Genome-wide association study of maize plant architecture using F1 populations

Zhao, Yang; Wang, Hengsheng; Chen, Bo; Dai, Wei; Zhang, Xingen; Cai, Ronghao; Gu, Longjiang; Ma, Qibin; Jiang, Haiyang; Zhu, Jun; Cheng, Beijiu

doi:10.1007/s11103-018-0797-7

Cited by 18 publications

(14 citation statements)

References 54 publications

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“…Importantly, it provides GLAI traits that exhibit similar or higher heritability than their ground based counterparts. The heritability of MA big is comparable with that of maize ear leaf width and length measured manually (Tian et al, 2011; Wang et al, 2017, 2018; Zhao et al, 2019), while stay-green traits, related to δ, are generally associated with lower heritability, whether assessed manually (Yang et al, 2017b), by visual scoring (Messmer et al, 2011; Ziyomo and Bernardo, 2013; Almeida et al, 2014; Trachsel et al, 2016) or proximal sensing (Christopher et al, 2014; Yang et al, 2017b). Similarly, lower heritability is generally reported for traits similar to AUC , such as the NDVI AUC before flowering (Trachsel et al, 2016 for maize) and after flowering (Christopher et al, 2014 for wheat) estimated by proximal sensing.…”

Section: Resultssupporting

confidence: 61%

A High-Throughput Model-Assisted Method for Phenotyping Maize Green Leaf Area Index Dynamics Using Unmanned Aerial Vehicle Imagery

Blancon

Dutartre²,

Tixier

et al. 2019

Front. Plant Sci.

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The dynamics of the Green Leaf Area Index (GLAI) is of great interest for numerous applications such as yield prediction and plant breeding. We present a high-throughput model-assisted method for characterizing GLAI dynamics in maize ( Zea mays subsp. mays ) using multispectral imagery acquired from an Unmanned Aerial Vehicle (UAV). Two trials were conducted with a high diversity panel of 400 lines under well-watered and water-deficient treatments in 2016 and 2017. For each UAV flight, we first derived GLAI estimates from empirical relationships between the multispectral reflectance and ground level measurements of GLAI achieved over a small sample of microplots. We then fitted a simple but physiologically sound GLAI dynamics model over the GLAI values estimated previously. Results show that GLAI dynamics was estimated accurately throughout the cycle (R 2 > 0.9). Two parameters of the model, biggest leaf area and leaf longevity, were also estimated successfully. We showed that GLAI dynamics and the parameters of the fitted model are highly heritable (0.65 ≤ H 2 ≤ 0.98), responsive to environmental conditions, and linked to yield and drought tolerance. This method, combining growth modeling, UAV imagery and simple non-destructive field measurements, provides new high-throughput tools for understanding the adaptation of GLAI dynamics and its interaction with the environment. GLAI dynamics is also a promising trait for crop breeding, and paves the way for future genetic studies.

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Section: Resultssupporting

confidence: 61%

A High-Throughput Model-Assisted Method for Phenotyping Maize Green Leaf Area Index Dynamics Using Unmanned Aerial Vehicle Imagery

Blancon

Dutartre²,

Tixier

et al. 2019

Front. Plant Sci.

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“…Since the advent of high-throughput NGS sequencing technologies and large scale genotyping array platforms, GWAS has played a pivotal role in identifying informative trait-associated genes and genomic loci in many crops [6,18,[51][52][53][54]. On the other side, many programs relevant to GWAS analysis, such as PLINK, EMMAX, GAPIT and MLMM, have been developed towards extracting more accurate genomic information within the context of phenotype-togenotype correlations [37,41,55,56].…”

Section: Discussionmentioning

confidence: 99%

Dissecting seed pigmentation-associated genomic loci and genes by employing dual approaches of reference-based and k-mer-based GWAS with 438 Glycine accessions

et al. 2020

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The soybean is agro-economically the most important among all cultivated legume crops, and its seed color is considered one of the most attractive factors in the selection-by-breeders. Thus, genome-wide identification of genes and loci associated with seed colors is critical for the precision breeding of crop soybeans. To dissect seed pigmentation-associated genomic loci and genes, we employed dual approaches by combining reference-based genome-wide association study (rbGWAS) and k-mer-based reference-free GWAS (rfGWAS) with 438 Glycine accessions. The dual analytical strategy allowed us to identify four major genomic loci (designated as SP1-SP4 in this study) associated with the seed colors of soybeans. The k-mer analysis enabled us to find an important recombination event that occurred between subtilisin and I-cluster B in the soybean genome, which could describe a special structural feature of ii allele within the I locus (SP3). Importantly, mapping analyses of both mRNAs and small RNAs allowed us to reveal that the subtilisin-CHS1/CHS3 chimeric transcripts generate and act as an initiator towards ‘mirtron (i.e., intron-harboring miRNA precursor)’-triggered silencing of chalcone synthase (CHS) genes. Consequently, the results led us to propose a working model of ‘mirtron-triggered gene silencing (MTGS)’ to elucidate a long-standing puzzle in the genome-wide CHS gene silencing mechanism. In summary, our study reports four major genomic loci, lists of key genes and genome-wide variations that are associated with seed pigmentation in soybeans. In addition, we propose that the MTGS mechanism plays a crucial role in the genome-wide silencing of CHS genes, thereby suggesting a clue to currently predominant soybean cultivars with the yellow seed coat. Finally, this study will provide a broad insight into the interactions and correlations among seed color-associated genes and loci within the context of anthocyanin biosynthetic pathways.

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“…At present, the cost of sequencing remains very high, but using the F1 population can reduce the cost of genotyping. As only the parental inbred lines be genotyped, the genotype of the hybrids could be inferred by bi-parental genotype [21]. For example, 100 inbred lines can be used to generate [(100 − 1) × 100]/2 = 4500 hybrids.…”

Section: Discussionmentioning

confidence: 99%

“…However, genes previously identified did not overlap with the candidate genes in this study. A possible reason for this finding is that GWAS is relatively insensitive to detecting low-frequency loci with significant effects, although the low-frequency or rare allelic variants seem to be important for the target trait [21].…”

Section: Discussionmentioning

confidence: 99%

“…SNPs with heterozygous or missing were expurgated; only SNPs markers that were homozygous in 99 inbred lines were reserved. Finally, 29,328 SNPs markers were obtained and used for inferring genotype of the hybrids, which was generated according to the genotype of their parents referenced to Zhao, et al [21]. SNPs with a minor allele frequency (MAF) <0.05 were expurgated, and only biallelic sites were reserved.…”

Section: Methodsmentioning

confidence: 99%

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Genome-Wide Association Analysis of Plant Height Using the Maize F1 Population

Zhang

Wan

Ling

et al. 2019

Plants

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Drastic changes in plant height (PH) are observed when maize adapt to a higher plant density. Most importantly, PH is an important factor affecting maize yield. Although the genetic basis of PH has been extensively studied using different populations during the past decades, genetic basis remains unclear in the F1 population, which was a widely used population in production. In this study, a genome-wide association study (GWAS) was conducted using an F1 population consisting of 300 maize hybrids with 17,652 single nucleotide polymorphisms (SNPs) makers to identify candidate genes for controlling PH. A total of nine significant SNPs makers and two candidate genes were identified for PH. The candidate genes, Zm00001d018617 and Zm00001d023659, were the genes most probable to be involved in the development of PH. Our results provide new insights into the genetic basis of PH in maize.

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Genome-wide association study of maize plant architecture using F1 populations

Cited by 18 publications

References 54 publications

A High-Throughput Model-Assisted Method for Phenotyping Maize Green Leaf Area Index Dynamics Using Unmanned Aerial Vehicle Imagery

A High-Throughput Model-Assisted Method for Phenotyping Maize Green Leaf Area Index Dynamics Using Unmanned Aerial Vehicle Imagery

Dissecting seed pigmentation-associated genomic loci and genes by employing dual approaches of reference-based and k-mer-based GWAS with 438 Glycine accessions

Genome-Wide Association Analysis of Plant Height Using the Maize F1 Population

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