Population-based association and gene by environment interactions in Genetic Analysis Workshop 18.

Population-based association and gene by environment interactions in Genetic Analysis Workshop 18.
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遗传分析研讨会 18 中基于群体的关联和环境相互作用的基因。

DOI:
10.1002/gepi.21825
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发表时间:
2014
影响因子:
2.1
通讯作者:
König,InkeR
König,InkeR
中科院分区:
医学4区
文献类型:
--
作者:
Satten,GlenA;Biswas,Swati;Papachristou,Charalampos;Turkmen,Asuman;König,InkeR

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在过去的十年中,全基因组关联研究已经成功地确定了在许多复杂疾病中发挥作用的遗传位点。尽管如此,很明显,对于许多性状,对单个常见变异的研究并不能给出对表型遗传贡献的完整描述。因此,目前正在研究一些新的方法,以进一步寻找易感基因座或区域。我们总结了遗传分析研讨会18(GAW 18)的贡献,这些贡献涉及使用基于人群的关联分析方法进行搜索。GAW 18工作组的许多成员使用了最近才通过使用下一代测序技术获得的数据类型,其中许多人专注于研究罕见变异,而不是常见变异或与常见变异相结合。一些贡献者使用了基于单倍型的方法,迄今为止,该方法使用相对较少,但对于分析罕见变异关联数据可能变得更加重要。其他人分析了基因-基因或基因-环境相互作用,需要新的统计方法来充分利用现有信息,而不需要过多的计算负担。GAW 18为参与者提供了利用最先进的数据、统计技术和技术的机会。我们在这里报告了研讨会参与者的一些经验和结论,他们分析了GAW 18数据作为基于人群的关联研究。
In the past decade, genome‐wide association studies have been successful in identifying genetic loci that play a role in many complex diseases. Despite this, it has become clear that for many traits, investigation of single common variants does not give a complete picture of the genetic contribution to the phenotype. Therefore a number of new approaches are currently being investigated to further the search for susceptibility loci or regions. We summarize the contributions to Genetic Analysis Workshop 18 (GAW18) that concern this search using methods for population‐based association analysis. Many of the members of our GAW18 working group made use of data types that have only recently become available through the use of next‐generation sequencing technologies, with many focusing on the investigation of rare variants instead of or in combination with common variants. Some contributors used a haplotype‐based approach, which to date has been used relatively infrequently but may become more important for analyzing rare variant association data. Others analyzed gene‐gene or gene‐environment interactions, where novel statistical approaches were needed to make the best use of the available information without requiring an excessive computational burden. GAW18 provided participants with the chance to make use of state‐of‐the‐art data, statistical techniques, and technology. We report here some of the experiences and conclusions that were reached by workshop participants who analyzed the GAW18 data as a population‐based association study.
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