Polygenic Risk Scores Tracks
 
Polygenic Risk Scores tracks   (All Phenotype and Literature tracks)

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PRS eMERGE  Polygenic Risk Scores from NHGRI Electronic Medical Records and Genomics (eMERGE) project  Source data version: Received from nlennon@broadinstitute.org, July 13 2023
Assembly: Human Feb. 2009 (GRCh37/hg19)

Description

The Polygenic Risk Scores eMERGE track shows variants that are part of selected polygenic risk scores for ten common diseases. Polygenic risk scores (PRS) have clinical utility and are the result of many years of GWAS studies. A score is given for a combination of SNPs to calculate the risk of getting a disease in a healthy population. The risk scores were selected by the NHGRI eMERGE project, and the selection process is described in Lennon et al. 2023. Many PRS models were evaluated, and the 9 models shown here were selected based on quality and are part of this track:

  • Asthma
  • Atrial Fibrillation
  • Breast Cancer
  • Coronary Heart Disease
  • Chronic Kidney Disease
  • Hypercholesterolemia
  • Prostate Cancer
  • T1 Diabetes
  • T2 Diabetes

The BMI (body mass index) model cannot currently be shown on the browser, pending publication.

Methods

Text files provided by eMerge were converted to bigBed format. The scripts are available in our GitHub repo.

Data access

The raw data can be explored interactively with the Table Browser or the Data Integrator. The data can be accessed from scripts through our API, the track name is "prsEmerge".

For automated download and analysis, the genome annotations are stored in files that can be obtained from our download server. The data is stored in our bigBed format. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can be used to obtain all features or only features within a given range, e.g.
bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg19/prsEmerge/t2d.bb -chrom=chr21 -start=0 -end=100000000 stdout

Credits

Thanks to Elisabeth McNally for advice, to Zia Truong for building this track and to Niall Lennon for sharing the data with us.

References

Lennon NJ, Kottyan LC, Kachulis C, Abul-Husn N, Arias J, Belbin G, Below JE, Berndt S, Chung W, Cimino JJ et al. Selection, optimization, and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse populations. medRxiv. 2023 Jun 5;. PMID: 37333246; PMC: PMC10275001