scholarly journals ShinyGPAS: Interactive genomic prediction accuracy simulator based on deterministic formulas

2017 ◽  
Author(s):  
Gota Morota
Keyword(s):  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Genetic Factors ◽  
Web Browser ◽  
Web Based ◽  
Factors Influencing ◽  
Scatter Plots ◽  
Potential Factors ◽  
Computationally Intensive ◽  
Shiny Application

AbstractBackgroundDeterministic formulas highlight the relationships among prediction accuracy and potential factors influencing prediction accuracy prior to performing computationally intensive cross-validation. Visualizing such deterministic formulas in an interactive manner may lead to a better understanding of how genetic factors control prediction accuracy.ResultsThe software to simulate deterministic formulas for genomic prediction accuracy was implemented in R and encapsulated as a web-based Shiny application. ShinyGPAS (Shiny Genomic Prediction Accuracy Simulator) simulates various deterministic formulas and delivers dynamic scatter plots of prediction accuracy vs. genetic factors impacting prediction accuracy, while requiring only mouse navigation in a web browser. ShinyGPAS is available at: https://chikudaisei.shinyapps.io/shinygpas/.ConclusionShinyGPAS is a shiny-based interactive genomic prediction accuracy simulator using deterministic formulas. It can be used for interactively exploring potential factors influencing prediction accuracy in genome-enabled prediction, simulating achievable prediction accuracy prior to genotyping individuals, or supporting in-class teaching. ShinyGPAS is open source software and it is hosted online as a freely available web-based resource with an intuitive graphical user interface.

Lipoprotein (a) Evolution: Possible Benefits and Harm. Genetic and Non-Genetic Factors Influencing its Plasma Levels

2017 ◽  
Vol 24 (10) ◽  
pp. 969-978 ◽  
Author(s):  
Peggy M. Kostakou ◽  
George Hatzigeorgiou ◽  
Vana Kolovou ◽  
Sophie Mavrogeni ◽  
Genovefa D. Kolovou
Keyword(s):  
Plasma Levels ◽  
Genetic Factors ◽  
Lipoprotein A ◽  
Factors Influencing

scholarly journals Optimal breeding-value prediction using a Sparse Selection Index

Genetics ◽  
2021 ◽  
Author(s):  
Marco Lopez-Cruz ◽  
Gustavo de los Campos
Keyword(s):  
Sample Size ◽  
Dna Sequences ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Regularization Parameter ◽  
Selection Index ◽  
Prediction Method ◽  
Training Data ◽  
Breeding Value ◽  
Data Set

Abstract Genomic prediction uses DNA sequences and phenotypes to predict genetic values. In homogeneous populations, theory indicates that the accuracy of genomic prediction increases with sample size. However, differences in allele frequencies and in linkage disequilibrium patterns can lead to heterogeneity in SNP effects. In this context, calibrating genomic predictions using a large, potentially heterogeneous, training data set may not lead to optimal prediction accuracy. Some studies tried to address this sample size/homogeneity trade-off using training set optimization algorithms; however, this approach assumes that a single training data set is optimum for all individuals in the prediction set. Here, we propose an approach that identifies, for each individual in the prediction set, a subset from the training data (i.e., a set of support points) from which predictions are derived. The methodology that we propose is a Sparse Selection Index (SSI) that integrates Selection Index methodology with sparsity-inducing techniques commonly used for high-dimensional regression. The sparsity of the resulting index is controlled by a regularization parameter (λ); the G-BLUP (the prediction method most commonly used in plant and animal breeding) appears as a special case which happens when λ = 0. In this study, we present the methodology and demonstrate (using two wheat data sets with phenotypes collected in ten different environments) that the SSI can achieve significant (anywhere between 5-10%) gains in prediction accuracy relative to the G-BLUP.

scholarly journals Potential factors influencing the development of thrombocytopenia and consumptive coagulopathy after genetically modified pig liver xenotransplantation

2012 ◽  
Vol 25 (8) ◽  
pp. 882-896 ◽  
Author(s):  
Burcin Ekser ◽  
Chih C. Lin ◽  
Cassandra Long ◽  
Gabriel J. Echeverri ◽  
Hidetaka Hara ◽  
...  
Keyword(s):  
Genetically Modified ◽  
Pig Liver ◽  
Consumptive Coagulopathy ◽  
Factors Influencing ◽  
Potential Factors

GENETIC FACTORS INFLUENCING THE COMMON NEUROBIOLOGICAL SUBSTRATE FOR MENTAL DISORDERS

2019 ◽  
Vol 29 ◽  
pp. S798
Author(s):  
Till Andlauer ◽  
Thomas Mühleisen ◽  
Felix Hoffstaedter ◽  
Alexander Teumer ◽  
Anja Teuber ◽  
...  
Keyword(s):  
Mental Disorders ◽  
Genetic Factors ◽  
Factors Influencing ◽  
The Common

scholarly journals Potential factors influencing lymphatic filariasis transmission in “hotspot” and “control” areas in Ghana: the importance of vectors

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Sellase Pi-Bansa ◽  
Joseph Harold Nyarko Osei ◽  
Kwadwo Kyeremeh Frempong ◽  
Elizabeth Elhassan ◽  
Osei Kweku Akuoko ◽  
...  
Keyword(s):  
Lymphatic Filariasis ◽  
Factors Influencing ◽  
Potential Factors ◽  
And Control

Mapping of genetic factors influencing the weight of cystic fibrosis knockout mice

2002 ◽  
Vol 13 (11) ◽  
pp. 614-618 ◽  
Author(s):  
Christina K. Haston ◽  
Mary Corey ◽  
Lap-Chee Tsui
Keyword(s):  
Cystic Fibrosis ◽  
Knockout Mice ◽  
Genetic Factors ◽  
Factors Influencing

scholarly journals Estimation of genomic prediction accuracy from reference populations with varying degrees of relationship

PLoS ONE ◽  
2017 ◽  
Vol 12 (12) ◽  
pp. e0189775 ◽  
Author(s):  
S. Hong Lee ◽  
Sam Clark ◽  
Julius H. J. van der Werf
Keyword(s):  
Genomic Prediction ◽  
Prediction Accuracy

scholarly journals Genetic factors influencing bone mineral content in a black South African population

2013 ◽  
Vol 31 (6) ◽  
pp. 708-716 ◽  
Author(s):  
Andrew May ◽  
John M. Pettifor ◽  
Shane A. Norris ◽  
Michèle Ramsay ◽  
Zané Lombard
Keyword(s):  
Bone Mineral Content ◽  
South African ◽  
Bone Mineral ◽  
Mineral Content ◽  
Genetic Factors ◽  
African Population ◽  
South African Population ◽  
Black South African ◽  
Factors Influencing

Investigation and Implementation of a Web-Based CAD System for Fabric Appearance

2010 ◽  
Vol 129-131 ◽  
pp. 645-647
Author(s):  
Fan Lei Yan ◽  
Lian He Yang ◽  
Hai Feng Chang
Keyword(s):  
Data Exchange ◽  
3D Model ◽  
Texture Mapping ◽  
Future Research ◽  
Web Browser ◽  
Research Directions ◽  
Web Based ◽  
Cad System ◽  
Key Technologies ◽  
Future Research Directions

The area of web-based CAD system has grown since the mid-1990s. This paper introduces a new web-based CAD system for fabric appearance. The system uses the Browser/Server structure, and the designer can employ this system installed on the server to build a 3D model of fabric appearance through the Web browser. The basic architecture is discussed in this paper. Some key technologies, such as graphics display, texture mapping and the data exchange, are also investigated. In the last, some future research directions are presented.

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