Semi-parametric empirical Bayes factor for genome-wide association studies

Morisawa, Junji; Nishino, Junichi; Emoto, Ryo; Takahashi, Kunihiko; Matsui, Shigeyuki

doi:10.1038/s41431-020-00800-x

Cited by 4 publications

(3 citation statements)

References 35 publications

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“…They are, for example, used in the assessment of different ELISA tests [26] or in the usage of ELISA data to assess the infection state of dairy herds [27]. Moreover, Bayes factors have been used in the analysis of genetic data [28] and, hence, in the selection of genetic markers associated with an individual's phenotype [29]. Bayes factors were also used in resistance breeding of dairy against Mycobacterium avium subspecies paratuberculosis [30].…”

Section: Introductionmentioning

confidence: 99%

Decision Strategies for Absorbance Readings from an Enzyme-Linked Immunosorbent Assay—A Case Study about Testing Genotypes of Sugar Beet (Beta vulgaris L.) for Resistance against Beet Necrotic Yellow Vein Virus (BNYVV)

et al. 2021

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The Beet necrotic yellow vein virus (BNYVV) causes rhizomania in sugar beet (Beta vulgaris L.), which is one of the most destructive diseases in sugar beet worldwide. In breeding projects towards resistance against BNYVV, the enzyme-linked immunosorbent assay (ELISA) is used to determine the virus concentration in plant roots and, thus, the resistance levels of genotypes. Here, we present a simulation study to generate 10,000 small samples from the estimated density functions of ELISA values from susceptible and resistant sugar beet genotypes. We apply receiver operating characteristic (ROC) analysis to these samples to optimise the cutoff values for sample sizes from two to eight and determine the false positive rates (FPR), true positive rates (TPR), and area under the curve (AUC). We present, furthermore, an alternative approach based upon Bayes factors to improve the decision procedure. The Bayesian approach has proven to be superior to the simple cutoff approach. The presented results could help evaluate or improve existing breeding programs and help design future selection procedures based upon ELISA. An R-script for the classification of sample data based upon Bayes factors is provided.

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Section: Introductionmentioning

confidence: 99%

Decision Strategies for Absorbance Readings from an Enzyme-Linked Immunosorbent Assay—A Case Study about Testing Genotypes of Sugar Beet (Beta vulgaris L.) for Resistance against Beet Necrotic Yellow Vein Virus (BNYVV)

et al. 2021

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“…Only Bayesian survival analysis can identify the difference between minor and major alleles based on time to events. In

is a semi-parametric statistic because it is based on MAF and allelic scores of genotypes simultaneously [ 34 ]. It seems that the semi-parametric empirical Bayes factor better controls both false positive and false negative errors in GWAS to identify allelic variations and find templates for significant SNPs.…”

Section: Discussionmentioning

confidence: 99%

Local and Bayesian Survival FDR Estimations to Identify Reliable Associations in Whole Genome of Bread Wheat

Sadeqi,

Ballvora,

Léon

2023

IJMS

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Estimating the FDR significance threshold in genome-wide association studies remains a major challenge in distinguishing true positive hypotheses from false positive and negative errors. Several comparative methods for multiple testing comparison have been developed to determine the significance threshold; however, these methods may be overly conservative and lead to an increase in false negative results. The local FDR approach is suitable for testing many associations simultaneously based on the empirical Bayes perspective. In the local FDR, the maximum likelihood estimator is sensitive to bias when the GWAS model contains two or more explanatory variables as genetic parameters simultaneously. The main criticism of local FDR is that it focuses only locally on the effects of single nucleotide polymorphism (SNP) in tails of distribution, whereas the signal associations are distributed across the whole genome. The advantage of the Bayesian perspective is that knowledge of prior distribution comes from other genetic parameters included in the GWAS model, such as linkage disequilibrium (LD) analysis, minor allele frequency (MAF) and call rate of significant associations. We also proposed Bayesian survival FDR to solve the multi-collinearity and large-scale problems, respectively, in grain yield (GY) vector in bread wheat with large-scale SNP information. The objective of this study was to obtain a short list of SNPs that are reliably associated with GY under low and high levels of nitrogen (N) in the population. The five top significant SNPs were compared with different Bayesian models. Based on the time to events in the Bayesian survival analysis, the differentiation between minor and major alleles within the association panel can be identified.

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“…The Bayesian decision procedure of Guindani et al [ 28 ] also resembles an EBF. Closest to the approach developed here is work by Morisawa et al [ 29 ], who constructed EBFs for multiple tests from a non-parametric estimate of the prior distribution. But they did not interpret their EBF beyond comparing its ranking of associations to that from P -values.…”

Section: Introductionmentioning

confidence: 99%

Empirical Bayes factors for common hypothesis tests

Dudbridge

2024

PLoS ONE

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Bayes factors for composite hypotheses have difficulty in encoding vague prior knowledge, as improper priors cannot be used and objective priors may be subjectively unreasonable. To address these issues I revisit the posterior Bayes factor, in which the posterior distribution from the data at hand is re-used in the Bayes factor for the same data. I argue that this is biased when calibrated against proper Bayes factors, but propose adjustments to allow interpretation on the same scale. In the important case of a regular normal model, the bias in log scale is half the number of parameters. The resulting empirical Bayes factor is closely related to the widely applicable information criterion. I develop test-based empirical Bayes factors for several standard tests and propose an extension to multiple testing closely related to the optimal discovery procedure. When only a P-value is available, an approximate empirical Bayes factor is 10p. I propose interpreting the strength of Bayes factors on a logarithmic scale with base 3.73, reflecting the sharpest distinction between weaker and stronger belief. This provides an objective framework for interpreting statistical evidence, and realises a Bayesian/frequentist compromise.

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Semi-parametric empirical Bayes factor for genome-wide association studies

Cited by 4 publications

References 35 publications

Decision Strategies for Absorbance Readings from an Enzyme-Linked Immunosorbent Assay—A Case Study about Testing Genotypes of Sugar Beet (Beta vulgaris L.) for Resistance against Beet Necrotic Yellow Vein Virus (BNYVV)

Decision Strategies for Absorbance Readings from an Enzyme-Linked Immunosorbent Assay—A Case Study about Testing Genotypes of Sugar Beet (Beta vulgaris L.) for Resistance against Beet Necrotic Yellow Vein Virus (BNYVV)

Local and Bayesian Survival FDR Estimations to Identify Reliable Associations in Whole Genome of Bread Wheat

Empirical Bayes factors for common hypothesis tests

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