Large-scale pathway specific polygenic risk and transcriptomic community network analysis identifies novel functional pathways in Parkinson disease.

Polygenic inheritance plays a central role in Parkinson disease (PD). A priority in elucidating PD etiology lies in defining the biological basis of genetic risk. Unraveling how risk leads to disruption will yield disease-modifying therapeutic targets that may be effective. Here, we utilized a high-throughput and hypothesis-free approach to determine biological processes underlying PD using the largest currently available cohorts of genetic and gene expression data from International Parkinson's Disease Genetics Consortium (IPDGC) and the Accelerating Medicines Partnership-Parkinson's disease initiative (AMP-PD), among other sources. We applied large-scale gene-set specific polygenic risk score (PRS) analyses to assess the role of common variation on PD risk focusing on publicly annotated gene sets representative of curated pathways. We nominated specific molecular sub-processes underlying protein misfolding and aggregation, post-translational protein modification, immune response, membrane and intracellular trafficking, lipid and vitamin metabolism, synaptic transmission, endosomal-lysosomal dysfunction, chromatin remodeling and apoptosis mediated by caspases among the main contributors to PD etiology. We assessed the impact of rare variation on PD risk in an independent cohort of whole-genome sequencing data and found evidence for a burden of rare damaging alleles in a range of processes, including neuronal transmission-related pathways and immune response. We explored enrichment linked to expression cell specificity patterns using single-cell gene expression data and demonstrated a significant risk pattern for dopaminergic neurons, serotonergic neurons, hypothalamic GABAergic neurons, and neural progenitors. Subsequently, we created a novel way of building de novo pathways by constructing a network expression community map using transcriptomic data derived from the blood of PD patients, which revealed functional enrichment in inflammatory signaling pathways, cell death machinery related processes, and dysregulation of mitochondrial homeostasis. Our analyses highlight several specific promising pathways and genes for functional prioritization and provide a cellular context in which such work should be done.

ASXL1 and DNMT3A mutation in a cytogenetically normal B3 thymoma.

The molecular drivers of thymoma are poorly understood. Outside of the identification of rarely occurring epidermal growth factor receptor and v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog mutations via candidate gene sequencing, mutations in common cancer genes have yet to be observed. Only a single thymoma genome sequence has been previously reported, with no mutations in known cancer genes identified. Thus, we attempted to identify somatic driver mutations in a cytogenetically normal thymoma. A stage IVB type B3 thymoma from a 47-year-old male of Asian descent with no history of myasthenia gravis or other autoimmune condition was genomically evaluated. Exome sequencing and low-pass whole-genome sequencing was performed to identify somatic point mutations, copy number changes and structural variants. Mutations in known tumor suppressors DNMT3A (p.G728D) and ASXL1 (p.E657fs), consistent with mutations of known consequence in acute myeloid leukemia, were identified. Contrary to a previous report, this finding suggests the genetic etiology of thymomas may not be fundamentally distinct from other tumor types. Rather, these findings suggest that further sequencing of cytogenetically normal thymoma samples should reveal the specific molecular drivers of thymoma.

Evidence for the role of EPHX2 gene variants in anorexia nervosa.

Anorexia nervosa (AN) and related eating disorders are complex, multifactorial neuropsychiatric conditions with likely rare and common genetic and environmental determinants. To identify genetic variants associated with AN, we pursued a series of sequencing and genotyping studies focusing on the coding regions and upstream sequence of 152 candidate genes in a total of 1205 AN cases and 1948 controls. We identified individual variant associations in the Estrogen Receptor-ß (ESR2) gene, as well as a set of rare and common variants in the Epoxide Hydrolase 2 (EPHX2) gene, in an initial sequencing study of 261 early-onset severe AN cases and 73 controls (P=0.0004). The association of EPHX2 variants was further delineated in: (1) a pooling-based replication study involving an additional 500 AN patients and 500 controls (replication set P=0.00000016); (2) single-locus studies in a cohort of 386 previously genotyped broadly defined AN cases and 295 female population controls from the Bogalusa Heart Study (BHS) and a cohort of 58 individuals with self-reported eating disturbances and 851 controls (combined smallest single locus P<0.01). As EPHX2 is known to influence cholesterol metabolism, and AN is often associated with elevated cholesterol levels, we also investigated the association of EPHX2 variants and longitudinal body mass index (BMI) and cholesterol in BHS female and male subjects (N=229) and found evidence for a modifying effect of a subset of variants on the relationship between cholesterol and BMI (P<0.01). These findings suggest a novel association of gene variants within EPHX2 to susceptibility to AN and provide a foundation for future study of this important yet poorly understood condition.

Background gene expression networks significantly enhance drug response prediction by transcriptional profiling.

A central goal of gene expression studies coupled with drug response screens is to identify predictive profiles that can be exploited to stratify patients. Numerous methods have been proposed towards this end, most of them focusing on novel statistical methods and model selection techniques that attempt to uncover groups of genes, whose expression profiles are directly and robustly correlated with drug response. However, biological systems process information through the crosstalk of multiple signaling networks, whose ultimate phenotypic consequences may only be determined by the combined input of relevant interacting systems. By restricting predictive signatures to direct gene-drug correlations, biologically meaningful interactions that may serve as superior predictors are ignored. Here we demonstrate that predictive signatures, which incorporate the interaction between background gene expression patterns and individual predictive probes, can provide superior models than those that directly relate gene expression levels to pharmacological response, and thus should be more widely utilized in pharmacogenetic studies.