Structured exploratory data analysis (SEDA) for determining mode of inheritance of quantitative traits. I. Simulation studies on the effect of background distributions.

We examine through simulations the effectiveness of a new methodology to help distinguish among monogenic, multifactorial, and sporadic trait transmission from parents to offspring in nuclear family data sets. The major gene index (MGI), which compares the deviation of the offspring from the midparental value with a function of the individual deviations between parents and offspring, aids in the discrimination of multifactorial from sporadic and monogenic models. In contrast with other methodologies, the ability of the MGI to separate multifactorial, monogenic, and sporadic models improves with increased skewness in the trait distribution. The midparental correlation coefficient serves as a further guide for indicating mode of inheritance. A new class of techniques, the offspring between parents function (OBP), is introduced that provides a more sensitive tool to help in assessing mode of transmission through the analysis of the level, shape, and undulation characteristics of the curves. Four data examples are used to illustrate the methodology: erythrocyte catechol-O-methyltransferase (COMT) activity, height, weight, and triglyceride measurements. Height appears largely multifactorial, and weight appears to be mostly sporadic, while COMT and triglyceride measurements suggest the presence of some major gene influences.