scholarly journals No association between genetic variants in MAOA, OXTR, and AVPR1a and cooperative strategies

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244189
Author(s):  
María I. Rivera-Hechem ◽  
Carlos Rodríguez-Sickert ◽  
Ricardo A. Guzmán ◽  
Tadeo Ramírez-Parada ◽  
Felipe Benavides ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Statistical Power ◽  
Genetic Basis ◽  
Small Samples ◽  
Social Traits ◽  
Cooperative Strategies ◽  
Social Motivations ◽  
Candidate Gene Studies ◽  
Power Problem

The effort to understand the genetic basis of human sociality has been encouraged by the diversity and heritability of social traits like cooperation. This task has remained elusive largely because most studies of sociality and genetics use sample sizes that are often unable to detect the small effects that single genes may have on complex social behaviors. The lack of robust findings could also be a consequence of a poor characterization of social phenotypes. Here, we explore the latter possibility by testing whether refining measures of cooperative phenotypes can increase the replication of previously reported associations between genetic variants and cooperation in small samples. Unlike most previous studies of sociality and genetics, we characterize cooperative phenotypes based on strategies rather than actions. Measuring strategies help differentiate between similar actions with different underlaying social motivations while controlling for expectations and learning. In an admixed Latino sample (n = 188), we tested whether cooperative strategies were associated with three genetic variants thought to influence sociality in humans—MAOA-uVNTR, OXTR rs53576, and AVPR1 RS3. We found no association between cooperative strategies and any of the candidate genetic variants. Since we were unable to replicate previous observations our results suggest that refining measurements of cooperative phenotypes as strategies is not enough to overcome the inherent statistical power problem of candidate gene studies.

scholarly journals Neurocognitive phenomics: examining the genetic basis of cognitive abilities

2012 ◽  
Vol 43 (10) ◽  
pp. 2027-2036 ◽  
Author(s):  
G. Donohoe ◽  
I. J. Deary ◽  
D. C. Glahn ◽  
A. K. Malhotra ◽  
K. E. Burdick
Keyword(s):  
Psychiatric Disorders ◽  
Cognitive Deficits ◽  
Genetic Variants ◽  
Cognitive Abilities ◽  
Genetic Basis ◽  
Phenotypic Characterization ◽  
Cognitive Studies ◽  
Risk Variants ◽  
Genome Wide

Cognitive deficits are core to the disability associated with many psychiatric disorders. Both variation in cognition and psychiatric risk show substantial heritability, with overlapping genetic variants contributing to both. Unsurprisingly, therefore, these fields have been mutually beneficial: just as cognitive studies of psychiatric risk variants may identify genes involved in cognition, so too can genome-wide studies based on cognitive phenotypes lead to genes relevant to psychiatric aetiology. The purpose of this review is to consider the main issues involved in the phenotypic characterization of cognition, and to describe the challenges associated with the transition to genome-wide approaches. We conclude by describing the approaches currently being taken by the international consortia involving many investigators in the field internationally (e.g. Cognitive Genomics Consortium; COGENT) to overcome these challenges.

Effect and in silico characterization of genetic variants associated with severe spermatogenic disorders in a large Iberian cohort

Andrology ◽  
2021 ◽  
Author(s):  
Miriam Cerván‐Martín ◽  
Lara Bossini‐Castillo ◽  
Rocío Rivera‐Egea ◽  
Nicolás Garrido ◽  
Saturnino Luján ◽  
...  
Keyword(s):  
Genetic Variants ◽  
In Silico ◽  
In Silico Characterization

scholarly journals Variation in the SERPINA6/SERPINA1 locus alters morning plasma cortisol, hepatic corticosteroid binding globulin expression, gene expression in peripheral tissues, and risk of cardiovascular disease

2021 ◽  
Author(s):  
Andrew A. Crawford ◽  
◽  
Sean Bankier ◽  
Elisabeth Altmaier ◽  
Catriona L. K. Barnes ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cardiovascular Disease ◽  
Genetic Variants ◽  
Plasma Cortisol ◽  
Statistical Power ◽  
Mendelian Randomisation ◽  
Eqtl Analysis ◽  
Causative Role ◽  
Peripheral Tissues ◽  
Corticosteroid Binding Globulin

AbstractThe stress hormone cortisol modulates fuel metabolism, cardiovascular homoeostasis, mood, inflammation and cognition. The CORtisol NETwork (CORNET) consortium previously identified a single locus associated with morning plasma cortisol. Identifying additional genetic variants that explain more of the variance in cortisol could provide new insights into cortisol biology and provide statistical power to test the causative role of cortisol in common diseases. The CORNET consortium extended its genome-wide association meta-analysis for morning plasma cortisol from 12,597 to 25,314 subjects and from ~2.2 M to ~7 M SNPs, in 17 population-based cohorts of European ancestries. We confirmed the genetic association with SERPINA6/SERPINA1. This locus contains genes encoding corticosteroid binding globulin (CBG) and α1-antitrypsin. Expression quantitative trait loci (eQTL) analyses undertaken in the STARNET cohort of 600 individuals showed that specific genetic variants within the SERPINA6/SERPINA1 locus influence expression of SERPINA6 rather than SERPINA1 in the liver. Moreover, trans-eQTL analysis demonstrated effects on adipose tissue gene expression, suggesting that variations in CBG levels have an effect on delivery of cortisol to peripheral tissues. Two-sample Mendelian randomisation analyses provided evidence that each genetically-determined standard deviation (SD) increase in morning plasma cortisol was associated with increased odds of chronic ischaemic heart disease (0.32, 95% CI 0.06–0.59) and myocardial infarction (0.21, 95% CI 0.00–0.43) in UK Biobank and similarly in CARDIoGRAMplusC4D. These findings reveal a causative pathway for CBG in determining cortisol action in peripheral tissues and thereby contributing to the aetiology of cardiovascular disease.

scholarly journals The effect of sertraline on emotional processing: secondary analyses of the PANDA randomised controlled trial

2021 ◽  
pp. 1-8
Author(s):  
Norin Ahmed ◽  
Jessica K. Bone ◽  
Gemma Lewis ◽  
Nick Freemantle ◽  
Catherine J. Harmer ◽  
...  
Keyword(s):  
Randomised Controlled Trial ◽  
Emotional Processing ◽  
Statistical Power ◽  
Controlled Trial ◽  
Negative Information ◽  
Small Samples ◽  
Double Blind ◽  
Treatment Allocation ◽  
Randomised Controlled ◽  
Cognitive Neuropsychological

Abstract Background According to the cognitive neuropsychological model, antidepressants reduce symptoms of depression and anxiety by increasing positive relative to negative information processing. Most studies of whether antidepressants alter emotional processing use small samples of healthy individuals, which lead to low statistical power and selection bias and are difficult to generalise to clinical practice. We tested whether the selective serotonin reuptake inhibitor (SSRI) sertraline altered recall of positive and negative information in a large randomised controlled trial (RCT) of patients with depressive symptoms recruited from primary care. Methods The PANDA trial was a pragmatic multicentre double-blind RCT comparing sertraline with placebo. Memory for personality descriptors was tested at baseline and 2 and 6 weeks after randomisation using a computerised emotional categorisation task followed by a free recall. We measured the number of positive and negative words correctly recalled (hits). Poisson mixed models were used to analyse longitudinal associations between treatment allocation and hits. Results A total of 576 participants (88% of those randomised) completed the recall task at 2 and 6 weeks. We found no evidence that positive or negative hits differed according to treatment allocation at 2 or 6 weeks (adjusted positive hits ratio = 0.97, 95% CI 0.90–1.05, p = 0.52; adjusted negative hits ratio = 0.99, 95% CI 0.90–1.08, p = 0.76). Conclusions In the largest individual placebo-controlled trial of an antidepressant not funded by the pharmaceutical industry, we found no evidence that sertraline altered positive or negative recall early in treatment. These findings challenge some assumptions of the cognitive neuropsychological model of antidepressant action.

scholarly journals G-computation and machine learning for estimating the causal effects of binary exposure statuses on binary outcomes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Florent Le Borgne ◽  
Arthur Chatton ◽  
Maxime Léger ◽  
Rémi Lenain ◽  
Yohann Foucher
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Statistical Power ◽  
Small Sample ◽  
Causal Effects ◽  
Small Samples ◽  
Support Vector ◽  
Sample Sizes ◽  
Super Learner ◽  
Small Sample Sizes

AbstractIn clinical research, there is a growing interest in the use of propensity score-based methods to estimate causal effects. G-computation is an alternative because of its high statistical power. Machine learning is also increasingly used because of its possible robustness to model misspecification. In this paper, we aimed to propose an approach that combines machine learning and G-computation when both the outcome and the exposure status are binary and is able to deal with small samples. We evaluated the performances of several methods, including penalized logistic regressions, a neural network, a support vector machine, boosted classification and regression trees, and a super learner through simulations. We proposed six different scenarios characterised by various sample sizes, numbers of covariates and relationships between covariates, exposure statuses, and outcomes. We have also illustrated the application of these methods, in which they were used to estimate the efficacy of barbiturates prescribed during the first 24 h of an episode of intracranial hypertension. In the context of GC, for estimating the individual outcome probabilities in two counterfactual worlds, we reported that the super learner tended to outperform the other approaches in terms of both bias and variance, especially for small sample sizes. The support vector machine performed well, but its mean bias was slightly higher than that of the super learner. In the investigated scenarios, G-computation associated with the super learner was a performant method for drawing causal inferences, even from small sample sizes.

scholarly journals Transcriptome-based identification and characterization of genes responding to imidacloprid in Myzus persicae

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Jianyu Meng ◽  
Xingjiang Chen ◽  
Changyu Zhang
Keyword(s):  
Genetic Basis ◽  
Expression Profiles ◽  
Control Measure ◽  
Effective Control ◽  
Agricultural Pest ◽  
Functional Classification ◽  
Quantitative Real Time Pcr ◽  
Underlying Mechanisms ◽  
Identification And Characterization

Abstract Myzus persicae is a serious and widespread agricultural pest, against which, imidacloprid remains an effective control measure. However, recent reports indicate that this aphid has evolved and developed resistance to imidacloprid. This study aimed to elucidate the underlying mechanisms and genetic basis of this resistance by conducting comparative transcriptomics studies on both imidacloprid-resistant (IR) and imidacloprid-susceptible (IS) M. persicae. The comparative analysis identified 252 differentially expressed genes (DEGs) among the IR and IS M. persicae transcriptomes. These candidate genes included 160 and 92 genes that were down- and up-regulated, respectively, in the imidacloprid-resistant strain. Using functional classification in the GO and KEGG databases, 187 DEGs were assigned to 303 functional subcategories and 100 DEGs were classified into 45 pathway groups. Moreover, several genes were associated with known insecticide targets, cuticle, metabolic processes, and oxidative phosphorylation. Quantitative real-time PCR of 10 DEGs confirmed the trends observed in the RNA sequencing expression profiles. These findings provide a valuable basis for further investigation into the complicated mechanisms of imidacloprid resistance in M. persicae.

scholarly journals Molecular Characterization of Pathogenicity Locus (PaLoc) and tcdC Genetic Diversity Among tcdA+B+ Clostridioides Difficile Clinical Isolates in Tehran, Iran

2020 ◽  
Author(s):  
Mansoor Kodori ◽  
Zohreh Ghalavand ◽  
Abbas Yadegar ◽  
Gita Eslami ◽  
Masoumeh Azimirad ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Molecular Characterization ◽  
Disease Severity ◽  
Genetic Variants ◽  
Rflp Analysis ◽  
Clostridioides Difficile ◽  
Pcr Rflp ◽  
The Common ◽  
Healthcare Associated

Abstract Background: Clostridioides difficile is the main cause of healthcare-associated diarrhea worldwide. It is proposed that certain C. difficile toxinotypes with distinct pathogenicity locus (PaLoc) variants are associated with disease severity and outcomes. Additionally, few studies have described the common C. difficile toxinotypes, and also little is known about the tcdC variants in Iranian isolates. We characterized the toxinotypes and the tcdC genotypes from a collection of Iranian clinical C. difficile tcdA+B+ isolates with known ribotypes (RTs).Methods: Fifty C. difficile isolates with known RTs and carrying the tcdA and tcdB toxin genes were analyzed. Toxinotyping was carried out based on a PCR-RFLP analysis of a 19.6 kb region encompassing the PaLoc. Genetic diversity of the tcdC gene was determined by the sequencing of the gene.Results: Of the 50 C. difficile isolates investigated, five distinct toxinotypes were recognized. Toxinotypes 0 (33/50, 66%) and V (11/50, 22%) were the most frequently found. C. difficile isolates of the toxinotype 0 mostly belonged to RT 001 (12/33, 36.4%), whereas toxinotype V consisted of RT 126 (9/11, 81.8%). The tcdC sequencing showed six variants (35/50, 70%); tcdC-sc3 (24%), tcdC-A (22%), tcdC-sc9 (18%), tcdC-B (2%), tcdC-sc14 (2%), and tcdC-sc15 (2%). The remaining isolates were wild-types (15/50, 30%) in the tcdC gene.Conclusions: The present study demonstrates that the majority of clinical tcdA+B+ isolates of C. difficile frequently harbor tcdC genetic variants. We also found that the RT 001/ toxinotype 0 and the RT 126/ toxinotype V are the most common types among Iranian isolates. Further studies are needed to investigate the putative association of various tcdC genotypes with CDI severity and its recurrence.

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