for 7 datasets, generated by re-analysis of individual-level data;
- multiple new datasets;
- a new interface displaying predicted trait- and disease-relevant tissues;
- new functionality in the custom burden test;
- and new video resources.
New genetic association results
Using the LoamStream genomic analysis pipeline, developed by the T2DKP team at the Accelerating Medicines Partnership in Type 2 Diabetes (AMP T2D) Data Coordinating Center (DCC) at the Broad Institute, we have re-analyzed several sets of individual-level genetic association data that were generated by AMP T2D collaborators:
- BioMe AMP T2D GWAS
- CAMP GWAS
- Diabetic Cohort - Singapore Prospective Study GWAS
- FUSION exome chip analysis
- FUSION GWAS
- FUSION Metabochip
- METSIM GWAS
The LoamStream software allowed T2DKP analysts to run these analyses rapidly, determining associations for many more phenotypes than were analyzed previously, and to use standard, state-of-the-art methods so that all of the results are comparable across datasets. Each dataset was analyzed for associations with glycemic, lipid, renal, anthropometric, and blood pressure phenotypes, and in addition, the type 2 diabetes cases in the BioMe AMP T2D GWAS set were analyzed for associations with three diabetic complications: chronic kidney disease, end-stage renal disease, and neuropathy. The Loamstream pipeline generates detailed Quality Control and Analysis reports, which may be downloaded from the dataset-specific sections of the T2DKP Data page.
Results from these re-analyses are integrated into the T2DKP and may be viewed on Gene and Variant pages and in Manhattan plots. They may be searched using the Variant Finder, and the individual-level data from the GWAS sets may be securely accessed for custom association analysis using the Genetic Association Interactive Tool (GAIT) on Variant pages.
We have also incorporated summary statistics from several studies into the T2DKP, including:
- IVGTT-based Insulin Secretion GWAS (Wood et al. 2017): genetic associations for first-phase insulin secretion, as measured by intravenous glucose tolerance tests in over 5,500 multi-ethnic non-diabetic individuals. Several of the phenotypes measured are new to the T2DKP: acute insulin response, insulin secretion rate, and peak insulin response.
- GIANT exome chip analysis (Turcot et al. 2018; Justice et al. 2019): genetic associations for BMI, height, and waist/hip ratio adjusted for BMI. BMI and height associations were determined in over 718,000 individuals, and waist/hip ratio in over 344,000.
- Global Lipids Genetics Consortium exome chip analysis (Liu et al. 2017), with associations for plasma lipid levels in over 347,000 participants.
- Chronic Inflammation GWAS (Ligthart et al. 2018): associations with plasma C-reactive protein, a measure of chronic inflammation, in more than 312,000 individuals.
- COGENT-Kidney Consortium eGFR GWAS (Morris et al. 2019): associations with estimated glomerular filtration rate (eGFR) in over 204,000 subjects.
FOCUS on tissue enrichments
Although the genetic association results in the T2DKP identify sequence variants that are associated with the risk of developing T2D, it is rarely straightforward to identify the genes that are responsible for these associations, and in which tissues they act. Making connections between variants, effector genes, and tissues is essential for a better understanding of disease genes and pathways and for the development of new therapeutics.
To help researchers make these connections, we are assembling a toolkit of cutting-edge computational methods and applying them across all of the data in the T2DKP. The methods integrate GWAS data with transcriptomic data, tissue-specific gene expression results, eQTL data, and more to predict the probability of associations between variants, genes, phenotypes, and tissues. We present these results in interactive FOCUS (Find Orthogonal Computational Support) tables.
We previously added to the Gene page a Gene FOCUS table that presents results to help researchers evaluate candidate causal genes around a genetic association signal (read our blog post describing this interface). Now, we have added a Tissue FOCUS table presenting results that can suggest which tissues or cell types may be relevant for a disease or trait of interest.
The table is currently accessible via a link on the home page:
To use the table, choose a phenotype of interest to see p-values for different tissues, denoting the significance with which variants associated with that phenotype are enriched in each tissue. The methods used for these predictions are DEPICT, GREGOR, and LD score regression (LDSR). Find complete details about the table and methods in our downloadable documentation.
Improvements to the custom burden test
Some sequence variants are biallelic or multiallelic: that is, the reference nucleotide may be substituted by two or more different nucleotides or indels. Previously, in the custom burden test (found on T2DKP Gene pages) these variants were treated as a single allele. Now, the software underlying the custom burden test has been updated so that it treats multiple alleles separately, offering the ability to choose whether each allele of a multiallelic variant should be included in or excluded from the custom burden test. Stay tuned for other major improvements to the burden test, including the ability to use several different aggregation test methods, coming to the T2DKP in the near future!
Continuing with our series of short videos documenting various features of the T2DKP, we are releasing a new video that describes the interactive table of predicted T2D effector genes. The recording of our most recent webinar, covering gene-specific resources in the T2DKP, is also now available. Links to both videos can be found on the T2DKP home page and Resources page, as well as on the Broad Institute YouTube channel.
Join us for our next webinar on Thursday September 26 at noon EDT! We'll announce the agenda and connection information in this space in the coming weeks.