The application of polygenic risk scores (PRS) has become routine across genetic research. Among a range of applications, PRS are exploited to assess shared aetiology between phenotypes, to evaluate the predictive power of genetic data for use in clinical settings, and as part of experimental studies in which, for example, experiments are performed on individuals, or their biological samples (eg. tissues, cells), at the tails of the PRS distribution and contrasted. As GWAS sample sizes increase and PRS become more powerful, they are set to play a key role in personalised medicine. However, despite the growing application and importance of PRS, there are limited guidelines for performing PRS analyses, which can lead to inconsistency between studies and misinterpretation of results. Here we provide detailed guidelines for performing polygenic risk score analyses relevant to different methods for their calculation, outlining standard quality control steps and offering recommendations for bestpractice. We also discuss different methods for the calculation of PRS, common misconceptions regarding the interpretation of results and future challenges.Genome-wide association studies (GWAS) have identified a large number of genetic variants, typically single nucleotide polymorphisms (SNP), associated with a wide range of complex traits [1-3]. However, the majority of these variants have a small effect and typically correspond to a small fraction of truly associated variants, meaning that they have limited predictive power [4][5][6]. Using a linear mixed model in the Genome-wide Complex Trait Analysis software (GCTA) [7], Yang et al (2010) demonstrated that much of the heritability of height can be explained by evaluating the effects of all SNPs simultaneously [6]. Subsequently, statistical techniques such as LD score regression (LDSC) [8,9] and the polygenic risk score (PRS) method [4,10] have also aggregated the effects of variants across the genome to estimate heritability, to infer genetic overlap between traits and to predict phenotypes based on genetic profile or that of other phenotypes [4,5,[8][9][10].While GCTA, LDSC and PRS can all be exploited to infer heritability and shared aetiology among complex traits, PRS is the only approach that provides an estimate of genetic propensity to a trait at the individual-level. In the standard approach [4,[11][12][13], polygenic risk scores are calculated by computing the sum of risk alleles corresponding to a phenotype of .

Polygenic scores (PGS) summarize the genetic contribution of a person's genotype to a disease or phenotype. They can be used to group participants into different risk categories for diseases, and are also used as covariates in epidemiological analyses. A number of possible ways of calculating PGS have been proposed, and recently there is much interest in methods that incorporate information available in published summary statistics. As there is no inherent information on linkage disequilibrium (LD) in summary statistics, a pertinent question is how we can use LD information available elsewhere to supplement such analyses. To answer this question, we propose a method for constructing PGS using summary statistics and a reference panel in a penalized regression framework, which we call lassosum. We also propose a general method for choosing the value of the tuning parameter in the absence of validation data. In our simulations, we showed that pseudovalidation often resulted in prediction accuracy that is comparable to using a dataset with validation phenotype and was clearly superior to the conservative option of setting the tuning parameter of lassosum to its lowest value. We also showed that lassosum achieved better prediction accuracy than simple clumping and P-value thresholding in almost all scenarios. It was also substantially faster and more accurate than the recently proposed LDpred.

Aggressive behaviour and victimization are common among severely mentally ill people requiring hospitalisation in the inner city. Rates of violent crime are higher than in the general population.

Men and women with severe mental illness who have a history of CD by mid-adolescence are at increased risk for aggressive behaviour and violent crime. These patients are easily identifiable and may benefit from learning-based treatments aimed at reducing antisocial behaviour. Longitudinal, prospective investigations are needed to understand why CD is more common among people with than without schizophrenia.

Supplementary data are available at Bioinformatics online.

Hibernating mammals conserve energy in the winter by undergoing prolonged bouts of torpor, interspersed with brief arousals back to euthermia. These bouts are accompanied by a suite of reversible physiological and biochemical changes; however, much remains to be discovered about the molecular mechanisms involved. Given the seasonal nature of hibernation, it stands to reason that underlying plastic epigenetic mechanisms should exist. One such form of epigenomic regulation involves the reversible modification of cytosine bases in DNA by methylation. DNA methylation is well known to be a mechanism that confers upon DNA its cellular identity during differentiation in response to innate developmental cues. However, it has recently been hypothesized that DNA methylation also acts as a mechanism for adapting genome function to changing external environmental and experiential signals over different time scales, including during adulthood. Here, we tested the hypothesis that DNA methylation is altered during hibernation in adult wild animals. This study evaluated global changes in DNA methylation in response to hibernation in the liver and skeletal muscle of thirteen-lined ground squirrels along with changes in expression of DNA methyltransferases (DNMT1/3B) and methyl binding domain proteins (MBDs). A reduction in global DNA methylation occurred in muscle during torpor phases whereas significant changes in DNMTs and MBDs were seen in both tissues. We also report dynamic changes in DNA methylation in the promoter of the myocyte enhancer factor 2C (mef2c) gene, a candidate regulator of metabolism in skeletal muscle. Taken together, these data show that genomic DNA methylation is dynamic across torporarousal bouts during winter hibernation, consistent with a role for this regulatory mechanism in contributing to the hibernation phenotype.

In order to elevate levels of psychosocial functioning among men with schizophrenia, clinicians need to assess victimisation experiences, and, when present, design and implement interventions to help patients protect themselves. In addition, depression needs to be identified and treated, and compliance with antipsychotic medication assured.

Targeted next generation sequencing of gene panels has become a popular tool for the genetic diagnosis of hypertrophic (HCM) and dilated cardiomyopathy (DCM). However, it is uncertain whether the use of Whole Exome Sequencing (WES) represents a more effective approach for diagnosis of cases with HCM and DCM. In this study, we performed indirect comparisons of the coverage and diagnostic yield of WES on genes and variants related to HCM and DCM versus 4 different commercial gene panels using 40 HCM and DCM patients, assuming perfect coverage in those panels. We identified 6 pathogenic or likely pathogenic among 14 HCM patients (diagnostic yield 43%). 3 pathogenic or likely pathogenic were found among the 26 DCM patients (diagnostic yield 12%). The coverage was similar to that of four existing commercial gene panels due to the clustering of mutation within MYH7, MYBPC3, TPM1, TNT2, and TTN. Moreover, the coverage of WES appeared inadequate for TNNI3 and PLN. We conclude that most of the pathogenic variants for HCM and DCM can be found within a small number of genes which were covered by all the commercial gene panels, and the application of WES did not increase diagnostic yield.