GWAMA: software for genome-wide association meta-analysis |
| |
| Authors: | Reedik Mägi Andrew P Morris |
| |
| Affiliation: | (1) Genetic and Genomic Epidemiology Unit, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK;(2) Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Headington, Oxford, OX3 7LJ, UK |
| |
| Abstract: | Background Despite the recent success of genome-wide association studies in identifying novel loci contributing effects to complex human traits, such as type 2 diabetes and obesity, much of the genetic component of variation in these phenotypes remains unexplained. One way to improving power to detect further novel loci is through meta-analysis of studies from the same population, increasing the sample size over any individual study. Although statistical software analysis packages incorporate routines for meta-analysis, they are ill equipped to meet the challenges of the scale and complexity of data generated in genome-wide association studies. |
| |
| Keywords: | |
| 本文献已被 SpringerLink 等数据库收录！ |
|
