Genetic Architecture and Meta-QTL Identification of Yield Traits in Maize (Zea mays L.)

TL;DR

This study identifies key genetic regions and candidate genes influencing maize yield traits, aiding future breeding efforts.

Contribution

The paper introduces 44 meta-QTLs and 159 candidate genes linked to maize yield components, revealing co-linked heredity patterns.

Findings

44 meta-QTLs were identified across 10 chromosomes, with 39 influencing multiple yield traits.

159 candidate genes were found, including 29 related to kernel size and weight via E3 ubiquitin protein ligase.

Traits like ear length and hundred-kernel weight showed co-linked heredity, supported by correlation analysis.

Abstract

Yield components are the most important breeding objectives, directly determining maize high-yield breeding. It is well known that these traits are controlled by a large number of quantitative trait loci (QTL). Therefore, deeply understanding the genetic basis of yield components and identifying key regulatory candidate genes can lay the foundation for maize marker-assisted selection (MAS) breeding. In this study, our aim was to identify the key genomic regions that regulate maize yield component formation through bioinformatic methods. Herein, 554 original QTLs related to 11 yield components, including ear length (EL), hundred-kernel weight (HKW), ear weight (EW), cob weight (CW), ear diameter (ED), cob diameter (CD), kernel row number (KRN), kernel number per row (KNR), kernel length (KL), grain weight per plant (GW), and kernel width (KW) in maize, were collected from the MaizeGDB, national center for biotechnology information (NCBI), and China national knowledge infrastructure (CNKI) databases. The consensus map was then constructed with a total length of 7154.30 cM. Approximately 80.32% of original QTLs were successfully projected on the consensus map, and they were unevenly distributed on the 10 chromosomes (Chr.). Moreover, 44 meta-QTLs (MQTLs) were identified by the meta-analysis. Among them, 39 MQTLs controlled two or more yield components, except for the MQTL4 in Chr. 1, which was associated with HKW; MQTL11 in Chr. 2, which was responsible for EL; MQTL19 in Chr. 3, which was related to KRN; MQTL26 in Chr. 5, which was involved in HKW; and MQTL36 in Chr. 7, which regulated EL. These findings were consistent with the Pearson correlation results, indicating that these traits exhibited co-linked heredity phenomena. Meanwhile, 159 candidate genes were found in all of the above MQTLs intervals, of which, 29 genes encoded E3 ubiquitin protein ligase, which was related with kernel size and weight. Other genes were involved in multiple metabolic processes, including plant hormones signaling transduction, plant growth and development, sucrose–starch synthesis and metabolism, and reproductive growth. Overall, the results will provide reliable genetic resources for high-yield molecular breeding in maize.

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