Gravitational Wave Consistency Relations for Multifield Inflation

We study the tensor spectral index n t and the tensor-to-scalar ratio r in the simplest multifield extension to single-field, slow-roll inflation models. We show that multifield models with potentials V ∼ ∑ i λ i | ϕ i | p have different predictions for n t / r than single-field models, even when all the couplings are equal λ i = λ j , due to the probabilistic nature of the fields’ initial values. We analyze well-motivated prior probabilities for the λ i and initial conditions to make detailed predictions for the marginalized probability distribution of n t / r . With O ( 100 ) fields and p > 3 / 4 , we find that n t / r differs from the single-field result of n t / r = − 1 / 8 at the 5 σ level. This gives a novel and testable prediction for the simplest multifield inflation models

MULTIMODECODE : an efficient numerical solver for multifield inflation

We present MULTIMODECODE, a Fortran 95/2000 package for the numerical exploration of multifield inflation models. This program facilitates efficient Monte Carlo sampling of prior probabilities for inflationary model parameters and initial conditions and is the first publicly available code that can efficiently generate large sample-sets for inflation models with Script O fields. The code numerically solves the equations of motion for the background and first-order perturbations of multi-field inflation models with canonical kinetic terms and arbitrary potentials, providing the adiabatic, isocurvature, and tensor power spectra at the end of inflation. For models with sum-separable potentials MULTIMODECODE also computes the slow-roll prediction via the δ N formalism for easy model exploration and validation. We pay particular attention to the isocurvature perturbations as the system approaches the adiabatic limit, showing how to avoid numerical instabilities that affect some other approaches to this problem. We demonstrate the use of MULTIMODECODE by exploring a few toy models. Finally, we give a concise review of multifield perturbation theory and a user's manual for the program

Aquatic community response to volcanic eruptions on the Ecuadorian Andean flank: evidence from the palaeoecological record

Aquatic ecosystems in the tropical Andes are under increasing pressure from human modification of the landscape (deforestation and dams) and climatic change (increase of extreme events and 1.5 °C on average temperatures are projected for AD 2100). However, the resilience of these ecosystems to perturbations is poorly understood. Here we use a multi-proxy palaeoecological approach to assess the response of aquatic ecosystems to a major mechanism for natural disturbance, volcanic ash deposition. Specifically, we present data from two Neotropical lakes located on the eastern Andean flank of Ecuador. Laguna Pindo (1°27.132′S–78°04.847′W) is a tectonically formed closed basin surrounded by a dense mid-elevation forest, whereas Laguna Baños (0°19.328′S–78°09.175′W) is a glacially formed lake with an inflow and outflow in high Andean Páramo grasslands. In each lake we examined the dynamics of chironomids and other aquatic and semi-aquatic organisms to explore the effect of thick (> 5 cm) volcanic deposits on the aquatic communities in these two systems with different catchment features. In both lakes past volcanic ash deposition was evident from four large tephras dated to c.850 cal year BP (Pindo), and 4600, 3600 and 1500 cal year BP (Baños). Examination of the chironomid and aquatic assemblages before and after the ash depositions revealed no shift in composition at Pindo, but a major change at Baños occurred after the last event around 1500 cal year BP. Chironomids at Baños changed from an assemblage dominated by Pseudochironomus and Polypedilum nubifer-type to Cricotopus/Paratrichocladius type-II, and such a dominance lasted for approximately 380 years. We suggest that, despite potential changes in the water chemistry, the major effect on the chironomid community resulted from the thickness of the tephra being deposited, which acted to shallow the water body beyond a depth threshold. Changes in the aquatic flora and fauna at the base of the trophic chain can promote cascade effects that may deteriorate the ecosystem, especially when already influenced by human activities, such as deforestation and dams, which is frequent in the high Andes

Advances of genomic science and systems biology in renal transplantation: a review

The diagnosis of rejection in kidney transplant patients is based on histologic classification of a graft biopsy. The current “gold standard” is the Banff 97 criteria; however, there are several limitations in classifying rejection based on biopsy samples. First, a biopsy involves an invasive procedure. Second, there is significant variance among blinded pathologists in the interpretation of a biopsy. And third, there is also variance between the histology and the molecular profiles of a biopsy. To increase the positive predictive value of classifiers of rejection, a Banff committee is developing criteria that integrate histologic and molecular data into a unified classifier that could diagnose and prognose rejection. To develop the most appropriate molecular criteria, there have been studies by multiple groups applying omics technologies in attempts to identify biomarkers of rejection. In this review, we discuss studies using genome-wide data sets of the transcriptome and proteome to investigate acute rejection, chronic allograft dysfunction, and tolerance. We also discuss studies which focus on genetic biomarkers in urine and peripheral blood, which will provide clinicians with minimally invasive methods for monitoring transplant patients. We also discuss emerging technologies, including whole-exome sequencing and RNA-Seq and new bioinformatic and systems biology approaches, which should increase the ability to develop both biomarkers and mechanistic understanding of the rejection process

Next-generation sequencing of vertebrate experimental organisms

Next-generation sequencing technologies are revolutionizing biology by allowing for genome-wide transcription factor binding-site profiling, transcriptome sequencing, and more recently, whole-genome resequencing. While it is currently not possible to generate complete de novo assemblies of higher-vertebrate genomes using next-generation sequencing, improvements in sequence read lengths and throughput, coupled with new assembly algorithms for large data sets, will soon make this a reality. These developments will in turn spawn a revolution in how genomic data are used to understand genetics and how model organisms are used for disease gene discovery. This review provides an overview of the current next-generation sequencing platforms and the newest computational tools for the analysis of next-generation sequencing data. We also describe how next-generation sequencing may be applied in the context of vertebrate model organism genetics

Commercially Available Outbred Mice for Genome-Wide Association Studies

Genome-wide association studies using commercially available outbred mice can detect genes involved in phenotypes of biomedical interest. Useful populations need high-frequency alleles to ensure high power to detect quantitative trait loci (QTLs), low linkage disequilibrium between markers to obtain accurate mapping resolution, and an absence of population structure to prevent false positive associations. We surveyed 66 colonies for inbreeding, genetic diversity, and linkage disequilibrium, and we demonstrate that some have haplotype blocks of less than 100 Kb, enabling gene-level mapping resolution. The same alleles contribute to variation in different colonies, so that when mapping progress stalls in one, another can be used in its stead. Colonies are genetically diverse: 45% of the total genetic variation is attributable to differences between colonies. However, quantitative differences in allele frequencies, rather than the existence of private alleles, are responsible for these population differences. The colonies derive from a limited pool of ancestral haplotypes resembling those found in inbred strains: over 95% of sequence variants segregating in outbred populations are found in inbred strains. Consequently it is possible to impute the sequence of any mouse from a dense SNP map combined with inbred strain sequence data, which opens up the possibility of cataloguing and testing all variants for association, a situation that has so far eluded studies in completely outbred populations. We demonstrate the colonies' potential by identifying a deletion in the promoter of H2-Ea as the molecular change that strongly contributes to setting the ratio of CD4+ and CD8+ lymphocytes

Statistical Epistasis and Functional Brain Imaging Support a Role of Voltage-Gated Potassium Channels in Human Memory

Despite the current progress in high-throughput, dense genome scans, a major portion of complex traits' heritability still remains unexplained, a phenomenon commonly termed “missing heritability.” The negligence of analytical approaches accounting for gene-gene interaction effects, such as statistical epistasis, is probably central to this phenomenon. Here we performed a comprehensive two-way SNP interaction analysis of human episodic memory, which is a heritable complex trait, and focused on 120 genes known to show differential, memory-related expression patterns in rat hippocampus. Functional magnetic resonance imaging was also used to capture genotype-dependent differences in memory-related brain activity. A significant, episodic memory-related interaction between two markers located in potassium channel genes (KCNB2 and KCNH5) was observed (Pnominal combined = 0.000001). The epistatic interaction was robust, as it was significant in a screening (Pnominal = 0.0000012) and in a replication sample (Pnominal = 0.01). Finally, we found genotype-dependent activity differences in the parahippocampal gyrus (Pnominal = 0.001) supporting the behavioral genetics finding. Our results demonstrate the importance of analytical approaches that go beyond single marker statistics of complex traits

Selenium, Selenoenzymes, Oxidative Stress and Risk of Neoplastic Progression from Barrett's Esophagus: Results from Biomarkers and Genetic Variants

Clinical trials have suggested a protective effect of selenium supplementation on the risk of esophageal cancer, which may be mediated through the antioxidant activity of selenoenzymes. We investigated whether serum selenium concentrations, selenoenzyme activity, oxidative stress and genetic variation in selenoenzymes were associated with the risk of neoplastic progression to esophageal adenocarcinoma (EA) and two intermediate endpoints, aneuploidy and tetraploidy. In this prospective cohort study, during an average follow-up of 7.3 years, 47 EA cases, 41 aneuploidy cases and 51 tetraploidy cases accrued among 361 participants from the Seattle Barrett's Esophagus Research Study who were free of EA at the time of blood draw and had at least one follow-up visit. Development to EA was assessed histologically and aneuploidy and tetraploidy by DNA content flow cytometry. Serum selenium concentrations were measured using atomic absorption spectrometry, activity of glutathione peroxidase (GPX) 1 and GPX3 by substrate-specific coupled test procedures, selenoprotein P (SEPP1) concentrations and protein carbonyl content by ELISA method and malondialdehyde concentrations by HPLC. Genetic variants in GPX1-4 and SEPP1 were genotyped. Serum selenium was not associated with the risk of neoplastic progression to EA, aneuploidy or tetraploidy (P for trend = 0.25 to 0.85). SEPP1 concentrations were positively associated with the risk of EA [hazard ratio (HR) = 3.95, 95% confidence intervals (CI) = 1.42–10.97 comparing the third tertile with the first] and with aneuploidy (HR = 6.53, 95% CI = 1.31–32.58), but not selenoenzyme activity or oxidative stress markers. No genetic variants, overall, were associated with the risk of neoplastic progression to EA (global p = 0.12–0.69). Our results do not support a protective effect of selenium on risk of neoplastic progression to EA. Our study is the first to report positive associations of plasma SEPP1 concentrations with the risk of EA and aneuploidy, which warrants further investigation

Twenty bone-mineral-density loci identified by large-scale meta-analysis of genome-wide association studies

To access publisher full text version of this article. Please click on the hyperlink in Additional Links fieldBone mineral density (BMD) is a heritable complex trait used in the clinical diagnosis of osteoporosis and the assessment of fracture risk. We performed meta-analysis of five genome-wide association studies of femoral neck and lumbar spine BMD in 19,195 subjects of Northern European descent. We identified 20 BMD loci that reached genome-wide significance (GWS; P < 5 x 10(-8)), of which 13 map to regions not previously associated with this trait: 1p31.3 (GPR177), 2p21 (SPTBN1), 3p22 (CTNNB1), 4q21.1 (MEPE), 5q14 (MEF2C), 7p14 (STARD3NL), 7q21.3 (FLJ42280), 11p11.2 (LRP4, ARHGAP1, F2), 11p14.1 (DCDC5), 11p15 (SOX6), 16q24 (FOXL1), 17q21 (HDAC5) and 17q12 (CRHR1). The meta-analysis also confirmed at GWS level seven known BMD loci on 1p36 (ZBTB40), 6q25 (ESR1), 8q24 (TNFRSF11B), 11q13.4 (LRP5), 12q13 (SP7), 13q14 (TNFSF11) and 18q21 (TNFRSF11A). The many SNPs associated with BMD map to genes in signaling pathways with relevance to bone metabolism and highlight the complex genetic architecture that underlies osteoporosis and variation in BMD