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.


Estimated posterior intensity of QTL positions along the genome (6 chromosomes, each of length 100 cM) for the QTL models of Table 2.
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Figure 1: Estimated posterior intensity of QTL positions along the genome (6 chromosomes, each of length 100 cM) for the QTL models of Table 2.

Mentions: The estimated intensity profile of indicated QTL had narrow peaks when all phenotypic data and a 1 cM marker density was used (Figure 1). For this marker density, the estimated position of the 2nd QTL on chromosome 1 was bimodal and 2 closely linked QTL were identified at the start of chromosome 4. The marker density of 5 cM resulted in much less narrow and lower QTL intensity profiles, while using phenotypic data partially (1665 records – Table 1) resulted in rather flat profiles (Figure 1).


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

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

Estimated posterior intensity of QTL positions along the genome (6 chromosomes, each of length 100 cM) for the QTL models of Table 2.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Estimated posterior intensity of QTL positions along the genome (6 chromosomes, each of length 100 cM) for the QTL models of Table 2.
Mentions: The estimated intensity profile of indicated QTL had narrow peaks when all phenotypic data and a 1 cM marker density was used (Figure 1). For this marker density, the estimated position of the 2nd QTL on chromosome 1 was bimodal and 2 closely linked QTL were identified at the start of chromosome 4. The marker density of 5 cM resulted in much less narrow and lower QTL intensity profiles, while using phenotypic data partially (1665 records – Table 1) resulted in rather flat profiles (Figure 1).

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.