TY - JOUR

T1 - Software for generating liability distributions for pedigrees conditional on their observed disease states and covariates

AU - Campbell, Desmond D.

AU - Sham, Pak C.

AU - Knight, Jo

AU - Wickham, Harvey

AU - Landau, Sabine

N1 - 2009 Wiley-Liss, Inc.

PY - 2010/2

Y1 - 2010/2

N2 - 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.

AB - 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.

KW - Depression

KW - Genetic Markers

KW - Genetic Predisposition to Disease

KW - Humans

KW - Models, Genetic

KW - Models, Statistical

KW - Pedigree

KW - Predictive Value of Tests

KW - Probability

KW - Risk Assessment

KW - Software

KW - Software Validation

U2 - 10.1002/gepi.20446

DO - 10.1002/gepi.20446

M3 - Journal article

C2 - 19771574

VL - 34

SP - 159

EP - 170

JO - Genetic Epidemiology

JF - Genetic Epidemiology

SN - 0741-0395

IS - 2

ER -