scholarly journals Evaluation of removable statistical interaction for binary traits

2012 ◽  
Vol 32 (7) ◽  
pp. 1164-1190 ◽  
Author(s):  
Jaya M. Satagopan ◽  
Robert C. Elston
Keyword(s):  
Statistical Interaction ◽  
Binary Traits

scholarly journals Maximum Likelihood Analysis of Rare Binary Traits Under Different Modes of Inheritance

Genetics ◽  
1996 ◽  
Vol 143 (4) ◽  
pp. 1819-1829 ◽  
Author(s):  
G Thaller ◽  
L Dempfle ◽  
I Hoeschele
Keyword(s):  
Maximum Likelihood ◽  
Mixed Model ◽  
Major Gene ◽  
Likelihood Ratio Statistic ◽  
Information Criterion ◽  
Genetic Models ◽  
Parameter Estimates ◽  
Gene Effects ◽  
Binary Traits ◽  
Deterministic Algorithms

Abstract Maximum likelihood methodology was applied to determine the mode of inheritance of rare binary traits with data structures typical for swine populations. The genetic models considered included a monogenic, a digenic, a polygenic, and three mixed polygenic and major gene models. The main emphasis was on the detection of major genes acting on a polygenic background. Deterministic algorithms were employed to integrate and maximize likelihoods. A simulation study was conducted to evaluate model selection and parameter estimation. Three designs were simulated that differed in the number of sires/number of dams within sires (10/10, 30/30, 100/30). Major gene effects of at least one SD of the liability were detected with satisfactory power under the mixed model of inheritance, except for the smallest design. Parameter estimates were empirically unbiased with acceptable standard errors, except for the smallest design, and allowed to distinguish clearly between the genetic models. Distributions of the likelihood ratio statistic were evaluated empirically, because asymptotic theory did not hold. For each simulation model, the Average Information Criterion was computed for all models of analysis. The model with the smallest value was chosen as the best model and was equal to the true model in almost every case studied.

scholarly journals Bayesian Mapping of Quantitative Trait Loci for Complex Binary Traits

Genetics ◽  
2000 ◽  
Vol 155 (3) ◽  
pp. 1391-1403
Author(s):  
Nengjun Yi ◽  
Shizhong Xu
Keyword(s):  
Quantitative Trait Loci ◽  
Bayesian Statistics ◽  
Quantitative Trait ◽  
Data Augmentation ◽  
Threshold Model ◽  
Binary Trait ◽  
Monte Carlo Algorithm ◽  
Binary Traits ◽  
Trait Loci ◽  
Bayesian Mapping

Abstract A complex binary trait is a character that has a dichotomous expression but with a polygenic genetic background. Mapping quantitative trait loci (QTL) for such traits is difficult because of the discrete nature and the reduced variation in the phenotypic distribution. Bayesian statistics are proved to be a powerful tool for solving complicated genetic problems, such as multiple QTL with nonadditive effects, and have been successfully applied to QTL mapping for continuous traits. In this study, we show that Bayesian statistics are particularly useful for mapping QTL for complex binary traits. We model the binary trait under the classical threshold model of quantitative genetics. The Bayesian mapping statistics are developed on the basis of the idea of data augmentation. This treatment allows an easy way to generate the value of a hypothetical underlying variable (called the liability) and a threshold, which in turn allow the use of existing Bayesian statistics. The reversible jump Markov chain Monte Carlo algorithm is used to simulate the posterior samples of all unknowns, including the number of QTL, the locations and effects of identified QTL, genotypes of each individual at both the QTL and markers, and eventually the liability of each individual. The Bayesian mapping ends with an estimation of the joint posterior distribution of the number of QTL and the locations and effects of the identified QTL. Utilities of the method are demonstrated using a simulated outbred full-sib family. A computer program written in FORTRAN language is freely available on request.

scholarly journals An assessment of the performance of the logistic mixed model for analyzing binary traits in maize and sorghum diversity panels

PLoS ONE ◽  
2018 ◽  
Vol 13 (11) ◽  
pp. e0207752 ◽  
Author(s):  
Esperanza Shenstone ◽  
Julian Cooper ◽  
Brian Rice ◽  
Martin Bohn ◽  
Tiffany M. Jamann ◽  
...  
Keyword(s):  
Mixed Model ◽  
Binary Traits

scholarly journals Statistical Interaction Model for Exchangeability of Food Folates in a Rat Growth Bioassay

1996 ◽  
Vol 126 (10) ◽  
pp. 2585-2592 ◽  
Author(s):  
Hans-Georg Müller ◽  
Matthew R. Facer ◽  
Nathan D. Bills ◽  
Andrew J. Clifford
Keyword(s):  
Interaction Model ◽  
Statistical Interaction ◽  
Rat Growth

Psychopathy as an Emergent Interpersonal Syndrome: What Is the Function of Fearlessness?

2019 ◽  
Vol 33 (5) ◽  
pp. 633-639 ◽  
Author(s):  
Susan C. South
Keyword(s):  
Personality Disorders ◽  
Quantitative Data ◽  
Personality Pathology ◽  
Symptom Dimensions ◽  
Statistical Interaction ◽  
Statistical Interactions ◽  
The Individual

Lilienfeld and colleagues (this issue) propose that some personality disorders can be conceptualized as emergent interpersonal syndromes (EIS). An EIS elicits negative interpersonal reactions in others. Further, an EIS results from statistical interactions between symptom dimensions that are uncorrelated. As a prototypical EIS, psychopathy is an interaction between boldness (or fearlessness) and interpersonal antagonism. The authors marshal many threads of research to develop an intriguing idea that suggests the “whole” of psychopathy is more than the sum of its parts. Unfortunately, the authors focus primarily on psychopathy, and fail to provide convincing quantitative data for the statistical interaction that forms the basis for their theory. Also missing from this model of personality pathology is a consideration of what function boldness serves; viewing boldness as a means to accomplish the (maladaptive) rewarding goals that motivate the individual high in antagonism and disinhibition may serve to flesh out this theory and our conceptualization of personality pathology more broadly.

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