QTL-based dissection of three key quality attributes in maize using double haploid populations
IntroductionMaize is a crucial source of nutrition, and the quality traits such as starch content, oil content, and lysine content are essential for meeting the demands of modern agricultural development. Understanding the genetic basis of these quality traits significantly contributes to improving...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-05-01
|
| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1599530/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850272632977489920 |
|---|---|
| author | Zitian He Jianping Wang Jianping Wang Jialei Li Jianwei Li Lei Chen Xiaolei Zhang Xiaolei Zhang |
| author_facet | Zitian He Jianping Wang Jianping Wang Jialei Li Jianwei Li Lei Chen Xiaolei Zhang Xiaolei Zhang |
| author_sort | Zitian He |
| collection | DOAJ |
| description | IntroductionMaize is a crucial source of nutrition, and the quality traits such as starch content, oil content, and lysine content are essential for meeting the demands of modern agricultural development. Understanding the genetic basis of these quality traits significantly contributes to improving maize yield and optimizing end-use quality. While previous studies have explored the genetic basis of these traits, further investigation into the quantitative trait loci (QTL) responsible for variations in starch content, oil content, and lysine content still requires additional attention.MethodsDouble haploid (DH) populations were developed via a nested association mapping (NAM) design. Phenotypic data for starch, oil, and lysine content were collected using near-infrared spectroscopy and analyzed via ANOVA. Genotyping employed a 3K SNP panel, and genetic maps were constructed using QTL IciMapping. QTL analysis integrated single linkage mapping (SLM) and NAM approaches, with candidate genes identified via maizeGDB annotation and transcriptome data.ResultsThe broad-sense heritability of the populations with a range of 63.98-80.72% indicated the majority of starch content, oil content and lysine content variations were largely controlled by genetic factors. The genetic maps were constructed and a total of 47 QTLs were identified. The phenotypic variation explained (PVE) of the three traits is in a range of 2.60-17.24% which suggested that the genetic component of starch content, oil content and lysine content was controlled by many small effect QTLs. Five genes encoding key enzymes in regulation of starch, oil and lysine synthesis and metabolism located within QTLs were proposed as candidate genes in this study.DiscussionThe information presented herein will establish a foundation for the investigation of candidate genes that regulate quality traits in maize kernels. These QTLs will prove beneficial for marker-assisted selection and gene pyramiding in breeding programs aimed at developing high-quality maize varieties. |
| format | Article |
| id | doaj-art-a9b3840453f44d95bb9f73f54b8bbb06 |
| institution | OA Journals |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-a9b3840453f44d95bb9f73f54b8bbb062025-08-20T01:51:44ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-05-011610.3389/fpls.2025.15995301599530QTL-based dissection of three key quality attributes in maize using double haploid populationsZitian He0Jianping Wang1Jianping Wang2Jialei Li3Jianwei Li4Lei Chen5Xiaolei Zhang6Xiaolei Zhang7Crop Stress Molecular Biology Laboratory, Heilongjiang Bayi Agricultural University, Daqin, Heilongjiang, ChinaQuality and Safety Institute of Agricultural Products, Heilongjiang Academy of Agricultural Sciences, Harbin, Heilongjiang, ChinaKey Laboratory of Quality and Safety of Cereals and Their Products, State Administration for Market Regulation, Harbin, Heilongjiang, ChinaFood Processing Institute, Heilongjiang Academy of Agricultural Sciences, Harbin, Heilongjiang, ChinaCrop Stress Molecular Biology Laboratory, Heilongjiang Bayi Agricultural University, Daqin, Heilongjiang, ChinaHeilongjiang Academy of Agricultural Sciences, Harbin, Heilongjiang, ChinaQuality and Safety Institute of Agricultural Products, Heilongjiang Academy of Agricultural Sciences, Harbin, Heilongjiang, ChinaKey Laboratory of Quality and Safety of Cereals and Their Products, State Administration for Market Regulation, Harbin, Heilongjiang, ChinaIntroductionMaize is a crucial source of nutrition, and the quality traits such as starch content, oil content, and lysine content are essential for meeting the demands of modern agricultural development. Understanding the genetic basis of these quality traits significantly contributes to improving maize yield and optimizing end-use quality. While previous studies have explored the genetic basis of these traits, further investigation into the quantitative trait loci (QTL) responsible for variations in starch content, oil content, and lysine content still requires additional attention.MethodsDouble haploid (DH) populations were developed via a nested association mapping (NAM) design. Phenotypic data for starch, oil, and lysine content were collected using near-infrared spectroscopy and analyzed via ANOVA. Genotyping employed a 3K SNP panel, and genetic maps were constructed using QTL IciMapping. QTL analysis integrated single linkage mapping (SLM) and NAM approaches, with candidate genes identified via maizeGDB annotation and transcriptome data.ResultsThe broad-sense heritability of the populations with a range of 63.98-80.72% indicated the majority of starch content, oil content and lysine content variations were largely controlled by genetic factors. The genetic maps were constructed and a total of 47 QTLs were identified. The phenotypic variation explained (PVE) of the three traits is in a range of 2.60-17.24% which suggested that the genetic component of starch content, oil content and lysine content was controlled by many small effect QTLs. Five genes encoding key enzymes in regulation of starch, oil and lysine synthesis and metabolism located within QTLs were proposed as candidate genes in this study.DiscussionThe information presented herein will establish a foundation for the investigation of candidate genes that regulate quality traits in maize kernels. These QTLs will prove beneficial for marker-assisted selection and gene pyramiding in breeding programs aimed at developing high-quality maize varieties.https://www.frontiersin.org/articles/10.3389/fpls.2025.1599530/fullmaizestarchoillysineQTLsgenetic analysis |
| spellingShingle | Zitian He Jianping Wang Jianping Wang Jialei Li Jianwei Li Lei Chen Xiaolei Zhang Xiaolei Zhang QTL-based dissection of three key quality attributes in maize using double haploid populations Frontiers in Plant Science maize starch oil lysine QTLs genetic analysis |
| title | QTL-based dissection of three key quality attributes in maize using double haploid populations |
| title_full | QTL-based dissection of three key quality attributes in maize using double haploid populations |
| title_fullStr | QTL-based dissection of three key quality attributes in maize using double haploid populations |
| title_full_unstemmed | QTL-based dissection of three key quality attributes in maize using double haploid populations |
| title_short | QTL-based dissection of three key quality attributes in maize using double haploid populations |
| title_sort | qtl based dissection of three key quality attributes in maize using double haploid populations |
| topic | maize starch oil lysine QTLs genetic analysis |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1599530/full |
| work_keys_str_mv | AT zitianhe qtlbaseddissectionofthreekeyqualityattributesinmaizeusingdoublehaploidpopulations AT jianpingwang qtlbaseddissectionofthreekeyqualityattributesinmaizeusingdoublehaploidpopulations AT jianpingwang qtlbaseddissectionofthreekeyqualityattributesinmaizeusingdoublehaploidpopulations AT jialeili qtlbaseddissectionofthreekeyqualityattributesinmaizeusingdoublehaploidpopulations AT jianweili qtlbaseddissectionofthreekeyqualityattributesinmaizeusingdoublehaploidpopulations AT leichen qtlbaseddissectionofthreekeyqualityattributesinmaizeusingdoublehaploidpopulations AT xiaoleizhang qtlbaseddissectionofthreekeyqualityattributesinmaizeusingdoublehaploidpopulations AT xiaoleizhang qtlbaseddissectionofthreekeyqualityattributesinmaizeusingdoublehaploidpopulations |