We introduce Multi-Trait Analysis of GWAS (MTAG), a method for joint analysis of summary statistics from GWASs of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (N eff = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). Compared to 32, 9, and 13 genome-wide significant loci in the single-trait GWASs (most of which are themselves novel), MTAG increases the number of loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase variance explained by polygenic scores by approximately 25%, matching theoretical expectations.

Corresponding authors, satterst@broadinstitute.org (FKS) and mjdaly@atgu.mgh.harvard.edu (MJD). Main Text Introductory paragraphAutism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are substantially heritable 1-4 , but individuals with psychiatric diagnoses often do not have blood drawn as part of routine medical procedure, making it difficult to collect large cohorts for genetic study. To overcome this challenge, we drew upon two Danish national resources: the Danish Neonatal Screening Biobank (DNSB) and the Danish national psychiatric registry. We have previously validated the use of archived bloodspots from the DNSB for genotyping 5 and sequencing 6,7 , and we recently performed common variant analysis on dried bloodspot material in both ASD 8 and ADHD 9 . Here, we present exome sequences from over 13,000 DNSB samples, finding that ASD and ADHD show a strikingly similar burden of rare protein-truncating variants, both significantly higher than controls. Additionally, the distributions of genes hit by these variants are not distinguishable between the two disorders, suggesting that many risk genes may be shared between them. These results motivate a combined analysis across ASD and ADHD, which-in conjunction with incorporation of the gnomAD reference database as additional population controls-leads to the identification of genes conferring general risk for childhood psychiatric disorders, including the novel gene MAP1A. Sample overviewExome sequences for individuals included in the iPSYCH research initiative 10 were obtained from archived dried blood samples stored by the DNSB. Individuals in this

We present a new method, Multi-Ancestry Meta-Analysis (MAMA), which combines genome-wide association study (GWAS) summary statistics from multiple populations to produce new summary statistics for each population, identifying novel loci that would not have been discovered in either set of GWAS summary statistics alone. In simulations, MAMA increases power with less bias and generally lower type-1 error rate than other multi-ancestry meta-analysis approaches. We apply MAMA to 23 phenotypes in East-Asian- and European-ancestry populations and find substantial gains in power. In an independent sample, novel genetic discoveries from MAMA replicate strongly.