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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.


Result of the Arabidopsis data analysis. (a) Shows the estimated QTL effects for the entire genome and the empirical thresholds drawn from permutation within the Markov chain analysis at α = 0.05 (2.5%–97.5%, wider interval) and α = 0.10 (5%–95%, narrower interval). (b)  Shows the plot of the squared prediction error (PE) against the Type I error obtained from the fivefold cross-validation test.
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fig8: Result of the Arabidopsis data analysis. (a) Shows the estimated QTL effects for the entire genome and the empirical thresholds drawn from permutation within the Markov chain analysis at α = 0.05 (2.5%–97.5%, wider interval) and α = 0.10 (5%–95%, narrower interval). (b) Shows the plot of the squared prediction error (PE) against the Type I error obtained from the fivefold cross-validation test.

Mentions: For the original data analysis, the burn-in period was 1000. The thinning rate was 10. The posterior sample size was 10000, and thus the total number of iterations was 1000 + 10000 × 10 = 101000. The posterior sample size of the within-chain permutation analysis was 80000, that is, 1000 + 80000 × 10 = 801000 iterations in total. The estimated QTL effects and the permutation generated 2.5%–97.5% and 5%–95% intervals are plotted in Figure 8(a). A total of 4 QTLs were detected on three chromosomes at α = 0.05. Chromosomes 1 and 4 each has one QTL and chromosome 5 has two QTL. When α = 0.10 was used, one more QTL on chromosome 1 was detected.


Significance test and genome selection in bayesian shrinkage analysis.

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

Result of the Arabidopsis data analysis. (a) Shows the estimated QTL effects for the entire genome and the empirical thresholds drawn from permutation within the Markov chain analysis at α = 0.05 (2.5%–97.5%, wider interval) and α = 0.10 (5%–95%, narrower interval). (b)  Shows the plot of the squared prediction error (PE) against the Type I error obtained from the fivefold cross-validation test.
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2902048&req=5

fig8: Result of the Arabidopsis data analysis. (a) Shows the estimated QTL effects for the entire genome and the empirical thresholds drawn from permutation within the Markov chain analysis at α = 0.05 (2.5%–97.5%, wider interval) and α = 0.10 (5%–95%, narrower interval). (b) Shows the plot of the squared prediction error (PE) against the Type I error obtained from the fivefold cross-validation test.
Mentions: For the original data analysis, the burn-in period was 1000. The thinning rate was 10. The posterior sample size was 10000, and thus the total number of iterations was 1000 + 10000 × 10 = 101000. The posterior sample size of the within-chain permutation analysis was 80000, that is, 1000 + 80000 × 10 = 801000 iterations in total. The estimated QTL effects and the permutation generated 2.5%–97.5% and 5%–95% intervals are plotted in Figure 8(a). A total of 4 QTLs were detected on three chromosomes at α = 0.05. Chromosomes 1 and 4 each has one QTL and chromosome 5 has two QTL. When α = 0.10 was used, one more QTL on chromosome 1 was detected.

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.