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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

(A) Linkage and (B) association profiles. (A) The linkage profile generated from a variance component linkage analysis. Y-axis shows the -LOG10 transformed p-values and the x-axis shows the positions of SNPs along the genome. Vertical lines denote the chromosome boundaries. The significance threshold of LOD = 3 is shown by the red line. (B) The association results produced by the single marker additive model using GRAMMAR. -LOG10 transformed p values are given for each marker position. The genome-wide significance threshold (p < 0.05) is shown with the red line.
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Figure 2: (A) Linkage and (B) association profiles. (A) The linkage profile generated from a variance component linkage analysis. Y-axis shows the -LOG10 transformed p-values and the x-axis shows the positions of SNPs along the genome. Vertical lines denote the chromosome boundaries. The significance threshold of LOD = 3 is shown by the red line. (B) The association results produced by the single marker additive model using GRAMMAR. -LOG10 transformed p values are given for each marker position. The genome-wide significance threshold (p < 0.05) is shown with the red line.

Mentions: Linkage analysis revealed strong evidence of QTL on chromosomes 4 and 5 (Figure 2A). Tests for within-family association pointed to QTL locations on chromosomes 1–5 In total 194 SNPs were significant. Since we have observed high LD between non-adjacent SNPs, we were concerned that multiple SNPs could be called significant simply due to LD. As a conservative measure, we grouped the significant SNPs into 9 QTL peaks. These peaks were defined as the most significant marker and its neighbouring significant SNPs being at least 10 cM from the next group of significant SNPs. Although the tests for total association gave more significant p-values (results not shown), the results of the QTDT tests for within-family association are considered more robust. The reason is that the way the nuclear families were created from the entire pedigree would have introduced some degree of population substructure. Testing for total association ignores the fact that some individuals sired multiple families and thus, the nuclear families are not independent.


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)

(A) Linkage and (B) association profiles. (A) The linkage profile generated from a variance component linkage analysis. Y-axis shows the -LOG10 transformed p-values and the x-axis shows the positions of SNPs along the genome. Vertical lines denote the chromosome boundaries. The significance threshold of LOD = 3 is shown by the red line. (B) The association results produced by the single marker additive model using GRAMMAR. -LOG10 transformed p values are given for each marker position. The genome-wide significance threshold (p < 0.05) is shown with the red line.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: (A) Linkage and (B) association profiles. (A) The linkage profile generated from a variance component linkage analysis. Y-axis shows the -LOG10 transformed p-values and the x-axis shows the positions of SNPs along the genome. Vertical lines denote the chromosome boundaries. The significance threshold of LOD = 3 is shown by the red line. (B) The association results produced by the single marker additive model using GRAMMAR. -LOG10 transformed p values are given for each marker position. The genome-wide significance threshold (p < 0.05) is shown with the red line.
Mentions: Linkage analysis revealed strong evidence of QTL on chromosomes 4 and 5 (Figure 2A). Tests for within-family association pointed to QTL locations on chromosomes 1–5 In total 194 SNPs were significant. Since we have observed high LD between non-adjacent SNPs, we were concerned that multiple SNPs could be called significant simply due to LD. As a conservative measure, we grouped the significant SNPs into 9 QTL peaks. These peaks were defined as the most significant marker and its neighbouring significant SNPs being at least 10 cM from the next group of significant SNPs. Although the tests for total association gave more significant p-values (results not shown), the results of the QTDT tests for within-family association are considered more robust. The reason is that the way the nuclear families were created from the entire pedigree would have introduced some degree of population substructure. Testing for total association ignores the fact that some individuals sired multiple families and thus, the nuclear families are not independent.

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