Redundancy in Genotyping Arrays

Despite their unprecedented density, current SNP genotyping arrays contain large amounts of redundancy, with up to 40 oligonucleotide features used to query each SNP. By using publicly available reference genotype data from the International HapMap, we show that 93.6% sensitivity at <5% false positive rate can be obtained with only four probes per SNP, compared with 98.3% with the full data set. Removal of this redundancy will allow for more comprehensive whole-genome association studies with increased SNP density and larger sample sizes

A Class Representative Model for Pure Parsimony Haplotyping under Uncertain Data

The Pure Parsimony Haplotyping (PPH) problem is a NP-hard combinatorial optimization problem that consists of finding the minimum number of haplotypes necessary to explain a given set of genotypes. PPH has attracted more and more attention in recent years due to its importance in analysis of many fine-scale genetic data. Its application fields range from mapping complex disease genes to inferring population histories, passing through designing drugs, functional genomics and pharmacogenetics. In this article we investigate, for the first time, a recent version of PPH called the Pure Parsimony Haplotype problem under Uncertain Data (PPH-UD). This version mainly arises when the input genotypes are not accurate, i.e., when some single nucleotide polymorphisms are missing or affected by errors. We propose an exact approach to solution of PPH-UD based on an extended version of Catanzaro et al. [1] class representative model for PPH, currently the state-of-the-art integer programming model for PPH. The model is efficient, accurate, compact, polynomial-sized, easy to implement, solvable with any solver for mixed integer programming, and usable in all those cases for which the parsimony criterion is well suited for haplotype estimation

Toll-like receptor gene polymorphisms are associated with susceptibility to graves' ophthalmopathy in Taiwan males

<p>Abstract</p> <p>Background</p> <p>Toll-like receptors (TLRs) are a family of pattern-recognition receptors, which plays a role in eliciting innate/adaptive immune responses and developing chronic inflammation. The polymorphisms of TLRs have been associated with the risk of various autoimmune diseases, including systemic lupus erythematosus (SLE), multiple sclerosis and rheumatorid arthritis. The aim of this study was to evaluate whether TLR genes could be used as genetic markers for the development of Graves' ophthalmopathy (GO).</p> <p>Methods</p> <p>6 TLR-4 and 2 TLR-9 gene polymorphisms in 471 GD patients (200 patients with GO and 271 patients without GO) from a Taiwan Chinese population were evaluated.</p> <p>Results</p> <p>No statistically significant difference was observed in the genotypic and allelic frequencies of TLR-4 and TLR-9 gene polymorphisms between the GD patients with and without GO. However, sex-stratified analyses showed that the association between TLR-9 gene polymorphism and GO phenotype was more pronounced in the male patients. The odds ratios (ORs) was 2.11 (95% confidence interval [CI] = 1.14-3.91) for rs187084 AàG polymorphism and 1.97 (95% CI = 1.07-3.62) for rs352140 AàG polymorphism among the male patients. Increasing one G allele of rs287084 and one A allele of rs352140 increased the risk of GO (<it>p </it>values for trend tests were 0.0195 and 0.0345, respectively). Further, in haplotype analyses, the male patients carrying the GA haplotype had a higher risk of GO (odds ratio [OR] = 2.02, 95% confidence interval [CI] = 1.09-3.73) than those not carrying the GA haplotype.</p> <p>Conclusion</p> <p>The present data suggest that TLR-9 gene polymorphisms were significantly associated with increased susceptibility of ophthalmopathy in male GD patients.</p

Automatic Diagnosis of Pathological Myopia from Heterogeneous Biomedical Data

10.1371/journal.pone.0065736PLoS ONE86

Massively Parallel Haplotyping on Microscopic Beads for the High-Throughput Phase Analysis of Single Molecules

In spite of the many advances in haplotyping methods, it is still very difficult to characterize rare haplotypes in tissues and different environmental samples or to accurately assess the haplotype diversity in large mixtures. This would require a haplotyping method capable of analyzing the phase of single molecules with an unprecedented throughput. Here we describe such a haplotyping method capable of analyzing in parallel hundreds of thousands single molecules in one experiment. In this method, multiple PCR reactions amplify different polymorphic regions of a single DNA molecule on a magnetic bead compartmentalized in an emulsion drop. The allelic states of the amplified polymorphisms are identified with fluorescently labeled probes that are then decoded from images taken of the arrayed beads by a microscope. This method can evaluate the phase of up to 3 polymorphisms separated by up to 5 kilobases in hundreds of thousands single molecules. We tested the sensitivity of the method by measuring the number of mutant haplotypes synthesized by four different commercially available enzymes: Phusion, Platinum Taq, Titanium Taq, and Phire. The digital nature of the method makes it highly sensitive to detecting haplotype ratios of less than 1∶10,000. We also accurately quantified chimera formation during the exponential phase of PCR by different DNA polymerases

Evolutionary Dynamics of Co-Segregating Gene Clusters Associated with Complex Diseases

BACKGROUND: The distribution of human disease-associated mutations is not random across the human genome. Despite the fact that natural selection continually removes disease-associated mutations, an enrichment of these variants can be observed in regions of low recombination. There are a number of mechanisms by which such a clustering could occur, including genetic perturbations or demographic effects within different populations. Recent genome-wide association studies (GWAS) suggest that single nucleotide polymorphisms (SNPs) associated with complex disease traits are not randomly distributed throughout the genome, but tend to cluster in regions of low recombination. PRINCIPAL FINDINGS: Here we investigated whether deleterious mutations have accumulated in regions of low recombination due to the impact of recent positive selection and genetic hitchhiking. Using publicly available data on common complex diseases and population demography, we observed an enrichment of hitchhiked disease associations in conserved gene clusters subject to selection pressure. Evolutionary analysis revealed that these conserved gene clusters arose by multiple concerted rearrangements events across the vertebrate lineage. We observed distinct clustering of disease-associated SNPs in evolutionary rearranged regions of low recombination and high gene density, which harbor genes involved in immunity, that is, the interleukin cluster on 5q31 or RhoA on 3p21. CONCLUSIONS: Our results suggest that multiple lineage specific rearrangements led to a physical clustering of functionally related and linked genes exhibiting an enrichment of susceptibility loci for complex traits. This implies that besides recent evolutionary adaptations other evolutionary dynamics have played a role in the formation of linked gene clusters associated with complex disease traits

Expression Profiling of Major Histocompatibility and Natural Killer Complex Genes Reveals Candidates for Controlling Risk of Graft versus Host Disease

Background: The major histocompatibility complex (MHC) is the most important genomic region that contributes to the risk of graft versus host disease (GVHD) after haematopoietic stem cell transplantation. Matching of MHC class I and II genes is essential for the success of transplantation. However, the MHC contains additional genes that also contribute to the risk of developing acute GVHD. It is difficult to identify these genes by genetic association studies alone due to linkage disequilibrium in this region. Therefore, we aimed to identify MHC genes and other genes involved in the pathophysiology of GVHD by mRNA expression profiling. Methodology/Principal Findings: To reduce the complexity of the task, we used genetically well-defined rat inbred strains and a rat skin explant assay, an in-vitro-model of the graft versus host reaction (GVHR), to analyze the expression of MHC, natural killer complex (NKC), and other genes in cutaneous GVHR. We observed a statistically significant and strong up or down regulation of 11 MHC, 6 NKC, and 168 genes encoded in other genomic regions, i.e. 4.9%, 14.0%, and 2.6% of the tested genes respectively. The regulation of 7 selected MHC and 3 NKC genes was confirmed by quantitative real-time PCR and in independent skin explant assays. In addition, similar regulations of most of the selected genes were observed in GVHD-affected skin lesions of transplanted rats and in human skin explant assays. Conclusions/Significance: We identified rat and human MHC and NKC genes that are regulated during GVHR in skin explant assays and could therefore serve as biomarkers for GVHD. Several of the respective human genes, including HLA-DMB, C2, AIF1, SPR1, UBD, and OLR1, are polymorphic. These candidates may therefore contribute to the genetic risk of GVHD in patients

Identification of Tumor Suppressors and Oncogenes from Genomic and Epigenetic Features in Ovarian Cancer

The identification of genetic and epigenetic alterations from primary tumor cells has become a common method to identify genes critical to the development and progression of cancer. We seek to identify those genetic and epigenetic aberrations that have the most impact on gene function within the tumor. First, we perform a bioinformatic analysis of copy number variation (CNV) and DNA methylation covering the genetic landscape of ovarian cancer tumor cells. We separately examined CNV and DNA methylation for 42 primary serous ovarian cancer samples using MOMA-ROMA assays and 379 tumor samples analyzed by The Cancer Genome Atlas. We have identified 346 genes with significant deletions or amplifications among the tumor samples. Utilizing associated gene expression data we predict 156 genes with altered copy number and correlated changes in expression. Among these genes CCNE1, POP4, UQCRB, PHF20L1 and C19orf2 were identified within both data sets. We were specifically interested in copy number variation as our base genomic property in the prediction of tumor suppressors and oncogenes in the altered ovarian tumor. We therefore identify changes in DNA methylation and expression for all amplified and deleted genes. We statistically define tumor suppressor and oncogenic features for these modalities and perform a correlation analysis with expression. We predicted 611 potential oncogenes and tumor suppressors candidates by integrating these data types. Genes with a strong correlation for methylation dependent expression changes exhibited at varying copy number aberrations include CDCA8, ATAD2, CDKN2A, RAB25, AURKA, BOP1 and EIF2C3. We provide copy number variation and DNA methylation analysis for over 11,500 individual genes covering the genetic landscape of ovarian cancer tumors. We show the extent of genomic and epigenetic alterations for known tumor suppressors and oncogenes and also use these defined features to identify potential ovarian cancer gene candidates

Global report on preterm birth and stillbirth (2 of 7): discovery science

<p>Abstract</p> <p>Background</p> <p>Normal and abnormal processes of pregnancy and childbirth are poorly understood. This second article in a global report explains what is known about the etiologies of preterm births and stillbirths and identifies critical gaps in knowledge. Two important concepts emerge: the continuum of pregnancy, beginning at implantation and ending with uterine involution following birth; and the multifactorial etiologies of preterm birth and stillbirth. Improved tools and data will enable discovery scientists to identify causal pathways and cost-effective interventions.</p> <p>Pregnancy and parturition continuum</p> <p>The biological process of pregnancy and childbirth begins with implantation and, after birth, ends with the return of the uterus to its previous state. The majority of pregnancy is characterized by rapid uterine and fetal growth without contractions. Yet most research has addressed only uterine stimulation (labor) that accounts for <0.5% of pregnancy.</p> <p>Etiologies</p> <p>The etiologies of preterm birth and stillbirth differ by gestational age, genetics, and environmental factors. Approximately 30% of all preterm births are indicated for either maternal or fetal complications, such as maternal illness or fetal growth restriction. Commonly recognized pathways leading to preterm birth occur most often during the gestational ages indicated: (1) inflammation caused by infection (22-32 weeks); (2) decidual hemorrhage caused by uteroplacental thrombosis (early or late preterm birth); (3) stress (32-36 weeks); and (4) uterine overdistention, often caused by multiple fetuses (32-36 weeks). Other contributors include cervical insufficiency, smoking, and systemic infections. Many stillbirths have similar causes and mechanisms. About two-thirds of late fetal deaths occur during the antepartum period; the other third occur during childbirth. Intrapartum asphyxia is a leading cause of stillbirths in low- and middle-income countries.</p> <p>Recommendations</p> <p>Utilizing new systems biology tools, opportunities now exist for researchers to investigate various pathways important to normal and abnormal pregnancies. Improved access to quality data and biological specimens are critical to advancing discovery science. Phenotypes, standardized definitions, and uniform criteria for assessing preterm birth and stillbirth outcomes are other immediate research needs.</p> <p>Conclusion</p> <p>Preterm birth and stillbirth have multifactorial etiologies. More resources must be directed toward accelerating our understanding of these complex processes, and identifying upstream and cost-effective solutions that will improve these pregnancy outcomes.</p