Genome-Wide Polygenic Score Predicts Large Number of High Risk Individuals in Monogenic Undiagnosed Young Onset Parkinson's Disease Patients from India.

Parkinson's disease (PD) is a genetically heterogeneous neurodegenerative disease with poorly defined environmental influences. Genomic studies of PD patients have identified disease-relevant monogenic genes, rare variants of significance, and polygenic risk-associated variants. In this study, whole genome sequencing data from 90 young onset Parkinson's disease (YOPD) individuals are analyzed for both monogenic and polygenic risk. The genetic variant analysis identifies pathogenic/likely pathogenic variants in eight of the 90 individuals (8.8%). It includes large homozygous coding exon deletions in PRKN and SNV/InDels in VPS13C, PLA2G6, PINK1, SYNJ1, and GCH1. Eleven rare heterozygous GBA coding variants are also identified in 13 (14.4%) individuals. In 34 (56.6%) individuals, one or more variants of uncertain significance (VUS) in PD/PD-relevant genes are observed. Though YOPD patients with a prioritized pathogenic variant show a low polygenic risk score (PRS), patients with prioritized VUS or no significant rare variants show an increased PRS odds ratio for PD. This study suggests that both significant rare variants and polygenic risk from common variants together may contribute to the genesis of PD. Further validation using a larger cohort of patients will confirm the interplay between monogenic and polygenic variants and their use in routine genetic PD diagnosis and risk assessment.

Cross-biome comparison of microbial association networks.

Clinical and environmental meta-omics studies are accumulating an ever-growing amount of microbial abundance data over a wide range of ecosystems. With a sufficiently large sample number, these microbial communities can be explored by constructing and analyzing co-occurrence networks, which detect taxon associations from abundance data and can give insights into community structure. Here, we investigate how co-occurrence networks differ across biomes and which other factors influence their properties. For this, we inferred microbial association networks from 20 different 16S rDNA sequencing data sets and observed that soil microbial networks harbor proportionally fewer positive associations and are less densely interconnected than host-associated networks. After excluding sample number, sequencing depth and beta-diversity as possible drivers, we found a negative correlation between community evenness and positive edge percentage. This correlation likely results from a skewed distribution of negative interactions, which take place preferentially between less prevalent taxa. Overall, our results suggest an under-appreciated role of evenness in shaping microbial association networks.