pathQTL: Integrative Multi-Omics Causal Inference of Molecular Mechanisms Leading to Neuropsychiatric Illness

pathQTL：导致神经精神疾病的分子机制的综合多组学因果推断

• 批准号: 10550143
• 负责人: Michael Isaiah Love
• 金额: $ 47.18万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-12-10 至 2023-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10550143
• 关键词: Adult; Affect; Alzheimer' s Disease; Bayesian Modeling; Bioconductor; Biological; Biology; Brain; Brain region; CRISPR-mediated transcriptional activation; Cells; Chromatin; Complex; Computer Models; Computer software; Computing Methodologies; Data; Data Set; Development; Disease; Engineering; Epigenetic Process; Gene Expression; Genes; Genetic; Genetic Variation; Genome; Genotype; Goals; Human; Human Development; Individual; Link; Love; Major Depressive Disorder; Measures; Mediating; Modeling; Molecular; Morphology; Multiomic Data; Neurons; Nonsense Mutation; Pathogenesis; Pathway interactions; Phenotype; Probability; Productivity; Quantitative Trait Loci; Regulator Genes; Regulatory Element; Regulatory Pathway; Risk; Schizophrenia; Statistical Distributions; Stream; Structure; System; Testing; Therapeutic Effect; Time; Update; Validation; Variant; Viral; Visualization; Work; brain abnormalities; causal model; cell type; cost; disorder risk; epigenome; experimental study; fetal; genetic association; genetic variant; genome wide association study; genomic locus; histone modification; inhibitor; multiple datasets; multiple omics; nerve stem cell; neurogenetics; neuropsychiatric disorder; neuropsychiatry; novel; open source; overexpression; risk variant; skills; statistics; success; therapeutic target; tool; trait; transcriptome; user-friendly; web interface

Project Summary A multitude of common genetic variants influencing risk for neuropsychiatric disorders (e.g., schizophrenia, major depressive disorder, and Alzheimer’s disease) have recently been identified and replicated, providing a foothold into the causes of these disorders. The critical next step in neuropsychiatric genetics is to move from a risk locus in the genome to an understanding of how this genetic variation influences molecules, cells, and circuits of the brain, leading to complex disorders. Many datasets, including those generated by our own labs, have established direct links between genotype and human brain traits at multiple levels of biology (molecular: chromatin accessibility, expression; cellular: morphology; circuit: gross brain structure), termed quantitative trait loci (QTLs). Here, we will integrate QTLs across multiple levels of biology in order to statistically prioritize causal pathways by which genetic variation creates risk for complex neuropsychiatric disorders. Causal modeling goes well beyond previous co-localization work, as it allows the prioritization of expensive functional validation experiments for cellular or molecular changes that are a cause of the disorder, rather than those that are a consequence or independent of the disorder. It additionally allows inference of key experimental parameters including cell-type and developmental time period. Finally, causal inference when combined across multiple levels of biology and multiple disorder risk loci allows for assessment of convergence at a biological level, cell-type, or developmental time period, which is critical information for therapeutic targeting. We will leverage the computational and statistical frameworks of Bayesian probabilistic networks and causal inference in a new framework that utilizes association summary statistics, as well-powered multi-level data collected on the same individuals is almost always infeasible. Subsequently, we will experimentally validate the molecular predictions of our model using epigenetic engineering in primary human neural progenitor cells, and in turn revising the computational models. Prioritizing causal molecular pathways of disorder associated variants, and identifying the relevant cell-type and developmental stage will increase the success rate of validation experiments and shed light on mechanisms of neuropsychiatric disorders in an unbiased manner.

项目概要 许多常见的遗传变异会影响神经精神疾病的风险（例如精神分裂症、 重度抑郁症和阿尔茨海默氏病）最近已被识别并复制，提供了 立足于这些疾病的原因。神经精神遗传学的下一步关键是从 基因组中的风险位点，以了解这种遗传变异如何影响分子、细胞和 大脑回路，导致复杂的疾病。许多数据集，包括我们自己的实验室生成的数据集， 在生物学的多个层面（分子： 染色质可及性、表达；细胞：形态学；电路：大体大脑结构），称为数量性状 位点（QTL）。在这里，我们将整合生物学多个层面的 QTL，以便在统计上优先排序 遗传变异造成复杂神经精神疾病风险的因果途径。因果关系 建模远远超出了以前的共定位工作，因为它允许优先考虑昂贵的功能 验证实验是针对导致疾病的细胞或分子变化，而不是那些导致疾病的细胞或分子变化。 是疾病的后果或独立于疾病。它还允许推断关键实验 参数包括细胞类型和发育时间段。最后，结合起来进行因果推断 多个生物学水平和多个疾病风险位点允许评估生物学的收敛性 水平、细胞类型或发育时间段，这是治疗靶向的关键信息。我们将 利用贝叶斯概率网络和因果推理的计算和统计框架 在一个新的框架中，利用关联汇总统计数据以及收集的有力的多级数据 同一个人几乎总是不可行的。随后，我们将通过分子实验验证 我们的模型在人类原代神经祖细胞中使用表观遗传工程进行预测，进而 修改计算模型。优先考虑疾病相关变异的因果分子途径，以及 识别相关的细胞类型和发育阶段将提高验证的成功率 实验并以公正的方式揭示神经精神疾病的机制。

项目成果

The Evolutionary History of Common Genetic Variants Influencing Human Cortical Surface Area.

• DOI: 10.1093/cercor/bhaa327
• 发表时间: 2021-03-05
• 期刊: Cerebral cortex (New York, N.Y. : 1991)
• 作者: Tilot AK;Khramtsova EA;Liang D;Grasby KL;Jahanshad N;Painter J;Colodro-Conde L;Bralten J;Hibar DP;Lind PA;Liu S;Brotman SM;Thompson PM;Medland SE;Macciardi F;Stranger BE;Davis LK;Fisher SE;Stein JL
• 通讯作者: Stein JL

Chromatin loop dynamics during cellular differentiation are associated with changes to both anchor and internal regulatory features.

• DOI: 10.1101/gr.277397.122
• 发表时间: 2023-08
• 期刊: GENOME RESEARCH
• 影响因子: 7
• 作者: Bond, Marielle L.;Davis, Eric S.;Quiroga, Ivana Y.;Dey, Anubha;Kiran, Manjari;Love, Michael I.;Won, Hyejung;Phanstiel, Douglas H.
• 通讯作者: Phanstiel, Douglas H.

Genetic variants associated with longitudinal changes in brain structure across the lifespan.

• DOI: 10.1038/s41593-022-01042-4
• 发表时间: 2022-04
• 期刊: NATURE NEUROSCIENCE
• 影响因子: 25
• 作者: Brouwer, Rachel M.;Klein, Marieke;Grasby, Katrina L.;Schnack, Hugo G.;Jahanshad, Neda;Teeuw, Jalmar;Thomopoulos, Sophia I.;Sprooten, Emma;Franz, Carol E.;Gogtay, Nitin;Kremen, William S.;Panizzon, Matthew S.;Olde Loohuis, Loes M.;Whelan, Christopher D.;Aghajani, Moji;Alloza, Clara;Alanaes, Dag;Artiges, Eric;Ayesa-Arriola, Rosa;Barker, Gareth J.;Bastin, Mark E.;Blok, Elisabet;Boen, Erlend;Breukelaar, Isabella A.;Bright, Joanna K.;Buimer, Elizabeth E. L.;Bulow, Robin;Cannon, Dara M.;Ciufolini, Simone;Crossley, Nicolas A.;Damatac, Christienne G.;Dazzan, Paola;de Mol, Casper L.;de Zwarte, Sonja M. C.;Desrivieres, Sylvane;Diaz-Caneja, Covadonga M.;Doan, Nhat Trung;Dohm, Katharina;Froehner, Juliane H.;Goltermann, Janik;Grigis, Antoine;Grotegerd, Dominik;Han, Laura K. M.;Harris, Mathew A.;Hartman, Catharina A.;Heany, Sarah J.;Heindel, Walter;Heslenfeld, Dirk J.;Hohmann, Sarah;Ittermann, Bernd;Jansen, Philip R.;Janssen, Joost;Jia, Tianye;Jiang, Jiyang;Jockwitz, Christiane;Karali, Temmuz;Keeser, Daniel;Koevoets, Martijn G. J. C.;Lenroot, Rhoshel K.;Malchow, Berend;Mandl, Rene C. W.;Medel, Vicente;Meinert, Susanne;Morgan, Catherine A.;Muehleisen, Thomas W.;Nabulsi, Leila;Opel, Nils;de la Foz, Victor Ortiz-Garcia;Overs, Bronwyn J.;Paillere Martinot, Marie-Laure;Redlich, Ronny;Marques, Tiago Reis;Repple, Jonathan;Roberts, Gloria;Roshchupkin, Gennady V.;Setiaman, Nikita;Shumskaya, Elena;Stein, Frederike;Sudre, Gustavo;Takahashi, Shun;Thalamuthu, Anbupalam;Tordesillas-Gutierrez, Diana;van der Lugt, Aad;van Haren, Neeltje E. M.;Wardlaw, Joanna M.;Wen, Wei;Westeneng, Henk-Jan;Wittfeld, Katharina;Zhu, Alyssa H.;Zugman, Andre;Armstrong, Nicola J.;Bonfiglio, Gaia;Bralten, Janita;Dalvie, Shareefa;Davies, Gail;Di Forti, Marta;Ding, Linda;Donohoe, Gary;Forstner, Andreas J.;Gonzalez-Penas, Javier;Guimaraes, Joao P. O. F. T.;Homuth, Georg;Hottenga, Jouke-Jan;Knol, Maria J.;Kwok, John B. J.;Le Hellard, Stephanie;Mather, Karen A.;Milaneschi, Yuri;Morris, Derek W.;Noethen, Markus M.;Papiol, Sergi;Rietschel, Marcella;Santoro, Marcos L.;Steen, Vidar M.;Stein, Jason L.;Streit, Fabian;Tankard, Rick M.;Teumer, Alexander;van 't Ent, Dennis;van der Meer, Dennis;van Eijk, Kristel R.;Vassos, Evangelos;Vazquez-Bourgon, Javier;Witt, Stephanie H.;Adams, Hieab H. H.;Agartz, Ingrid;Ames, David;Amunts, Katrin;Andreassen, Ole A.;Arango, Celso;Banaschewski, Tobias;Baune, Bernhard T.;Belangero, Sintia I.;Bokde, Arun L. W.;Boomsma, Dorret I.;Bressan, Rodrigo A.;Brodaty, Henry;Buitelaar, Jan K.;Cahn, Wiepke;Caspers, Svenja;Cichon, Sven;Crespo-Facorro, Benedicto;Cox, Simon R.;Dannlowski, Udo;Elvsashagen, Torbjorn;Espeseth, Thomas;Falkai, Peter G.;Fisher, Simon E.;Flor, Herta;Fullerton, Janice M.;Garavan, Hugh;Gowland, Penny A.;Grabe, Hans J.;Hahn, Tim;Heinz, Andreas;Hillegers, Manon;Hoare, Jacqueline;Hoekstra, Pieter J.;Ikram, Mohammad A.;Jackowski, Andrea P.;Jansen, Andreas;Jonsson, Erik G.;Kahn, Rene S.;Kircher, Tilo;Korgaonkar, Mayuresh S.;Krug, Axel;Lemaitre, Herve;Malt, Ulrik F.;Martinot, Jean-Luc;McDonald, Colm;Mitchell, Philip B.;Muetzel, Ryan L.;Murray, Robin M.;Nees, Frauke;Nenadic, Igor;Oosterlaan, Jaap;Ophoff, Roel A.;Pan, Pedro M.;Penninx, Brenda W. J. H.;Poustka, Luise;Sachdev, Perminder S.;Salum, Giovanni A.;Schofield, Peter R.;Schumann, Gunter;Shaw, Philip;Sim, Kang;Smolka, Michael N.;Stein, Dan J.;Trollor, Julian N.;van den Berg, Leonard H.;Veldink, Jan H.;Walter, Henrik;Westlye, Lars T.;Whelan, Robert;White, Tonya;Wright, Margaret J.;Medland, Sarah E.;Franke, Barbara;Thompson, Paul M.;Hulshoff Pol, Hilleke E.
• 通讯作者: Hulshoff Pol, Hilleke E.

MRLocus: Identifying causal genes mediating a trait through Bayesian estimation of allelic heterogeneity.

• DOI: 10.1371/journal.pgen.1009455
• 发表时间: 2021-04
• 期刊: PLoS genetics
• 影响因子: 4.5
• 作者: Zhu A;Matoba N;Wilson EP;Tapia AL;Li Y;Ibrahim JG;Stein JL;Love MI
• 通讯作者: Love MI