PSR-Genome-wide Association Studies and QTL Mapping for Traits Deviating from Normal Distribution

Part I. Supplementary Material.zip This package contains the supplementary materials supporting the results and analyses reported in the manuscript, including datasets, figures, codes, and methodological notes. All files in this part are directly related to data analysis, simulation studies, and method validation. Data S1.csv – Phenotype data for the RIL rice population (binary trait). Data S2.csv – Genotype (SNP) data for the RIL rice population. Data S3.csv – Kinship matrix for the RIL rice population. Data S4.csv – Phenotype data for the IMF2 hybrid rice population (quantitative KGW trait). Data S5.csv – Genotype (SNP) data for the IMF2 hybrid rice population. Data S6.csv – Kinship matrix for the IMF2 hybrid rice population. Figure S1.png – Genome-wide association analyses of binary and ordinal traits were performed using GMMAT and POLMM, and the results were compared with those obtained by the GLMM and PSR methods. The Manhattan plots show highly similar patterns across methods, indicating consistent association signals. Figure S2.tif – This figure compares the statistical properties of four methods (PSR, Laplace, BGLIMM, and BGLR) in terms of parameter estimation and test statistics. Although the PSR method shows slight bias in effect estimation, its test statistics remain unbiased and computationally efficient, supporting its use for association detection. Supplementary Code S1.sas – SAS code (PROC IML) for simulating ordinal traits from KGW. Supplementary Code S2.sas – SAS code for Bayesian analyses and simulation studies (PROC BGLIMM). Supplementary Code S3.zip – Complete package containing simulation scripts, GWAS pipelines, and power/type I error analyses. Supplementary Notes.docx – Mathematical and methodological notes supporting the study. Part II. Code, User Manual, and Example Data This part provides the PSR-GLMM/R software resources for general use, including the implementation code, documentation, and example materials. These files are intended to facilitate software usage, reproducibility, and extension to new datasets. Demo code/ – Example scripts demonstrating how to run the PSR-GLMM/R software. Demo Data/ – Example datasets for quick testing and demonstration purposes. Source code/ – Full source code of the PSR-GLMM/R software. User manual/ – Documentation describing data formats, parameters, and usage instructions.