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.

抽象的 该项目旨在进一步理解对重大抑郁症的遗传影响 （MDD）。增加样本量的一种方法，用于MDD的分子遗传研究，从而增加 检测遗传基因座的能力是使用比完整的调查评估个体 临床评估。这种“最小表型”导致了风险基因座的识别，这可能不是特定于 严格定义的MDD，可以与各种精神病表型有关。虽然这些发现是 重要的是要了解复杂的心理和精神病结果的整体生物学，它们几乎没有直接 以及对严格定义的MDD生物学基础的可行见解，该基础表明严重程度增加， 损害，复发风险以及对残疾和发病率的不成比例的影响 与自由定义的MDD进行比较。最近，大型生物库对数十万人进行了调查 数百至数千个变量和EHR记录的受试者已获得 科学界。将丰富的表型数据与全基因组的基因分型或测序相结合提供 空前的机会来利用这些资源来提高发现和理解遗传 对MDD的影响。一个主要挑战是缺乏允许评估严格定义的统一措施 MDD，损害，严重性和复发风险。这种缺乏“深度表型”，同时务实地允许 组装大型样品，在准确确定对照，非特异性轻度病例的准确确定中构成挑战 并严格定义的情况。我们以前已经展示了机器学习（ML）分析方法如何利用 这种类型的异构，广泛但信息收集的信息收集，以预测和量化受试者的风险 深入评估。尽管这些预测有很大的改进空间，但最终有效 应用此方法时，样本量和功率检测特定责任基因座的功率急剧增加。在 目的1，我们计划评估2个可用于严格预测未测量和特定特定方法的ML方法家族 定义的MDD风险。在AIM 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 常见的遗传变异会影响严格定义的MDD的风险。最后，我们将成为我们的生物库 适用于更广泛的精神遗传学研究社区的改编的ML方法管道可以预期 改善其他精神疾病的功率和基因座检测。