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

Makale detayları

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

Yazarlar:

Ebru Marzioğlu Özdemir | Ozkan Bagci

Yayıncı:

Selcuk University

Ana Başlıklar:

Tıbbi Genetik (Kanser Genetiği hariç)

Dergi:

Genel Tıp Dergisi Vol.35 Issue 4, pp.728-735

Belge Tipi:

Makale

Yayın Tarihi:

8/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001356610

pISSN:

2602-3741

Anahtar kelimeler:

Özet:

 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 models 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.

 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 models 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.

 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 models 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.

 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 PRSye 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 PRSnin klinik risk değerlendirmesi ve kişiselleştirilmiş tedavi planlarını destekleyebileceğini öne sürer. Ancak, klinik uygulamadan önce, çeşitli popülasyonlarda doğrulamak çok önemlidir.

Makale detayları

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

Yazarlar:

Ebru Marzioğlu Özdemir | Ozkan Bagci

Yayıncı:

Selcuk University

Ana Başlıklar:

Tıbbi Genetik (Kanser Genetiği hariç)

Dergi:

Genel Tıp Dergisi Vol.35 Issue 4, pp.728-735

Belge Tipi:

Makale

Yayın Tarihi:

8/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001356610

pISSN:

2602-3741

Phenylketonuria

polygenic risk score

genomic modeling

machine learning

phenotypic predictionFenilketonüri

poligenik risk skoru

genomik modelleme

makine öğrenimi

fenotipik öngörü

Sayı

Makale detayları

Yazarlar: Ebru Marzioğlu Özdemir | Ozkan Bagci

Yayıncı: Selcuk University

Ana Başlıklar: Tıbbi Genetik (Kanser Genetiği hariç)

Dergi: Genel Tıp Dergisi Vol.35 Issue 4, pp.728-735

Belge Tipi: Makale

Yayın Tarihi: 8/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001356610

pISSN: 2602-3741

Sayı

Yazarlar:

Ebru Marzioğlu Özdemir | Ozkan Bagci

Yayıncı:

Selcuk University

Ana Başlıklar:

Tıbbi Genetik (Kanser Genetiği hariç)

Dergi:

Genel Tıp Dergisi Vol.35 Issue 4, pp.728-735

Belge Tipi:

Makale

Yayın Tarihi:

8/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001356610

pISSN:

2602-3741