Deciphering the Relationship between Substance Use and Psychiatric Disorders from Whole Genome Sequencing Data

摘要 物质使用障碍与精神疾病（包括双相情感障碍）有很大程度的共病性 失调症、脑脊髓炎和抑郁症。理解物质使用的遗传基础的一个关键挑战 在遗传水平上理解它与精神疾病的关系。全基因组 物质使用和精神疾病个体的测序数据有可能提供 关于他们之间关系的大量信息。然而，有效地解释这些变体， 数据，特别是在人类基因组的巨大非编码区，将需要新的计算 更好地注释人类基因组的方法。我们将开发几种方法来产生更多的 用于解释这种全基因组测序数据的人类基因组的相关注释。一个限制 现有的基于表观基因组的基因组注释对于来自大脑区域的相关样本的重要性在于， 是由多种细胞类型的复杂混合物衍生而来的。我们将定义表观基因组注释，如染色质状态 通过在框架中对基于群体的ChIP-seq数据进行去卷积， 这项工作整合了单细胞RNA-seq数据和Hi-C或其他将远端区域与 基因.我们还将开发更好地绘制与物质使用高度相关的表观基因组数据的方法 通过一种新的方法，从模型生物到人类的疾病， 从现有的表观基因组数据的概要活性。我们还将制定方法， 高通量功能测试功能重要位置和变体的全基因组预测 即使在未使用表观基因组特征和特别构建的序列特征测试的细胞类型中， 在细胞类型之间进行泛化。通过合作，这里产生的基因组注释将应用于 分析多个全基因组测序数据集的个人与物质使用障碍，精神 疾病，或两者。我们将把注释类标识为与 物质使用障碍，精神障碍，或它们之间的联合，以获得生物学的见解， 疾病之间的生物学关系。开发的所有计算方法和基因组注释 制作的将广泛传播。

Abstract Substance use disorders have a large degree of co-morbidity with psychiatric disorders including bipolar disorder, schizopherina, and depression. A key challenge to understanding the genetic basis of substance use disorders is to understand at a genetic level its relationship with psychiatric disorders. Whole genome sequencing data of individuals with substance use and psychiatric disorders has the potential to provide extensive information on the relationship between them. However effectively interpreting variants from such data particularly in the vast non-coding regions of the human genome will require novel computational approaches to better annotate the human genome. We will develop several approaches to produce a more relevant annotation of the human genome for interpreting such whole genome sequencing data. One limitation of existing epigenome based annotations of the genome for relevant samples from brain regions is they are derived from a complex mixture of cell types. We will define epigenome annotations such as chromatin states computationally at a single cell type level by deconvoluting population based ChIP-seq data in a framework work that integrates single cell RNA-seq data and Hi-C or other information associating distal regions with genes. We will also develop approaches to better map highly relevant epigenomic data on substance use disorders from model organisms to human through a novel approach that learns a mapping based on common activity from a compendium of existing epigenomic data. We will also develop approaches that will learn from high-throughput functional testing genomewide predictions of the functionally important positions and variants even in cell types not tested using epigenomic features and specially constructed sequence features that will generalize across cell types. Through collaborations the genome annotations produced here will be applied to analyze multiple whole genome sequencing data sets of individuals with substance use disorders, psychiatric disorders, or both. We will identify annotation classes as being associated specifically with variants of substance use disorders, psychiatric disorders, or jointly between them to gain biological insights into the biological relationship between the disorders. All computational methods developed and genome annotations produced will be broadly disseminated.