Brain aging research relies mostly on cross-sectional studies, which infer true changes from age differences. We present longitudinal measures of five-year change in the regional brain volumes in healthy adults. Average and individual differences in volume changes and the effects of age, sex and hypertension were assessed with latent difference score modeling. The caudate, the cerebellum, the hippocampus and the association cortices shrunk substantially. There was minimal change in the entorhinal and none in the primary visual cortex. Longitudinal measures of shrinkage exceeded cross-sectional estimates. All regions except the inferior parietal lobule showed individual differences in change. Shrinkage of the cerebellum decreased from young to middle adulthood, and increased from middle adulthood to old age. Shrinkage of the hippocampus, the entorhinal cortices, the inferior temporal cortex and the prefrontal white matter increased with age. Moreover, shrinkage in the hippocampus and the cerebellum accelerated with age. In the hippocampus, both linear and quadratic trends in incremental age-related shrinkage were limited to the hypertensive participants. Individual differences in shrinkage correlated across some regions, suggesting common causes. No sex differences in age trends except for the caudate were observed. We found no evidence of neuroprotective effects of larger brain size or educational attainment.

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.

Most psychiatric disorders are moderately to highly heritable. The degree to which genetic variation is unique to individual disorders or shared across disorders is unclear. To examine shared genetic etiology, we use genome-wide genotype data from the Psychiatric Genomics Consortium (PGC) for cases and controls in schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). We apply univariate and bivariate methods for the estimation of genetic variation within and covariation between disorders. SNPs explained 17–29% of the variance in liability. The genetic correlation calculated using common SNPs was high between schizophrenia and bipolar disorder (0.68 ± 0.04 s.e.), moderate between schizophrenia and major depressive disorder (0.43 ± 0.06 s.e.), bipolar disorder and major depressive disorder (0.47 ± 0.06 s.e.), and ADHD and major depressive disorder (0.32 ± 0.07 s.e.), low between schizophrenia and ASD (0.16 ± 0.06 s.e.) and non-significant for other pairs of disorders as well as between psychiatric disorders and the negative control of Crohn’s disease. This empirical evidence of shared genetic etiology for psychiatric disorders can inform nosology and encourages the investigation of common pathophysiologies for related disorders.

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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.

We conducted a combined genome-wide association (GWAS) analysis of 7,481 individuals affected with bipolar disorder and 9,250 control individuals within the Psychiatric Genomewide Association Study Consortium Bipolar Disorder group (PGC-BD). We performed a replication study in which we tested 34 independent SNPs in 4,493 independent bipolar disorder cases and 42,542 independent controls and found strong evidence for replication. In the replication sample, 18 of 34 SNPs had P value < 0.05, and 31 of 34 SNPs had signals with the same direction of effect (P = 3.8 × 10−7). In the combined analysis of all 63,766 subjects (11,974 cases and 51,792 controls), genome-wide significant evidence for association was confirmed for CACNA1C and found for a novel gene ODZ4. In a combined analysis of non-overlapping schizophrenia and bipolar GWAS samples we observed strong evidence for association with SNPs in CACNA1C and in the region of NEK4/ITIH1,3,4. Pathway analysis identified a pathway comprised of subunits of calcium channels enriched in the bipolar disorder association intervals. The strength of the replication data implies that increasing samples sizes in bipolar disorder will confirm many additional loci.

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Objective Treatment resistance complicates the management of schizophrenia. Research and clinical translation is limited by inconsistent definitions. To address this we evaluated current approaches and then developed consensus criteria and guidelines. Method A systematic review of randomized antipsychotic clinical trials in treatment resistant schizophrenia was performed. Definitions of treatment resistance were extracted. Subsequently, consensus operationalized criteria were developed by a working group of researchers and clinicians through i) a multi-phase, mixed methods approach; ii) identifying key criteria via an online survey; and iii) meetings to achieve consensus. Results 42 studies met inclusion criteria. Of these, 21 (50%) studies did not provide operationalized criteria, whilst in others, criteria varied considerably, particularly regarding symptom severity, prior treatment duration and antipsychotic dose thresholds. Important for the inability to compare results, only two (5%) studies utilized the same criteria. The consensus group identified minimum and optimal criteria, employing the following principles: 1) current symptoms of a minimum duration and severity determined by a standardized rating scale; 2) ≥moderate functional impairment; 3) prior treatment consisting of ≥2 different antipsychotic trials, each for a minimum duration and dose; 4) adherence systematically assessed and meeting minimum criteria; 5) ideally at least one prospective treatment trial; 6) criteria that clearly separated responsive from treatment resistant patients. Conclusions There is considerable variation in current approaches to defining treatment resistance in schizophrenia. We present consensus guidelines that operationalize criteria for determining and reporting treatment resistance, adequate treatment and treatment response in schizophrenia, providing a benchmark for research and clinical translation.