The Relationship Between Polygenic Risk Scores and Clinical Phenotype in Patients with Phenylketonuria: Genetic Prediction with the Random Forest Model

Ebru Marzioğlu Özdemir; Ozkan Bagci

doi:10.54005/geneltip.1697947

EN TR

The Relationship Between Polygenic Risk Scores and Clinical Phenotype in Patients with Phenylketonuria: Genetic Prediction with the Random Forest Model

Abstract

Summary: Background /Aims: Pathogenic variations in the PAH gene cause phenylketonuria (PKU), a monogenic metabolic disorder. Individuals with the same mutation often exhibit phenotypic variability despite the monogenic nature of the condition. The aim of this study is to create a prediction model using the Random Forest (RF) machine learning algorithm and to examine the relationship between polygenic risk scores (PRS) and phenotypic severity in PKU patients. Methods: In this study, clinical exome sequencing data obtained from 174 PKU patients with molecular validation were retrospectively examined. Approximately 18,000 common variants were retained after being filtered by population frequency and quality for individual-level analysis. All eligible variants (excluding PAH mutations) were used to calculate PRS, and RF (1000 trees, maximum depth = 5) was used for modeling. International criteria were used to classify patients into mild, moderate, and severe phenotypes. Pearson correlation and ROC analysis were used to evaluate the model's performance. Findings: The RF-based PRS model had a high accuracy rate in predicting phenotypic severity (AUC = 0.91, overall accuracy = 84.3%). There was a significant correlation between PRS values and the severity of the phenotype (r = 0.68, p < 0.001). Severe clinical phenotypes were more common in patients with higher PRS. Variants in genes associated with phenylalanine metabolism (e.g., GCH1, QDPR, PTS) were the most significant contributors to risk prediction according to feature importance analysis results. Results: The results indicate that PRS modeling combined with machine learning could be a useful method for predicting the severity of phenotypes in monogenic disorders such as PKU. This integrative approach highlights the regulatory effect of a polygenic background and suggests that PRS could support clinical risk assessment and personalized treatment plans. However, before clinical application, it is very important to validate in various populations.

Keywords

Phenylketonuria , polygenic risk score , genomic modeling , machine learning , phenotypic prediction

Fenilketonürili Hastalarda Poligenik Risk Skorlarının Klinik Fenotiple İlişkisi: Random Forest Modeli ile Genetik Öngörü

Öz

Özet: Arka Plan / Amaçlar: PAH genindeki patojenik varyasyonlar, monogenik bir metabolik bozukluk olan fenilketonüriye (PKU) neden olur. Aynı mutasyona sahip bireyler, durumun monogenik doğasına rağmen genellikle fenotipik değişkenlik gösterirler. Bu çalışmanın amacı, Random Forest (RF) makine öğrenimi algoritmasını kullanarak bir tahmin modeli oluşturmak ve PKU hastalarında poligenik risk skorları (PRS) ile fenotipik şiddet arasındaki ilişkiyi incelemektir. Metod Bu çalışmada, 174 PKU hastasından elde edilen klinik ekzom dizileme verileri retrospektif olarak moleküler doğrulama ile incelendi. Yaklaşık 18.000 yaygın varyant, bireysel düzeyde analiz için popülasyon frekansı ve kaliteye göre filtrelendikten sonra tüm uygun varyantlar (PAH mutasyonları hariç) PRS hesaplamak için kullanıldı ve modelleme için RF (1000 ağaç, maksimum derinlik = 5) kullanıldı. Hastaları hafif, orta ve şiddetli fenotiplere sınıflandırmak için uluslararası kriterler kullanıldı. Pearson korelasyon analizi ve ROC analizi, modelin performansını değerlendirmek için kullanıldı. Bulgular: RF tabanlı PRS modeli, fenotipik şiddetin tahmininde yüksek bir doğruluk oranına sahipti (AUC = 0.91, genel doğruluk = %84.3). PRS değerleri ile fenotipin şiddeti arasında anlamlı bir korelasyon vardı (r = 0.68, p < 0.001). Klinik fenotiplerin şiddetli olanları, daha yüksek bir PRS'ye sahip hastalarda daha yaygındı. Fenilalanin metabolizması ile ilişkili genlerdeki varyantlar (örneğin, GCH1, QDPR, PTS), özelliklerin önem analizi sonuçlarına göre risk tahminine en önemli katkıda bulunanlardı. Sonuçlar: Sonuçlar, PRS modellemesinin makine öğrenimi ile birleştirilmesinin, PKU gibi monogenik bozukluklarda fenotiplerin şiddetini tahmin etmek için yararlı bir yöntem olabileceğini göstermektedir. Bu bütünleştirici yaklaşım, çok genli bir arka planın düzenleyici etkisini vurgular ve PRS'nin klinik risk değerlendirmesi ve kişiselleştirilmiş tedavi planlarını destekleyebileceğini öne sürer. Ancak, klinik uygulamadan önce, çeşitli popülasyonlarda doğrulamak çok önemlidir.

Anahtar Kelimeler

Fenilketonüri , poligenik risk skoru , genomik modelleme , makine öğrenimi , fenotipik öngörü

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Details

Primary Language

English

Subjects

Medical Genetics (Excl. Cancer Genetics)

Journal Section

Research Article

Authors

Ebru Marzioğlu Özdemir ^*
0000-0001-5125-2855
Türkiye

Ozkan Bagci
0000-0002-9896-6764
Türkiye

Early Pub Date

August 29, 2025

Publication Date

August 29, 2025

Submission Date

May 14, 2025

Acceptance Date

July 28, 2025

Published in Issue

Year 2025 Volume: 35 Number: 4

DOI

https://doi.org/10.54005/geneltip.1697947

IZ

https://izlik.org/JA98DY69WN

Vancouver

1.Ebru Marzioğlu Özdemir, Ozkan Bagci. The Relationship Between Polygenic Risk Scores and Clinical Phenotype in Patients with Phenylketonuria: Genetic Prediction with the Random Forest Model. Genel Tıp Derg. 2025 Aug. 1;35(4):728-35. doi:10.54005/geneltip.1697947

The Journal of General Medicine is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY NC).

The Relationship Between Polygenic Risk Scores and Clinical Phenotype in Patients with Phenylketonuria: Genetic Prediction with the Random Forest Model

Abstract

Keywords

Fenilketonürili Hastalarda Poligenik Risk Skorlarının Klinik Fenotiple İlişkisi: Random Forest Modeli ile Genetik Öngörü

Öz

Anahtar Kelimeler

References

Details

Primary Language

Subjects

Journal Section

Authors

Early Pub Date

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

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