Association mapping across a multitude of traits collected in diverse environments in maize

Classical genetic studies have identified many cases of pleiotropy where mutations in individual genes alter many different phenotypes. Quantitative genetic studies of natural genetic variants frequently examine one or a few traits, limiting their potential to identify pleiotropic effects of natural genetic variants. Widely adopted community association panels have been employed by plant genetics communities to study the genetic basis of naturally occurring phenotypic variation in a wide range of traits. High-density genetic marker data—18M markers—from 2 partially overlapping maize association panels comprising 1,014 unique genotypes grown in field trials across at least 7 US states and scored for 162 distinct trait data sets enabled the identification of of 2,154 suggestive marker-trait associations and 697 confident associations in the maize genome using a resampling-based genome-wide association strategy. The precision of individual marker-trait associations was estimated to be 3 genes based on a reference set of genes with known phenotypes. Examples were observed of both genetic loci associated with variation in diverse traits (e.g., above-ground and below-ground traits), as well as individual loci associated with the same or similar traits across diverse environments. Many significant signals are located near genes whose functions were previously entirely unknown or estimated purely via functional data on homologs. This study demonstrates the potential of mining community association panel data using new higher-density genetic marker sets combined with resampling-based genome-wide association tests to develop testable hypotheses about gene functions, identify potential pleiotropic effects of natural genetic variants, and study genotype-by-environment interaction

Brd1 Gene in Maize Encodes a Brassinosteroid C-6 Oxidase

The role of brassinosteroids in plant growth and development has been well-characterized in a number of plant species. However, very little is known about the role of brassinosteroids in maize. Map-based cloning of a severe dwarf mutant in maize revealed a nonsense mutation in an ortholog of a brassinosteroid C-6 oxidase, termed brd1, the gene encoding the enzyme that catalyzes the final steps of brassinosteroid synthesis. Homozygous brd1–m1 maize plants have essentially no internode elongation and exhibit no etiolation response when germinated in the dark. These phenotypes could be rescued by exogenous application of brassinolide, confirming the molecular defect in the maize brd1-m1 mutant. The brd1-m1 mutant plants also display alterations in leaf and floral morphology. The meristem is not altered in size but there is evidence for differences in the cellular structure of several tissues. The isolation of a maize mutant defective in brassinosteroid synthesis will provide opportunities for the analysis of the role of brassinosteroids in this important crop system

Advances in plant phenomics: From data and algorithms to biological insights

The measurement of the characteristics of living organisms is re- ferred to as phenotyping (Singh et al., 2016). While the use of phe- notyping in plant biology and genetics can be traced back at least to Gregor Mendel sorting and counting peas by shape and pod color 160 years ago, addressing current questions in plant biology, genet- ics, and breeding often requires increasingly precise phenotyping of a wide range of traits. Accurate phenotyping has played a role in both novel discoveries about the fundamental biology of plants and the development of improved crop varieties around the world. With the advent of inexpensive genotyping tools, crop functional genomics has entered the “big data” era, but efficient large-scale phenotyping is still an impediment hindering plant functional genomics. The precise measurement of plant traits both throughout the growth cycle and across environments is expensive and labor intensive. A convergence of interdisciplinary efforts has led to the development of new technologies for nondestructive phenotyping in plants to measure large numbers of traits accurately with higher throughput (Close and Last, 2011). Improvements in imaging and automation, as well as in data processing and analytics, are helping to fill significant gaps in efforts to employ these new technologies to connect genetic variation with phenotypes (Yang et al., 2020). In recent years, plant phenomics research has transitioned from the development of methods and molecular genetic analysis of model plants in controlled environments toward accelerated efforts for applications in plant breeding, association studies, and stress phenotyping in crops grown under complex field conditions (Costaet al., 2018). In this special issue, “Advances in Plant Phenomics: From Data and Algorithms to Biological Insights,” we present six papers that capture plant phenomics extending to multiple scales, from field-wide traits, to individual plots or plants, to specific gene interactions. In the context of field-scale image acquisition and processing, one of the first challenges that must be addressed in drone-based imaging of agricultural fields is turning free-flown images acquired over an area into a single mosaic image from which phenotypes can be extracted. Current methods rely mostly on the ability to locate each pixel in space, requiring costly global positioning systems (GPS) and/or inertial measurement units (IMU) to track the posi- tion of ground control points relative to the image acquisition de- vice. These approaches are computationally taxing, demand larger data storage, and require the purchase of software licenses, lead- ing to a high barrier of entry. Aktar et al. (2020) have developed a method called Video Mosaicking and summariZation (VMZ) to provide an alternative pipeline that is faster, less computationally de- manding, and much cheaper to implement. The authors show that compared to other methods, VMZ not only works faster but also produces mosaics with superior quality. This work, demonstrated here in maize, begins to democratize drone-based phenotyping for large- and small-scale field researchers across multiple species

Advances in plant phenomics: From data and algorithms to biological insights

The measurement of the characteristics of living organisms is referred to as phenotyping (Singh et al., 2016). While the use of phenotyping in plant biology and genetics can be traced back at least to Gregor Mendel sorting and counting peas by shape and pod color 160 years ago, addressing current questions in plant biology, genetics, and breeding often requires increasingly precise phenotyping of a wide range of traits. Accurate phenotyping has played a role in both novel discoveries about the fundamental biology of plants and the development of improved crop varieties around the world. With the advent of inexpensive genotyping tools, crop functional genomics has entered the “big data” era, but efficient large-scale phenotyping is still an impediment hindering plant functional genomics. The precise measurement of plant traits both throughout the growth cycle and across environments is expensive and labor intensive. A convergence of interdisciplinary efforts has led to the development of new technologies for nondestructive phenotyping in plants to measure large numbers of traits accurately with higher throughput (Close and Last, 2011). Improvements in imaging and automation, as well as in data processing and analytics, are helping to fill significant gaps in efforts to employ these new technologies to connect genetic variation with phenotypes (Yang et al., 2020). In recent years, plant phenomics research has transitioned from the development of methods and molecular genetic analysis of model plants in controlled environments toward accelerated efforts for applications in plant breeding, association studies, and stress phenotyping in crops grown under complex field conditions (Costa et al., 2018). In this special issue, “Advances in Plant Phenomics: From Data and Algorithms to Biological Insights,” we present six papers that capture plant phenomics extending to multiple scales, from field-wide traits, to individual plots or plants, to specific gene interactions

Supplementing the growth media with 10⁻⁶ M brassinolide (BL) partially rescues the <i>brd1 –m1</i> phenotype in maize.

<p>Plants were germinated and grown in the darkness on MS medium with or without 10⁻⁶ M brassinolide. At 12 days after planting the seedlings were genotyped and wild type (+/+) and mutant (−/−) plants were identified. <b>A </b><i>brd1-m1</i> plants grown on media supplemented with brassinolide show epicotyl elongation, while mutant plants grown without brassinolide fail to show etiolation response. Arrows indicate the positions of the internodes. <b>B</b> Epicotyl length was measured in 12 day old seedlings germinated and grown in the darkness. The results are presented as mean values +/− standard deviation from four to eight plants. All of the groups exhibit statistically significant differences at p<0.01 or less (t-test). Number of plants in each category is shown above each of the columns. <b>C</b> qRT-PCR analysis of the <i>brd1</i> expression level. The expression level of <i>brd1</i> was normalized to the expression of the house-keeping <i>mez2</i> gene and shown relative to the expression of the wild type plants grown without brassinolide. No RT controls were all negative (data not shown). The difference between the <i>brd1</i> expression in mutant plants grown without brassinolide supplement and all other growth conditions is statistically significant at p<0.001 (t-test). The data are presented as mean values +/− standard deviation from three samples for each growth condition.</p

Molecular markers used to map <i>brd1</i> gene in maize.

<p>Molecular markers used to map <i>brd1</i> gene in maize.</p

Mapping the maize <i>brd1-m1</i> mutant.

<p><b>A</b> Comparison of a gross morphology of a severe dwarf mutant <i>brd1-m1</i> (on the right) and a wild type maize plant (on the left). <b>B</b> Physical location of the mapped gene relative to the molecular markers used for mapping. M1 through M8 are markers tightly linked to the gene (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030798#pone-0030798-t001" target="_blank">Table 1</a>). Approximate genetic map distances between the outside markers and the mutation are shown. The star designates the location of the mutation in <i>brd1-m1</i> mapped between markers M4 and M5. Each line in the lower panel designates a particular recombinant event, where Mo17 alleles are shown in blue and B73 alleles are shown in red. <b>C</b> Predicted genes in the region between markers M4 and M5. The star indicates the location of the only mutation found in the coding regions of these predicted genes. <b>D</b> The predicted protein sequence encoded by maize <i>brd1</i> (GRMZM2G103773) gene. A nonsense mutation found in <i>brd1-m1</i> leads to the synthesis of a truncated protein of only 165 amino acids long. A cyp450 domain is shown in gray.</p

Expression pattern of maize <i>brd1</i> gene in various plant tissues.

<p><b>A</b> Relative expression levels of maize <i>brd1</i> based on qRT-PCR analysis. cDNA was synthesized from total RNA isolated from 14 days after pollination embryo (Emb) and endosperm (endo), immature ear (Imm ear), leaf (14 day old seedling), shoot apical meristem-enriched tissue (SAM), and root (14 day old seedling) tissue. The expression level of <i>brd1</i> was normalized to the expression of the house-keeping <i>mez2</i> gene and shown relative to the expression of the wild type plants grown without brassinolide. Bars represent standard deviation values. <b>B</b> Expression levels of <i>Zmbrd1</i> in various plant tissues based on Maize Expression Atlas <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030798#pone.0030798-Sekhon1" target="_blank">[28]</a>.</p

Maize <i>brd1</i> gene encodes a cytochrome P450 protein.

<p><b>A</b> Alignment of a portion of maize BRD1 with brC-6 oxidases from other plants. <b>B</b> Phylogenetic relationships between a maize BRD1protein and other P450 proteins in maize and other plants. An unrooted tree was constructed using the neighbor-joining method. Accession numbers are as follows: rice brd1 (AB084385), OsDwarf4 (Q5CCK3), At Dwarf (AB035868), AtBRC-6 ox2 (NP_566852), Tomato Dwarf (U54770), At CPD (X87367), At Rot3 (AB008097), At Dwarf4 (AF044216), ZmDWF1 (AAS90832), ZmDwarf3 (U32579), barley dwarf3 (AF326277), At KAO1 (AF318500), At KAO2 (AF318501), ZmDWF4 (GRMZM2G065635), CPD Vigna (AF279252).</p