Abstract：
BACKGROUND: Genome-wide association studies (GWAS) have identified hundreds of common variants associated with alcohol consumption. In contrast, genetic studies of alcohol consumption that use rare variants are still in their early stages. No prior studies of alcohol consumption have examined whether common and rare variants implicate the same genes and molecular networks, leaving open the possibility that the two approaches might identify distinct biology.
METHODS: To address this knowledge gap, we used publicly available alcohol consumption GWAS summary statistics (GSCAN, N = 666,978) and whole exome sequencing data (Genebass, N = 393,099) to identify a set of common and rare variants for alcohol consumption. We used gene-based analysis to implicate genes from common and rare variant analyses, which we then propagated onto a shared molecular network using a network colocalization procedure.
RESULTS: Gene-based analysis of each dataset implicated 294 (common variants) and 35 (rare variants) genes, including ethanol metabolizing genes ADH1B and ADH1C, which were identified by both analyses, and ANKRD12, GIGYF1, KIF21B, and STK31, which were identified in only the rare variant analysis, but have been associated with other neuropsychiatric traits. Network colocalization revealed significant network overlap between the genes identified via common and rare variants. The shared network identified gene families that function in alcohol metabolism, including ADH, ALDH, CYP, and UGT. Seventy-one of the genes in the shared network were previously implicated in neuropsychiatric or substance use disorders but not alcohol-related behaviors (e.g. EXOC2, EPM2A, and CACNG4). Differential gene expression analysis showed enrichment in the liver and several brain regions.
CONCLUSIONS: Genes implicated by network colocalization identify shared biology relevant to alcohol consumption, which also underlie neuropsychiatric traits and substance use disorders that are comorbid with alcohol use, providing a more holistic understanding of two disparate sources of genetic information.
METHODS: To address this knowledge gap, we used publicly available alcohol consumption GWAS summary statistics (GSCAN, N = 666,978) and whole exome sequencing data (Genebass, N = 393,099) to identify a set of common and rare variants for alcohol consumption. We used gene-based analysis to implicate genes from common and rare variant analyses, which we then propagated onto a shared molecular network using a network colocalization procedure.
RESULTS: Gene-based analysis of each dataset implicated 294 (common variants) and 35 (rare variants) genes, including ethanol metabolizing genes ADH1B and ADH1C, which were identified by both analyses, and ANKRD12, GIGYF1, KIF21B, and STK31, which were identified in only the rare variant analysis, but have been associated with other neuropsychiatric traits. Network colocalization revealed significant network overlap between the genes identified via common and rare variants. The shared network identified gene families that function in alcohol metabolism, including ADH, ALDH, CYP, and UGT. Seventy-one of the genes in the shared network were previously implicated in neuropsychiatric or substance use disorders but not alcohol-related behaviors (e.g. EXOC2, EPM2A, and CACNG4). Differential gene expression analysis showed enrichment in the liver and several brain regions.
CONCLUSIONS: Genes implicated by network colocalization identify shared biology relevant to alcohol consumption, which also underlie neuropsychiatric traits and substance use disorders that are comorbid with alcohol use, providing a more holistic understanding of two disparate sources of genetic information.
摘要：
背景：全基因组关联研究（GWAS）已经确定了数百种与饮酒相关的常见变异。相比之下,使用罕见变异的酒精消费的遗传研究仍处于早期阶段。以前没有关于饮酒的研究检查过常见和罕见的变异是否涉及相同的基因和分子网络，留下了这两种方法可能识别不同生物学的可能性。
方法：为了解决这个知识差距，我们使用了公开可用的饮酒量GWAS汇总统计数据(GSCAN，N=666,978)和整个外显子组测序数据(Genebass，N=393,099），以确定一组常见和罕见的饮酒变体。我们使用基于基因的分析来暗示来自常见和罕见变异分析的基因，然后，我们使用网络共定位程序将其传播到共享的分子网络上。
结果：每个数据集的基于基因的分析涉及294个（常见变异）和35个（罕见变异）基因，包括乙醇代谢基因ADH1B和ADH1C，这两种分析都确定了，ANKRD12、GIGYF1、KIF21B、和STK31，仅在罕见的变异分析中鉴定，但与其他神经精神特征有关。网络共定位揭示了通过常见和罕见变异鉴定的基因之间的显著网络重叠。共享网络确定了在酒精代谢中起作用的基因家族，包括ADH,ALDH,CYP,和UGT。Seven-oneofthegenesinthesharednetworkwerepreviouslyimplicatedinneurochemicalorsubstanceusedisordersbutnotalcohol-relatedbehavior(e.e.exoC2,EPM2A,和CACNG4）。差异基因表达分析显示在肝脏和几个脑区富集。
结论：网络共定位所涉及的基因识别与饮酒相关的共享生物学，这也是与饮酒共存的神经精神特征和物质使用障碍的基础，提供对两种不同的遗传信息来源的更全面的理解。
方法：为了解决这个知识差距，我们使用了公开可用的饮酒量GWAS汇总统计数据(GSCAN，N=666,978)和整个外显子组测序数据(Genebass，N=393,099），以确定一组常见和罕见的饮酒变体。我们使用基于基因的分析来暗示来自常见和罕见变异分析的基因，然后，我们使用网络共定位程序将其传播到共享的分子网络上。
结果：每个数据集的基于基因的分析涉及294个（常见变异）和35个（罕见变异）基因，包括乙醇代谢基因ADH1B和ADH1C，这两种分析都确定了，ANKRD12、GIGYF1、KIF21B、和STK31，仅在罕见的变异分析中鉴定，但与其他神经精神特征有关。网络共定位揭示了通过常见和罕见变异鉴定的基因之间的显著网络重叠。共享网络确定了在酒精代谢中起作用的基因家族，包括ADH,ALDH,CYP,和UGT。Seven-oneofthegenesinthesharednetworkwerepreviouslyimplicatedinneurochemicalorsubstanceusedisordersbutnotalcohol-relatedbehavior(e.e.exoC2,EPM2A,和CACNG4）。差异基因表达分析显示在肝脏和几个脑区富集。
结论：网络共定位所涉及的基因识别与饮酒相关的共享生物学，这也是与饮酒共存的神经精神特征和物质使用障碍的基础，提供对两种不同的遗传信息来源的更全面的理解。
