Improving disease prediction by incorporating family disease history in risk prediction models with large-scale genetic data

Jungsoo Gim, Wonji Kim, Soo Heon Kwak, Hosik Choi, Changyi Park, Kyong Soo Park, Sunghoon Kwon, Taesung Park, Sungho Won

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Despite the many successes of genome-wide association studies (GWAS), the known susceptibility variants identified by GWAS have modest effect sizes, leading to notable skepticism about the effectiveness of building a risk prediction model from large-scale genetic data. However, in contrast to genetic variants, the family history of diseases has been largely accepted as an important risk factor in clinical diagnosis and risk prediction. Nevertheless, the complicated structures of the family history of diseases have limited their application in clinical practice. Here, we developed a new method that enables incorporation of the general family history of diseases with a liability threshold model, and propose a new analysis strategy for risk prediction with penalized regression analysis that incorporates both large numbers of genetic variants and clinical risk factors. Application of our model to type 2 diabetes in the Korean population (1846 cases and 1846 controls) demonstrated that single-nucleotide polymorphisms accounted for 32.5% of the variation explained by the predicted risk scores in the test data set, and incorporation of family history led to an additional 6.3% improvement in prediction. Our results illustrate that family medical history provides valuable information on the variation of complex diseases and improves prediction performance.

Original languageEnglish
Pages (from-to)1147-1155
Number of pages9
JournalGenetics
Volume207
Issue number3
DOIs
StatePublished - Nov 2017

Keywords

  • Family history
  • Genetic variability in complex binary traits
  • Liability threshold model
  • Penalized prediction model
  • Risk prediction in complex disease

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