Abstract

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.

Highlights

  • Rapid progress in next-generation whole-exome and whole-genome sequencing technologies provides new opportunities for detection of rare genetic variants that control complex traits

  • We proposed a new weighted functional linear model for gene-based association analysis and demonstrated that the power of existing methods can be increased by introducing weights to functional linear models

  • Weighting of predictors into the complete multiple linear regression model is meaningless, we showed how weights can be introduced into reduced models such as functional linear regression model (FLM)

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Summary

Introduction

Rapid progress in next-generation whole-exome and whole-genome sequencing technologies provides new opportunities for detection of rare genetic variants that control complex traits. Statistical methods using single-variant association tests that are commonly adopted in genome-wide association studies are generally underpowered for rare variants. Scientific Organizations, 0324-2016-008, http:// fano.gov.ru/en/; Chief Scientist Office of the Scottish Government, CZB/4/276 and CZB/4/710 to JFW, www.cso.scot.nhs.uk, and the European Union framework program 6 EUROSPAN, LSHGCT-2006-018947, to JFW. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

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