Combining the transmission disequilibrium test and case–control methodology using generalized logistic regression

Abstract

To study the role of genetic factors in the etiology, susceptibility, or severity of disease, several methods are available. In a transmission disequilibrium test, genotypes of cases are compared to those of their parents to explore whether a specific allele, or marker, at a locus of interest appears to be transmitted in excess of what is expected on the basis of Mendelian inheritance. Such apparent excess transmission indicates that cases are being selected for that allele, thereby providing evidence that this allele is a risk factor for disease. In case-control studies, genotypes of cases are compared to those of controls from the same population to identify whether a specific allele is associated with disease. If so, either the allele at this locus or one in linkage disequilibrium with it may be causally related to the etiology of the disease. Here, we discuss the problem of combining a transmission disequilibrium test and a case -control comparison, in order to integrate all available information, and thereby increase statistical power. As the same cases are used in both approaches, the two results are not independent. However, parents of cases can be independently compared to controls. Both the issue of testing for a genetic effect and the estimation of relative risks under the multiplicative model using generalized logistic regression are discussed.

Analysis of Family‐ and Population‐Based Samples Using Multiple Linkage Disequilibrium Mapping

“…In this scenario, the entire sample for association mapping would consist of trios, unrelated controls, and perhaps unrelated cases. Nagelkerke et al (2004) proposed a joint analysis of such data using a likelihood-based approach, demonstrating that power is increased in comparison to methods that analyze trios and unrelated subjects separately. Epstein et al (2005) showed that power is improved after relaxing the assumption of parental mating-type distribution compared to the more general parental mating-type used by Nagelkerke et al (2004).…”

“…Nagelkerke et al (2004) proposed a joint analysis of such data using a likelihood-based approach, demonstrating that power is increased in comparison to methods that analyze trios and unrelated subjects separately. Epstein et al (2005) showed that power is improved after relaxing the assumption of parental mating-type distribution compared to the more general parental mating-type used by Nagelkerke et al (2004). In addition, they developed formal tests to determine whether data types can be combined, as well as for analyses of incomplete triad data.…”

“…Glaser and Holmans (2009) compared the power of six combined methods through simulation studies and concluded that the combined statistics were more powerful than single-sample statistics when risk parameters were similar in single samples. They concluded that the four methods by Schaid and Rowland (1998), Nagelkerke et al (2004), Kazeem and Farrall (2005), and Dudbridge (2008) had similar performance when combining unrelated cases/controls with trios (TDTCC design) as they are all likelihood-based methods. In addition, they found that all combined analysis methods under the TDTCC scenario had better or equal (adjusted) power than single-sample statistics and the pooledχ 2 -statistics.…”

Combining the case–control methodology with the small size transmission/disequilibrium test for multiallelic markers

“…In a TDT test, genotypes of cases are compared to those of their parents to explore whether a specific allele, or marker, at a locus of interest is transmitted to a greater degree than Mendelian inheritance would warrant. In order to integrate all available information and thereby increase the statistical power, Nagelkerke et al 5 proposed a new TDT statistic by combining a TDT test and a case -control result using the generalized logistic regression. For multiallelic markers, there are a number of extensions to the TDT after its debut.…”

Review and Evaluation of Methods Correcting for Population Stratification with a Focus on Underlying Statistical Principles

“…In 2004, Nagelkerke et al [138] proposed a likelihoodbased association analysis of the data comprising of trio data, with unrelated controls, and possibly some unrelated cases. Nagelkerke et al [138] provided ad hoc procedures to determine whether trios and unrelated data can be safely combined.…”