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3 results on '"Jonathan L. Hess"'

1. BrainGENIE: The Brain Gene Expression and Network Imputation Engine

Author

Jonathan L. Hess, Thomas P. Quinn, Chunling Zhang, Gentry C. Hearn, Samuel Chen, Neuropsychiatric Consortium for Analysis and Sharing of Transcriptomes, Sek Won Kong, Murray Cairns, Ming T. Tsuang, Stephen V. Faraone, and Stephen J. Glatt

Subjects
Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Abstract In vivo experimental analysis of human brain tissue poses substantial challenges and ethical concerns. To address this problem, we developed a computational method called the Brain Gene Expression and Network-Imputation Engine (BrainGENIE) that leverages peripheral-blood transcriptomes to predict brain tissue-specific gene-expression levels. Paired blood–brain transcriptomic data collected by the Genotype-Tissue Expression (GTEx) Project was used to train BrainGENIE models to predict gene-expression levels in ten distinct brain regions using whole-blood gene-expression profiles. The performance of BrainGENIE was compared to PrediXcan, a popular method for imputing gene expression levels from genotypes. BrainGENIE significantly predicted brain tissue-specific expression levels for 2947–11,816 genes (false-discovery rate-adjusted p

Published
2023
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2. Polygenic resilience scores capture protective genetic effects for Alzheimer’s disease

Author

Jiahui Hou, Jonathan L. Hess, Nicola Armstrong, Joshua C. Bis, Benjamin Grenier-Boley, Ida K. Karlsson, Ganna Leonenko, Katya Numbers, Eleanor K. O’Brien, Alexey Shadrin, Anbupalam Thalamuthu, Qiong Yang, Ole A. Andreassen, Henry Brodaty, Margaret Gatz, Nicole A. Kochan, Jean-Charles Lambert, Simon M. Laws, Colin L. Masters, Karen A. Mather, Nancy L. Pedersen, Danielle Posthuma, Perminder S. Sachdev, Julie Williams, the Alzheimer’s Disease Neuroimaging Initiative, Chun Chieh Fan, Stephen V. Faraone, Christine Fennema-Notestine, Shu-Ju Lin, Valentina Escott-Price, Peter Holmans, Sudha Seshadri, Ming T. Tsuang, William S. Kremen, and Stephen J. Glatt

Subjects
Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Abstract Polygenic risk scores (PRSs) can boost risk prediction in late-onset Alzheimer’s disease (LOAD) beyond apolipoprotein E (APOE) but have not been leveraged to identify genetic resilience factors. Here, we sought to identify resilience-conferring common genetic variants in (1) unaffected individuals having high PRSs for LOAD, and (2) unaffected APOE-ε4 carriers also having high PRSs for LOAD. We used genome-wide association study (GWAS) to contrast “resilient” unaffected individuals at the highest genetic risk for LOAD with LOAD cases at comparable risk. From GWAS results, we constructed polygenic resilience scores to aggregate the addictive contributions of risk-orthogonal common variants that promote resilience to LOAD. Replication of resilience scores was undertaken in eight independent studies. We successfully replicated two polygenic resilience scores that reduce genetic risk penetrance for LOAD. We also showed that polygenic resilience scores positively correlate with polygenic risk scores in unaffected individuals, perhaps aiding in staving off disease. Our findings align with the hypothesis that a combination of risk-independent common variants mediates resilience to LOAD by moderating genetic disease risk.

Published
2022
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3. An integrated analysis of genes and functional pathways for aggression in human and rodent models

Author

Karim Malki, Bru Cormand, Yanli Zhang-James, Noèlia Fernàndez-Castillo, Stephen J. Glatt, Jonathan L. Hess, and Stephen V. Faraone

Subjects
0301 basic medicine, Candidate gene, Emotions, Genome-wide association study, Computational biology, Biology, Polymorphism, Single Nucleotide, Article, Transcriptome, Mice, 03 medical and health sciences, Cellular and Molecular Neuroscience, 0302 clinical medicine, Aggressiveness, Risk Factors, Stress, Physiological, Databases, Genetic, medicine, OMIM : Online Mendelian Inheritance in Man, Genetics, Animals, Humans, Gene Regulatory Networks, Genetic Predisposition to Disease, Molecular Biology, Gene, Genetic association, Aggression, Gene Expression Profiling, Genetic architecture, Rats, Reelin Protein, Psychiatry and Mental health, 030104 developmental biology, medicine.symptom, Agressivitat, 030217 neurology & neurosurgery, Genètica, Genome-Wide Association Study
Abstract

Human genome-wide association studies (GWAS), transcriptome analyses of animal models, and candidate gene studies have advanced our understanding of the genetic architecture of aggressive behaviors. However, each of these methods presents unique limitations. To generate a more confident and comprehensive view of the complex genetics underlying aggression, we undertook an integrated, cross-species approach. We focused on human and rodent models to derive eight gene lists from three main categories of genetic evidence: two sets of genes identified in GWAS studies, four sets implicated by transcriptome-wide studies of rodent models, and two sets of genes with causal evidence from online Mendelian inheritance in man (OMIM) and knockout (KO) mice reports. These gene sets were evaluated for overlap and pathway enrichment to extract their similarities and differences. We identified enriched common pathways such as the G-protein coupled receptor (GPCR) signaling pathway, axon guidance, reelin signaling in neurons, and ERK/MAPK signaling. Also, individual genes were ranked based on their cumulative weights to quantify their importance as risk factors for aggressive behavior, which resulted in 40 top-ranked and highly interconnected genes. The results of our cross-species and integrated approach provide insights into the genetic etiology of aggression.

Published
2018

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