Fine mapping rheumatic disease variants using functional genomic sequencing

使用功能基因组测序精细绘制风湿病变异图谱

• 批准号: 10115944
• 负责人: Chun Jimmie Ye
• 金额: $ 13.27万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-05-01 至 2022-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10115944
• 关键词: ATAC-seq; Accounting; Address; Alleles; Antiviral Response; Autoimmune Process; Awareness; Bayesian Method; Biological; Biological Assay; Biological Models; Biology; CD4 Positive T Lymphocytes; CRISPR/Cas technology; Cell Line; Cell model; Cells; Cellular biology; Chromatin; Clustered Regularly Interspaced Short Palindromic Repeats; Computer Models; Computer software; Computing Methodologies; DNA; Data; Data Set; Dendritic Cells; Development; Disease; Emerging Technologies; Enhancers; Etiology; European; Genes; Genetic; Genetic Determinism; Genome; Genome engineering; IL2RA gene; Individual; Intervention; Knock-in; Knock-out; Lead; Linkage Disequilibrium; Maps; Methods; Modeling; Molecular; Molecular Mechanisms of Action; Molecular Profiling; Outcome; Pathway interactions; Phase; Phenotype; Process; Public Health; Publishing; Quantitative Trait Loci; RNA Splicing; Reporter; Resolution; Rheumatism; Rheumatoid Arthritis; Scheme; Science; Signal Transduction; Single Nucleotide Polymorphism; Sjogren' s Syndrome; System; Systemic Lupus Erythematosus; T cell differentiation; T-Cell Activation; T-Lymphocyte; Testing; Untranslated RNA; Validation; Variant; Writing; XCL1 gene; base; candidate validation; causal variant; cell type; computerized tools; disease-causing mutation; functional genomics; genome editing; genome wide association study; genomic data; improved; programs; synthetic biology; synthetic construct; trait; transcriptomics

PROJECT SUMMARY ABSTRACT Here, we propose to develop a two-step computational strategy to improve the power and resolution of identifying non-coding variants causal for autoimmune rheumatic disease by integrating functional genomic data. The computational methods developed here address an important problem in disease biology: pinpointing the precise disease-causing mutations implicated by genome-wide association studies (GWAS) and understanding the biological mechanisms by which they act. We will develop our program using activated CD4+ T cells as a model system because of their relevance to autoimmune rheumatic disease, the availability of functional genomic data, and the ability to experimentally manipulate primary T cells and related cell lines. The three overlapping aims are: 1. Leveraging allele-specific reads to increase the power of detecting functional genomic quantitative trait loci (fgQTLs). We will (i) develop an approach to accurately quantify allele-specific reads from functional genomic sequencing data while accounting for sequencing and mapping biases, (ii) develop a linear mixed model (LMM) method to perform phase-aware association tests for functional genomic traits, and (iii) apply the method to identify expression and chromatin accessibility QTLs in activated CD4+ T cells in ~100 individuals. 2. Nominate causal non-coding variants in autoimmune rheumatic disease-associated loci. We will (i) develop a method that leverages functional genomic QTLs to fine map disease-causing variants in a locus, (ii) apply the method to integrate expression and chromatin accessibility QTLs from Aim 1 with three autoimmune rheumatic disease GWAS datasets to identify disease-causing variants most likely associated with CD4+ T cell activation, (iii) computationally refine and annotate causal variants using orthogonal functional genomic data in CD4+ T cells. 3. Validate predictions using synthetic biology and genome engineering. We will (i) use massively parallel reporter assays (MPRAs) to test in activated Jurkats, ~500 synthetic constructs harboring predicted causal variants from Aims 1 and 2 prioritized for GWAS loci, and use CRISPR/Cas9 to (ii) knock out 25 enhancers harboring causal variants (a subset of the MPRA hits) in Jurkats and CD4+ primary T cells and (iii) knock-in 10 predicted causal variants in CD4+ primary T cells. We will observe the endogenous effects of genome edits by profiling molecular and cellular phenotypes during CD4+ T cell activation and differentiation.

项目摘要 在这里，我们建议开发一种两步计算策略来提高计算能力和分辨率 通过整合功能基因组鉴定导致自身免疫性风湿病的非编码变体 数据这里开发的计算方法解决了疾病生物学中的一个重要问题： 精确定位全基因组关联研究（GWAS）所涉及的致病突变 并了解它们的生物学机制。我们将使用激活的 由于CD 4 + T细胞与自身免疫性风湿病的相关性， 功能基因组数据，以及实验操作原代T细胞和相关细胞系的能力。 这三个相互重叠的目标是： 1.利用等位基因特异性读段来增加检测功能性基因组定量的能力 性状基因座（fgQTL）。我们将（i）开发一种方法来准确地定量来自功能基因组的等位基因特异性读段， 基因组测序数据，同时考虑测序和作图偏差，（ii）开发线性混合 模型（LMM）方法来执行功能基因组性状的相位感知关联测试，以及（iii）应用 方法鉴定约100个个体中活化的CD 4 + T细胞中的表达和染色质可及性QTL。 2.命名自身免疫性风湿病相关基因座中的非编码变异。我们将（一） 开发利用功能基因组QTL来精细定位基因座中的致病变体的方法，（ii） 应用该方法整合来自Aim 1的表达和染色质可及性QTL， 风湿性疾病GWAS数据集，以确定最可能与CD 4 + T细胞相关的致病变体 激活，（iii）使用正交功能基因组数据在计算上精炼和注释因果变体， CD 4 + T淋巴细胞。 3.利用合成生物学和基因组工程进行预测。我们将（i）大量使用 平行报告基因测定（MPRA），以测试活化的Jurkats，约500个合成的构建体， 来自GWAS基因座优先化的目标1和2的致病变体，并使用CRISPR/Cas9来（ii）敲除25 在Jurkats和CD 4+原代T细胞中携带因果变体（MPRA命中的子集）的增强子，和（iii） 在CD 4+原代T细胞中敲入10个预测的因果变体。我们将观察的内源性影响， 通过分析CD 4 + T细胞活化和分化过程中的分子和细胞表型进行基因组编辑。

项目成果

Single-cell RNA-sequencing of peripheral blood mononuclear cells reveals widespread, context-specific gene expression regulation upon pathogenic exposure.

• DOI: 10.1038/s41467-022-30893-5
• 发表时间: 2022-06-07
• 期刊: Nature communications
• 影响因子: 16.6

Genetic analysis of isoform usage in the human anti-viral response reveals influenza-specific regulation of ERAP2 transcripts under balancing selection.

• DOI: 10.1101/gr.240390.118
• 发表时间: 2018-12
• 期刊: Genome research
• 影响因子: 7
• 作者: Ye CJ;Chen J;Villani AC;Gate RE;Subramaniam M;Bhangale T;Lee MN;Raj T;Raychowdhury R;Li W;Rogel N;Simmons S;Imboywa SH;Chipendo PI;McCabe C;Lee MH;Frohlich IY;Stranger BE;De Jager PL;Regev A;Behrens T;Hacohen N
• 通讯作者: Hacohen N

Combined Single Cell Transcriptome and Surface Epitope Profiling Identifies Potential Biomarkers of Psoriatic Arthritis and Facilitates Diagnosis via Machine Learning.

• DOI: 10.3389/fimmu.2022.835760
• 发表时间: 2022
• 期刊: Frontiers in immunology
• 影响因子: 7.3
• 作者: Liu J;Kumar S;Hong J;Huang ZM;Paez D;Castillo M;Calvo M;Chang HW;Cummins DD;Chung M;Yeroushalmi S;Bartholomew E;Hakimi M;Ye CJ;Bhutani T;Matloubian M;Gensler LS;Liao W
• 通讯作者: Liao W

Genetic determinants of co-accessible chromatin regions in activated T cells across humans.

• DOI: 10.1038/s41588-018-0156-2
• 发表时间: 2018-08
• 期刊: Nature genetics
• 影响因子: 30.8
• 作者: Gate RE;Cheng CS;Aiden AP;Siba A;Tabaka M;Lituiev D;Machol I;Gordon MG;Subramaniam M;Shamim M;Hougen KL;Wortman I;Huang SC;Durand NC;Feng T;De Jager PL;Chang HY;Aiden EL;Benoist C;Beer MA;Ye CJ;Regev A
• 通讯作者: Regev A

Multiplexed droplet single-cell RNA-sequencing using natural genetic variation.

使用自然遗传变异的多重液滴单细胞RNA序列。

• DOI: 10.1038/nbt.4042
• 发表时间: 2018-01
• 期刊: Nature biotechnology
• 影响因子: 46.9
• 作者: Kang HM;Subramaniam M;Targ S;Nguyen M;Maliskova L;McCarthy E;Wan E;Wong S;Byrnes L;Lanata CM;Gate RE;Mostafavi S;Marson A;Zaitlen N;Criswell LA;Ye CJ
• 通讯作者: Ye CJ