Using Crop Databases to Explore Phenotypes: From QTL to Candidate Genes

Seeds, especially those of certain grasses and legumes, provide the majority of the protein and carbohydrates for much of the world’s population. Therefore, improvements in seed quality and yield are important drivers for the development of new crop varieties to feed a growing population. Quantitative Trait Loci (QTL) have been identified for many biologically interesting and agronomically important traits, including many seed quality traits. QTL can help explain the genetic architecture of the traits and can also be used to incorporate traits into new crop cultivars during breeding. Despite the important contributions that QTL have made to basic studies and plant breeding, knowing the exact gene(s) conditioning each QTL would greatly improve our ability to study the underlying genetics, biochemistry and regulatory networks. The data sets needed for identifying these genes are increasingly available and often housed in species- or clade-specific genetics and genomics databases. In this demonstration, we present a generalized walkthrough of how such databases can be used in these studies using SoyBase, the USDA soybean Genetics and Genomics Database, as an example.

QTL Mapping of Seed Quality Traits Including Cooking Time, Flavor, and Texture in a Yellow Dry Bean (Phaseolus vulgaris L.) Population

Manteca yellow dry beans (Phaseolus vulgaris L.) have many quality traits that appeal to consumers, including fast cooking times, creamy texture, and sweet, buttery flavor. They are native to Chile and consumed in regions of South America and Africa but are largely unfamiliar to United States consumers. While cooking time, flavor, and texture have not been prioritized in United States dry bean breeding programs, genetic variability exists such that these traits could be addressed through breeding. In this study, a recombinant inbred line (RIL) population was developed from a cross between Ervilha (Manteca) and PI527538 (Njano), yellow dry beans with contrasting cooking time and sensory attributes. The population and parents were grown for 2 years in Michigan and evaluated for cooking time and sensory attribute intensities, including total flavor, beany, vegetative, earthy, starchy, sweet, bitter, seed-coat perception, and cotyledon texture. Cooking time ranged 19–34 min and exhibited high broad-sense heritability (0.68). Sensory attribute intensities also exhibited variation among RILs, although broad-sense heritability was low, with beany and total flavor exhibiting the highest (0.33 and 0.27). A linkage map of 870 single nucleotide polymorphisms markers across 11 chromosomes was developed for quantitative trait loci (QTL) mapping, which revealed QTL for water uptake (3), cooking time (6), sensory attribute intensities (28), color (13), seed-coat postharvest non-darkening (1), seed weight (5), and seed yield (2) identified from data across 2 years. Co-localization was identified for starchy, sweet, and seed-coat perception on Pv01; for total flavor, beany, earthy, starchy, sweet, bitter, seed-coat perception, cotyledon texture, and color on Pv03; water uptake and color on Pv04; total flavor, vegetative, sweet, and cotyledon texture on Pv07; cooking time, starchy, sweet, and color on Pv08; and water uptake, cooking time, total flavor, beany, starchy, bitter, seed-coat perception, cotyledon texture, color, and seed-coat postharvest non-darkening on Pv10. The QTL identified in this work, in particular CT8.2 and CT10.2, can be used to develop molecular markers to improve seed quality traits in future dry bean varieties. Considering yellow dry beans already excel in quality and convenience, they might be an ideal market class to signal a new focus on consumer-valued traits in the United States.

AtFAHD1a: A New Player Influencing Seed Longevity and Dormancy in Arabidopsis?

Fumarylacetoacetate hydrolase (FAH) proteins form a superfamily found in Archaea, Bacteria, and Eukaryota. However, few fumarylacetoacetate hydrolase domain (FAHD)-containing proteins have been studied in Metazoa and their role in plants remains elusive. Sequence alignments revealed high homology between two Arabidopsis thaliana FAHD-containing proteins and human FAHD1 (hFAHD1) implicated in mitochondrial dysfunction-associated senescence. Transcripts of the closest hFAHD1 orthologue in Arabidopsis (AtFAHD1a) peak during seed maturation drying, which influences seed longevity and dormancy. Here, a homology study was conducted to assess if AtFAHD1a contributes to seed longevity and vigour. We found that an A. thaliana T-DNA insertional line (Atfahd1a-1) had extended seed longevity and shallower thermo-dormancy. Compared to the wild type, metabolite profiling of dry Atfahd1a-1 seeds showed that the concentrations of several amino acids, some reducing monosaccharides, and δ-tocopherol dropped, whereas the concentrations of dehydroascorbate, its catabolic intermediate threonic acid, and ascorbate accumulated. Furthermore, the redox state of the glutathione disulphide/glutathione couple shifted towards a more reducing state in dry mature Atfahd1a-1 seeds, suggesting that AtFAHD1a affects antioxidant redox poise during seed development. In summary, AtFAHD1a appears to be involved in seed redox regulation and to affect seed quality traits such as seed thermo-dormancy and longevity.

Seed Germination and Seedling Vigour Improvement by Halophytic Seed Treatment in Black gram (Vigna mungo L.)

Background: Chenopodium plant is halophytic in nature in which the plant absorbs salt from soil and secrets the salts in aerial parts particularly in leaves and also has lot of macro and micro nutrients. This salt secretion by salt glands helps to survive the plants in saline conditions. The morpho-physiological characters act as barrier against mechanical damages, insects, excessive light and loss of water. Therefore, an experiment was conducted to enhance the seed quality traits viz., germination, speed of germination and seedling vigour in black gram by treating with the salt glands of Chenopodium.Methods: The experiment was conducted in the Department of Seed Science and Technology, Tamil Nadu Agricultural University, Coimbatore during 2019 - 2020. The black gram variety VBN 8 seeds were treated with different concentrations of Chenopodium leaf extract and salt bladders. Then, the seeds were assessed for its quality traits.Result: The experimental results showed that seeds soaked in Chenopodium leaf extract along with salt bladders @ 1.0% or salt bladders alone @ 0.2% for 3 h at 1:0.3 (w/v) ratio have recorded highest germination (97% and 96%) and seedling vigour (2280 and 2102). Nevertheless, analytical results indicated that the Chenopodium leaf extract and its salt bladders contain more amount of minerals particularly phosphorous (0.50%, 0.15%), potassium (0.83%, 1.11%), nitrogen (2.52%, 2.21%), calcium (16.00 ppm, 22.40 ppm), magnesium (190.56 ppm, 193.40 ppm), sodium (4.14 mg 100 g-1, 6.57 mg 100 g-1), chloride (0.14 mol. L-1, 0.17 mol. L-1), respectively, which favored the enhancement of seed qualities in black gram.

Translating insights from the seed metabolome into improved prediction for lipid-composition traits in oat (Avena sativa L.)

Oat (Avena sativa L.) seed is a rich resource of beneficial lipids, soluble fiber, protein, and antioxidants, and is considered a healthful food for humans. Little is known regarding the genetic controllers of variation for these compounds in oat seed. We characterized natural variation in the mature seed metabolome using untargeted metabolomics on 367 diverse lines and leveraged this information to improve prediction for seed quality traits. We used a latent factor approach to define unobserved variables that may drive covariance among metabolites. One hundred latent factors were identified, of which 21% were enriched for compounds associated with lipid metabolism. Through a combination of whole-genome regression and association mapping, we show that latent factors that generate covariance for many metabolites tend to have a complex genetic architecture. Nonetheless, we recovered significant associations for 23% of the latent factors. These associations were used to inform a multi-kernel genomic prediction model, which was used to predict seed lipid and protein traits in two independent studies. Predictions for 8 of the 12 traits were significantly improved compared to genomic best linear unbiased prediction when this prediction model was informed using associations from lipid-enriched factors. This study provides new insights into variation in the oat seed metabolome and provides genomic resources for breeders to improve selection for health-promoting seed quality traits. More broadly, we outline an approach to distill high-dimensional “omics” data to a set of biologically meaningful variables and translate inferences on these data into improved breeding decisions.