title: The impact of population heterogeneity on risk estimation in genetic counseling.
abstract: BACKGROUND
  Genetic counseling has been an important tool for evaluating and communicating disease susceptibility for decades, and it has been applied to predict risks for a wide class of hereditary disorders. Most diseases are complex in nature and are affected by multiple genes and environmental conditions; it is highly likely that DNA tests alone do not define all the genetic factors responsible for a disease, so that persons classified into the same risk group by DNA testing actually could have different disease susceptibilities. Ignorance of population heterogeneity may lead to biased risk estimates, whereas additional information on population heterogeneity may improve the precision of such estimates.
  
  
  METHODS
  Although DNA tests are widely used, few studies have investigated the accuracy of the predicted risks. We examined the impact of population heterogeneity on predicted disease risks by simulation of three different heterogeneity scenarios and studied the precision and accuracy of the risks estimated from a logistic regression model that ignored population heterogeneity. Moreover, we also incorporated information about population heterogeneity into our original model and investigated the resulting improvement in the accuracy of risk estimation.
  
  
  RESULTS
  We found that heterogeneity in one or more categories could lead to biased estimates not only in the "contaminated" categories but also in other homogeneous categories. Incorporating information about population heterogeneity into the original model greatly improved the accuracy of risk estimation.
  
  
  CONCLUSIONS
  Our findings imply that without thorough knowledge about genetic basis of the disease, risks estimated from DNA tests may be misleading. Caution should be taken when evaluating the predicted risks obtained from genetic counseling. On the other hand, the improved accuracy of risk estimates after incorporating population heterogeneity information into the model did point out a promising direction for genetic counseling, since more and more new techniques are being invented and disease etiology is being better understood.
authors.affiliation: Department of Health Evaluation Sciences, Penn State College of Medicine, A210, Suite 2200, 600 Centerview Drive, Hershey, PA 17033, USA. wliu@hes.hmc.psu.edu
authors.forename: Wenlei
authors.initials: W
authors.lastname: Liu
authors.forename: Nikolina
authors.initials: N
authors.lastname: Icitovic
authors.forename: Michele L
authors.initials: ML
authors.lastname: Shaffer
authors.forename: Gary A
authors.initials: GA
authors.lastname: Chase
mesh_terms: D015982:Bias; D005817:Genetic Counseling / Q000379:methods / Q000592:standards*; D030342:Genetic Diseases, Inborn / Q000453:epidemiology / Q000235:genetics; D018740:Genetic Heterogeneity; D020022:Genetic Predisposition to Disease / Q000235:genetics; D005820:Genetic Testing / Q000379:methods / Q000592:standards; D005838:Genotype; D006801:Humans; D016015:Logistic Models; D008957:Models, Genetic; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods / Q000592:standards; D012307:Risk Factors