New statistical selection method for pleiotropic variants associated with both quantitative and qualitative traits

Kipoong Kim; Tae-Hwan Jun; Bo-Keun Ha; Shuang Wang; Hokeun Sun

doi:10.1186/s12859-023-05505-8

New statistical selection method for pleiotropic variants associated with both quantitative and qualitative traits

BMC Bioinformatics. 2023 Oct 10;24(1):381. doi: 10.1186/s12859-023-05505-8.

Authors

Kipoong Kim¹, Tae-Hwan Jun², Bo-Keun Ha³, Shuang Wang⁴, Hokeun Sun⁵

Affiliations

¹ Department of Statistic, Pusan National University, 46241, Busan, Korea.
² Department of Plant Bioscience, Pusan National University, 50463, Miryang, Korea.
³ Department of Applied Plant Science, Chonnam National University, 61186, Gwangju, Korea.
⁴ Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, 10032, USA.
⁵ Department of Statistic, Pusan National University, 46241, Busan, Korea. hsun@pusan.ac.kr.

Abstract

Background: Identification of pleiotropic variants associated with multiple phenotypic traits has received increasing attention in genetic association studies. Overlapping genetic associations from multiple traits help to detect weak genetic associations missed by single-trait analyses. Many statistical methods were developed to identify pleiotropic variants with most of them being limited to quantitative traits when pleiotropic effects on both quantitative and qualitative traits have been observed. This is a statistically challenging problem because there does not exist an appropriate multivariate distribution to model both quantitative and qualitative data together. Alternatively, meta-analysis methods can be applied, which basically integrate summary statistics of individual variants associated with either a quantitative or a qualitative trait without accounting for correlations among genetic variants.

Results: We propose a new statistical selection method based on a unified selection score quantifying how a genetic variant, i.e., a pleiotropic variant associates with both quantitative and qualitative traits. In our extensive simulation studies where various types of pleiotropic effects on both quantitative and qualitative traits were considered, we demonstrated that the proposed method outperforms the existing meta-analysis methods in terms of true positive selection. We also applied the proposed method to a peanut dataset with 6 quantitative and 2 qualitative traits, and a cowpea dataset with 2 quantitative and 6 qualitative traits. We were able to detect some potentially pleiotropic variants missed by the existing methods in both analyses.

Conclusions: The proposed method is able to locate pleiotropic variants associated with both quantitative and qualitative traits. It has been implemented into an R package 'UNISS', which can be downloaded from http://github.com/statpng/uniss.

Keywords: Genetic association; Multiple phenotypes; Pleiotropic variants; Regularization; Selection probability.

MeSH terms

Computer Simulation
Genetic Association Studies
Genome-Wide Association Study*
Phenotype
Polymorphism, Single Nucleotide*

Abstract

MeSH terms

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