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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

Accuracy and bias in estimates of QTL effects. In (A), a function of the square root of the estimated genetic variance of the QTL divided by the simulated genetic variance is shown. In (B)-(G), the difference between the estimated and simulated effect (average effect of allelic substitution) as a percentage of the simulated effect is shown. Potential relationships between the degree of inaccuracy and the simulated effect, minor allele frequency and percentage phenotypic variance explained by the QTL were tested by least squares linear regression for each study and the one with the lowest p-value (not dependent on outliers) is illustrated in (A)-(F). In (A), a relationship with the square of minor allele frequency and a zero intercept was fitted. Lines show significant relationships (p < 0.05) and regression equations and R2 values are given. Symbols indicate which values were overestimated and which were underestimated. (G) shows the comparable relationship with simulated effect for our multiple regression model. P-values were: (A) 0.003, (B) 0.6, (C) 0.3, (D) 0.006, (E) 0.15, (F) 0.07, (G) 0.0005.
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Figure 4: Accuracy and bias in estimates of QTL effects. In (A), a function of the square root of the estimated genetic variance of the QTL divided by the simulated genetic variance is shown. In (B)-(G), the difference between the estimated and simulated effect (average effect of allelic substitution) as a percentage of the simulated effect is shown. Potential relationships between the degree of inaccuracy and the simulated effect, minor allele frequency and percentage phenotypic variance explained by the QTL were tested by least squares linear regression for each study and the one with the lowest p-value (not dependent on outliers) is illustrated in (A)-(F). In (A), a relationship with the square of minor allele frequency and a zero intercept was fitted. Lines show significant relationships (p < 0.05) and regression equations and R2 values are given. Symbols indicate which values were overestimated and which were underestimated. (G) shows the comparable relationship with simulated effect for our multiple regression model. P-values were: (A) 0.003, (B) 0.6, (C) 0.3, (D) 0.006, (E) 0.15, (F) 0.07, (G) 0.0005.

Mentions: Whilst all the studies had good estimates of QTL location for at least some of the M-QTL, there were bigger differences between studies in how well they estimated QTL effects. The most accurate estimates of effect size were in LDLA1, LDHap and LDBayes, which were generally within 30% of the simulated values (Figure 4). They were of comparable accuracy to estimates from our multiple regression for small effect sizes, but slightly less accurate for larger effects.


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)

Accuracy and bias in estimates of QTL effects. In (A), a function of the square root of the estimated genetic variance of the QTL divided by the simulated genetic variance is shown. In (B)-(G), the difference between the estimated and simulated effect (average effect of allelic substitution) as a percentage of the simulated effect is shown. Potential relationships between the degree of inaccuracy and the simulated effect, minor allele frequency and percentage phenotypic variance explained by the QTL were tested by least squares linear regression for each study and the one with the lowest p-value (not dependent on outliers) is illustrated in (A)-(F). In (A), a relationship with the square of minor allele frequency and a zero intercept was fitted. Lines show significant relationships (p < 0.05) and regression equations and R2 values are given. Symbols indicate which values were overestimated and which were underestimated. (G) shows the comparable relationship with simulated effect for our multiple regression model. P-values were: (A) 0.003, (B) 0.6, (C) 0.3, (D) 0.006, (E) 0.15, (F) 0.07, (G) 0.0005.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Accuracy and bias in estimates of QTL effects. In (A), a function of the square root of the estimated genetic variance of the QTL divided by the simulated genetic variance is shown. In (B)-(G), the difference between the estimated and simulated effect (average effect of allelic substitution) as a percentage of the simulated effect is shown. Potential relationships between the degree of inaccuracy and the simulated effect, minor allele frequency and percentage phenotypic variance explained by the QTL were tested by least squares linear regression for each study and the one with the lowest p-value (not dependent on outliers) is illustrated in (A)-(F). In (A), a relationship with the square of minor allele frequency and a zero intercept was fitted. Lines show significant relationships (p < 0.05) and regression equations and R2 values are given. Symbols indicate which values were overestimated and which were underestimated. (G) shows the comparable relationship with simulated effect for our multiple regression model. P-values were: (A) 0.003, (B) 0.6, (C) 0.3, (D) 0.006, (E) 0.15, (F) 0.07, (G) 0.0005.
Mentions: Whilst all the studies had good estimates of QTL location for at least some of the M-QTL, there were bigger differences between studies in how well they estimated QTL effects. The most accurate estimates of effect size were in LDLA1, LDHap and LDBayes, which were generally within 30% of the simulated values (Figure 4). They were of comparable accuracy to estimates from our multiple regression for small effect sizes, but slightly less accurate for larger effects.

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