The combined effect of SNP-marker and phenotype attributes in genome-wide association studies

The last decade has seen rapid improvements in high-throughput single nucleotide polymorphism (SNP) genotyping technologies that have consequently made genome-wide association studies (GWAS) possible. With tens to hundreds of thousands of SNP markers being tested simultaneously in GWAS, it is imperative to appropriately pre-process, or filter out, those SNPs that may lead to false associations. This paper explores the relationships between various SNP genotype and phenotype attributes and their effects on false associations. We show that (i) uniformly distributed ordinal data as well as binary data are more easily influenced, though not necessarily negatively, by differences in various SNP attributes compared with normally distributed data; (ii) filtering SNPs on minor allele frequency (MAF) and extent of Hardy–Weinberg equilibrium (HWE) deviation has little effect on the overall false positive rate; (iii) in some cases, filtering on MAF only serves to exclude SNPs from the analysis without reduction of the overall proportion of false associations; and (iv) HWE, MAF and heterozygosity are all dependent on minor genotype frequency, a newly proposed measure for genotype integrity

Candidate gene prioritization based on spatially mapped gene expression: an application to XLMR

Motivation: The identification of genes involved in specific phenotypes, such as human hereditary diseases, often requires the time-consuming and expensive examination of a large number of positional candidates selected by genome-wide techniques such as linkage analysis and association studies. Even considering the positive impact of next-generation sequencing technologies, the prioritization of these positional candidates may be an important step for disease-gene identification

A Jet-ADAF model for Sgr A*

The recent {\em Chandra} observation of the radio source at the center of our Galaxy, Sgr A$^*$, puts new constraints on its theoretical models. The spectrum is very soft, and the source is rapidly variable. We consider different models to explain the observations. We find that the features of the x-ray spectrum can be marginally explained with an advection-dominated accretion flow (ADAF) model while it does not well fit the radio spectrum. An ADAF with strong winds (ADIOS) model is not favored if we assume that the wind does not radiate. Alternatively, we propose a coupled jet plus accretion disk model to explain the observations for Sgr A$^*$. The accretion flow is described as an ADAF fed by Bondi-Hoyle accretion of hot plasma in the Galactic Center region. A small fraction of the accretion flow is ejected near the black hole, forming a jet after passing through a shock. As a result, the electron temperature increases to $\sim 2 \times 10^{11}{\rm K}$, which is about 10 times higher than the highest temperature attained in the ADAF. The model is self-consistent since the main jet parameters are determined by the underlying accretion disk at the inner edge. The emergent spectrum of Sgr A$^*$ is the sum of the emission from jet and underlying ADAF. The very strong Comptonization of synchrotron emission from the jet dominates the bremsstrahlung from the ADAF, therefore, a very short variability timescale is expected and the predicted X-ray slope and the radio spectrum is in very good agreement with the observations.Comment: 10 pages, 3 figures, accepted by Astronomy & Astrophysic

Effector CD4+ T Cell Expression Signatures and Immune-Mediated Disease Associated Genes

Genome-wide association studies (GWAS) in immune-mediated diseases have identified over 150 associated genomic loci. Many of these loci play a role in T cell responses, and regulation of T cell differentiation plays a critical role in immune-mediated diseases; however, the relationship between implicated disease loci and T cell differentiation is incompletely understood. To further address this relationship, we examined differential gene expression in naïve human CD4+ T cells, as well as in in vitro differentiated Th1, memory Th17-negative and Th17-enriched CD4+ T cells subsets using microarray and RNASeq. We observed a marked enrichment for increased expression in memory CD4+ compared to naïve CD4+ T cells of genes contained among immune–mediated disease loci. Within memory T cells, expression of disease-associated genes was typically increased in Th17-enriched compared to Th17-negative cells. Utilizing RNASeq and promoter methylation studies, we identified a differential regulation pattern for genes solely expressed in Th17 cells (IL17A and CCL20) compared to genes expressed in both Th17 and Th1 cells (IL23R and IL12RB2), where high levels of promoter methylation are correlated to near zero RNASeq levels for IL17A and CCL20. These findings have implications for human Th17 celI plasticity and for the regulation of Th17-Th1 expression signatures. Importantly, utilizing RNASeq we found an abundant isoform of IL23R terminating before the transmembrane domain that was enriched in Th17 cells. In addition to molecular resolution, we find that RNASeq provides significantly improved power to define differential gene expression and identify alternative gene variants relative to microarray analysis. The comprehensive integration of differential gene expression between cell subsets with disease-association signals, and functional pathways provides insight into disease pathogenesis

Assessing batch effects of genotype calling algorithm BRLMM for the Affymetrix GeneChip Human Mapping 500 K array set using 270 HapMap samples

<p>Abstract</p> <p>Background</p> <p>Genome-wide association studies (GWAS) aim to identify genetic variants (usually single nucleotide polymorphisms [SNPs]) across the entire human genome that are associated with phenotypic traits such as disease status and drug response. Highly accurate and reproducible genotype calling are paramount since errors introduced by calling algorithms can lead to inflation of false associations between genotype and phenotype. Most genotype calling algorithms currently used for GWAS are based on multiple arrays. Because hundreds of gigabytes (GB) of raw data are generated from a GWAS, the samples are typically partitioned into batches containing subsets of the entire dataset for genotype calling. High call rates and accuracies have been achieved. However, the effects of batch size (i.e., number of chips analyzed together) and of batch composition (i.e., the choice of chips in a batch) on call rate and accuracy as well as the propagation of the effects into significantly associated SNPs identified have not been investigated. In this paper, we analyzed both the batch size and batch composition for effects on the genotype calling algorithm BRLMM using raw data of 270 HapMap samples analyzed with the Affymetrix Human Mapping 500 K array set.</p> <p>Results</p> <p>Using data from 270 HapMap samples interrogated with the Affymetrix Human Mapping 500 K array set, three different batch sizes and three different batch compositions were used for genotyping using the BRLMM algorithm. Comparative analysis of the calling results and the corresponding lists of significant SNPs identified through association analysis revealed that both batch size and composition affected genotype calling results and significantly associated SNPs. Batch size and batch composition effects were more severe on samples and SNPs with lower call rates than ones with higher call rates, and on heterozygous genotype calls compared to homozygous genotype calls.</p> <p>Conclusion</p> <p>Batch size and composition affect the genotype calling results in GWAS using BRLMM. The larger the differences in batch sizes, the larger the effect. The more homogenous the samples in the batches, the more consistent the genotype calls. The inconsistency propagates to the lists of significantly associated SNPs identified in downstream association analysis. Thus, uniform and large batch sizes should be used to make genotype calls for GWAS. In addition, samples of high homogeneity should be placed into the same batch.</p

New polymorphisms associated with response to anti-TNF drugs in patients with moderate-to-severe plaque psoriasis

Anti-tumor necrosis factor (anti-TNF) drugs are effective against psoriasis, although 20–30% of patients are nonresponders. Few pharmacogenomic studies have been performed to predict the response to anti-TNF drugs in psoriasis. We studied 173 polymorphisms to establish an association with the response to anti-TNF drugs in patients with moderate-to-severe plaque psoriasis (N=144). We evaluated the response using PASI75 at 3, 6 and 12 months. The results of the multivariate analysis showed an association between polymorphisms in PGLYR4, ZNF816A, CTNNA2, IL12B, MAP3K1 and HLA-C genes and the response at 3 months. Besides, the results for polymorphisms in IL12B and MAP3K1 were replicated at 6 months. We also obtained significant results for IL12B polymorphism at 1 year. Moreover, polymorphisms in FCGR2A, HTR2A and CDKAL1 were significant at 6 months. This is the first study to show an association with these polymorphisms. However, these biomarkers should be validated in large-scale studies before implementation in clinical practiceThis study was supported by Instituto de Salud Carlos III (FIS PI10/01740), Fundación Teófilo Hernando, and AbbVie. RPP has a grant from Universidad Autónoma de Madrid (FPI program 2013

Assessing sources of inconsistencies in genotypes and their effects on genome-wide association studies with HapMap samples

The discordance in results of independent genome-wide association studies (GWAS) indicates the potential for Type I and Type II errors. We assessed the repeatibility of current Affymetrix technologies that support GWAS. Reasonable reproducibility was observed for both raw intensity and the genotypes/copy number variants. We also assessed consistencies between different SNP arrays and between genotype calling algorithms. We observed that the inconsistency in genotypes was generally small at the specimen level. To further examine whether the differences from genotyping and genotype calling are possible sources of variation in GWAS results, an association analysis was applied to compare the associated SNPs. We observed that the inconsistency in genotypes not only propagated to the association analysis, but was amplified in the associated SNPs. Our studies show that inconsistencies between SNP arrays and between genotype calling algorithms are potential sources for the lack of reproducibility in GWAS results

β-Defensin-2 Protein Is a Serum Biomarker for Disease Activity in Psoriasis and Reaches Biologically Relevant Concentrations in Lesional Skin

BACKGROUND: Previous studies have extensively documented antimicrobial and chemotactic activities of beta-defensins. Human beta-defensin-2 (hBD-2) is strongly expressed in lesional psoriatic epidermis, and recently we have shown that high beta-defensin genomic copy number is associated with psoriasis susceptibility. It is not known, however, if biologically and pathophysiologically relevant concentrations of hBD-2 protein are present in vivo, which could support an antimicrobial and proinflammatory role of beta-defensins in lesional psoriatic epidermis. METHODOLOGY/PRINCIPAL FINDINGS: We found that systemic levels of hBD-2 showed a weak but significant correlation with beta defensin copy number in healthy controls but not in psoriasis patients with active disease. In psoriasis patients but not in atopic dermatitis patients, we found high systemic hBD-2 levels that strongly correlated with disease activity as assessed by the PASI score. Our findings suggest that systemic levels in psoriasis are largely determined by secretion from involved skin and not by genomic copy number. Modelling of the in vivo epidermal hBD-2 concentration based on the secretion rate in a reconstructed skin model for psoriatic epidermis provides evidence that epidermal hBD-2 levels in vivo are probably well above the concentrations required for in vitro antimicrobial and chemokine-like effects. CONCLUSIONS/SIGNIFICANCE: Serum hBD-2 appears to be a useful surrogate marker for disease activity in psoriasis. The discrepancy between hBD-2 levels in psoriasis and atopic dermatitis could explain the well known differences in infection rate between these two diseases

Supernova remnants: the X-ray perspective

Supernova remnants are beautiful astronomical objects that are also of high scientific interest, because they provide insights into supernova explosion mechanisms, and because they are the likely sources of Galactic cosmic rays. X-ray observations are an important means to study these objects.And in particular the advances made in X-ray imaging spectroscopy over the last two decades has greatly increased our knowledge about supernova remnants. It has made it possible to map the products of fresh nucleosynthesis, and resulted in the identification of regions near shock fronts that emit X-ray synchrotron radiation. In this text all the relevant aspects of X-ray emission from supernova remnants are reviewed and put into the context of supernova explosion properties and the physics and evolution of supernova remnants. The first half of this review has a more tutorial style and discusses the basics of supernova remnant physics and thermal and non-thermal X-ray emission. The second half offers a review of the recent advances.The topics addressed there are core collapse and thermonuclear supernova remnants, SN 1987A, mature supernova remnants, mixed-morphology remnants, including a discussion of the recent finding of overionization in some of them, and finally X-ray synchrotron radiation and its consequences for particle acceleration and magnetic fields.Comment: Published in Astronomy and Astrophysics Reviews. This version has 2 column-layout. 78 pages, 42 figures. This replaced version has some minor language edits and several references have been correcte