Juan P. Steibel

A powerful and flexible linear mixed model framework for the analysis of relative quantification RT-PCR data

Quantitative reverse transcription polymerase chain reaction (qRT-PCR) is currently viewed as the most precise technique to quantify levels of messenger RNA. Relative quantification compares the expression of a target gene under two or more experimental conditions normalized to the measured expression of a control gene. The statistical methods and software currently available for the analysis of relative quantification of RT-PCR data lack the flexibility and statistical properties to produce valid inferences in a wide range of experimental situations. In this paper we present a novel method for the analysis of relative quantification of qRT-PCR data, which consists of the analysis of cycles to threshold values (C(T)) for a target and a control gene using a general linear mixed model methodology. Our method allows testing of a broader class of hypotheses than traditional analyses such as the classical comparative C(T). Moreover, a simulation study using plasmode datasets indicated that the estimated fold-change in pairwise comparisons was the same using either linear mixed models or a comparative C(T) method, but the linear mixed model approach was more powerful. In summary, the method presented in this paper is more accurate, powerful and flexible than the traditional methods for analysis of qRT-PCR data. This new method is especially useful for studies involving multiple experimental factors and complex designs.

Estimation of linkage disequilibrium in four US pig breeds

BackgroundThe success of marker assisted selection depends on the amount of linkage disequilibrium (LD) across the genome. To implement marker assisted selection in the swine breeding industry, information about extent and degree of LD is essential. The objective of this study is to estimate LD in four US breeds of pigs (Duroc, Hampshire, Landrace, and Yorkshire) and subsequently calculate persistence of phase among them using a 60 k SNP panel. In addition, we report LD when using only a fraction of the available markers, to estimate persistence of LD over distance.ResultsAverage r2 between adjacent SNP across all chromosomes was 0.36 for Landrace, 0.39 for Yorkshire, 0.44 for Hampshire and 0.46 for Duroc. For markers 1 Mb apart, r2 ranged from 0.15 for Landrace to 0.20 for Hampshire. Reducing the marker panel to 10% of its original density, average r2 ranged between 0.20 for Landrace to 0.25 for Duroc. We also estimated persistence of phase as a measure of prediction reliability of markers in one breed by those in another and found that markers less than 10 kb apart could be predicted with a maximal accuracy of 0.92 for Landrace with Yorkshire.ConclusionsOur estimates of LD, although in good agreement with previous reports, are more comprehensive and based on a larger panel of markers. Our estimates also confirmed earlier findings reporting higher LD in pigs than in American Holstein cattle, especially at increasing marker distances (> 1 Mb). High average LD (r2 > 0.4) between adjacent SNP found in this study is an important precursor for the implementation of marker assisted selection within a livestock species.Results of this study are relevant to the US purebred pig industry and critical for the design of programs of whole genome marker assisted evaluation and selection. In addition, results indicate that a more cost efficient implementation of marker assisted selection using low density panels with genotype imputation, would be feasible for these breeds.

Reassessing design and analysis of two-colour microarray experiments using mixed effects models

Gene expression microarray studies have led to interesting experimental design and statistical analysis challenges. The comparison of expression profiles across populations is one of the most common objectives of microarray experiments. In this manuscript we review some issues regarding design and statistical analysis for two-colour microarray platforms using mixed linear models, with special attention directed towards the different hierarchical levels of replication and the consequent effect on the use of appropriate error terms for comparing experimental groups. We examine the traditional analysis of variance (ANOVA) models proposed for microarray data and their extensions to hierarchically replicated experiments. In addition, we discuss a mixed model methodology for power and efficiency calculations of different microarray experimental designs.

Effects of early weaning and social isolation on the expression of glucocorticoid and mineralocorticoid receptor and 11β-hydroxysteroid dehydrogenase 1 and 2 mRNAs in the frontal cortex and hippocampus of piglets

Pigs weaned at young ages show more abnormal and aggressive behaviors and cognitive deficits compared to later weaned pigs. We investigated the effects of age, weaning and/or social isolation on the expression of genes regulating glucocorticoid response [glucocorticoid receptor (GR), mineralocorticoid receptor (MR), 11beta-hydroxysteroid dehydrogenases 1 and 2 (11beta-HSD1 and 11beta-HSD2)] in the frontal cortex and hippocampus. Early- (EW; n = 6) and conventionally-weaned (CW; n = 6) piglets were weaned at 10 and 21 days after birth, respectively. Non-weaned (NW) piglets of both ages (NW; n = 6/group) remained with their dams. Immediately before euthanasia, half of CW, EW and NW animals were socially isolated for 15 min at 12 (EW, NW) and 23 (CW, NW) days of age. Differences in amounts of 11beta-HSD1, 11beta-HSD2, GR and MR mRNA were determined by quantitative real-time RT-PCR and data subjected to multivariate linear mixed model analysis. When compared with NW piglets at 12 days of age, the hippocampi of EW piglets showed decreased gene expression (P < 0.01). Social isolation decreased gene expression (P < 0.05) in the frontal cortex of all piglets. Twelve-day-old piglets showed higher MR mRNA in the frontal cortex (P < 0.01) and lower 11beta-HSD2 and GR mRNA (P < 0.05) in the hippocampus compared to 23-day-old animals. Results indicate that EW affected the hippocampus of piglets at 12 days of age, while social isolation affected frontal cortex regardless of age. These results may be correlated with behavioral and cognitive changes reported in EW piglets.

Probing genetic control of swine responses to PRRSV infection: current progress of the PRRS host genetics consortium

BackgroundUnderstanding the role of host genetics in resistance to porcine reproductive and respiratory syndrome virus (PRRSV) infection, and the effects of PRRS on pig health and related growth, are goals of the PRRS Host Genetics Consortium (PHGC).MethodsThe project uses a nursery pig model to assess pig resistance/susceptibility to primary PRRSV infection. To date, 6 groups of 200 crossbred pigs from high health farms were donated by commercial sources. After acclimation, the pigs were infected with PRRSV in a biosecure facility and followed for 42 days post infection (dpi). Blood samples were collected at 0, 4, 7, 10, 14, 21, 28, 35 and 42 dpi for serum and whole blood RNA gene expression analyses; weekly weights were recorded for growth traits. All data have been entered into the PHGC relational database. Genomic DNAs from all PHGC1-6 pigs were prepared and genotyped with the Porcine SNP60 SNPchip.ResultsResults have affirmed that all challenged pigs become PRRSV infected with peak viremia being observed between 4-21 dpi. Multivariate statistical analyses of viral load and weight data have identified PHGC pigs in different virus/weight categories. Sera are now being compared for factors involved in recovery from infection, including speed of response and levels of immune cytokines. Genome-wide association studies (GWAS) are underway to identify genes and chromosomal locations that identify PRRS resistant/susceptible pigs and pigs able to maintain growth while infected with PRRSV.ConclusionsOverall, the PHGC project will enable researchers to discover and verify important genotypes and phenotypes that predict resistance/susceptibility to PRRSV infection. The availability of PHGC samples provides a unique opportunity to continue to develop deeper phenotypes on every PRRSV infected pig.

Deciphering the luteal transcriptome: potential mechanisms mediating stage-specific luteolytic response of the corpus luteum to prostaglandin F2α

The objective of this study was to identify prostaglandin F(2α) (PG)-induced changes in the transcriptome of bovine corpora lutea (CL) that are specific to mature, PG-responsive (day 11) CL vs. developing (day 4) CL, which do not undergo luteolysis in response to PG administration. CL were collected at 0, 4, and 24 h after PG injection on days 4 and 11 of the estrous cycle (n = 5 per day and time point), and microarray analysis was performed with GeneChip Bovine Genome Arrays. Data normalization was performed with affy package and significance testing with maanova from Bioconductor. Significance (relative to 0 h time point) was declared at fold change >2.0 or <0.5 and false discovery rate of <5%. At 4 and 24 h after PG, 221 (day 4) and 661 (day 11) and 248 (day 4) and 1,421 (day 11) regulated genes, respectively, were identified. The accentuated gene expression response in day 11 CL was accompanied by specific enrichment of PG-regulated genes in distinctive gene ontology categories (immune related and other), particularly at 24 h after injection. Specificity in putative transcription factor binding sites was observed among PG-regulated genes on day 11 vs. day 4, including a potential association of ETS transcription factors with acute PG-induced gene expression specific to day 11 CL. Temporal and PG-induced regulation of abundance of mRNA for ETS transcription factor family members linked to the stage-specific response to PG was not observed. Increased abundance of protein and/or mRNA for six PG-regulated putative ETS-responsive genes was noted in day 11 but not day 4 CL. Results reveal insight into stage-specific gene expression in bovine CL in response to PG and potential transcriptional mediators of luteolysis.

Energy Balance Modulates Colon Tumor Growth: Interactive Roles of Insulin and Estrogen

Obesity increases colorectal cancer (CRC) risk and progression. However, the impact of obesity on CRC in women is dependent on ovarian hormone status. The purpose of this study was to determine the interactive roles of obesity and ovarian hormones on serum markers of inflammation, cell signaling, and transplanted colon tumor growth. Female C57BL/6 mice (6 wk) were either ovariectomized (OVX) or ovaries left intact (nonovariectomized, NOVX) and randomized to receive a (1) control, (2) 30% calorie‐restricted (CR), or (3) diet‐induced obese (DIO) diet regimen for 20 wk to induce differing levels of adiposity. Serum was collected and inflammatory and metabolic markers were measured using an antibody array (62 proteins) and ELISAs. Mice were subcutaneously injected with syngeneic MC38 colon cancer cells after 20 wk and sacrificed 4 wk later. CR mice had the smallest tumors irrespective of hormone status, whereas the largest tumors were observed in DIO‐OVX mice. Glucose tolerance was impaired in OVX mice, being most severe in the DIO‐OVX group. Cytokine arrays suggested that in CR animals, inhibition of tumor growth paralleled insulin sensitivity and associated changes in leptin, adiponectin, and IGF‐BPs. Conversely, in DIO‐OVX animals, tumor growth was associated with insulin and leptin resistance as well as higher levels of pro‐inflammatory proteins. In vitro, leptin and adiponectin had no effect, whereas insulin induced MC38 cell proliferation and MAPK activation. Co‐treatment with estrogen blocked the stimulatory effects of insulin. Thus, our in vitro and in vivo data indicate female reproductive hormones have a modulating effect on obesity‐induced insulin resistance and inflammation, which may directly or indirectly influence CRC progression. ©2010 Wiley‐Liss, Inc.

Molecular advances in QTL discovery and application in pig breeding

Thousands of quantitative trait loci (QTL) have been identified for a wide range of economically important phenotypes in pigs. Recently, QTL analyses have begun to use high-density single nucleotide polymorphism (SNP) panels and applications have extended beyond experimental intercrosses to outbred populations by exploiting long-range linkage disequilibrium that results in higher resolution QTL mapping. Relevant phenotypes generally fall under categories of growth and body composition, carcass and meat quality, reproduction, and disease resistance. A few expression QTL (eQTL) studies have been performed that integrate transcriptional profiles with genotype data by considering expression levels as response variables in QTL analyses for identifying genes controlling important trait phenotypes. Rapidly evolving genomics technologies, including RNAseq, provide tremendous opportunities for QTL and eQTL discovery. In this review, we discuss recent progress in pig QTL and eQTL discovery, including approaches for allele-specific expression, and implications of these discoveries for pig breeding and genetics.

Characterization of a Canine Model of Autosomal Recessive Retinitis Pigmentosa due to a PDE6A Mutation

PURPOSE To characterize a canine model of autosomal recessive RP due to a PDE6A gene mutation. METHODS Affected and breed- and age-matched control puppies were studied by electroretinography (ERG), light and electron microscopy, immunohistochemistry, and assay for retinal PDE6 levels and enzymatic activity. RESULTS The mutant puppies failed to develop normal rod-mediated ERG responses and had reduced light-adapted a-wave amplitudes from an early age. The residual ERG waveforms originated primarily from cone-driven responses. Development of photoreceptor outer segments stopped, and rod cells were lost by apoptosis. Immunohistochemistry demonstrated a marked reduction in rod opsin immunostaining outer segments and relative preservation of cones early in the disease process. With exception of rod bipolar cells, which appeared to be reduced in number relatively early in the disease process, other inner retinal cells were preserved in the early stages of the disease, although there was marked and early activation of Müller glia. Western blot analysis showed that the PDE6A mutation not only resulted in a lack of PDE6A protein but the affected retinas also lacked the other PDE6 subunits, suggesting expression of PDE6A is essential for normal expression of PDE6B and PDE6G. Affected retinas lacked PDE6 enzymatic activity. CONCLUSIONS This represents the first characterization of a PDE6A model of autosomal recessive retinitis pigmentosa, and the PDE6A mutant dog shows promise as a large animal model for investigation of therapies to rescue mutant rod photoreceptors and to preserve cone photoreceptors in the face of a rapid loss of rod cells.

Genotype imputation accuracy in a F2 pig population using high density and low density SNP panels

BackgroundF2 resource populations have been used extensively to map QTL segregating between pig breeds. A limitation associated with the use of these resource populations for fine mapping of QTL is the reduced number of founding individuals and recombinations of founding haplotypes occurring in the population. These limitations, however, become advantageous when attempting to impute unobserved genotypes using within family segregation information. A trade-off would be to re-type F2 populations using high density SNP panels for founding individuals and low density panels (tagSNP) in F2 individuals followed by imputation. Subsequently a combined meta-analysis of several populations would provide adequate power and resolution for QTL mapping, and could be achieved at relatively low cost. Such a strategy allows the wealth of phenotypic information that has previously been obtained on experimental resource populations to be further mined for QTL identification. In this study we used experimental and simulated high density genotypes (HD-60K) from an F2 cross to estimate imputation accuracy under several genotyping scenarios.ResultsSelection of tagSNP using physical distance or linkage disequilibrium information produced similar imputation accuracies. In particular, tagSNP sets averaging 1 SNP every 2.1 Mb (1,200 SNP genome-wide) yielded imputation accuracies (IA) close to 0.97. If instead of using custom panels, the commercially available 9K chip is used in the F2, IA reaches 0.99. In order to attain such high imputation accuracy the F0 and F1 generations should be genotyped at high density. Alternatively, when only the F0 is genotyped at HD, while F1 and F2 are genotyped with a 9K panel, IA drops to 0.90.ConclusionsCombining 60K and 9K panels with imputation in F2 populations is an appealing strategy to re-genotype existing populations at a fraction of the cost.