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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.
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Related Experiment Video

Updated: Jun 26, 2025

Isolation and Quantification of Zika Virus from Multiple Organs in a Mouse

Isolation and Quantification of Zika Virus from Multiple Organs in a Mouse

Published on: August 15, 2019

Association Between Institution ZIP Code Characteristics and NIH Funding.

Ryan Huebinger¹, Ryan A Coute², Mandy J Hill³

¹The University of New Mexico, Albuquerque, NM, USA.

Health Promotion Practice

|May 11, 2024

Summary

This summary is machine-generated.

Community demographics of institutions are linked to National Institutes of Health (NIH) funding, but ZIP code data alone is insufficient for assessing funding disparities. Improved data transparency is needed to address inequities in grant funding.

Keywords:

NIH disparities grant race research funding socioeconomic status

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Last Updated: Jun 26, 2025

Isolation and Quantification of Zika Virus from Multiple Organs in a Mouse

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Published on: August 15, 2019

Peptide-based Identification of Functional Motifs and their Binding Partners

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Published on: June 30, 2013

Zika Virus Specific Diagnostic Epitope Discovery

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Published on: December 12, 2017

Area of Science:

Health Services Research
Health Disparities
Biomedical Research Funding

Background:

Racial disparities in National Institutes of Health (NIH) funding are documented.
The relationship between institutional community demographics and NIH funding remains underexplored.

Purpose of the Study:

To investigate the association between U.S. Census-defined characteristics of institutional ZIP codes and NIH grant funding levels.
To determine if community demographics predict NIH funding allocation.

Main Methods:

Linked NIH RePORTER data (2011-2021) with U.S. Census data for 3,971 ZIP codes.
Calculated total NIH funding per institution and stratified institutions into funding quartiles.
Employed ordinal regression to analyze associations between ZIP code race/ethnicity, socioeconomic status, and grant funding quartile.

Main Results:

Institutions in ZIP codes with higher unemployment rates, higher high school graduation rates, lower proportions of White residents, and higher proportions of Black and Hispanic/Latine residents received higher NIH grant funding quartiles.
No significant association was found between household income within a ZIP code and NIH grant funding quartile.
Funding distribution showed a steep decline from the highest quartile (US$43.5 million) to the lowest (US$0.25 million).

Conclusions:

ZIP code demographics alone are insufficient for a comprehensive evaluation of NIH funding disparities.
The complex interplay between institutional ZIP code demographics and investigator demographics requires further investigation.
Enhanced accessibility and transparency of grant recipient data are crucial for accurately assessing and rectifying grant funding inequities.