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GWAMA: software for genome-wide association meta-analysis
2010
BMC Bioinformatics
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 metaanalysis of studies from the same population, increasing the sample size over any individual study. Although statistical software analysis packages incorporate routines for
doi:10.1186/1471-2105-11-288
pmid:20509871
pmcid:PMC2893603
fatcat:hb2y22ijajcgbbbaswlroykxoy