Statistical methods that integrate between-individual (IA) and within-family (FA) genetic association analyses can increase statistical power to identify disease susceptibility genes, however combining IA and FA is valid only when the IA are free of population stratification bias (PSB). Existing methods initially test for PSB by comparing IA and FA results using an arbitrary testing level αPSB, typically 5%. Combined analyses are performed if no significant PSB is detected, otherwise analyses are restricted to FA. As a novel alternative, we propose a weighted (WGT) framework that combines the estimate from the most powerful analysis subject to PSB with the most powerful robust FA estimate, using weights based on the p-value from the PSB test. The WGT approach generalizes existing methods by using a continuous weighting function that depends only on the observed PSB p-value instead of a binary one that also depends on specification of an arbitrary PSB testing level αPSB. Simulations of quantitative trait and case-control data show that in comparison to existing methods, the WGT approach has 5% type I error closer to the nominal level, increased (decreased) accuracy for larger (smaller) PSB levels, and overall increased positive predictive value. The resulting PSB correction is SNP-specific and provides a good compromise between type I error control and power in candidate gene or confirmation studies limited to few loci, when PSB is likely and there are no additional empirical data available to correct PSB. We applied the WGT approach to a case-control study of childhood leukemia and a study of diabetes complications with time-to-event outcomes derived from repeated measurements obtained over 17 years of follow-up. To directly analyze the longitudinal measurements without specification of event thresholds, we developed fully Bayesian latent change-point time (LCPT) models for IA and FA. In analogy with the WGT approach, we also considered an extended LCPT model incorporating PSB evidence in analyses combining IA and FA.

Statistical methods that integrate between-individual (IA) and within-family (FA) genetic association analyses can increase statistical power to identify disease susceptibility genes, however combining IA and FA is valid only when the IA are free of population stratification bias (PSB). Existing methods initially test for PSB by comparing IA and FA results using an arbitrary testing level αPSB, typically 5%. Combined analyses are performed if no significant PSB is detected, otherwise analyses are restricted to FA. As a novel alternative, we propose a weighted (WGT) framework that combines the estimate from the most powerful analysis subject to PSB with the most powerful robust FA estimate, using weights based on the p-value from the PSB test. The WGT approach generalizes existing methods by using a continuous weighting function that depends only on the observed PSB p-value instead of a binary one that also depends on specification of an arbitrary PSB testing level αPSB. Simulations of quantitative trait and case-control data show that in comparison to existing methods, the WGT approach has 5% type I error closer to the nominal level, increased (decreased) accuracy for larger (smaller) PSB levels, and overall increased positive predictive value. The resulting PSB correction is SNP-specific and provides a good compromise between type I error control and power in candidate gene or confirmation studies limited to few loci, when PSB is likely and there are no additional empirical data available to correct PSB. We applied the WGT approach to a case-control study of childhood leukemia and a study of diabetes complications with time-to-event outcomes derived from repeated measurements obtained over 17 years of follow-up. To directly analyze the longitudinal measurements without specification of event thresholds, we developed fully Bayesian latent change-point time (LCPT) models for IA and FA. In analogy with the WGT approach, we also considered an extended LCPT model incorporating PSB evidence in analyses combining IA and FA.