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A comparison of different linkage statistics in small to moderate sized pedigrees with complex diseases.

Flaquer A, Strauch K - BMC Res Notes (2012)

Bottom Line: In the last years GWA studies have successfully identified common SNPs associated with complex diseases.Furthermore, we found that the best performing statistic depends not only on the type of pedigrees but also on the true mode of inheritance.We provide recommendations regarding the most favorable test statistics, in terms of power, for a given mode of inheritance and type of pedigrees under study, in order to reduce the probability to miss a true linkage.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universit├Ąt (LMU) Munich, Germany. antonia.flaquer@lmu.de

ABSTRACT

Background: In the last years GWA studies have successfully identified common SNPs associated with complex diseases. However, most of the variants found this way account for only a small portion of the trait variance. This fact leads researchers to focus on rare-variant mapping with large scale sequencing, which can be facilitated by using linkage information. The question arises why linkage analysis often fails to identify genes when analyzing complex diseases. Using simulations we have investigated the power of parametric and nonparametric linkage statistics (KC-LOD, NPL, LOD and MOD scores), to detect the effect of genes responsible for complex diseases using different pedigree structures.

Results: As expected, a small number of pedigrees with less than three affected individuals has low power to map disease genes with modest effect. Interestingly, the power decreases when unaffected individuals are included in the analysis, irrespective of the true mode of inheritance. Furthermore, we found that the best performing statistic depends not only on the type of pedigrees but also on the true mode of inheritance.

Conclusions: When applied in a sensible way linkage is an appropriate and robust technique to map genes for complex disease. Unlike association analysis, linkage analysis is not hampered by allelic heterogeneity. So, why does linkage analysis often fail with complex diseases? Evidently, when using an insufficient number of small pedigrees, one might miss a true genetic linkage when actually a real effect exists. Furthermore, we show that the test statistic has an important effect on the power to detect linkage as well. Therefore, a linkage analysis might fail if an inadequate test statistic is employed. We provide recommendations regarding the most favorable test statistics, in terms of power, for a given mode of inheritance and type of pedigrees under study, in order to reduce the probability to miss a true linkage.

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Influence of the pedigree structure on the distribution of NPL, KC-LOD and MOD score under H0. Legend: Plots for the empirical distributions regarding the different pedigree sizes (P-values on a logarithmic scale). Horizontal gray lines refer to suggestive evidence for linkage (P-value of 0.0017), the classic 'LOD-3-criterion' (P-value of 0.0001), and significant evidence for linkage (P-value of 0.000049), respectively. The horizontal lines shown at the bottom of each graph represent the 95% confidence interval at the suggestive and LOD-3 level.
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Figure 2: Influence of the pedigree structure on the distribution of NPL, KC-LOD and MOD score under H0. Legend: Plots for the empirical distributions regarding the different pedigree sizes (P-values on a logarithmic scale). Horizontal gray lines refer to suggestive evidence for linkage (P-value of 0.0017), the classic 'LOD-3-criterion' (P-value of 0.0001), and significant evidence for linkage (P-value of 0.000049), respectively. The horizontal lines shown at the bottom of each graph represent the 95% confidence interval at the suggestive and LOD-3 level.

Mentions: A graphical overview of the results is shown in Figure2 for the nonparametric statistics (NPL, KC-LOD) and the MOD score. The horizontal lines at the bottom of the graph represent the 95% confidence interval at the suggestive and LOD-3 level. The parametric LOD score is shown in Figure3, with the 95% confidence intervals at the LOD-3 level. Two non-overlapping intervals for two pedigree structures at the same level represent statistically significant differences for the test statistic. Although an effect of the pedigree structure on the distributions of the NPL and KC-LOD score can be appreciated at the LOD-3 level, this effect is not statistically significant. However, there is a significant effect of the pedigree structure on the distribution of the MOD score at the suggestive level (Figure2). In summary, adding one affected offspring to the ASPs (ASTs, red line) leads to an increase of the MOD score and adding another affected offspring (ASQ, yellow line) leads to a further increase (3.39 vs. 3.53 vs. 3.72; c.f. Figure2). These results corroborate the findings by Mattheisen et al.[10]. We also see an increase of type I error when analyzing a mixture of pedigrees corresponding to a very similar critical value as DSQ pedigrees (3.81, 3.82 respectively, violet and orange lines). Although the differences among ASP, AST and ASQ are not significant, this group shows significant differences compared to DST and DSQ, i.e., when adding one or two unaffected siblings to ASPs. Significant differences are also found between the three-generation pedigrees (A3G and D3G).


A comparison of different linkage statistics in small to moderate sized pedigrees with complex diseases.

Flaquer A, Strauch K - BMC Res Notes (2012)

Influence of the pedigree structure on the distribution of NPL, KC-LOD and MOD score under H0. Legend: Plots for the empirical distributions regarding the different pedigree sizes (P-values on a logarithmic scale). Horizontal gray lines refer to suggestive evidence for linkage (P-value of 0.0017), the classic 'LOD-3-criterion' (P-value of 0.0001), and significant evidence for linkage (P-value of 0.000049), respectively. The horizontal lines shown at the bottom of each graph represent the 95% confidence interval at the suggestive and LOD-3 level.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3475142&req=5

Figure 2: Influence of the pedigree structure on the distribution of NPL, KC-LOD and MOD score under H0. Legend: Plots for the empirical distributions regarding the different pedigree sizes (P-values on a logarithmic scale). Horizontal gray lines refer to suggestive evidence for linkage (P-value of 0.0017), the classic 'LOD-3-criterion' (P-value of 0.0001), and significant evidence for linkage (P-value of 0.000049), respectively. The horizontal lines shown at the bottom of each graph represent the 95% confidence interval at the suggestive and LOD-3 level.
Mentions: A graphical overview of the results is shown in Figure2 for the nonparametric statistics (NPL, KC-LOD) and the MOD score. The horizontal lines at the bottom of the graph represent the 95% confidence interval at the suggestive and LOD-3 level. The parametric LOD score is shown in Figure3, with the 95% confidence intervals at the LOD-3 level. Two non-overlapping intervals for two pedigree structures at the same level represent statistically significant differences for the test statistic. Although an effect of the pedigree structure on the distributions of the NPL and KC-LOD score can be appreciated at the LOD-3 level, this effect is not statistically significant. However, there is a significant effect of the pedigree structure on the distribution of the MOD score at the suggestive level (Figure2). In summary, adding one affected offspring to the ASPs (ASTs, red line) leads to an increase of the MOD score and adding another affected offspring (ASQ, yellow line) leads to a further increase (3.39 vs. 3.53 vs. 3.72; c.f. Figure2). These results corroborate the findings by Mattheisen et al.[10]. We also see an increase of type I error when analyzing a mixture of pedigrees corresponding to a very similar critical value as DSQ pedigrees (3.81, 3.82 respectively, violet and orange lines). Although the differences among ASP, AST and ASQ are not significant, this group shows significant differences compared to DST and DSQ, i.e., when adding one or two unaffected siblings to ASPs. Significant differences are also found between the three-generation pedigrees (A3G and D3G).

Bottom Line: In the last years GWA studies have successfully identified common SNPs associated with complex diseases.Furthermore, we found that the best performing statistic depends not only on the type of pedigrees but also on the true mode of inheritance.We provide recommendations regarding the most favorable test statistics, in terms of power, for a given mode of inheritance and type of pedigrees under study, in order to reduce the probability to miss a true linkage.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universit├Ąt (LMU) Munich, Germany. antonia.flaquer@lmu.de

ABSTRACT

Background: In the last years GWA studies have successfully identified common SNPs associated with complex diseases. However, most of the variants found this way account for only a small portion of the trait variance. This fact leads researchers to focus on rare-variant mapping with large scale sequencing, which can be facilitated by using linkage information. The question arises why linkage analysis often fails to identify genes when analyzing complex diseases. Using simulations we have investigated the power of parametric and nonparametric linkage statistics (KC-LOD, NPL, LOD and MOD scores), to detect the effect of genes responsible for complex diseases using different pedigree structures.

Results: As expected, a small number of pedigrees with less than three affected individuals has low power to map disease genes with modest effect. Interestingly, the power decreases when unaffected individuals are included in the analysis, irrespective of the true mode of inheritance. Furthermore, we found that the best performing statistic depends not only on the type of pedigrees but also on the true mode of inheritance.

Conclusions: When applied in a sensible way linkage is an appropriate and robust technique to map genes for complex disease. Unlike association analysis, linkage analysis is not hampered by allelic heterogeneity. So, why does linkage analysis often fail with complex diseases? Evidently, when using an insufficient number of small pedigrees, one might miss a true genetic linkage when actually a real effect exists. Furthermore, we show that the test statistic has an important effect on the power to detect linkage as well. Therefore, a linkage analysis might fail if an inadequate test statistic is employed. We provide recommendations regarding the most favorable test statistics, in terms of power, for a given mode of inheritance and type of pedigrees under study, in order to reduce the probability to miss a true linkage.

Show MeSH