How trophic interaction strength depends on traits

A key problem in community ecology is to understand how individual-level traits give rise to population-level trophic interactions. Here, we propose a synthetic framework based on ecological considerations to address this question systematically. We derive a general functional form for the dependence of trophic interaction coefficients on trophically relevant quantitative traits of consumers and resources. The derived expression encompasses-and thus allows a unified comparison of-several functional forms previously proposed in the literature. Furthermore, we show how a community's, potentially low-dimensional, effective trophic niche space is related to its higher-dimensional phenotypic trait space. In this manner, we give ecological meaning to the notion of the "dimensionality of trophic niche space." Our framework implies a method for directly measuring this dimensionality. We suggest a procedure for estimating the relevant parameters from empirical data and for verifying that such data matches the assumptions underlying our derivation. © Springer Science+Business Media B.V. 2009.


--------------------------------------------------------------------------------

Reaxys Database Information|

--------------------------------------------------------------------------------

Rigorous conditions for food-web intervality in high-dimensional trophic niche spaces

Food webs represent trophic (feeding) interactions in ecosystems. Since the late 1970s, it has been recognized that food-webs have a surprisingly close relationship to interval graphs. One interpretation of food-web intervality is that trophic niche space is low-dimensional, meaning that the trophic character of a species can be expressed by a single or at most a few quantitative traits. In a companion paper we demonstrated, by simulating a minimal food-web model, that food webs are also expected to be interval when niche-space is high-dimensional. Here we characterize the fundamental mechanisms underlying this phenomenon by proving a set of rigorous conditions for food-web intervality in high-dimensional niche spaces. Our results apply to a large class of food-web models, including the special case previously studied numerically.

Clinical features of psychotic disorders and polymorphisms in HT2A, DRD2, DRD4, SLC6A3 (DAT1), and BDNF:a family based association study

Schizophrenia is clinically heterogeneous and multidimensional, but it is not known whether this is due to etiological heterogeneity. Previous studies have not consistently reported association between any specific polymorphisms and clinical features of schizophrenia, and have primarily used case-control designs. We tested for the presence of association between clinical features and polymorphisms in the genes for the serotonin 2A receptor (HT2A), dopamine receptor types 2 and 4, dopamine transporter (SLC6A3), and brain-derived neurotrophic factor (BDNF). Two hundred seventy pedigrees were ascertained on the basis of having two or more members with schizophrenia or poor outcome schizoaffective disorder. Diagnoses were made using a structured interview based on the SCID. All patients were rated on the major symptoms of schizophrenia scale (MSSS), integrating clinical and course features throughout the course of illness. Factor analysis revealed positive, negative, and affective symptom factors. The program QTDT was used to implement a family-based test of association for quantitative traits, controlling for age and sex. We found suggestive evidence of association between the His452Tyr polymorphism in HT2A and affective symptoms (P = 0.02), the 172-bp allele of BDNF and negative symptoms (P = 0.04), and the 480-bp allele in SLC6A3 (= DAT1) and negative symptoms (P = 0.04). As total of 19 alleles were tested, we cannot rule out false positives. However, given prior evidence of involvement of the proteins encoded by these genes in psychopathology, our results suggest that more attention should be focused on the impact of these alleles on clinical features of schizophrenia.

Quantitative Analysis of Adsorbate Concentrations by Diffuse Reflectance FT-IR

Fully quantitative analyses of DRIFTS data are required when the surface concentrations and the specific rate constants of reaction (or desorption) of adsorbates are needed to validate microkinetic models. The relationship between the surface coverage of adsorbates and various functions derived from the signal collected by DRIFTS is discussed here. The Kubelka-Munk and pseudoabsorbance (noted here as absorbance, for the sake of brevity) transformations were considered, since those are the most commonly used functions when data collected by DRIFTS are reported. Theoretical calculations and experimental evidence based on the study of CO adsorption on Pt/SiO2 and formate species adsorbed on Pt/CeO2 showed that the absorbance (i.e., ) log 1/R������¢, with R������¢ ) relative reflectance) is the most appropriate, yet imperfect, function to give a linear representation of the adsorbate surface concentration in the examples treated here, for which the relative reflectance R������¢ is typically > 60%. When the adsorbates lead to a strong signal absorption (e.g., R������¢ < 60%), the Kubelka-Munk function is actually more appropriate. The absorbance allows a simple correction of baseline drifts, which often occur during time-resolved data collection over catalytic materials. Baseline corrections are markedly more complex in the case of the other mathematical transforms, including the function proposed by Matyshak and Krylov (Catal. Today 1995, 25, 1-87), which has been proposed as an appropriate representation of surface concentrations in DRIFTS spectroscopy.

Blue luminous stars in nearby galaxies: Quantitative spectral analysis of M33 B-type supergiant stars

We present the detailed spectral analysis of a sample of M33 B-type supergiant stars, aimed at the determination of their fundamental parameters and chemical composition. The analysis is based on a grid of non-LTE metal line-blanketed model atmospheres including the effects of stellar winds and spherical extension computed with the code FASTWIND. Surface abundance ratios of C, N, and O are used to discuss the chemical evolutionary status of each individual star. The comparison of observed stellar properties with theoretical predictions of massive star evolutionary models shows good agreement within the uncertainties of the analysis. The spatial distribution of the sample allows us to investigate the existence of radial abundance gradients in the disk of M33. The comparison of stellar and H II region O abundances ( based on direct determinations of the electron temperature of the nebulae) shows good agreement. Using a simple linear radial representation, the stellar oxygen abundances result in a gradient of -0.0145 +/- 0.005 dex arcmin(-1) (or -0.06 +/- 0.02 dex kpc(-1)) up to a distance equal to similar to 1.1 times the isophotal radius of the galaxy. A more complex representation cannot be completely discarded by our stellar sample. The stellar Mg and Si abundances follow the trend displayed by O abundances, although with shallower gradients. These differences in gradient slope cannot be explained at this point. The derived abundances of the three alpha-elements yield solar metallicity in the central regions of the disk of M33. A comparison with recent planetary nebula data from Magrini and coworkers indicates that the disk of M33 has not suffered from a significant O enrichment in the last 3 Gyr.

An Excess of Risk-Increasing Low-Frequency Variants Can Be a Signal of Polygenic Inheritance in Complex Diseases

In most complex diseases, much of the heritability remains unaccounted for by common variants. It has been postulated that lower-frequency variants contribute to the remaining heritability. Here, we describe a method to test for polygenic inheritance from lower-frequency variants by using GWAS summary association statistics. We explored scenarios with many causal low-frequency variants and showed that there is more power to detect risk variants than to detect protective variants, resulting in an increase in the ratio of detected risk to protective variants (R/P ratio). Such an excess can also occur if risk variants are present and kept at lower frequencies because of negative selection. The R/P ratio can be falsely elevated because of reasons unrelated to polygenic inheritance, such as uneven sample sizes or asymmetric population stratification, so precautions to correct for these confounders are essential. We tested our method on published GWAS results and observed a strong signal in some diseases (schizophrenia and type 2 diabetes) but not others. We also explored the shared genetic component in overlapping phenotypes related to inflammatory bowel disease (Crohn disease [CD] and ulcerative colitis [UC]) and diabetic nephropathy (macroalbuminuria and end-stage renal disease [ESRD]). Although the signal was still present when both CD and UC were jointly analyzed, the signal was lost when macroalbuminuria and ESRD were jointly analyzed, suggesting that these phenotypes should best be studied separately. Thus, our method may also help guide the design of future genetic studies of various traits and diseases.

The role of adiposity in cardiometabolic traits:a Mendelian randomization analysis

The association between adiposity and cardiometabolic traits is well known from epidemiological studies. Whilst the causal relationship is clear for some of these traits, for others it is not. We aimed to determine whether adiposity is causally related to various cardiometabolic traits using the Mendelian randomization approach.

Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture

Approaches exploiting trait distribution extremes may be used to identify loci associated with common traits, but it is unknown whether these loci are generalizable to the broader population. In a genome-wide search for loci associated with the upper versus the lower 5th percentiles of body mass index, height and waist-to-hip ratio, as well as clinical classes of obesity, including up to 263,407 individuals of European ancestry, we identified 4 new loci (IGFBP4, H6PD, RSRC1 and PPP2R2A) influencing height detected in the distribution tails and 7 new loci (HNF4G, RPTOR, GNAT2, MRPS33P4, ADCY9, HS6ST3 and ZZZ3) for clinical classes of obesity. Further, we find a large overlap in genetic structure and the distribution of variants between traits based on extremes and the general population and little etiological heterogeneity between obesity subgroups.

Identification of quantitative trait loci for rice grain element composition on an arsenic impacted soil:Influence of flowering time on genetic loci

Elements in grain crops such as iron, zinc and selenium are essential in the human diet, whereas elements such as arsenic are potentially toxic to humans. This study aims to identify quantitative trait loci (QTLs) for trace elements in rice grain. A field experiment was conducted in an arsenic enriched field site in Qiyang, China using the Bala x Azucena mapping population grown under standard field conditions. Grains were subjected to elemental analysis by inductively coupled plasma mass spectroscopy. QTLs were detected for the elemental composition within the rice grains, including for iron and selenium, which have previously been detected in this population grown at another location, indicating the stability of these QTLs. A correlation was observed between flowering time and a number of the element concentrations in grains, which was also revealed as co-localisation between flowering time QTLs and grain element QTLs. Unravelling the environmental conditions that influence the grain ionome appears to be complex, but from the results in this study one of the major factors which controls the accumulation of elements within the grain is flowering time.

Developing the clinical components of a complex intervention for a glaucoma screening trial:a mixed methods study

BACKGROUND: Glaucoma is a leading cause of avoidable blindness worldwide. Open angle glaucoma is the most common type of glaucoma. No randomised controlled trials have been conducted evaluating the effectiveness of glaucoma screening for reducing sight loss. It is unclear what the most appropriate intervention to be evaluated in any glaucoma screening trial would be. The purpose of this study was to develop the clinical components of an intervention for evaluation in a glaucoma (open angle) screening trial that would be feasible and acceptable in a UK eye-care service.

METHODS: A mixed-methods study, based on the Medical Research Council (MRC) framework for complex interventions, integrating qualitative (semi-structured interviews with 46 UK eye-care providers, policy makers and health service commissioners), and quantitative (economic modelling) methods. Interview data were synthesised and used to revise the screening interventions compared within an existing economic model.

RESULTS: The qualitative data indicated broad based support for a glaucoma screening trial to take place in primary care, using ophthalmic trained technical assistants supported by optometry input. The precise location should be tailored to local circumstances. There was variability in opinion around the choice of screening test and target population. Integrating the interview findings with cost-effectiveness criteria reduced 189 potential components to a two test intervention including either optic nerve photography or screening mode perimetry (a measure of visual field sensitivity) with or without tonometry (a measure of intraocular pressure). It would be more cost-effective, and thus acceptable in a policy context, to target screening for open angle glaucoma to those at highest risk but for both practicality and equity arguments the optimal strategy was screening a general population cohort beginning at age forty.

CONCLUSIONS: Interventions for screening for open angle glaucoma that would be feasible from a service delivery perspective were identified. Integration within an economic modelling framework explicitly highlighted the trade-off between cost-effectiveness, feasibility and equity. This study exemplifies the MRC recommendation to integrate qualitative and quantitative methods in developing complex interventions. The next step in the development pathway should encompass the views of service users.

Quantifying heritable variation in fitness-related traits of wild, farmed and hybrid Atlantic salmon families in a wild river environment

Farmed fish are typically genetically different from wild conspecifics. Escapees from fish farms may contribute one-way gene flow from farm to wild gene pools, which can depress population productivity, dilute local adaptations and disrupt coadapted gene complexes. Here, we reanalyse data from two experiments (McGinnity et al., 1997, 2003) where performance of Atlantic salmon (Salmo salar) progeny originating from experimental crosses between farm and wild parents (in three different cohorts) were measured in a natural stream under common garden conditions. Previous published analyses focussed on group-level differences but did not account for pedigree structure, as we do here using modern mixed-effect models. Offspring with one or two farm parents exhibited poorer survival in their first and second year of life compared with those with two wild parents and these group-level inferences were robust to excluding outlier families. Variation in performance among farm, hybrid and wild families was generally similar in magnitude. Farm offspring were generally larger at all life stages examined than wild offspring, but the differences were moderate (5–20%) and similar in magnitude in the wild versus hatchery environments. Quantitative genetic analyses conducted using a Bayesian framework revealed moderate heritability in juvenile fork length and mass and positive genetic correlations (>0.85) between these morphological traits. Our study confirms (using more rigorous statistical techniques) previous studies showing that offspring of wild fish invariably have higher fitness and contributes fresh insights into family-level variation in performance of farm, wild and hybrid Atlantic salmon families in the wild. It also adds to a small, but growing, number of studies that estimate key evolutionary parameters in wild salmonid populations. Such information is vital in modelling the impacts of introgression by escaped farm salmon.

Detection of quantitative trait loci for androstenone, skatole and boar taint in a cross between Large White and Meishan pigs

'Boar taint' is a strong perspiration-like, urine-like unpleasant odour given off upon heating or cooking of meat from some intact (uncastrated) male pigs. Data from the F(2) generation of a Large White (LW) x Meishan (MS) crossbred population were analysed to detect quantitative trait loci (QTL) for traits associated with boar taint. Fat samples from 178 intact male pigs slaughtered at 85 +/- 5 kg were analysed for the major contributors to boar taint (androstenone, indole and skatole). Fat and lean samples from cooked meat were scored for boar, abnormal and pork flavour and odour by a trained sensory panel (SP). A scan with 117 markers covering the whole genome was performed in the F(2) individuals, together with their F(1) parents and purebred grandparents. At the 5% chromosomal significance threshold (approximately equal to the genome-wide suggestive significance threshold), QTL were detected for the laboratory estimate of androstenone on chromosomes 2, 4, 6, 7 and 9. However, only on chromosome 6 were there QTL for boar flavour (BF) traits in the same or adjacent marker intervals as a QTL for the laboratory estimate of androstenone. On chromosome 14, QTL were detected for the laboratory estimates of indole and skatole, the SP score for skatole and the scores for BF in lean and BF in fat. In all five cases, the MS allele generally increased the estimate or score, compared with the LW allele, but it appeared that desirable and undesirable alleles were present in both breeds. This locus on chromosome 14 has considerable potential for use to reduce the incidence of boar taint, especially if further research can identify the causative polymorphism or strongly associated markers.

Gene regulatory mechanisms underpinning prostate cancer susceptibility

Molecular characterization of genome-wide association study (GWAS) loci can uncover key genes and biological mechanisms underpinning complex traits and diseases. Here we present deep, high-throughput characterization of gene regulatory mechanisms underlying prostate cancer risk loci. Our methodology integrates data from 295 prostate cancer chromatin immunoprecipitation and sequencing experiments with genotype and gene expression data from 602 prostate tumor samples. The analysis identifies new gene regulatory mechanisms affected by risk locus SNPs, including widespread disruption of ternary androgen receptor (AR)-FOXA1 and AR-HOXB13 complexes and competitive binding mechanisms. We identify 57 expression quantitative trait loci at 35 risk loci, which we validate through analysis of allele-specific expression. We further validate predicted regulatory SNPs and target genes in prostate cancer cell line models. Finally, our integrated analysis can be accessed through an interactive visualization tool. This analysis elucidates how genome sequence variation affects disease predisposition via gene regulatory mechanisms and identifies relevant genes for downstream biomarker and drug development.