Adapting machine learning methods to detect genetic loci specific to strictly defined MDD

摘要 这个项目试图加深我们对基因对严重抑郁障碍的影响的理解 (MDD)。一种增加MDD分子遗传学研究样本量的方法 检测基因座的能力是使用比完整调查更短和更有效的调查来评估个体 临床评估。这种“最小表型”导致了风险基因的识别，这些基因可能不是特定于 MDD定义严格，可与多种精神疾病表型相关。虽然这些发现是 对于理解复杂的精神和精神结果的整体生物学很重要，它们几乎没有提供直接的 以及对严格定义的MDD的生物学基础的可操作洞察力，这表明MDD的严重性增加， 损害和复发风险，并占不成比例的影响残疾和发病率 与自由定义的MDD进行比较。最近，大型生物库调查了数万到数十万 涉及成百上千个变量的受试者和电子病历记录已提供给 科学界。将丰富的表型数据与全基因组基因分型或测序相结合，可以提供 利用这些资源来推进对基因的发现和理解的前所未有的机会 对MDD的影响。一个主要挑战是缺乏统一的措施来评估严格定义的 MDD、损害、严重程度和复发风险。这种缺乏“深层表型”的现象，虽然在实际中允许 大样本的收集，在准确确定对照、非特定的轻微病例、 以及严格界定的案例。我们之前已经展示了机器学习(ML)分析方法如何利用 这种类型的异质、广泛但很少的信息收集来预测和量化受试者的风险 经过深思熟虑。虽然这些预测有很大的改进空间，但由此产生的有效 当应用这种方法时，样本大小和检测特定易感基因的能力显著增加。在……里面 目的1.我们计划严格评估两个可用于不可测量和特定预测的最大似然方法 已定义MDD风险。在目标2中，我们建议在遗传关联分析中使用这些风险预测来检测 影响特定于严格定义的MDD的风险的常见遗传变异。最后，我们将使我们的生物库 经过调整的ML方法管道可用于更广泛的精神病学遗传学研究社区，预计 以提高对其他精神疾病的能力和基因定位。

Abstract This project seeks to further our understanding of the genetic influences on Major Depressive Disorder (MDD). One approach to increasing sample sizes for molecular genetic studies of MDD and thereby increasing power to detect genetic loci is to assess individuals using surveys that are shorter and more efficient than full clinical assessments. This `minimal phenotyping' leads to identification of risk loci that may not be specific to strictly defined MDD and can be associated with a variety of psychiatric phenotypes. While these discoveries are important to understand the overall biology of complex mental and psychiatric outcomes, they offer little direct and actionable insight into the biological underpinning of strictly defined MDD which shows increased severity, impairment, and recurrence risk and accounts for a disproportionate impact on disability and morbidity in comparison to liberally defined MDD. Recently, large biobanks surveying tens to hundreds of thousands of subjects across hundreds to thousands of variables and EHR records have been become available to the scientific community. Combining rich phenotype data with genome-wide genotyping or sequencing offers an unprecedented opportunity to leverage these resources to advance discovery and understanding of the genetic influences on MDD. One major challenge is the lack of uniform measures that allow assessment of strictly defined MDD, impairment, severity, and recurrence risk. This lack of `deep phenotyping' while pragmatic in allowing the assembly of large samples, creates challenges in accurate determinations of controls, non-specific mild cases, and strictly defined cases. We have previously shown how machine learning (ML) analysis methods can leverage this type of heterogeneous, broad, but light collection of information to predict and quantify risk in subjects not deeply assessed. While there is significant room for improvement in these predictions, the resulting effective sample size and power to detect specific liability loci increased dramatically when this method was applied. In Aim 1, we plan to evaluate 2 families of ML methods that can be used to predict unmeasured and specific strictly defined MDD risk. In Aim 2, we propose to use these predictions of risk in genetic association analyses to detect common genetic variation that influences risk specific to strictly defined MDD. Finally, we will make our biobank adapted ML method pipeline available to the broader psychiatric genetics research community which is expected to improve power and loci detection for other psychiatric disorders.