scholarly journals Statistical modeling of phenotypic, pedigree and genomic information for improved genetic evaluation in modern plant breeding: a case study with sorghum

2020 ◽  
Author(s):  
Julio G. Velazco
Keyword(s):  
Plant Breeding ◽  
Statistical Modeling ◽  
Genetic Evaluation ◽  
Genomic Information

scholarly journals Statistics in the Service of Science: Don't let the Tail Wag the Dog

2021 ◽  
Author(s):  
Henrik Singmann ◽  
Gregory Edward Cox ◽  
David Kellen ◽  
Suyog Chandramouli ◽  
Clintin Davis-Stober ◽  
...  
Keyword(s):  
Statistical Modeling ◽  
Statistical Models ◽  
Mixed Models ◽  
Model Comparison ◽  
Bayes Factors ◽  
Causal Mechanisms ◽  
Science Models ◽  
Scientific Goal ◽  
Coordination Function

Statistical modeling is generally meant to describe patterns in data in service of the broader scientific goal of developing theories to explain those patterns. Statistical models support meaningful inferences when models are built so as to align parameters of the model with potential causal mechanisms and how they manifest in data. When statistical models are instead based on assumptions chosen by default, Attempts to draw inferences can be uninformative or even paradoxical—in essence, the tail is trying to wag the dog.These issues are illustrated by van Doorn et al. (in press) in the context of using BayesFactors to identify effects and interactions in linear mixed models. We show that the problems identified in their applications can be circumvented by using priors over inherently meaningful units instead of default priors on standardized scales. This case study illustrates how researchers must directly engage with a number of substantive issues in order to support meaningful inferences, of which we highlight two: The first is the problem of coordination, which requires a researcher to specify how the theoretical constructs postulated by a model are functionally related to observable variables. The second is the problem of generalization, which requires a researcher to consider how a model may represent theoretical constructs shared across similar but non-identical situations, along with the fact that model comparison metrics like Bayes Factors do not directly address this form of generalization. For statistical modeling to serve the goals of science, models cannot be based on default assumptions, but should instead be based on an understanding of their coordination function and on how they represent causal mechanisms that may be expected to generalize to other related scenarios.

scholarly journals Genetic evaluation of sheep for resistance to gastrointestinal nematodes and body size including genomic information

2020 ◽  
Author(s):  
Tatiana Saraiva Torres ◽  
Luciano Silva Sena ◽  
Gleyson Vieira dos Santos ◽  
Luiz Antonio Silva Figueiredo Filho ◽  
Bruna Lima Barbosa ◽  
...  
Keyword(s):  
Body Size ◽  
Gastrointestinal Nematodes ◽  
Genetic Evaluation ◽  
Genomic Information

Statistical modeling the effect of sediment physicochemical properties on the concentration of heavy metal (case study: Musa Creek, SW Iran)

2018 ◽  
Vol 77 (3) ◽  
Author(s):  
Maryam Ghazanfari Shabankareh ◽  
Hakimeh Amanipoor ◽  
Sedigheh Battaleb-Looie ◽  
Javad Dravishi Khatooni
Keyword(s):  
Heavy Metal ◽  
Physicochemical Properties ◽  
Statistical Modeling ◽  
Metal Case ◽  
Sw Iran

scholarly journals Prediction of breeding values for group-recorded traits including genomic information and an individually recorded correlated trait

Heredity ◽  
2020 ◽  
Vol 126 (1) ◽  
pp. 206-217
Author(s):  
Xiang Ma ◽  
Ole F. Christensen ◽  
Hongding Gao ◽  
Ruihua Huang ◽  
Bjarne Nielsen ◽  
...  
Keyword(s):  
Egg Production ◽  
Quantitative Traits ◽  
Group Structure ◽  
Genetic Evaluation ◽  
Genomic Information ◽  
Bivariate Model ◽  
Breeding Values ◽  
Lower Accuracy ◽  
Small Group Size ◽  
Estimated Breeding Values

AbstractRecords on groups of individuals could be valuable for predicting breeding values when a trait is difficult or costly to measure on single individuals, such as feed intake and egg production. Adding genomic information has shown improvement in the accuracy of genetic evaluation of quantitative traits with individual records. Here, we investigated the value of genomic information for traits with group records. Besides, we investigated the improvement in accuracy of genetic evaluation for group-recorded traits when including information on a correlated trait with individual records. The study was based on a simulated pig population, including three scenarios of group structure and size. The results showed that both the genomic information and a correlated trait increased the accuracy of estimated breeding values (EBVs) for traits with group records. The accuracies of EBV obtained from group records with a size 24 were much lower than those with a size 12. Random assignment of animals to pens led to lower accuracy due to the weaker relationship between individuals within each group. It suggests that group records are valuable for genetic evaluation of a trait that is difficult to record on individuals, and the accuracy of genetic evaluation can be considerably increased using genomic information. Moreover, the genetic evaluation for a trait with group records can be greatly improved using a bivariate model, including correlated traits that are recorded individually. For efficient use of group records in genetic evaluation, relatively small group size and close relationships between individuals within one group are recommended.

Proper Statistical Modeling and Validation in QSAR: A Case Study in the Prediction of Rat Fat-Air Partitioning

2007 ◽  
Author(s):  
Subhash C. Basak ◽  
Denise Mills ◽  
Douglas M. Hawkins ◽  
Jessica J. Kraker ◽  
Theodore E. Simos ◽  
...  
Keyword(s):  
Statistical Modeling

scholarly journals Analysis and assessment of productivity in the parquet production by applying the methods of statistical modeling: Case study of selected production system in Serbia

2019 ◽  
pp. 37-56
Author(s):  
Miljan Kalem ◽  
Slobodanka Mitrovic ◽  
Aleksandra Lazarevic
Keyword(s):  
Statistical Modeling ◽  
Production System ◽  
Strong Influence ◽  
Statistical Significance ◽  
Statistical Program ◽  
Main Factors ◽  
The Republic ◽  
Factor Interaction ◽  
Factor Dimension

In this paper, researching results of the selected factors? influence on productivity of parquet production on the example of the selected company in the Republic of Serbia are presented. The analysis has included the influence of the two main factors, dimension and class of parquet quality on productivity. This research has had an aim to determine dependency between the productivity and the mentioned factors, to get corresponding conclusions, to give expert recommendation and to propose suitable management decisions, which would provide an increase in the productivity in the selected company. In order to determine the dependency between the productivity and the mentioned factors, statistical modeling is performed in the program SPSS v.20. Interpretation of the results in the statistical program SPSS, established a strong influence of the analyzed factors on productivity. Statistical significance of the factor dimension influence on productivity is Sig.1= 0.010, while the statistical significance of the parquet class quality factor influence on productivity is Sig. = 0,000. Statistical significance of the factor interaction influence of these factors on productivity is Sig. = 0,028. According to the researching results, it is concluded that parquet productivity depends on the mentioned factors.

As Others See Us: A Case Study in Path Analysis

1987 ◽  
Vol 12 (2) ◽  
pp. 101-128 ◽  
Author(s):  
D. A. Freedman
Keyword(s):  
United States ◽  
Comparative Study ◽  
Path Analysis ◽  
Statistical Modeling ◽  
Social Mobility ◽  
The United States ◽  
Path Model ◽  
Social Data ◽  
Complex Phenomena

In 1967, Blau and Duncan proposed a path model for education and stratification. This is one of the most influential applications of statistical modeling technique to social data. There is recent use of the same technique in Hope’s (1984) comparative study of Scotland and the United States, As Others See Us: Schooling and Social Mobility in Scotland and the United States. A review of path analysis is offered here, with Hope’s model used as an example, the object being to suggest the limits of the method in analyzing complex phenomena.

Sign in / Sign up

Export Citation Format

Share Document