Translational pediatrics · 2026
Machine learning-based genome-wide association analysis to construct a clinical decision model for severe neonatal jaundice.
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Ma Haiyan, Chen Xianhong, Zhang Peng, Dong Xinran, Wu Bingbing, Chen Guoqiang, Zhou Wenhao, Wang Mingbang
Аннотация
BACKGROUND: Early identification of severe unconjugated hyperbilirubinemia is critical to prevent bilirubin encephalopathy and long-term neurological damage. The etiology of neonatal jaundice is complex. This study aims to identify genetic variants associated with severe neonatal jaundice (SNJ) through a genome-wide association approach and to evaluate their potential for risk prediction.
METHODS: We conducted a genome-wide association study using whole-exome sequencing data from 155 SNJ cases and 160 controls without SNJ. Genetic and clinical variables were integrated using a LASSO-based machine learning approach to assess their importance in classifying SNJ. Causal inference methods were applied to evaluate the relationship between identified single-nucleotide polymorphisms (SNPs) and SNJ.
RESULTS: In our cohort, SNJ was associated with increased erythrocyte count and hemoglobin (Hb) concentration. A positive correlation between erythrocyte count and Hb was also observed. Seventeen SNPs were found to be significantly associated with total blood erythrocyte count. A missense mutation in the gene haptoglobin-related protein (HPR), rs144648182, was enriched in SNJ. This mutation may affect the ability of HPR to bind free Hb, and a machine learning causal inference approach confirmed the potential causal effect of rs144648182 with serum total bilirubin. Nine genotypes and clinical phenotypes associated with SNJ were identified by the LASSO method and used to construct a clinical prediction model for SNJ, which enables accurate prediction of high-risk individuals in neonatal jaundice and aids clinical decisions.
CONCLUSIONS: We applied machine learning causal inference to GWAS data and identified potential erythroid-related genetic factors, including the HPR variant rs144648182, that may contribute to SNJ. This finding represents a testable hypothesis requiring experimental validation. A prediction model based on genetic and clinical variables demonstrated potential for risk stratification among jaundiced neonates, though external validation is needed before clinical application.
METHODS: We conducted a genome-wide association study using whole-exome sequencing data from 155 SNJ cases and 160 controls without SNJ. Genetic and clinical variables were integrated using a LASSO-based machine learning approach to assess their importance in classifying SNJ. Causal inference methods were applied to evaluate the relationship between identified single-nucleotide polymorphisms (SNPs) and SNJ.
RESULTS: In our cohort, SNJ was associated with increased erythrocyte count and hemoglobin (Hb) concentration. A positive correlation between erythrocyte count and Hb was also observed. Seventeen SNPs were found to be significantly associated with total blood erythrocyte count. A missense mutation in the gene haptoglobin-related protein (HPR), rs144648182, was enriched in SNJ. This mutation may affect the ability of HPR to bind free Hb, and a machine learning causal inference approach confirmed the potential causal effect of rs144648182 with serum total bilirubin. Nine genotypes and clinical phenotypes associated with SNJ were identified by the LASSO method and used to construct a clinical prediction model for SNJ, which enables accurate prediction of high-risk individuals in neonatal jaundice and aids clinical decisions.
CONCLUSIONS: We applied machine learning causal inference to GWAS data and identified potential erythroid-related genetic factors, including the HPR variant rs144648182, that may contribute to SNJ. This finding represents a testable hypothesis requiring experimental validation. A prediction model based on genetic and clinical variables demonstrated potential for risk stratification among jaundiced neonates, though external validation is needed before clinical application.