Testing for association between ordinal traits and genetic variants in pedigree-structured samples by collapsing and kernel methods
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Li-Chu Chien
Abstract
In genome-wide association studies (GWAS), logistic regression is one of the most popular analytics methods for binary traits. Multinomial regression is an extension of binary logistic regression that allows for multiple categories. However, many GWAS methods have been limited application to binary traits. These methods have improperly often been used to account for ordinal traits, which causes inappropriate type I error rates and poor statistical power. Owing to the lack of analysis methods, GWAS of ordinal traits has been known to be problematic and gaining attention. In this paper, we develop a general framework for identifying ordinal traits associated with genetic variants in pedigree-structured samples by collapsing and kernel methods. We use the local odds ratios GEE technology to account for complicated correlation structures between family members and ordered categorical traits. We use the retrospective idea to treat the genetic markers as random variables for calculating genetic correlations among markers. The proposed genetic association method can accommodate ordinal traits and allow for the covariate adjustment. We conduct simulation studies to compare the proposed tests with the existing models for analyzing the ordered categorical data under various configurations. We illustrate application of the proposed tests by simultaneously analyzing a family study and a cross-sectional study from the Genetic Analysis Workshop 19 (GAW19) data.
Funding source: Ministry of Science and Technology, Taiwan
Award Identifier / Grant number: MOST 110-2118-M-037-001-MY2
Acknowledgments
We thank the GAW19 data provider for their generosity in sharing their data with us. The Genetic Analysis Workshops are supported by NIH grant R01 GM031575. The GAW19 whole genome sequence data were provided by the T2D-GENES Consortium, which is supported by NIH grants U01 DK085524, U01 DK085584, U01 DK085501, U01 DK085526, and U01 DK085545. The other genetic and phenotypic data for GAW19 were provided by the San Antonio Family Heart Study and San Antonio Family Diabetes/Gallbladder study, which are supported by NIH grants P01 HL045222, R01 DK047482, and R01 DK053889. Andrew R. Wood is supported by European Research Council grant SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC. We are grateful to the editor and the referees for their helpful comments and suggestions in improving the paper.
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Research ethics: Not applicable.
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Author contributions: The author has accepted responsibility for the entire content of this manuscript and approved its submission.
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Competing interests: The author states no conflict of interest.
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Research funding: This work is supported by grant MOST 110-2118-M-037-001- MY2 of Ministry of Science and Technology, Taiwan, R.O.C.
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Data availability: Not applicable.
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/ijb-2022-0123).
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