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A combined strategy for quantitative trait loci detection by genome-wide association.

Lam AC, Powell J, Wei WH, de Koning DJ, Haley CS - BMC Proc (2009)

Bottom Line: Further multiple-marker and haplotype analyses confirmed the results with higher significance.Two-locus interaction analysis detected two epistatic pairs of markers that were not significant by marginal effects.Extensive simulations are still required to determine appropriate thresholds for more advanced model including epistasis.

View Article: PubMed Central - HTML - PubMed

Affiliation: Division of Genetics and Genomics, Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Roslin, Midlothian, EH25 9PS, UK. Alex.Lam@roslin.ed.ac.uk

ABSTRACT

Background: We applied a range of genome-wide association (GWA) methods to map quantitative trait loci (QTL) in the simulated dataset provided by the 12th QTLMAS workshop in order to derive an effective strategy.

Results: A variance component linkage analysis revealed QTLs but with low resolution. Three single-marker based GWA methods were then applied: Transmission Disequilibrium Test and single marker regression, fitting an additive model or a genotype model, on phenotypes pre-corrected for pedigree and fixed effects. These methods detected QTL positions with high concordance to each other and with greater refinement of the linkage signals. Further multiple-marker and haplotype analyses confirmed the results with higher significance. Two-locus interaction analysis detected two epistatic pairs of markers that were not significant by marginal effects. Overall, using stringent Bonferroni thresholds we identified 9 additive QTL and 2 epistatic interactions, which together explained about 12.3% of the corrected phenotypic variance.

Conclusion: The combination of methods that are robust against population stratification, like QTDT, with flexible linear models that take account of the family structure provided consistent results. Extensive simulations are still required to determine appropriate thresholds for more advanced model including epistasis.

No MeSH data available.


Related in: MedlinePlus

Comparison of single-marker methods. (A) Additive single marker method compared to the within family QTDT. (B) Additive single marker method compared to the genotypic single marker method. The scatter plots show high correlations between the different single-marker methods used, despite the difference in the magnitude of p-values.
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Figure 3: Comparison of single-marker methods. (A) Additive single marker method compared to the within family QTDT. (B) Additive single marker method compared to the genotypic single marker method. The scatter plots show high correlations between the different single-marker methods used, despite the difference in the magnitude of p-values.

Mentions: The 108 qSNPs and the two epistatic pairs were integrated using the forward selection regression method to remove possible redundancy among those SNPs. The two epistatic pairs and 9 qSNPs remained significant in the final model and jointly explained 12.3% of the phenotypic variance (Table 1). Each of the 9 qSNPs was the most significant marker of the corresponding QTL peak shown in Figure 2B. The focus of our study was the detection of loci rather than the precise estimation of their effects. Previous work [2] has shown that grammar correction is effective at controlling type I error with little loss of power, but leads to underestimation of effects. Hence the full procedure requires the re-estimation of effects in a full mixed model. The latter was not performed in this study and so considering the use of GRAMMAR corrected trait, our results may have identified a considerable proportion of the actual phenotypic variance. The results from the three single-marker methods had high concordance (Figure 3), which suggested the final results to be fairly robust. Some difference in power of detection was found (Figure 4). However, in spite of the expectation that QTDT being more conservative, it does not seem to suffer from loss of power when many large full-sib families are available. QTDT may have the advantage of added protection against bias due to the unknown relatedness amongst the founders. On the other hand, QTDT is unable to handle the half-sib relationships that GRAMMAR can. Therefore, a combination of these approaches would compensate the shortcomings in each of the individual methods. In this case, they yielded very similar results. Thus, we favour the GRAMMAR method for its speed and flexibility.


A combined strategy for quantitative trait loci detection by genome-wide association.

Lam AC, Powell J, Wei WH, de Koning DJ, Haley CS - BMC Proc (2009)

Comparison of single-marker methods. (A) Additive single marker method compared to the within family QTDT. (B) Additive single marker method compared to the genotypic single marker method. The scatter plots show high correlations between the different single-marker methods used, despite the difference in the magnitude of p-values.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2654500&req=5

Figure 3: Comparison of single-marker methods. (A) Additive single marker method compared to the within family QTDT. (B) Additive single marker method compared to the genotypic single marker method. The scatter plots show high correlations between the different single-marker methods used, despite the difference in the magnitude of p-values.
Mentions: The 108 qSNPs and the two epistatic pairs were integrated using the forward selection regression method to remove possible redundancy among those SNPs. The two epistatic pairs and 9 qSNPs remained significant in the final model and jointly explained 12.3% of the phenotypic variance (Table 1). Each of the 9 qSNPs was the most significant marker of the corresponding QTL peak shown in Figure 2B. The focus of our study was the detection of loci rather than the precise estimation of their effects. Previous work [2] has shown that grammar correction is effective at controlling type I error with little loss of power, but leads to underestimation of effects. Hence the full procedure requires the re-estimation of effects in a full mixed model. The latter was not performed in this study and so considering the use of GRAMMAR corrected trait, our results may have identified a considerable proportion of the actual phenotypic variance. The results from the three single-marker methods had high concordance (Figure 3), which suggested the final results to be fairly robust. Some difference in power of detection was found (Figure 4). However, in spite of the expectation that QTDT being more conservative, it does not seem to suffer from loss of power when many large full-sib families are available. QTDT may have the advantage of added protection against bias due to the unknown relatedness amongst the founders. On the other hand, QTDT is unable to handle the half-sib relationships that GRAMMAR can. Therefore, a combination of these approaches would compensate the shortcomings in each of the individual methods. In this case, they yielded very similar results. Thus, we favour the GRAMMAR method for its speed and flexibility.

Bottom Line: Further multiple-marker and haplotype analyses confirmed the results with higher significance.Two-locus interaction analysis detected two epistatic pairs of markers that were not significant by marginal effects.Extensive simulations are still required to determine appropriate thresholds for more advanced model including epistasis.

View Article: PubMed Central - HTML - PubMed

Affiliation: Division of Genetics and Genomics, Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Roslin, Midlothian, EH25 9PS, UK. Alex.Lam@roslin.ed.ac.uk

ABSTRACT

Background: We applied a range of genome-wide association (GWA) methods to map quantitative trait loci (QTL) in the simulated dataset provided by the 12th QTLMAS workshop in order to derive an effective strategy.

Results: A variance component linkage analysis revealed QTLs but with low resolution. Three single-marker based GWA methods were then applied: Transmission Disequilibrium Test and single marker regression, fitting an additive model or a genotype model, on phenotypes pre-corrected for pedigree and fixed effects. These methods detected QTL positions with high concordance to each other and with greater refinement of the linkage signals. Further multiple-marker and haplotype analyses confirmed the results with higher significance. Two-locus interaction analysis detected two epistatic pairs of markers that were not significant by marginal effects. Overall, using stringent Bonferroni thresholds we identified 9 additive QTL and 2 epistatic interactions, which together explained about 12.3% of the corrected phenotypic variance.

Conclusion: The combination of methods that are robust against population stratification, like QTDT, with flexible linear models that take account of the family structure provided consistent results. Extensive simulations are still required to determine appropriate thresholds for more advanced model including epistasis.

No MeSH data available.


Related in: MedlinePlus