TY - JOUR T1 - GWIS: Genome Wide Inferred Statistics for non-linear functions of multiple phenotypes JF - bioRxiv DO - 10.1101/035329 SP - 035329 AU - H. A. Nieuwboer AU - R. Pool AU - C. V. Dolan AU - D. I. Boomsma AU - M. G. Nivard Y1 - 2015/01/01 UR - http://biorxiv.org/content/early/2015/12/24/035329.abstract N2 - Here we present a method of genome wide inferred study (GWIS) that provides an approximation of genome wide association study (GWAS) summary statistics for a variable that is a function of phenotypes for which GWAS summary statistics, phenotypic means and covariances are available. GWIS can be performed regardless of sample overlap between the GWAS of the phenotypes on which the function depends. As GWIS provides association estimates and their standard errors for each SNP, GWIS can form the basis for polygenic risk scoring, LD score regression1, Mendelian randomization studies, biological annotation and other analyses. Here, we replicate a body mass index (BMI) GWAS using GWIS based on a height GWAS and a weight GWAS. We proceed to use a GWIS to further our understanding of the genetic architecture of schizophrenia and bipolar disorder. ER -