Biology subjects

Giase, G.

Publications and source records attributed to Giase, G..

4 recordsLinked to original sources

bioRxiv2023

The impact of common variants on gene expression in the human brain: from RNA to protein to schizophrenia risk

BackgroundThe impact of genetic variants on gene expression has been intensely studied at the transcription level, yielding invaluable insights into the association between genes and the risk of complex disorders, such as schizophrenia (SCZ). However, the downstream impact of these variants and the molecular mechanisms connecting transcription variation to disease risk are not well understood. ResultsWe quantitated ribosome occupancy in prefrontal cortex samples of the BrainGVEX cohort. Together with transcriptomics and proteomics data from the same cohort, we performed cis-Quantitative Trait Locus (QTL) mapping and identified, at 10% false discovery rate, 3,253 expression QTLs (eQTLs), 1,344 ribosome occupancy QTLs (rQTLs), and 657 protein QTLs (pQTLs) out of 7,458 genes from 185 samples. Of the eQTLs identified, only 34% have their effects propagated to the protein level. Further analysis on the effect size of prefrontal cortex eQTLs identified from an independent dataset clearly replicated the post-transcriptional attenuation of eQTL effects. We identified omics-specific QTLs and investigated their potential in driving disease risks. Using both a variant-based approach and a gene-based approach, we identified genes containing expression-specific QTLs (esQTLs), ribosome-occupancy-specific QTLs (rsQTLs), and protein-specific QTLs (psQTLs). Among the variant-based omics-specific QTL, 38 showed strong colocalization with brain associated disorder GWAS signals, 29 of them are esQTLs. From the gene-based approach, we found 11 brain associated disorder risk genes that are driven predominantly by omics-specific QTL, all of them are driven by variants impacting transcriptional regulation. To take a complementary approach to further investigate the functional relevance of genes driven predominantly by attenuated eQTL signals, we identified SCZ risk genes using each omics independently and then investigated the omics-specificity of the driver regulatory process for each risk gene. Using S-PrediXcan we identified 74 SCZ risk genes across the three omics, 30% of which were novel, and 67% of these risk genes were confirmed to be causal in a MR-Egger test. Notably, 52 out of the 74 risk genes were identified using eQTL data and 68% of these SCZ-risk-gene-driving eQTLs show little to no evidence of driving corresponding variations at the protein level. ConclusionThe effect of eQTLs on gene expression in the prefrontal cortex is commonly attenuated post-transcriptionally. Many of the attenuated eQTLs still correlate with GWAS signals of brain associated complex disorders, indicating the possibility that these eQTL variants drive disease risk through mechanisms other than regulating protein expression level. Further investigation is needed to elucidate the mechanistic link between attenuated eQTLs and brain associated complex disorders.

genetics↗

bioRxiv2020

Risperidone-induced changes in DNA methylation from peripheral blood in first-episode schizophrenia parallel neuroimaging and cognitive phenotype

Today, second generation anti-psychotics such as clozapine and risperidone are the favored treatment for schizophrenia. Yet, the absence of relevant biomarkers that can decode their neurobiological effect shackles our ability to accurately predict and track response to treatment. While researchers have investigated DNA methylation as a biomarker for schizophrenia risk, none have performed a systematic analysis of the effect of antipsychotics upon DNA methylation. We hypothesize that disease-related methylation changes occur before treatment, and that acute antipsychotic treatment may affect DNA methylation. We designed a longitudinal DNA methylation study to estimate risperidones effect on DNA methylation and how changes in DNA methylation might influence risperidones therapeutic effect on behavioral and neuroimaging phenotypes. Thirty-eight patients with first-episode drug-naive schizophrenia (FES) and 38 demographically-matched individuals (healthy controls) participated. We identified brain related pathways enriched in 8,204 FES-associated methylation sites. Risperidone administration altered methylation in 6,143 CpG DNA sites. Post-treatment FES associated with methylation in 6760 CpG sites. Majority of the DNA methylation changes were treatment effect in the overall CpG sites, the FES associated CpG sites, and risperidone associated CpG sites, except for the post-treatment FES associated CpG sites. There were 590 DNA methylation cites normalized by risperidone treatment. The methylation changes of these 590 CpG sites were related to alterations in symptom severity, spontaneous neurophysiological activity, and cognitive function. To our knowledge, this is the first longitudinal methylation study of drug treatment effect and side effect in psychiatric disorders to include parallel studies of neuroimaging and cognitive phenotypes. We identified FES-associated CpG sites not confounded by drug treatment as potential SCZ biomarkers. The normalization effect of risperidone monotherapy suggests that DNA methylation changes may serve as a predictive biomarker for treatment effect. The constructed methylation-phenotype network revealed a relationship between methylation and a wide range of biological and psychological variables.

pharmacology and toxicology↗

bioRxiv2019

DRAMS: A Tool to Detect and Re-Align Mixed-up Samples for Integrative Studies of Multi-omics Data

Studies of complex disorders benefit from integrative analyses of multiple omics data. Yet, sample mix-ups frequently occur in multi-omics studies, weakening statistical power and risking false findings. Accurately aligning sample information, genotype, and corresponding omics data is critical for integrative analyses. We developed DRAMS (https://github.com/Yi-Jiang/DRAMS) to Detect and Re-Align Mixed-up Samples to address the sample mix-up problem. It uses a logistic regression model followed by a modified topological sorting algorithm to identify the potential true IDs based on data relationships of multi-omics. According to tests using simulated data, the more types of omics data used or the smaller the proportion of mix-ups, the better that DRAMS performs. Applying DRAMS to real data from the PsychENCODE BrainGVEX project, we detected and corrected 201 (12.5% of total data generated) mix-ups. Of the 21 mix-ups involving errors of racial identity, DRAMS re-assigned all samples to the correct racial cluster in the 1000 Genomes project. In doing so, quantitative trait loci (QTL) (FDR<0.01) increased by an average of 1.62-fold. The use of DRAMS in multi-omics studies will strengthen statistical power of the study and improve quality of the results. Even though very limited studies have multi-omics data in place, we expect such data will increase quickly with the needs of DRAMS.\n\nAuthor summarySample mix-up happens inevitably during sample collection, processing, and data management. It leads to reduced statistical power and sometimes false findings. It is of great importance to correct mixed-up samples before conducting any downstream analyses. We developed DRAMS to detect and re-align mixed-up samples in multi-omics studies. The basic idea of DRAMS is to align the data and labels for each sample leveraging the genetic information of multi-omics data. DRAMS corrects sample IDs following a two-step strategy. At first, it estimates pairwise genetic relatedness among all the data generated from all the individuals. Because the different data generated from the same individual should share the same genetics, we can cluster all the highly related data and consider that the data from one cluster have only one potential ID. Then, we used a \"majority vote\" strategy to infer the potential ID for individuals in each cluster. Other information, such as match of genetics-based and reported sexes, omics priorority, etc., were also used to direct identifying the potential IDs. It has been proved that DRAMS performs very well in both simulation and PsychENCODE BrainGVEX multi-omics data.

bioinformatics↗

bioRxiv2017

Positional effects revealed in Illumina Methylation Array and the impact on analysis

With the evolution of rapid epigenetic research, Illumina Infinium HumanMethylation BeadChips have been widely used to study DNA methylation. However, in evaluating the accuracy of this method, we found that the commonly used Illumina HumanMethylation BeadChips are substantially affected by positional effects; the DNA samples location in a chip affects the measured methylation levels. We analyzed three HumanMethylation450 and three HumanMethylation27 datasets by using four methods to prove the existence of positional effects. Three datasets were analyzed further for technical replicate analysis or differential methylation CpG sites analysis. The pre- and post-correction comparisons indicate that the positional effects could alter the measured methylation values and downstream analysis results. Nevertheless, ComBat, linear regression and functional normalization could all be used to minimize such artifact. We recommend performing ComBat to correct positional effects followed by the correction of batch effects in data preprocessing as this procedure slightly outperforms the others. In addition, randomizing the sample placement should be a critical laboratory practice for using such experimental platforms. Code for our method is freely available at: https://github.com/ChuanJ/posibatch.

bioinformatics↗