At the individual level, there is substantial variation in cortical thickness and surface area which has been implicated in a wide range of psychiatric and neurological traits. While cortical thickness and surface area are both strongly heritable, the two processes are genetically independent and there is little known about the loci influencing these morphological characteristics. Here we present results from GWAS meta-analyses of the thickness and surface area of cortical regions of interest derived from magnetic resonance imaging (MRI) scans from over 25,000 individuals.Across cohorts, structural T1-weighted MRI brain scans were analyzed locally using harmonized analysis and quality-control protocols (http://enigma.ini.usc.edu/protocols). The structural T1 images from the UK Biobank are included in these analyses; however, all images were reprocessed and reQc’ed to ensure homogeneity and quality of segmentation. All cortical parcellations were performed with the freely available and validated segmentation software. Cortical thickness and surface area were calculated for 68 (34 left and 34 right) cortical gray matter regions were visually inspected and statistically evaluated for outliers. The genotypic data from each cohort were quality-control and imputed to the 1000 genome reference panel and all cohorts computed ancestry multi-dimensional scores following harmonized protocols.To improve measurement accuracy, the cortical measurements were averaged across the hemispheres resulting in the average thickness and surface area of 34 regions. We also analyzed two summary measures, average cortical thickness across regions and total surface area, resulting in a total of 70 cortical phenotypes. Corrections for sex, age (linear and quadratic effects) and their interactions were included in all analyses, for the regional measures corrections for the corresponding summary measures were also included to account for the omnibus effects of brain size.The GWAS meta analyses presented will provide an overview of the loci influencing univariate region level thickness and surface area. We will also present the results of the multivariate analyses which focus on identifying patterns of genetic variation across regions. These results provide insight into genetic influences on the morphology of the human cortex and the relationship between cortical structure, psychiatric and behavioural traits.