Thalamic inputs to dorsomedial striatum are involved in inhibitory control: evidence from the five-choice serial reaction time task in rats

Rationale Corticostriatal circuits are widely implicated in the top-down control of attention including inhibitory control and behavioural flexibility. However, recent neurophysiological evidence also suggests a role for thalamic inputs to striatum in behaviours related to salient, reward-paired cues. Objectives Here, we used designer receptors exclusively activated by designer drugs (DREADDs) to investigate the role of parafascicular (Pf) thalamic inputs to the dorsomedial striatum (DMS) using the five-choice serial reaction time task (5CSRTT) in rats. Methods The 5CSRTT requires sustained attention in order to detect spatially and temporally distributed visual cues and provides measures of inhibitory control related to impulsivity (premature responses) and compulsivity (perseverative responses). Rats underwent bilateral Pf injections of the DREADD vector, AAV2-CaMKIIa-HA-hM4D(Gi)-IRES-mCitrine. The DREADD agonist, clozapine N-oxide (CNO; 1 μl bilateral; 3 μM) or vehicle, was injected into DMS 1 h before behavioural testing. Task parameters were manipulated to increase attention load or reduce stimulus predictability respectively. Results We found that inhibition of the Pf-DMS projection significantly increased perseverative responses when stimulus predictability was reduced but had no effect on premature responses or response accuracy, even under increased attentional load. Control experiments showed no effects on locomotor activity in an open field. Conclusions These results complement previous lesion work in which the DMS and orbitofrontal cortex were similarly implicated in perseverative responses and suggest a specific role for thalamostriatal inputs in inhibitory control

Attenuation of Congenital Portosystemic Shunt Reduces Inflammation in Dogs

Liver disease is a major cause of morbidity and mortality. One of the most significant complications in patients with liver disease is the development of neurological disturbances, termed hepatic encephalopathy. The pathogenesis of hepatic encephalopathy is incompletely understood, which has resulted in the development of a wide range of experimental models. Congenital portosystemic shunt is one of the most common congenital disorders diagnosed in client owned dogs. Our recent studies have demonstrated that the pathophysiology of canine hepatic encephalopathy is very similar to human hepatic encephalopathy, which provides strong support for the use of dogs with a congenital portosystemic shunt as a naturally occurring model of human hepatic encephalopathy. Specifically, we have demonstrated an important role for ammonia and inflammation in the development of hepatic encephalopathy in dogs with a congenital portosystemic shunt. Despite the apparent importance of inflammation in driving hepatic encephalopathy in dogs, it is unclear whether inflammation resolves following the successful treatment of liver disease. We hypothesized that haematological and biochemical evidence of inflammation, as gauged by neutrophil, lymphocyte and monocyte concentrations together with C-reactive protein concentrations, would decrease following successful treatment of congenital portosystemic shunts in dogs. One hundred and forty dogs with a congenital portosystemic shunt were enrolled into the study. We found that the proportion of dogs with a monocyte concentration above the reference range was significantly greater in dogs with hepatic encephalopathy at time of initial diagnosis. Importantly, neutrophil and monocyte concentrations significantly decreased following surgical congenital portosystemic shunt attenuation. We also found a significant decrease in C-reactive protein concentrations following surgical attenuation of congenital portosystemic shunts. Our study demonstrates that haematological and biochemical indices of inflammation reduce following successful treatment of the underlying liver disorder

GWAS on longitudinal growth traits reveals different genetic factors influencing infant, child, and adult BMI

Early childhood growth patterns are associated with adult health, yet the genetic factors and the developmental stages involved are not fully understood. Here, we combine genome-wide association studies with modeling of longitudinal growth traits to study the genetics of infant and child growth, followed by functional, pathway, genetic correlation, risk score, and colocalization analyses to determine how developmental timings, molecular pathways, and genetic determinants of these traits overlap with those of adult health. We found a robust overlap between the genetics of child and adult body mass index (BMI), with variants associated with adult BMI acting as early as 4 to 6 years old. However, we demonstrated a completely distinct genetic makeup for peak BMI during infancy, influenced by variation at the LEPR/LEPROT locus. These findings suggest that different genetic factors control infant and child BMI. In light of the obesity epidemic, these findings are important to inform the timing and targets of prevention strategies.Peer reviewe

Genome-wide mega-analysis identifies 16 loci and highlights diverse biological mechanisms in the common epilepsies

The epilepsies affect around 65 million people worldwide and have a substantial missing heritability component. We report a genome-wide mega-analysis involving 15,212 individuals with epilepsy and 29,677 controls, which reveals 16 genome-wide significant loci, of which 11 are novel. Using various prioritization criteria, we pinpoint the 21 most likely epilepsy genes at these loci, with the majority in genetic generalized epilepsies. These genes have diverse biological functions, including coding for ion-channel subunits, transcription factors and a vitamin-B6 metabolism enzyme. Converging evidence shows that the common variants associated with epilepsy play a role in epigenetic regulation of gene expression in the brain. The results show an enrichment for monogenic epilepsy genes as well as known targets of antiepileptic drugs. Using SNP-based heritability analyses we disentangle both the unique and overlapping genetic basis to seven different epilepsy subtypes. Together, these findings provide leads for epilepsy therapies based on underlying pathophysiology

The Early Growth Genetics (EGG) and EArly Genetics and Lifecourse Epidemiology (EAGLE) consortia: design, results and future prospects

The impact of many unfavorable childhood traits or diseases, such as low birth weight and mental disorders, is not limited to childhood and adolescence, as they are also associated with poor outcomes in adulthood, such as cardiovascular disease. Insight into the genetic etiology of childhood and adolescent traits and disorders may therefore provide new perspectives, not only on how to improve wellbeing during childhood, but also how to prevent later adverse outcomes. To achieve the sample sizes required for genetic research, the Early Growth Genetics (EGG) and EArly Genetics and Lifecourse Epidemiology (EAGLE) consortia were established. The majority of the participating cohorts are longitudinal population-based samples, but other cohorts with data on early childhood phenotypes are also involved. Cohorts often have a broad focus and collect(ed) data on various somatic and psychiatric traits as well as environmental factors. Genetic variants have been successfully identified for multiple traits, for example, birth weight, atopic dermatitis, childhood BMI, allergic sensitization, and pubertal growth. Furthermore, the results have shown that genetic factors also partly underlie the association with adult traits. As sample sizes are still increasing, it is expected that future analyses will identify additional variants. This, in combination with the development of innovative statistical methods, will provide detailed insight on the mechanisms underlying the transition from childhood to adult disorders. Both consortia welcome new collaborations. Policies and contact details are available from the corresponding authors of this manuscript and/or the consortium websites

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis.

BACKGROUND: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. RESULTS: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3-5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. CONCLUSIONS: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk

A saturated map of common genetic variants associated with human height

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes(1). Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel(2)) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.A large genome-wide association study of more than 5 million individuals reveals that 12,111 single-nucleotide polymorphisms account for nearly all the heritability of height attributable to common genetic variants

A saturated map of common genetic variants associated with human height.

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries