Functional genomics study of neuropsychiatric disorders through integration of chromatin regulation, transcriptomics, and metabolomics

This dissertation examines significant risk loci identified through GWAS for neuropsychiatric illness to prioritize causal variants and genes for future validation in functional assays. This investigation focuses on the impact of risk loci on molecular traits, and whether these functionally relevant variants are associated with GWAS-SNPs for neuropsychiatric phenotypes. Chapter 1 explores the integration of three complementary next-generation sequencing approaches, including genotyping, gene expression, and chromatin accessibility in hundreds of fibroblast cell lines of a multi-ancestry cohort of bipolar disorder patients and controls. Chapter 2 investigates the blood transcriptomes of a larger cohort, including a broader spectrum of bipolar disorder and schizophrenia diagnoses, and how application of computational cell type deconvolution methods can further highlight potentially relevant genes. Chapter 3 delves into metabolomics of cerebrospinal fluid in Alzheimer’s patients and healthy controls, allowing for the investigation of neurobiological mechanisms in vivo. We find that the integration of multiple omics levels significantly enhances our understanding of GWAS risk loci by uncovering the functional consequences and molecular mechanisms of disease-associated loci

Links between critical proteins drive the controllability of protein interaction networks

Focusing on the interactomes of Homo sapiens, Saccharomyces cerevisiae, and Escherichia coli, we investigated interactions between controlling proteins. In particular, we determined critical, intermittent, and redundant proteins based on their tendency to participate in minimum dominating sets. Independently of the organisms considered, we found that interactions that involved critical nodes had the most prominent effects on the topology of their corresponding networks. Furthermore, we observed that phosphorylation and regulatory events were considerably enriched when the corresponding transcription factors and kinases were critical proteins, while such interactions were depleted when they were redundant proteins. Moreover, interactions involving critical proteins were enriched with essential genes, disease genes, and drug targets, suggesting that such characteristics may be key for the detection of novel drug targets as well as assess their efficacy

Polygenic Risk Associations with Clinical Characteristics and Recurrence of Dupuytren Disease

Background: Dupuytren disease (DD) is a common complex trait, with varying severity and incompletely understood cause. Genome-wide association studies (GWAS) have identified risk loci. In this article, we examine whether genetic risk profiles of DD in patients are associated with clinical variation and disease severity and with patient genetic risk profiles of genetically correlated traits, including body mass index (BMI), triglycerides, high-density lipoproteins, type 2 diabetes mellitus, and endophenotypes fasting glucose and glycated hemoglobin.Methods: The authors used a well-characterized cohort of 1461 DD patients with available phenotypic and genetic data. Phenotype data include age at onset, recurrence, and family history of disease. Polygenic risk scores (PRSs) of DD, BMI, triglycerides, high-density lipoprotein, type 2 diabetes, fasting glucose, and hemoglobin A1c using various significance thresholds were calculated with PRSice using the most recent GWAS summary statistics. Control data from LifeLines were used to determine P value cutoffs for PRS generation explaining most variance. Results: The PRS for DD was significantly associated with a positive family history for DD, age at onset, disease onset before the age of 50, and recurrence. We also found a significant negative correlation between the PRSs for DD and BMI. Conclusions: Although GWAS studies of DD are designed to identify genetic risk factors distinguishing case/control status, we show that the genetic risk profile for DD also explains part of its clinical variation and disease severity. The PRS may therefore aid in accurate prognostication, choosing initial treatment and in personalized medicine in the future. CLINICAL QUESTION/LEVEL OF EVIDENCE: Risk, III.</p

Gene Expression Imputation Across Multiple Tissue Types Provides Insight Into the Genetic Architecture of Frontotemporal Dementia and Its Clinical Subtypes

Background: The etiology of frontotemporal dementia (FTD) is poorly understood. To identify genes with predicted expression levels associated with FTD, we integrated summary statistics with external reference gene expression data using a transcriptome-wide association study approach. Methods: FUSION software was used to leverage FTD summary statistics (all FTD: n = 2154 cases, n = 4308 controls; behavioral variant FTD: n = 1337 cases, n = 2754 controls; semantic dementia: n = 308 cases, n = 616 controls; progressive nonfluent aphasia: n = 269 cases, n = 538 controls; FTD with motor neuron disease: n = 200 cases, n = 400 controls) from the International FTD-Genomics Consortium with 53 expression quantitative loci tissue type panels (n = 12,205; 5 consortia). Significance was assessed using a 5% false discovery rate threshold. Results: We identified 73 significant gene–tissue associations for FTD, representing 44 unique genes in 34 tissue types. Most significant findings were derived from dorsolateral prefrontal cortex splicing data (n = 19 genes, 26%). The 17q21.31 inversion locus contained 23 significant associations, representing 6 unique genes. Other top hits included SEC22B (a gene involved in vesicle trafficking), TRGV5, and ZNF302. A single gene finding (RAB38) was observed for behavioral variant FTD. For other clinical subtypes, no significant associations were observed. Conclusions: We identified novel candidate genes (e.g., SEC22B) and previously reported risk regions (e.g., 17q21.31) for FTD. Most significant associations were observed in dorsolateral prefrontal cortex splicing data despite the modest sample size of this reference panel. This suggests that our findings are specific to FTD and are likely to be biologically relevant highlights of genes at different FTD risk loci that are contributing to the disease pathology

Polygenic Risk Associations with Clinical Characteristics and Recurrence of Dupuytren Disease

BACKGROUND: Dupuytren's disease (DD) is a common complex trait, with varying severity and incompletely understood etiology. Genome-wide association studies (GWAS) have identified risk loci. Here, we examine whether genetic risk profiles of DD in patients are associated with clinical variation and disease severity as well as with patient genetic risk profiles of genetically correlated traits, including body mass index (BMI), triglycerides (TG), high-density lipoproteins (HDL), type 2 diabetes mellitus (T2D), and endophenotypes fasting glucose (FG), and glycated hemoglobin (HbA1c).METHODS: We used a well-characterized cohort of 1,461 DD patients with available phenotypic and genetic data. Phenotype data include age of onset, recurrence, and family history of disease. Polygenic risk scores (PRSs) of DD, BMI, TG, HDL, T2D, FG, and HbA1c using various significance thresholds were calculated with PRSice using the most recent GWAS summary statistics. Control data from LifeLines were used to determine p-value cut-offs for PRSs generation explaining most variance.RESULTS: The PRS for DD was significantly associated with a positive family history for DD, age of onset, disease onset before the age of 50, and recurrence. We also found a significant negative correlation between the PRSs for DD and BMI.CONCLUSIONS: While GWAS studies of DD are designed to identify genetic risk factors distinguishing case/control status, we show that the genetic risk profile for DD also explains part of its clinical variation and disease severity. The PRS may therefore aid in accurate prognostication, choosing initial treatment and in personalized medicine in future.</p

Cell-type deconvolution of bulk-blood RNA-seq reveals biological insights into neuropsychiatric disorders

Genome-wide association studies (GWASs) have uncovered susceptibility loci associated with psychiatric disorders such as bipolar disorder (BP) and schizophrenia (SCZ). However, most of these loci are in non-coding regions of the genome, and the causal mechanisms of the link between genetic variation and disease risk is unknown. Expression quantitative trait locus (eQTL) analysis of bulk tissue is a common approach used for deciphering underlying mechanisms, although this can obscure cell-type-specific signals and thus mask trait-relevant mechanisms. Although single-cell sequencing can be prohibitively expensive in large cohorts, computationally inferred cell-type proportions and cell-type gene expression estimates have the potential to overcome these problems and advance mechanistic studies. Using bulk RNA-seq from 1,730 samples derived from whole blood in a cohort ascertained from individuals with BP and SCZ, this study estimated cell-type proportions and their relation with disease status and medication. For each cell type, we found between 2,875 and 4,629 eGenes (genes with an associated eQTL), including 1,211 that are not found on the basis of bulk expression alone. We performed a colocalization test between cell-type eQTLs and various traits and identified hundreds of associations that occur between cell-type eQTLs and GWASs but that are not detected in bulk eQTLs. Finally, we investigated the effects of lithium use on the regulation of cell-type expression loci and found examples of genes that are differentially regulated according to lithium use. Our study suggests that applying computational methods to large bulk RNA-seq datasets of non-brain tissue can identify disease-relevant, cell-type-specific biology of psychiatric disorders and psychiatric medication.</p

Gene Expression Imputation Across Multiple Tissue Types Provides Insight Into the Genetic Architecture of Frontotemporal Dementia and Its Clinical Subtypes.

BackgroundThe etiology of frontotemporal dementia (FTD) is poorly understood. To identify genes with predicted expression levels associated with FTD, we integrated summary statistics with external reference gene expression data using a transcriptome-wide association study approach.MethodsFUSION software was used to leverage FTD summary statistics (all FTD: n&nbsp;= 2154 cases, n&nbsp;= 4308 controls; behavioral variant FTD: n&nbsp;= 1337 cases, n&nbsp;= 2754 controls; semantic dementia: n&nbsp;= 308 cases, n&nbsp;= 616 controls; progressive nonfluent aphasia: n&nbsp;= 269 cases, n&nbsp;= 538 controls; FTD with motor neuron disease: n&nbsp;= 200 cases, n&nbsp;= 400 controls) from the International FTD-Genomics Consortium with 53 expression quantitative loci tissue type panels (n&nbsp;= 12,205; 5 consortia). Significance was assessed using a 5% false discovery rate threshold.ResultsWe identified 73 significant gene-tissue associations for FTD, representing 44 unique genes in 34 tissue types. Most significant findings were derived from dorsolateral prefrontal cortex splicing data (n&nbsp;= 19 genes, 26%). The 17q21.31 inversion locus contained 23 significant associations, representing 6 unique genes. Other top hits included SEC22B (a gene involved in vesicle trafficking), TRGV5, and ZNF302. A single gene finding (RAB38) was observed for behavioral variant FTD. For other clinical subtypes, no significant associations were observed.ConclusionsWe identified novel candidate genes (e.g., SEC22B) and previously reported risk regions (e.g., 17q21.31) for FTD. Most significant associations were observed in dorsolateral prefrontal cortex splicing data despite the modest sample size of this reference panel. This suggests that our findings are specific to FTD and are likely to be biologically relevant highlights of genes at different FTD risk loci that are contributing to the disease pathology