Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2018 Jan 8;13(1):e0190486.
doi: 10.1371/journal.pone.0190486. eCollection 2018.

Weighted functional linear regression models for gene-based association analysis

Nadezhda M Belonogova  1 , Gulnara R Svishcheva  1   2 , James F Wilson  3   4 , Harry Campbell  3 , Tatiana I Axenovich  1   5
Affiliations

Weighted functional linear regression models for gene-based association analysis

Nadezhda M Belonogova et al. PLoS One. .

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.

PubMed Disclaimer

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Weights calculated as Beta(MAF; a1, a2) for three weighting modes.
Numbers in parentheses are the values of the beta function parameters a1 and a2.
Fig 2
Fig 2. The statistical power of regional association analysis with weighted FLM on the familial data with effect modeled as |βj| = log(s)|log10(MAFj)|/2 and all causal variants having MAFs ≤ 0.03.
Proportion of causal variants is the proportion of all rare variants (MAF ≤ 0.03) within the region (all rare variants = 100%). B—B-spline basis functions; F—Fourier basis functions; (1, 1)—the unweighted model; (0.5, 0.5)—the weighted model with a1 = a2 = 0.5; (1, 25)—the weighted model with a1 = 1 and a2 = 25.
Fig 3
Fig 3. The statistical power of regional association analysis with weighted FLM on the familial data with effect modeled as |βj|=s/2MAFj(1-MAFj) and all causal variants having MAFs ≤ 0.03.
Other model parameters and the notations are the same as in Fig 2.
Fig 4
Fig 4. The results of regional association analysis of the known Mendelian BP genes having P < 0.1 in at least one analysis.
The differently weighted FLM based on the Fourier basis functions was used.
See this image and copyright information in PMC

References

    1. Eichler EE, Flint J, Gibson G, Kong A, Leal SM, Moore JH, et al. Missing heritability and strategies for finding the underlying causes of complex disease. Nature reviews Genetics. 2010;11(6):446–50. doi: 10.1038/nrg2809 . - DOI - PMC - PubMed
    1. Li B, Leal SM. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am J Hum Genet. 2008;83(3):311–21. doi: 10.1016/j.ajhg.2008.06.024 . - DOI - PMC - PubMed
    1. Han F, Pan W. A data-adaptive sum test for disease association with multiple common or rare variants. Hum Hered. 2010;70(1):42–54. doi: 10.1159/000288704 . - DOI - PMC - PubMed
    1. Madsen BE, Browning SR. A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genet. 2009;5(2):e1000384 doi: 10.1371/journal.pgen.1000384 . - DOI - PMC - PubMed
    1. Morris AP, Zeggini E. An evaluation of statistical approaches to rare variant analysis in genetic association studies. Genet Epidemiol. 2010;34(2):188–93. doi: 10.1002/gepi.20450 . - DOI - PMC - PubMed

Publication types