Inclusion of a priori information in genome-wide association analysis.
Inclusion of a priori information in genome-wide association analysis.
复制标题
DOI：
10.1002/gepi.20476
复制
发表时间：
2009
影响因子：
2.1
通讯作者：
Bickeboeller, Heike
中科院分区：
文献类型：
--
作者：
Tintle, Nathan;Lantieri, Francesca;Lebrec, Jeremie;Sohns, Melanie;Ballard, David;Bickeboeller, Heike
关键词：
全基因组关联研究（GWAS）继续受到欢迎。为了更有效地利用所创建的大量数据，最近已经提出了各种方法来包括先验信息（例如，生物学上可解释的基因组、候选基因信息或基因表达）。遗传分析研讨会16第11组的六项贡献将新的或最近提出的方法应用于类风湿性关节炎和心脏病相关表型的GWAS。这些分析的结果是各种新的候选基因和基因集，除了众所周知的基因型-表型关联的验证。然而，由于许多方法相对较新，它们将受益于进一步的方法学研究，以确保它们保持I类错误率，同时增加发现其他关联的能力。当方法已从其他研究类型（例如，基因表达数据分析或连锁分析），应利用从中吸取的经验教训来指导技术的实施。最后，许多开放的研究问题存在的逻辑细节的起源的先验信息和方式，以将其纳入整体，我们的小组已经证明了强大的潜力，确定新的基因型-表型的关系，包括先验数据的分析GWAS，同时也揭示了一系列问题，需要进一步研究。
Genome-wide association studies (GWAS) continue to gain in popularity. To utilize the wealth of data created more effectively, a variety of methods have recently been proposed to include a priori information (e.g., biologically interpretable sets of genes, candidate gene information, or gene expression) in GWAS analysis. Six contributions to Genetic Analysis Workshop 16 Group 11 applied novel or recently proposed methods to GWAS of rheumatoid arthritis and heart disease related phenotypes. The results of these analyses were a variety of novel candidate genes and sets of genes, in addition to the validation of well known genotype-phenotype associations. However, because many methods are relatively new, they would benefit from further methodological research to ensure that they maintain type I error rates while increasing power to find additional associations. When methods have been adapted from other study types (e.g., gene expression data analysis or linkage analysis) the lessons learned there should be used to guide implementation of techniques. Lastly, many open research questions exist concerning the logistic details of the origin of the a priori information and the way to incorporate it. Overall, our group has demonstrated a strong potential for identifying novel genotype-phenotype relationships by including a priori data in the analysis of GWAS, while also uncovering a series of questions requiring further research.
登录
查看更多内容
影响因子：
158.5
作者：
Remmers, Elaine F.;Plenge, Robert M.;Gregersen, Peter K.
通讯作者：
Gregersen, Peter K.
影响因子：
--
作者：
Sohns M;Rosenberger A;Bickeböller H
通讯作者：
Bickeböller H
基因设定分析用于解释原核生物的微阵列实验。
影响因子：
3
作者：
Tintle, Nathan L.;Best, Aaron A.;DeJongh, Matthew;Van Bruggen, Dirk;Heffron, Fred;Porwollik, Steffen;Taylor, Ronald C.
通讯作者：
Taylor, Ronald C.
影响因子：
5.3
作者：
Bosse, Yohan;Bacot, Francois;Desrosiers, Martin
通讯作者：
Desrosiers, Martin
影响因子：
158.5
作者：
Plenge, Robert M.;Seielstad, Mark;Gregersen, Peter K.
通讯作者：
Gregersen, Peter K.