Assessing the role of polygenic background on the penetrance of monogenic forms in Parkinson’s disease

Abstract

Background: Several rare and common variants are associated with Parkinson's disease. However, there is still an incomplete penetrance in the carriers of rare variants associated with Parkinson's disease. To address this issue, we investigated whether a PRS calculated from significant GWAS SNPs affects the penetrance of Parkinson's disease among carriers of rare monogenic variants in known Parkinson's disease genes and those with a family history. Methods: We calculated the PRS based on common variants and selected the carriers of rare monogenic variants by using the exome data from UK Biobank. Individuals were divided into three risk categories based on PRS: low (<10%), intermediate (10%-90%), and high (>90%) risk groups. We then compared how PRS affects Parkinson's disease risk among carriers of rare monogenic variants and those with family-history. Results: We observed a two-fold higher odds ratio for a carrier of a monogenic variant that had a high PRS (OR 4.07,95% CI, 1.72-8.08) compared to carriers with a low PRS (OR 1.91, 95% CI, 0.31-6.05). In the same line, carriers with a first-degree family history and with >90% PRS have even a higher risk of developing PD (OR 23.53, 95%CI 5.39-71.54) compared to those with <90% PRS (OR 9.54, 95% CI 3.32-21.65). Conclusions: Our results show that PRS, carrier status, and family history contribute independently and additively to the Parkinson's disease risk.

Incomplete Penetrance and Variable Expressivity: From Clinical Studies to Population Cohorts

“…An overlapping genetic basis between complex traits and monogenic conditions is becoming increasingly apparent across the genome. Deleterious variants in genes causative of monogenic disease can be further dysregulated by non-coding variants that are associated with common traits, and monogenic forms of numerous common complex diseases have been identified ( Peltonen et al, 2006 ; Chami et al, 2020 ; Hassanin et al, 2021 ). This overlap can cause considerable complexity when it comes to determining genotype–phenotype relationships ( Freund et al, 2018 ).…”

Trans-ancestry polygenic models for the prediction of LDL blood levels: an analysis of the United Kingdom Biobank and Taiwan Biobank

“…We used the available imputed genotype array data through the UKB (Bycroft et al, 2018). We excluded outliers with high genotype missing rates, putative sex chromosome aneuploidy, and discordant reported sex vs. genotypic sex (Hassanin et al, 2021). We randomly excluded one from each pair of related individuals if the genetic relationship was closer than the second degree, defined as kinship coefficient >0.0884 as calculated by the UKB.…”

Genetics in parkinson’s disease: From better disease understanding to machine learning based precision medicine

“…Hence, predictive machine learning models are needed, which can potentially also overcome one of the typical limitations of PRS, namely lacking variant interactions and thus non-linearities. A recent study shows the combined role of PRS, rare high-impact variants, and family history in PD risk ( Hassanin et al, 2021 ). Cope et al demonstrated that a non-linear machine learning algorithm purely trained on genetic variants can result in dramatically improved prediction performances compared to a classical PRS ( Cope et al, 2021 ).…”