Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways

Genome-wide association studies (GWAS) of psychiatric disorders have identified multiple genetic associations with such disorders, but better methods are needed to derive the underlying biological mechanisms that these signals indicate. We sought to identify biological pathways in GWAS data from over 60,000 participants from the Psychiatric Genomics Consortium. We developed an analysis framework to rank pathways that requires only summary statistics. We combined this score across disorders to find common pathways across three adult psychiatric disorders: schizophrenia, major depression and bipolar disorder. Histone methylation processes showed the strongest association, and we also found statistically significant evidence for associations with multiple immune and neuronal signaling pathways and with the postsynaptic density. Our study indicates that risk variants for psychiatric disorders aggregate in particular biological pathways and that these pathways are frequently shared between disorders. Our results confirm known mechanisms and suggest several novel insights into the etiology of psychiatric disorders

Elucidating the relationship between DISC1, NDEL1 and NDE1 and the risk for schizophrenia: Evidence of epistasis and competitive binding

DISC1 influences susceptibility to psychiatric disease and related phenotypes. Intact functions of DISC1 and its binding partners, NDEL1 and NDE1, are critical to neurodevelopmental processes aberrant in schizophrenia (SZ). Despite evidence of an NDEL1–DISC1 protein interaction, there have been no investigations of the NDEL1 gene or the relationship between NDEL1 and DISC1 in SZ. We genotyped six NDEL1 single-nucleotide polymorphisms (SNPs) in 275 Caucasian SZ patients and 200 controls and tested for association and interaction between the functional SNP Ser704Cys in DISC1 and NDEL1. We also evaluated the relationship between NDE1 and DISC1 genotype and SZ. Finally, in a series of in vitro assays, we determined the binding profiles of NDEL1 and NDE1, in relation to DISC1 Ser704Cys. We observed a single haplotype block within NDEL1; the majority of variation was captured by NDEL1 rs1391768. We observed a significant interaction between rs1391768 and DISC1 Ser704Cys, with the effect of NDEL1 on SZ evident only against the background of DISC1 Ser704 homozygosity. Secondary analyses revealed no direct relationship between NDE1 genotype and SZ; however, there was an opposite pattern of risk for NDE1 genotype when conditioned on DISC1 Ser704Cys, with NDE1 rs3784859 imparting a significant effect but only in the context of a Cys-carrying background. In addition, we report opposing binding patterns of NDEL1 and NDE1 to Ser704 versus Cys704, at the same DISC1 binding domain. These data suggest that NDEL1 significantly influences risk for SZ via an interaction with DISC1. We propose a model where NDEL1 and NDE1 compete for binding with DISC1

Independent evidence for an association between general cognitive ability and a genetic locus for educational attainment

Cognitive deficits and reduced educational achievement are common in psychiatric illness; understanding the genetic basis of cognitive and educational deficits may be informative about the etiology of psychiatric disorders. A recent, large genome-wide association study (GWAS) reported a genome-wide significant locus for years of education, which subsequently demonstrated association to general cognitive ability (g) in overlapping cohorts. The current study was designed to test whether GWAS hits for educational attainment are involved in general cognitive ability in an independent, large-scale collection of cohorts. Using cohorts in the Cognitive Genomics Consortium (COGENT; up to 20,495 healthy individuals), we examined the relationship between g and variants associated with educational attainment. We next conducted meta-analyses with 24,189 individuals with neurocognitive data from the educational attainment studies, and then with 53,188 largely independent individuals from a recent GWAS of cognition. A SNP (rs1906252) located at chromosome 6q16.1, previously associated with years of schooling, was significantly associated with g (P=1.47x10(-4)) in COGENT. The first joint analysis of 43,381 non-overlapping individuals for this a priori-designated locus was strongly significant (P=4.94x10(-7)), and the second joint analysis of 68,159 non-overlapping individuals was even more robust (P=1.65x10(-9)). These results provide independent replication, in a large-scale dataset, of a genetic locus associated with cognitive function and education. As sample sizes grow, cognitive GWAS will identify increasing numbers of associated loci, as has been accomplished in other polygenic quantitative traits, which may be relevant to psychiatric illness. (c) 2015 Wiley Periodicals, Inc

Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder

Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved. The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk

Concerns about the use of polygenic embryo screening for psychiatric and cognitive traits

Private companies have begun offering services to allow parents undergoing in-vitro fertilisation to screen embryos for genetic risk of complex diseases, including psychiatric disorders. This procedure, called polygenic embryo screening, raises several difficult scientific and ethical issues, as discussed in this Personal View. Polygenic embryo screening depends on the statistical properties of polygenic risk scores, which are complex and not well studied in the context of this proposed clinical application. The clinical, social, and ethical implications of polygenic embryo screening have barely been discussed among relevant stakeholders. To our knowledge, the International Society of Psychiatric Genetics is the first professional biomedical organisation to issue a statement regarding polygenic embryo screening. For the reasons discussed in this Personal View, the Society urges caution and calls for additional research and oversight on the use of polygenic embryo screening

Specific star-formation and the relation to stellar mass from 0<z<2 as seen in the far-infrared at 70 and 160mu

We use the Spitzer Wide-area InfraRed Extragalactic Legacy Survey (SWIRE) to explore the specific star-formation activity of galaxies and their evolution near the peak of the cosmic far-infrared (FIR) background at 70 and 160um. We use a stacking analysis to determine the mean FIR properties of well defined subsets of galaxies at flux levels well below the FIR catalogue detection limits of SWIRE and other Spitzer surveys. We tabulate the contribution of different subsets of galaxies to the FIR background at 70um and 160um. These long wavelengths provide a good constraint on the bolometric, obscured emission. The large area provides good constraints at low z and in finer redshift bins than previous work. At all redshifts we find that the specific FIR Luminosity (sLFIR) decreases with increasing mass, following a trend L_FIR/M* propto M_* ^beta with beta =-0.38\pm0.14. This is a more continuous change than expected from the {Delucia2007} semi-analytic model suggesting modifications to the feedback prescriptions. We see an increase in the sLFIR by about a factor of ~100 from 0<z<2 and find that the sLFIR evolves as (1+z)^alpha with alpha=4.4\pm0.3 for galaxies with 10.5 < log M*/Msun < 12. This is considerably steeper than the {Delucia2007} semi-analytic model (alpha \sim 2.5). When separating galaxies into early and late types on the basis of the optical/IR spectral energy distributions we find that the decrease in sLFIR with stellar mass is stronger in early type galaxies (beta ~ -0.46), while late type galaxies exhibit a flatter trend (beta \sim -0.15). The evolution is strong for both classes but stronger for the early type galaxies. The early types show a trend of decreasing strength of evolution as we move from lower to higher masses while the evolution of the late type galaxies has little dependence on stellar mass. We suggest that in late-type galaxies we are seeing a consistently declining sSFR..Comment: v2 Update doesn't change the content of the paper, but now includes data files for the plots Fig 5-13 (all.plotdat, spi.plotdat and ell.plotdat on arXiv package