MARV: a tool for genome-wide multi-phenotype analysis of rare variants

详细信息    查看全文

• 作者：Marika Kaakinen ; Reedik Mägi ; Krista Fischer ; Jani Heikkinen…
• 关键词：Rare variant analysis ; Multi ; phenotype analysis ; High ; dimensional data
• 刊名：BMC Bioinformatics
• 出版年：2017
• 出版时间：December 2017
• 年：2017
• 卷：18
• 期：1
• 全文大小：1037KB
• 刊物主题：Bioinformatics; Microarrays; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Algorithms;
• 出版者：BioMed Central
• ISSN：1471-2105
• 卷排序：18

文摘

BackgroundGenome-wide association studies have enabled identification of thousands of loci for hundreds of traits. Yet, for most human traits a substantial part of the estimated heritability is unexplained. This and recent advances in technology to produce high-dimensional data cost-effectively have led to method development beyond standard common variant analysis, including single-phenotype rare variant and multi-phenotype common variant analysis, with the latter increasing power for locus discovery and providing suggestions of pleiotropic effects. However, there are currently no optimal methods and tools for the combined analysis of rare variants and multiple phenotypes.