Brain expression quantitative trait locus mapping informs genetic studies of psychiatric diseases

Genome-wide association study（GWAS） can be used to identify genes that increase the risk of psychiatric diseases.However,much of the disease heritability is still unexplained,suggesting that there are genes to be discovered.Functional annotation of the genetic variants may increase the power of GWAS to identify disease genes,by providing prior information that can be used in Bayesian analysis or in reducing the number of tests.Expression quantitative trait loci（eQTLs） are genomic loci that regulate gene expression.Genetic mapping of eQTLs can help reveal novel functional effects of thousands of single nucleotide polymorphisms（SNPs）.The present review mainly focused on the current knowledge on brain eQTL mapping,and discussed some major methodological issues and their possible solutions.The frequently ignored problems of batch effects,covariates,and multiple testing were emphasized,since they can lead to false positives and false negatives.The future application of eQTL data in GWAS analysis was also discussed.

Hidden Genetic Regulation of Human Complex Traits via Brain Isoforms

Alternative splicing exists in most multi-exonic genes,and exploring these complex alternative splicing events and their resultant isoform expressions is essential.However,it has become conventional that RNA sequencing results have often been summarized into gene-level expression counts mainly due to the multiple ambiguous mapping of reads at highly similar regions.Transcript-level quantification and interpretation are often overlooked,and biological interpretations are often deduced based on combined transcript information at the gene level.Here,for the most variable tissue of alternative splicing,the brain,we estimate isoform expressions in 1,191 samples collected by the Genotype-Tissue Expression(GTEx)Consortium using a powerful method that we previously developed.We perform genome-wide association scans on the isoform ratios per gene and identify isoform-ratio quantitative trait loci(irQTL),which could not be detected by studying gene-level expressions alone.By analyzing the genetic architecture of the irQTL,we show that isoform ratios regulate edu-cational attainment via multiple tissues including the frontal cortex(BA9),cortex,cervical spinal cord,and hippocampus.These tissues are also associated with different neuro-related traits,including Alzheimer’s or dementia,mood swings,sleep duration,alcohol intake,intelligence,anxiety or depression,etc.Mendelian randomization(MR)analysis revealed 1,139 pairs of isoforms and neuro-related traits with plausible causal relationships,showing much stronger causal effects than on general diseases measured in the UK Biobank(UKB).Our results highlight essential transcript-level biomarkers in the human brain for neuro-related complex traits and diseases,which could be missed by merely investigating overall gene expressions.

Genetic variants in the 6p21.3 region influence hepatitis B virus clearance and chronic hepatitis B risk in the Han Chinese population

Background and aim:A genome-wide association study has indicated the association of numerous genes in the 6p21.3 region with chronic hepatitis B virus(HBV)infection.In this study,we screened 12 representative single-nucleotide polymorphisms(SNPs)from the 6p21.3 region and investigated their association with the risk of chronic hepatitis B(CHB)to better understand the molecular etiology un-derlying CHB risk in the Han Chinese population.Methods:Between March 2021 and November 2022,we included 183 patients with CHB(case group)and 196 with natural HBV clearance(control group).Allele typing of the selected SNPs was performed using snapshot technology.The correlation between the 12 chosen SNPs and the risk of chronic HBV infection was examined using binary logistic regression analysis.Interacting genes of the variants were identified,and expression quantitative trait loci(eQTL)were analyzed using the 3DSNP database.Results:We validated 12 previously reported CHB susceptibility sites,including rs1419881 of tran-scription factor 19(TCF19),rs3130542 and rs2853953 of human leukocyte antigen(HLA)-C,rs652888 of euchromatic histone-lysine-methyltransferase 2(EHMT2),rs2856718,rs9276370,rs7756516,and rs7453920 of HLA-DQ,rs378352 of HLA-DOA,and rs3077,rs9277535,and rs9366816 of HLA-DP.Logistic regression analyses revealed that polymorphisms such as rs9276370,rs7756516,rs7453920,rs3077,rs9277535,and rs9366816 were positively correlated with natural HBV clearance in the dominant model.Conversely,rs3130542 and rs378352 were identified as risk factors for CHB.Haplotype analysis revealed that rs9276370,rs7756516,and rs7453920 in HLA-DQ were TTG and GCA haplotypes.Although the TTG haplotype was positively correlated with a higher risk of CHB,the GCA haplotype significantly influenced the natural clearance of HBV.Bioinformatics analysis demonstrated that rs378352,rs3077,and rs9366816 were located within enhancer states;rs3077 and rs9366816 overlapped with nine tran-scription factor-binding sites,whereas rs378352 altered five sequence motifs.Furthermore,eQTL analysis demonstrated the functional tendencies of eight statistically significant SNPs(rs3130542,rs9276370,rs7756516,rs7453920,rs378352,rs3077,rs9277535,and rs9366816).Conclusions:Genetic variations within the 6p21.3 region were associated with chronic HBV infection in the Han Chinese population in southern China.Furthermore,the GCA haplotype including rs9276370,rs7756516,and rs7453920 of HLA-DQ contributed significantly to natural HBV clearance,implying that multiple SNPs exert a cumulative allelic effect on HBV infection.

Genome-wide association analysis reveals regulation of at-risk loci by DNA methylation in prostate cancer

Epigenetic changes are potentially important for the ontogeny and progression of tumors but are not usually studied because of the complexity of analyzing transcript regulation resulting from epigenetic alterations.Prostate cancer(PCa)is characterized by variable clinical manifestations and frequently unpredictable outcomes.We performed an expression quantitative trait loci(eQTL)analysis to identify the genomic regions that regulate gene expression in PCa and identified a relationship between DNA methylation and clinical information.Using multi-level information published in The Cancer Genome Atlas,we performed eQTL-based analyses on DNA methylation and gene expression.To better interpret these data,we correlated loci and clinical indexes to identify the important loci for both PCa development and progression.Our data demonstrated that although only a small proportion of genes are regulated via DNA methylation in PCa,these genes are enriched in important cancer-related groups.In addition,single nucleotide polymorphism analysis identified the locations of CpG sites and genes within at-risk loci,including the 19q13.2-q13.43 and 16q22.2-q23.1 loci.Further,an epigenetic association study of clinical indexes detected risk loci and pyrosequencing for site validation.Although DNA methylation-regulated genes across PCa samples are a small proportion,the associated genes play important roles in PCa carcinogenesis.

Genome-wide Analysis of Transcriptional Variability in a Large Maize-Teosinte Population

Gene expression regulation plays an important role in controlling plant phenotypes and adaptation. Here, we report a comprehensive assessment of gene expression variation through the transcriptome analyses of a large maize-teosinte experimental population. Genome-wide mapping identified 25 660 expression quantitative trait loci （eQTL） for 17 311 genes, capturing an unprecedented range of expression variation. We found that local eQTL were more frequently mapped to adjacent genes, displaying a mode of expression piggybacking, which consequently created co-regulated gene clusters. Genes within the co-regulated gene clusters tend to have relevant functions and shared chromatin modifications. Distant eQTL formed 125 significant distant eQTL hotspots with their targets significantly enriched in specific functional cate- gories. By integrating different sources of information, we identified putative trans- regulators for a variety of metabolic pathways. We demonstrated that the bHLH transcription factor R1 and hexokinase HEX9 might act as crucial regulators for flavonoid biosynthesis and glycolysis, respectively. Moreover, we showed that domestication or improvement has significantly affected global gene expression, with many genes targeted by selection. Of particular interest, the Bx genes for benzoxazinoid biosynthesis may have undergone coordinated cis-regulatory divergence between maize and teosinte, and a transposon insertion that inactivates Bx12 was under strong selection as maize spread into temperate environments with a distinct herbivore community.

Brain Banks Spur New Frontiers in Neuropsychiatric Research and Strategies for Analysis and Validation

Neuropsychiatric disorders affect hundreds of millions of patients and families worldwide.To decode the molecular framework of these diseases,many studies use human postmortem brain samples.These studies reveal brain-specific genetic and epigenetic patterns via highthroughput sequencing technologies.Identifying best practices for the collection of postmortem brain samples,analyzing such large amounts of sequencing data,and interpreting these results are critical to advance neuropsychiatry.We provide an overview of human brain banks worldwide,including progress in China,highlighting some well-known projects using human postmortem brain samples to understand molecular regulation in both normal brains and those with neuropsychiatric disorders.Finally,we discuss future research strategies,as well as state-of-the-art statistical and experimental methods that are drawn upon brain bank resources to improve our understanding of the agents of neuropsychiatric disorders.

Methods for Population-Based eQTL Analysis in Human Genetics

Gene expression is a critical process in biological system that is influenced and modulated by many factors including genetic variation. Expression Quantitative Trait Loci（e QTL） analysis provides a powerful way to understand how genetic variants affect gene expression. For genome wide e QTL analysis, the number of genetic variants and that of genes are large and thus the search space is tremendous. Therefore, e QTL analysis brings about computational and statistical challenges. In this paper, we provide a comprehensive review of recent advances in methods for e QTL analysis in population-based studies. We first present traditional pairwise association methods, which are widely used in human genetics. To account for expression heterogeneity, we investigate the methods for correcting confounding factors. Next, we discuss newly developed statistical learning methods including Lasso-based models. In the conclusion, we provide an overview of future method development in analyzing e QTL associations. Although we focus on human genetics in this review, the methods are applicable to many other organisms.