From developing new computational methods to applying them at scale, expert geneticists are using Terra to access data, share tools, and accelerate research
Alexander Bick, MD, PhD, Assistant Professor
The recent tidal wave of genomic data has dramatically increased researchers’ ability to identify genetic risk factors of diseases. Terra’s built-in access to key research datasets enables Natarajan Lab members to access the data they need, run their analyses on large cohorts, and collaborate securely with external collaborators, leading to important discoveries such as massive-scale detection of clonal hematopoiesis from raw genetic data.
Alexander G. Bick and collaborators in the Natarajan Lab analyzed 97,691 high coverage whole genome sequences to identify the root causes of clonal haematopoiesis of indeterminate potential (CHIP), an age-related disorder of stem cell populations has recently been associated with both haematological cancer and coronary heart disease. The manuscript describes the discovery of associations with blood cell, lipid and inflammatory traits that are specific to different CHIP driver genes, and the identification of three genetic loci associated with CHIP status. Overall, Bick et al. observed that germline genetic variation shapes haematopoietic stem cell function, leading to CHIP through mechanisms that are specific to clonal haematopoiesis as well as shared mechanisms that lead to somatic mutations across tissues.
This research group uses genomics, biomarkers, bioinformatics, mobile technology, and deep phenotyping to discover and understand the causal factors of atherosclerotic cardiovascular disease across diverse populations. They also implement these insights in prospective studies to improve preventive cardiovascular care.
10 samples from the 1000 Genomes Project High Coverage phase 3 panel, synthetically mutated to stand in for the access-controlled TOPMed samples used in the original study
Reproducible workflow implementing the GATK Best Practices for somatic short variant discovery with GATK4 Mutect2
This public workspace includes the GATK4 Mutect2 workflow preconfigured to run on example data with the analysis parameters used in the study, so anyone can clone the workspace and reproduce the analysis on their own data.
Genome wide association study (GWAS) on 1000 Genomes data (tutorial)
Variant analysis of Tetralogy of Fallot (reproducibility case study)
GATK Best Practices for SNP/Indel Variant Calling in Mitochondria
GATK Best Practices for Germline SNPs and Indels
GATK Best Practices for Germline Copy Number Variation