Leveraging Family History in Case-Control Analyses of Rare Variation

Abstract

Standard methods for case-control association studies of rare variation often treat disease outcome as a dichotomous phenotype. However, both theoretical and experimental studies have demonstrated that subjects with a family history of disease can be enriched for risk variation relative to subjects without such history. Assuming family history information is available, this observation motivates the idea of replacing the standard dichotomous outcome variable used in case-control studies with a more informative ordinal outcome variable that distinguishes controls (0), sporadic cases (1), and cases with a family history (2), with the expectation that we should observe increasing number of risk variants with increasing category of the ordinal variable. To leverage this expectation, we propose a novel rare-variant association test that incorporates family history information based on our previous GAMuT framework for rare-variant association testing of multivariate phenotypes. We use simulated data to show that, when family history information is available, our new method outperforms standard rare-variant association methods, like burden and SKAT tests, that ignore family history. We further illustrate our method using a rare-variant study of cleft lip and palate.

Keywords: complex human traits; gene mapping; rare variant.

Figure 1 Q–Q

plots of P-values for gene-based tests of rare variants for three methods: burden test, SKAT, and the ordinal GAMuT proposed here. Simulated datasets (10,000) assumed a 10-kb region and rare variants defined as those with MAF < 3%. (A) A total of 750 subjects per group, disease prevalence of 0.01, and $\lambda =2$. (B) A total of 750 subjects per group, disease prevalence of 0.05, and $\lambda =2$. (C) A total of 750 subjects per group, disease prevalence of 0.01, and $\lambda =4$. (D) A total of 750 subjects per group, disease prevalence of 0.05 and $\lambda =4$.

Figure 2

Power for gene-based testing comparing three methods: burden test (blue, square), SKAT (green, triangle), and ordinal GAMuT (red, circle). We compared two disease prevalences 0.01, 0.05 (rows) with different conditional recurrence risk ratio $\lambda =2,4,8$ (columns).

Figure 3 Q–Q

plots of P-values for gene-based tests of rare variants for three methods: burden test (Li and Leal 2008; Madsen and Browning 2009), SKAT (Wu et al. 2011), and the ordinal GAMuT proposed here, in the Pittsburgh Orofacial Cleft (POFC) Multiethnic GWAS.

Figure 4

Gene-based test on POFC Multiethnic GWAS using burden, SKAT, and ordinal GAMuT approach. Manhattan plots for each of the three tests. Red line: genome-wide significance level $\left(-{\text{log}}_{10}\left(0.05/5137\right)=5.0117\right)$. Blue line: suggestive level $\left(-{\text{log}}_{10}\left(1\times {10}^{-4}\right)=4\right)$.