Weighted functional linear regression models for gene-based association analysis. PLoS One 2018;13(1):e0190486
Date
01/09/2018Pubmed ID
29309409Pubmed Central ID
PMC5757938DOI
10.1371/journal.pone.0190486Scopus ID
2-s2.0-85040309060 (requires institutional sign-in at Scopus site) 6 CitationsAbstract
Functional linear regression models are effectively used in gene-based association analysis of complex traits. These models combine information about individual genetic variants, taking into account their positions and reducing the influence of noise and/or observation errors. To increase the power of methods, where several differently informative components are combined, weights are introduced to give the advantage to more informative components. Allele-specific weights have been introduced to collapsing and kernel-based approaches to gene-based association analysis. Here we have for the first time introduced weights to functional linear regression models adapted for both independent and family samples. Using data simulated on the basis of GAW17 genotypes and weights defined by allele frequencies via the beta distribution, we demonstrated that type I errors correspond to declared values and that increasing the weights of causal variants allows the power of functional linear models to be increased. We applied the new method to real data on blood pressure from the ORCADES sample. Five of the six known genes with P < 0.1 in at least one analysis had lower P values with weighted models. Moreover, we found an association between diastolic blood pressure and the VMP1 gene (P = 8.18×10-6), when we used a weighted functional model. For this gene, the unweighted functional and weighted kernel-based models had P = 0.004 and 0.006, respectively. The new method has been implemented in the program package FREGAT, which is freely available at https://cran.r-project.org/web/packages/FREGAT/index.html.
Author List
Belonogova NM, Svishcheva GR, Wilson JF, Campbell H, Axenovich TIAuthor
J Frank Wilson MD Professor Emeritus in the Radiation Oncology department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
Genome-Wide Association StudyHumans
Models, Genetic
Regression Analysis