Schizophrenia is a highly heritable disorder. Genetic risk is conferred by a large number of alleles, including common alleles of small effect that might be detected by genome-wide association studies. Here we report a multi-stage schizophrenia genome-wide association study of up to 36,989 cases and 113,075 controls. We identify 128 independent associations spanning 108 conservatively defined loci that meet genome-wide significance, 83 of which have not been previously reported. Associations were enriched among genes expressed in brain, providing biological plausibility for the findings. Many findings have the potential to provide entirely new insights into aetiology, but associations at DRD2 and several genes involved in glutamatergic neurotransmission highlight molecules of known and potential therapeutic relevance to schizophrenia, and are consistent with leading pathophysiological hypotheses. Independent of genes expressed in brain, associations were enriched among genes expressed in tissues that have important roles in immunity, providing support for the speculated link between the immune system and schizophrenia.

/pdf/biological-insights-from-108-schizophrenia-associated-3rfvth8dm9.pdf

Biological insights from 108 schizophrenia-associated genetic loci

Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.

/pdf/ld-score-regression-distinguishes-confounding-from-58i9nc2cv7.pdf

LD score regression distinguishes confounding from polygenicity in genome-wide association studies :

Identifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual-level genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique-cross-trait LD Score regression-for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use this method to estimate 276 genetic correlations among 24 traits. The results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity, and educational attainment and several diseases. These results highlight the power of genome-wide analyses, as there currently are no significantly associated SNPs for anorexia nervosa and only three for educational attainment.

/pdf/an-atlas-of-genetic-correlations-across-human-diseases-and-11fyqwnh4c.pdf

An atlas of genetic correlations across human diseases and traits.

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.

https://sites.oxy.edu/clint/physio/article/Geneticrelationshipbetweenfivepsychiatricdisorders.pdf

Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs

Major depressive disorder (MDD) is a debilitating disease that is characterized by depressed mood, diminished interests, impaired cognitive function and vegetative symptoms, such as disturbed sleep or appetite. MDD occurs about twice as often in women than it does in men and affects one in six adults in their lifetime. The aetiology of MDD is multifactorial and its heritability is estimated to be approximately 35%. In addition, environmental factors, such as sexual, physical or emotional abuse during childhood, are strongly associated with the risk of developing MDD. No established mechanism can explain all aspects of the disease. However, MDD is associated with alterations in regional brain volumes, particularly the hippocampus, and with functional changes in brain circuits, such as the cognitive control network and the affective-salience network. Furthermore, disturbances in the main neurobiological stress-responsive systems, including the hypothalamic-pituitary-adrenal axis and the immune system, occur in MDD. Management primarily comprises psychotherapy and pharmacological treatment. For treatment-resistant patients who have not responded to several augmentation or combination treatment attempts, electroconvulsive therapy is the treatment with the best empirical evidence. In this Primer, we provide an overview of the current evidence of MDD, including its epidemiology, aetiology, pathophysiology, diagnosis and treatment.

/pdf/major-depressive-disorder-4dnf4f9a0d.pdf

Major depressive disorder

http://news.medlive.cn/uploadfile/20130709/13733376781389.pdf

Jordan W. Smoller

Papers

Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis