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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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Pedigree structures used for simulations. Legend: 1) affected sib pair (ASP), 2) affected sib triplet (AST), 3) affected sib quadruplet (ASQ), 4) discordant sib triplet (DST), 5) discordant sib quadruplet (DSQ), 6) affected three-generation (A3G), 7) discordant three-generation (D3G).
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Figure 1: Pedigree structures used for simulations. Legend: 1) affected sib pair (ASP), 2) affected sib triplet (AST), 3) affected sib quadruplet (ASQ), 4) discordant sib triplet (DST), 5) discordant sib quadruplet (DSQ), 6) affected three-generation (A3G), 7) discordant three-generation (D3G).

Mentions: Samples of different pedigree structures and sizes were considered, the same structures as by Mattheisen et al.[10]. Five pedigree structures represent nuclear families, varying the number of affected and unaffected siblings. Furthermore, two structures represent three-generation families (Figure1). Genotypes are available for all family members. The annotation to each pedigree structure is as follows: affected sib pair (ASP), affected sib triplet (AST), affected sib quadruplet (ASQ), discordant sib triplet (DST), discordant sib quadruplet (DSQ), affected three-generation (A3G) and discordant three-generation (D3G). We conducted also simulations with a mixture of different pedigrees (100 AST, 100 ASQ, 100 DST, 100 DSQ).


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

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

Pedigree structures used for simulations. Legend: 1) affected sib pair (ASP), 2) affected sib triplet (AST), 3) affected sib quadruplet (ASQ), 4) discordant sib triplet (DST), 5) discordant sib quadruplet (DSQ), 6) affected three-generation (A3G), 7) discordant three-generation (D3G).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Pedigree structures used for simulations. Legend: 1) affected sib pair (ASP), 2) affected sib triplet (AST), 3) affected sib quadruplet (ASQ), 4) discordant sib triplet (DST), 5) discordant sib quadruplet (DSQ), 6) affected three-generation (A3G), 7) discordant three-generation (D3G).
Mentions: Samples of different pedigree structures and sizes were considered, the same structures as by Mattheisen et al.[10]. Five pedigree structures represent nuclear families, varying the number of affected and unaffected siblings. Furthermore, two structures represent three-generation families (Figure1). Genotypes are available for all family members. The annotation to each pedigree structure is as follows: affected sib pair (ASP), affected sib triplet (AST), affected sib quadruplet (ASQ), discordant sib triplet (DST), discordant sib quadruplet (DSQ), affected three-generation (A3G) and discordant three-generation (D3G). We conducted also simulations with a mixture of different pedigrees (100 AST, 100 ASQ, 100 DST, 100 DSQ).

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