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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 analysis and the estimated QTL effects (simulated data)and empirical threshold values generated from permutation 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) shows the result of “permutation outside the Markov chain” (b) Result of “permutation within the Markov chain” with phenotype reshuffling in every iteration (h = 1).
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fig3: Empirical threshold values generated from permutation analysis and the estimated QTL effects (simulated data)and empirical threshold values generated from permutation 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) shows the result of “permutation outside the Markov chain” (b) Result of “permutation within the Markov chain” with phenotype reshuffling in every iteration (h = 1).

Mentions: We generated a total of 5000 permuted samples. Each permuted sample was subject to the same MCMC analysis as the original data (51000 iterations). The SAS/IML program took approximately 20 days in a Dell PC (2.5 GHz and 3.25 Go of RAM). For each marker, the 2.5%–97.5% and 5%–95% intervals (corresponding to α = 0.05 and α = 0.10) were calculated. The profiles of these percentiles along with the estimated QTL effects are given in Figure 3(a). Using the 2.5%–97.5% interval, we can detect 15 QTL out of the 20 simulated QTL. A few more QTLs with small effects were detected when 5%–95% interval was used. The results here are more reasonable than those when the equal tail credible interval was used. The conclusion is that permutation test applies well to the Bayesian shrinkage mapping.


Significance test and genome selection in bayesian shrinkage analysis.

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

Empirical threshold values generated from permutation analysis and the estimated QTL effects (simulated data)and empirical threshold values generated from permutation 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) shows the result of “permutation outside the Markov chain” (b) Result of “permutation within the Markov chain” with phenotype reshuffling in every iteration (h = 1).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig3: Empirical threshold values generated from permutation analysis and the estimated QTL effects (simulated data)and empirical threshold values generated from permutation 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) shows the result of “permutation outside the Markov chain” (b) Result of “permutation within the Markov chain” with phenotype reshuffling in every iteration (h = 1).
Mentions: We generated a total of 5000 permuted samples. Each permuted sample was subject to the same MCMC analysis as the original data (51000 iterations). The SAS/IML program took approximately 20 days in a Dell PC (2.5 GHz and 3.25 Go of RAM). For each marker, the 2.5%–97.5% and 5%–95% intervals (corresponding to α = 0.05 and α = 0.10) were calculated. The profiles of these percentiles along with the estimated QTL effects are given in Figure 3(a). Using the 2.5%–97.5% interval, we can detect 15 QTL out of the 20 simulated QTL. A few more QTLs with small effects were detected when 5%–95% interval was used. The results here are more reasonable than those when the equal tail credible interval was used. The conclusion is that permutation test applies well to the Bayesian shrinkage mapping.

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.