Limits...
A Bayesian QTL linkage analysis of the common dataset from the 12th QTLMAS workshop.

Bink MC, van Eeuwijk FA - BMC Proc (2009)

Bottom Line: Decreasing the number of phenotyped individuals from 4665 to 1665 and/or the number of SNPs in the analysis from 600 to 120 dramatically reduced the power to identify and locate QTL.Our analysis identified all regions that contained QTL with effects explaining more than one percent of the phenotypic variance.We showed how the results of a Bayesian QTL mapping can be used in genomic prediction.

View Article: PubMed Central - HTML - PubMed

Affiliation: Biometris, Wageningen University & Research centre, Bornsesteeg 47, 6708 PD, Wageningen, Netherlands. marco.bink@wur.nl

ABSTRACT

Background: To compare the power of various QTL mapping methodologies, a dataset was simulated within the framework of 12th QTLMAS workshop. A total of 5865 diploid individuals was simulated, spanning seven generations, with known pedigree. Individuals were genotyped for 6000 SNPs across six chromosomes. We present an illustration of a Bayesian QTL linkage analysis, as implemented in the special purpose software FlexQTL. Most importantly, we treated the number of bi-allelic QTL as a random variable and used Bayes Factors to infer plausible QTL models. We investigated the power of our analysis in relation to the number of phenotyped individuals and SNPs.

Results: We report clear posterior evidence for 12 QTL that jointly explained 30% of the phenotypic variance, which was very close to the total of included simulation effects, when using all phenotypes and a set of 600 SNPs. Decreasing the number of phenotyped individuals from 4665 to 1665 and/or the number of SNPs in the analysis from 600 to 120 dramatically reduced the power to identify and locate QTL. Posterior estimates of genome-wide breeding values for a small set of individuals were given.

Conclusion: We presented a successful Bayesian linkage analysis of a simulated dataset with a pedigree spanning several generations. Our analysis identified all regions that contained QTL with effects explaining more than one percent of the phenotypic variance. We showed how the results of a Bayesian QTL mapping can be used in genomic prediction.

No MeSH data available.


Posterior inference on QTL characteristics along the genome for the model 1 cM_Q5a. (I) Posterior QTL intensity; (II) Posterior genotype probabilities of 1st thirty individuals of the dataset (QQ = red; Qq/qQ = green; qq = blue; ambiguous = gray, see also equation (3)); (III) Estimates of posterior mean (black line) and 90%quantiles (gray lines) of additive QTL effects; (IV) Estimated breeding values of 1st thirty individuals.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2654498&req=5

Figure 2: Posterior inference on QTL characteristics along the genome for the model 1 cM_Q5a. (I) Posterior QTL intensity; (II) Posterior genotype probabilities of 1st thirty individuals of the dataset (QQ = red; Qq/qQ = green; qq = blue; ambiguous = gray, see also equation (3)); (III) Estimates of posterior mean (black line) and 90%quantiles (gray lines) of additive QTL effects; (IV) Estimated breeding values of 1st thirty individuals.

Mentions: The posterior mean estimates of additive QTL effects in the twelve regions varied from 0.31 up to 0.78 (Table 4). The posterior 90% quantiles (of the distribution within bins) for the additive QTL effects are depicted in Figure 2 and the QTL at the end of chromosome 5 had the tightest quantile region.


A Bayesian QTL linkage analysis of the common dataset from the 12th QTLMAS workshop.

Bink MC, van Eeuwijk FA - BMC Proc (2009)

Posterior inference on QTL characteristics along the genome for the model 1 cM_Q5a. (I) Posterior QTL intensity; (II) Posterior genotype probabilities of 1st thirty individuals of the dataset (QQ = red; Qq/qQ = green; qq = blue; ambiguous = gray, see also equation (3)); (III) Estimates of posterior mean (black line) and 90%quantiles (gray lines) of additive QTL effects; (IV) Estimated breeding values of 1st thirty individuals.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2654498&req=5

Figure 2: Posterior inference on QTL characteristics along the genome for the model 1 cM_Q5a. (I) Posterior QTL intensity; (II) Posterior genotype probabilities of 1st thirty individuals of the dataset (QQ = red; Qq/qQ = green; qq = blue; ambiguous = gray, see also equation (3)); (III) Estimates of posterior mean (black line) and 90%quantiles (gray lines) of additive QTL effects; (IV) Estimated breeding values of 1st thirty individuals.
Mentions: The posterior mean estimates of additive QTL effects in the twelve regions varied from 0.31 up to 0.78 (Table 4). The posterior 90% quantiles (of the distribution within bins) for the additive QTL effects are depicted in Figure 2 and the QTL at the end of chromosome 5 had the tightest quantile region.

Bottom Line: Decreasing the number of phenotyped individuals from 4665 to 1665 and/or the number of SNPs in the analysis from 600 to 120 dramatically reduced the power to identify and locate QTL.Our analysis identified all regions that contained QTL with effects explaining more than one percent of the phenotypic variance.We showed how the results of a Bayesian QTL mapping can be used in genomic prediction.

View Article: PubMed Central - HTML - PubMed

Affiliation: Biometris, Wageningen University & Research centre, Bornsesteeg 47, 6708 PD, Wageningen, Netherlands. marco.bink@wur.nl

ABSTRACT

Background: To compare the power of various QTL mapping methodologies, a dataset was simulated within the framework of 12th QTLMAS workshop. A total of 5865 diploid individuals was simulated, spanning seven generations, with known pedigree. Individuals were genotyped for 6000 SNPs across six chromosomes. We present an illustration of a Bayesian QTL linkage analysis, as implemented in the special purpose software FlexQTL. Most importantly, we treated the number of bi-allelic QTL as a random variable and used Bayes Factors to infer plausible QTL models. We investigated the power of our analysis in relation to the number of phenotyped individuals and SNPs.

Results: We report clear posterior evidence for 12 QTL that jointly explained 30% of the phenotypic variance, which was very close to the total of included simulation effects, when using all phenotypes and a set of 600 SNPs. Decreasing the number of phenotyped individuals from 4665 to 1665 and/or the number of SNPs in the analysis from 600 to 120 dramatically reduced the power to identify and locate QTL. Posterior estimates of genome-wide breeding values for a small set of individuals were given.

Conclusion: We presented a successful Bayesian linkage analysis of a simulated dataset with a pedigree spanning several generations. Our analysis identified all regions that contained QTL with effects explaining more than one percent of the phenotypic variance. We showed how the results of a Bayesian QTL mapping can be used in genomic prediction.

No MeSH data available.