Software for generating liability distributions for pedigrees conditional on their observed disease states and covariates

Abstract

For many multifactorial diseases, aetiology is poorly understood. A major research aim is the identification of disease predictors (environmental, biological, and genetic markers). In order to achieve this, a two-stage approach is proposed. The initial or synthesis stage combines observed pedigree data with previous genetic epidemiological research findings, to produce estimates of pedigree members' disease risk and predictions of their disease liability. A further analysis stage uses the latter as inputs to look for associations with potential disease markers. The incorporation of previous research findings into an analysis should lead to power gains. It also allows separate predictions for environmental and genetic liabilities to be generated. This should increase power for detecting disease predictors that are environmental or genetic in nature. Finally, the approach brings pragmatic benefits in terms of data reduction and synthesis, improving comprehensibility, and facilitating the use of existing statistical genetics tools. In this article we present a statistical model and Gibbs sampling approach to generate liability predictions for multifactorial disease for the synthesis stage. We have implemented the approach in a software program. We apply this program to a specimen disease pedigree, and discuss the results produced, comparing its results with those generated under a more naïve model. We also detail simulation studies that validate the software's operation.

Power in the phenotypic extremes: a simulation study of power in discovery and replication of rare variants

“…Genetic factors could be easily incorporated into the liability score [Plomin et al, 2009], although it is unclear how much power would be gained with this approach. Family history could improve estimates of disease risk and liability scores [Campbell et al, 2010;Falconer, 1965;Feng et al, 2009]; however, researchers should a priori decide how family history should be incorporated in the liability model. If the ascertainment strategy is to select cases that have little to no risk but have a family history of disease (and conversely controls who are at high risk of being affected but have no family history of disease), then the directionality of family history should be reversed in the risk model.…”

“…Recently, an approach was described that estimates the proportion of genetic and environmental variance contributing to an outcome variable, per individual, using Monte Carlo simulation with Gibbs or Rejection Sampling within pedigree data [Campbell et al, 2010]. The information of focus in our work (identifying phenotypic extremes given a set of risk factors) and the information estimated broadly in that work are similar in spirit.…”

“…1). Indeed such liability scores estimated from disease status, risk factors, and residual heritability have already been proposed for pedigrees [Campbell et al, 2010;Falconer, 1965].…”

Multifactorial disease risk calculator: Risk prediction for multifactorial disease pedigrees

“…Combining family history with known risk factors can improve risk prediction, and family history and polygenic score have been found to contribute to risk prediction in a complimentary manner (Do, Hinds, Francke, & Eriksson, ). Several similar risk prediction methodologies that combine family history with known risk factors have been developed (Campbell, Sham, Knight, Wickham, & Landau, ; Ruderfer, Korn, & Purcell, ; So, Kwan, Cherny, & Sham, ). Here, we describe a web tool based on one of these.…”

“…The existing methodology (Campbell et al., ) has been extended in several ways: Derivation of the appropriate disease model from epidemiological findings is difficult when categorical risk factors are involved. This has now been automated.…”