TY - JOUR

T1 - Weighted functional linear regression models for gene-based association analysis

AU - Belonogova, Nadezhda M.

AU - Svishcheva, Gulnara R.

AU - Wilson, James F.

AU - Campbell, Harry

AU - Axenovich, Tatiana I.

N1 - Publisher Copyright: © 2018 Belonogova et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

PY - 2018/1/8

Y1 - 2018/1/8

N2 - 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.

AB - 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.

KW - Genome-Wide Association Study/methods

KW - Humans

KW - Models, Genetic

KW - Regression Analysis

KW - TESTS

KW - QUANTITATIVE TRAITS

KW - RARE VARIANTS

KW - DISEASE

KW - COMMON

KW - POWERFUL

UR - http://www.scopus.com/inward/record.url?scp=85040309060&partnerID=8YFLogxK

U2 - 10.1371/journal.pone.0190486

DO - 10.1371/journal.pone.0190486

M3 - Article

C2 - 29309409

AN - SCOPUS:85040309060

VL - 13

SP - e0190486

JO - PLoS ONE

JF - PLoS ONE

SN - 1932-6203

IS - 1

M1 - 0190486

ER -