Neuropsychological effects of the CSMD1 genome‐wide associated schizophrenia risk variant rs10503253

The single‐nucleotide polymorphism (SNP) rs10503253, located within the CUB and Sushi multiple domains‐1 (CSMD1) gene on 8p23.2, was recently identified as genome‐wide significant for schizophrenia (SZ), but is of unknown function. We investigated the neurocognitive effects of this CSMD1 variant in vivo in patients and healthy participants using behavioral and imaging measures of brain structure and function. We compared carriers and non‐carriers of the risk ‘A’ allele on measures of neuropsychological performance typically impaired in SZ (general cognitive ability, episodic and working memory and attentional control) in independent samples of Irish patients (n = 387) and controls (n = 171) and German patients (205) and controls (n = 533). Across these groups, the risk ‘A’ allele at CSMD1 was associated with deleterious effects across a number of neurocognitive phenotypes. Specifically, the risk allele was associated with poorer performance on neuropsychological measures of general cognitive ability and memory function but not attentional control. These effects, while significant, were subtle, and varied between samples. Consistent with previous evidence suggesting that CSMD1 may be involved in brain mechanisms related to memory and learning, these data appear to reflect the deleterious effects of the identified ‘A’ risk allele on neurocognitive function, possibly as part of the mechanism by which CSMD1 is associated with SZ risk.     

Functional validation of a finding from a mouse genome-wide association study shows that Azi2 influences the acute locomotor stimulant response to methamphetamine

In a previous genome-wide association study (GWAS) using outbred Carworth Farms White (CFW) mice, we identified a locus that influenced the stimulant response to methamphetamine and colocalized with an eQTL for Azi2. Based on those findings, we hypothesized that heritable differences in Azi2 expression were causally related to the differential response to methamphetamine. To test that hypothesis, we created a mutant Azi2 allele on an inbred C57BL/6J background. The mutant allele enhanced the locomotor response to methamphetamine. However, the GWAS had suggested that lower Azi2 would decrease the locomotor response to methamphetamine. We also sought to explore the mechanism by which Azi2 influenced methamphetamine sensitivity. A recent publication reported that the 3′UTR of Azi2 mRNA downregulates the expression of Slc6a3, which encodes the dopamine transporter, which is a key target of methamphetamine. We evaluated the relationship between Azi2, Azi2 3′UTR and Slc6a3 expression in the ventral tegmental area of wildtype, mutant Azi2 heterozygotes and mutant Azi2 homozygotes and in a new cohort of outbred CFW mice where both allele mapped in our prior GWAS were segregating. We did not observe any correlation between Azi2 and Slc6a3 in either cohort. However, RNA sequencing confirmed that the Azi2 mutation altered Azi2 expression and also revealed a number of potentially important genes and pathways that were regulated by Azi2, including the metabotropic glutamate receptor group III pathway and nicotinic acetylcholine receptor signaling pathway. Our results support a role for Azi2 in methamphetamine sensitivity; however, the exact mechanism does not appear to involve regulation of Slc6a3.     

MultiGWAS: An integrative tool for Genome Wide Association Studies in tetraploid organisms

The genome‐wide association studies (GWASs) are essential to determine the genetic bases of either ecological or economic phenotypic variation across individuals within populations of the model and nonmodel organisms. For this research question, the GWAS replication testing different parameters and models to validate the results'' reproducibility is common. However, straightforward methodologies that manage both replication and tetraploid data are still missing. To solve this problem, we designed the MultiGWAS, a tool that does GWAS for diploid and tetraploid organisms by executing in parallel four software packages, two designed for polyploid data (GWASpoly and SHEsis) and two designed for diploid data (GAPIT and TASSEL). MultiGWAS has several advantages. It runs either in the command line or in a graphical interface; it manages different genotype formats, including VCF. Moreover, it allows control for population structure, relatedness, and several quality control checks on genotype data. Besides, MultiGWAS can test for additive and dominant gene action models, and, through a proprietary scoring function, select the best model to report its associations. Finally, it generates several reports that facilitate identifying false associations from both the significant and the best‐ranked association Single Nucleotide Polymorphisms (SNPs) among the four software packages. We tested MultiGWAS with public tetraploid potato data for tuber shape and several simulated data under both additive and dominant models. These tests demonstrated that MultiGWAS is better at detecting reliable associations than using each of the four software packages individually. Moreover, the parallel analysis of polyploid and diploid software that only offers MultiGWAS demonstrates its utility in understanding the best genetic model behind the SNP association in tetraploid organisms. Therefore, MultiGWAS probed to be an excellent alternative for wrapping GWAS replication in diploid and tetraploid organisms in a single analysis environment.     

Candidate biomarkers in psychiatric disorders: state of the field

The field of psychiatry is hampered by a lack of robust, reliable and valid biomarkers that can aid in objectively diagnosing patients and providing individualized treatment recommendations. Here we review and critically evaluate the evidence for the most promising biomarkers in the psychiatric neuroscience literature for autism spectrum disorder, schizophrenia, anxiety disorders and post-traumatic stress disorder, major depression and bipolar disorder, and substance use disorders. Candidate biomarkers reviewed include various neuroimaging, genetic, molecular and peripheral assays, for the purposes of determining susceptibility or presence of illness, and predicting treatment response or safety. This review highlights a critical gap in the biomarker validation process. An enormous societal investment over the past 50 years has identified numerous candidate biomarkers. However, to date, the overwhelming majority of these measures have not been proven sufficiently reliable, valid and useful to be adopted clinically. It is time to consider whether strategic investments might break this impasse, focusing on a limited number of promising candidates to advance through a process of definitive testing for a specific indication. Some promising candidates for definitive testing include the N170 signal, an event-related brain potential measured using electroencephalography, for subgroup identification within autism spectrum disorder; striatal resting-state functional magnetic resonance imaging (fMRI) measures, such as the striatal connectivity index (SCI) and the functional striatal abnormalities (FSA) index, for prediction of treatment response in schizophrenia; error-related negativity (ERN), an electrophysiological index, for prediction of first onset of generalized anxiety disorder, and resting-state and structural brain connectomic measures for prediction of treatment response in social anxiety disorder. Alternate forms of classification may be useful for conceptualizing and testing potential biomarkers. Collaborative efforts allowing the inclusion of biosystems beyond genetics and neuroimaging are needed, and online remote acquisition of selected measures in a naturalistic setting using mobile health tools may significantly advance the field. Setting specific benchmarks for well-defined target application, along with development of appropriate funding and partnership mechanisms, would also be crucial. Finally, it should never be forgotten that, for a biomarker to be actionable, it will need to be clinically predictive at the individual level and viable in clinical settings.     

A new method for post Genome-Wide Association Study (GWAS) analysis of colorectal cancer in Taiwan

Recently, single nucleotide polymorphisms (SNPs) located in specific loci or genes have been identified associated with susceptibility to colorectal cancer (CRC) in Genome-Wide Association Studies (GWAS). However, in different ethnicities and regions, the genetic variations and the environmental factors can widely vary. Therefore, here we propose a post-GWAS analysis method to investigate the CRC susceptibility SNPs in Taiwan by conducting a replication analysis and bioinformatics analysis. One hundred and forty-four significant SNPs from published GWAS results were collected by a literature survey, and two hundred and eighteen CRC samples and 385 normal samples were collected for post-GWAS analysis. Finally, twenty-six significant SNPs were identified and reported as associated with susceptibility to colorectal cancer, other cancers, obesity, and celiac disease in a previous GWAS study. Functional analysis results of 26 SNPs indicate that most biological processes identified are involved in regulating immune responses and apoptosis. In addition, an efficient prediction model was constructed by applying Jackknife feature selection and ANOVA testing. As compared to another risk prediction model of CRC for European Caucasians population, which performs 0.616 of AUC by using 54 SNPs, the proposed model shows good performance in predicting CRC risk within the Taiwanese population, i.e., 0.724 AUC by using 16 SNPs. We believe that the proposed risk prediction model is highly promising for predicting CRC risk within the Taiwanese population. In addition, the functional analysis results could be helpful to explore the potential associated regulatory mechanisms that may be involved in CRC development.     

Challenges in calculation of the gamma index in radiotherapy – Towards good practice

The gamma index (γ) is one of the most commonly used metrics for the verification of complex modulated radiotherapy. The mathematical definition of the γ is computationally expensive and various techniques have been reported to speed up the calculation either by mathematically refining the γ or employing various computational techniques. These techniques can cause variation in output with different software implementations. The γ has traditionally been used to compare a 2D measured plane against a 2D or 3D dose distribution. Recently, software algorithm and hardware improvements have led to the possibility of using measured 2D data from commercial detector arrays to reconstruct a 3D-dose distribution and perform a volumetric comparison against the treatment planning system (TPS). A limitation in this approach is that commercial detector arrays have so far been limited by their spatial resolution which may affect the accuracy of the reconstructed 3D volume and subsequently the γ calculation. Additionally, 3D versus 3D γ comparison adds a layer of complication in the calculation of the γ given the increase in the number of calculation points and the result cannot be as easily interpreted in the same way as 2D comparison. This review summarises and highlights the computational challenges of the γ calculation and sheds light on some of these issues by means of a bespoke MATLAB software to demonstrate the impact of interpolation, γ search distance, resolution and 2D and 3D calculations. Finally, a recommendation is made on the minimum information that should be reported when publishing γ results.     

Quantification of the effect of electrical and thermal parameters on radiofrequency ablation for concentric tumour model of different sizes

Radiofrequency ablation (RFA) has been increasingly used in treating cancer for multitude of situations in various tissue types. To perform the therapy safely and reliably, the effect of critical parameters needs to be known beforehand. Temperature plays an important role in the outcome of the therapy and any uncertainties in temperature assessment can be lethal. This study presents the RFA case of fixed tip temperature where we’ve analysed the effect of electrical conductivity, thermal conductivity and blood perfusion rate of the tumour and surrounding normal tissue on the radiofrequency ablation. Ablation volume was chosen as the characteristic to be optimised and temperature control was achieved via PID controller. The effect of all 6 parameters each having 3 levels was quantified with minimum number of experiments harnessing the fractional factorial characteristic of Taguchi’s orthogonal arrays. It was observed that as the blood perfusion increases the ablation volume decreases. Increasing electrical conductivity of the tumour results in increase of ablation volume whereas increase in normal tissue conductivity tends to decrease the ablation volume and vice versa. Likewise, increasing thermal conductivity of the tumour results in enhanced ablation volume whereas an increase in thermal conductivity of the surrounding normal tissue has a debilitating effect on the ablation volume and vice versa. With increase in the size of the tumour (i.e., 2–3 cm) the effect of each parameter is not linear. The parameter effect varies with change in size of the tumour that is manifested by the different gradient observed in ablation volume. Most important is the relative insensitivity of ablation volume to blood perfusion rate for smaller tumour size (2 cm) that is also in accordance with the previous results presented in literature. These findings will provide initial insight for safe, reliable and improved treatment planning perceptively.