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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.


False positive rate profiles for the simulated markers obtained from 100 replicated experiments. (a) False positive rate at α = 0.05. (b) False positive rate at α = 0.10.
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fig6: False positive rate profiles for the simulated markers obtained from 100 replicated experiments. (a) False positive rate at α = 0.05. (b) False positive rate at α = 0.10.

Mentions: For the 241 marker effects included in the model, 20 × 3 = 60 loci were reserved for the true QTL (20 true QTL plus 40 flanking markers), leaving 241 − 60 = 181 model effects as false QTL. If a false QTL was detected in a particular sample, it was counted as one false positive. For each false QTL, we counted the total number of false positives among the 100 replicated experiments. The proportion of false positive (false positive rate or type I error) was recorded for each false QTL simulated. The false positive rate (FPR) profiles are depicted in Figure 6. Figure 6(a) shows the observed false positive rate when α = 0.05. Only two markers had false positive rate larger than the controlled value of 0.05. All other markers had false positive rate less than 0.05. The average false positive rate of all markers was about 0.02. The observed false positive rate is indeed less than 0.05, confirming our previous conclusion that the within-chain permutation approach is conservative. Figure 6(b) shows the observed false positive rate at α = 0.10. Only four markers had false positive rates larger than 0.10. The average false positive rate for all these markers was about 0.05, again confirming the conservativeness of the within chain permutation approach.


Significance test and genome selection in bayesian shrinkage analysis.

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

False positive rate profiles for the simulated markers obtained from 100 replicated experiments. (a) False positive rate at α = 0.05. (b) False positive rate at α = 0.10.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig6: False positive rate profiles for the simulated markers obtained from 100 replicated experiments. (a) False positive rate at α = 0.05. (b) False positive rate at α = 0.10.
Mentions: For the 241 marker effects included in the model, 20 × 3 = 60 loci were reserved for the true QTL (20 true QTL plus 40 flanking markers), leaving 241 − 60 = 181 model effects as false QTL. If a false QTL was detected in a particular sample, it was counted as one false positive. For each false QTL, we counted the total number of false positives among the 100 replicated experiments. The proportion of false positive (false positive rate or type I error) was recorded for each false QTL simulated. The false positive rate (FPR) profiles are depicted in Figure 6. Figure 6(a) shows the observed false positive rate when α = 0.05. Only two markers had false positive rate larger than the controlled value of 0.05. All other markers had false positive rate less than 0.05. The average false positive rate of all markers was about 0.02. The observed false positive rate is indeed less than 0.05, confirming our previous conclusion that the within-chain permutation approach is conservative. Figure 6(b) shows the observed false positive rate at α = 0.10. Only four markers had false positive rates larger than 0.10. The average false positive rate for all these markers was about 0.05, again confirming the conservativeness of the within chain permutation approach.

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.