Additional file 4 of Genome-wide association mapping and genomic prediction analyses reveal the genetic architecture of grain yield and agronomic traits under drought and optimum conditions in maize

Supplementary Material 4: Supplementary Table S1. Detailed information about pedigree of 236 lines and groupings based on markers used in the study. Supplementary Table S2. Summary of SNPs distribution across the ten maize chromosomes. Supplementary Table S3. Significant QTNs identified for eight traits across multi-environment trials under optimum conditions using six multi-locus GWAS models. Supplementary Table S4. Significant QTNs identified for eight traits across multi-environment trials under drought conditions using six multi-locus GWAS models. Supplementary Table S5. Significant QTNs associated with eight traits under drought and optimum condition using Farm CPU GWAS model from GAPIT. Supplementary Table S6. Number of stable QTNs detected by at least two GWAS models for grain yield, flowering traits and other agronomic traits under drought and optimum conditions.

补充材料4：补充表S1. 本研究中使用的236个品系的系谱详细信息及基于标记的分组情况。补充表S2. 单核苷酸多态性（SNPs）在玉米10条染色体上的分布总结。补充表S3. 采用6种多位点全基因组关联分析（GWAS）模型，在适宜条件下多环境试验中鉴定出的8个性状相关的显著数量性状核苷酸（QTNs）。补充表S4. 采用6种多位点GWAS模型，在干旱条件下多环境试验中鉴定出的8个性状相关的显著QTNs。补充表S5. 利用GAPIT中的Farm CPU GWAS模型，在干旱和适宜条件下鉴定出的与8个性状相关的显著QTNs。补充表S6. 在干旱和适宜条件下，至少两种GWAS模型检测到的籽粒产量、开花性状及其他农艺性状相关的稳定QTNs数量。