Comparison of generalized estimating equations and quadratic inference functions using data from the National Longitudinal Survey of Children and Youth (NLSCY) database

<p>Abstract</p> <p>Background</p> <p>The generalized estimating equations (GEE) technique is often used in longitudinal data modeling, where investigators are interested in population-averaged effects of covariates on responses of interest. GEE involves specifying a model relating covariates to outcomes and a plausible correlation structure between responses at different time periods. While GEE parameter estimates are consistent irrespective of the true underlying correlation structure, the method has some limitations that include challenges with model selection due to lack of absolute goodness-of-fit tests to aid comparisons among several plausible models. The quadratic inference functions (QIF) method extends the capabilities of GEE, while also addressing some GEE limitations.</p> <p>Methods</p> <p>We conducted a comparative study between GEE and QIF via an illustrative example, using data from the "National Longitudinal Survey of Children and Youth (NLSCY)" database. The NLSCY dataset consists of long-term, population based survey data collected since 1994, and is designed to evaluate the determinants of developmental outcomes in Canadian children. We modeled the relationship between hyperactivity-inattention and gender, age, family functioning, maternal depression symptoms, household income adequacy, maternal immigration status and maternal educational level using GEE and QIF. Basis for comparison include: (1) ease of model selection; (2) sensitivity of results to different working correlation matrices; and (3) efficiency of parameter estimates.</p> <p>Results</p> <p>The sample included 795, 858 respondents (50.3% male; 12% immigrant; 6% from dysfunctional families). QIF analysis reveals that gender (male) (odds ratio [OR] = 1.73; 95% confidence interval [CI] = 1.10 to 2.71), family dysfunctional (OR = 2.84, 95% CI of 1.58 to 5.11), and maternal depression (OR = 2.49, 95% CI of 1.60 to 2.60) are significantly associated with higher odds of hyperactivity-inattention. The results remained robust under GEE modeling. Model selection was facilitated in QIF using a goodness-of-fit statistic. Overall, estimates from QIF were more efficient than those from GEE using AR (1) and Exchangeable working correlation matrices (Relative efficiency = 1.1117; 1.3082 respectively).</p> <p>Conclusion</p> <p>QIF is useful for model selection and provides more efficient parameter estimates than GEE. QIF can help investigators obtain more reliable results when used in conjunction with GEE.</p

Highly Parallel and Short-Acting Amplification with Locus-Specific Primers to Detect Single Nucleotide Polymorphisms by the DigiTag2 Assay

The DigiTag2 assay enables analysis of a set of 96 SNPs using Kapa 2GFast HotStart DNA polymerase with a new protocol that has a total running time of about 7 hours, which is 6 hours shorter than the previous protocol. Quality parameters (conversion rate, call rate, reproducibility and concordance) were at the same levels as when genotype calls were acquired using the previous protocol. Multiplex PCR with 192 pairs of locus-specific primers was available for target preparation in the DigiTag2 assay without the optimization of reaction conditions, and quality parameters had the same levels as those acquired with 96-plex PCR. The locus-specific primers were able to achieve sufficient (concentration of target amplicon ≥5 nM) and specific (concentration of unexpected amplicons <2 nM) amplification within 2 hours, were also able to achieve detectable amplifications even when working in a 96-plex or 192-plex form. The improved DigiTag2 assay will be an efficient platform for screening an intermediate number of SNPs (tens to hundreds of sites) in the replication analysis after genome-wide association study. Moreover, highly parallel and short-acting amplification with locus-specific primers may thus facilitate widespread application to other PCR-based assays

The Effect of Epstein-Barr Virus Latent Membrane Protein 2 Expression on the Kinetics of Early B Cell Infection

Infection of human B cells with wild-type Epstein-Barr virus (EBV) in vitro leads to activation and proliferation that result in efficient production of lymphoblastoid cell lines (LCLs). Latent Membrane Protein 2 (LMP2) is expressed early after infection and previous research has suggested a possible role in this process. Therefore, we generated recombinant EBV with knockouts of either or both protein isoforms, LMP2A and LMP2B (Δ2A, Δ2B, Δ2A/Δ2B) to study the effect of LMP2 in early B cell infection. Infection of B cells with Δ2A and Δ2A/Δ2B viruses led to a marked decrease in activation and proliferation relative to wild-type (wt) viruses, and resulted in higher percentages of apoptotic B cells. Δ2B virus infection showed activation levels comparable to wt, but fewer numbers of proliferating B cells. Early B cell infection with wt, Δ2A and Δ2B viruses did not result in changes in latent gene expression, with the exception of elevated LMP2B transcript in Δ2A virus infection. Infection with Δ2A and Δ2B viruses did not affect viral latency, determined by changes in LMP1/Zebra expression following BCR stimulation. However, BCR stimulation of Δ2A/Δ2B cells resulted in decreased LMP1 expression, which suggests loss of stability in viral latency. Long-term outgrowth assays revealed that LMP2A, but not LMP2B, is critical for efficient long-term growth of B cells in vitro. The lowest levels of activation, proliferation, and LCL formation were observed when both isoforms were deleted. These results suggest that LMP2A appears to be critical for efficient activation, proliferation and survival of EBV-infected B cells at early times after infection, which impacts the efficient long-term growth of B cells in culture. In contrast, LMP2B did not appear to play a significant role in these processes, and long-term growth of infected B cells was not affected by the absence of this protein. © 2013 Wasil et al

Detection of regulator genes and eQTLs in gene networks

Genetic differences between individuals associated to quantitative phenotypic traits, including disease states, are usually found in non-coding genomic regions. These genetic variants are often also associated to differences in expression levels of nearby genes (they are "expression quantitative trait loci" or eQTLs for short) and presumably play a gene regulatory role, affecting the status of molecular networks of interacting genes, proteins and metabolites. Computational systems biology approaches to reconstruct causal gene networks from large-scale omics data have therefore become essential to understand the structure of networks controlled by eQTLs together with other regulatory genes, and to generate detailed hypotheses about the molecular mechanisms that lead from genotype to phenotype. Here we review the main analytical methods and softwares to identify eQTLs and their associated genes, to reconstruct co-expression networks and modules, to reconstruct causal Bayesian gene and module networks, and to validate predicted networks in silico.Comment: minor revision with typos corrected; review article; 24 pages, 2 figure

A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease

Existing knowledge of genetic variants affecting risk of coronary artery disease (CAD) is largely based on genome-wide association studies (GWAS) analysis of common SNPs. Leveraging phased haplotypes from the 1000 Genomes Project, we report a GWAS meta-analysis of 185 thousand CAD cases and controls, interrogating 6.7 million common (MAF>0.05) as well as 2.7 million low frequency (0.005<MAF<0.05) variants. In addition to confirmation of most known CAD loci, we identified 10 novel loci, eight additive and two recessive, that contain candidate genes that newly implicate biological processes in vessel walls. We observed intra-locus allelic heterogeneity but little evidence of low frequency variants with larger effects and no evidence of synthetic association. Our analysis provides a comprehensive survey of the fine genetic architecture of CAD showing that genetic susceptibility to this common disease is largely determined by common SNPs of small effect siz

Regulatory T Cells in γ Irradiation-Induced Immune Suppression

Sublethal total body γ irradiation (TBI) of mammals causes generalized immunosuppression, in part by induction of lymphocyte apoptosis. Here, we provide evidence that a part of this immune suppression may be attributable to dysfunction of immune regulation. We investigated the effects of sublethal TBI on T cell memory responses to gain insight into the potential for loss of vaccine immunity following such exposure. We show that in mice primed to an MHC class I alloantigen, the accelerated graft rejection T memory response is specifically lost several weeks following TBI, whereas identically treated naïve mice at the same time point had completely recovered normal rejection kinetics. Depletion in vivo with anti-CD4 or anti-CD25 showed that the mechanism involved cells consistent with a regulatory T cell (T reg) phenotype. The loss of the T memory response following TBI was associated with a relative increase of CD4+CD25+ Foxp3+ expressing T regs, as compared to the CD8+ T effector cells requisite for skin graft rejection. The radiation-induced T memory suppression was shown to be antigen-specific in that a third party ipsilateral graft rejected with normal kinetics. Remarkably, following the eventual rejection of the first MHC class I disparate skin graft, the suppressive environment was maintained, with markedly prolonged survival of a second identical allograft. These findings have potential importance as regards the immunologic status of T memory responses in victims of ionizing radiation exposure and apoptosis-inducing therapies

Facile Synthesis of Monodisperse CdS Nanocrystals via Microreaction

CdS-based nanocrystals (NCs) have attracted extensive interest due to their potential application as key luminescent materials for blue and white LEDs. In this research, the continuous synthesis of monodisperse CdS NCs was demonstrated utilizing a capillary microreactor. The enhanced heat and mass transfer in the microreactor was useful to reduce the reaction temperature and residence time to synthesize monodisperse CdS NCs. The superior stability of the microreactor and its continuous operation allowed the investigation of synthesis parameters with high efficiency. Reaction temperature was found to be a key parameter for balancing the reactivity of CdS precursors, while residence time was shown to be an important factor that governs the size and size distribution of the CdS NCs. Furthermore, variation of OA concentration was demonstrated to be a facile tuning mechanism for controlling the size of the CdS NCs. The variation of the volume percentage of OA from 10.5 to 51.2% and the variation of the residence time from 17 to 136 s facilitated the synthesis of monodisperse CdS NCs in the size range of 3.0–5.4 nm, and the NCs produced photoluminescent emissions in the range of 391–463 nm