An Extended Application of the Fast Multi-Locus Ridge Regression Algorithm in Genome-Wide Association Studies of Categorical Phenotypes

Overview

Journal Plants (Basel)

Date 2024 Sep 14

PMID 39274004

Authors

Jin Zhang

Bolin Shen

Ziyang Zhou

Mingzhi Cai

Xinyi Wu

Le Han

Yangjun Wen

Affiliations

Soon will be listed here.

Abstract

Categorical (either binary or ordinal) quantitative traits are widely observed to measure count and resistance in plants. Unlike continuous traits, categorical traits often provide less detailed insights into genetic variation and possess a more complex underlying genetic architecture, which presents additional challenges for their genome-wide association studies. Meanwhile, methods designed for binary or continuous phenotypes are commonly used to inappropriately analyze ordinal traits, which leads to the loss of original phenotype information and the detection power of quantitative trait nucleotides (QTN). To address these issues, fast multi-locus ridge regression (FastRR), which was originally designed for continuous traits, is used to directly analyze binary or ordinal traits in this study. FastRR includes three stages of continuous transformation, variable reduction, and parameter estimation, and it can computationally handle categorical phenotype data instead of link functions introduced or methods inappropriately used. A series of simulation studies demonstrate that, compared with four other continuous or binary or ordinal approaches, including logistic regression, FarmCPU, FaST-LMM, and POLMM, the FastRR method outperforms in the detection of small-effect QTN, accuracy of estimated effect, and computation speed. We applied FastRR to 14 binary or ordinal phenotypes in the real dataset and identified 479 significant loci and 76 known genes, at least seven times as many as detected by other algorithms. These findings underscore the potential of FastRR as a very useful tool for genome-wide association studies and novel gene mining of binary and ordinal traits.

References

Atwell S, Huang Y, Vilhjalmsson B, Willems G, Horton M, Li Y . Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature. 2010; 465(7298):627-31. PMC: 3023908. DOI: 10.1038/nature08800. View

Loh P, Tucker G, Bulik-Sullivan B, Vilhjalmsson B, Finucane H, Salem R . Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat Genet. 2015; 47(3):284-90. PMC: 4342297. DOI: 10.1038/ng.3190. View

Xu Y, Xing L, Su J, Zhang X, Qiu W . Model-based clustering for identifying disease-associated SNPs in case-control genome-wide association studies. Sci Rep. 2019; 9(1):13686. PMC: 6757104. DOI: 10.1038/s41598-019-50229-6. View

Tamba C, Ni Y, Zhang Y . Iterative sure independence screening EM-Bayesian LASSO algorithm for multi-locus genome-wide association studies. PLoS Comput Biol. 2017; 13(1):e1005357. PMC: 5308866. DOI: 10.1371/journal.pcbi.1005357. View

Zhang J, Chen M, Wen Y, Zhang Y, Lu Y, Wang S . A Fast Multi-Locus Ridge Regression Algorithm for High-Dimensional Genome-Wide Association Studies. Front Genet. 2021; 12:649196. PMC: 8041068. DOI: 10.3389/fgene.2021.649196. View

Huang A, Xu S, Cai X . Empirical Bayesian LASSO-logistic regression for multiple binary trait locus mapping. BMC Genet. 2013; 14:5. PMC: 3771412. DOI: 10.1186/1471-2156-14-5. View

Wang S, Feng J, Ren W, Huang B, Zhou L, Wen Y . Improving power and accuracy of genome-wide association studies via a multi-locus mixed linear model methodology. Sci Rep. 2016; 6:19444. PMC: 4726296. DOI: 10.1038/srep19444. View

Zhang Y, Jia Z, Dunwell J . Editorial: The Applications of New Multi-Locus GWAS Methodologies in the Genetic Dissection of Complex Traits. Front Plant Sci. 2019; 10:100. PMC: 6378272. DOI: 10.3389/fpls.2019.00100. View

Xing L, Lesperance M, Zhang X . Simultaneous prediction of multiple outcomes using revised stacking algorithms. Bioinformatics. 2019; 36(1):65-72. DOI: 10.1093/bioinformatics/btz531. View

10.

Kaur S, Gill H, Breiland M, Kolmer J, Gupta R, Sehgal S . Identification of leaf rust resistance loci in a geographically diverse panel of wheat using genome-wide association analysis. Front Plant Sci. 2023; 14:1090163. PMC: 9929074. DOI: 10.3389/fpls.2023.1090163. View

11.

Kang H, Zaitlen N, Wade C, Kirby A, Heckerman D, Daly M . Efficient control of population structure in model organism association mapping. Genetics. 2008; 178(3):1709-23. PMC: 2278096. DOI: 10.1534/genetics.107.080101. View

12.

Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira M, Bender D . PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007; 81(3):559-75. PMC: 1950838. DOI: 10.1086/519795. View

13.

Bi W, Zhou W, Dey R, Mukherjee B, Sampson J, Lee S . Efficient mixed model approach for large-scale genome-wide association studies of ordinal categorical phenotypes. Am J Hum Genet. 2021; 108(5):825-839. PMC: 8206161. DOI: 10.1016/j.ajhg.2021.03.019. View

14.

Wang M, Li R, Xu S . Deshrinking ridge regression for genome-wide association studies. Bioinformatics. 2020; 36(14):4154-4162. DOI: 10.1093/bioinformatics/btaa345. View

15.

Wang X, Philip V, Ananda G, White C, Malhotra A, Michalski P . A Bayesian Framework for Generalized Linear Mixed Modeling Identifies New Candidate Loci for Late-Onset Alzheimer's Disease. Genetics. 2018; 209(1):51-64. PMC: 5937180. DOI: 10.1534/genetics.117.300673. View

16.

Jiang L, Zheng Z, Qi T, Kemper K, Wray N, Visscher P . A resource-efficient tool for mixed model association analysis of large-scale data. Nat Genet. 2019; 51(12):1749-1755. DOI: 10.1038/s41588-019-0530-8. View

17.

Zhang J, Feng J, Ni Y, Wen Y, Niu Y, Tamba C . pLARmEB: integration of least angle regression with empirical Bayes for multilocus genome-wide association studies. Heredity (Edinb). 2017; 118(6):517-524. PMC: 5436030. DOI: 10.1038/hdy.2017.8. View

18.

Chen H, Wang C, Conomos M, Stilp A, Li Z, Sofer T . Control for Population Structure and Relatedness for Binary Traits in Genetic Association Studies via Logistic Mixed Models. Am J Hum Genet. 2016; 98(4):653-66. PMC: 4833218. DOI: 10.1016/j.ajhg.2016.02.012. View

19.

Wen Y, Zhang Y, Zhang J, Feng J, Dunwell J, Zhang Y . An efficient multi-locus mixed model framework for the detection of small and linked QTLs in F2. Brief Bioinform. 2018; 20(5):1913-1924. PMC: 6917223. DOI: 10.1093/bib/bby058. View

20.

Feng J, Zhang J, Zhang W, Wang S, Han S, Zhang Y . An efficient hierarchical generalized linear mixed model for mapping QTL of ordinal traits in crop cultivars. PLoS One. 2013; 8(4):e59541. PMC: 3614919. DOI: 10.1371/journal.pone.0059541. View