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.

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.

Numerous lines of inquiry implicate connectivity as a central abnormality in schizophrenia. Myelination and factors that affect myelination, such as the function of oligodendroglia, are critical processes that could profoundly affect neuronal connectivity, especially given the diffuse distribution of oligodendrocytes and the widespread distribution of brain regions that have been implicated in schizophrenia. Multiple lines of evidence now converge to implicate oligodendroglia and myelin in schizophrenia. Imaging and neurocytochemical evidence, similarities with demyelinating diseases, age-related changes in white matter, myelin-related gene abnormalities, and morphologic abnormalities in the oligodendroglia demonstrated in schizophrenic brains are all examined in light of the hypothesis that oligodendroglial dysfunction and even death, with subsequent abnormalities in myelin maintenance and repair, contribute to the schizophrenic syndrome.

Schizophrenia is a psychiatric disorder with onset in late adolescence and unclear etiology characterized by both positive and negative symptoms, as well as cognitive deficits. To identify copy number variations (CNVs) that increase the risk of schizophrenia, we performed a whole-genome CNV analysis on a cohort of 977 schizophrenia cases and 2,000 healthy adults of European ancestry who were genotyped with 1.7 million probes. Positive findings were evaluated in an independent cohort of 758 schizophrenia cases and 1,485 controls. The Gene Ontology synaptic transmission family of genes was notably enriched for CNVs in the cases (P = 1.5 × 10 −7 ). Among these, CACNA1B and DOC2A, both calcium-signaling genes responsible for neuronal excitation, were deleted in 16 cases and duplicated in 10 cases, respectively. In addition, RET and RIT2, both ras-related genes important for neural crest development, were significantly affected by CNVs. RET deletion was exclusive to seven cases, and RIT2 deletions were overrepresented common variant CNVs in the schizophrenia cases. Our results suggest that novel variations involving the processes of synaptic transmission contribute to the genetic susceptibility of schizophrenia.genetics | genomics | neurobiology