Genetic risk discovery using WGS from a population-based resource of 10,000 suicide deaths with DNA

摘要 自杀是第十大死因，仅在美国每年就有超过4.7万人死于可预防的死亡。这个 在过去的二十年里，美国的自杀死亡率上升了33%。尽管这是戏剧性的 公共健康危机，自杀研究远远落后于其他主要健康状况，因为人们认为 风险因素过于复杂和不可控，难以研究。重要的是，虽然环境具有不可否认的 有证据表明，遗传因素在自杀死亡中起着重要作用。而对基因的研究 因此，风险是有希望的，大多数自杀遗传学的研究都集中在自杀遗传学更常见的特征上。 自杀的念头和行为。这一战略使其他研究小组能够获得足够的 由统计数据提供动力的样本。然而，自杀行为可能很难量化，也很难代表 具有广泛的自杀死亡风险的个人。使用犹他州可用的独特资源 自杀遗传风险研究(USGRS)，使我们能够研究明确、影响大的遗传风险 直接自杀死亡的健康结局。USGRS目前拥有来自6,000个人口的DNA-已确定 自杀死亡；在史无前例的20年里，这一资源每年增长约650例 与犹他州卫生部的中央法医办公室(OME)合作。我们有 犹他州281例自杀死亡病例的完整全基因组序列(WGS)数据 遗传风险很高。我们有Illumina心理阵列关于这些案件和犹他州其他自杀事件的数据(总计 N=4,382)。所有病例都链接到犹他州人口数据库(UPDB)，这是一个全州范围的资源，包括 人口统计数据和全面的医疗记录。UPDB表型数据还包括唯一的 关于家族风险的信息远远超过其他数据资源通过家谱记录 回到17世纪。真正了解自杀死亡风险，并实施高效干预措施 为那些最有可能死亡的人提供适当的、有针对性的服务，我们必须具体了解其中的风险 与自杀死亡有关。本建议书侧重于确定、验证、表征和 复制具有高功能影响的变异，这些变异涉及对风险具有重要意义的基因和基因途径 自杀身亡。从我们的WGS数据中，我们已经检测到了高影响结构变体(SVS)和单一 核苷酸变异(SNV)显示全基因组显着的基因途径浓缩和蛋白质-蛋白质 互动。这些途径也受到与我们全基因组关联有关的基因的支持 对犹他州3413例自杀死亡的分析表明，罕见和常见风险的功能水平存在重叠 变种。广泛的家族性风险数据和大样本容量将使我们能够选择760个额外的子集 具有更高遗传风险的自杀，以复制和扩展我们目前的发现，为 确定高危个人，并制定有针对性的干预措施。

ABSTRACT Suicide is the 10th leading cause of death, with over 47,000 preventable deaths per year in the U.S. alone. The rate of suicide death across the U.S. has risen by 33% over the past two decades. In spite of this dramatic public health crisis, suicide research lags far behind other major health conditions due to the perception that risk factors are too complex and uncontrollable for study. Importantly, while environment has undeniable impact, evidence suggests that genetic factors play a major role in suicide death. While the study of genetic risks is therefore promising, most studies of suicide genetics have focused on the much more common traits of suicidal thoughts and behaviors. This strategy has allowed other research groups to acquire sufficiently statistically-powered samples. However, suicidal behaviors can be difficult to quantify, and represent individuals with a wide range of risk for later suicide death. Using the unique resources available to the Utah Suicide Genetic Risk Study (USGRS), we are able to study the genetic risks of the unambiguous, high-impact health outcome of suicide death directly. The USGRS currently has DNA from >6,000 population-ascertained suicide deaths; this resource grows by ~650 cases per year through an unprecedented two-decade collaboration with the Utah Department of Health’s centralized Office of the Medical Examiner (OME). We have completed whole genome sequence (WGS) data on a subset of 281 of the Utah suicide deaths selected for high genetic risk. We have Illumina PsychArray data on these cases and additional Utah suicides (total N=4,382). All cases are linked to the Utah Population Database (UPDB), a statewide resource that includes demographic data and comprehensive medical records. The UPDB phenotypic data also includes unique information on familial risk far exceeding that of other data resources through genealogical records that go back to the 1700s. To truly understand risk of suicide death and to implement highly effective interventions that provide appropriate, targeted services to those most likely to die, we must understand the risks specifically associated with suicide deaths. This proposal focuses on the identification, validation, characterization, and replication of variants with high functional impact that implicate genes and gene pathways important for risk of suicide death. From our WGS data, we have already detected high-impact structural variants (SVs) and single nucleotide variants (SNVs) showing genome-wide significant gene pathway enrichment and protein-protein interactions. These pathways are also supported by genes implicated in our genome-wide association analyses of 3,413 Utah suicide deaths, suggesting overlap at the functional level of rare and common risk variation. Extensive familial risk data and large sample size will allow us to select an additional subset of 760 suicides with enhanced genetic risk to replicate and extend our current findings, setting the stage for identification of high-risk individuals, and for development of targeted interventions.