Juan P Steibel

Publications (42) View all

• Article: Evaluation of QTL for carcass merit and meat quality traits in a US commercial Duroc population.

  Igseo Choi, Ronald O Bates, Nancy E Raney, Juan P Steibel, Catherine W Ernst
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  ABSTRACT: Putative quantitative trait loci (QTL) regions on 5 chromosomes (SSC3, 6, 12, 15, and 18) were selected from our previous genome scans of a Duroc×Pietrain F(2) resource population for further evaluation in a US commercial Duroc population (n=331). A total of 81 gene-specific single nucleotide polymorphism (SNP) markers were genotyped and 33 markers were segregating. The MDH1 SNP on SSC3 was associated with 45-min and ultimate pH (pHu), and pH decline. PRKAG3 on SSC15 was associated with pHu. The HSPG2 SNP on SSC6 was associated with marbling score and days to 113kg. Markers for NUP88 and FKBP10 on SSC12 were associated with 45-min pH and L*, respectively. The SSC15 marker SF3B1 was associated with L* and LMA, and the SSC18 marker ARF5 was associated with pHu and color score. These results in a commercial Duroc population showed a general consistency with our previous genome scan.
  Meat Science 04/2012; 92(2):132-8. · 2.28 Impact Factor
• Article: Application of alternative models to identify QTL for growth traits in an F₂Duroc x Pietrain pig resource population

  Igseo Choi, Juan Steibel, Ronald Bates, Nancy Raney, Janice Rumph, Catherine Ernst
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  ABSTRACT: Abstract Background A variety of analysis approaches have been applied to detect quantitative trait loci (QTL) in experimental populations. The initial genome scan of our Duroc x Pietrain F2 resource population included 510 F2 animals genotyped with 124 microsatellite markers and analyzed using a line-cross model. For the second scan, 20 additional markers on 9 chromosomes were genotyped for 954 F2 animals and 20 markers used in the first scan were genotyped for 444 additional F2 animals. Three least-squares Mendelian models for QTL analysis were applied for the second scan: a line-cross model, a half-sib model, and a combined line-cross and half-sib model. Results In total, 26 QTL using the line-cross model, 12 QTL using the half-sib model and 3 additional QTL using the combined line-cross and half-sib model were detected for growth traits with a 5% false discovery rate (FDR) significance level. In the line-cross analysis, highly significant QTL for fat deposition at 10-, 13-, 16-, 19-, and 22-wk of age were detected on SSC6. In the half-sib analysis, a QTL for loin muscle area at 19-wk of age was detected on SSC7 and QTL for 10th-rib backfat at 19- and 22-wk of age were detected on SSC15. Conclusions Additional markers and animals contributed to reduce the confidence intervals and increase the test statistics for QTL detection. Different models allowed detection of new QTL which indicated differing frequencies for alternative alleles in parental breeds.
  BMC Genetics. 01/2010;
• Article: Hierarchical Bayesian modeling of heterogeneous cluster- and subject-level associations between continuous and binary outcomes in dairy production.

  Nora M Bello, Juan P Steibel, Robert J Tempelman
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  ABSTRACT: The augmentation of categorical outcomes with underlying Gaussian variables in bivariate generalized mixed effects models has facilitated the joint modeling of continuous and binary response variables. These models typically assume that random effects and residual effects (co)variances are homogeneous across all clusters and subjects, respectively. Motivated by conflicting evidence about the association between performance outcomes in dairy production systems, we consider the situation where these (co)variance parameters may themselves be functions of systematic and/or random effects. We present a hierarchical Bayesian extension of bivariate generalized linear models whereby functions of the (co)variance matrices are specified as linear combinations of fixed and random effects following a square-root-free Cholesky reparameterization that ensures necessary positive semidefinite constraints. We test the proposed model by simulation and apply it to the analysis of a dairy cattle data set in which the random herd-level and residual cow-level effects (co)variances between a continuous production trait and binary reproduction trait are modeled as functions of fixed management effects and random cluster effects.
  Biometrical Journal 03/2012; 54(2):230-48. · 1.25 Impact Factor
• Source

  Available from: Ailam Lim

  Article: Differential gene expression segregates cattle confirmed positive for bovine tuberculosis from antemortem tuberculosis test-false positive cattle originating from herds free of bovine tuberculosis.

  Ailam Lim, Juan P Steibel, Paul M Coussens, Daniel L Grooms, Steven R Bolin
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  ABSTRACT: Antemortem tests for bovine tuberculosis (bTB) currently used in the US measure cell-mediated immune responses against Mycobacterium bovis. Postmortem tests for bTB rely on observation of gross and histologic lesions of bTB, followed by bacterial isolation or molecular diagnostics. Cumulative data from the state of Michigan indicates that 98 to 99% of cattle that react positively in antemortem tests are not confirmed positive for bTB at postmortem examination. Understanding the fundamental differences in gene regulation between antemortem test-false positive cattle and cattle that have bTB may allow identification of molecular markers that can be exploited to better separate infected from noninfected cattle. An immunospecific cDNA microarray was used to identify altered gene expression (P ≤ 0.01) of 122 gene features between antemortem test-false positive cattle and bTB-infected cattle following a 4-hour stimulation of whole blood with tuberculin. Further analysis using quantitative real-time PCR assays validated altered expression of 8 genes that had differential power (adj  P ≤ 0.05) to segregate cattle confirmed positive for bovine tuberculosis from antemortem tuberculosis test-false positive cattle originating from herds free of bovine tuberculosis.
  Veterinary medicine international. 01/2012; 2012:192926.
• Article: Energy balance modulates colon tumor growth: Interactive roles of insulin and estrogen.

  Elizabeth A Rondini, Alison E Harvey, Juan P Steibel, Stephen D Hursting, Jenifer I Fenton
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  ABSTRACT: 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.
  Molecular Carcinogenesis 05/2011; 50(5):370-82. · 3.16 Impact Factor