In conclusion, we have developed LD Score regression, a method to distinguish between inflated test statistics from confounding bias and polygenicity. Application of LD Score regression to over 20 complex traits confirms that polygenicity accounts for the majority of test statistic inflation in GWAS results and this approach can be used to generate a correction factor for GWAS that retains more power than λGC, especially at large sample sizes. We have made available for download a Python command line tool for estimating LD Score and performing LD Score regression, and a database of LD Scores suitable for European-ancestry samples (URLs). Research in progress aims to apply this method to estimation of components of heritability, genetic correlation and the calibration of mixed model association statistics.
