Immunoseq: the identification of functionally relevant variants through targeted capture and sequencing of active regulatory regions in human immune cells

$\textbf{BACKGROUND}$: The observation that the genetic variants identified in genome-wide association studies (GWAS) frequently lie in non-coding regions of the genome that contain cis-regulatory elements suggests that altered gene expression underlies the development of many complex traits. In order to efficiently make a comprehensive assessment of the impact of non-coding genetic variation in immune related diseases we emulated the whole-exome sequencing paradigm and developed a custom capture panel for the known DNase I hypersensitive site (DHS) in immune cells - "Immunoseq". $\textbf{RESULTS}$: We performed Immunoseq in 30 healthy individuals where we had existing transcriptome data from T cells. We identified a large number of novel non-coding variants in these samples. Relying on allele specific expression measurements, we also showed that our selected capture regions are enriched for functional variants that have an impact on differential allelic gene expression. The results from a replication set with 180 samples confirmed our observations. $\textbf{CONCLUSIONS}$: We show that Immunoseq is a powerful approach to detect novel rare variants in regulatory regions. We also demonstrate that these novel variants have a potential functional role in immune cells.This work was supported by grants from the Canadian Institute of Health Research (CIHR), the UK Medical Research Council (G1100125), the Swedish Research Council (DO283001) and Knut and Alice Wallenberg Foundation (KAW). We also acknowledge the use of subjects from the Cambridge BioResource and the support of the Cambridge NIHR Biomedical Research Centre. AM was supported by the Fond de Recherche Santé Québec Doctoral training award. TP and CL holds a Canada Research Chair

Genome-wide association study of angioedema induced by angiotensin-converting enzyme inhibitor and angiotensin receptor blocker treatment

Angioedema in the mouth or upper airways is a feared adverse reaction to angiotensin-converting enzyme inhibitor (ACEi) and angiotensin receptor blocker (ARB) treatment, which is used for hypertension, heart failure and diabetes complications. This candidate gene and genome-wide association study aimed to identify genetic variants predisposing to angioedema induced by these drugs. The discovery cohort consisted of 173 cases and 4890 controls recruited in Sweden. In the candidate gene analysis, ETV6, BDKRB2, MME, and PRKCQ were nominally associated with angioedema (p < 0.05), but did not pass Bonferroni correction for multiple testing (p < 2.89 × 10−5). In the genome-wide analysis, intronic variants in the calcium-activated potassium channel subunit alpha-1 (KCNMA1) gene on chromosome 10 were significantly associated with angioedema (p < 5 × 10−8). Whilst the top KCNMA1 hit was not significant in the replication cohort (413 cases and 599 ACEi-exposed controls from the US and Northern Europe), a meta-analysis of the replication and discovery cohorts (in total 586 cases and 1944 ACEi-exposed controls) revealed that each variant allele increased the odds of experiencing angioedema 1.62 times (95% confidence interval 1.05–2.50, p = 0.030). Associated KCNMA1 variants are not known to be functional, but are in linkage disequilibrium with variants in transcription factor binding sites active in relevant tissues. In summary, our data suggest that common variation in KCNMA1 is associated with risk of angioedema induced by ACEi or ARB treatment. Future whole exome or genome sequencing studies will show whether rare variants in KCNMA1 or other genes contribute to the risk of ACEi- and ARB-induced angioedema

A risk haplotype of STAT4 for systemic lupus erythematosus is over-expressed, correlates with anti-dsDNA and shows additive effects with two risk alleles of IRF5

Systemic lupus erythematosus (SLE) is the prototype autoimmune disease where genes regulated by type I interferon (IFN) are over-expressed and contribute to the disease pathogenesis. Because signal transducer and activator of transcription 4 (STAT4) plays a key role in the type I IFN receptor signaling, we performed a candidate gene study of a comprehensive set of single nucleotide polymorphism (SNPs) in STAT4 in Swedish patients with SLE. We found that 10 out of 53 analyzed SNPs in STAT4 were associated with SLE, with the strongest signal of association (P = 7.1 × 10−8) for two perfectly linked SNPs rs10181656 and rs7582694. The risk alleles of these 10 SNPs form a common risk haplotype for SLE (P = 1.7 × 10−5). According to conditional logistic regression analysis the SNP rs10181656 or rs7582694 accounts for all of the observed association signal. By quantitative analysis of the allelic expression of STAT4 we found that the risk allele of STAT4 was over-expressed in primary human cells of mesenchymal origin, but not in B-cells, and that the risk allele of STAT4 was over-expressed (P = 8.4 × 10−5) in cells carrying the risk haplotype for SLE compared with cells with a non-risk haplotype. The risk allele of the SNP rs7582694 in STAT4 correlated to production of anti-dsDNA (double-stranded DNA) antibodies and displayed a multiplicatively increased, 1.82-fold risk of SLE with two independent risk alleles of the IRF5 (interferon regulatory factor 5) gene

Improved Resolution Haplogroup G Phylogeny in the Y Chromosome, Revealed by a Set of Newly Characterized SNPs

Background: Y-SNP haplogroup G (hgG), defined by Y-SNP marker M201, is relatively uncommon in the United States general population, with only 8 additional sub-markers characterized. Many of the previously described eight sub-markers are either very rare (2–4%) or do not distinguish between major populations within this hg. In fact, prior to the current study, only 2 % of our reference Caucasian population belonged to hgG and all of these individuals were in sub-haplogroup G2a, defined by P15. Additional Y-SNPs are needed in order to differentiate between individuals within this haplogroup. Principal Findings: In this work we have investigated whether we could differentiate between a population of 63 hgG individuals using previously uncharacterized Y-SNPs. We have designed assays to test these individuals using all known hgG SNPs (n = 9) and an additional 16 unreported/undefined Y-SNPS. Using a combination of DNA sequence and genetic genealogy databases, we have uncovered a total of 15 new hgG SNPs that had been previously reported but not phylogenetically characterized. Ten of the new Y-SNPs are phylogenetically equivalent to M201, one is equivalent to P15 and, interestingly, four create new, separate haplogroups. Three of the latter are more common than many of the previously defined Y-SNPs. Y-STR data from these individuals show that DYS385*12 is present in (70%) of G2a3b1-U13 individuals while only 4 % of non-G2a3b1-U13 individuals posses the DYS385*12 allele. Conclusions: This study uncovered several previously undefined Y-SNPs by using data from several database sources. Th

Genotype calling in tetraploid species from bi-allelic marker data using mixture models

<p>Abstract</p> <p>Background</p> <p>Automated genotype calling in tetraploid species was until recently not possible, which hampered genetic analysis. Modern genotyping assays often produce two signals, one for each allele of a bi-allelic marker. While ample software is available to obtain genotypes (homozygous for either allele, or heterozygous) for diploid species from these signals, such software is not available for tetraploid species which may be scored as five alternative genotypes (aaaa, baaa, bbaa, bbba and bbbb; nulliplex to quadruplex).</p> <p>Results</p> <p>We present a novel algorithm, implemented in the R package fitTetra, to assign genotypes for bi-allelic markers to tetraploid samples from genotyping assays that produce intensity signals for both alleles. The algorithm is based on the fitting of several mixture models with five components, one for each of the five possible genotypes. The models have different numbers of parameters specifying the relation between the five component means, and some of them impose a constraint on the mixing proportions to conform to Hardy-Weinberg equilibrium (HWE) ratios. The software rejects markers that do not allow a reliable genotyping for the majority of the samples, and it assigns a missing score to samples that cannot be scored into one of the five possible genotypes with sufficient confidence.</p> <p>Conclusions</p> <p>We have validated the software with data of a collection of 224 potato varieties assayed with an Illumina GoldenGate™ 384 SNP array and shown that all SNPs with informative ratio distributions are fitted. Almost all fitted models appear to be correct based on visual inspection and comparison with diploid samples. When the collection of potato varieties is analyzed as if it were a population, almost all markers seem to be in Hardy-Weinberg equilibrium. The R package fitTetra is freely available under the GNU Public License from <url>http://www.plantbreeding.wur.nl/UK/software_fitTetra.html</url> and as Additional files with this article.</p

Sequence-selective detection of double-stranded DNA sequences using pyrrole-imidazole polyamide microarrays

We describe a microarray format that can detect double-stranded DNA sequences with a high degree of sequence selectivity. Cyclooctyne-derivatized pyrrole-imidazole polyamides were immobilized on azide-modified glass substrates using microcontact printing and a strain-promoted azide-alkyne cycloaddition (SPAAC) reaction. These polyamide-immobilized substrates selectively detected a seven-base-pair binding site incorporated within a double-stranded oligodeoxyribonucleotide sequence even in the presence of an excess of a sequence with a single-base-pair mismatc

A pipeline for high throughput detection and mapping of SNPs from EST databases

Single nucleotide polymorphisms (SNPs) represent the most abundant type of genetic variation that can be used as molecular markers. The SNPs that are hidden in sequence databases can be unlocked using bioinformatic tools. For efficient application of these SNPs, the sequence set should be error-free as much as possible, targeting single loci and suitable for the SNP scoring platform of choice. We have developed a pipeline to effectively mine SNPs from public EST databases with or without quality information using QualitySNP software, select reliable SNP and prepare the loci for analysis on the Illumina GoldenGate genotyping platform. The applicability of the pipeline was demonstrated using publicly available potato EST data, genotyping individuals from two diploid mapping populations and subsequently mapping the SNP markers (putative genes) in both populations. Over 7000 reliable SNPs were identified that met the criteria for genotyping on the GoldenGate platform. Of the 384 SNPs on the SNP array approximately 12% dropped out. For the two potato mapping populations 165 and 185 SNPs segregating SNP loci could be mapped on the respective genetic maps, illustrating the effectiveness of our pipeline for SNP selection and validation

Origin of structural difference in metabolic networks with respect to temperature

<p>Abstract</p> <p>Background</p> <p>Metabolism is believed to adaptively shape-shift with changing environment. In recent years, a structural difference with respect to temperature, which is an environmental factor, has been revealed in metabolic networks, implying that metabolic networks transit with temperature. Subsequently, elucidatation of the origin of these structural differences due to temperature is important for understanding the evolution of life. However, the origin has yet to be clarified due to the complexity of metabolic networks.</p> <p>Results</p> <p>Consequently, we propose a simple model with a few parameters to explain the transitions. We first present mathematical solutions of this model using mean-field approximation, and demonstrate that this model can reproduce structural properties, such as heterogeneous connectivity and hierarchical modularity, in real metabolic networks both qualitatively and quantitatively. We next show that the model parameters correlate with optimal growth temperature. In addition, we present a relationship between multiple cyclic properties and optimal growth temperature in metabolic networks.</p> <p>Conclusion</p> <p>From the proposed model, we find that such structural properties are determined by the emergence of a short-cut path, which reduces the minimum distance between two nodes on a graph. Furthermore, we investigate correlations between model parameters and growth temperature; as a result, we find that the emergence of the short-cut path tends to be inhibited with increasing temperature. In addition, we also find that the short-cut path bypasses a relatively long path at high temperature when the emergence of the new path is not inhibited. Even further, additional network analysis provides convincing evidence of the reliability of the proposed model and its conclusions on the possible origins of differences in metabolic network structure.</p

High-Resolution Genotyping via Whole Genome Hybridizations to Microarrays Containing Long Oligonucleotide Probes

To date, microarray-based genotyping of large, complex plant genomes has been complicated by the need to perform genome complexity reduction to obtain sufficiently strong hybridization signals. Genome complexity reduction techniques are, however, tedious and can introduce unwanted variables into genotyping assays. Here, we report a microarray-based genotyping technology for complex genomes (such as the 2.3 GB maize genome) that does not require genome complexity reduction prior to hybridization. Approximately 200,000 long oligonucleotide probes were identified as being polymorphic between the inbred parents of a mapping population and used to genotype two recombinant inbred lines. While multiple hybridization replicates provided ∼97% accuracy, even a single replicate provided ∼95% accuracy. Genotyping accuracy was further increased to >99% by utilizing information from adjacent probes. This microarray-based method provides a simple, high-density genotyping approach for large, complex genomes