1. Genome-wide Association Studies of Grain Traits in Sorghum [Sorghum bicolor (L.) Moench]
Richard Boyles1, Geoffrey Morris2, Davina Rhodes2, Leo Hoffman Jr3,
William Rooney3, & StephenKresovich4¶
1University of South Carolina, 2Kansas State University, 3Texas A&M University, 4Clemson Unversity, ¶Principal Investigator
INTRODUCTION
A priori Gene Candidates from Maize (Zea mays)
Table 2. Displaying top 5 SNPs associated with grain starch composition.
SNP
CHR
Position
P-value
MAF
S2_ 2
2.62E-08
S1_ 1
2.93E-08
S5_ 5
8.76E-08
S9_ 9
1.53E-07
S7_ 7
1.58E-07
Sorghum orthologs to known genes coding for enzymes within the starch biosynthesis pathway and proteins affecting sink strength in maize kernels are listed below (Table 1).
These orthologs provide a starting point for developing markers associated with important grain yield and quality traits.
Novel peaks across the genome will also be included in the study and verified by complementing the association study with QTL mapping in multiple recombinant inbred line (RIL) populations segregating for several endosperm traits.
Cereal grains account for two-thirds of human caloric consumption and are the main food source for livestock.  Locating genes in sorghum [Sorghum bicolor (L.) Moench] associated with grain yield and quality will be useful for marker-assisted breeding and allow for the use of genomic tools to understand and manipulate these traits.  To identify the genetic variation underlying grain yield and quality, a genome-wide association study (GWAS) on a diverse panel of approximately 400 accessions, all of which have been genotyped-by-sequencing for over 265,000 single nucleotide polymorphisms (SNPs), will be used to locate QTL throughout the sorghum genome associated with grain number per panicle, thousand grain weight, starch quantity and composition, and total digestible energy.  The goal of this project is to develop markers across the genome that account for substantial phenotypic variance for important grain yield and grain quality traits to be used to aid cereal crop improvement for food and feed end-uses.
SNP = Single Nucleotide Polymorphism, CHR = Chromosome, MAF = Minor Allele Frequency
Table 1. Descriptions of candidate genes known to affect grain starch composition in Maize.
Gene
Description
Sorghum Ortholog
Similarity (%)
Shrunken2 (SH2)
Large subunit of ADP-glc pyrophosphorylase
Sb03g028850
73.1
Brittle2 (BT2)
Small subunit of ADP-glc pyrophosphorylase
Sb07g012320
94.6
Waxy1 (WX1)
Granule-bound starch synthase I
Sb10g002140
95.9
Brittle1 (BT1)
Amyloplast membrane transporter protein
Sb04g007010
84.7
Shrunken (SH1)
Sucrose synthase
Sb10g006330
87.7
Amylose extender (AE1)
Starch branching enzyme IIb
Sb04g021540
97.9
Sugary1 (SU1)
Debranching enzyme
Sb07g027200
92.9
Sugary2 (SU2)
Starch synthase IIa
Sb10g008200
70.9
Opaque2 (O2)
Leucine zipper motif that binds to zein DNA
Sb02g004590
55.7
Sucrose synthase1 (SUS1)
Sb01g033060
99.6
Starch synthase (SS1)
Starch synthase IIb
Sb10g004160
57.2
Dull1 (DU1)
Starch synthase II
Sb07g005400
87.3
UDP-glucose pyrophosphorylase1 (UGP1)
UTP-glucose-1-phosphate uridylyltransferase
Sb02g032250
84.5
Miniature seed1 (MN1)
Cell wall invertase
Sb04g021810
71.8
Floury2 (FL2)
Unknown; mutation creates reduction in zeins
Sb05g025050
69.6
OBJECTIVES
Figure 5. Manhattan plot displaying associations on grain starch content across the sorghum genome using mixed linear modeling. Vertical lines correspond to locations of sorghum orthologs to maize gene candidates. The horizontal dashed line signifies the Bonferroni correction for statistical significance used when performing simultaneous, independent t-tests.
Objective 1. Examine the natural variation of (1) grain size and grain number per panicle using high-speed seed counters, (2) endosperm starch content and (3) starch composition (amylopectin/amylose) of sorghum grain using near-infrared reflectance spectroscopy (NIRS), all across a diverse panel consisting of approximately 400 accessions.
Objective 2. Identify genes contributing to starch concentration and composition in sorghum grain, namely those functioning in the starch biosynthesis pathway and develop these markers for breeding purposes.
Objective 3. Conduct a QTL analysis, using the same collection of SNP markers on the diversity panel, for yield components and starch composition using recombinant inbred line (RIL) populations and compare QTL locations to the corresponding genome-wide association peaks.
GLM for Grain Starch
Figure 6. Manhattan plot of chromosome 10 on starch content in sorghum grain evaluated in 300 genotypes using NIRS and the single kernel characterization system. This data was published in Sukumaran et al. (2012). Top associated SNPs lie adjacent to starch candidates genes (vertical lines) encoding a starch synthase (SU2), membrane transporter of ADP-glucose (BT1), the substrate for starch, and branching enzyme (AE1).
Figure 2. A picture of diluted starch samples. Pure amylopectin starch (left) is easily dissolved in distilled water at room temperature while starch containing pure amylose (right), unbranched glucose monomers connected by α-1,4 glycosidic bonds, is much more tightly packed preventing water from penetrating leading to its relative insolubility. The ratio of amylose/amylopectin varies greatly depending on genotype and contributes to altered cooking and pasting properties among sorghums.
CONCLUSIONS
By introducing genomic selection into breeding approaches, the time and effort required for developing high-yielding, high-quality hybrid seed for important agronomic crops can be decreased without sacrificing value. This ‘front end’ technique will not replace but rather complement and enhance traditional plant breeding by screening the genotypes (selecting for particular traits) first and then planting a higher number of lines that have desirable phenotypes in the field for further testing.
The substantial variation existing within yield components and grain macronutrient composition in cereals reinforces the need to elucidate markers associated with these important phenotypic traits.
By combining the markers developed for both grain yield and grain quality, we can produce sorghum genotypes, tailored for food and feed uses, to meet the needs from the increasing population demand.
PRELIMINARY RESULTS Natural Diversity
Yield Components: *2013 Diversity Panel
Grain number ranged from 225 to 2800 seeds per panicle
Thousand-grain weight ranged from 13.8 to 79.6 grams
Grain Composition: *2012 Diversity Panel using NIRS
Starch range within the diversity panel was 13% (60-73%)
Protein range within the diversity panel was slightly over 16% (7.5-24%)
Figure 1. Sorghum grain diversity panel in Florence, South Carolina
MATERIALS & METHODS
Figure 4 (below). A look into the endosperm of sorghum grains. The white (left) grain has a high amount of vitreous endosperm and little corneous endosperm is seen. The red sorghum grain (right) has a ‘floury’ texture due to the high amount of corneous endosperm present.
Two replicates of approximately 400 sorghum genotypes representing a wide array of sorghum's natural diversity were planted in Florence, SC in a randomized complete block design.  Plot dimensions were two-rows, m in length with m row spacing for a total of 9.29 m2.  A planting density of approximately 75,000 plants/ha (8.61 plants m-2) was used.
5 main culm panicles per plot were randomly harvested and threshed by hand (average panicle number per plant was also recorded).
The threshed grain was then cleaned using a Clipper Office Tester (AT Ferrell Company, Inc.) and run through a seed counter (Model 900-2, IMB Co.) to obtain the following yield components: grain number per primary panicle and thousand grain weight (TGW).
To determine grain composition, NIRS was used on whole kernels to estimate macronutrient, amylose, amylopectin, and total digestible nutrient (TDN) percentages.
FUTURE RESEARCH
Genome-wide association studies will be used to locate regions throughout the genome for yield component traits: grain number per panicle and grain size (TGW) to develop higher-yielding sorghum.
NIRS on grain from the 2013 diversity panel in addition to the existing data will improve power for the ongoing GWAS on grain quality as well as provide information on environmental effect for these traits.
A QTL mapping study will be conducted using multiple RIL populations segregating for endosperm traits to complement the GWAS by eliminating false positives created from spurious associations.
Figure 3 (above). An image taken of two genotypes that have very different panicle size and architecture to show a breadth of the natural diversity existing within sorghum. The ruler used for scale is one foot in length.
REFERENCES
Funding for this project is provided by:
*North Carolina Biotechnology Center
*USDA-DOE Plant Feedstocks Genomics
1Sukumaran (2012). Plant Genome, 5(3), Nordborg & Weigel (2008). Nature, 456(1), 720–723.
2Morris (2012). PNAS, 110(2), Séne (2000). Plant Physiology and Biochemistry, 38(6), 459–472.
3Rooney (2005). Field Crops Research, 91(1), Sang (2008). Ag and Food Chemistry, 56(15), 6680–6685.