Bipolar disorder (BD) is a heritable mental illness with complex etiology. We performed a genome-wide association study (GWAS) of 41,917 BD cases and 371,549 controls, which identified 64 associated genomic loci. BD risk alleles were enriched in genes in synaptic and calcium signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers and antiepileptics. Integrating eQTL data implicated 15 genes robustly linked to BD via gene expression, including druggable genes such as HTR6, MCHR1, DCLK3 and FURIN. This GWAS provides the best-powered BD polygenic scores to date, when applied in both European and diverse ancestry samples. Together, these results advance our understanding of the biological etiology of BD, identify novel therapeutic leads and prioritize genes for functional follow-up studies.

Numerous genetic and environmental factors contribute to psychiatric disorders and other brain disorders. Common risk factors likely converge on biological pathways regulating the optimization of brain structure and function across the lifespan. Here, using structural magnetic resonance imaging and machine learning, we estimated the gap between brain age and chronological age in 36,891 individuals aged 3 to 96 years, including individuals with different brain disorders. We show that several disorders are associated with accentuated brain aging, with strongest effects in schizophrenia, multiple sclerosis and dementia, and document differential regional patterns of brain age gaps between disorders. In 16,269 healthy adult individuals, we show that brain age gap is heritable with a polygenic architecture overlapping those observed in common brain disorders. Our results identify brain age gap as a genetically modulated trait that offers a window into shared and distinct mechanisms in different brain disorders.

Cryptic relatedness is inherently a feature of large genome-wide association studies (GWAS), and can give rise to considerable inflation in summary statistics for single nucleotide polymorphism (SNP) associations with phenotypes. It has proven difficult to disentangle these inflationary effects from true polygenic effects. Here we present results of a model that enables estimation of polygenicity, mean strength of association, and residual inflation in GWAS summary statistics. We show that there is substantial residual inflation in recent large GWAS of height and schizophrenia; correcting for this reduces the number of independent genome-wide significant loci from the reported values of 697 for height and 108 for schizophrenia to 368 and 61, respectively. In contrast, a larger GWAS of educational attainment shows no residual inflation. Additionally, we find that height has a relatively low polygenicity, with approximately 8k SNPs having causal association, more than an order of magnitude less than has been reported. The residual inflation in GWAS summary statistics can be corrected using the standard genomic control procedure with the estimated residual inflation factor.

1Determining the contribution of functional genetic categories is fundamental to understanding the 2 genetic etiology of complex human traits and diseases. Here we present Annotation Informed MiXeR: a 3 likelihood-based method to estimate the number of variants influencing a phenotype and their effect 4 sizes across different functional annotation categories of the genome using summary statistics from 5 genome-wide association studies. Applying the model to 11 complex phenotypes suggests diverse 6 patterns of functional category-specific genetic architectures across human diseases and traits. 7 8 Keywords: genetic architecture, complex traits, functional genetic categories, polygenicity, variant effect 9 size 10 11 Background 12 The rapid technological advances of the last years have provided an enormous amount of genetic data 13 thereby promoting the development of statistical methods aimed to unravel the genetic architecture of 14 complex traits [1]. A key effort has been to estimate SNP-based heritability, either using individual-level 15 genotype data [2], or summary-level statistics from genome-wide association studies (GWAS) [3]. 16 However, heritability estimates provide a limited picture of the genetic architecture underlying complex 17 phenotypes. For example, they are agnostic about the number of genetic variants influencing a 18 phenotype and their effect sizes [4]. Both of these quantities can vary and still result in the same 19heritability, which is proportional to their product [5,6]. 20Recently, we developed a model which allows the breakdown of SNP-heritability into the number of 21 variants influencing a given phenotype (non-null variants) and the distribution of their effect sizes using 22 summary statistics from GWAS and detailed population-specific linkage disequilibrium (LD) structure [5, 23 6]. This model assumes a uniform distribution of non-null variants with the same expected effect size 24 throughout the genome. However, prior genetic studies suggest that non-null variants are differentially for the same phenotype may differ depending on a GWAS's sample size, as well as on the coverage of 2 the tested variants. The samples of the GWAS tested here vary by more than one order of magnitude in 3 size, from approximately 5×10 4 for BD and ADHD to more than 7×10 5 for EA and height. In all 4 simulations, we kept the sample size constant (N = 10 5 ) and varied only the heritability (ℎ 2 =0.1, 0.4, 0.7). 5Since these quantities contribute to the GWAS z-scores distribution only through their product (follows 6 from formula (5)), our simulation scenario with N=10 5 and ℎ 2 =0.7 is equivalent to a scenario with, for 7 example, N=7×10 5 and ℎ 2 =0.1. Other aspects of potential GWAS-related issues (e.g. coverage of tested 8 Declarations 1