Qian Fu, Jing Jianguo, Zhang Zhanqin, Chen Shubin, Sang Zhiqin, Li Weihua
Xinjiang Academy of Agricultural and Reclamation Science, Shihezi 832000, China.
The Key Laboratory of Oasis Eco-Agriculture, College of Agriculture, Shihezi University, Shihezi 832003, China.
Plants (Basel). 2023 Nov 8;12(22):3806. doi: 10.3390/plants12223806.
Maize ear traits are an important component of yield, and the genetic basis of ear traits facilitates further yield improvement. In this study, a panel of 580 maize inbred lines were used as the study material, eight ear-related traits were measured through three years of planting, and whole genome sequencing was performed using the maize 40 K breeding chip based on genotyping by targeted sequencing (GBTS) technology. Five models were used to conduct a genome-wide association study (GWAS) on best linear unbiased estimate (BLUE) of ear traits to find the best model. The FarmCPU (Fixed and random model Circulating Probability Unification) model was the best model for this study; a total of 104 significant single nucleotide polymorphisms (SNPs) were detected, and 10 co-location SNPs were detected simultaneously in more than two environments. Through gene function annotation and prediction, a total of nine genes were identified as potentially associated with ear traits. Moreover, a total of 760 quantitative trait loci (QTL) associated with yield-related traits reported in 37 different articles were collected. Using the collected 760 QTL for meta-QTL analysis, a total of 41 MQTL (meta-QTL) associated with yield-related traits were identified, and 19 MQTL detected yield-related ear trait functional genes and candidate genes that have been reported in maize. Five significant SNPs detected by GWAS were located within these MQTL intervals, and another three significant SNPs were close to MQTL (less than 1 Mb). The results provide a theoretical reference for the analysis of the genetic basis of ear-related traits and the improvement of maize yield.
玉米穗部性状是产量的重要组成部分，穗部性状的遗传基础有助于进一步提高产量。本研究以580份玉米自交系为研究材料，通过三年种植测定了8个与穗部相关的性状，并基于靶向测序基因分型（GBTS）技术，利用玉米40K育种芯片进行了全基因组测序。采用5种模型对穗部性状的最佳线性无偏估计值（BLUE）进行全基因组关联研究（GWAS），以寻找最佳模型。FarmCPU（固定和随机模型循环概率统一）模型是本研究的最佳模型；共检测到104个显著单核苷酸多态性（SNP），其中10个共定位SNP在两个以上环境中同时被检测到。通过基因功能注释和预测，共鉴定出9个可能与穗部性状相关的基因。此外，收集了37篇不同文章中报道的760个与产量相关性状的数量性状位点（QTL）。利用收集到的760个QTL进行元QTL分析，共鉴定出41个与产量相关性状的MQTL（元QTL），其中19个MQTL检测到了玉米中已报道的与产量相关的穗部性状功能基因和候选基因。GWAS检测到的5个显著SNP位于这些MQTL区间内，另外3个显著SNP靠近MQTL（小于1 Mb）。研究结果为分析穗部相关性状的遗传基础和提高玉米产量提供了理论参考。
