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: 10196078
• 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 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10196078
• 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; improved; indexing; insight; interest; large datasets; lifetime risk; machine learning method; 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.

抽象的 该项目旨在进一步了解遗传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，从而提高 检测遗传位点的能力是使用比全面调查更短、更有效的调查来评估个体 临床评估。这种“最小表型分析”导致识别出可能不特定于的风险位点 严格定义的 MDD 可能与多种精神表型相关。虽然这些发现是 对于理解复杂的精神和精神病结果的整体生物学很重要，但它们几乎没有提供直接的帮助 以及对严格定义的 MDD 的生物学基础的可操作的见解，该疾病的严重性有所增加， 损害和复发风险，并对残疾和发病率产生不成比例的影响 与自由定义的 MDD 进行比较。最近，大型生物银行调查了数万至数十万 数百到数千个变量和 EHR 记录的受试者已可供 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，提供了 利用这些资源推进对遗传的发现和理解的前所未有的机会 对 MDD 的影响。一项主要挑战是缺乏统一的措施来评估严格定义的 MDD、损伤、严重程度和复发风险。这种缺乏“深度表型分析”的做法虽然务实，但允许 大样本的组装，给准确确定对照、非特异性轻微病例带来了挑战， 和严格定义的情况。我们之前已经展示了机器学习 (ML) 分析方法如何利用 这种异质的、广泛的但少量的信息收集来预测和量化受试者的风险 深入评估。虽然这些预测还有很大的改进空间，但由此产生的有效结果 当应用这种方法时，样本量和检测特定责任位点的能力显着增加。在 目标 1，我们计划评估 2 个 ML 方法系列，这些方法可用于严格预测未测量的和特定的 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 改编后的机器学习方法管道可用于更广泛的精神科遗传学研究界，这是预期的 提高其他精神疾病的功效和基因座检测。