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Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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Identification of Individuals With Hereditary Cancer Risk Through Multiple Data Sources: A Population-Based Method

Guilherme Del Fiol^1,2, Michael J Madsen², Richard L Bradshaw¹

¹Department of Biomedical Informatics, University of Utah, Salt Lake City, UT.

JCO Clinical Cancer Informatics

|November 21, 2024

Summary

This summary is machine-generated.

Integrating comprehensive family history data significantly boosts the identification of individuals eligible for hereditary cancer genetic testing. However, disparities in detection rates persist, indicating a need for broader data collection and diverse research.

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Area of Science:

Genetics and Genomics
Public Health
Health Informatics

Background:

The GARDE platform utilizes electronic health record (EHR) family history data to identify patients for hereditary cancer syndrome genetic testing.
Underdocumentation of family history in EHRs can limit the effectiveness of such identification platforms.

Purpose of the Study:

To evaluate the impact of incorporating comprehensive family history data on the effectiveness of the GARDE platform.
To quantify the extent to which underdocumentation affects the identification of eligible individuals for genetic testing.

Main Methods:

A cohort of 133,764 patients at University of Utah Health was analyzed using the GARDE platform.
Identification rates were compared between using EHR data alone versus EHR data combined with the Utah Population Database (UPDB).

Main Results:

Combining EHR and UPDB data increased the identification rate of eligible individuals from 4.1% to 9.2%.
For individuals with the most complete family history, eligibility quadrupled from 4.6% to 19.3% with the addition of UPDB data.
While improvements were seen across demographics, significant disparities remained for minority groups compared to White individuals.

Conclusions:

Augmenting EHRs with comprehensive family history data substantially improves the detection of individuals eligible for hereditary cancer genetic testing.
Persistent disparities suggest that incomplete family history alone does not explain the inequities; historical biases in genetic research are also implicated.
Addressing disparities requires intentional data collection from underrepresented groups and more inclusive genetic research to ensure equitable healthcare.