WO2024110412A1 - Identification of root traits associated with plant performance - Google Patents

Abstract

The present invention relates to the field of identifying maize plants with an improved root phenotype. Further, there is provided a specific set of markers diagnostic for a chromosomal region of about or less than 0.65 Mb on chromosome 1 of Zea mays, said region defining a quantitative trait locus (QTL) for an improved root phenotype. Methods and means for detecting the desired phenotype are also provided. Finally, uses of the QTL- bearing chromosomal region for the manufacture and/or identification of a plant of the genus Zea having an improved root phenotype are provided.

Description

Identification of root traits associated with plant performance

Technical Field

The present invention relates to the field of identifying maize plants with an improved root phenotype. Further, there is provided a specific set of markers diagnostic for a chromosomal region of about or less than 0.65 Mb on chromosome 1 of Zea mays, said region defining a quantitative trait locus (QTL) for an improved root phenotype. Methods and means for detecting the desired phenotype are also provided. Finally, uses of the QTL- bearing chromosomal region for the manufacture and/or identification of a plant of the genus Zea having an improved root phenotype are provided.

Background Soil resource acquisition is a primary limitation to crop production. Availability and uptake rates of water and/or nutrient resources are important factors determining plant performance, including growth, yield, and resistance to biotic and abiotic stress. It is a plant’s root systems and root network that predominantly determines the ability to use given resources efficiently and effectively in the specific growth scenario. For example, there is a tight relationship between plant root architecture and/or root surface area and multiple resource acquisition, specifically water and mineral nutrients from soil, including phosphorus and nitrogen, in spatially heterogeneous environments. Furthermore, root morphology and an initial establishment of a vigorous nodal root system during early root development, for example in Zea mays (maize or corn) as relevant crop plant, are of utmost importance for the emergence and ultimately for the later yield that can be achieved. Particularly maize is known to form a complex root system with structurally and functionally diverse root types formed at different developmental stages and needed to extract water and nutrients from soil and to guarantee anchoring, growth and ultimately yield.

Still, root traits influencing root characteristics and biomass in a positive way for plant breeding are difficult to address, as either complex monitoring systems in controlled conditions must be in place, or extensive and very laborious field trials need to be carried out.

Phenotypic analysis supported by molecular tools, like transcriptome analysis, and proteomics, are used to analyze root traits (see, for example, Comparative Proteomic Analysis Provides New Insights Into Low Nitrogen-Promoted Primary Root Growth in Hexapioid Wheat, Frontiers in Plant Science, vol. 10, 2019;

DOI=10.3389/fpls.2019.00151 ).

More recently, in silico assisted techniques and marker-assisted breeding techniques are used to predict and study root traits, for example to study genetic variation in quantitative root yield and root related traits as determined by ShovelOMICs (Trachsel et. al., 2011) and digital root phenotype analysis using the Software suites GiaRoot (Galkovsky et al., 2012) & Rest (Colombi etal., 2015).

Still, in view of the complexity of crop plant genomes, a successful identification of novel root traits, e.g., by identifying a causative gene or a quantitative trait locus (QTL), and the characterization of the phenotypic effect is cumbersome. Moreover, even the availability of a locus or a as predictions and extrapolations starting from known candidate genes and loci usually fail in the context of a complex plant genome in a typical breeding set-up.

WO 2015/081075 discloses an approximately 10 Mb segment on chromosome 1 that is associated with salt and drought tolerance. While the effect of this large genomic region could at least partially be attributed to a gene encoding the Salt Overly Sensitive 1 (SOS7) Na⁺ Z H⁺ antiporter, this antiporter is described to affect salt tolerance through its effect on the ion homeostasis and osmoregulation (https://doi.Org/10.1016/j.jplph.2011.10.007; https://doi.org/10.1104/pp.19.00324). Therefore, this chromosomal region was characterized and studied in the context of root ion flow and ion homeostasis, but not for root architecture.

It was thus an aim of the present invention to identify and localize candidate genes and/or at least one QTL associated with a significantly optimized root trait characteristics phenotype, in particular, in a Zea mays plant as basis for breeding strategies. Further, it was an object to fine map a root phenotype associated region to a tight interval to avoid undesired linkage drag effects to optimize precision breeding.

Summary of the invention

In a first aspect, there is provided a method of detecting a plant of the genus Zea having an improved root phenotype, the method comprising: a) providing a plant or a plant population of the genus Zea comprising genomic DNA; b) providing a set of markers diagnostic for a chromosomal region of about or less than 0.65 Mb on chromosome 1 within a window flanked by position 180693189 and position 181340039, according to the Zea AGPvO2 reference annotation, said region defining a quantitative trait locus (QTL) for an improved root phenotype; c) detecting the presence of at least one allele, preferably at least two, at least three, at least four, at least five, or more alleles associated with an improved root phenotype and located in a chromosomal region of about or less than 0.65 Mb within a window on chromosome 1 flanked by position 180693189 and position 181340039, according to said Zea AGPvO2 reference annotation, wherein the at least one allele(s) is/are as defined in Table 2; and d) optionally: using the detection of the presence of the at least one allele in step c) to select a plant having an improved root phenotype.

In one embodiment of the first aspect, for detection in step c) at least one allele, preferably at least two, at least three, at least four, at least five or more alleles is/are selected from any one of number 10 to number 27, preferably number 18 to number 27, more preferably number 18 to number 23, as defined in Table 2.

In one embodiment of the first aspect, in step b) there is provided a set of markers selected from the group consisting of SEQ ID NO: 6 to SEQ ID NO: 27 or a sequence having at least 95%, 96%, 97%, 98% or at least 99% sequence identity thereto; and wherein in step c) the presence of at least one allele, preferably at least two, at least three, at least four, at least five, or more alleles associated with an improved root phenotype, using the markers of b), is/are detected, wherein the at least one allele(s) is/are defined as in Table 2. In another embodiment of the first aspect, at least one marker, preferably at least two, at least three, at least four, at least five or more markers is/are selected from any one of SEQ ID NO: 10 to SEQ ID NO: 27 or a sequence having at least 95%, 96%, 97%, 98% or at least 99% sequence identity thereto, preferably selected from any one of SEQ ID NO: 18 to SEQ ID NO: 27 or a sequence having at least 95%, 96%, 97%, 98% or at least 99% sequence identity thereto, more preferably selected from any one of SEQ ID NO: 18 to SEQ ID NO: 23 or a sequence having at least 95%, 96%, 97%, 98% or at least 99% sequence identity thereto, said markers being defined as above.

In another embodiment of the first aspect, the method comprises the detection of the allele number 23 as defined in Table 2 and/or comprises the detection with the marker of SEQ ID NO: 23 or a sequence having at least 95%, 96%, 97%, 98% or at least 99% sequence identity thereto; or wherein the method, in step c), consists of the detection of only the allele number 23 as defined in Table 2 and/or wherein step b), consists of the provision of only the marker of SEQ ID NO: 23 or a sequence having at least 95%, 96%, 97%, 98% or at least 99% sequence identity thereto, said marker being defined as above.

In another embodiment of the first aspect, the QTL comprises a nucleotide sequence encoding at least one SALT-OVERLY-SENSITIVE1 (SOS1) Na⁺/H⁺ antiporter, having a mutant and/or variant sequence conferring an improved root phenotype, preferably wherein the reference nucleotide sequence comprises the sequence of SEQ ID NO: 46 or SEQ ID NO: 49, or a sequence having at least 90%, 91 %, 92%, 93%, 94%, 95%, 96%, 97%, 98% or at least 99% sequence identity thereto, or wherein the reference nucleotide sequence encodes an RNA comprising a sequence corresponding to the cDNA sequence of SEQ ID NO: 47 or SEQ ID NO: 50, or a sequence having at least 90%, 91 %, 92%, 93%, 94%, 95%, 96%, 97%, 98% or at least 99% sequence identity thereto, or wherein the reference nucleotide sequence encodes a protein comprising the amino acid sequence of SEQ ID NO: 48 or SEQ ID NO: 51 , or a sequence having at least 90%, 91 %, 92%, 93%, 94%, 95%, 96%, 97%, 98% or at least 99% sequence identity thereto.

In another embodiment of the first aspect, the nucleic acid sequence encoding the at least one SOS1 Na⁺/H⁺ antiporter is selected from SEQ ID NO: 55, or a sequence having at least 90%, 91 %, 92%, 93%, 94%, 95%, 96%, 97%, 98% or at least 99% sequence identity thereto.

In another embodiment of the first aspect, the improved root phenotype is independently selected from average root width (diameter), network bushiness, network depth, ellipse axes ratio, network length distribution, major ellipse axis, maximum number of roots, network width, median number of roots, minor ellipse axis, network area, network convex area, network perimeter, network solidity, specific root length, network surface area, network length, network volume, network width to depth ratio, stem diameter, plant height, or any combination thereof, preferably wherein the improved root phenotype is independently selected from network bushiness, network depth, maximum number of roots, median number of roots, network area, network perimeter, network solidity, network surface area, network length, network volume, root dry weight, stem diameter, plant height, or any combination thereof, more preferably wherein the improved root phenotype is independently selected from maximum number of roots, network area, network perimeter, network surface area, network length, network volume, root dry weight, stem diameter, plant height, or any combination thereof.

In another embodiment of the first aspect, the plant of the genus Zea is a Zea mays plant.

In a second aspect, there is provided use of at least one marker, preferably at least two, at least three, at least four, or at least five, or more markers selected from SEQ ID NO: 6 to SEQ ID NO: 27, for the manufacture and/or identification of a plant of the genus Zea, including a Zea mays plant, with an improved root phenotype, said markers being defined as in Table 3.

In one embodiment of the second aspect, the use comprises the use of at least one marker, preferably at least two, at least three, at least four, or at least five, or more markers selected from SEQ ID NO: 10 to SEQ ID NO: 27, said markers being defined as above.

In one embodiment of the second aspect, the use comprises the use of at least one marker, preferably at least two, at least three, or more markers selected from SEQ ID NO: 18 to SEQ ID NO: 27, said markers being defined as above, and/or comprising the use of at least one marker, preferably at least two, at least three, or more markers selected from SEQ ID NO: 18 to SEQ ID NO: 23, said markers being defined as above.

In another embodiment of the second aspect, the use consists of the use of the marker of SEQ ID NO: 23, said marker being defined as above.

In a third aspect, there is provided a use of a chromosomal region as defined above or a use of a mutant SOS1 Na⁺/H⁺ antiporter gene for the manufacture and/or identification of a plant of the genus Zea, including a Zea mays plant, having an improved root phenotype. In one embodiment of the second or third aspect, the improved root phenotype is independently selected from average root width (diameter), network bushiness, network depth, ellipse axes ratio, network length distribution, major ellipse axis, maximum number of roots, network width, median number of roots, minor ellipse axis, network area, network convex area, network perimeter, network solidity, specific root length, network surface area, network length, network volume, network width to depth ratio, stem diameter, plant height, or any combination thereof, preferably wherein the improved root phenotype is independently selected from network bushiness, network depth, maximum number of roots, median number of roots, network area, network perimeter, network solidity, network surface area, network length, network volume, root dry weight, plant height, or any combination thereof, more preferably wherein the improved root phenotype is independently selected from maximum number of roots, network area, network perimeter, network surface area, network length, network volume, root dry weight, stem diameter, plant height, or any combination thereof.

Definitions

Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

The term “about” means +/- 10% of the recited value, preferably +/- 5% of the recited value.

The term “AGPvO2 reference annotation” refers to the physical map from maize as described in Ganal et al., 201 1). Unless specifically stated otherwise, numbers specifying one or more positions on a chromosome are always given in base pairs (bp) and always refer the chromosome 1 of the AGPvO2 reference annotation/genome. Table 2 specifies, for each allele therein the position according to the AGPvO2 reference annotation and the corresponding position according to the AGPvO4 reference annotation (Yang et al. 2021). Distances spanning from a first position to a second position, i.e. the flanking positions, are given in the unit megabase(s) (Mb), wherein windows and/or regions specified herein always include the flanking positions.

The term ’’improved root phenotype” refers to an increase, preferably an increase of at least 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%, 25%, 30%, 35%, or at least 40 % , in any of the quantitative root traits specified in Table 1 , as defined below for each of the root phenotypes (cf. also Trachsel et. al., 2011 , Galkovsky etal., 2012 and Colombi etal., 2015), or - if specified - in the specified on or more quantitative root traits, wherein the improved root phenotype refers to an increase in any of said traits as conferred or conferrable by one or more alleles associated with an improved root phenotype in comparison to a plant not comprising said one or more allele(s), for instance instead comprising the corresponding one or more “A” alleles as specified in Table 2 at the same position(s), in an otherwise, at least outside of the specified QTL, identical or nearly identical genetic background, i.e. the genotype outside of the specified QTL being identical or nearly identical, if grown under the same conditions within reasonable limits. An improved root phenotype encompasses situations in which one or more alleles associated with an improved root phenotype as defined in Table 2 are present or introduced, e.g. through crossing, by mutagenesis, by targeted genome editing or as a transgene, wherein the improved root phenotype conferred by said one or more al lele(s) may not be directly phenotypically visible due to alleles outside of the defined QTL negatively affecting one the root phenotype that may also have been introduced, and the improved root phenotype may only become visible, for instance, though backcrossing to remove such adverse alleles outside of the defined QTL.

General plant performance as used herein includes but is not limited to, increasing or decreasing the plant's root characteristics and plant growth.

The terms “root trait(s)”, “root characteristic(s)”, “root architecture” and “root phenotype” are used interchangeably herein, and the term “root yield” refers to those root phenotypes that reflect the overall mass and/or weight and/or size of the roots. The root traits are defined and/or determined as follows, wherein root phenotypes are nowadays usually determined /quantified by image analysis or any other digital means. Initially, root phenotypes can be measured directly by physical means, by mechanistic and/or physiological measurement. ShovelOMICs can be used for determining root traits in a combination of physical and digital means, wherein the final step usual is a scoring step of a given root-related parameter to provide for standardized procedures to determine root traits. ShovelOMICs thus allows for standardization by providing a software-assisted digital scoring and documentation tool. Still, ShovelOMICs still needs physical plant root provision and preparation steps.

Average root width (diameter) (cm) - the mean value of the root width estimation computed for all pixels of the medial axis of the entire root system. This trait corresponds to the diameter of a root.

Ellipse axes ratio (cm/cm) - the ratio of the minor to the major axis of best fitting ellipse. Major ellipse axis (cm) - the length of the major axis of the best fitting ellipse to the network.

Maximum number of roots (n) - after sorting the number of roots crossing a horizontal line from smallest to largest, the maximum number is considered to be the 84th-percentile value (one standard deviation).

Median number of roots (n) - the result of a vertical line sweep in which the number of roots that crossed a horizontal line was estimated, and then the median of all values forthe extent of the network was calculated.

Minor ellipse axis (cm) - the length of the minor axis of the best fitting ellipse to the network.

Network area (cm2) - the number of network pixels in the image.

Network bushiness (n/n) -the ratio of the maximum to the median number of roots.

Network convex area (cm2) the area of the convex hull that encompasses the root.

Network depth (cm) - the number of pixels in the vertical direction from the upper-most network pixel to the lower-most network pixel.

Network length (cm) - the total number of pixels in the network skeleton.

Network length distribution (n/n) - the fraction of network pixels found in the lower 2/3 of the network. The lower 2/3 of the network is defined based on the network depth.

Network perimeter (cm) the total number of network pixels connected to a background pixel (using an 8-nearest neighbor neighborhood).

Network solidity (cm2/cm2) - the total network area divided by the network convex area.

Network surface area (cm2) - the sum of the local surface area at each pixel of the network skeleton, as approximated by a tubular shape whose radius is estimated from the image.

Network volume (cm3) - the sum of the local volume at each pixel of the network skeleton, as approximated by a tubular shape whose radius is estimated from the image. Network width (cm) - the number of pixels in the horizontal direction from the left-most network pixel to the right-most network pixel. Only pixels lying in the same row are considered.

Network width to depth ratio (cm/cm) - the value of network width divided by the value of network depth.

Root dry weight (kg ) - measured 5-6 d after harvesting (stalks were cut at ca. 25 cm from the soil, then clumps were uprooted by handpulling, washed, and weighed).

Specific root length (cm/cm3) - total network length divided by network volume. Volume is estimated as the sum of cross sectional areas for all pixels of the medial axis of the root system. The total root length is the number of pixels in the medial axis of the root system.

Stem diameter (cm) - the mean value of the stem thickness, wherein both stalk diameters are measured in the middle of the second elongated internode and the arithmetic average of both values is formed.

Plant height (cm) - the height of a freestanding plant, measured after pollen shedding from the soil level to the tassel tip.

An allele “associated with an improved root phenotype” confers or contributes, compared to at least one different allele, such as alleles “A” in Table 2, an increase and/or improvement of at least one root phenotype according to Table 1 .

The term “marker” as used herein refers to nucleic acid molecules, such as an allelespecific PCR primer or a detection probe, capable of hybridizing to a genomic location of interest, such as a polymorphic site, including a single nucleotide polymorphism, allowing the detection of an allele at or near or immediately next to the site of hybridization. Alleles of polymorphic sites of a population can be detected by various methods well known in the art. These include, e.g., PCR-based and/or amplification-based methods, such as competitive allele specific PCR (KASP; Semagn et al 2013) , restriction fragment length polymorphisms (RFLP I PCR-RFLP) detection (Hashim and Al-Shuhaib 2019, single-strand conformation polymorphism (SSCP I PCR-SSCP) detection (Hashim and Al-Shuhaib 2019), allele specific hybridization (ASH; Poulsen er al 2011), or allele-specific oligonucleotide (ASO) hybridization, detection of simple sequence repeats (SSRs; Qu and Liu 2013), or conventional sequencing methods, such as sanger sequencing or next generation sequencing, including high-throughput sequencing, and the likes. Accordingly, a “marker” as used herein may refer to any primer, such as a PCR primer, that may, be used (typically as part of a primer pair) for amplification of a polymorphic region of interest, followed by a detection method, to determine one or more alleles at one or more positions of interest. In certain embodiments, a marker is an allele-specific primer. The skilled person is well aware of the design and use of various different types of nucleic acid molecules for the detection of alleles and/or genotyping of nucleic acid sequences.

The term “sequence identify” as used herein refers to a comparison over the entire length of the respective nucleic acid or amino acid sequence to be compared to another, the sequence of interest or subject representing the reference sequence (e.g., in the form of a SEQ ID NO as disclosed herein) wherein these identity or homology values define those as obtained by using the EMBOSS Water Pairwise Sequence Alignments (nucleotide) programme (http://www.ebi.ac.uk/Tools/psa/emboss_water/nucleotide.html) nucleic acids or the EMBOSS Water Pairwise Sequence Alignments (protein) programme (http://www.ebi.ac.uk/Tools/psa/emboss_water/) for amino acid sequences. Those tools provided by the European Molecular Biology Laboratory (EMBL) European Bioinformatics Institute (EBI) for local sequence alignments use a modified Smith-Waterman algorithm (see http://www.ebi.ac.uk/Tools/psa/ and Smith, T.F. & Waterman, M.S. “Identification of common molecular subsequences” Journal of Molecular Biology, 1981 147 (1):195-197). When conducting an alignment, the default parameters defined by the EMBL-EBI are used. Those parameters are (i) for amino acid sequences: Matrix = BLOSUM62, gap open penalty = 10 and gap extend penalty = 0.5 or (ii) for nucleic acid sequences: Matrix = DNAfull, gap open penalty = 10 and gap extend penalty = 0.5.

The term "allele" as used herein refers to a nucleic acid sequence variant at a specific location, such as an allele of a single nucleotide polymorphism. Generally, an allele can be understood as any one of two or more genes and/or loci that may occur alternatively at a given site on a chromosome. Alleles may occur in pairs, or there may be multiple alleles affecting the expression (phenotype) of a particular trait.

The term “allelic variation” as used herein describes the presence or number of different allele forms at a particular locus on a chromosome.

The term “genetic variation” as used herein describes the presence and/or number of differences in sequences of genes between individual organisms of a species. The term "haplotype" refers to the genotype of an individual at a plurality of loci, i.e. a combination of more than one alleles defined in Table 2.

The terms "plant" as used herein refer to a plant organism, a plant organ, differentiated and undifferentiated plant tissues, plant cells, seeds, and derivatives and progenies thereof. Plant cells include without limitation, for example, cells from seeds, from mature and immature embryos, meristematic tissues, seedlings, callus tissues in different differentiation 5 states, leaves, flowers, roots, shoots, male or female gametophytes, sporophytes, pollen, pollen tubes and microspores, protoplasts, macroalgae and microalgae. Different plant cells can have any degree of ploidity, i.e. they may be either haploid, diploid, tetrapioid, hexapioid or polyploid.

The term "quantitative trait locus" or "QTL" refers to a region of genomic DNA that is associated with and/or influences a quantitative phenotypic trait, such as the root phenotypes listed in Table 2 and defined herein, in at least one genetic background, e.g., in at least one breeding population. A plant or plant line specified as QTL(-Z-) herein comprises, in a homozygous manner, all alleles “A” as defined in Table 2. A plant or plant line specified as QTL(+/+) herein comprises, in a homozygous manner, all alleles “B” as defined in Table 2.

Brief Description of Figures

Figure 1 (Fig. 1) shows an overview of chromosome 1 and the position of the identified QTL therein. Horizontal oriented numbers indicate physical positions on Chromosome 1 in Mb. Bottom row represents the complete chromosome 1 . Middle row shows a magnification spanning approximately 5 Mb as indicated by the diagonal lines between rows. Top row shows a magnification of the middle row as indicated by the diagonal lines between rows. Vertical oriented numbers indicate SEQ ID NOs of markers as well as the matching allele numbers according to Table 2. The box directly below the asterisk represents the SOS1 gene, boxes to the left and right represent positions of further genes on the (+) strand (black boxes) and on the (-) strand (white boxes).

Figure 2 (Fig. 2) Effect of the QTL according to the current disclosure in root stocks collected during summer 2018 field trials and treated with ShovelOMICS. A and B: example of QTL(+/+) and QTL(-/-) lines. C and D: root stocks of nearly isogenic QTL(+/+) and QTL(-/-) parental lines. Figure 3A (Fig. 3A) shows Kruskal-Wallis rank sum test results representative for traits a, e and o according to Table 1. Y-axis shows the p-value plotted as negative logarithm of base 10 and X-axis denotes the alleles according to Table 2.

Figure 3B (Fig. 3B) shows Kruskal-Wallis rank sum test results representative for traits b, c, h, I, j, n and s according to Table 1. Y-axis shows the p-value plotted as negative logarithm of base 10 and X-axis denotes the alleles according to Table 2.

Figure 3C (Fig. 3C) shows Kruskal-Wallis rank sum test results representative for trait d according to Table 1. Y-axis shows the p-value plotted as negative logarithm of base 10 and X-axis denotes the alleles according to Table 2.

Figure 3D (Fig. 3D) shows Kruskal-Wallis rank sum test results representative for traits f, g, k, I, m, p, q, r, and v according to Table 1 . Y-axis shows the p-value plotted as negative logarithm of base 10 and X-axis denotes the alleles according to Table 2.

Figure 3E (Fig. 3E) shows Kruskal-Wallis rank sum test results representative for trait t according to Table 1. Y-axis shows the p-value plotted as negative logarithm of base 10 and X-axis denotes the alleles according to Table 2.

Figure 3F (Fig 3F) shows Kruskal-Wallis rank sum test results representative for trait u according to Table 1. Y-axis shows the p-value plotted as negative logarithm of base 10 and X-axis denotes the alleles according to Table 2.

Figure 4A (Fig. 4A) shows the allele I trait correlation for trait a according to Table 1 . Y- axis shows the allele I trait correlation in % change to mean and X-axis denotes the alleles according to Table 2.

Figure 4B (Fig. 4B) shows the allele I trait correlation for trait b according to Table 1 . Y- axis shows the allele I trait correlation in % change to mean and X-axis denotes the alleles according to Table 2.

Figure 4C (Fig. 4C) shows the allele I trait correlation for trait c according to Table 1 . Y- axis shows the allele I trait correlation in % change to mean and X-axis denotes the alleles according to Table 2. Figure 4D (Fig. 4D) shows the allele / trait correlation for trait d according to Table 1 . Y- axis shows the allele I trait correlation in % change to mean and X-axis denotes the alleles according to Table 2.

Figure 4E (Fig. 4E) shows the allele / trait correlation for trait e according to Table 1 . Y- axis shows the allele I trait correlation in % change to mean and X-axis denotes the alleles according to Table 2.

Figure 4F (Fig. 4F) shows the allele / trait correlation for trait f according to Table 1 . Y-axis shows the allele I trait correlation in % change to mean and X-axis denotes the alleles according to Table 2.

Figure 4G (Fig. 4G) shows the allele I trait correlation for trait g according to Table 1 . Y- axis shows the allele I trait correlation in % change to mean and X-axis denotes the alleles according to Table 2.

Figure 4H (Fig. 4H) shows the allele I trait correlation for trait h according to Table 1 . Y- axis shows the allele I trait correlation in % change to mean and X-axis denotes the alleles according to Table 2.

Figure 4I (Fig. 4I) shows the allele I trait correlation for trait i according to Table 1 . Y-axis shows the allele I trait correlation in % change to mean and X-axis denotes the alleles according to Table 2.

Figure 4J (Fig. 4J) shows the allele / trait correlation for trait j according to Table 1 . Y-axis shows the allele I trait correlation in % change to mean and X-axis denotes the alleles according to Table 2.

Figure 4K (Fig. 4K) shows the allele I trait correlation for trait k according to Table 1 . Y- axis shows the allele I trait correlation in % change to mean and X-axis denotes the alleles according to Table 2.

Figure 4L (Fig. 4L) shows the allele / trait correlation for trait I according to Table 1 . Y-axis shows the allele I trait correlation in % change to mean and X-axis denotes the alleles according to Table 2. Figure 4M (Fig. 4M) shows the allele / trait correlation for trait m according to Table 1 . Y- axis shows the allele I trait correlation in % change to mean and X-axis denotes the alleles according to Table 2.

Figure 4N (Fig. 4N) shows the allele / trait correlation for trait n according to Table 1 . Y- axis shows the allele I trait correlation in % change to mean and X-axis denotes the alleles according to Table 2.

Figure 40 (Fig. 40) shows the allele I trait correlation for trait o according to Table 1 . Y- axis shows the allele I trait correlation in % change to mean and X-axis denotes the alleles according to Table 2.

Figure 4P (Fig. 4P) shows the allele I trait correlation for trait p according to Table 1 . Y- axis shows the allele I trait correlation in % change to mean and X-axis denotes the alleles according to Table 2.

Figure 4Q (Fig. 4Q) shows the allele I trait correlation for trait q according to Table 1 . Y- axis shows the allele I trait correlation in % change to mean and X-axis denotes the alleles according to Table 2.

Figure 4R (Fig. 4R) shows the allele I trait correlation for trait r according to Table 1 . Y- axis shows the allele I trait correlation in % change to mean and X-axis denotes the alleles according to Table 2.

Figure 4S (Fig. 4S) shows the allele I trait correlation for trait s according to Table 1 . Y- axis shows the allele I trait correlation in % change to mean and X-axis denotes the alleles according to Table 2.

Figure 4T (Fig. 4T) shows the allele / trait correlation for trait t according to Table 1 . Y-axis shows the allele I trait correlation in % change to mean and X-axis denotes the alleles according to Table 2.

Figure 4U (Fig. 4U) shows the allele I trait correlation for trait u according to Table 1 . Y- axis shows the allele I trait correlation in % change to mean and X-axis denotes the alleles according to Table 2. Figure 4V (Fig. 4V) shows the allele / trait correlation for trait s according to Table 1 . Y- axis shows the allele I trait correlation in % change to mean and X-axis denotes the alleles according to Table 2.

Figure 5 (Fig. 5) shows trait correlation matrix as deduced from the recombinant analysis. Black represents a correlation of 1 (self-correlation), white represents a negative correlation of 0 (no correlation), different shades of gray represent correlations in between. Colorcoding does not distinguish between positive and negative correlation.

Figure 6 (Fig. 6) shows the SOS1 expression differences between QTL(-/-) NIL (grey bars) and QTL(+/+) NIL (black bars) under control (WW) and water withholding (WS) conditions for 7 and 11 days (d) as well as after rehydration (RE; sampling on day 13). Expression differences are given as fold changes on the logarithmic base 2 and are leveled relatively to the expression of QTL(-/-) NIL at control conditions at sampling day 7 (log2FC = 0; fold change of 1).

Figure 7A (Fig. 7A) shows phenotypic differences (traits t, u, and v according to table 1) between the SOS7(W130*) mutant (MUT, light gray, SEQ ID NO: 53) and corresponding SOS1 wild type (WT, dark gray, SEQ ID NO: 49) plants. Single phenotypic values are plotted as jittered points within bar charts. Significant differences in phenotype between MUT and WT are indicated (***), “RDW’ denotes root dry weight and “diameter” denotes stem diameter.

Figure 7B (Fig. 7B) shows phenotypic differences (traits c, k and q according to table 1) between the SOS7(W130*) mutant (MUT, light gray, SEQ ID NO: 53) and corresponding SOS1 wild type (WT, dark gray, SEQ ID NO: 49) plants. Single phenotypic values are plotted as jittered points within bar charts. Significant differences in phenotype between MUT and WT are indicated (***).

Figure 7C (Fig. 7C) shows phenotypic differences (trait m according to table 1) between the SOS7(W130*) mutant (MUT, light gray, SEQ ID NO: 53) and corresponding SOS1 wild type (WT, dark gray, SEQ ID NO: 49) plants. Single phenotypic values are plotted as jittered points within bar charts. Significant differences in phenotype between MUT and WT are indicated (***). Figure 8 (Fig. 8) shows haplotype counts in the DROPS Dent diversity set. Count of the respective haplotype is given on the X-axis. Grey bar shows the QTL- haplotype, black bar represents the QTL+ haplotype. Other haplotypes are indicated by white bars.

Figure 9 (Fig.9) shows graphical genotype of isogenic lines NIL120, NIL129 and recombinant isogenic lines REC227 and REC935 used in the transcriptome analysis, at the target QTL. Markers are identified by number from 1 to 45. ‘A’ identifies the SNP allele from QTL -/- NIL (NIL120), ‘B’ identifies the SNP allele from QTL +/+ NIL(NIL129). Grey colour shading indicates flanking markers SOS1 gene locus.

Figure 10 (Fig. 10) Venn diagrams showing the number of differentially expressed genes at the differentiation zone (LZ) identified in each genetic background and shared amongst QTL+/+ NIL vs -/- NIL paired of lines (NIL129-NIL120), recombinant homozygous lines REC227-REC935 and TILLING mutant lines TILL94-TILL85 (MUT, SEQ ID NO: 53; WT, SEQ ID NO: 49), respectively.

List of Sequences

SEQ ID NO: 1 : Nucleic acid sequence of marker SYN9635

SEQ ID NO: 3: Nucleic acid sequence of marker PZE-101137688

SEQ ID NO: 4: Nucleic acid sequence of marker PZE-101139064

SEQ ID NO: 6: Nucleic acid sequence of marker ZmSYNBREED_15493_963

SEQ ID NO: 7: Nucleic acid sequence of marker ZmSYNBREED_15494_103

SEQ ID NO: 8: Nucleic acid sequence of marker PZE-101139993

SEQ ID NO: 9: Nucleic acid sequence of marker ZmSYNBREED_15494_185

SEQ ID NO: 10: Nucleic acid sequence of marker ZmSYNBREED_15494_255

SEQ ID NO: 11 : Nucleic acid sequence of marker ZmSYNBREED_15494_256 SEQ ID NO: 12: Nucleic acid sequence of marker PZE-101140058

SEQ ID NO: 13: Nucleic acid sequence of marker ZmSYNBREED_15495_338

SEQ ID NO: 14: Nucleic acid sequence of marker ZmSYNBREED_15495_364

SEQ ID NO: 15: Nucleic acid sequence of marker ZmSYNBREED_15495_383 SEQ ID NO: 16: Nucleic acid sequence of marker ZmSYNBREED_15495_392

SEQ ID NO: 17: Nucleic acid sequence of marker ZmSYNBREED_15495_472

SEQ ID NO: 18: Nucleic acid sequence of marker ZmSYNBREED_15497_423

SEQ ID NO: 19: Nucleic acid sequence of marker ZmSYNBREED_15497_431

SEQ ID NO: 20: Nucleic acid sequence of marker ZmSYNBREED_15497_468 SEQ ID NO: 21 : Nucleic acid sequence of marker ZmSYNBREED_15497_554

SEQ ID NO: 22: Nucleic acid sequence of marker SYN13130

SEQ ID NO: 23: Nucleic acid sequence of marker ZmSYNBREED_15503_496

SEQ ID NO: 24: Nucleic acid sequence of marker ZmSYNBREED_15513_300

SEQ ID NO: 25: Nucleic acid sequence of marker ZmSYNBREED_15514_671 SEQ ID NO: 26: Nucleic acid sequence of marker ZmSYNBREED_15517_103

SEQ ID NO: 27: Nucleic acid sequence of marker ZmSYNBREED_15518_526

SEQ ID NO: 28: Nucleic acid sequence of marker PZE-101140439

SEQ ID NO: 29: Nucleic acid sequence of marker ZmSYNBREED_15523_932

SEQ ID NO: 30: Nucleic acid sequence of marker ZmSYNBREED_15529_448 SEQ ID NO: 31 : Nucleic acid sequence of marker ZmSYNBREED_15531 _448

SEQ ID NO: 32: Nucleic acid sequence of marker ZmSYNBREED_15533_115

SEQ ID NO: 33: Nucleic acid sequence of marker ZmSYNBREED_15537_488

SEQ ID NO: 34: Nucleic acid sequence of marker PZE-101140678 SEQ ID NO: 35: Nucleic acid sequence of marker PZE-101140689

SEQ ID NO: 36: Nucleic acid sequence of marker PZE-101140751

SEQ ID NO: 37: Nucleic acid sequence of marker SYN35729

SEQ ID NO: 38: Nucleic acid sequence of marker ZmSYNBREED_15549_397

SEQ ID NO: 39: Nucleic acid sequence of marker SYN25883 SEQ ID NO: 40: Nucleic acid sequence of marker SYN25881

SEQ ID NO: 41 : Nucleic acid sequence of marker ZmSYNBREED_15550_693

SEQ ID NO: 42: Nucleic acid sequence of marker PZE-101140981

SEQ ID NO: 43: Nucleic acid sequence of marker PZE-101141198

SEQ ID NO: 44: Nucleic acid sequence of marker SYN8998 SEQ ID NO: 45: Nucleic acid sequence of marker ZmSYNBREED_15598_559

SEQ ID NO: 46: Nucleic acid sequence of the SOS1 gene of Zea mays inbred line B73

SEQ ID NO: 47: Nucleic acid sequence of cDNA of the transcript encoded by the SOS1 gene of Zea mays inbred line B73

SEQ ID NO: 48: Amino acid sequence of SOS1 protein gene of Zea mays inbred line B73 SEQ ID NO: 49: Nucleic acid sequence of the SOS1 gene of Zea mays inbred line PH207

SEQ ID NO: 50: Nucleic acid sequence of cDNA of the transcript encoded by the SOS1 gene of Zea mays inbred line PH207

SEQ ID NO: 51 : Amino acid sequence of SOS1 protein gene of Zea mays inbred line PH207

SEQ ID NO: 52: Nucleic acid sequence of the SOS1 gene of Zea mays inbred line PH207 with an additional Q1019* mutation (the asterisk indicates a stop codon)

SEQ ID NO: 53: Nucleic acid sequence of the SOS1 gene of Zea mays inbred line PH207 with an additional W130* mutation (the asterisk indicates a stop codon)

SEQ ID NO: 54: Nucleic acid sequence of the SOS1 gene of Zea mays QTL(-/-) recurrent parent

SEQ ID NO: 55: Nucleic acid sequence of the SOS1 gene of Zea mays QTL(+/+) donor parent

Detailed description

The invention relates to identification and localization of a quantitative trait locus (QTL) on maize chromosome 1 contributing to genetic variation in quantitative root yield and root related traits.

Generally quantitative root traits are very hard to characterize and to fine map. Although, certain techniques, for example, ShovelOMICs and digital root phenotype analysis using the Software suites GiaRoot & Rest are available, it is still rather hard to fine map root associated traits.

The present inventors succeeded in defining a very narrow QTL of only ~0.65 Mb that was characterized on DNA sequence level and its phenotypic effects were verified at a molecular, biochemical, and physiological level. This sets the basis for using this QTL for breeding purposes without the risk of undesired linkage drag in view of the concise region of the locus of interest. Additionally, a new role of the gene SOS1 was identified, and functional validation studies based on TILLING mutants and gene expression studies were conducted. Finally, molecular marker data integration and application allows detection of positive and negative haplotypes at the locus and gene level, characterizing material, and monitoring diversity at and surrounding the locus as such.

In addition, markers are disclosed that allow determining and/or detecting the genomic state at this locus or parts thereof. This information can be linked to phenotype values of parental lines; NILs; recombinants and generated recombinants to use the disclosed marker/trait correlations. Altogether, the findings can be integrated into selection processes to select for specific allele compositions and to characterize germplasm in regard to root yield, architecture, and associated parameters.

In particular, the present invention can e.g. be used for "Marker assisted selection" (of MAS) of plants having an improved root phenotype. Benefits of an improved root phenotype according to the present disclosure are for example better nutrient uptake, increased plant height, better plant vigor, less lodging, higher chlorophyll content (due to better nutrient uptake) and higher root to shoot ratio.

The invention allows to use molecular markers or other genotyping and/or sequencing techniques to infer the genomic state of a QTL of 0.65 Mb between (and including) the flanking allele numbers: 6 and 27 (cf Table 2) to achieve an improved root phenotype as determined by ShovelOMICs and digital root phenotype analysis as well as general plant performance. In addition, genetic variation in quantitative root yield and root related traits determined by ShovelOMICs and digital root phenotype analysis, the haplotype states, the expression level and the physiological constitution contributing to the observed trait variation as determined by the candidate gene SOS1 mapping to the above mentioned interval can be deduced. Generally, the invention allows using the allele information to characterize material based on the haplotypic constitution contributing to variation in root dry matter yield and root architectural parameters. Correspondingly, using single allele state information as well as binned information resulting in haplotypes is the basis for a fast, precise, and improved classification of genetic material during a common selection process. Finally, allelic variation at the SOS1 gene level can be used to improve the above- mentioned phenotypes by either modulating expression of the SOS1 genes, modifying the molecular activity of such genes and gene products or generating any allelic versions derived from such genes.

In a first aspect, there is thus provided a method of detecting a plant of the genus Zea having an improved root phenotype, the method comprising: a) providing a plant or a plant population of the genus Zea comprising genomic DNA; b) providing a set of markers diagnostic for a chromosomal region of about or less than 0.65 Mb on chromosome 1 within a window flanked by position 180693189 and position 181340039, according to the Zea AGPvO2 reference annotation, said region defining a quantitative trait locus (QTL) for an improved root phenotype; c) detecting the presence of at least one allele, preferably at least two, at least three, at least four, at least five, or more alleles associated with an improved root phenotype and located in a chromosomal region of about or less than 0.65 Mb within a window on chromosome 1 flanked by position 180693189 and position 181340039, according to said Zea AGPvO2 reference annotation, wherein the at least one allele(s) is/are as defined in Table 2; and d) optionally: using the detection of the presence of the at least one allele in step c) to select a plant having an improved root phenotype.

An allele can be detected by any means known in the art, including hybridization to allelespecific oligonucleotides, such as competitive allele specific PCR (KASP), or simply by nucleotide sequencing of the respective polymorphic region, including next generation sequencing and/or high throughput sequencing methods.

In certain embodiments, the method may comprise an amplification step, such as PCR amplification or LCR (ligase chain reaction, a technique well known to the skilled person) amplification, to genotype one or more polymorphic positions and/one to detect one or more alleles associated with an improved root phenotype DNA amplifications methods are well known in the art.

Embodiments relating to allele-specific amplification, including KASP, may comprise a non- allele-specific amplification step followed by allele-specific amplification or may use allelespecific amplification direction on the provided genomic DNA.

The method according to the present invention may be utilized for marker-assisted selection (MAS; Sun et al., 2020), including genomic selection (GS) also referred to as genome wide selection (GWS).

In certain embodiments, a donor plant or donor plant population, comprising one or more alleles associated with an improved root phenotype according to the present invention, may be crossed with a recipient plant or a recipient plant population, such as a plant of an elite line or any plant of interest, to introduce one or more alleles associated with an improved root phenotype in the recipient plant or plant population, e.g. as part of a breeding program. The method according to the present invention may be used to identify one or more progenies of such crossings, having an improved root phenotype.

In certain embodiments, one or more selected plants are used for one or more further breeding/crossing steps, including backcrossing, the one or more progenies of said one or more further crossing steps may be selected again for an improved root phenotype by methods according to the invention. Cycles of crossing/breeding and selection of plants having an improved root phenotype according to the present invention may be repeated multiple times.

The term "crossed" or "cross" refers to a sexual cross and involves the fusion of two haploid gametes via pollination to produce diploid progeny (e.g., cells, seeds or plants). The term encompasses both the pollination of one plant by another and selfing (or self-pollination, e.g., when the pollen and ovule are from the same plant).

“Backcrossing" refers to the process by which progeny are repeatedly crossed back to one of the parents, such as the (donor) parent comprising one or more alleles associated with an improved root phenotype according to the present invention. In a backcrossing scheme, the "donor" parent refers to the parental plant with the desired gene/genes, locus/loci, or specific phenotype to be introgressed. The "recipient" parent (used one or more times) or "recurrent" parent (used two or more times) refers to the parental plant into which the gene or locus is being introgressed.

In certain embodiments, one or more plants having an improved root phenotype according to the present disclosure may be identified directly, i.e. one or more parts of the plant, comprising genomic DNA of said one or more plants, is removed and used for the method according to the present invention, thereby identifying the presence of one or more alleles associated with an improved root phenotype in, or representing the situation in, said one or more plants itself.

In certain embodiments, the method may comprise regeneration of an entire plant, preferably a fertile plant, from a plant cell, preferably derived from somatic tissue, embryonic tissue, callus tissue or protoplast. Regeneration may also be somatic embryogenesis, which is an artificial process in which a plant or embryo is derived from a single somatic cell or group of somatic cells. Somatic embryos are formed from plant cells that are not normally involved in the development of embryos, i.e. plant tissue like buds, leaves, shoots etc. In certain embodiments, the method comprises the step of analyzing and/or verifying the root phenotype of one or more selected plants as disclosed herein and/or as described in Trachsel et. al., 2011 , Galkovsky et al., 2012 and Colombi et al., 2015.

In one embodiment of the first aspect, for detection in step c) at least one allele, preferably at least two, at least three, at least four, at least five or more alleles is/are selected from any one of number 7 to number 27, preferably number 10 to number 27, more preferably number 18 to number 27, even more preferably number 18 to number 23, as defined in Table 2.

In another embodiment of the first aspect, in step b) there is provided a set of markers selected from the group consisting of SEQ ID NO: 6 to SEQ ID NO: 27 or a sequence having at least 95%, 96%, 97%, 98% or at least 99% sequence identity thereto; and wherein in step c) the presence of at least one allele, preferably at least two, at least three, at least four, at least five, or more alleles associated with an improved root phenotype, using the markers of b), is/are detected, wherein the at least one allele(s) is/are defined as in Table 3.

In another embodiment of the first aspect, at least one marker, preferably at least two, at least three, at least four, at least five or more markers is/are selected from any one of SEQ ID NO: 7 to SEQ ID NO: 27 or a sequence having at least 95%, 96%, 97%, 98% or at least 99% sequence identity thereto, preferably SEQ ID NO: 10 to SEQ ID NO: 27 or a sequence having at least 95%, 96%, 97%, 98% or at least 99% sequence identity thereto, more preferably selected from any one of SEQ ID NO: 18 to SEQ ID NO: 27 or a sequence having at least 95%, 96%, 97%, 98% or at least 99% sequence identity thereto, even more preferably selected from any one of SEQ ID NO: 18 to SEQ ID NO: 23 or a sequence having at least 95%, 96%, 97%, 98% or at least 99% sequence identity thereto, said markers being defined as in Table 3.

The markers disclosed herein may be used for detection of alleles using allele-specific PCR, including competitive allele-specific PCR.

In certain embodiments according to the first aspect, in step b) there is provided a set of markers diagnostic for a chromosomal region of about or less than 0.647 Mb on chromosome 1 within a window flanked by position 180693557 and position 181340039, according to the Zea AGPvO2 reference annotation, said region defining a quantitative trait locus (QTL) for an improved root phenotype; and in step c) there is detected the presence of at least one allele, preferably at least two, at least three, at least four, at least five, or more alleles associated with an improved root phenotype and located in a chromosomal region of about or less than 0.647 Mb within a window on chromosome 1 flanked by position 180693557 and position 181340039, according to said Zea AGPvO2 reference annotation, wherein the at least one allele(s) is/are as defined in Table 2.

In certain embodiments according to the first aspect, in step b) there is provided a set of markers diagnostic for a chromosomal region of about or less than 0.642 Mb on chromosome 1 within a window flanked by position 180697181 and position 181340039, according to the Zea AGPvO2 reference annotation, said region defining a quantitative trait locus (QTL) for an improved root phenotype; and in step c) there is detected the presence of at least one allele, preferably at least two, at least three, at least four, at least five, or more alleles associated with an improved root phenotype and located in a chromosomal region of about or less than 0.642 Mb within a window on chromosome 1 flanked by position 180697181 and position 181340039, according to said Zea AGPvO2 reference annotation, wherein the at least one allele(s) is/are as defined in Table 2.

In other embodiments according to the first aspect, in step b) there is provided a set of markers diagnostic for a chromosomal region of about or less than 0.53 Mb on chromosome 1 within a window flanked by position 180813196 and position 181340039, according to the Zea AGPvO2 reference annotation, said region defining a quantitative trait locus (QTL) for an improved root phenotype; and in step c) there is detected the presence of at least one allele, preferably at least two, at least three, at least four, at least five, or more alleles associated with an improved root phenotype and located in a chromosomal region of about or less than 0.53 Mb within a window on chromosome 1 flanked by position 180813196 and position 181340039, according to said Zea AGPvO2 reference annotation, wherein the at least one allele(s) is/are as defined in Table 2.

In other embodiments according to the first aspect, in step b) there is provided a set of markers diagnostic for a chromosomal region of about or less than 0.14 Mb on chromosome 1 within a window flanked by position 180813196 and position 180952015, according to the Zea AGPvO2 reference annotation, said region defining a quantitative trait locus (QTL) for an improved root phenotype; and in step c) there is detected the presence of at least one allele, preferably at least two, at least three, at least four, at least five, or more alleles associated with an improved root phenotype and located in a chromosomal region of about or less than 0.14 Mb within a window on chromosome 1 flanked by position 180813196 and position 180952015, according to said Zea AGPvO2 reference annotation, wherein the at least one allele(s) is/are as defined in Table 2.

In another embodiment of the first aspect, the method comprises the detection of the allele number 23 as defined in Table 2 and/or comprises the detection with the marker of SEQ ID NO: 23 or a sequence having at least 95%, 96%, 97%, 98% or at least 99% sequence identity thereto; or wherein the method, in step c), consists of the detection of only the allele number 23 as defined in Table 2 and/or wherein step b), consists of the provision of only the marker of SEQ ID NO: 23 or a sequence having at least 95%, 96%, 97%, 98% or at least 99% sequence identity thereto, said marker being defined as in Table 3.

In another embodiment of the first aspect, the QTL comprises a nucleotide sequence encoding at least one SALT-OVERLY-SENSITIVE1 (SOS1) Na⁺/H⁺ antiporter, having a mutant and/or variant sequence leading to reduced or absent expression of the SOS1 Na⁺/H⁺ antiporter protein and/or mRNA, or a mutation leading to a SOS1 Na⁺/H⁺ antiporter protein having reduced activity, preferably wherein the reference nucleotide sequence comprises the sequence of SEQ ID NO: 46 or SEQ ID NO: 49, or a sequence having at least 90%, 91 %, 92%, 93%, 94%, 95%, 96%, 97%, 98% or at least 99% sequence identity thereto, or wherein the reference nucleotide sequence encodes an RNA comprising a sequence corresponding to the cDNA sequence of SEQ ID NO: 47 or SEQ ID NO: 50, or a sequence having at least 90%, 91 %, 92%, 93%, 94%, 95%, 96%, 97%, 98% or at least 99% sequence identity thereto, or wherein the reference nucleotide sequence encodes a protein comprising the amino acid sequence of SEQ ID NO: 48 or SEQ ID NO: 51 , or a sequence having at least 90%, 91 %, 92%, 93%, 94%, 95%, 96%, 97%, 98% or at least 99% sequence identity thereto.

In another embodiment of the first aspect, the nucleic acid sequence encoding the at least one SOS1 Na⁺/H⁺ antiporter is selected from SEQ ID NO 55 or a sequence having at least 90%, 91 %, 92%, 93%, 94%, 95%, 96%, 97%, 98% or at least 99% sequence identity thereto.

In another embodiment of the first aspect, the improved root phenotype is independently selected from average root width (diameter), network bushiness, network depth, ellipse axes ratio, network length distribution, major ellipse axis, maximum number of roots, network width, median number of roots, minor ellipse axis, network area, network convex area, network perimeter, network solidity, specific root length, network surface area, network length, network volume, network width to depth ratio, stem diameter, plant height, or any combination thereof, preferably wherein the improved root phenotype is independently selected from maximum number of roots, network area, network perimeter, network surface area, network length, network volume, root dry weight, plant height, or any combination thereof.

In another embodiment of the first aspect, the plant of the genus Zea is a Zea mays plant.

In a second aspect, there is provided a use of at least one marker, preferably at least two, at least three, at least four, or at least five, or more markers selected from SEQ ID NO: 6 to SEQ ID NO: 27, for the manufacture and/or identification of a plant of the genus Zea, including a Zea mays plant, with an improved root phenotype, said markers being defined as in Table 3.

In one embodiment of the second aspect, the use comprises at least one marker, preferably at least two, at least three, at least four, or at least five, or more markers selected from SEQ ID NO: 7 to SEQ ID NO: 27, preferably selected from SEQ ID NO: 10 to SEQ ID NO: 27, said markers being defined as in Table 3.

In one embodiment of the second aspect, the use comprises at least one marker, preferably at least two, at least three, or more markers selected from SEQ ID NO: 18 to SEQ ID NO: 27, said markers being defined as in Table 3 and/or comprising the use of at least one marker, preferably at least two, at least three, or more markers selected from SEQ ID NO: 18 to SEQ ID NO: 23, said markers being defined as in Table 3.

In one embodiment of the second aspect, the use consists of the use of the marker of SEQ ID NO: 23, said marker being defined as in Table 3.

In a third aspect, there is provided a use of a chromosomal region as defined in the first aspect or a use of a mutant SOS1 Na⁺/H⁺ antiporter gene for the manufacture and/or identification of a plant of the genus Zea, including a Zea mays plant, having an improved root phenotype.

In preferred embodiment of the second and/or third aspect, the improved root phenotype is independently selected from average root width (diameter), network bushiness, network depth, ellipse axes ratio, network length distribution, major ellipse axis, maximum number of roots, network width, median number of roots, minor ellipse axis, network area, network convex area, network perimeter, network solidity, specific root length, network surface area, network length, network volume, network width to depth ratio, stem diameter, plant height, or any combination thereof, preferably wherein the improved root phenotype is independently selected from maximum number of roots, network area, network perimeter, network surface area, network length, network volume, root dry weight, plant height, or any combination thereof.

The use according to the second or the third aspect may comprise marker-assisted selection (MAS) strategies, including genomic selection (GS; Nakaya and Isobe, 2012, Van Vleck et al., 1992 and Heffner et al., 2009) also referred to as genome wide selection (GWS).

In certain embodiments of the second or third aspect, the use may relate to a donor plant or donor plant population, comprising one or more alleles associated with an improved root phenotype according to the present invention, may be crossed with a recipient plant or a recipient plant population, such as a plant of an elite line or any plant of interest, to introduce one or more alleles associated with an improved root phenotype in the recipient plant, e.g. as part of a breeding program, for the identification of one or more progenies of such crossings, having an improved root phenotype. The use may additionally comprise one or more further breeding/crossing, including backcrossing, the progenies of said further crossing steps may be selected again for an improved root phenotype by methods according to the invention.

In certain embodiments of the second orthird aspect, the use may relate to the identification of a provided plant or plant population itself, having an improved root phenotype according to the present disclosure, wherein one or more parts of the plant or plant population, comprising genomic DNA of said plant or plant population, is taken and used for detection of presence of one or more alleles associated with an improved root phenotype in or representing the situation in said plant or plant population itself.

The use according to the second or the third aspect may comprise regeneration of an entire plant, preferably a fertile plant, from a plant cell, preferably derived from somatic tissue, embryonic tissue, callus tissue or protoplast. Regeneration may also be somatic embryogenesis, which is an artificial process in which a plant or embryo is derived from a single somatic cell or group of somatic cells. Somatic embryos are formed from plant cells that are not normally involved in the development of embryos, i.e. plant tissue like buds, leaves, shoots etc. In a fourth aspect there is provided a molecular marker suitable to detect a QTL, or parts thereof, as defined in the first aspect associated with an improved root phenotype, the molecular marker comprising a sequence selected from the group consisting of SEQ ID NO: 6 to SEQ ID NO: 27, or a sequence having at least 95%, 96%, 97%, 98%, or at least 99% sequence identity thereto, preferably a sequence selected from the group consisting of SEQ ID NO: 7 to SEQ ID NO: 27 or a sequence having at least 95%, 96%, 97%, 98%, at least 99% sequence identity thereto, more preferably a sequence selected from the group consisting of SEQ ID NO: 10 to SEQ ID NO: 27 or a sequence having at least 95%, 96%, 97%, 98%, at least 99% sequence identity thereto, even more preferably a sequence selected from the group consisting of SEQ ID NO: 18 to SEQ ID NO: 27 or a sequence having at least 95%, 96%, 97%, 98%, at least 99% sequence identity thereto, most preferably a sequence selected from the group consisting of SEQ ID NO: 18 to SEQ ID NO: 23, or a sequence having at least 95%, 96%, 97%, 98%, at least 99% sequence identity thereto.

In another aspect, there is provided a method of generating a plant, wherein the method comprises introducing the QTL according to the first aspect described herein, or at least one allele characterizing the QTL and leading to an improved root phenotype, into a germplasm to provide optimized germplasm for breeding purposes and/or analytical purposes. The introduction can be independently selected from classical breeding, molecular marker assisted breeding, using chemical mutagenesis (including TILLING), using targeted mutagenesis by genome editing, including base editing and prime editing, to modify a locus of interest or at least one SNP position of interest, insertion of the QTL as at least one transgene, or any combination of the aforementioned methods, or a stepwise introduction using at least one, or a combination of the aforementioned introduction methods to insert a QTL of interest into a germplasm and/or genome of interest and thereby, optionally by a step of regeneration, obtaining at least one plant cell, tissue, organ or seed or whole plant comprising and capable of expressing at least one allele of the QTL as defined according to the first aspect, the plant showing an improved root phenotype.

The present invention will now be further illustrated by the following not limiting Examples.

Examples

Example 1 : Material and Methods Development of recombinant NILs

Specific recombinants from biparental crosses were selected using the HIF method (Tuinstra et al., 1997) and selfed, followed by selection homozygous lines to maximize the recurrent parent genome contribution with exception of the QTL region (all alleles according to Table 2), wherein the aim was to have a contrast between the QTL(+/+) near isogenic line (NIL), homozygously carrying all “B” alleles according to Table 2, and the QTL(-/-) NIL, homozygously carrying all “A” alleles according to Table 2.

Recombinants (F4 to F6 generation) originating from the cross of QTL(-/-) NIL to QTL(+/+) NIL were identified using Affx-90451971 and SEQ ID NO:27 as flanking markers as well as additional KASP markers (e.g. SEQ ID NO:22, SEQ ID NO:23, SEQ ID NO:24).

Plant materials - Mapping population

In order to increase mapping resolution it was necessary to develop and phenotype a population of homozygous recombinant lines at the QTL’s region. The mapping population consisted of F4 homozygous recombinant lines that were developed over period 2016 - 2019 along with the two pairs of isogenic lines (-/- and +/+ at the QTL (alleles 6 to 27 according to Table 2)). The recombinant isogenic lines used for fine mapping were eventually derived from these crosses. F1 progeny were self-pollinated and marker assisted selection (MAS) was performed on F2 plants which were screened using SSR flanking markers (umc 1601 and umc 1709). Recombinants individuals were self-pollinated to F3 and then characterized with markers along the QTL interval. F3 homozygous recombinant individuals were self-pollinated to increase the seeds stock.

Preparation of genomic DNA for preliminary marker analysis was carried out using the CTAB protocol (Doyle and Doyle, 1987).

Field experiment for analysis of root architecture

Phenotypic evaluations were conducted during the summer seasons at the experimental station of Cadriano (Bo) (Po Valley, Northern Italy; 11 °24’E, 44°33’N, 33 m asl) on a flat clay loam soil (28% sand, 44% clay, and 28% silt). A completely randomised block experimental design was used to assign the genotype to the single plot in each block.

The trials were manually sown at the end of April. In every plot 26 (13x2) seeds were planted maintaining a distance of 0.25 m apart plants and a row spacing of 0.80 m. Plants were thinned 15 d after emergence. In order to reduce edge effects, trials were surrounded by at least three borders plots consisting of inbreed lines with similar vegetative vigour to the recombinants. The traditional maize cultivation techniques applied in the Northern Po valley were adopted for cultivation and pest control. Weeds were manually removed where necessary.

Phenotype collection

Phenotypic data were collected on three representative plants for each plot at the R1 stage (generally at the end of July), when maximum vegetative growth had been reached. During the selection process, the first and the last plants of the row were discarded as well as weak plants and/or plants showing symptoms of diseases. Phenotypic evaluation

Phenotypic evaluation of plant material has been carried out in field trials using ShovelOMICs (Trachsel et al., 2011) and digital analysis of root traits using the Software suites GiaRoot (Galkovsky et al., 2012) & Rest (Colombi et al., 2015). Table 2 gives an overview about the recorded traits.

Table 1 - Trait overview

Root traits phenotyping using ShovelOMICs

Root system architecture traits were analyzed by integrating ShovelOMICs (Trachsel etal., 2011) with a high-resolution root image analysis (Bucksch etal., 2014) as described below.

At flowering time, the stems of the selected plants were marked with red color on the stem east side and cut at 40 cm above the soil surface. After cutting, roots were excavated by removing a soil cylinder of 30 cm diameter and 25 cm depth with the plant base as the horizontal center of the soil cylinder by using a standard shovel. Then the roots were shaken to remove most of the coarse soil adhering to the roots and placed into tanks filled with water to moisten the remaining soil. Finally, the root crowns were washed using a low- pressure water nozzle attached to a hose.

After the washing process, each root system was photographed with a high-resolution digital camera (Nikon D3500, with lens AS-S DX NIKKOR 15-55 mm f/3.5-5.6G VR II). In order to enhance image analysis performance each root apparatus was pictured on two side, firstly on red marked side and then by turning it of 90°. The images were saved in fine .jpeg format.

A special photographic set was created to capture the root images. It was based on a wooden panel covered with black velvet (1 .5 m width x 1 m high) to serve as background, a clamp to attach the roots to be photographed and a tripod on which was mounted the camera. The background panel was assembled horizontally to a wooden frame, at the middle of which was fixed the clamp. Camera was positioned in front of the black panel at the fixed distance of 1 m away to the imaging sensor. For each picture, a label was placed on the field of view of the camera as a root tag and as a scale to transform pixels into metric units.

Root Images analysis

Root images were processed using GiA Root (General Image Analysis of Roots) software (Galkovskyi etal., 2012). Images were manually cropped and then loaded into the software. Scale calibration was applied in terms of pixels/cm before the segmentation process was performed by using the adaptive thresholding algorithm. Basic thresholding algorithm parameter were manually optimized to improve segmentation performance. Data reproducibility of from images analysis was ensured by standardized setting of GiA Root software. Finally, traits are computed on binary images. Numerical measurement of selected traits was saved in a .csv file at the end of the analysis process. In addition to image-collected traits, cleaned root-stocks were dried for three days in the oven at 105 °C, and then root dry weight (RDW) was collected and added to the list of root traits.

Shoot traits phenotyping

Shoot traits such as plant height and stem diameter were collected. Plant height (PH, cm) was measured from the soil level to the tassel tip through a hard meter. Stem diameter (Diam, mm) was measured by an electronic gauge caliper. Both stalk diameters were measured in the middle of the second elongated internode and values were mediated. Analysis of phenotypic data of F4 recombinant population and fine mapping

Analysis of experimental data collected from root images and plant trait was computed with packages from R software (version 3.6.3). Simple Pearson correlation between traits was assessed with stats package (version 3.6.3). Hereditability (repeatability) was calculated on single-plot based mean value with heritability package (version 1.3), and correct for number of observations. Outliers were removed from raw data applying simple linear model-based Bonferroni test p<0.05 by using package car (version 3.0-11).

In order to reduce effects of spatial-temporal variation linear, mixed models were fitted including effects of genotype, year, replicate and single plant. AIC stepwise model selection (MASS package version 7.3-54) was applied to determine which model retain for prediction. The best linear unbiased predictor (BLUP) for each near isogenic lines was predicted with Ime4 package (version 1.1-27.1) and BLUPs data were used as phenotype for fine mapping.

For fine mapping study, marker effect based on allelic substitution was analyzed over the QTL region. This method allowed overcoming problems arising from use of statistical tests that suffer from imbalances in the sense of variability between the number of samples for each marker group. “B” allele (cf. Table 2) effect was calculate based on single marker locus. Percentage of increase from the mean value of “A” and “B” alleles was plotted. Additionally, a single marker association analysis was performed by applying the nonparametric Kruskal-Wallis test computed through the package stats (version 3.6.3). The P- values at single marker position was graphed as -log10(P).

Sequencing of near isogenic lines QTL(-Z-) NIL and QTL(+/+) NIL

Whole-genome sequencing was performed on the isogenic lines QTL(-/-) NIL and QTL(+/+) NIL. Total genomic DNA was isolated from fresh young leaf using NucleoSpinPlant II (Machery Nagel) extraction kit following the manufacture instructions. DNA quality and concentration were assessed by electrophoresis on 1 % agarose gel and Qubit 4 Fluorometer (ThermoFisher Scientific). In collaboration with NOVOGENE, Illumina HiSeq PE50 (20x coverage) (Illumina, Inc; San Diego, CA) was used to obtain reads sequence. Reads were aligned to the maize B73 reference genome Zm-B73-REFERENCE-NAM-5.0 (Hufford et al., 2021) using BWA v.7.17 (Li and Durbin, 2009). Variants were called with BCFtools v. 1.10.2 (Li and Barrett, 2011) and were filtered for a minimum reads depth of 10x, PHRED quality > 40 and a minimum DV/AD[2] ratio of 0.8, where DP is the coverage depth at the variant position and AD[2] is the allelic depth of the alternate allele. Variant effects were predicted with SNPEff v.3.0.7 (Cingolani et al., 2012) and among variants in the gene space, only high or moderate effects were considered.

Additionally, alleles sequences of candidate genes were extracted for QTL(+/+) NIL and QTL(-/-) NIL from their whole .vcf files and the fasta sequences were obtained with the command bcftools consensus. The 25 NAM founder sequences were downloaded from www.maizegdb.org. The fasta sequences were aligned using MUSCLE (Edgar, 2004) from MEGAX (Kumar et al., 2018).

RNA-Seq analysis and candidate gene extraction

An experiment with parental NIL(+/+) and NIL(-/-) was conducted in the glasshouse. Control (well-watered) and treatment conditions (water-withholding) were included in the experimental setup. The experiment consisted of growing both genotypes under controlled conditions a described in Talame et al., 2007. J. Exp. Bot. 58(2): 229-240. Briefly, after germination and transplanting to pots, the plants were shifted after reaching the V4 stage to either control or treatment conditions. A first sampling has been carried out after seven days of water withholding (~55% soil water content) followed by a second sampling time point after 11 days of water withholding (~40% soil water content). Subsequently plants were re-watered on day twelve and a final sampling (rehydration) is carried out on day thirteen after water withholding. Leaf tissue samples, each of two independent biological replicates has been subjected to RNA sequencing using single end Illumina short reads (120bp). After mapping, read counting and differential expression analysis, candidate genes within the target region were evaluated.

RNA-Seq analysis of root meristem and lateral root zone

The transcriptome analysis was performed on root tissues collected from six lines, namely the near isogenic lines QTL -/- NIL (NIL120) and QTL +/+ NIL(NIL129), the recombinant isogenic lines REC935 and REC227 (-/- or +/+ at the QTL, respectively (Figure 9), and the tilling mutant lines TILL85 and TILL94 (carrying a stop codon mutation at W130* of ZmSOSI or the ZmSOSI wild type allele, respectively). Samples were collected at 15, 25 and 40 days after germination (DAG). At each time point root tissues from the meristematic plus elongation zone (MZ + EZ) and differentiation zone (LZ) (where lateral root emerges) were collected from the first crown below the soil level and immediately frozen in liquid nitrogen. Plants were cultivated in greenhouse (16-h light at ~25 °C and 8-h dark at ~18 °C) by using pots filled with a mixed substrate composed by peat, perlite, and vermiculite (2:0.5:0.5). A total of 108 samples were collected (6 genotypes x 3 time points x 2 tissues x 3 biological replications), each sample including root tissues collected from three independent plants. Total RNA was isolated using NucleoSpin RNA Plant kit (Machery Nagel) following manufacturer’s instructions.

Strand specific root RNA libraries were prepared with the Universal Plus mRNA-Seq kit (Tecan Genomics, Redwood City, CA) and sequenced on paired-end 150 bp mode on NovaSeq 6000 (IGA Technology, Udine Italy), producing on average 40 to 60 million paired reads per sample. After quality assessment using FastQC (Wingett & Andrews, 2018), raw reads were pre-processed for rRNA contaminant reads filtering with ERNEFILTER 2.1.1 (Del Fabbro et al., 2013), and then Trimmomatic (Bolger et al., 2014) was applied for adapter clipping and low-quality sequence filter and trimming. High quality reads were finally mapped to the Zea mays B73 RefGen_v5 genome (Woodhouse et al., 2021) genome and annotated transcriptome using STAR v 2.7.10b aligner (Dobin et al. ,2013). Gene expression estimates were obtained from transcriptome uniquely aligned reads using RSEM v1 .3.1 (Li and Dewey, 2011), including sequencing bias estimation in the data (-- estimate-rspd option). The differential expression analysis was carried out using DESeq2 (Love et al., 2014): after estimation of size factors and dispersion between samples and genes, differentially expressed genes were identified applying the default Wald test in pairwise comparison between lines with contrasting alleles at the candidate locus. Genes with Iog2 fold change ratio > |1 |and FDR- adjusted p-value < 0.05 were considered as differentially expressed genes (DEGs).

Plant materials -functional characterization of candidate gene NHX7ISOS1 using TILLING

A targeting induced local lesion in genome (TILLING) population, in the background of inbred line PH207 was screened with primers targeting nhx7 (ZmOOOOl eb033240). Mutant lines, along with their respective segregating wild-type sister lines, were tested during 2020 and 2021 field trials in Cadriano (Bo). Example 2: Marker/phe notype correlations within the set of identified recombinants

Based on 157 unique recombinants in the target region of the QTL interval allele/phenotype correlations were calculated (see also Figure 2 for root stocks of QTL(+/+) and QTL(-/-) plants). Two NIL pairs (QTL(+/+) I QTL(-/-)) were included as QTL contrast controls. This refers to the NILs meaning being the same over most of the genome and just differing at the QTL region. Single allele I trait correlations are evaluated between contrasting recombinant groups having a QTL- (recurrent parent Lo964) or a QTL+ (donor parent Lo1016) call at the respective SNP site. Test statistics using a Kruskal-Wallis rank sum test for the contrasting groups of genotypes were calculated. The P-value for each corresponding allele of 44 single nucleotide polymorphisms (SNPs) covering the QTL and its up-stream / down-stream boundaries is plotted as negative logarithm of base 10 (Figure 3A to Figure 3F) for the given traits (see Table 1). Forty-four SNPs were used to have a larger interval and to better resolve the QTL interval (see it rise and decline).

It can be clearly deduced, that for root dry weight (trait t according to Table 1) the region between markers SEQ ID NO: 10 and SEQ ID NO: 27 (position with reference to AGPvO2_Chr1 : 180697181-181340039; 0.642Mb) shows a correlation according to the p_value profile. The Peak of main association is located within the interval flanked by markers SEQ ID NO: 18 and SEQ ID NO: 23 (position with reference to AGPvO2_Chr1 :180813196-180952015; 140kb). For most other traits a core association interval ranging from markers SEQ ID NO: 18 to SEQ ID NO: 27 (position with reference to AGPvO2_Chr1 : 180813196-181340039; 0.53Mb) is observed. Additionally, the traits average root width (diameter), network length distribution, maximum number of roots, specific root length and, network volume (trait letter codes: a, e, g, 0, r) show an additional association peak at marker SEQ ID NO: 6 (position with reference to AGPvO2_Chr1 : 180693189).

In addition to the calculated marker I trait correlation profiles for each trait, the effect of carrying a donor allele at each corresponding marker position compared to the mean trait value at the given position has been calculated. Figure 4A to Figure 4V show that a clear positive effect for multiple traits can be observed for recombinants having the donor allele between markers SEQ ID NO: 18 and SEQ ID NO: 27 (position with reference to AGPvO2_Chr1 : 180813196-181340039; 0.53Mb). For root dry weight (trait letter code: t) a constant positive effect of about +20% for the whole QTL region has been detected. Table 2 - Alleles, wherein alleles denoted as “B” are alleles associated with an improved root phenotype

Table 3 - KASP Markers for allele detection

Markers were generated using the publicly available 600k Axiom™ Maize Genotyping Array (Unterseer et al., 2014).

As shown in Figure 5, two major clusters of traits show correlations ranging from positive correlation of 0.00052 to 1 (self-correlation) and negative correlation of -0.01972 to -0.9407215.

Example 3: Expression analysis of Zm00001d031232 - GRMZM2G098494 - NHX7ISOS1

The SOS1 gene, also called NHX7 ,GRMZM2G098494 (AGPvO2_ID) or Zm00001d031232 (AGPvO4_ID), shows a similar expression profile for QTL(+/+) NIL and QTL(-/-) NIL with exception of the rehydration time point. At rehydration, the QTL(+/+) NIL is characterized by a stronger expression than those in the QTL(-/-) NIL (Figure 6). The gene is a component of the so-called “Salt Overly Sensitive” (SOS) pathway in plant species. This pathway is important to sense and respond environmental stimuli notably, soil salinity. The SOS signaling pathway comprises three components, SOS3, SOS2, and SOS1 that together assure the maintenance of ion homeostasis. How cellularly heterogenous organs couple the salt signals to homeostasis maintenance of different types of cells and to appropriate growth of the entire organ and plant is less well known. Recent evidence strongly indicates that different regulatory mechanisms are adopted by roots and shoots in response to salt stress. Several reports have stated that the SOS proteins may have novel roles in addition to their functions in sodium homeostasis, but systematic studies for root phenotypes are lacking so far for relevant crop plants. SOS3, for example, was said to play a critical role in plastic development of lateral roots through modulation of auxin gradients and maxima in roots under mild salt conditions. The function of SOS1 is that of a classical sodium transporter activity (Ji et al., 2013). This gene has been previously described in WO 2015/081075 as being causal for a salt sensitivity phenotype in the non-stiff stalk heterotic pool. The effect can be attributed to a 4bp deletion mutant in the corresponding nhx7 sequence. The same 4bp deletion can be found in the QTL(-Z-) NIL. A salt stress experiment also revealed that the indeed the NILs show contrasting reactions to elevated sodium concentrations. In this respect, QTL(-/-) NILs showed weaker plant performance than QTL(+/+) NILs. In addition to this effect, the impaired activity due to the 4bp allele in QTL(-/-) genotypes could lead to an altered SOS pathway activity that also affect the SOS components SOS2 and SOS3. Via perturbations in SOS3 and root auxin levels the root architecture phenotype could manifest, especially by variation in lateral root architecture and number. In consequence this could lead to reduced root dry matter weight and differences in plant performance.

To validate this hypothesis and NHX7/SOS1 as being the causal candidate gene, two different mutants were identified - SEQ ID NO: 52 as mimicking the natural situation of a truncated/altered protein C-terminus, as it is the case for the 4 bp deletion mentioned above, by a premature stop at position Q1019 and SEQ ID NO: 53 with an early premature Stop at position W130 leading to a full gene knock-out. Allele variants were tested in field trials by ShovelOMICs and mutant SEQ ID NO: 53 phenocopied the QTL effect (Figure 7A to Figure 7C). The mutant displayed weaker root clumps with lower lateral roots, lower root dry matter weight and weaker plant performance. This data indicates that indeed the SOS1 gene has a biological relevance in root yield and root architecture. The genetic variant of SOS1 in the QTL(-/-) NIL appears to negatively affect general plant performance through decreasing the plant's root yield and alteration of root architecture.

Example 4: Haplotypes

As indicated in Table 4 specific haplotypes for the region contributing to trait variation can be identified. Allele information and corresponding haplotypes in Table 4 can be applied to different set of materials to screen for presence/absence alleles associated with an improved root phenotype. In addition, Figure 8 depicts, that QTL- and QTL+ haplotypes reflect major haplotypes within the specific public material resource DROPS Dent diversity set that is composed of ~350 Dent genotypes (Millet et al., 2016) The most prominent haplotype in the DROPS panel is different from both QTL+ and QTL- haplotypes.

Table 4 - Overview over haplotype constitution and corresponding identified alleles. Haplotypes are represented in in single nucleotide base format showing the corresponding alleles (18, 19, 20, 21 , 22, 23, 25 and 26) in sequential order.

Example 5: RNA-Seq analysis for the identification of differentially expressed genes in root tissues.

To identify potential regulatory targets of the SOS1 perturbation, a transcriptomic analysis was conducted in QTL (-/-) NIL, QTL (+/+) NIL, -/- and +/+ recombinant isogenic lines and mutant Tilling lines 85 (SEQ ID NO: 53) and WT 94 (SEQ ID NO: 49) root meristematic and differentiation portion. Three genes commonly downregulated in genotypes with QTL(- /-) haplotypes and SEQ ID NO: 53 allele were identified at 15, 25 and 40 DAG roots (Figure 10, Table 5 and Table 6). Table 5 - Differentially expressed genes (DEGs) shared by NILs, RILs and TILLING mutant lines in LZ root tissues. ID and Gene position are according to B73RefGen_v5; relevant literature is reported too.

Zm00001 eb031850 encodes for a cation/H+ antiporter exchanger (chx15) involved in maize salt tolerance, Zm00001 eb153640 which Myb/SANT-like domain was shown to be involved in abiotic stress resistance (Li et al., 2019; Liu et al., 2022), and Zm00001 eb339970 encoding for stress-responsive S-adenosyl-L-methionine-dependent methyltransferase protein (Barua et al. 2022). Interestingly its ortholog in Arabidopsis thaliana (AT2G41380) is involved in root epidermis cell differentiation (Bruex et al., 2012). These data indicate that SOS1 perturbation in different maize genetic background affect cation exchanger downstream pathways and genes related to root development.

Table 6 - Representation of Iog2fold change of pairwise comparisons (QTL -/- NIL- QTL +/+ NIL I -I- and +/+ recombinant isogenic lines REC935-REC227 I TILLING Mut- TILLING WT) at each time points (15, 25 and 40 DAG). Column indicate samples included in the pairwise differential expression comparison while shared DEG are reported on rows. Log2fold change expression values are reported for each gene in each comparison a from -2 (down-regulation) to +2 (up-regulation).

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Claims

Claims

1 . A method of detecting a plant of the genus Zea having an improved root phenotype, the method comprising: a) providing a plant or a plant population of the genus Zea comprising genomic DNA; b) providing a set of markers diagnostic for a chromosomal region of about or less than 0.65 Mb on chromosome 1 within a window flanked by position 180693189 and position 181340039, according to the Zea AGPvO2 reference annotation, said region defining a quantitative trait locus (QTL) for an improved root phenotype; c) detecting the presence of at least one allele, preferably at least two, at least three, at least four, at least five, or more alleles associated with an improved root phenotype and located in a chromosomal region of about or less than 0.65 Mb within a window on chromosome 1 flanked by position 180693189 and position 181340039, according to said Zea AGPvO2 reference annotation, wherein the at least one allele(s) is/are as defined in Table 2; and d) optionally: using the detection of the presence of the at least one allele in step c) to select a plant having an improved root phenotype.

2. The method of claim 1 , wherein for detection in step c) at least one allele, preferably at least two, at least three, at least four, at least five or more alleles is/are selected from any one of number 10 to number 27, preferably number 18 to number 27, more preferably number 18 to number 23, as defined in Table 2.

3. The method of claim 1 or 2, wherein in step b) there is provided a set of markers selected from the group consisting of SEQ ID NO: 6 to SEQ ID NO: 27 or a sequence having at least 95%, 96%, 97%, 98% or at least 99% sequence identity thereto; and wherein in step c) the presence of at least one allele, preferably at least two, at least three, at least four, at least five, or more alleles associated with an improved root phenotype, using the markers of b), is/are detected, wherein the at least one allele(s) is/are defined as in Table 2.

4. The method of claim 3, wherein at least one marker, preferably at least two, at least three, at least four, at least five or more markers is/are selected from any one of SEQ ID NO: 10 to SEQ ID NO: 27 or a sequence having at least 95%, 96%, 97%, 98% or at least 99% sequence identity thereto, preferably selected from any one of SEQ ID NO: 18 to SEQ ID NO: 27 or a sequence having at least 95%, 96%, 97%, 98% or at least 99% sequence identity thereto, more preferably selected from any one of SEQ ID NO: 18 to SEQ ID NO: 23 or a sequence having at least 95%, 96%, 97%, 98% or at least 99% sequence identity thereto, said markers being defined as in claim 3.

5. The method of any of the preceding claims, wherein the method comprises the detection of the allele number 23 as defined in Table 2 and/or comprises the detection with the marker of SEQ ID NO: 23 or a sequence having at least 95%, 96%, 97%, 98% or at least 99% sequence identity thereto; or wherein the method, in step c), consists of the detection of only the allele number 23 as defined in Table 2 and/orwherein step b), consists of the provision of only the marker of SEQ ID NO: 23 or a sequence having at least 95%, 96%, 97%, 98% or at least 99% sequence identity thereto, said marker being defined as in claim 3.

6. The method of any of the preceding claims, wherein the QTL comprises a nucleotide sequence encoding at least one SALT-OVERLY-SENSITIVE1 (SOS1) Na⁺/H⁺ antiporter, having a mutant and/or variant sequence conferring an improved root phenotype, preferably wherein the reference nucleotide sequence comprises the sequence of SEQ ID NO: 46 or SEQ ID NO: 49, or a sequence having at least 90%, 91 %, 92%, 93%, 94%, 95%, 96%, 97%, 98% or at least 99% sequence identity thereto, or wherein the reference nucleotide sequence encodes an RNA comprising a sequence corresponding to the cDNA sequence of SEQ ID NO: 47 or SEQ ID NO: 50, or a sequence having at least 90%, 91 %, 92%, 93%, 94%, 95%, 96%, 97%, 98% or at least 99% sequence identity thereto, or wherein the reference nucleotide sequence encodes a protein comprising the amino acid sequence of SEQ ID NO: 48 or SEQ ID NO: 51 , or a sequence having at least 90%, 91 %, 92%, 93%, 94%, 95%, 96%, 97%, 98% or at least 99% sequence identity thereto.

7. The method of claim 6, wherein the nucleic acid sequence encoding the at least one SOS1 Na⁺/H⁺ antiporter is selected from SEQ ID NO: 55, or a sequence having at least 90%, 91 %, 92%, 93%, 94%, 95%, 96%, 97%, 98% or at least 99% sequence identity thereto.

8. The method of any of the preceding claims, wherein the improved root phenotype is independently selected from average root width (diameter), network bushiness, network depth, ellipse axes ratio, network length distribution, major ellipse axis, maximum number of roots, network width, median number of roots, minor ellipse axis, network area, network convex area, network perimeter, network solidity, specific root length, network surface area, network length, network volume, network width to depth ratio, stem diameter, plant height, or any combination thereof, preferably wherein the improved root phenotype is independently selected from network bushiness, network depth, maximum number of roots, median number of roots, network area, network perimeter, network solidity, network surface area, network length, network volume, root dry weight, stem diameter, plant height, or any combination thereof, more preferably wherein the improved root phenotype is independently selected from maximum number of roots, network area, network perimeter, network surface area, network length, network volume, root dry weight, stem diameter, plant height, or any combination thereof.

9. The method of any of the preceding claims, wherein the plant of the genus Zea is a Zea mays plant.

10. A use of at least one marker, preferably at least two, at least three, at least four, or at least five, or more markers selected from SEQ ID NO: 6 to SEQ ID NO: 27, for the manufacture and/or identification of a plant of the genus Zea, including a Zea mays plant, with an improved root phenotype, said markers being defined as in Table 3.

11 . The use of claim 10, comprising the use of at least one marker, preferably at least two, at least three, at least four, or at least five, or more markers selected from SEQ ID NO: 10 to SEQ ID NO: 27, said markers being defined as in claim 10.

12. The use of claim 10 or 11 , comprising the use of at least one marker, preferably at least two, at least three, or more markers selected from SEQ ID NO: 18 to SEQ ID NO: 27, said markers being defined as in claim 10, and/or comprising the use of at least one marker, preferably at least two, at least three, or more markers selected from SEQ ID NO: 18 to SEQ ID NO: 23, said markers being defined as in claim 10.

13. The use of any one of claims 10 to 12, wherein the use consists of the use of the marker of SEQ ID NO: 23, said marker being defined as in claim 10.

14. A use of a chromosomal region as defined in claim 1 or a use of a mutant SOS1 Na⁺/H⁺ antiporter gene for the manufacture and/or identification of a plant of the genus Zea, including a Zea mays plant, having an improved root phenotype.

15. The use of any one of claims 10 to 14, wherein the improved root phenotype is independently selected from average root width (diameter), network bushiness, network depth, ellipse axes ratio, network length distribution, major ellipse axis, maximum number of roots, network width, median number of roots, minor ellipse axis, network area, network convex area, network perimeter, network solidity, specific root length, network surface area, network length, network volume, network width to depth ratio, stem diameter, plant height, or any combination thereof, preferably wherein the improved root phenotype is independently selected from network bushiness, network depth, maximum number of roots, median number of roots, network area, network perimeter, network solidity, network surface area, network length, network volume, root dry weight, plant height, or any combination thereof, more preferably wherein the improved root phenotype is independently selected from maximum number of roots, network area, network perimeter, network surface area, network length, network volume, root dry weight, stem diameter, plant height, or any combination thereof.