A new strategy for enhancing imputation quality of rare variants from next-generation sequencing data via combining SNP and exome chip data

Background: Rare variants have gathered increasing attention as a possible alternative source of missing heritability. Since next generation sequencing technology is not yet cost-effective for large-scale genomic studies, a widely used alternative approach is imputation. However, the imputation approach may be limited by the low accuracy of the imputed rare variants. To improve imputation accuracy of rare variants, various approaches have been suggested, including increasing the sample size of the reference panel, using sequencing data from study-specific samples (i.e., specific populations), and using local reference panels by genotyping or sequencing a subset of study samples. While these approaches mainly utilize reference panels, imputation accuracy of rare variants can also be increased by using exome chips containing rare variants. The exome chip contains 250 K rare variants selected from the discovered variants of about 12,000 sequenced samples. If exome chip data are available for previously genotyped samples, the combined approach using a genotype panel of merged data, including exome chips and SNP chips, should increase the imputation accuracy of rare variants. Results: In this study, we describe a combined imputation which uses both exome chip and SNP chip data simultaneously as a genotype panel. The effectiveness and performance of the combined approach was demonstrated using a reference panel of 848 samples constructed using exome sequencing data from the T2D-GENES consortium and 5,349 sample genotype panels consisting of an exome chip and SNP chip. As a result, the combined approach increased imputation quality up to 11 %, and genomic coverage for rare variants up to 117.7 % (MAF < 1 %), compared to imputation using the SNP chip alone. Also, we investigated the systematic effect of reference panels on imputation quality using five reference panels and three genotype panels. The best performing approach was the combination of the study specific reference panel and the genotype panel of combined data. Conclusions: Our study demonstrates that combined datasets, including SNP chips and exome chips, enhances both the imputation quality and genomic coverage of rare variants

Identification and Functional Characterization of G6PC2 Coding Variants Influencing Glycemic Traits Define an Effector Transcript at the G6PC2-ABCB11 Locus

Genome wide association studies (GWAS) for fasting glucose (FG) and insulin (FI) have identified common variant signals which explain 4.8% and 1.2% of trait variance, respectively. It is hypothesized that low-frequency and rare variants could contribute substantially to unexplained genetic variance. To test this, we analyzed exome-array data from up to 33,231 non-diabetic individuals of European ancestry. We found exome-wide significant (P&lt;5&times;10-7) evidence for two loci not previously highlighted by common variant GWAS: GLP1R (p.Ala316Thr, minor allele frequency (MAF)=1.5%) influencing FG levels, and URB2 (p.Glu594Val, MAF = 0.1%) influencing FI levels. Coding variant associations can highlight potential effector genes at (non-coding) GWAS signals. At the G6PC2/ABCB11 locus, we identified multiple coding variants in G6PC2 (p.Val219Leu, p.His177Tyr, and p.Tyr207Ser) influencing FG levels, conditionally independent of each other and the non-coding GWAS signal. In vitro assays demonstrate that these associated coding alleles result in reduced protein abundance via proteasomal degradation, establishing G6PC2 as an effector gene at this locus. Reconciliation of single-variant associations and functional effects was only possible when haplotype phase was considered. In contrast to earlier reports suggesting that, paradoxically, glucose-raising alleles at this locus are protective against type 2 diabetes (T2D), the p.Val219Leu G6PC2 variant displayed a modest but directionally consistent association with T2D risk. Coding variant associations for glycemic traits in GWAS signals highlight PCSK1, RREB1, and ZHX3 as likely effector transcripts. These coding variant association signals do not have a major impact on the trait variance explained, but they do provide valuable biological insights

Isolation, expansion and differentiation of mesenchymal stromal cells from rabbits' bone marrow

Abstract: Tissue engineering has been a fundamental technique in the regenerative medicine field, once it permits to build tri-dimensional tissue constructs associating undifferentiated mesenchymal cells (or mesenchymal stromal cells - MSCs) and scaffolds in vitro. Therefore, many studies have been carried out using these cells from different animal species, and rabbits are often used as animal model for in vivo tissue repair studies. However, most of the information available about MSCs harvesting and characterization is about human and murine cells, which brings some doubts to researchers who desire to work with a rabbit model in tissue repair studies based on MSCs. In this context, this study aimed to add and improve the information available in the scientific literature providing a complete technique for isolation, expansion and differentiation of MSCs from rabbits. Bone marrow mononuclear cells (BMMCs) from humerus and femur of rabbits were obtained and to evaluate their proliferation rate, three different culture media were tested, here referred as DMEM-P, DMEM´S and α-MEM. The BMMCs were also cultured in osteogenic, chondrogenic and adipogenic induction media to prove their multipotentiality. It was concluded that the techniques suggested in this study can provide a guideline to harvest and isolate MSCs from bone marrow of rabbits in enough amount to allow their expansion and, based on the laboratory experience where the study was developed, it is also suggested a culture media formulation to provide a better cell proliferation rate with multipotentiality preservation

Functionally oriented analysis of cardiometabolic traits in a trans-ethnic sample

Interpretation of genetic association results is difficult because signals often lack biological context. To generate hypotheses of the functional genetic etiology of complex cardiometabolic traits, we estimated the genetically determined component of gene expression from common variants using PrediXcan (1) and determined genes with differential predicted expression by trait. PrediXcan imputes tissue-specific expression levels from genetic variation using variant-level effect on gene expression in transcriptome data. To explore the value of imputed genetically regulated gene expression (GReX) models across different ancestral populations, we evaluated imputed expression levels for predictive accuracy genome-wide in RNA sequence data in samples drawn from European-Ancestry and African-Ancestry populations and identified substantial predictive power using European-derived models in a non-European target population.We then tested the association of GReX on 15 cardiometabolic traits including blood lipid levels, body mass index, height, blood pressure, fasting glucose and insulin, RR interval, fibrinogen level, factor VII level and white blood cell and platelet counts in 15 755 individuals across three ancestry groups, resulting in 20 novel gene-phenotype associations reaching experiment-wide significance across ancestries. In addition, we identified 18 significant novel gene-phenotype associations in our ancestry-specific analyses. Top associations were assessed for additional support via query of S-PrediXcan (2) results derived from publicly available genome-wide association studies summary data. Collectively, these findings illustrate the utility of transcriptome-based imputation models for discovery of cardiometabolic effect genes in a diverse dataset

Validity of models for Dreicer generation of runaway electrons in dynamic scenarios

Abstract Runaway electron modelling efforts are motivated by the risk these energetic particles pose to large fusion devices. The sophisticated kinetic models can capture most features of the runaway electron generation but have high computational costs, which can be avoided by using computationally cheaper reduced kinetic codes. This paper compares the reduced kinetic and kinetic models to determine when the former solvers, based on analytical calculations assuming quasi-stationarity, can be used. The Dreicer generation rate is calculated by two different solvers in parallel in a workflow developed in the European integrated modelling framework, and this is complemented by calculations of a third code that is not yet integrated into the framework. Runaway Fluid, a reduced kinetic code, NORSE, a kinetic code using non-linear collision operator, and DREAM, a linearized Fokker–Planck solver, are used to investigate the effect of a dynamic change in the electric field for different plasma scenarios spanning across the whole tokamak-relevant range. We find that on time scales shorter than or comparable to the electron–electron collision time at the critical velocity for runaway electron generation, kinetic effects not captured by reduced kinetic models play an important role. This characteristic time scale is easy to calculate and can reliably be used to determine whether there is a need for kinetic modelling or cheaper reduced kinetic codes are expected to deliver sufficiently accurate results. This criterion can be automated, and thus it can be of great benefit for the comprehensive self-consistent modelling frameworks that are attempting to simulate complex events such as tokamak start-up or disruptions.</jats:p

Extracellular Calcium Modulates Chondrogenic and Osteogenic Differentiation of Human Adipose-Derived Stem Cells: A Novel Approach for Osteochondral Tissue Engineering Using a Single Stem Cell Source

We have previously shown that elevating extracellular calcium from a concentration of 1.8 to 8 mM accelerates and increases human adipose-derived stem cell (hASC) osteogenic differentiation and cell-mediated calcium accretion, even in the absence of any other soluble osteogenic factors in the culture medium. However, the effects of elevated calcium on hASC chondrogenic differentiation have not been reported. The goal of this study was to determine the effects of varied calcium concentrations on chondrogenic differentiation of hASC. We hypothesized that exposure to elevated extracellular calcium (8 mM concentration) in a chondrogenic differentiation medium (CDM) would inhibit chondrogenesis of hASC when compared to basal calcium (1.8 mM concentration) controls. We further hypothesized that a full osteochondral construct could be engineered by controlling local release of calcium to induce site-specific chondrogenesis and osteogenesis using only hASC as the cell source. Human ASC was cultured as micromass pellets in CDM containing transforming growth factor-β1 and bone morphogenetic protein 6 for 28 days at extracellular calcium concentrations of either 1.8 mM (basal) or 8 mM (elevated). Our findings indicated that elevated calcium induced osteogenesis and inhibited chondrogenesis in hASC. Based on these findings, stacked polylactic acid nanofibrous scaffolds containing either 0% or 20% tricalcium phosphate (TCP) nanoparticles were electrospun and tested for site-specific chondrogenesis and osteogenesis. Histological assays confirmed that human ASC differentiated locally to generate calcified tissue in layers containing 20% TCP, and cartilage in the layers with no TCP when cultured in CDM. This is the first study to report the effects of elevated calcium on chondrogenic differentiation of hASC, and to develop osteochondral nanofibrous scaffolds using a single cell source and controlled calcium release to induce site-specific differentiation. This approach holds great promise for osteochondral tissue engineering using a single cell source (hASC) and single scaffold