SNV and haplotype analysis reveals new CSRP1 variants associated with growth and carcass traits

The cysteine and glycine-rich protein 1 and 2 genes (CSRP1 and CSRP2) are an effective growth factor in promoting skeletal muscle growth in vitro and vivo. However, in cattle, the information on the CSRP1 and CSRP2 genes is very limited. The aim of this study was to examine the association of the CSRP1 and CSRP2 variants with growth and carcass traits in cattle breeds. Three single nucleotide variants (SNVs) were identified within the bovine CSRP1 gene, whereas CSRP2 gene has not detected any SNVs, using DNA pooled sequencing, PCR-RFLP, and forced PCR-RFLP methods. These SNVs include g. 801T>C (Intron 2), g. 46T>C (Exon 3) and g. 99C>G (Intron 3). Besides, we also investigated haplotype frequencies and linkage disequilibrium (LD) coefficients for three SNVs in all study populations. LD and haplotype structure of CSRP1 were different between breeds. The result of haplotype analysis demonstrated eight haplotype present in QC (Qinchuan) and one haplotype in CH (Chinese Holstein). Only haplotype 1 (TTC), shared by all two populations, comprised 10.74% and 100.00%, of all haplotypes observed in QC and CH, respectively. Haplotype 5 (CTC) had the highest haplotype frequencies in QC (30.98%) and haplotype 1 had the highest haplotype frequencies in CH (100.00%). The statistical analyses indicated that one single SNV and 19 combined haplotypes were significantly or highly significantly associated with growth and carcass traits in the QC cattle population (P < 0.05 or P < 0.01). Quantitative real-time PCR (qRT-PCR) analyses showed that the bovine CSRP1 and CSRP2 genes were widely expressed in many tissues. The results of this study suggest that the CSRP1 gene possibly is a strong candidate gene that affects growth and carcass traits in the Chinese beef cattle breeding.     

Dealing with uncertain absences in habitat modelling: a case study of a rare ground-dwelling parrot

In the development of a species distribution model based on regression techniques such as generalized linear or additive modelling (GLM/GAM), a basic assumption is that records of species presence and absence are real. However, a common concern in many studies examining species distributions is that absences cannot be inferred with certainty. This is particularly the case where the species is rare, difficult to detect and/or does not occupy all available habitat considered suitable. The western ground parrot ( Pezoporus wallicus flaviventris ) of southern Western Australia, Australia, is a case in point, as not only is it rare and difficult to detect, but it is also unlikely to occupy all available suitable habitat. A recent survey of ground parrots provided the opportunity to develop a predictive distribution model. As the data were susceptible to false absences, these were replaced with randomly selected 'pseudo' absences and modelled using GLM. As a comparison, presence-only information was modelled using a relatively new approach, MAXENT, a machine-learning technique that has been shown to perform comparatively well. The predictive performance of both models, as assessed by the receiver operating characteristic plot (ROC) was high (AUC > 0.8), with MAXENT performing only marginally better than the GLM. These approaches both indicated that the ground parrot prefers areas relatively high in altitude, distant from rivers, gently sloping to level habitat, with an intermediate cover of vegetation and where there is a mosaic of vegetation ages. In this case, the use of presence-only information resulted in the identification of important environmental attributes defining the occurrence of the ground parrot, but additional factors that account for the inability of the bird to occupy all suitable habitat should be a component of model refinement.     

Assessing plant-nutrient relationships in highly invaded Californian grasslands using non-normal probability distributions

Question: Is native species occurrence related to soil nutrients in highly invaded Californian annual grasslands? What is the best method to analyze this relationship, given that native species occur in very low numbers and are absent from many locations? Location: California, USA. Methods: We investigated the effects of soil characteristics and livestock grazing on native plant occurrence at 40 plots from six sites during the period 2003–2005. Low absolute cover (< 5.8%) of native species resulted in strongly skewed, zero-inflated data sets. To overcome problems in the analysis created by non-normality and correlations within plots, we used GLMs and GLMMs, either with a Poisson or a negative binomial distribution, to analyse native species richness and Nassella pulchra cover. Results: N. pulchra cover was strongly associated with low phosphorus in sandy soils, whereas native species richness was highest in soils with low available nitrogen (high C:N). Conclusion: Under current conditions, phosphorus seems to be a critical factor influencing abundance of N. pulchra. Low fertility soils may provide refugia for native species in highly invaded California grasslands as they may be below a threshold required for non-native annuals to completely dominate. By using non-normal distributions in linear models with random components, we report well fitted models with more accurately tested significant covariates.     

Identification and siting of native vegetation types on disturbed land: demonstration of statistical methods

Question: How does one best choose native vegetation types and site them in reclamation of disturbed sites ranging from cropland and strip mines? Application: World‐wide, demonstrated in SE Montana. Methods: We assumed that pre‐disturbance native communities are the best targets for revegetation, and that the environmental facet each occupies naturally provides its optimal habitat. Given this assumption, we used pre‐strip‐mine data (800 points from a 88 km² site) to demonstrate statistical methods for identifying native communities, describing them, and determining their environments. Results and conclusions: Classification and pruning analysis provided an objective method for choosing the number of target community types to be used in reclamation. The composition of eight target types, identified with these analyses, was described with a relevé table to provide a species list, target cover levels and support the choice of species to be seeded. As a basis for siting communities, we modeled community presence as a function of topography, slope/aspect, and substrate. Logistic GLMs identified the optimal environment for each community. Classification and Regression Tree (CART) analysis identified the most probable community in each environmental facet. Topography and slope were generally the best predictors in these models. Because our analyses relate native vegetation to undisturbed environments, our results may apply best to sites with minimal substrate disturbance (i.e. better to abandoned cropland than to strip‐mined sites).     

Assessing the effect of prevalence on the predictive performance of species distribution models using simulated data

Aim The proportion of sampled sites where a species is present is known as prevalence. Empirical studies have shown that prevalence can affect the predictive performance of species distribution models. This paper uses simulated species data to examine how prevalence and the form of species environmental dependence affect the assessment of the predictive performance of models. Methods Simulated species data were based on various functions of simulated environmental data with differing degrees of spatial correlation. Seven model performance measures – sensitivity, specificity, class‐average (CA), overall prediction success, kappa (κ), normalized mutual information (NMI) and area under the receiver operating characteristic curve (AUC) – were applied to species models fitted by three regression methods. The response of the performance measures to prevalence was then assessed. Three probability threshold selection methods used to convert fitted logistic model values to presence or absence were also assessed. Results The study shows that the extent to which prevalence affects model performance depends on the modelling technique and its degree of success in capturing dominant environmental determinants. It also depends on the statistic used to measure model performance and the probability threshold method. The response based on κ generally preferred models with medium prevalence. All performance measures were least affected by prevalence when the probability threshold was chosen to maximize predictive performance or was based directly on prevalence. In these cases, the responses based on AUC, CA and NMI generally preferred models with small or large prevalence. Main conclusions The effect of prevalence on the predictive performance of species distribution models has a methodological basis. Relevant factors include the success of the fitted distribution model in capturing the dominant environmental determinant, the model performance measure and the probability threshold selection method. The fixed probability threshold method yields a marked response of model performance to prevalence and is therefore not recommended. The study explains previous empirical results obtained with real data.     

The R453Q and D151A polymorphisms of hexose-6-phosphate dehydrogenase gene (H6PD) influence the polycystic ovary syndrome (PCOS) and obesity

Hexose-6-phosphate dehydrogenase (H6PDH) influences 11β-hydroxysteroid dehydrogenase activity, a key enzyme in the peripheral metabolism of cortisol that modulates insulin sensitivity in adipose tissue. To study the associations of R453Q and D151A polymorphisms in the H6PDH gene (H6PD) with polycystic ovary syndrome (PCOS) and their influence on clinical and metabolic variables, we genotyped 237 patients with PCOS and 135 control women for the R453Q (rs6688832) and D151A (rs34603401) variants in H6PD. The R453Q genotypes were distributed differently in patients and controls (χ(2)=9.55, P=0.002). Genotypes of D151A were distributed evenly in women with PCOS and controls, but showed a different distribution in non-obese and obese women (χ(2)=3.95, P=0.047), especially within the PCOS subgroup (χ(2)=4.65, P=0.031). A backward stepwise likelihood ratio logistic regression model (Nagelkerke's R(2)=0.490; χ(2)=164; P<0.0001) retained free testosterone (OR=1.13; 95% CI: 1.10-1.17) and H6PD Q453 alleles (OR=0.46; 95% CI: 0.27-0.79) as statistically significant predictors for PCOS, whereas homeostasis model assessment of insulin resistance and the H6PD D151A variant were excluded by the model. Both H6PD variants were associated with several phenotypic variables, including fasting insulin, homeostasis model assessment of insulin resistance and androstenedione levels. In summary, the R453Q and D151A variants of the H6PD gene are associated with PCOS and obesity, respectively, and may contribute to the PCOS phenotype by influencing obesity, insulin resistance and hyperandrogenism.     

SORL1 and SIRT1 mRNA expression and promoter methylation levels in aging and Alzheimer’s Disease

Alzheimer’s Disease (AD) is a neurodegenerative disorder and the most common cause of dementia among the elderly. Efforts have been made to understand the genetic and epigenetic mechanisms involved in the development of this disease. As SORL1 (sortilin-related receptor) and SIRT1 (sirtuin 1) genes have been linked to AD pathogenesis, we aimed to investigate their mRNA expression and promoter DNA methylation in post mortem brain tissues (entorhinal and auditory cortices and hippocampus) from healthy elderly subjects and AD patients. We also evaluated these levels in peripheral blood leukocytes from young, healthy elderly and AD patients, investigating whether there was an effect of age on these profiles. The comparative CT method by Real Time PCR and MALDI-TOF mass spectrometry were used to analyze gene expression and DNA methylation, respectively. SORL1 gene was differently expressed in the peripheral blood leukocytes and might act as a marker of aging in this tissue. Furthermore, we found that SORL1 promoter DNA methylation might act as one of the mechanisms responsible for the differences in expression observed between blood and brain for both healthy elderly and AD patients groups. The impact of these studied genes on AD pathogenesis remains to be better clarified.     

Impacts of climate, host and landscape factors on Culicoides species in Scotland

Culicoides biting midges (Diptera: Ceratopogonidae) vector a wide variety of internationally important arboviral pathogens of livestock and represent a widespread biting nuisance. This study investigated the influence of landscape, host and remotely-sensed climate factors on local abundance of livestock-associated species in Scotland, within a hierarchical generalized linear model framework. The Culicoides obsoletus group and the Culicoides pulicaris group accounted for 56% and 41%, respectively, of adult females trapped. Culicoides impunctatus Goetghebuer and C. pulicaris s.s. Linnaeus were the most abundant and widespread species in the C. pulicaris group (accounting for 29% and 10%, respectively, of females trapped). Abundance models performed well for C. impunctatus, Culicoides deltus Edwards and Culicoides punctatus Meigen (adjusted R(2) : 0.59-0.70), but not for C. pulicaris s.s. (adjusted R(2) : 0.36) and the C. obsoletus group (adjusted R(2) : 0.08). Local-scale abundance patterns were best explained by models combining host, landscape and climate factors. The abundance of C. impunctatus was negatively associated with cattle density, but positively associated with pasture cover, consistent with this species' preference in the larval stage for lightly grazed, wet rush pasture. Predicted abundances of this species varied widely among farms even over short distances (less than a few km). Modelling approaches that may facilitate the more accurate prediction of local abundance patterns for a wider range of Culicoides species are discussed.