Ordered-subset analysis (OSA) for family-based association mapping of complex traits

Abstract

Association analysis provides a powerful tool for complex disease gene mapping. However, in the presence of genetic heterogeneity, the power for association analysis can be low since only a fraction of the collected families may carry a specific disease susceptibility allele. Ordered-subset analysis (OSA) is a linkage test that can be powerful in the presence of genetic heterogeneity. OSA uses trait-related covariates to identify a subset of families that provide the most evidence for linkage. A similar strategy applied to genetic association analysis would likely result in increased power to detect association. Association in the presence of linkage (APL) is a family-based association test (FBAT) for nuclear families with multiple affected siblings that properly infers missing parental genotypes when linkage is present. We propose here APL-OSA, which applies the OSA method to the APL statistic to identify a subset of families that provide the most evidence for association. A permutation procedure is used to approximate the distribution of the APL-OSA statistic under the null hypothesis that there is no relationship between the family-specific covariate and the family-specific evidence for allelic association. We performed a comprehensive simulation study to verify that APL-OSA has the correct type I error rate under the null hypothesis. This simulation study also showed that APL-OSA can increase power relative to other commonly used association tests (APL, FBAT and FBAT with covariate adjustment) in the presence of genetic heterogeneity. Finally, we applied APL-OSA to a family study of age-related macular degeneration, where cigarette smoking was used as a covariate.

Fig. 1

Power comparison between APL-OSA, APL, FBAT and PBAT over 500 replicates. Power is calculated for significance level of 0.05 (APL-OSA_0.05, APL, FBAT and PBAT) and 0.025 (APL-OSA_0.025) for recessive (Models 5-7) and dominant (Models 8-10) models with different GRRs. APL-OSA, association in the presence of linkage-ordered-subset analysis; FBAT, family-based association test; GRR, genotypic relative risk.

Fig. 2

Power comparison between APL-OSA, APL, FBAT and PBAT over 500 replicates. Power is calculated for significance level of 0.05 (APL-OSA_0.05, APL, FBAT and PBAT) and 0.025 (APL-OSA_0.025) for recessive (Models 5-7) and dominant (Models 8-10) models with different GRRs. The covariate means and standard deviations for the population 1 in Models 5-10 are set to 30 and 10, respectively. APL-OSA, association in the presence of linkage-ordered-subset analysis; FBAT, family-based association test; GRR, genotypic relative risk.

Fig. 3

Power comparison between APL-OSA, APL, FBAT and PBAT over 500 replicates. Power is calculated for significance level of 0.05 (APL-OSA_0.05, APL, FBAT and PBAT) and 0.025 (APL-OSA_0.025) for recessive (Models 7, 11 and 12) and dominant (Models 10, 13 and 14) models with different levels of LD. APL-OSA, association in the presence of linkage-ordered-subset analysis; FBAT, family-based association test; LD, linkage disequilibrium.

Fig. 4

Power comparison between APL-OSA, APL, FBAT and PBAT over 500 replicates. Power is calculated for significance level of 0.05 (APL-OSA_0.05, APL, FBAT and PBAT) and 0.025 (APL-OSA_0.025) for recessive (Models 5 and 15) and dominant (Models 8 and 16) models with different proportions of population admixture. APL-OSA, association in the presence of linkage-ordered-subset analysis; FBAT, family-based association test.