Deleterious GRM1 mutations in schizophrenia

We analysed a phenotypically well-characterised sample of 450 schziophrenia patients and 605 controls for rare non-synonymous single nucleotide polymorphisms (nsSNPs) in the GRM1 gene, their functional effects and family segregation. GRM1 encodes the metabotropic glutamate receptor 1 (mGluR1), whose documented role as a modulator of neuronal signalling and synaptic plasticity makes it a plausible schizophrenia candidate. In a recent study, this gene was shown to harbour a cluster of deleterious nsSNPs within a functionally important domain of the receptor, in patients with schizophrenia and bipolar disorder. Our Sanger sequencing of the GRM1 coding regions detected equal numbers of nsSNPs in cases and controls, however the two groups differed in terms of the potential effects of the variants on receptor function: 6/6 case-specific and only 1/6 control-specific nsSNPs were predicted to be deleterious. Our in-vitro experimental follow-up of the case-specific mutants showed that 4/6 led to significantly reduced inositol phosphate production, indicating impaired function of the major mGluR1 signalling pathway; 1/6 had reduced cell membrane expression; inconclusive results were obtained in 1/6. Family segregation analysis indicated that these deleterious nsSNPs were inherited. Interestingly, four of the families were affected by multiple neuropsychiatric conditions, not limited to schizophrenia, and the mutations were detected in relatives with schizophrenia, depression and anxiety, drug and alcohol dependence, and epilepsy. Our findings suggest a possible mGluR1 contribution to diverse psychiatric conditions, supporting the modulatory role of the receptor in such conditions as proposed previously on the basis of in vitro experiments and animal studies.     

Rheumatoid factor and anti-citrullinated protein antibody positivity,but not level,are associated with increased mortality in patients with rheumatoid arthritis: results from two large independent cohorts

Introduction

This study aimed to investigate rheumatoid factor (RF) and anti-citrullinated protein antibody (ACPA) status and levels as predictors of mortality in two large cohorts of patients with early inflammatory arthritis (EIA).

Methods

Data from the Norfolk Arthritis Register (NOAR) and Leiden Early Arthritis Clinic (EAC) cohorts were used. At baseline, patients had demographic data and smoking status recorded; RF, ACPA and inflammatory markers were measured in the local laboratories. Patients were flagged with national death registers until death or censor date. Antibody status was stratified as negative, low or high positive by RF and ACPA levels individually. In addition, patients were grouped as seronegative, RF positive, ACPA positive or double antibody (RF and ACPA) positive. Cox regression models explored associations between antibody status and mortality adjusting for age, sex, smoking status, inflammatory markers and year of enrolment.

Results

A total of 4962 patients were included, 64% were female. Median age at onset was 56 (NOAR) and 54 (EAC) years. In NOAR and EAC respectively, 35% and 42% of patients were ACPA/RF positive. When antibody status was stratified as negative, low or high positive, there were no consistent findings between the two cohorts. Double antibody positivity was associated with excess mortality in both cohorts compared to seronegative patients: NOAR and EAC respective adjusted HR (95% confidence interval) 1.35 (1.09 to 1.68) and 1.58 (1.16 to 2.15).

Conclusions

Patients with EIA who are seropositive for both RF and ACPA have increased mortality compared to those who are single positive or seronegative. Antibody level in seropositive patients was not consistently associated with excess mortality.

Electronic supplementary material

The online version of this article (doi:10.1186/s13075-014-0483-3) contains supplementary material, which is available to authorized users.     

Statistical Power to Detect Genetic (Co)Variance of Complex Traits Using SNP Data in Unrelated Samples

We have recently developed analysis methods (GREML) to estimate the genetic variance of a complex trait/disease and the genetic correlation between two complex traits/diseases using genome-wide single nucleotide polymorphism (SNP) data in unrelated individuals. Here we use analytical derivations and simulations to quantify the sampling variance of the estimate of the proportion of phenotypic variance captured by all SNPs for quantitative traits and case-control studies. We also derive the approximate sampling variance of the estimate of a genetic correlation in a bivariate analysis, when two complex traits are either measured on the same or different individuals. We show that the sampling variance is inversely proportional to the number of pairwise contrasts in the analysis and to the variance in SNP-derived genetic relationships. For bivariate analysis, the sampling variance of the genetic correlation additionally depends on the harmonic mean of the proportion of variance explained by the SNPs for the two traits and the genetic correlation between the traits, and depends on the phenotypic correlation when the traits are measured on the same individuals. We provide an online tool for calculating the power of detecting genetic (co)variation using genome-wide SNP data. The new theory and online tool will be helpful to plan experimental designs to estimate the missing heritability that has not yet been fully revealed through genome-wide association studies, and to estimate the genetic overlap between complex traits (diseases) in particular when the traits (diseases) are not measured on the same samples.     

Role of energy systems in two intermittent field tests in women field hockey players

The energetics of 2 field tests that reflect physical performance in intermittent sports (i.e., the Interval Shuttle Sprint Test [ISST] and the Interval Shuttle Run Test [ISRT]) were examined in 21 women field hockey players. The ISST required the players to perform 10 shuttle sprints starting every 20 seconds. During the ISRT, players alternately ran 20-m shuttles for 30 seconds and walked for 15 seconds with increasing speed. Anaerobic and aerobic power tests included Wingate cycle sprints and a .V(O2)max cycle test, respectively. Based on correlation and regression analyses, it was concluded that for the ISST, anaerobic energetic pathways contribute mainly to energy supply for peak sprint time, while aerobic energetic pathways also contribute to energy supply for total sprint time. Energy during the ISRT is supplied mainly by the aerobic energy system. Depending on the aspect of physical performance a coach wants to determine, the ISST or ISRT can be used.     

Genetic mapping and developmental timing of transmission ratio distortion in a mouse interspecific backcross

Background

Sheep carcasses with yellow fat are sporadically observed at Norwegian slaughter houses. This phenomenon is known to be inherited as a recessive trait, and is caused by accumulation of carotenoids in adipose tissue. Two enzymes are known to be important in carotenoid degradation in mammals, and are therefore potential candidate genes for this trait. These are beta-carotene 15,15'-monooxygenase 1 (BCMO1) and the beta-carotene oxygenase 2 (BCO2).

Results

In the present study the coding region of the BCMO1 and the BCO2 gene were sequenced in yellow fat individuals and compared to the corresponding sequences from control animals with white fat. In the yellow fat individuals a nonsense mutation was found in BCO2 nucleotide position 196 (c.196C>T), introducing a stop codon in amino acid position 66. The full length protein consists of 575 amino acids. In spite of a very low frequency of this mutation in the Norwegian AI-ram population, 16 out of 18 yellow fat lambs were found to be homozygous for this mutation.

Conclusion

In the present study a nonsense mutation (c.196C>T) in the beta-carotene oxygenase 2 (BCO2) gene is found to strongly associate with the yellow fat phenotype in sheep. The existence of individuals lacking this mutation, but still demonstrating yellow fat, suggests that additional mutations may cause a similar phenotype in this population. The results demonstrate a quantitatively important role for BCO2 in carotenoid degradation, which might indicate a broad enzyme specificity for carotenoids. Animals homozygous for the mutation are not reported to suffer from any negative health or development traits, pointing towards a minor role of BCO2 in vitamin A formation. Genotyping AI rams for c.196C>T can now be actively used in selection against the yellow fat trait.     

Analysis of pooled DNA samples on high density arrays without prior knowledge of differential hybridization rates

Array based DNA pooling techniques facilitate genome-wide scale genotyping of large samples. We describe a structured analysis method for pooled data using internal replication information in large scale genotyping sets. The method takes advantage of information from single nucleotide polymorphisms (SNPs) typed in parallel on a high density array to construct a test statistic with desirable statistical properties. We utilize a general linear model to appropriately account for the structured multiple measurements available with array data. The method does not require the use of additional arrays for the estimation of unequal hybridization rates and hence scales readily to accommodate arrays with several hundred thousand SNPs. Tests for differences between cases and controls can be conducted with very few arrays. We demonstrate the method on 384 endometriosis cases and controls, typed using Affymetrix Genechip© HindIII 50 K arrays. For a subset of this data there were accurate measures of hybridization rates available. Assuming equal hybridization rates is shown to have a negligible effect upon the results. With a total of only six arrays, the method extracted one-third of the information (in terms of equivalent sample size) available with individual genotyping (requiring 768 arrays). With 20 arrays (10 for cases, 10 for controls), over half of the information could be extracted from this sample.     

Bias, precision and heritability of self-reported and clinically measured height in Australian twins

Many studies of quantitative and disease traits in human genetics rely upon self-reported measures. Such measures are based on questionnaires or interviews and are often cheaper and more readily available than alternatives. However, the precision and potential bias cannot usually be assessed. Here we report a detailed quantitative genetic analysis of stature. We characterise the degree of measurement error by utilising a large sample of Australian twin pairs (857 MZ, 815 DZ) with both clinical and self-reported measures of height. Self-report height measurements are shown to be more variable than clinical measures. This has led to lowered estimates of heritability in many previous studies of stature. In our twin sample the heritability estimate for clinical height exceeded 90%. Repeated measures analysis shows that 2–3 times as many self-report measures are required to recover heritability estimates similar to those obtained from clinical measures. Bivariate genetic repeated measures analysis of self-report and clinical height measures showed an additive genetic correlation >0.98. We show that the accuracy of self-report height is upwardly biased in older individuals and in individuals of short stature. By comparing clinical and self-report measures we also showed that there was a genetic component to females systematically reporting their height incorrectly; this phenomenon appeared to not be present in males. The results from the measurement error analysis were subsequently used to assess the effects of error on the power to detect linkage in a genome scan. Moderate reduction in error (through the use of accurate clinical or multiple self-report measures) increased the effective sample size by 22%; elimination of measurement error led to increases in effective sample size of 41%.