Evaluation of Genotype-Based Gene Expression Model Performance: A Cross-Framework and Cross-Dataset Study

Tavares, Vânia; Monteiro, Joana; Vassos, Evangelos; Coleman, Jonathan R. I.; Prata, Diana

doi:10.3390/genes12101531

Cited by 3 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the present study, instead of actual gene expression measures we used a proxy for a-genetically regulated component of the expression of genes, the eQTL scores. Although we have computed eQTL scores only for the genes having a validated eQTL score model ( 43 ), this does not guarantee that the estimated gene expression represents (or correlates perfectly with) the real levels of the expression. Furthermore, although we have selected the initial list of genes as the ones most associated with schizophrenia (vs. healthy controls), this selection did not take into account the expression profile of these genes in the brain, and we have computed an eQTL score for several brain tissues.…”

Section: Discussionmentioning

confidence: 99%

“…Both SNPs and genes’ eQTL scores were chosen as the ones most associated with psychosis as ascertained in a recent meta-analysis ( 27 ). The eQTL score of each gene was extracted with the eGenScore which we developed and published previously ( 43 ) and it was computed as the sum of the alleles of SNPs showing a statistically significant association with the brain gene expression in a standard genomic and transcriptomic sample weighted by the size of that effect (further details available in Supplementary material ).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of transition to psychosis from an at-risk mental state using structural neuroimaging, genetic, and environmental data

Tavares

Vassos²,

Marquand³

et al. 2023

Front. Psychiatry

Self Cite

View full text Add to dashboard Cite

IntroductionPsychosis is usually preceded by a prodromal phase in which patients are clinically identified as being at in an “At Risk Mental State” (ARMS). A few studies have demonstrated the feasibility of predicting psychosis transition from an ARMS using structural magnetic resonance imaging (sMRI) data and machine learning (ML) methods. However, the reliability of these findings is unclear due to possible sampling bias. Moreover, the value of genetic and environmental data in predicting transition to psychosis from an ARMS is yet to be explored.MethodsIn this study we aimed to predict transition to psychosis from an ARMS using a combination of ML, sMRI, genome-wide genotypes, and environmental risk factors as predictors, in a sample drawn from a pool of 246 ARMS subjects (60 of whom later transitioned to psychosis). First, the modality-specific values in predicting transition to psychosis were evaluated using several: (a) feature types; (b) feature manipulation strategies; (c) ML algorithms; (d) cross-validation strategies, as well as sample balancing and bootstrapping. Subsequently, the modalities whose at least 60% of the classification models showed an balanced accuracy (BAC) statistically better than chance level were included in a multimodal classification model.Results and discussionResults showed that none of the modalities alone, i.e., neuroimaging, genetic or environmental data, could predict psychosis from an ARMS statistically better than chance and, as such, no multimodal classification model was trained/tested. These results suggest that the value of structural MRI data and genome-wide genotypes in predicting psychosis from an ARMS, which has been fostered by previous evidence, should be reconsidered.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Prediction of transition to psychosis from an at-risk mental state using structural neuroimaging, genetic, and environmental data

Tavares

Vassos²,

Marquand³

et al. 2023

Front. Psychiatry

Self Cite

View full text Add to dashboard Cite

show abstract

“…The elastic net models from PredictDB 13 were used to impute organspecific gene expression levels from individual-level genotypes. These are well-established models that have been extensively validated 13,[110][111][112] . The latest release of PredictDB models, which had been trained with GTEx v8 data, were obtained from https://predictdb.…”

Section: Imputing Individual-specific Gene Expression Datamentioning

confidence: 99%

Genetically personalised organ-specific metabolic models in health and disease

Foguet

Ritchie

et al. 2022

Nat Commun

View full text Add to dashboard Cite

Understanding how genetic variants influence disease risk and complex traits (variant-to-function) is one of the major challenges in human genetics. Here we present a model-driven framework to leverage human genome-scale metabolic networks to define how genetic variants affect biochemical reaction fluxes across major human tissues, including skeletal muscle, adipose, liver, brain and heart. As proof of concept, we build personalised organ-specific metabolic flux models for 524,615 individuals of the INTERVAL and UK Biobank cohorts and perform a fluxome-wide association study (FWAS) to identify 4312 associations between personalised flux values and the concentration of metabolites in blood. Furthermore, we apply FWAS to identify 92 metabolic fluxes associated with the risk of developing coronary artery disease, many of which are linked to processes previously described to play in role in the disease. Our work demonstrates that genetically personalised metabolic models can elucidate the downstream effects of genetic variants on biochemical reactions involved in common human diseases.

show abstract

Tissue-specific atlas of trans-models for gene regulation elucidates complex regulation patterns

Dagostino,

Gottlieb

2024

BMC Genomics

View full text Add to dashboard Cite

Background Deciphering gene regulation is essential for understanding the underlying mechanisms of healthy and disease states. While the regulatory networks formed by transcription factors (TFs) and their target genes has been mostly studied with relation to cis effects such as in TF binding sites, we focused on trans effects of TFs on the expression of their transcribed genes and their potential mechanisms. Results We provide a comprehensive tissue-specific atlas, spanning 49 tissues of TF variations affecting gene expression through computational models considering two potential mechanisms, including combinatorial regulation by the expression of the TFs, and by genetic variants within the TF. We demonstrate that similarity between tissues based on our discovered genes corresponds to other types of tissue similarity. The genes affected by complex TF regulation, and their modelled TFs, were highly enriched for pharmacogenomic functions, while the TFs themselves were also enriched in several cancer and metabolic pathways. Additionally, genes that appear in multiple clusters are enriched for regulation of immune system while tissue clusters include cluster-specific genes that are enriched for biological functions and diseases previously associated with the tissues forming the cluster. Finally, our atlas exposes multilevel regulation across multiple tissues, where TFs regulate other TFs through the two tested mechanisms. Conclusions Our tissue-specific atlas provides hierarchical tissue-specific trans genetic regulations that can be further studied for association with human phenotypes.

show abstract

Evaluation of Genotype-Based Gene Expression Model Performance: A Cross-Framework and Cross-Dataset Study

Cited by 3 publications

References 24 publications

Prediction of transition to psychosis from an at-risk mental state using structural neuroimaging, genetic, and environmental data

Prediction of transition to psychosis from an at-risk mental state using structural neuroimaging, genetic, and environmental data

Genetically personalised organ-specific metabolic models in health and disease

Tissue-specific atlas of trans-models for gene regulation elucidates complex regulation patterns

Contact Info

Product

Resources

About