Abstract
OBJECTIVE: To establish a model for estimating genetic risk using known and unknown family data. METHODS: Four simulated datasets were generated for four paternal and maternal chromosomes. The simulated data for children were generated from the parental data according to the Mendelian law. The correlation coefficient between the children's and paternal data was calculated, and 2R was defined as the heredity index for continuous data (HIC). The simulated continuous data were transformed into binary data according to the gene accumulation threshold (incidence); the incidences of children in the parental no-disease group and the disease onset group were obtained; the correlation coefficient (R) was calculated as expected R (Re). The ratio of observed R (Ro) and Re was defined as the Heredity index for binary data (HIB). RESULTS: Different actual pedigree data (lunula and holding a hammer in the right or left hand) were successfully used to verify the accuracy of the model. The genetic risk was estimated according to the incidence in a population using a lookup table. CONCLUSION: Our findings indicate the reliability of the model based on the fact that the multigene effect constitutes the normal distribution. Thus, this model can be used for comprehensive analysis of the influence of genetic and nongenetic factors on the genetic phenotype and to estimate genetic risk using known and unknown family data.