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.

全基因组关联研究是识别相关遗传变异的关键 然而，每当这些 GWAS 是基于大样本量时， 它们意味着存在过多的单核苷酸多态性（SNP）风险。 将风险变化映射到易患疾病的生物机制上提出了挑战 许多研究将基因组和转录组变异与疾病进行了整合。 共定位 GWAS SNP 关联和顺式转录模式的目标 在某些情况下，这些研究还包括表达数量性状基因座 (eQTL) 以及其他 QTL。 突出显示一个或多个基因，其转录组变异主要由特定基因的变异驱动 然而，对于大部分风险位点来说，共定位是不一致的。 研究或没有观察到遗传风险变异之间缺失的联系。 生物变异可能是由于许多因素造成的，包括细胞类型特异性、发育 模式，或缺失组学特征，尤其是大量组织甚至单细胞 mRNA。 水平并不能完美地预测它们所编码的蛋白质的细胞水平。 这些缺失环节的很大一部分是由于我们对蛋白质组如何运作的了解有限 变异与人类大脑中的遗传变异有关，SNP 可以通过调节蛋白质组。 “跳过”转录水平和蛋白质水平的机制受到翻译后严格调节 不易从转录组中预测的修饰（PTM）。 我们建议表征人类死后的转录组和蛋白质组变异 大脑，特别是蛋白质表达（目标 1）；将遗传变异映射到 转录组 (eQTL) 和蛋白质组和 PTM 变异 (pQTL 和 PTMQTL) 并评估它们 相互关系（目标 3）；然后进行共定位分析以告知生物学 遗传变异导致精神疾病风险的途径（目标 4）。 蛋白质组学实验中，我们将蛋白质组学与 SNP 基因分型相结合来鉴定 pQTL。 我们发现很大一部分 pQTL 绕过转录组（约 50%），与 最近的另一项人脑 pQTL 研究和我们的假设。 我们的目标与 RFA-MH-21-100 的目标一致：(1) 开发新型蛋白质组学 和其他组学资源；（2）利用它们来绘制遗传风险变异如何影响的图谱 神经组织和细胞类型的组学特征；(3) 提供一组高置信度的组学特征； 可能导致疾病风险的因果变异、基因和亚型，增强我们的 对直接疾病机制的见解。