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dbRUSP: An Interactive Database to Investigate Inborn Metabolic Differences for Improved Genetic Disease Screening
Gang Peng1,2, Yunxuan Zhang1, Hongyu Zhao1,2
1Department of Biostatistics, Yale University School of Public Health, New Haven, CT 06520, USA.
International Journal of Neonatal Screening
|September 22, 2022
Summary
Newborn screening accuracy improves by analyzing how factors like gestational age affect metabolite levels. A new tool, dbRUSP, helps establish reference ranges and identify new markers for metabolic disorders.
Area of Science:
- Biochemistry
- Genetics
- Public Health
Background:
- Newborn screening (NBS) uses tandem-mass spectrometry to detect over forty metabolic disorders recommended by the Recommended Uniform Screening Panel (RUSP).
- Screening accuracy can be reduced by covariates like gestational age, birth weight, and sex, leading to false positives and negatives.
- Accurate identification of metabolic disorders in newborns is crucial for early intervention and improved health outcomes.
Purpose of the Study:
- To develop a database and web-based tools (dbRUSP) for analyzing NBS metabolite levels.
- To investigate the influence of covariates on metabolite levels in a large cohort of newborns.
- To provide tools for establishing reference ranges and identifying novel screening markers for metabolic conditions.
Main Methods:
- Developed dbRUSP, an interactive R shiny package database, analyzing 41 NBS metabolites and six covariates.
- Utilized data from 500,539 screen-negative newborns from the California NBS program.
- Created modules to study single and joint effects of covariates on metabolite levels.
Main Results:
- dbRUSP allows users to input individual variables to obtain personalized metabolite reference ranges.
- The tool facilitates the selection of new candidate markers for metabolic disorder detection.
- Demonstrated the impact of covariates on metabolite levels, crucial for refining screening algorithms.
Conclusions:
- dbRUSP enhances the analysis of NBS data by accounting for covariate influences.
- The open-source tool supports the development of data mining algorithms to improve NBS accuracy.
- Accurate newborn screening is vital for early detection and management of metabolic disorders.
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