Risk Prediction of Complex Diseases from Family History and Known Susceptibility Loci, with Applications for Cancer Screening

Abstract

Risk prediction based on genomic profiles has raised a lot of attention recently. However, family history is usually ignored in genetic risk prediction. In this study we proposed a statistical framework for risk prediction given an individual's genotype profile and family history. Genotype information about the relatives can also be incorporated. We allow risk prediction given the current age and follow-up period and consider competing risks of mortality. The framework allows easy extension to any family size and structure. In addition, the predicted risk at any percentile and the risk distribution graphs can be computed analytically. We applied the method to risk prediction for breast and prostate cancers by using known susceptibility loci from genome-wide association studies. For breast cancer, in the population the 10-year risk at age 50 ranged from 1.1% at the 5th percentile to 4.7% at the 95th percentile. If we consider the average 10-year risk at age 50 (2.39%) as the threshold for screening, the screening age ranged from 62 at the 20th percentile to 38 at the 95th percentile (and some never reach the threshold). For women with one affected first-degree relative, the 10-year risks ranged from 2.6% (at the 5th percentile) to 8.1% (at the 95th percentile). For prostate cancer, the corresponding 10-year risks at age 60 varied from 1.8% to 14.9% in the population and from 4.2% to 23.2% in those with an affected first-degree relative. We suggest that for some diseases genetic testing that incorporates family history can stratify people into diverse risk categories and might be useful in targeted prevention and screening.

Pitfalls of predicting complex traits from SNPs

“…For example, from a risk predictor for type 1 diabetes (T1D), created from risk variants known up to 2011, a risk group comprising the top ranked 18% of individuals would need to be monitored in order to capture 80% of future cases, yet because T1D is not common (prevalence 0.4%) the probability of disease for individuals in this risk group is still less than 2% 14 . Nonetheless, cost-effective public health strategies could result from use of genetic predictors to identify high-risk strata where disease prevention interventions should be focussed 15, 16 . In agriculture, genetic risk prediction is geared mostly towards selection of breeding stock based on estimates of additive genetic values (‘ estimated breeding values’ ) in the parent generation with the aim of eliciting changes in the phenotype of the of the offspring generation on average.…”

“…Methods that model the distribution of SNP effects 40 and the correlation between SNPs in the presence of single as well as multiple causal variants will be more accurate 1, 39–43, 45 . In human applications, sometimes only genome-wide significant SNPs are included in the predictor 15, 46–49 , yet greater accuracy results from the use of less stringent thresholds 1, 37, 40 and in animal and plant breeding it is typical to use all available SNPs. Better SNP estimation methods exist and are used in plant and animal breeding 1, 2, 37, 44, 50 and such methods have been proposed for applications to human data 1, 43 .…”

Transforming Summary Statistics from Logistic Regression to the Liability Scale: Application to Genetic and Environmental Risk Scores

“…Equations for risk estimation incorporating a PRS and family history have been derived using the liability model [21]. Although we do not consider family history here, we acknowledge that family history is important for stratifying individuals by risk.…”

Prediction of Melanoma Risk in a Southern European Population Based on a Weighted Genetic Risk Score

“…In addition, we did not include family history as a risk factor because this information was not available for the vast majority of our controls. Risk prediction algorithms in other cancers (e.g., breast cancer) suggest that inclusion of family history in a polygenic risk score leads to further substantial improvement of the risk prediction model (Mavaddat et al, 2015;So et al, 2011). In addition, we did not take into account possible gene-environment interactions or gene-gene interactions.…”