Improved detection of gene-diet interactions via longitudinal data, metabolomic proxies, and polygenic scores
Improved detection of gene-diet interactions via longitudinal data, metabolomic proxies, and polygenic scores
批准号:
10506410
负责人:
Kenneth E Westerman
金额:
$15.53万
依托单位国家:
美国
项目类别:
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-07-01 至 2027-04-30
关键词:
AddressAffectAllelesAreaAwarenessBehaviorComplementConsumptionDataData AnalysesData ReportingDetectionDevelopmentDiabetes MellitusDietDietary AssessmentDietary FactorsDietary FatsDietary PracticesDoctor of PhilosophyFellowshipFoundationsFutureGenesGeneticGenomeGrantIndividualIntakeLeadershipLife StyleLinkLiteratureMeasurementMentorsMentorshipMetabolicMetabolic DiseasesMethodsModelingModificationNational Heart, Lung, and Blood InstituteNational Institute of Diabetes and Digestive and Kidney DiseasesNon-Insulin-Dependent Diabetes MellitusNutritional StudyOutcomeOutputPatient Self-ReportPatternPerformancePersonsPopulation HeterogeneityPositioning AttributePrincipal InvestigatorProxyPublicationsRecommendationReportingReproducibilityResearchResearch PersonnelResearch TrainingRiskRisk FactorsScientistScoring MethodSerumStatistical MethodsStrategic PlanningTCF7L2 geneTechniquesTestingTrainingTraining ProgramsTrans-Omics for Precision MedicineUnited States National Institutes of HealthVariantWorkanalytical methodbasebiobankcareercatalystcohortdiabetes riskdietarydisorder riskexperiencefasting glucosegenetic makeupgenetic variantgenome sequencinggenome-wideimprovedinter-individual variationmetabolomicsnovelnutritionnutritional epidemiologynutritional genomicspolygenic risk scoreprecision nutritionprediction algorithmpreventrare variantresponsesimulationskillsstatisticssuccesstooltraitwaist circumferencewhole genome
中文摘要
总结
饮食是代谢性疾病发生的关键因素,但饮食因素对疾病风险的影响不同
在不同的个体之间。基因-饮食相互作用(GxDs)可以帮助解决饮食反应中的个体间差异
通过识别改变饮食行为和代谢危险因素之间关联的遗传变异,
(e.g.,血糖特征和腰围)。识别许多这样的相互作用将使基因组引导的
精确的营养建议(例如,地中海式饮食(MedDiet)与低脂饮食模式,
降低糖尿病风险)。然而,自我报告的饮食行为的不精确性和偏差以及小的
变异体特异性效应在很大程度上阻碍了对强GxD相互作用的鉴定。这两个关键
这些障碍可以通过使用营养流行病学和基因组学的技术来改善GxD
识别.具体地说,饮食测量可以通过以下方式得到改善:(1)纵向饮食
测量更稳定的摄入量估计和(2)客观代谢组学代理饮食补充
自我报告的数据。小效应量的问题可以通过调整多基因风险评分(PRS)来改善
创建多基因相互作用评分(iPRS)的方法,该评分结合了许多变体之间的联合收割机相互作用效应。
这些策略可以在Trans-Omics for Precision Medicine(TOPMed)队列中进行探索,
多个时间点的自我报告饮食和血清代谢组学。MedDiet是一种非常适合的接触,
探索这些方法:它通常被消费,影响代谢疾病的风险,有证据表明,
GxD相互作用,并具有经验证的代谢组学特征。要探讨的假设是,
膳食测量方法和iPRS将允许复制已知的和发现新的GxD。的
首席研究员肯尼斯·韦斯特曼博士是唯一能够完成这项工作的早期-
具有营养学背景的职业科学家,GxD分析专业知识,以及TOPMed膳食经验
数据他需要额外的培训,以解决目前在纵向数据分析,代谢组学,
PRS和领导力,促进完成以下研究目标。目标1:改善
通过结合纵向研究,研究代谢危险因素的标准基因-饮食相互作用模型的重现性
测量和代谢组学饮食代理。目标2:改进交互测试的标准PRS
通过开发一种多基因评分方法,该方法聚集了遗传互作效应。
新的遗传因素改变地中海式饮食和代谢危险因素之间的关联。
拟议的研究将验证GxD研究的改进饮食建模方法,并创建遗传模型。
预测饮食反应的分数。相关的培训将帮助韦斯特曼博士实现独立,
精确营养领域,并在未来的赠款中进一步探索这些想法。
英文摘要
SUMMARY
Diet is a critical factor in the development of metabolic diseases, but dietary factors affect disease risk differently
across individuals. Gene-diet interactions (GxDs) can help resolve this inter-individual variability in diet response
by identifying genetic variants that modify the association between dietary behaviors and metabolic risk factors
(e.g., glycemic traits and waist circumference). Identifying many such interactions would enable genome-guided
precision nutrition recommendations (e.g., Mediterranean-style diet (MedDiet) vs. lower-fat dietary pattern to
decrease diabetes risk). However, imprecision and bias in self-reported dietary behaviors as well as small
variant-specific effects have largely impeded the identification of robust GxD interactions. These two key
obstacles can be addressed by using techniques from nutritional epidemiology and genomics to improve GxD
identification. Specifically, diet measurement can be improved by incorporating (1) longitudinal diet
measurements for more stable intake estimates and (2) objective metabolomic proxies for diet to complement
self-reported data. The problem of small effect sizes can be improved by adapting the polygenic risk score (PRS)
approach to create polygenic interaction scores (iPRS) that combine interaction effects across many variants.
These strategies can be explored in the Trans-Omics for Precision Medicine (TOPMed) cohorts, which have
self-reported diet at multiple timepoints and serum metabolomics. The MedDiet is a well-suited exposure with
which to explore these approaches: it is commonly consumed, impacts metabolic disease risk, has evidence of
GxD interactions, and has a validated metabolomic signature. The hypothesis to be explored is that improved
dietary measurement approaches and iPRS will allow replication of known and discovery of novel GxDs. The
principal investigator, Dr. Kenneth Westerman, Ph.D., is uniquely positioned to complete this work as an early-
career scientist with a nutrition background, expertise in GxD analysis, and prior experience with TOPMed dietary
data. He requires additional training to address current skill gaps in longitudinal data analysis, metabolomics,
PRS, and leadership, facilitating the completion of the following research aims. Aim 1: To improve the
reproducibility of standard gene-diet interaction models for metabolic risk factors by combining longitudinal
measurements and metabolomic dietary proxies. Aim 2: To improve upon standard PRS for interaction testing
by developing a polygenic score method that aggregates over genetic interaction effects Aim 3: To discover
novel genetic factors modifying the association between a Mediterranean-style diet and metabolic risk factors.
The proposed research will validate improved dietary modeling methods for GxD studies and create genetic
scores that predict diet response. The associated training will help Dr. Westerman achieve independence in the
field of precision nutrition and explore these ideas further in dietary trials in future grants.
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会议论文
Improved detection of gene-diet interactions via longitudinal data, metabolomic proxies, and polygenic scores
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批准号:10653260
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项目类别:
-
资助金额:$15.28万
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财政年份:2022
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负责人:Kenneth E Westerman
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依托单位:
海外基金