Biology subjects

Zeng, B.

Publications and source records attributed to Zeng, B..

3 recordsLinked to original sources

bioRxiv2018

Unraveling the polygenic architecture of complex traits using blood eQTL meta-analysis

SummaryWhile many disease-associated variants have been identified through genome-wide association studies, their downstream molecular consequences remain unclear.\n\nTo identify these effects, we performed cis- and trans-expression quantitative trait locus (eQTL) analysis in blood from 31,684 individuals through the eQTLGen Consortium.\n\nWe observed that cis-eQTLs can be detected for 88% of the studied genes, but that they have a different genetic architecture compared to disease-associated variants, limiting our ability to use cis-eQTLs to pinpoint causal genes within susceptibility loci.\n\nIn contrast, trans-eQTLs (detected for 37% of 10,317 studied trait-associated variants) were more informative. Multiple unlinked variants, associated to the same complex trait, often converged on trans-genes that are known to play central roles in disease etiology.\n\nWe observed the same when ascertaining the effect of polygenic scores calculated for 1,263 genome-wide association study (GWAS) traits. Expression levels of 13% of the studied genes correlated with polygenic scores, and many resulting genes are known to drive these traits.

genomics↗

bioRxiv2017

Deficit in parietal memory network underlies auditory hallucination: a longitudinal study

Auditory hallucination is a prominent and common symptom in schizophrenia. Previous neuroimaging studies have yielded mixed results of its brain network deficits. We proposed a novel hypothesis that parietal memory network, centered at the precuneus, plays a critical role in auditory hallucination. This network is adjacent and partially overlaps with the default mode network, and has been associated with brain function of familiarity labelling in memory processing. Using a longitudinal design and a large cohort of first-episode, drug-naive schizophrenia patients, we examined this hypothesis and further investigated whether the functional connectivity patterns of the parietal memory network can serve as a neuroimaging marker for auditory hallucination and help to predict future treatment effects. Resting-state scans from 59 first-episode drug-naive schizophrenic patients (27 with and 32 without hallucination) and 53 healthy control subjects were acquired at the baseline test, and 56 of them were scanned again after two months. Functional connectivity strength within the parietal memory network and between this network and memory hubs was across the three groups at baseline and follow-up scans. Results showed that decreased functional connectivity strength within the parietal memory network was specific to the auditory hallucination group (p = 0.009, compare to the healthy subjects; p = 0.029, compare to the patients without hallucination), with the precuneus representing the largest group difference. The intra-network connectivity strength of the precuneus negatively correlated with the severity of hallucination at the baseline scan (r = -0.437, p = 0.029), and it was significantly increased after two-month medication (p = 0.039). Logistic regression analysis and crossvalidation test demonstrated that the functional connectivity strength of the precuneus and precuneus-hippocampus connectivity could differentiate patients with or without auditory hallucination with a sensitivity of 0.750 and a specificity of 0.708. Moreover, crossvalidation test showed that these imaging features at the baseline scan well predicted the extents of positive symptom improvement in the hallucination group after the two-month medication (R2 = 0.433, p = 0.022). Our results provide evidence for a critical role of the parietal memory network underlying auditory hallucination, and further propose a novel neuroimaging marker for identifying patients, accessing severity, and prognosis of treatment effect for auditory hallucination.\n\nAbbreviations

neuroscience↗

bioRxiv2016

Constraints on eQTL fine mapping in the presence of multi-site local regulation of gene expression

Expression QTL (eQTL) detection has emerged as an important tool for unravelling of the relationship between genetic risk factors and disease or clinical phenotypes. Most studies use single marker linear regression to discover primary signals, followed by sequential conditional modeling to detect secondary genetic variants affecting gene expression. However, this approach assumes that functional variants are sparsely distributed and that close linkage between them has little impact on estimation of their precise location and magnitude of effects. In this study, we address the prevalence of secondary signals and bias in estimation of their effects by performing multi-site linear regression on two large human cohort peripheral blood gene expression datasets (each greater than 2,500 samples) with accompanying whole genome genotypes, namely the CAGE compendium of Illumina microarray studies, and the Framingham Heart Study Affymetrix data. Stepwise conditional modeling demonstrates that multiple eQTL signals are present for ~40% of over 3500 eGenes in both datasets, and the number of loci with additional signals reduces by approximately two-thirds with each conditioning step. However, the concordance of specific signals between the two studies is only ~30%, indicating that expression profiling platform is a large source of variance in effect estimation. Furthermore, a series of simulation studies imply that in the presence of multi-site regulation, up to 10% of the secondary signals could be artefacts of incomplete tagging, and at least 5% but up to one quarter of credible intervals may not even include the causal site, which is thus mis-localized. Joint multi-site effect estimation recalibrates effect size estimates by just a small amount on average. Presumably similar conclusions apply to most types of quantitative trait. Given the strong empirical evidence that gene expression is commonly regulated by more than one variant, we conclude that the fine-mapping of causal variants needs to be adjusted for multi-site influences, as conditional estimates can be highly biased by interference among linked sites.

genetics↗