Analysis of Family‐ and Population‐Based Samples Using Multiple Linkage Disequilibrium Mapping

Abstract

SummaryWe report two methods for linkage disequilibrium mapping that involve incorporation of covariates through parametric modeling to utilize combined case-parent trios and unrelated case and/or control data. The proposed two combined methods were used to map the disease locus of hypertension in the angiotensin-converting enzyme (ACE) gene with incorporation of ACE activity. The efficiencies in estimating the disease locus increased by 351-and 100-fold in the hybrid study with respect to the two proposed methods when compared to the estimates from the trios study; and they changed by 1.4-and 0.4-fold, respectively, when compared to the case-control study. Efficiency of disease locus estimates was greatly improved in both simulations and hypertension studies based on the hybrid data, compared to case-parent trio studies only. These newly developed methods preserve the advantages of the previous methods, including flexible modeling and assessment of gene-gene and gene-covariate effects, while providing more power by using all the data combined. The computing program for analysis using the separate and hybrid data sets is freely available on the author's website.

General retrospective mega‐analysis framework for rare variant association tests

“…Properly integrating all available data can possibly improve statistical power or efficiency of parameter estimates (Mirea, Infante-Rivard, Sun, & Bull, 2012). A number of methods have been developed to combine cross-sectional and longitudinal cohorts (Carrillo & Karr, 2013) or to analyze unrelated casecontrol individuals together with case control family trios (Chiu, Lee, Kao, Pan, & Hsu, 2013;Mirea et al, 2012). However, the presence of heterogeneity such as population stratification or population structure bias would produce spurious results in the absence of association or may mask or reverse a true association.…”

Multipoint association mapping for longitudinal family data: an application to hypertension phenotypes

“…The variance of the disease locus position estimate was estimated to make inferences about the disease locus position (τ) and its genetic effect (C) [2]. Theoretically, the genetic effect of τ , characterized by C , is the transmission probability that the affected offspring will carry the disease allele, H , at τ .…”

“…Theoretically, the genetic effect of τ , characterized by C , is the transmission probability that the affected offspring will carry the disease allele, H , at τ . Detailed derivations for case-parent trios in a cross-sectional design can be found in Chiu et al [2, 5]. We will present the details of this proposed methodology elsewhere.…”

“…The semiparametric linkage disequilibrium mapping for the hybrid family design we developed previously [2] uses all markers simultaneously to localize the disease locus without making an assumption about genetic mechanism, except that only 1 disease gene lies in the region under study. The advantages of this approach are (a) it does not require the specification of an underlying genetic model, so estimating the position of a disease locus and its standard error is robust to a wide variety of genetic mechanisms; (b) it provides estimates of disease locus positions, along with a confidence interval for further fine mapping; and (c) it uses linkage disequilibrium between markers to localize the disease locus, which may not have been typed.…”