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

采用机器学习方法来检测严格定义的 MDD 特有的遗传位点

• 批准号: 10378100
• 负责人: BRADLEY Todd WEBB
• 金额: $ 20.84万
• 依托单位: RESEARCH TRIANGLE INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10378100
• 关键词: Address; Biological; Biology; Body mass index; Characteristics; Clinical; Clinical assessments; Collection; Communities; Complex; Data; Data Set; Detection; Disease; Family; Foundations; Genetic; Genetic Research; Genetic Variation; Genetic study; Genotype; Heritability; Impairment; Individual; Lead; Light; Machine Learning; Major Depressive Disorder; Measures; Mental disorders; Methodology; Methods; Modeling; Molecular Genetics; Morbidity - disease rate; Neurotic Disorders; Nucleotides; Outcome; Pattern; Performance; Phenotype; Pilot Projects; Probability; Process; Psyche structure; Records; Recurrence; Resources; Risk; Risk Estimate; Sample Size; Sampling; Severities; Smoking Behavior; Solid; Structure; Surveys; Techniques; Training; Variant; Weight; biobank; disability; disorder risk; genetic analysis; genetic association; genome wide association study; genome-wide; genomic locus; gradient boosting; improved; indexing; insight; interest; large datasets; lifetime risk; machine learning method; machine learning prediction; novel; phenotypic data; psychogenetics; risk prediction; risk variant; sample collection; statistical and machine learning; supervised learning; theories; trait; vector

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.

摘要 这个项目旨在进一步了解遗传因素对重度抑郁症的影响 （MDD）。一种增加MDD分子遗传学研究样本量的方法， 检测基因位点的能力是使用比完整的调查更短、更有效的调查来评估个体。 临床评估。这种“最小表型”导致识别可能不是特异性的风险基因座， 严格定义的MDD，并可能与各种精神病表型相关。虽然这些发现是 重要的是要了解复杂的心理和精神疾病的结果的整体生物学，他们提供了很少的直接 以及对严格定义的MDD的生物学基础的可行见解，其显示出严重程度增加， 残疾和复发风险，并对残疾和发病率造成不成比例的影响， 与自由定义的MDD相比。最近，大型生物库调查了数万至数十万个 涉及数百到数千个变量的主题和EHR记录已可供 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， 前所未有的机会，利用这些资源，以推进发现和理解的遗传 对MDD的影响一个主要挑战是缺乏统一的措施，无法对严格界定的 MDD、损伤、严重程度和复发风险。这种缺乏“深度表型”的做法虽然务实地允许 大样本的组装，在准确测定对照，非特异性轻度病例， 以及严格定义的案例。我们之前已经展示了机器学习（ML）分析方法如何利用 这种类型的异质的，广泛的，但轻的信息收集来预测和量化受试者的风险， 深刻评价。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，样本量和检测特定易感基因座的能力显著增加。在 目的1，我们计划评估2个ML方法家族，它们可用于严格预测不可测量的和特定的 定义的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 影响严格定义的MDD特定风险的常见遗传变异。最后，我们将建立我们的生物样本库， 适应ML方法管道可用于更广泛的精神病遗传学研究社区，预计 以提高其他精神疾病的检测能力和基因位点。