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Significance test and genome selection in bayesian shrinkage analysis.

Che X, Xu S - Int J Plant Genomics (2010)

Bottom Line: A nice property of the shrinkage analysis is that it can estimate effects of QTL as small as explaining 2% of the phenotypic variance in a typical sample size of 300-500 individuals.In most cases, QTL can be detected with simple visual inspection of the entire genome for the effect because the false positive rate is low.However, it is still desirable to put some confidences on the estimated QTL effects.

View Article: PubMed Central - PubMed

Affiliation: Department of Statistics, University of California, Riverside, California 92521, USA.

ABSTRACT
Bayesian shrinkage analysis is the state-of-the-art method for whole genome analysis of quantitative traits. It can estimate the genetic effects for the entire genome using a dense marker map. The technique is now called genome selection. A nice property of the shrinkage analysis is that it can estimate effects of QTL as small as explaining 2% of the phenotypic variance in a typical sample size of 300-500 individuals. In most cases, QTL can be detected with simple visual inspection of the entire genome for the effect because the false positive rate is low. As a Bayesian method, no significance test is needed. However, it is still desirable to put some confidences on the estimated QTL effects. We proposed to use the permutation test to draw empirical thresholds to declare significance of QTL under a predetermined genome wide type I error. With the permutation test, Bayesian shrinkage analysis can be routinely used for QTL detection.

No MeSH data available.


Empirical threshold values generated from “permutation within Markov chain” and the estimated QTL effects (simulated data) and empirical threshold values generated from “permutation within Markov chain” analysis at α = 0.05 (2.5%–97.5%) and α = 0.10 (5%–95%) along with the estimated QTL effects (simulated data). Percentiles for the 2.5%–97.5% interval are plotted against the genome location as dashed lines (wider interval). Percentiles of the 5%–95% interval are plotted against the genome location as solid lines (narrower interval). (a) Phenotype reshuffling in every 5 iterations (h = 5). (b) Phenotype reshuffling in every 10 iterations (h = 10). (c) Phenotype reshuffling in every 100 iterations (h = 100).
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fig4: Empirical threshold values generated from “permutation within Markov chain” and the estimated QTL effects (simulated data) and empirical threshold values generated from “permutation within Markov chain” analysis at α = 0.05 (2.5%–97.5%) and α = 0.10 (5%–95%) along with the estimated QTL effects (simulated data). Percentiles for the 2.5%–97.5% interval are plotted against the genome location as dashed lines (wider interval). Percentiles of the 5%–95% interval are plotted against the genome location as solid lines (narrower interval). (a) Phenotype reshuffling in every 5 iterations (h = 5). (b) Phenotype reshuffling in every 10 iterations (h = 10). (c) Phenotype reshuffling in every 100 iterations (h = 100).

Mentions: We now evaluate situations where h is greater than one. This time we chose three different levels, h = 5,10, and 100. The 2.5%, 5%, 95%, and 97.5% percentiles plotted against the genome location are shown in Figure 4. These intervals appear to be similar to h = 1 except that the higher h's tend to generate rougher percentile profiles. Therefore, h = 1 is more preferable than other values of h. Hereafter, we chose h = 1 for all subsequent analysis.


Significance test and genome selection in bayesian shrinkage analysis.

Che X, Xu S - Int J Plant Genomics (2010)

Empirical threshold values generated from “permutation within Markov chain” and the estimated QTL effects (simulated data) and empirical threshold values generated from “permutation within Markov chain” analysis at α = 0.05 (2.5%–97.5%) and α = 0.10 (5%–95%) along with the estimated QTL effects (simulated data). Percentiles for the 2.5%–97.5% interval are plotted against the genome location as dashed lines (wider interval). Percentiles of the 5%–95% interval are plotted against the genome location as solid lines (narrower interval). (a) Phenotype reshuffling in every 5 iterations (h = 5). (b) Phenotype reshuffling in every 10 iterations (h = 10). (c) Phenotype reshuffling in every 100 iterations (h = 100).
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2902048&req=5

fig4: Empirical threshold values generated from “permutation within Markov chain” and the estimated QTL effects (simulated data) and empirical threshold values generated from “permutation within Markov chain” analysis at α = 0.05 (2.5%–97.5%) and α = 0.10 (5%–95%) along with the estimated QTL effects (simulated data). Percentiles for the 2.5%–97.5% interval are plotted against the genome location as dashed lines (wider interval). Percentiles of the 5%–95% interval are plotted against the genome location as solid lines (narrower interval). (a) Phenotype reshuffling in every 5 iterations (h = 5). (b) Phenotype reshuffling in every 10 iterations (h = 10). (c) Phenotype reshuffling in every 100 iterations (h = 100).
Mentions: We now evaluate situations where h is greater than one. This time we chose three different levels, h = 5,10, and 100. The 2.5%, 5%, 95%, and 97.5% percentiles plotted against the genome location are shown in Figure 4. These intervals appear to be similar to h = 1 except that the higher h's tend to generate rougher percentile profiles. Therefore, h = 1 is more preferable than other values of h. Hereafter, we chose h = 1 for all subsequent analysis.

Bottom Line: A nice property of the shrinkage analysis is that it can estimate effects of QTL as small as explaining 2% of the phenotypic variance in a typical sample size of 300-500 individuals.In most cases, QTL can be detected with simple visual inspection of the entire genome for the effect because the false positive rate is low.However, it is still desirable to put some confidences on the estimated QTL effects.

View Article: PubMed Central - PubMed

Affiliation: Department of Statistics, University of California, Riverside, California 92521, USA.

ABSTRACT
Bayesian shrinkage analysis is the state-of-the-art method for whole genome analysis of quantitative traits. It can estimate the genetic effects for the entire genome using a dense marker map. The technique is now called genome selection. A nice property of the shrinkage analysis is that it can estimate effects of QTL as small as explaining 2% of the phenotypic variance in a typical sample size of 300-500 individuals. In most cases, QTL can be detected with simple visual inspection of the entire genome for the effect because the false positive rate is low. As a Bayesian method, no significance test is needed. However, it is still desirable to put some confidences on the estimated QTL effects. We proposed to use the permutation test to draw empirical thresholds to declare significance of QTL under a predetermined genome wide type I error. With the permutation test, Bayesian shrinkage analysis can be routinely used for QTL detection.

No MeSH data available.