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
Genetics ◽  
1996 ◽  
Vol 143 (4) ◽  
pp. 1819-1829 ◽  
Author(s):  
G Thaller ◽  
L Dempfle ◽  
I Hoeschele

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.


Genetics ◽  
2000 ◽  
Vol 155 (3) ◽  
pp. 1391-1403
Author(s):  
Nengjun Yi ◽  
Shizhong Xu

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.


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

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

2019 ◽  
Vol 33 (5) ◽  
pp. 633-639 ◽  
Author(s):  
Susan C. South

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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