Comparison of Family History and SNPs for Predicting Risk of Complex Disease

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Chuong B. Do¹^*, David A. Hinds¹, Uta Francke¹^,², Nicholas Eriksson¹

1 23andMe, Mountain View, California, United States of America, 2 Department of Genetics, Stanford University, Stanford, California, United States of America

Abstract Top

The clinical utility of family history and genetic tests is generally well understood for simple Mendelian disorders and rare subforms of complex diseases that are directly attributable to highly penetrant genetic variants. However, little is presently known regarding the performance of these methods in situations where disease susceptibility depends on the cumulative contribution of multiple genetic factors of moderate or low penetrance. Using quantitative genetic theory, we develop a model for studying the predictive ability of family history and single nucleotide polymorphism (SNP)–based methods for assessing risk of polygenic disorders. We show that family history is most useful for highly common, heritable conditions (e.g., coronary artery disease), where it explains roughly 20%–30% of disease heritability, on par with the most successful SNP models based on associations discovered to date. In contrast, we find that for diseases of moderate or low frequency (e.g., Crohn disease) family history accounts for less than 4% of disease heritability, substantially lagging behind SNPs in almost all cases. These results indicate that, for a broad range of diseases, already identified SNP associations may be better predictors of risk than their family history–based counterparts, despite the large fraction of missing heritability that remains to be explained. Our model illustrates the difficulty of using either family history or SNPs for standalone disease prediction. On the other hand, we show that, unlike family history, SNP–based tests can reveal extreme likelihood ratios for a relatively large percentage of individuals, thus providing potentially valuable adjunctive evidence in a differential diagnosis.

Author Summary Top

In clinical practice, obtaining a detailed family history is often considered the standard-of-care for characterizing the inherited component of an individual's disease risk. Recently, genetic risk assessments based on the cumulative effect of known single nucleotide polymorphism (SNP) disease associations have been proposed as another potentially useful source of information. To date, however, little is known regarding the predictive power of each approach. In this study, we develop models based on quantitative genetic theory to analyze and compare family history and SNP–based models. Our models explain the impact of disease frequency and heritability on performance for each method, and reveal a wide range of scenarios (16 out of the 23 diseases considered) where SNP associations may already be better predictors of risk than family history. Our results confirm the difficulty of obtaining accurate prediction when SNP or family history–based methods are used alone, and they show the benefits of combining information from the two approaches. They also suggest that, in some situations, SNP associations may be potentially useful as supporting evidence alongside other types of clinical information. To our knowledge, this study is the first broad comparison of family history– and SNP–based methods across a wide range of health conditions.

Citation: Do CB, Hinds DA, Francke U, Eriksson N (2012) Comparison of Family History and SNPs for Predicting Risk of Complex Disease. PLoS Genet 8(10): e1002973. doi:10.1371/journal.pgen.1002973
Editor: Nicholas J. Schork, University of California San Diego and The Scripps Research Institute, United States of America

Received: February 24, 2012; Accepted: August 8, 2012; Published: October 11, 2012
Copyright: © Do et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was funded by 23andMe. Other than the authors of this paper, who are all employed by 23andMe, the funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: CBD, DAH, UF, and NE are employed by 23andMe and own either stock or stock options in the company. PLOS co-founder Michael B. Eisen is a member of the 23andMe Scientific Advisory Board and also holds stock options in the company.
* E-mail: cdo@23andme.com