Fine-Mapping Genome-Wide Associated Loci using Multi-omics Data to Identify Mechanisms Affecting Serious Mental Illness

使用多组学数据精细绘制全基因组相关基因座，以确定影响严重精神疾病的机制

• 批准号: 10322735
• 负责人: BERNIE DEVLIN
• 金额: $ 67.57万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2024-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10322735
• 关键词: Adult; Affect; Anterior; Autopsy; Binding; Biological; Brain; Brain region; Bypass; Cell model; Cells; Code; Communities; DNA; Data; Data Set; Development; Disease; Dorsal; Elements; Genes; Genetic Risk; Genetic Transcription; Genetic Variation; Genome Mappings; Genomics; Goals; Human; Individual; Investigation; Knowledge; Lateral; Link; Maps; Mass Spectrum Analysis; Measures; Mental disorders; Messenger RNA; Methods; Modeling; Multiomic Data; Neurons; Pathology; Pathway interactions; Pattern; Phosphorylation; Post-Translational Protein Processing; Prefrontal Cortex; Protein Analysis; Protein Isoforms; Proteins; Proteome; Proteomics; Quantitative Trait Loci; RNA; Regulation; Resources; Ribosomes; Risk; Risk Factors; SNP genotyping; Sample Size; Set protein; Single Nucleotide Polymorphism; Site; Specificity; Testing; Tissues; Transcript; Translations; Ubiquitination; Untranslated RNA; Variant; base; brain tissue; causal variant; cell type; cingulate cortex; cohort; disorder risk; experimental study; genome wide association study; genome-wide; glycosylation; insight; learning strategy; multiple omics; novel; protein expression; proteogenomics; relating to nervous system; risk variant; severe mental illness; tool; transcriptome; transcriptome sequencing; transcriptomics

Genome-wide association studies have been key for identifying genetic variation associated with psychiatric disorders. Whenever these GWAS are based on large sample sizes, however, they implicate a plethora of single nucleotide polymorphisms (SNPs) in risk. This polygenicity presents challenges for mapping risk variation onto the biological mechanisms that predispose individuals to illness. Many studies have integrated genomic and transcriptomic variation with the goal of colocalizing the GWAS SNP associations and cis transcriptional patterns determined by expression quantitative trait loci (eQTLs), as well as other QTLs. In some instances, these studies highlight one or more genes whose transcriptomic variation is driven largely by variation in specific risk SNPs. For a substantial fraction of the risk loci, however, colocalization is inconsistent across studies or no effect on transcription is observed. These missing links between genetic risk variation and biological variation could be due to many factors, including cell-type specificity, developmental patterns, or missing -omics characterizations. Notably, bulk tissue and even single cell mRNA levels are imperfect predictors of the cellular levels of the proteins they code for. We hypothesize that a substantial portion of these missing links is due to our limited knowledge of how proteomic variation relates to genetic variation in the human brain. SNPs can regulate the proteome via mechanisms that “skip” transcript levels and protein levels are tightly regulated by posttranslational modifications (PTMs) that are not readily predictable from the transcriptome. We propose to characterize transcriptomic and proteomic variation in human post-mortem brain, specifically protein expression (Aim 1); PTMs (Aim 2); map genetic variation onto transcriptomic (eQTLs) and proteome and PTM variation (pQTLs and PTMQTLs) and evaluate their interrelationships (Aim 3); and then perform colocalization analysis to inform the biological pathways by which genetic variation confers risk to psychiatric disorders (Aim 4). In our preliminary proteogenomic experiments, we combined proteomics with SNP genotyping to identify pQTLs. We discovered that a substantial fraction of pQTLs bypass the transcriptome (~50%), in line with another recent human brain pQTL study and our hypothesis. Our aims are consistent with goals from RFA-MH-21-100: (1) develop novel proteomic and other omics resources; (2) use them to map how genetic risk variation influences omics features in neural tissue and cell types; and (3) provide a high confidence set of causal variants, genes, and isoforms that likely contribute to disease risk, enhancing our insights into proximate disease mechanisms.

全基因组关联研究一直是鉴定遗传变异相关的关键 患有精神疾病。但是，每当这些GWA基于大型样本量时， 它们暗示了大量的单一核苷酸多态性（SNP）的风险。这种多基因 提出了将风险变化映射到易感生物学机制上的挑战 患病的人。许多研究已将基因组和转录组变异与 将GWAS SNP关联和顺式转录模式共定位的目标 表达定量性状位置（EQTL）以及其他QTL。在某些情况下，这些研究 突出显示一个或多个基因的转录组变异主要由特定的变化驱动 风险SNP。但是，对于大部分风险基因座，共定位在整个环境中并不一致 研究或未观察到对转录的影响。这些遗传风险变化之间的缺少联系 生物学变异可能是由于许多因素，包括细胞类型的特异性，发育 模式或丢失的 - 组字符。值得注意的是，散装组织甚至单细胞mRNA 水平是其代码蛋白质的细胞水平的不完善预测指标。我们假设 这些缺失链接的很大一部分是由于我们对蛋白质组学的了解有限 与人脑遗传变异的变异关系。 SNP可以通过 “跳过”转录水平和蛋白质水平的机制受到翻译后严格调节 从转录组不容易预测的修改（PTM）。 我们建议表征人类后人类的转录组和蛋白质组学变异 大脑，特别是蛋白质表达（AIM 1）； PTMS（AIM 2）；将遗传变异映射到 转录组（EQTL），蛋白质组和PTM变异（PQTL和PTMQTL），并评估它们的 相互关系（目标3）；然后进行共定位分析以告知生物学 遗传变异承认精神疾病风险的途径（AIM 4）。在我们的初步中 蛋白质实验，我们将蛋白质组学与SNP基因分型相结合以鉴定PQTL。 我们发现，很大一部分PQTL绕过转录组（〜50％），与 人类脑PQTL的另一项最近研究和我们的假设。 我们的目标与RFA-MH-21-100的目标一致：（1）开发新颖的蛋白质组学 和其他OMICS资源； （2）用它们来绘制遗传风险变化的影响 神经组织和细胞类型中的法术特征； （3）提供了高信心集 可能导致疾病风险的因果变异，基因和同工型，增强我们 对近端疾病机制的见解。