Biology subjects

Hess, J. L.

Publications and source records attributed to Hess, J. L..

3 recordsLinked to original sources

bioRxiv2024

Genetic and environmental contributions to eigengene expression

Multivariate network-based analytic methods such as weighted gene co-expression network analysis are being increasingly applied to human and animal gene-expression data to estimate module eigengenes (MEs). MEs represent multivariate summaries of correlated gene-expression patterns and network connectivity across genes within a module. Although this approach has the potential to elucidate the mechanisms by which molecular genomic variations contribute to individual differences in complex traits, the genetic etiology of MEs has never been empirically established. It is unclear if and to what degree individual differences in blood derived MEs reflect random variation versus familial aggregation arising from heritable or shared environmental influences. We used biometrical genetic analyses to estimate the contribution of genetic and environmental influences on MEs derived from blood lymphocytes collected on a sample of N=661 older male twins from the Vietnam Era Twin Study of Aging (VETSA) whose mean age at assessment was 67.7 years (SD=2.6 years, range=62-74 years). Of the 26 detected MEs, 14 (56%) had statistically significant additive genetic variation with an average heritability of 44% (SD=0.08, range=35-64%). Despite the relatively small sample size, this demonstration of significant family aggregation including estimates of heritability in 14 of the 26 MEs suggests that blood-based MEs are reliable and merit further exploration in terms of their associations with complex traits and diseases.

genetics↗

bioRxiv2020

BrainGENIE: The Brain Gene Expression and Network Imputation Engine

In vivo experimental analysis of human brain tissue poses substantial challenges and ethical concerns. We developed a novel method called the Brain Gene Expression and Network Imputation Engine (BrainGENIE) that uses peripheral-blood transcriptomes to predict brain-tissue-specific gene-expression levels. BrainGENIE reliably predicted brain-tissue-specific expression levels for 1,733 - 11,569 genes (false-discovery rate-adjusted p<0.05), including many transcripts that cannot be predicted reliably by a transcriptome imputation method such as PrediXcan. We tested the generalizability of BrainGENIE in external within-individual data from ex vivo peripheral blood and postmortem brain samples from the Religious Orders Study and Memory and Aging Project, wherein we validated 39% of predicted gene expression levels as concordant with observed expression levels in dorsolateral prefrontal cortex and 23% in caudate. BrainGENIE recapitulated diagnosis-related gene expression changes in brain better than direct correlations from blood and predictions from PrediXcan. BrainGENIE complements and, in some ways, outperforms existing transcriptome-imputation tools, providing biologically meaningful predictions and opening new research avenues.

bioinformatics↗

bioRxiv2019

Structural Brain Imaging Studies Offer Clues about the Effects of the Shared Genetic Etiology among Neuropsychiatric Disorders

BackgroundGenomewide association studies have found significant genetic correlations among many neuropsychiatric disorders. In contrast, we know much less about the degree to which structural brain alterations are similar among disorders and, if so, the degree to which such similarities have a genetic etiology. MethodsFrom the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium, we acquired standardized mean differences (SMDs) in regional brain volume and cortical thickness between cases and controls. We had data on 41 brain regions for: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), epilepsy, major depressive disorder (MDD), obsessive compulsive disorder (OCD) and schizophrenia (SCZ). These data had been derived from 24,360 patients and 37,425 controls. ResultsThe SMDs were significantly correlated between SCZ and BD, OCD, MDD, and ASD. MDD was positively correlated with BD and OCD. BD was positively correlated with OCD and negatively correlated with ADHD. These pairwise correlations among disorders were correlated with the corresponding pairwise correlations among disorders derived from genomewide association studies (r = 0.49). ConclusionsOur results show substantial similarities in sMRI phenotypes among neuropsychiatric disorders and suggest that these similarities are accounted for, in part, by corresponding similarities in common genetic variant architectures.

neuroscience↗