Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R(2) increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.

Copy number variants (CNVs) have been strongly implicated in the genetic etiology of schizophrenia (SCZ). However, genome-wide investigation of the contribution of CNV to risk has been hampered by limited sample sizes. We sought to address this obstacle by applying a centralized analysis pipeline to a SCZ cohort of 21,094 cases and 20,227 controls. A global enrichment of CNV burden was observed in cases (OR=1.11, P=5.7×10−15), which persisted after excluding loci implicated in previous studies (OR=1.07, P=1.7 ×10−6). CNV burden was enriched for genes associated with synaptic function (OR = 1.68, P = 2.8 ×10−11) and neurobehavioral phenotypes in mouse (OR = 1.18, P= 7.3 ×10−5). Genome-wide significant evidence was obtained for eight loci, including 1q21.1, 2p16.3 (NRXN1), 3q29, 7q11.2, 15q13.3, distal 16p11.2, proximal 16p11.2 and 22q11.2. Suggestive support was found for eight additional candidate susceptibility and protective loci, which consisted predominantly of CNVs mediated by non-allelic homologous recombination.

BACKGROUND: Alzheimer 's disease is a progressive, irreversible, and fatal disease for which accumulation of amyloid beta is thought to play a key role in pathogenesis. Aducanumab is a human monoclonal antibody directed against aggregated soluble and insoluble forms of amyloid beta. OBJECTIVES: We evaluated the efficacy and safety of aducanumab in early Alzheimer's disease. DESIGN: EMERGE and ENGAGE were two randomized, double-blind, placebo-controlled, global, phase 3 studies of aducanumab in patients with early Alzheimer's disease. SETTING: These studies involved 348 sites in 20 countries. PARTICIPANTS: Participants included 1638 (EMERGE) and 1647 (ENGAGE) patients (aged 50-85 years, confirmed amyloid pathology) who met clinical criteria for mild cognitive impairment due to Alzheimer's disease or mild Alzheimer's disease dementia, of which 1812 (55.2%) completed the study. INTERVENTION: Participants were randomly assigned 1:1:1 to receive aducanumab low dose (3 or 6 mg/kg target dose), high dose (10 mg/kg target dose), or placebo via IV infusion once every 4 weeks over 76 weeks. MEASUREMENTS: The primary outcome measure was change from baseline to week 78 on the Clinical Dementia Rating Sum of Boxes (CDR-SB), an integrated scale that assesses both function and cognition. Other measures included safety assessments; secondary and tertiary clinical outcomes that assessed cognition, function, and behavior; and biomarker endpoints. RESULTS: EMERGE and ENGAGE were halted based on futility analysis of data pooled from the first approximately 50% of enrolled patients; subsequent efficacy analyses included data from a larger data set collected up to futility declaration and followed prespecified statistical analyses. The primary endpoint was met in EMERGE (difference of −0.39 for highdose aducanumab vs placebo [95% CI, −0.69 to −0.09; P=.012; 22% decrease]) but not in ENGAGE (difference of 0.03, [95% CI, −0.26 to 0.33; P=.833; 2% increase]). Results of biomarker substudies confirmed target engagement and dose-dependent reduction in markers of Alzheimer's disease pathophysiology. The most common adverse event was amyloid-related imaging abnormalities-edema. CONCLUSIONS: Data from EMERGE demonstrated a statistically significant change across all four primary and secondary clinical endpoints. ENGAGE did not meet its primary or secondary endpoints. A dose-and time-dependent reduction in pathophysiological markers of Alzheimer 's disease was observed in both trials.

Objective Although twin and family studies have shown Attention Deficit/Hyperactivity Disorder (ADHD) to be highly heritable, genetic variants influencing the trait at a genome-wide significant level have yet to be identified. As prior genome-wide association scans (GWAS) have not yielded significant results, we conducted a meta-analysis of existing studies to boost statistical power. Method We used data from four projects: a) the Children’s Hospital of Philadelphia (CHOP), b) phase I of the International Multicenter ADHD Genetics project (IMAGE), c) phase II of IMAGE (IMAGE II), and d) the Pfizer funded study from the University of California, Los Angeles, Washington University and the Massachusetts General Hospital (PUWMa). The final sample size consisted of 2,064 trios, 896 cases and 2,455 controls. For each study, we imputed HapMap SNPs, computed association test statistics and transformed them to Z-scores, and then combined weighted Z-scores in a meta-analysis. Results No genome-wide significant associations were found, although an analysis of candidate genes suggests they may be involved in the disorder. Conclusions Given that ADHD is a highly heritable disorder, our negative results suggest that the effects of common ADHD risk variants must, individually, be very small or that other types of variants, e.g. rare ones, account for much of the disorder’s heritability.

Schizophrenia and bipolar disorder are two distinct diagnoses that share symptomology. Understanding the genetic factors contributing to the shared and disorder-specific symptoms will be crucial for improving diagnosis and treatment. In genetic data consisting of 53,555 cases (20,129 bipolar disorder [BD], 33,426 schizophrenia [SCZ]) and 54,065 controls, we identified 114 genome-wide significant loci implicating synaptic and neuronal pathways shared between disorders. Comparing SCZ to BD (23,585 SCZ, 15,270 BD) identified four genomic regions including one with disorder-independent causal variants and potassium ion response genes as contributing to differences in biology between the disorders. Polygenic risk score (PRS) analyses identified several significant correlations within case-only phenotypes including SCZ PRS with psychotic features and age of onset in BD. For the first time, we discover specific loci that distinguish between BD and SCZ and identify polygenic components underlying multiple symptom dimensions. These results point to the utility of genetics to inform symptomology and potential treatment.