Empirical Bayes ranking and selection methods via semiparametric hierarchical mixture models in microarray studies

2012 ◽  
Vol 32 (11) ◽  
pp. 1904-1916 ◽  
Author(s):  
Hisashi Noma ◽  
Shigeyuki Matsui
Keyword(s):  
Mixture Models ◽  
Empirical Bayes ◽  
Ranking And Selection ◽  
Selection Methods ◽  
Hierarchical Mixture Models ◽  
Microarray Studies

scholarly journals Empirical Bayes estimation of semi-parametric hierarchical mixture models for unbiased characterization of polygenic disease architectures

2016 ◽  
Author(s):  
Jo Nishino ◽  
Yuta Kochi ◽  
Daichi Shigemizu ◽  
Mamoru Kato ◽  
Katsunori Ikari ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Mixture Models ◽  
Effect Size ◽  
Empirical Bayes ◽  
Complex Diseases ◽  
Bayes Estimation ◽  
Empirical Bayes Estimation ◽  
Risk Variants ◽  
Genome Wide ◽  
Hierarchical Mixture Models

AbstractGenome-wide association studies (GWAS) suggest that the genetic architecture of complex diseases consists of unexpectedly numerous variants with small effect sizes. However, the polygenic architectures of many diseases have not been well characterized due to lack of simple and fast methods for unbiased estimation of the underlying proportion of disease-associated variants and their effect-size distribution. Applying empirical Bayes estimation of semi-parametric hierarchical mixture models to GWAS summary statistics, we confirmed that schizophrenia was extremely polygenic (∼ 40% risk variants of independent genome-wide SNPs, most within odds ratio (OR)=1.03), whereas rheumatoid arthritis was less polygenic (∼ 4 to 8% risk variants, significant portion reaching OR=1.05 to 1.1). For rheumatoid arthritis, stratified estimations revealed that expression quantitative loci in blood explained large genetic variance, and low- and high-frequency derived alleles were prone to be risk and protective, respectively, suggesting a predominance of deleterious-risk and advantageous-protective mutation. Despite genetic correlation, effect-size distributions for schizophrenia and bipolar disorder differed across allele frequency. These analyses distinguished disease polygenic architectures and provided clues for etiological differences in complex diseases.

Nonparametric hierarchical mixture models based on asymmetric Gaussian distribution

2020 ◽  
Vol 106 ◽  
pp. 102829
Author(s):  
Ziyang Song ◽  
Samr Ali ◽  
Nizar Bouguila ◽  
Wentao Fan
Keyword(s):  
Mixture Models ◽  
Gaussian Distribution ◽  
Hierarchical Mixture Models

scholarly journals An overview on selection methods

1982 ◽  
Vol 1 (2) ◽  
pp. 91-96 ◽  
Author(s):  
J. W. H. Swanepoel
Keyword(s):  
Major Part ◽  
Ranking And Selection ◽  
Regression Analyses ◽  
Selection Methods ◽  
Selection Procedures ◽  
Indifference Zone ◽  
Selection Problems ◽  
Fixed Sample ◽  
Sequential Procedures ◽  
Ranking And Selection Procedures

In many studies the experimenter has under consideration several (two or more) alternatives, and is studying them in order to determine which is the best (with regard to certain specified criteria of “goodness”). Such an experimenter does not wish basically to test hypotheses, or construct confidence intervals, or perform regression analyses (though these may be appropriate parts of his analysis); he does wish to select the best of several alternatives, and the major part of his analysis should therefore be directed towards this goal. It is precisely for this problem that ranking and selection procedures were developed. This paper presents an overview of some recent work in this field, with emphasis on aspects important to experimenters confronted with selection problems. Fixed sample size and sequential procedures for both the indifference zone and subset formulations of the selection problem are discussed.

scholarly journals Hierarchical Mixture Models for Expression Profiles

2009 ◽  
pp. 40-52 ◽  
Author(s):  
Michael A. Newton ◽  
Ping Wang ◽  
Christina Kendziorski ◽  
Marina Vannucci
Keyword(s):  
Mixture Models ◽  
Expression Profiles ◽  
Hierarchical Mixture Models
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