Genetics in medicine : official journal of the American College of Medical Genetics · 2026
Genotype-phenotype relationships in phenylalanine hydroxylase deficiency: Functional annotation-enhanced analysis of 23,427 individuals.
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Blau Nenad, Himmelreich Nastassja
Аннотация
PURPOSE: Phenylalanine hydroxylase deficiency spans from mild hyperphenylalaninemia (MHP) to mild PKU phenylketonuria (mPKU) and classic PKU (cPKU). Genotype-phenotype inference is complicated by allelic heterogeneity and the incomplete functional annotation of complementary DNA-only variant strings.
METHODS: We analyzed 23,427 individuals with 2 PAH alleles and metabolic phenotype (MHP [n = 4208, 18.0%], mPKU [n = 5295, 22.6%], and cPKU [n = 13,924, 59.4%]; 10,108 [43.2%] individuals had blood phenylalanine values). Variants were functionally annotated with Ensembl variant effect predictor and SpliceAI and mapped to 3 functional classes: predicted loss-of-function, splice-uncertain, and missense/other. We quantified genotype-phenotype concordance and evaluated phenotype prediction using ordinal and multinomial models.
RESULTS: Variant effect predictor provided functional consequences for 1007 unique variants and annotated >99% of alleles. The genotype functional class showed a strong relationship with phenotype, with 0/0 genotypes predominantly classified as cPKU. Genotype-phenotype concordance increased with genotype frequency, and common genotypes displayed high phenotype consistency. An ordinal ridge model using allele identity plus functional class achieved an accuracy of 0.790 (quadratic weighted κ = 0.784) under genotype-held-out evaluation. A multinomial logistic model achieved an accuracy of 0.836 on a random patient split. Continuous Phe prediction using ridge regression on log(Phenylalanine) achieved an R2 of 0.673 with a mean absolute error of 357 μmol/L. Benchmarking against the published allelic phenotype value/genotypic phenotype value system yielded an accuracy of 0.849 in 22,656 individuals with allelic phenotype values for both alleles; performance was high for cPKU and MHP but lower for mPKU, consistent with prior reports.
CONCLUSION: In this large cohort, PAH genotype is strongly associated with metabolic phenotype. Functional consequence annotation enables mechanistic interpretation (loss-of-function and splice effects) and improves the portability of genotype-based predictions to previously unseen genotypes.
METHODS: We analyzed 23,427 individuals with 2 PAH alleles and metabolic phenotype (MHP [n = 4208, 18.0%], mPKU [n = 5295, 22.6%], and cPKU [n = 13,924, 59.4%]; 10,108 [43.2%] individuals had blood phenylalanine values). Variants were functionally annotated with Ensembl variant effect predictor and SpliceAI and mapped to 3 functional classes: predicted loss-of-function, splice-uncertain, and missense/other. We quantified genotype-phenotype concordance and evaluated phenotype prediction using ordinal and multinomial models.
RESULTS: Variant effect predictor provided functional consequences for 1007 unique variants and annotated >99% of alleles. The genotype functional class showed a strong relationship with phenotype, with 0/0 genotypes predominantly classified as cPKU. Genotype-phenotype concordance increased with genotype frequency, and common genotypes displayed high phenotype consistency. An ordinal ridge model using allele identity plus functional class achieved an accuracy of 0.790 (quadratic weighted κ = 0.784) under genotype-held-out evaluation. A multinomial logistic model achieved an accuracy of 0.836 on a random patient split. Continuous Phe prediction using ridge regression on log(Phenylalanine) achieved an R2 of 0.673 with a mean absolute error of 357 μmol/L. Benchmarking against the published allelic phenotype value/genotypic phenotype value system yielded an accuracy of 0.849 in 22,656 individuals with allelic phenotype values for both alleles; performance was high for cPKU and MHP but lower for mPKU, consistent with prior reports.
CONCLUSION: In this large cohort, PAH genotype is strongly associated with metabolic phenotype. Functional consequence annotation enables mechanistic interpretation (loss-of-function and splice effects) and improves the portability of genotype-based predictions to previously unseen genotypes.