Therapy-, gender- and race-specific microRNA markers, target genes and networks related to glioblastoma recurrence and survival.

AIM: To identify and study targets of microRNA biomarkers of glioblastoma survival across events (death and recurrence) and phases (life expectancy or post-diagnostic) using functional and network analyses. MATERIALS AND METHODS: microRNAs associated with glioblastoma survival within and across race, gender, recurrence, and therapy cohorts were identified using 253 individuals, 534 microRNAs, Cox survival model, cross-validation, discriminant analyses, and cross-study comparison. RESULTS: All 45 microRNAs revealed as being associated with survival were confirmed in independent cancer studies and 25 in glioblastoma studies. Thirty-nine and six microRNAs (including hsa-miR-222) were associated with one and multiple glioblastoma survival indicators, respectively. Nineteen and 26 microRNAs exhibited cohort-dependent (including hsa-miR-10b with therapy and hsa-miR-486 with race) and independent associations with glioblastoma, respectively. CONCLUSION: Sensory perception and G protein-coupled receptor processes were enriched among microRNA gene targets also associated with survival and network visualization highlighted their relations. These findings can help to improve prognostic tools and personalized treatments.

Genome-wide census and expression profiling of chicken neuropeptide and prohormone convertase genes.

Neuropeptides regulate cell-cell signaling and influence many biological processes in vertebrates, including development, growth, and reproduction. The complex processing of neuropeptides from prohormone proteins by prohormone convertases, combined with the evolutionary distance between the chicken and mammalian species that have experienced extensive neuropeptide research, has led to the empirical confirmation of only 18 chicken prohormone proteins. To expand our knowledge of the neuropeptide and prohormone convertase gene complement, we performed an exhaustive survey of the chicken genomic, EST, and proteomic databases using a list of 95 neuropeptide and 7 prohormone convertase genes known in other species. Analysis of the EST resources and 22 microarray studies offered a comprehensive portrait of gene expression across multiple conditions. Five neuropeptide genes (apelin, cocaine-and amphetamine-regulated transcript protein, insulin-like 5, neuropeptide S, and neuropeptide B) previously unknown in chicken were identified and 62 genes were confirmed. Although most neuropeptide gene families known in human are present in chicken, there are several gene not present in the chicken. Conversely, several chicken neuropeptide genes are absent from mammalian species, including C-RF amide, c-type natriuretic peptide 1 precursor, and renal natriuretic peptide. The prohormone convertases, with one exception, were found in the chicken genome. Bioinformatic models used to predict prohormone cleavages confirm that the processing of prohormone proteins into neuropeptides is similar between species. Neuropeptide genes are most frequently expressed in the brain and head, followed by the ovary and small intestine. Microarray analyses revealed that the expression of adrenomedullin, chromogranin-A, augurin, neuromedin-U, platelet-derived growth factor A and D, proenkephalin, relaxin-3, prepronociceptin, and insulin-like growth factor I was most susceptible (P-value<0.005) to changes in developmental stage, gender, and genetic line among other conditions studied. Our complete survey and characterization facilitates understanding of neuropeptides genes in the chicken, an animal of importance to biomedical and agricultural research.

Advancing the understanding of the embryo transcriptome co-regulation using meta-, functional, and gene network analysis tools.

Embryo development is a complex process orchestrated by hundreds of genes and influenced by multiple environmental factors. We demonstrate the application of simple and effective meta-study and gene network analyses strategies to characterize the co-regulation of the embryo transcriptome in a systems biology framework. A meta-analysis of nine microarray experiments aimed at characterizing the effect of agents potentially harmful to mouse embryos improved the ability to accurately characterize gene co-expression patterns compared with traditional within-study approaches. Simple overlap of significant gene lists may result in under-identification of genes differentially expressed. Sample-level meta-analysis techniques are recommended when common treatment levels or samples are present in more than one study. Otherwise, study-level meta-analysis of standardized estimates provided information on the significance and direction of the differential expression. Cell communication pathways were highly represented among the genes differentially expressed across studies. Mixture and dependence Bayesian network approaches were able to reconstruct embryo-specific interactions among genes in the adherens junction, axon guidance, and actin cytoskeleton pathways. Gene networks inferred by both approaches were mostly consistent with minor differences due to the complementary nature of the methodologies. The top-down approach used to characterize gene networks can offer insights into the mechanisms by which the conditions studied influence gene expression. Our work illustrates that further examination of gene expression information from microarray studies including meta- and gene network analyses can help characterize transcript co-regulation and identify biomarkers for the reproductive and embryonic processes under a wide range of conditions.

Impact of biological and economic variables on optimal parity for replacement in swine breed-to-wean herds.

Voluntary and involuntary culling practices determine the average parity when sows are replaced in a herd. Underlying these practices is the economic effect of replacing a sow at different parities. A dynamic programming model was used to find the optimal parity and net present value in breed-to-wean swine herds. The model included income and costs per parity weighted by the discount rate and sow removal rate. Three scenarios that reflect a wide range of cases were considered: low removal rates per parity with no salvage value (LRNS), high removal rates per parity with no salvage value (HRNS), and high removal rates per parity with a percentage of the sows having a salvage value (HRYS). The optimal parity of replacement for the base biological and economic conditions was 4 and 5 parities in the high and low removal scenarios, respectively. Sensitivity analyses identified the variables influencing the optimal replacement parity. Optimal parity of replacement ranged from 3 to 7 parities in the low replacement scenario, compared with 1 to 5 parities in the high replacement scenarios. Sow replacement cost and salvage value had the greatest impact on optimal parity of replacement followed by revenues per piglet weaned. The discount rate and number of parities per year generally had little influence on optimal parity. For situations with high sow costs, low salvage values, and low revenues per piglet, the optimal parity at removal was as high as 6 to 10 parities, and for situations with low sow cost, high salvage values, and high revenues per piglet, the optimal parity at removal was as low as 1 to 2 parities depending on removal rates. The modified internal rate of return suggested that, for most LRNS and HRYS scenarios considered, investment in a swine breed-to-wean enterprise was favored over other investments involving a similar risk profile. Our results indicate that in US breeding herds, sows are culled on average near the optimal parity of 4. However, the optimization process should be a dynamic one that adapts to changes in replacement rates, salvage value, replacement cost, and revenues per piglet.

Evaluation of approaches to detect quantitative trait loci for growth, carcass, and meat quality on swine chromosomes 2, 6, 13, and 18. II. Multivariate and principal component analyses.

The merits of complementary multivariate techniques to identify QTL associated with multiple traits were evaluated. Records from 806 F2 pigs pertaining to a Berkshire x Duroc three-generation population were available. Six multitrait groups on SSC 2, 6, 13, and 18 with information on 30 markers were studied. Multivariate techniques studied included multivariate models and principal components analysis of each multitrait group. All models included, in addition to systematic effects, additive, dominance, and imprinting coefficients corresponding to a one-QTL model and a random family effect. Multivariate analysis identified QTL associated with genomewise significant variation in four of the multitrait groups. The majority of the multivariate analysis provided greater precision of parameter estimates and higher statistical significance in some cases than univariate approaches, because of the greater parameterization of the multivariate models and moderate information content of the data. Principal component analysis results were consistent with univariate and multivariate analyses and recovered the levels of statistical significance observed in univariate analyses on the original data. In addition, principal component analysis was able to provide a location associated with LM area not detected by other analyses. The relative advantage of multivariate over the univariate approaches varied with the level of genetic covariance between traits because of the modeled QTL effect and information contained in the data; however, multivariate approaches have the unique capability to identify pleiotropic effects or multiple linked QTL.

Evaluation of approaches to detect quantitative trait loci for growth, carcass, and meat quality on swine chromosomes 2, 6, 13, and 18. I. Univariate outbred F2 and sib-pair analyses.

Results from univariate outbred F2 interval mapping and sib-pair analyses of 12 growth and 28 carcass traits to identify QTL on SSC 2, 6, 13, and 18 were compared. Phenotypic and genetic data were recorded on a three-generation resource population including 832 F2 pigs from a cross between three Berkshire sires and 18 Duroc dams. Thirty markers with an average spacing of approximately 16 cM were genotyped across the four chromosomes. The outbred F2 mixed model included the effects of sex, birth month, and year, one-QTL additive, dominance and imprinting coefficients calculated every 1 cM using interval mapping, and a random family effect. The general sib-pair model used to describe the phenotypic differences between sib-pairs included the same systematic and random effects and a one-QTL additive coefficient calculated every 1 cM. The outbred F2 analysis found significant evidence of QTL on SSC 2 associated with 105-d weight, backfat thicknesses, LM area, fat percent, shear force, juiciness, marbling, and tenderness. In addition, QTL were identified on SSC 6 relating to 42-d weight and LM area, and on SSC 18 for fat and moisture percents. In most instances, the outbred F2 approach offered greater power to detect QTL; however, the sib-pair analysis offered greater power in several instances. The trait-specific superiority could be due to the relative advantage of each model within a trait data set. The two approaches provided complementary evidence for QTL segregating between the Berkshire and Duroc breeds used in the study that may be used to aid marker-assisted introgression and selection and candidate gene studies to improve swine growth and meat quality characteristics.

Competing risks analysis of lamb mortality in a terminal sire composite population.

Mortality records from birth to weaning of 8,301 lambs from a composite population at the U.S. Meat Animal Research Center were analyzed using a competing risks model. The advantage of the competing risks model over traditional survival analyses is that different hazards of mortality can be assigned to different causes, such as disease, dystocia, and starvation. In this study, specific causes of mortality were grouped into dam-related (DAMR; e.g., dystocia and starvation), pneumonia (PNEU), disease (DIS; excluding pneumonia), and other (OTHER) categories. The hazard of mortality was analyzed using a competing risk approach, where each mortality category was assumed to be independent. Continuous- and discrete-time survival analyses were implemented using sire, animal, and maternal effects mixed models. The continuous-time survival analysis used the Weibull model to describe the hazard of mortality for each category of mortality. Under the discrete-time survival analysis, a complementary log-log link function was used to analyze animal-time data sets using weekly intervals for each category of mortality. Explanatory variables were sex, type of birth, contemporary group, and age of dam. The significant influences of type of birth and age of dam effects were consistent across category of mortality, and the sex effect was significant for all categories except the OTHER category. Estimates of variance components indicated strong maternal effects for all categories except for PNEU. Estimates of additive genetic heritabilities from the discrete maternal effects models were 0.08+/-0.04, 0.09+/-0.18, 0.16+/-0.12, 0.19+/-0.09, and 0.14+/-0.10 for OVERALL (all causes combined), DIS, DAMR, PNEU, and OTHER categories, respectively. Ignoring the cause of the defining event in mortality and longevity studies may hide important genetic differences. Therefore, the effectiveness of breeding programs relying on models that ignore multiple causes of an event in time-to-event data, such as mortality and longevity, could be affected.

Controlling the proportion of false positives in multiple dependent tests.

Genome scan mapping experiments involve multiple tests of significance. Thus, controlling the error rate in such experiments is important. Simple extension of classical concepts results in attempts to control the genomewise error rate (GWER), i.e., the probability of even a single false positive among all tests. This results in very stringent comparisonwise error rates (CWER) and, consequently, low experimental power. We here present an approach based on controlling the proportion of false positives (PFP) among all positive test results. The CWER needed to attain a desired PFP level does not depend on the correlation among the tests or on the number of tests as in other approaches. To estimate the PFP it is necessary to estimate the proportion of true null hypotheses. Here we show how this can be estimated directly from experimental results. The PFP approach is similar to the false discovery rate (FDR) and positive false discovery rate (pFDR) approaches. For a fixed CWER, we have estimated PFP, FDR, pFDR, and GWER through simulation under a variety of models to illustrate practical and philosophical similarities and differences among the methods.

Bioeconomic evaluation of sow longevity and profitability.

Sow production indicators, including litter size, litter weight, and the length of time that sows remained in the herd (sow longevity), were used to characterize sow performance and profitability. Sow longevity and production records from 148,568 sows in 32 commercial herds from Central Illinois from January 1995 to May 2001 were analyzed using survival and repeatability models, respectively. The factors studied included sow genetics (32 genetic lines), with eight major lines present in multiple herds, and the combination of herd and year of entry in the herd. The largest difference in longevity between the major genetic lines was approximately one parity. There were differences (P < 0.05) in the instantaneous sow removal rate or hazard from the major lines. These differences constitute evidence that sow longevity could be improved by using replacements from specific genetic lines. The net present value per sow (present value of future cash flows and the present value of the sow) was used to evaluate the effect of sow longevity and production traits on economic returns. Assuming a zero discount rate per parity, genetic lines with longer herd life resulted in greater profit than genetic lines with shorter herd life. This difference was reduced with increasing discount rates and was reversed with high discount rates and low net income per litter. These results suggest that the magnitude of the economic improvement attained through the use of sow genetic lines with longer longevity depends on the economic context under which the evaluation is made.

Discrete time survival analysis of lamb mortality in a terminal sire composite population.

Mortality records of 8,642 lambs from a composite population at the U.S. Meat Animal Research Center during the first year of life were studied using discrete survival analyses. Lamb mortality was studied across periods from birth to weaning, birth to 365 d of age, and weaning to 365 d of age. Animal-time data sets were created for each period using different time intervals: daily, weekly, fortnightly, and monthly. Each data set was analyzed using logistic and complementary log-log sire, animal, and maternal effects models. Explanatory variables included in the models were duration of time interval, sex, type of birth, contemporary group, age of dam, and type of upbringing (nursery or not). Similar estimates of explanatory variables were obtained within the same period across models and different time intervals. Heritability estimates from the complementary log-log models were greater than those from the comparable logistic models because of the difference in variance of the respective link functions. Heritability estimates from the complementary log-log sire model ranged from 0.13 to 0.21 for all periods. These estimates were greater than the complementary log-log animal model estimates that ranged from 0.04 to 0.12. Maternal effects were important early in life, with the maternal heritability slightly greater than the direct additive heritability. Negative correlations (-0.72 to -0.65) between direct additive and maternal effects was estimated. The similarity of results among survival analysis methods demonstrates that the discrete methodology is a viable alternative to estimate variance components in livestock survival data.

Interval and composite interval mapping of somatic cell score, yield, and components of milk in dairy cattle.

Single-marker, interval-mapping (IM) and composite interval mapping (CIM) were used to detect quantitative trait loci (QTL) controlling milk, fat and protein yields, and somatic cell score (SCS). A granddaughter design was used to combine molecular genetic information with predicted transmitting abilities (PTA) and estimated daughter yield deviations (DYD) from eight Dairy Bull DNA Repository Holstein families. Models that included and excluded weights accounting for the uncertainty of the response variable were evaluated in each trait, family and phenotype (DYD and PTA) combination. The genotypic information consisted of 174 microsatellite markers along 29 Bos taurus autosomes. The average number of informative markers per autosome was three and the number of informative sons per family and marker varied between 21 and 173. Within-family results from the least squares single-marker analyses were used in expectation-maximization likelihood IM and CIM implemented with QTL Cartographer. Different CIM model specifications, offering complementary control on the background QTL outside the interval under study, were evaluated. Permutation techniques were used to calculate the genome-wide threshold test statistic values based on 1,000 samples. Results from the DYD and PTA analyses were highly consistent across traits and families. The minor differences in the estimates from the models that accounted for or ignored the uncertainty of the DYD (variance) and PTA (inverse of reliability) may be associated to the elevated and consistent precision of the DYD and PTA among sons. The CIM model best supported by the data had 10 markers controlling for background effects. On autosome (BTA) three, a QTL at 32 cM influencing protein yield was located in family five and a QTL at 74 cM for fat yield was located in family eight. Two map positions associated with SCS were detected on BTA 21, one at 33 cM in family one and the other at 84 cM in family three. A QTL for protein yield was detected between 26 and 36 cM on BTA six, family six, and a QTL for milk yield was detected at 116 cM on BTA seven in family three. The IM and CIM approaches detected a QTL at 3 cM on BTA 14 influencing fat yield in family four. Two map positions on BTA 29 were associated with significant variation of milk (0 cM) and fat yield (14 cM) in family seven. These results suggest the presence of one QTL with pleiotropic effects on multiple traits or multiple QTL within the marker interval. Findings from this study could be used in subsequent fine-mapping work and applied to marker-assisted selection of dairy production and health traits.

Detection of quantitative trait loci influencing dairy traits using a model for longitudinal data.

A longitudinal-linkage analysis approach was developed and applied to an outbred population. Nonlinear mixed-effects models were used to describe the lactation patterns and were extended to include marker information following single-marker and interval mapping models. Quantitative trait loci (QTL) affecting the shape and scale of lactation curves for production and health traits in dairy cattle were mapped in three U.S. Holstein families (Dairy Bull DNA Repository families one, four, and five) using the granddaughter design. Information on 81 informative markers on six Bos taurus autosomes (BTA) was combined with milk yield, fat, and protein percentage and somatic cell score (SCS) test-day records. Six percent of the single-marker tests surpassed the experiment-wise significance threshold. Marker BL41 on BTA3 was associated with decrease in milk yield during mid-lactation in family one. The scale and shape of the protein percentage lactation curve in family four varied with BMC4203 (BTA6) allele that the son received from the grandsire. Some map locations were associated with variation in the lactation pattern of multiple traits. In family four, the marker HUJI177 (BTA3) was associated with changes in the milk yield and protein percentage curves suggesting a QTL with pleiotropic effects or multiple QTL in the region. The interval mapping model uncovered a QTL on BTA7 associated with variation in milk-yield pattern in family four and a QTL on BTA21 affecting SCS in family five. The developed approach can be extended to random regressions, covariance functions, spline, gametic and variance component models. The results from the longitudinal-QTL approach will help to understand the genetic factors acting at different stages of lactation and will assist in positional candidate gene research. Identified positions can be incorporated into marker-assisted selection decisions to alter the persistency and peak production or the fluctuation of SCS during a lactation.

Survival analysis of lamb mortality in a terminal sire composite population.

Records of mortality during the first year of life of 8,642 lambs from a composite population at the U.S. Meat Animal Research Center were studied using survival and logistic analyses. The traditional logistic approach analyzes the binary response of whether or not a lamb survived until a particular time point, thus disregarding information on the actual age at death. Survival analysis offers an alternative way to study mortality, wherein the response variable studied is the precise age at death while accounting for possible record censoring. Lamb mortality was studied across five periods based on management practices: birth to weaning, birth to 120 d of age, birth to 365 d of age, weaning to 365 d of age, and 120 to 365 d of age. Explanatory variables included in the models were sex, type of birth, age of dam, and whether or not a lamb was raised in a nursery. The survival analysis was implemented using Weibull and Cox proportional hazards models with sire as random effect. The logistic approach evaluated sire, animal, and maternal effects models. Lambs culled during any period were treated as censored in the survival analyses and were assumed alive in the logistic analyses. Similar estimates of the explanatory variables were obtained from the survival and logistic analyses, but the survival analyses had lower standard errors than the logistic analyses, suggesting a slight superiority of the former approach. Heritability estimates were generally consistent across all periods ranging from 0.15 to 0.21 in the Weibull model, 0.12 to 0.20 in the Cox model, 0.08 to 0.11 in the logistic sire model, 0.04 to 0.05 in the logistic animal model, and 0.03 to 0.07 in the maternal effects logistic model. Maternal effects were important in the early stages of lamb life, but the maternal heritability was less than 0.07 in all the stages studied with a negative correlation (-0.86 to -0.61) between direct and maternal effects. The estimates of additive genetic variance indicate that the use of survival analysis estimates in breeding schemes could allow for effective selection against mortality, thereby improving sheep productivity, welfare, and profitability.

Analysis of nucleotide sequence data using mixed model methodology.

Linear, logistic, and multivariate mixed model analyses were applied to simulated data of five quantitative traits and a binary liability trait to detect associations with sequence variants in seven genes. Infrequent site variants (< 1%) were eliminated and conservative step-wise procedures were used to reduce the number of variants fitted. Random effects accounting for additive genetic relationships between individuals and for common environment effects were fitted to reduce spurious significant results. Five sites in genes 1, 2, and 6 had significant effects (p < 0.0001) on the traits and were found in both replicates studied. Survival analysis using a Weibull model identified two significant sites for disease age at onset. Other less significant sites may be false positives or due to founder effects. This approach was effective in identifying putative sites while accounting for polygenic and environmental sources of variation.