Limits...
Comparison of analyses of the QTLMAS XII common dataset. II: genome-wide association and fine mapping.

Crooks L, Sahana G, de Koning DJ, Lund MS, Carlborg O - BMC Proc (2009)

Bottom Line: Generally the power to detect QTL was high and the Type 1 error was low.Estimates of QTL locations were generally very accurate.No epistasis was simulated, but the two studies that included searches for epistasis reported several interacting loci, indicating a problem with controlling the Type I error rate in these analyses.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Box 7023, SE-75007 Uppsala, Sweden. Lucy.Crooks@hgen.slu.se

ABSTRACT
As part of the QTLMAS XII workshop, a simulated dataset was distributed and participants were invited to submit analyses of the data based on genome-wide association, fine mapping and genomic selection. We have evaluated the findings from the groups that reported fine mapping and genome-wide association (GWA) efforts to map quantitative trait loci (QTL). Generally the power to detect QTL was high and the Type 1 error was low. Estimates of QTL locations were generally very accurate. Some methods were much better than others at estimating QTL effects, and with some the accuracy depended on simulated effect size or minor allele frequency. There were also indications of bias in the effect estimates. No epistasis was simulated, but the two studies that included searches for epistasis reported several interacting loci, indicating a problem with controlling the Type I error rate in these analyses. Although this study is based on a single dataset, it indicates that there is a need to improve fine mapping and GWA methods with respect to estimation of genetic effects, appropriate choice of significance thresholds and analysis of epistasis.

No MeSH data available.


Related in: MedlinePlus

QTL detected by each study. In (A)-(F) the M-QTL identified, and not found, by each study are shown, in relation to the simulated average effect of allelic substitution and minor allele frequency.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2654496&req=5

Figure 2: QTL detected by each study. In (A)-(F) the M-QTL identified, and not found, by each study are shown, in relation to the simulated average effect of allelic substitution and minor allele frequency.

Mentions: We looked for trends in which QTL were detected. Figure 2 shows the M-QTL, ordered by simulated effect size and minor allele frequency that were found by each study. The QTL found in LDLA2 were those with highest minor allele frequency (MAF) for a given chromosome in the last two generations. In LDmulti and LDHap, the same QTL plus two more (M2, M12) were detected. M2 and M12 had large simulated effects and were the next most significant M-QTL in individual regressions. An additional two QTL were found in LDHap than in LDmulti (M9, M13). M9 and M13 were the most significant of the remaining M-QTL in individual regressions. M9 had the second highest simulated effect of all the QTL but the lowest MAF and was only identified in LDHap. Hence, in LDmulti and LDHap, power to detect QTL appears to have been mostly limited by effect size.


Comparison of analyses of the QTLMAS XII common dataset. II: genome-wide association and fine mapping.

Crooks L, Sahana G, de Koning DJ, Lund MS, Carlborg O - BMC Proc (2009)

QTL detected by each study. In (A)-(F) the M-QTL identified, and not found, by each study are shown, in relation to the simulated average effect of allelic substitution and minor allele frequency.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2654496&req=5

Figure 2: QTL detected by each study. In (A)-(F) the M-QTL identified, and not found, by each study are shown, in relation to the simulated average effect of allelic substitution and minor allele frequency.
Mentions: We looked for trends in which QTL were detected. Figure 2 shows the M-QTL, ordered by simulated effect size and minor allele frequency that were found by each study. The QTL found in LDLA2 were those with highest minor allele frequency (MAF) for a given chromosome in the last two generations. In LDmulti and LDHap, the same QTL plus two more (M2, M12) were detected. M2 and M12 had large simulated effects and were the next most significant M-QTL in individual regressions. An additional two QTL were found in LDHap than in LDmulti (M9, M13). M9 and M13 were the most significant of the remaining M-QTL in individual regressions. M9 had the second highest simulated effect of all the QTL but the lowest MAF and was only identified in LDHap. Hence, in LDmulti and LDHap, power to detect QTL appears to have been mostly limited by effect size.

Bottom Line: Generally the power to detect QTL was high and the Type 1 error was low.Estimates of QTL locations were generally very accurate.No epistasis was simulated, but the two studies that included searches for epistasis reported several interacting loci, indicating a problem with controlling the Type I error rate in these analyses.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Box 7023, SE-75007 Uppsala, Sweden. Lucy.Crooks@hgen.slu.se

ABSTRACT
As part of the QTLMAS XII workshop, a simulated dataset was distributed and participants were invited to submit analyses of the data based on genome-wide association, fine mapping and genomic selection. We have evaluated the findings from the groups that reported fine mapping and genome-wide association (GWA) efforts to map quantitative trait loci (QTL). Generally the power to detect QTL was high and the Type 1 error was low. Estimates of QTL locations were generally very accurate. Some methods were much better than others at estimating QTL effects, and with some the accuracy depended on simulated effect size or minor allele frequency. There were also indications of bias in the effect estimates. No epistasis was simulated, but the two studies that included searches for epistasis reported several interacting loci, indicating a problem with controlling the Type I error rate in these analyses. Although this study is based on a single dataset, it indicates that there is a need to improve fine mapping and GWA methods with respect to estimation of genetic effects, appropriate choice of significance thresholds and analysis of epistasis.

No MeSH data available.


Related in: MedlinePlus