Latent rank change detection for analysis of splice-junction microarrays with nonlinear effects

Alternative splicing of gene transcripts greatly expands the functional capacity of the genome, and certain splice isoforms may indicate specific disease states such as cancer. Splice junction microarrays interrogate thousands of splice junctions, but data analysis is difficult and error prone because of the increased complexity compared to differential gene expression analysis. We present Rank Change Detection (RCD) as a method to identify differential splicing events based upon a straightforward probabilistic model comparing the over- or underrepresentation of two or more competing isoforms. RCD has advantages over commonly used methods because it is robust to false positive errors due to nonlinear trends in microarray measurements. Further, RCD does not depend on prior knowledge of splice isoforms, yet it takes advantage of the inherent structure of mutually exclusive junctions, and it is conceptually generalizable to other types of splicing arrays or RNA-Seq. RCD specifically identifies the biologically important cases when a splice junction becomes more or less prevalent compared to other mutually exclusive junctions. The example data is from different cell lines of glioblastoma tumors assayed with Agilent microarrays.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS389 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Bayesian Model-based Methods for the Analysis of DNA Microarrays with Survival, Genetic, and Sequence Data

DNA microarrays measure the expression of thousands of genes or DNA fragments simultaneously in which probes have specific complementary hybridization. Gene expression or microarray data analysis problems have a prominent role in the biostatistics, biological sciences, and clinical medicine. The first paper proposes a method for finding associations between the survival time of the subjects and the gene expression of tumor microarrays. Measurement error is known to bias the estimates for survival regression coefficients, and this method minimizes bias. The latent variable model is shown to detect associations between potentially important genes and survival in a breast cancer dataset that conventional models did not detect, and the method is demonstrated to have robustness to misspecification with simulated data. The second paper considers the Expression Quantitative Trait Loci (eQTL) detection problem. An eQTL is a genetic locus that influences gene expression, and the major challenges with this type of data are multiple testing and computational issues. The proposed method extends the Mixture Over Marker (MOM) model to include a structured prior probability that accounts for the transcript location. The new technique exploits the fact that genetic markers are more likely to influence transcripts that share the same location on the genome. The third paper improves the analysis of Chromatin (Ch)-Immunoprecipitation (IP) (ChIP) microarray data. ChIP-chip data analysis estimates the motif of specific Transcription Factor Binding Sites (TFBSs) by comparing the IP DNA sample that is enriched for the TFBS and a control sample of general genomic DNA. The probes on the ChIP-chip array are uniformly spaced on the genome, and the probes that have relatively high intensity in the IP sample will have corresponding sequences that are likely to contain the TFBS motif. Present analytical methods use the array data to discover peaks or regions of IP enrichment then analyze the sequences of these peaks in a separate procedure to discover the motif. The proposed model will integrate enrichment peak finding and motif discovery through a Hidden Markov Model (HMM). Performance comparisons are made between the proposed HMM and the previously developed methods

Reproducibility of quantitative RT-PCR array in miRNA expression profiling and comparison with microarray analysis

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) have critical functions in various biological processes. MiRNA profiling is an important tool for the identification of differentially expressed miRNAs in normal cellular and disease processes. A technical challenge remains for high-throughput miRNA expression analysis as the number of miRNAs continues to increase with <it>in silico </it>prediction and experimental verification. Our study critically evaluated the performance of a novel miRNA expression profiling approach, quantitative RT-PCR array (qPCR-array), compared to miRNA detection with oligonucleotide microchip (microarray).</p> <p>Results</p> <p>High reproducibility with qPCR-array was demonstrated by comparing replicate results from the same RNA sample. Pre-amplification of the miRNA cDNA improved sensitivity of the qPCR-array and increased the number of detectable miRNAs. Furthermore, the relative expression levels of miRNAs were maintained after pre-amplification. When the performance of qPCR-array and microarrays were compared using different aliquots of the same RNA, a low correlation between the two methods (r = -0.443) indicated considerable variability between the two assay platforms. Higher variation between replicates was observed in miRNAs with low expression in both assays. Finally, a higher false positive rate of differential miRNA expression was observed using the microarray compared to the qPCR-array.</p> <p>Conclusion</p> <p>Our studies demonstrated high reproducibility of TaqMan qPCR-array. Comparison between different reverse transcription reactions and qPCR-arrays performed on different days indicated that reverse transcription reactions did not introduce significant variation in the results. The use of cDNA pre-amplification increased the sensitivity of miRNA detection. Although there was variability associated with pre-amplification in low abundance miRNAs, the latter did not involve any systemic bias in the estimation of miRNA expression. Comparison between microarray and qPCR-array indicated superior sensitivity and specificity of qPCR-array.</p

The development of a specific pathogen free (SPF) barrier colony of marmosets (Callithrix jacchus) for aging research

A specific pathogen free (SPF) barrier colony of breeding marmosets (Callithrix jacchus) was established at the Barshop Institute for Longevity and Aging Studies. Rodent and other animal models maintained as SPF barrier colonies have demonstrated improved health and lengthened lifespans enhancing the quality and repeatability of aging research. The marmosets were screened for two viruses and several bacterial pathogens prior to establishing the new SPF colony. Twelve founding animals successfully established a breeding colony with increased reproductive success, improved health parameters, and increased median lifespan when compared to a conventionally housed, open colony. The improved health and longevity of marmosets from the SPF barrier colony suggests that such management can be used to produce a unique resource for future studies of aging processes in a nonhuman primate model

A Pilot Study of Parent Mentors for Early Childhood Obesity

Objective. To assess the feasibility of a parent mentor model of intervention for early childhood obesity using positive deviance-based methods to inform the intervention. Methods. In this pilot, randomized clinical trial, parent-child dyads (age: 2–5) with children whose body mass index (BMI) was ≥95th percentile were randomized to parent mentor intervention or community health worker comparison. The child’s height and weight were measured at baseline, after the six-month intervention, and six months after the intervention. Feasibility outcomes were recruitment, participation, and retention. The primary clinical outcome was BMI z-score change. Results. Sixty participants were enrolled, and forty-eight completed the six-month intervention. At baseline, the BMI z-score in the parent mentor group was 2.63 (SD = 0.65) and in the community health worker group it was 2.61 (SD = 0.89). For change in BMI z-score over time, there was no difference by randomization group at the end of the intervention: −0.02 (95% CI: −0.26, 0.22). At the end of the intervention, the BMI z-score for the parent mentor group was 2.48 (SD = 0.58) and for the community health worker group it was 2.45 (SD = 0.91), both reduced from baseline, p<0.001. Conclusion. The model of a parent mentor clinical trial is feasible, and both randomized groups experienced small, sustained effects on adiposity in an obese, Hispanic population

Maternal Diabetes and Obesity Influence the Fetal Epigenome in a Largely Hispanic Population

BACKGROUND: Obesity and diabetes mellitus are directly implicated in many adverse health consequences in adults as well as in the offspring of obese and diabetic mothers. Hispanic Americans are particularly at risk for obesity, diabetes, and end-stage renal disease. Maternal obesity and/or diabetes through prenatal programming may alter the fetal epigenome increasing the risk of metabolic disease in their offspring. The aims of this study were to determine if maternal obesity or diabetes mellitus during pregnancy results in a change in infant methylation of CpG islands adjacent to targeted genes specific for obesity or diabetes disease pathways in a largely Hispanic population. METHODS: Methylation levels in the cord blood of 69 newborns were determined using the Illumina Infinium MethylationEPIC BeadChip. Over 850,000 different probe sites were analyzed to determine whether maternal obesity and/or diabetes mellitus directly attributed to differential methylation; epigenome-wide and regional analyses were performed for significant CpG sites. RESULTS: Following quality control, agranular leukocyte samples from 69 newborns (23 normal term (NT), 14 diabetes (DM), 23 obese (OB), 9 DM/OB) were analyzed for over 850,000 different probe sites. Contrasts between the NT, DM, OB, and DM/OB were considered. After correction for multiple testing, 15 CpGs showed differential methylation from the NT, associated with 10 differentially methylated genes between the diabetic and non-diabetic subgroups, CCDC110, KALRN, PAG1, GNRH1, SLC2A9, CSRP2BP, HIVEP1, RALGDS, DHX37, and SCNN1D. The effects of diabetes were partly mediated by the altered methylation of HOOK2, LCE3C, and TMEM63B. The effects of obesity were partly mediated by the differential methylation of LTF and DUSP22. CONCLUSIONS: The presented data highlights the associated altered methylation patterns potentially mediated by maternal diabetes and/or obesity. Larger studies are warranted to investigate the role of both the identified differentially methylated loci and the effects on newborn body composition and future health risk factors for metabolic disease. Additional future consideration should be targeted to the role of Hispanic inheritance. Potential future targeting of transgenerational propagation and developmental programming may reduce population obesity and diabetes risk

Methods for building a staff workforce of quantitative scientists in academic health care.

Collaborative quantitative scientists, including biostatisticians, epidemiologists, bio-informaticists, and data-related professionals, play vital roles in research, from study design to data analysis and dissemination. It is imperative that academic health care centers (AHCs) establish an environment that provides opportunities for the quantitative scientists who are hired as staff to develop and advance their careers. With the rapid growth of clinical and translational research, AHCs are charged with establishing organizational methods, training tools, best practices, and guidelines to accelerate and support hiring, training, and retaining this staff workforce. This paper describes three essential elements for building and maintaining a successful unit of collaborative staff quantitative scientists in academic health care centers: (1) organizational infrastructure and management, (2) recruitment, and (3) career development and retention. Specific strategies are provided as examples of how AHCs can excel in these areas

Differential expression analysis with global network adjustment

&lt;p&gt;Background: Large-scale chromosomal deletions or other non-specific perturbations of the transcriptome can alter the expression of hundreds or thousands of genes, and it is of biological interest to understand which genes are most profoundly affected. We present a method for predicting a gene’s expression as a function of other genes thereby accounting for the effect of transcriptional regulation that confounds the identification of genes differentially expressed relative to a regulatory network. The challenge in constructing such models is that the number of possible regulator transcripts within a global network is on the order of thousands, and the number of biological samples is typically on the order of 10. Nevertheless, there are large gene expression databases that can be used to construct networks that could be helpful in modeling transcriptional regulation in smaller experiments.&lt;/p&gt; &lt;p&gt;Results: We demonstrate a type of penalized regression model that can be estimated from large gene expression databases, and then applied to smaller experiments. The ridge parameter is selected by minimizing the cross-validation error of the predictions in the independent out-sample. This tends to increase the model stability and leads to a much greater degree of parameter shrinkage, but the resulting biased estimation is mitigated by a second round of regression. Nevertheless, the proposed computationally efficient “over-shrinkage” method outperforms previously used LASSO-based techniques. In two independent datasets, we find that the median proportion of explained variability in expression is approximately 25%, and this results in a substantial increase in the signal-to-noise ratio allowing more powerful inferences on differential gene expression leading to biologically intuitive findings. We also show that a large proportion of gene dependencies are conditional on the biological state, which would be impossible with standard differential expression methods.&lt;/p&gt; &lt;p&gt;Conclusions: By adjusting for the effects of the global network on individual genes, both the sensitivity and reliability of differential expression measures are greatly improved.&lt;/p&gt

Acarbose improves health and lifespan in aging HET3 mice

To follow‐up on our previous report that acarbose (ACA), a drug that blocks postprandial glucose spikes, increases mouse lifespan, we studied ACA at three doses: 400, 1,000 (the original dose), and 2,500 ppm, using genetically heterogeneous mice at three sites. Each dose led to a significant change (by log‐rank test) in both sexes, with larger effects in males, consistent with the original report. There were no significant differences among the three doses. The two higher doses produced 16% or 17% increases in median longevity of males, but only 4% or 5% increases in females. Age at the 90th percentile was increased significantly (8%–11%) in males at each dose, but was significantly increased (3%) in females only at 1,000 ppm. The sex effect on longevity is not explained simply by weight or fat mass, which were reduced by ACA more in females than in males. ACA at 1,000 ppm reduced lung tumors in males, diminished liver degeneration in both sexes and glomerulosclerosis in females, reduced blood glucose responses to refeeding in males, and improved rotarod performance in aging females, but not males. Three other interventions were also tested: ursolic acid, 2‐(2‐hydroxyphenyl) benzothiazole (HBX), and INT‐767; none of these affected lifespan at the doses tested. The acarbose results confirm and extend our original report, prompt further attention to the effects of transient periods of high blood glucose on aging and the diseases of aging, including cancer, and should motivate studies of acarbose and other glucose‐control drugs in humans.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148418/1/acel12898.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148418/2/acel12898_am.pd