Sample size calculations for classical association and TDT-type methods using family data
Related Experiment Video
Updated: Oct 5, 2026

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
Published on: January 31, 2014
D A Tregouet1, C Pallaud, C Sass
1INSERM Unite 525, Paris, France. tregouet@idf.inserm.fr
Abstract:
Transmission Disequilibrium Test (TDT)-based methods have been advocated by several authors for testing that a marker-phenotype association is actually due to linkage and not to uncontrolled stratification. As a pre-requisite of TDT-type methods is the presence of an association between marker and phenotype, one may wish to first investigate the association using a classical association study, and then to check by a TDT approach whether this association is actually due to linkage. We propose an estimating equation (EE) procedure, to compute analytically the minimum sample size of sibship data required to detect the association between a marker and a quantitative phenotype, and that required to confirm it by two TDT methods. We show that, when the marker allele frequency is low or high, the number of informative sibs needed in TDT-type methods can be lower than the number required in an association analysis, and even more so when the familial clustering is strong. However, in all cases, the number of sibs that need to be sampled to get the appropriate number of informative sibs for analysis is always larger for TDT methods than for an association study. In a phenotype-first strategy, this number may be critical when investigating costly phenotypes.
Related Concept Videos
Choosing Between z and t Distribution
Distributions to Estimate Population Parameter
Comparing Experimental Results: Student's t-Test
Student t Distribution
The Student t distribution was developed by William S. Goset (1876–1937) of the...
Estimating Population Mean with Unknown Standard Deviation
William S. Gosset (1876–1937) of the Guinness...
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with data...
