Department of Psychological Sciences, University of Missouri, Columbia, MO, USA.
Departments of Neurology and Genetics, University of North Carolina, Chapel Hill, NC, USA.
Addict Biol. 2018 Jan;23(1):461-473. doi: 10.1111/adb.12489. Epub 2017 Jan 23.
Recent advances in genome wide sequencing techniques and analytical methods allow for more comprehensive examinations of the genome than microarray-based genome-wide association studies (GWAS). The present report provides the first application of whole genome sequencing (WGS) to identify low frequency variants involved in cannabis dependence across two independent cohorts. The present study used low-coverage whole genome sequence data to conduct set-based association and enrichment analyses of low frequency variation in protein-coding regions as well as regulatory regions in relation to cannabis dependence. Two cohorts were studied: a population-based Native American tribal community consisting of 697 participants nested within large multi-generational pedigrees and a family-based sample of 1832 predominantly European ancestry participants largely nested within nuclear families. Participants in both samples were assessed for Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) lifetime cannabis dependence, with 168 and 241 participants receiving a positive diagnosis in each sample, respectively. Sequence kernel association tests identified one protein-coding region, C1orf110 and one regulatory region in the MEF2B gene that achieved significance in a meta-analysis of both samples. A regulatory region within the PCCB gene, a gene previously associated with schizophrenia, exhibited a suggestive association. Finally, a significant enrichment of regions within or near genes with multiple splice variants or involved in cell adhesion or potassium channel activity were associated with cannabis dependence. This initial study demonstrates the potential utility of low pass whole genome sequencing for identifying genetic variants involved in the etiology of cannabis use disorders.
近年来，基因组测序技术和分析方法的进步使得人们能够比基于微阵列的全基因组关联研究（GWAS）更全面地研究基因组。本报告首次应用全基因组测序（WGS）来鉴定与大麻依赖相关的低频变异体，该研究涉及两个独立的队列。本研究使用低覆盖率全基因组序列数据，对蛋白质编码区域和与大麻依赖相关的调控区域的低频变异进行基于集合的关联和富集分析。研究了两个队列：一个是由 697 名参与者组成的基于人群的美国原住民部落社区，这些参与者嵌套在大型多代系谱中；另一个是基于家族的 1832 名主要为欧洲血统的参与者样本，这些参与者大部分嵌套在核心家庭中。两个样本中的参与者都接受了《精神障碍诊断与统计手册》第四版（DSM-IV）的终生大麻依赖评估，每个样本中有 168 名和 241 名参与者被诊断为阳性。序列核关联测试确定了一个蛋白质编码区域 C1orf110 和一个 MEF2B 基因中的调控区域，这两个区域在两个样本的荟萃分析中都达到了显著水平。PCCB 基因内的一个调控区域，该基因先前与精神分裂症有关，表现出了暗示性的关联。最后，与大麻依赖相关的基因内或附近的区域显著富集了具有多个剪接变体或参与细胞黏附或钾通道活性的基因。这项初步研究表明，低通全基因组测序在鉴定大麻使用障碍病因相关的遗传变异方面具有潜在的应用价值。
