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
基本信息
- 批准号:10196078
- 负责人:
- 金额:$ 20.84万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:AddressBiologicalBiologyBody mass indexCharacteristicsClinicalClinical assessmentsCollectionCommunitiesComplexDataData SetDetectionDiseaseFamilyFoundationsGeneticGenetic ResearchGenetic VariationGenetic studyGenotypeHeritabilityImpairmentIndividualLeadLightMachine LearningMajor Depressive DisorderMeasuresMental disordersMethodologyMethodsModelingMolecular GeneticsMorbidity - disease rateNeurotic DisordersNucleotidesOutcomePatternPerformancePhenotypePilot ProjectsProbabilityProcessPsyche structureRecordsRecurrenceResourcesRiskRisk EstimateSample SizeSamplingSeveritiesSmoking BehaviorSolidStructureSurveysTechniquesTrainingVariantWeightbiobankdisabilitydisorder riskgenetic analysisgenetic associationgenome wide association studygenome-widegenomic locusimprovedindexinginsightinterestlarge datasetslifetime riskmachine learning methodnovelphenotypic datapsychogeneticsrisk predictionrisk variantsample collectionstatistical and machine learningsupervised learningtheoriestraitvector
项目摘要
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,
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。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
BRADLEY Todd WEBB其他文献
BRADLEY Todd WEBB的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('BRADLEY Todd WEBB', 18)}}的其他基金
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 - 财政年份:2021
- 资助金额:
$ 20.84万 - 项目类别:
Project 5 - Genetic architecture of alcohol use disorder using cross-trait genetic correlations and public next-generation sequencing studies
项目 5 - 使用跨性状遗传相关性和公共下一代测序研究的酒精使用障碍的遗传结构
- 批准号:
10429957 - 财政年份:2014
- 资助金额:
$ 20.84万 - 项目类别:
Project 5 - Genetic architecture of alcohol use disorder using cross-trait genetic correlations and public next-generation sequencing studies
项目 5 - 使用跨性状遗传相关性和公共下一代测序研究的酒精使用障碍的遗传结构
- 批准号:
10633321 - 财政年份:2014
- 资助金额:
$ 20.84万 - 项目类别:
相似海外基金
Elucidating the molecular basis and expanding the biological applications of the glycosyltransferases using biochemical and structural biology approaches
利用生化和结构生物学方法阐明糖基转移酶的分子基础并扩展其生物学应用
- 批准号:
23K14138 - 财政年份:2023
- 资助金额:
$ 20.84万 - 项目类别:
Grant-in-Aid for Early-Career Scientists
Upsampling of low-resolution/large-volume 3D tomographic images using generative adversarial neural networks applied to biological anthropology, medical imaging, and evolutionary biology
使用应用于生物人类学、医学成像和进化生物学的生成对抗神经网络对低分辨率/大容量 3D 断层扫描图像进行上采样
- 批准号:
571519-2021 - 财政年份:2022
- 资助金额:
$ 20.84万 - 项目类别:
Alliance Grants
The biology of Ciceribacter spp. and their adaptations as biological chassis for engineered nitrogen fixation
西塞里杆菌属的生物学。
- 批准号:
2735213 - 财政年份:2022
- 资助金额:
$ 20.84万 - 项目类别:
Studentship
NSF Postdoctoral Fellowship in Biology: Symbiosis as a Means of Survival for Biological Soil Crust Microbes
NSF 生物学博士后奖学金:共生作为生物土壤结皮微生物的生存手段
- 批准号:
2209217 - 财政年份:2022
- 资助金额:
$ 20.84万 - 项目类别:
Fellowship Award
Conference: 2023 Stochastic Physics in Biology: Bridging Stochastic Physical Theories with Biological Experiments
会议:2023 年生物学中的随机物理学:将随机物理理论与生物实验联系起来
- 批准号:
2242530 - 财政年份:2022
- 资助金额:
$ 20.84万 - 项目类别:
Standard Grant
From Big Biological Data to Tangible Insights: Designing tangible and multi-display interactions to support data analysis and model building in the biology domain
从生物大数据到有形洞察:设计有形和多显示交互以支持生物学领域的数据分析和模型构建
- 批准号:
RGPIN-2021-03987 - 财政年份:2022
- 资助金额:
$ 20.84万 - 项目类别:
Discovery Grants Program - Individual
Engineering of next-generation synthetic biology tools for biological applications
用于生物应用的下一代合成生物学工具的工程
- 批准号:
RGPIN-2019-07002 - 财政年份:2022
- 资助金额:
$ 20.84万 - 项目类别:
Discovery Grants Program - Individual
BEORHN: Biological Enzymatic Oxidation of Reactive Hydroxylamine in Nitrification via Combined Structural Biology and Molecular Simulation
BEORHN:通过结合结构生物学和分子模拟对硝化反应中的活性羟胺进行生物酶氧化
- 批准号:
BB/V01577X/1 - 财政年份:2022
- 资助金额:
$ 20.84万 - 项目类别:
Research Grant
NSF Postdoctoral Fellowship in Biology FY 2020: Integrating biological collections and observational data sources to estimate long-term butterfly population trends
2020 财年 NSF 生物学博士后奖学金:整合生物收藏和观测数据源来估计蝴蝶种群的长期趋势
- 批准号:
2010698 - 财政年份:2021
- 资助金额:
$ 20.84万 - 项目类别:
Fellowship Award
Engineering of next-generation synthetic biology tools for biological applications
用于生物应用的下一代合成生物学工具的工程
- 批准号:
RGPIN-2019-07002 - 财政年份:2021
- 资助金额:
$ 20.84万 - 项目类别:
Discovery Grants Program - Individual