Strange Quark Matter and Compact Stars

Astrophysicists distinguish between three different types of compact stars. These are white dwarfs, neutron stars, and black holes. The former contain matter in one of the densest forms found in the Universe which, together with the unprecedented progress in observational astronomy, make such stars superb astrophysical laboratories for a broad range of most striking physical phenomena. These range from nuclear processes on the stellar surface to processes in electron degenerate matter at subnuclear densities to boson condensates and the existence of new states of baryonic matter--like color superconducting quark matter--at supernuclear densities. More than that, according to the strange matter hypothesis strange quark matter could be more stable than nuclear matter, in which case neutron stars should be largely composed of pure quark matter possibly enveloped in thin nuclear crusts. Another remarkable implication of the hypothesis is the possible existence of a new class of white dwarfs. This article aims at giving an overview of all these striking physical possibilities, with an emphasis on the astrophysical phenomenology of strange quark matter. Possible observational signatures associated with the theoretically proposed states of matter inside compact stars are discussed as well. They will provide most valuable information about the phase diagram of superdense nuclear matter at high baryon number density but low temperature, which is not accessible to relativistic heavy ion collision experiments.Comment: 58 figures, to appear in "Progress in Particle and Nuclear Physics"; References added for sections 1,2,3,5; Equation (116) corrected; Figs. 1 and 58 update

Improved power by collapsing rare and common variants based on a data-adaptive forward selection strategy

Genome-wide association studies have been used successfully to detect associations between common genetic variants and complex diseases, but common single-nucleotide polymorphisms (SNPs) detected by these studies explain only 5–10% of disease heritability. Alternatively, the common disease/rare variants hypothesis suggests that complex diseases are often caused by multiple rare variants with moderate to high effects. Under this hypothesis, the analysis of the cumulative effect of rare variants may thus help us discover the missing genetic variations. Collapsing all rare variants across a functional region is currently a popular method to find rare variants that may have a causal effect on certain diseases. However, the power of tests based on collapsing methods is often impaired by misclassification of functional variants. We develop a data-adaptive forward selection procedure that selectively chooses only variants that improve the association signal between functional regions and the disease risk. We apply our strategy to the Genetic Analysis Workshop 17 unrelated individuals data with quantitative traits. The type I error rate and the power of different collapsing functions are evaluated. The substantially higher power of the proposed strategy was demonstrated. The new method provides a useful strategy for the association study of sequencing data by taking advantage of the selection of rare variants

Incorporating predicted functions of nonsynonymous variants into gene-based analysis of exome sequencing data: a comparative study

Next-generation sequencing has opened up new avenues for the genetic study of complex traits. However, because of the small number of observations for any given rare allele and high sequencing error, it is a challenge to identify functional rare variants associated with the phenotype of interest. Recent research shows that grouping variants by gene and incorporating computationally predicted functions of variants may provide higher statistical power. On the other hand, many algorithms are available for predicting the damaging effects of nonsynonymous variants. Here, we use the simulated mini-exome data of Genetic Analysis Workshop 17 to study and compare the effects of incorporating the functional predictions of single-nucleotide polymorphisms using two popular algorithms, SIFT and PolyPhen-2, into a gene-based association test. We also propose a simple mixture model that can effectively combine test results based on different functional prediction algorithms

Design Considerations for Massively Parallel Sequencing Studies of Complex Human Disease

Massively Parallel Sequencing (MPS) allows sequencing of entire exomes and genomes to now be done at reasonable cost, and its utility for identifying genes responsible for rare Mendelian disorders has been demonstrated. However, for a complex disease, study designs need to accommodate substantial degrees of locus, allelic, and phenotypic heterogeneity, as well as complex relationships between genotype and phenotype. Such considerations include careful selection of samples for sequencing and a well-developed strategy for identifying the few “true” disease susceptibility genes from among the many irrelevant genes that will be found to harbor rare variants. To examine these issues we have performed simulation-based analyses in order to compare several strategies for MPS sequencing in complex disease. Factors examined include genetic architecture, sample size, number and relationship of individuals selected for sequencing, and a variety of filters based on variant type, multiple observations of genes and concordance of genetic variants within pedigrees. A two-stage design was assumed where genes from the MPS analysis of high-risk families are evaluated in a secondary screening phase of a larger set of probands with more modest family histories. Designs were evaluated using a cost function that assumes the cost of sequencing the whole exome is 400 times that of sequencing a single candidate gene. Results indicate that while requiring variants to be identified in multiple pedigrees and/or in multiple individuals in the same pedigree are effective strategies for reducing false positives, there is a danger of over-filtering so that most true susceptibility genes are missed. In most cases, sequencing more than two individuals per pedigree results in reduced power without any benefit in terms of reduced overall cost. Further, our results suggest that although no single strategy is optimal, simulations can provide important guidelines for study design

Genetic and other factors determining mannose-binding lectin levels in American Indians: the Strong Heart Study

<p>Abstract</p> <p>Background</p> <p>Mannose-binding lectin (MBL) forms an integral part of the innate immune system. Persistent, subclinical infections and chronic inflammatory states are hypothesized to contribute to the pathogenesis of atherosclerosis. MBL gene (<it>MBL2</it>) variants with between 12 to 25% allele frequency in Caucasian and other populations, result in markedly reduced expression of functional protein. Prospective epidemiologic studies, including a nested, case-control study from the present population, have demonstrated the ability of <it>MBL2 </it>genotypes to predict complications of atherosclerosis,. The genetic control of <it>MBL2 </it>expression is complex and genetic background effects in specific populations are largely unknown.</p> <p>Methods</p> <p>The Strong Heart Study is a longitudinal, cohort study of cardiovascular disease among American Indians. A subset of individuals genotyped for the above mentioned case-control study were selected for analysis of circulating MBL levels by double sandwich ELISA method. Mean MBL levels were compared between genotypic groups and multivariate regression was used to determine other independent factors influencing <it>MBL2 </it>expression.</p> <p>Results</p> <p>Our results confirm the effects of variant structural (B, C, and D) and promoter (H and Y) alleles that have been seen in other populations. In addition, MBL levels were found to be positively associated with male gender and hemoglobin A1c levels, but inversely related to triglyceride levels. Correlation was not found between MBL and other markers of inflammation.</p> <p>Conclusion</p> <p>New data is presented concerning the effects of known genetic variants on MBL levels in an American Indian population, as well as the relationship of <it>MBL2 </it>expression to clinical and environmental factors, including inflammatory markers.</p

Insulin and GH Signaling in Human Skeletal Muscle In Vivo following Exogenous GH Exposure: Impact of an Oral Glucose Load

GH induces acute insulin resistance in skeletal muscle in vivo, which in rodent models has been attributed to crosstalk between GH and insulin signaling pathways. Our objective was to characterize time course changes in signaling pathways for GH and insulin in human skeletal muscle in vivo following GH exposure in the presence and absence of an oral glucose load.Eight young men were studied in a single-blinded randomized crossover design on 3 occasions: 1) after an intravenous GH bolus 2) after an intravenous GH bolus plus an oral glucose load (OGTT), and 3) after intravenous saline plus OGTT. Muscle biopsies were taken at t = 0, 30, 60, and 120. Blood was sampled at frequent intervals for assessment of GH, insulin, glucose, and free fatty acids (FFA).GH increased AUC(glucose) after an OGTT (p<0.05) without significant changes in serum insulin levels. GH induced phosphorylation of STAT5 independently of the OGTT. Conversely, the OGTT induced acute phosphorylation of the insulin signaling proteins Akt (ser(473) and thr(308)), and AS160.The combination of OGTT and GH suppressed Akt activation, whereas the downstream expression of AS160 was amplified by GH. WE CONCLUDED THE FOLLOWING: 1) A physiological GH bolus activates STAT5 signaling pathways in skeletal muscle irrespective of ambient glucose and insulin levels 2) Insulin resistance induced by GH occurs without a distinct suppression of insulin signaling proteins 3) The accentuation of the glucose-stimulated activation of AS 160 by GH does however indicate a potential crosstalk between insulin and GH.ClinicalTrials.gov NCT00477997

A Genetic Signature of Spina Bifida Risk from Pathway-Informed Comprehensive Gene-Variant Analysis

Despite compelling epidemiological evidence that folic acid supplements reduce the frequency of neural tube defects (NTDs) in newborns, common variant association studies with folate metabolism genes have failed to explain the majority of NTD risk. The contribution of rare alleles as well as genetic interactions within the folate pathway have not been extensively studied in the context of NTDs. Thus, we sequenced the exons in 31 folate-related genes in a 480-member NTD case-control population to identify the full spectrum of allelic variation and determine whether rare alleles or obvious genetic interactions within this pathway affect NTD risk. We constructed a pathway model, predetermined independent of the data, which grouped genes into coherent sets reflecting the distinct metabolic compartments in the folate/one-carbon pathway (purine synthesis, pyrimidine synthesis, and homocysteine recycling to methionine). By integrating multiple variants based on these groupings, we uncovered two provocative, complex genetic risk signatures. Interestingly, these signatures differed by race/ethnicity: a Hispanic risk profile pointed to alterations in purine biosynthesis, whereas that in non-Hispanic whites implicated homocysteine metabolism. In contrast, parallel analyses that focused on individual alleles, or individual genes, as the units by which to assign risk revealed no compelling associations. These results suggest that the ability to layer pathway relationships onto clinical variant data can be uniquely informative for identifying genetic risk as well as for generating mechanistic hypotheses. Furthermore, the identification of ethnic-specific risk signatures for spina bifida resonated with epidemiological data suggesting that the underlying pathogenesis may differ between Hispanic and non-Hispanic groups