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Related Concept Videos

Jaundice01:25

Jaundice

Jaundice, or icterus, is the yellow discoloration of the skin, sclerae, and mucous membranes. It happens when plasma bilirubin levels rise above 2.5-3 mg/dL, leading to bilirubin deposition in tissue.Bilirubin is a byproduct of hemoglobin degradation. In macrophages, hemoglobin breaks down into globin and heme. Globin is converted into amino acids, while heme is turned into biliverdin by heme oxygenase, which is then reduced to unconjugated bilirubin by biliverdin reductase.Unconjugated...
Genome-wide Association Studies-GWAS01:11

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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Related Experiment Video

Updated: Jun 16, 2026

In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Machine learning-based genome-wide association analysis to construct a clinical decision model for severe neonatal

Haiyan Ma1,2, Xianhong Chen3,4, Peng Zhang5

  • 1Center for Molecular Medicine, Children's Hospital of Fudan University, National Center for Children's Health, Shanghai, China.

Translational Pediatrics
|June 15, 2026
PubMed
Summary

Early identification of severe neonatal jaundice (SNJ) is crucial. A genome-wide study identified an HPR gene variant (rs144648182) potentially causing SNJ, aiding in risk prediction for jaundiced neonates.

Keywords:
Least Absolute Shrinkage and Selection Operator (LASSO)Neonatal jaundicecausal inferencegenome-wide association study (GWAS)whole-exome sequencing (WES)

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Area of Science:

  • Genetics
  • Neonatology
  • Machine Learning

Background:

  • Severe neonatal jaundice (SNJ) poses risks for neurological damage.
  • Identifying genetic factors is key for early SNJ detection.
  • Understanding SNJ etiology is complex and requires advanced approaches.

Purpose of the Study:

  • To identify genetic variants associated with SNJ using genome-wide association.
  • To evaluate the predictive potential of these variants for SNJ risk.
  • To develop a clinical prediction model for SNJ.

Main Methods:

  • Genome-wide association study (GWAS) with whole-exome sequencing data.
  • LASSO-based machine learning for variable integration and classification.
  • Causal inference methods to assess SNP-SNJ relationships.

Main Results:

  • SNJ associated with higher erythrocyte count and hemoglobin.
  • Seventeen SNPs linked to total blood erythrocyte count.
  • Haptoglobin-related protein (HPR) variant rs144648182 potentially causal for SNJ.
  • A nine-feature model accurately predicted SNJ risk.

Conclusions:

  • GWAS and machine learning identified HPR variant rs144648182 as a potential SNJ contributor.
  • A predictive model using genetic and clinical data shows promise for SNJ risk stratification.
  • Further experimental validation and external validation of the model are necessary.