Abstract
Family-based study designs are again becoming popular as new next-generation sequencing technologies make whole-exome and whole-genome sequencing projects economically and temporally feasible. Here we evaluate the statistical properties of linkage analyses and family-based tests of association for the Genetic Analysis Workshop 17 mini-exome sequence data. Based on our results, the linkage methods using relative pairs or nuclear families had low power, with the best results coming from variance components linkage analysis in nuclear families and Elston-Stewart model-based linkage analysis in extended pedigrees. For family-based tests of association, both ASSOC and ROMP performed well for genes with large effects, but ROMP had the advantage of not requiring parental genotypes in the analysis. For the linkage analyses we conclude that genome-wide significance levels appear to control type I error well but that “suggestive” significance levels do not. Methods that make use of the extended pedigrees are well powered to detect major loci segregating in the families even when there is substantial genetic heterogeneity and the trait is mainly polygenic. However, large numbers of such pedigrees will be necessary to detect all major loci. The family-based tests of association found the same major loci as the linkage analyses and detected low-frequency loci with moderate effect sizes, but control of type I error was not as stringent.
Highlights
Studies have been an integral part of genetic research since the 1950s
In the simulation process, the 202 founders of the eight extended pedigrees were a random sample from the 697 individuals in the simulated unrelated data set, so only a subset of the total trait-generating sequence variants (SVs) were present in the family data, with rare variants being underrepresented [1]
There were no nonmonomorphic SVs with major effects on the qualitative trait except for the three SVs acting through the effects of Q1 and Q2 on the qualitative trait
Summary
The 202 founders of the eight extended pedigrees were a random sample from the 697 individuals in the simulated unrelated data set, so only a subset of the total trait-generating SVs were present in the family data, with rare variants being underrepresented [1]. The simulated SV genotypes were identical for all 200 replicates. Phenotypes were simulated using the same age, sex, and causal SVs in all replicates; smoking status differed across replicates. All SVs were tested for Hardy-Weinberg equilibrium. SVs not in Hardy-Weinberg equilibrium were flagged for further consideration. The traits were adjusted for covariates that had a significant effect on the trait (Age and Smoking for Q1; none for Q2; Age, Sex, and Smoking for Q4). Haseman-Elston regression, Lander-Green nonparametric linkage analysis, and Elston-Stewart parametric linkage analysis were performed on replicate 1 only, whereas variance components (VC) analyses were performed with all 200 replicates. The three quantitative traits in each replicate were adjusted for Age, Sex, and Smoking and were centered using linear regression, with the residuals being used in all association analyses
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
