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PhenX:一个跨学科遗传学研究的工具包。

PhenX: a toolkit for interdisciplinary genetics research.

机构信息

Cornell University, Ithaca, New York, USA.

出版信息

Curr Opin Lipidol. 2010 Apr;21(2):136-40. doi: 10.1097/MOL.0b013e3283377395.

Abstract

PURPOSE OF REVIEW

To highlight standard PhenX (consensus measures for Phenotypes and eXposures) measures for nutrition, dietary supplements, and cardiovascular disease research and to demonstrate how these and other PhenX measures can be used to further interdisciplinary genetics research.

RECENT FINDINGS

PhenX addresses the need for standard measures in large-scale genomic research studies by providing investigators with high-priority, well established, low-burden measurement protocols in a web-based toolkit (https://www.phenxtoolkit.org). Cardiovascular and Nutrition and Dietary Supplements are just 2 of 21 research domains and accompanying measures included in the PhenX Toolkit.

SUMMARY

Genome-wide association studies (GWAS) provide promise for the identification of genomic markers associated with different disease phenotypes, but require replication to validate results. Cross-study comparisons typically increase statistical power and are required to understand the roles of comorbid conditions and environmental factors in the progression of disease. However, the lack of comparable phenotypic, environmental, and risk factor data forces investigators to infer and to compare metadata rather than directly combining data from different studies. PhenX measures provide a common currency for collecting data, thereby greatly facilitating cross-study analysis and increasing statistical power for identification of associations between genotypes, phenotypes, and exposures.

摘要

目的综述

强调营养、饮食补充剂和心血管疾病研究的标准 PhenX(表型和暴露的共识测量)措施,并展示如何将这些和其他 PhenX 措施用于进一步开展跨学科遗传学研究。

最近的发现

PhenX 通过在基于网络的工具包(https://www.phenxtoolkit.org)中为研究人员提供高优先级、成熟、低负担的测量方案,满足了大型基因组研究中对标准措施的需求。心血管疾病和营养与饮食补充剂只是 PhenX 工具包中包含的 21 个研究领域和相关措施中的 2 个。

总结

全基因组关联研究(GWAS)为识别与不同疾病表型相关的基因组标记提供了希望,但需要复制以验证结果。跨研究比较通常会增加统计功效,并需要了解共病和环境因素在疾病进展中的作用。然而,缺乏可比的表型、环境和风险因素数据迫使研究人员推断和比较元数据,而不是直接合并来自不同研究的数据。PhenX 措施提供了收集数据的通用货币,从而极大地促进了跨研究分析,并提高了识别基因型、表型和暴露之间关联的统计功效。

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