In silico exploration of the impact of pasture larvae contamination and anthelmintic treatment on genetic parameter estimates for parasite resistance in grazing sheep1

2012 ◽  
Vol 90 (7) ◽  
pp. 2167-2180 ◽  
Author(s):  
Y. C. S. M. Laurenson ◽  
I. Kyriazakis ◽  
S. C. Bishop
Keyword(s):  
In Silico ◽  
Genetic Parameter ◽  
Anthelmintic Treatment ◽  
Parameter Estimates ◽  
Parasite Resistance ◽  
The Impact

Genetic parameter estimates for gastrointestinal nematode parasite resistance and maternal efficiency indicator traits in Santa Inês breed

2019 ◽  
Vol 136 (6) ◽  
pp. 495-504
Author(s):  
Mariana P. Berton ◽  
Rosiane P. Silva ◽  
Felipe E. Carvalho ◽  
Hermenegildo Lucas Justino Chiaia ◽  
Priscila S. Oliveira ◽  
...  
Keyword(s):  
Genetic Parameter ◽  
Gastrointestinal Nematode ◽  
Nematode Parasite ◽  
Parameter Estimates ◽  
Parasite Resistance ◽  
Santa Inês ◽  
Indicator Traits ◽  
Gastrointestinal Nematode Parasite

Effects of trait definition on genetic parameter estimates and sire evaluation for clinical mastitis with threshold models

2004 ◽  
Vol 79 (3) ◽  
pp. 355-363 ◽  
Author(s):  
Y. M. Chang ◽  
D. Gianola ◽  
B. Heringstad ◽  
G. Klemetsdal
Keyword(s):  
Genetic Parameter ◽  
Genetic Correlations ◽  
Clinical Mastitis ◽  
Residual Effects ◽  
Parameter Estimates ◽  
Threshold Models ◽  
Posterior Probabilities ◽  
Sire Evaluation ◽  
Single Interval ◽  
The Impact

AbstractClinical mastitis records on 36 178 first-lactation Norwegian dairy cattle (NRF) cows, daughters of 245 sires from 5286 herds, were analysed to study the impact of trait definition on estimates of genetic parameters and sire evaluations for clinical mastitis. The opportunity interval for infection, going from 30 days pre-calving to 300 days post partum, was divided into either 11 periods (each 30 days long); four periods ((-30, 0), (1, 30), (31, 120), (121, 300)); a single period (-30, 300) or defined as the interval currently used for sire evaluation in Norway (-15,120). Within each period, clinical mastitis was scored as 1 if it occurred at least once and 0 otherwise. Analysis was with Bayesian threshold models, assuming that mastitis (presence v. absence) was a different trait in each period. By use of multivariate or univariate normal link functions, unobserved liabilities to disease were modelled as a linear function of year of calving, age-season of calving, herd, sire of cow and residual effects. Estimates of heritability of liability to clinical mastitis ranged from 0-06 to 0-14, depending on the model and stage of lactation. In multi-period models, estimates of genetic correlations between periods were positive and ranged from 0-13 to 0-55. This suggests that clinical mastitis resistance is not the same trait in different periods of the first lactation, which is not captured by the single-interval models. The single-interval (-30, 300) model gave slightly smaller sire-specific posterior probabilities of clinical mastitis during the first lactation than the multi-period models. Furthermore, the interval used in current Norwegian sire evaluation understated the posterior probabilities of clinical mastitis, relative to the multi-period specifications. This led to some differences in sire rankings between the four models, although there was agreement between the four- and 11-period models. In conclusion, the multi-period models captured more genetic variation than the single-interval models, but the four-period model gave sire rankings that differed little from those obtained with an 11-period definition of clinical mastitis.

The Impact of Missing and Error-Prone Auxiliary Information on Sparse-Matrix Sub-Population Parameter Estimates

Methodology ◽  
2015 ◽  
Vol 11 (3) ◽  
pp. 89-99 ◽  
Author(s):  
Leslie Rutkowski ◽  
Yan Zhou
Keyword(s):  
Sparse Matrix ◽  
Small Body ◽  
Auxiliary Information ◽  
Poor Quality ◽  
Quality Data ◽  
Estimation Methods ◽  
Parameter Estimates ◽  
Population Parameter ◽  
Conditioning Model ◽  
The Impact

Abstract. Given a consistent interest in comparing achievement across sub-populations in international assessments such as TIMSS, PIRLS, and PISA, it is critical that sub-population achievement is estimated reliably and with sufficient precision. As such, we systematically examine the limitations to current estimation methods used by these programs. Using a simulation study along with empirical results from the 2007 cycle of TIMSS, we show that a combination of missing and misclassified data in the conditioning model induces biases in sub-population achievement estimates, the magnitude and degree to which can be readily explained by data quality. Importantly, estimated biases in sub-population achievement are limited to the conditioning variable with poor-quality data while other sub-population achievement estimates are unaffected. Findings are generally in line with theory on missing and error-prone covariates. The current research adds to a small body of literature that has noted some of the limitations to sub-population estimation.

Examining Nonnormal Latent Variable Distributions for Non-Ignorable Missing Data

2021 ◽  
Vol 45 (3) ◽  
pp. 159-177
Author(s):  
Chen-Wei Liu
Keyword(s):  
Missing Data ◽  
Latent Variable ◽  
Bivariate Normal Distribution ◽  
Parameter Estimates ◽  
Missing Responses ◽  
Variable Distribution ◽  
Item Responses ◽  
Nonnormal Distribution ◽  
The Impact ◽  
Biased Estimates

Missing not at random (MNAR) modeling for non-ignorable missing responses usually assumes that the latent variable distribution is a bivariate normal distribution. Such an assumption is rarely verified and often employed as a standard in practice. Recent studies for “complete” item responses (i.e., no missing data) have shown that ignoring the nonnormal distribution of a unidimensional latent variable, especially skewed or bimodal, can yield biased estimates and misleading conclusion. However, dealing with the bivariate nonnormal latent variable distribution with present MNAR data has not been looked into. This article proposes to extend unidimensional empirical histogram and Davidian curve methods to simultaneously deal with nonnormal latent variable distribution and MNAR data. A simulation study is carried out to demonstrate the consequence of ignoring bivariate nonnormal distribution on parameter estimates, followed by an empirical analysis of “don’t know” item responses. The results presented in this article show that examining the assumption of bivariate nonnormal latent variable distribution should be considered as a routine for MNAR data to minimize the impact of nonnormality on parameter estimates.

scholarly journals Realized Sampling Variances of Estimates of Genetic Parameters and the Difference Between Genetic and Phenotypic Correlations

Genetics ◽  
1996 ◽  
Vol 143 (3) ◽  
pp. 1409-1416 ◽  
Author(s):  
Kenneth R Koots ◽  
John P Gibson
Keyword(s):  
Genetic Correlation ◽  
Genetic Parameters ◽  
Genetic Parameter ◽  
Genetic Correlations ◽  
Phenotypic Correlation ◽  
Parameter Estimates ◽  
Sampling Variance ◽  
Phenotypic Correlations ◽  
Genetic And Phenotypic Correlations ◽  
Heritability Estimates

Abstract A data set of 1572 heritability estimates and 1015 pairs of genetic and phenotypic correlation estimates, constructed from a survey of published beef cattle genetic parameter estimates, provided a rare opportunity to study realized sampling variances of genetic parameter estimates. The distribution of both heritability estimates and genetic correlation estimates, when plotted against estimated accuracy, was consistent with random error variance being some three times the sampling variance predicted from standard formulae. This result was consistent with the observation that the variance of estimates of heritabilities and genetic correlations between populations were about four times the predicted sampling variance, suggesting few real differences in genetic parameters between populations. Except where there was a strong biological or statistical expectation of a difference, there was little evidence for differences between genetic and phenotypic correlations for most trait combinations or for differences in genetic correlations between populations. These results suggest that, even for controlled populations, estimating genetic parameters specific to a given population is less useful than commonly believed. A serendipitous discovery was that, in the standard formula for theoretical standard error of a genetic correlation estimate, the heritabilities refer to the estimated values and not, as seems generally assumed, the true population values.

scholarly journals Impact of Carbohydrate Counting Error on Glycemic Control in Open-Loop Management of Type 1 Diabetes: Quantitative Assessment Through an in silico Trial

2021 ◽  
pp. 193229682110123
Author(s):  
Chiara Roversi ◽  
Martina Vettoretti ◽  
Simone Del Favero ◽  
Andrea Facchinetti ◽  
Pratik Choudhary ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
In Silico ◽  
Open Loop ◽  
Random Errors ◽  
Linear Regression Models ◽  
Reference Case ◽  
Counting Error ◽  
The Impact

Background: In the management of type 1 diabetes (T1D), systematic and random errors in carb-counting can have an adverse effect on glycemic control. In this study, we performed an in silico trial aiming at quantifying the impact of different levels of carb-counting error on glycemic control. Methods: The T1D patient decision simulator was used to simulate 7-day glycemic profiles of 100 adults using open-loop therapy. The simulation was repeated for different values of systematic and random carb-counting errors, generated with Gaussian distribution varying the error mean from -10% to +10% and standard deviation (SD) from 0% to 50%. The effect of the error was evaluated by computing the difference of time inside (∆TIR), above (∆TAR) and below (∆TBR) the target glycemic range (70-180mg/dl) compared to the reference case, that is, absence of error. Finally, 3 linear regression models were developed to mathematically describe how error mean and SD variations result in ∆TIR, ∆TAR, and ∆TBR changes. Results: Random errors globally deteriorate the glycemic control; systematic underestimations lead to, on average, up to 5.2% more TAR than the reference case, while systematic overestimation results in up to 0.8% more TBR. The different time in range metrics were linearly related with error mean and SD ( R2>0.95), with slopes of [Formula: see text], [Formula: see text] for ∆TIR, [Formula: see text], [Formula: see text] for ∆TAR, and [Formula: see text], [Formula: see text] for ∆TBR. Conclusions: The quantification of carb-counting error impact performed in this work may be useful understanding causes of glycemic variability and the impact of possible therapy adjustments or behavior changes in different glucose metrics.

Insight into the impact of EGFR L792Y/F/H mutations on sensitivity to osimertinib: an in silico study

2021 ◽  
Vol 45 (10) ◽  
pp. 4756-4765
Author(s):  
Daoxing Chen ◽  
Liting Zhang ◽  
Yanan Liu ◽  
Jiali Song ◽  
Jingwen Guo ◽  
...  
Keyword(s):  
In Silico ◽  
In Silico Study ◽  
The Impact ◽  
Insight Into

EGFR L792Y/F/H mutation makes it difficult for Osimertinib to recognize ATP pockets.

Examining the Impact of and Sensitivity of Fit Indices to Omitting Covariates Interaction Effect in Multilevel Multiple-Indicator Multiple-Cause Models

2021 ◽  
pp. 001316442199240
Author(s):  
Chunhua Cao ◽  
Eun Sook Kim ◽  
Yi-Hsin Chen ◽  
John Ferron
Keyword(s):  
Root Mean Square ◽  
Interaction Effect ◽  
Intraclass Correlation ◽  
Information Criteria ◽  
Parameter Estimates ◽  
Mean Square ◽  
Fit Indices ◽  
Cluster Number ◽  
Multiple Indicator ◽  
The Impact

This study examined the impact of omitting covariates interaction effect on parameter estimates in multilevel multiple-indicator multiple-cause models as well as the sensitivity of fit indices to model misspecification when the between-level, within-level, or cross-level interaction effect was left out in the models. The parameter estimates produced in the correct and the misspecified models were compared under varying conditions of cluster number, cluster size, intraclass correlation, and the magnitude of the interaction effect in the population model. Results showed that the two main effects were overestimated by approximately half of the size of the interaction effect, and the between-level factor mean was underestimated. None of comparative fit index, Tucker–Lewis index, root mean square error of approximation, and standardized root mean square residual was sensitive to the omission of the interaction effect. The sensitivity of information criteria varied depending majorly on the magnitude of the omitted interaction, as well as the location of the interaction (i.e., at the between level, within level, or cross level). Implications and recommendations based on the findings were discussed.

scholarly journals Common Treatment, Common Variant: Evolutionary Prediction of Functional Pharmacogenomic Variants

2021 ◽  
Vol 11 (2) ◽  
pp. 131
Author(s):  
Laura B. Scheinfeldt ◽  
Andrew Brangan ◽  
Dara M. Kusic ◽  
Sudhir Kumar ◽  
Neda Gharani
Keyword(s):  
In Silico ◽  
Drug Efficacy ◽  
Common Variant ◽  
Genomic Research ◽  
Whole Genome Sequencing Data ◽  
Mendelian Disease ◽  
Allele Frequency Distribution ◽  
Sequencing Data ◽  
Patient Race ◽  
The Impact

Pharmacogenomics holds the promise of personalized drug efficacy optimization and drug toxicity minimization. Much of the research conducted to date, however, suffers from an ascertainment bias towards European participants. Here, we leverage publicly available, whole genome sequencing data collected from global populations, evolutionary characteristics, and annotated protein features to construct a new in silico machine learning pharmacogenetic identification method called XGB-PGX. When applied to pharmacogenetic data, XGB-PGX outperformed all existing prediction methods and identified over 2000 new pharmacogenetic variants. While there are modest pharmacogenetic allele frequency distribution differences across global population samples, the most striking distinction is between the relatively rare putatively neutral pharmacogene variants and the relatively common established and newly predicted functional pharamacogenetic variants. Our findings therefore support a focus on individual patient pharmacogenetic testing rather than on clinical presumptions about patient race, ethnicity, or ancestral geographic residence. We further encourage more attention be given to the impact of common variation on drug response and propose a new ‘common treatment, common variant’ perspective for pharmacogenetic prediction that is distinct from the types of variation that underlie complex and Mendelian disease. XGB-PGX has identified many new pharmacovariants that are present across all global communities; however, communities that have been underrepresented in genomic research are likely to benefit the most from XGB-PGX’s in silico predictions.

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