Plant Breeding Programs Research Articles

Sweetpotato cultivation: characteristics, constraints and preferred traits of producers and consumers in Côte d'Ivoire.

In an environment where the adoption of improved varieties resulting from plant breeding programs is limited, it is essential to identify end-user preferences beforehand. A participatory survey was conducted in eight localities in Korhogo and Bouaké regions (central and northern Cote d'Ivoire respectively) to identify producers' preferences and increase the adoption of improved varieties. The study involved 160 producers and consumers through focus group discussions and individual interviews. Sweetpotato is mostly grown on small plots (<1 ha) of land (89.2%), with women (66%) as the main producers. In the Bouaké region, sweetpotatoes are grown on mounds (100%), whereas in Korhogo they are grown on ridges (86.2%). The main food products or forms of preparation from roots are fries (34.7%), boiled (34.3%), mashed (12.4%), and sweetpotato stew (9.1%). Major constraints, including low price of roots (26.3%), low productivity (16.2%), and post-harvest storage issues (14.5%), were identified as affecting sweetpotato production. The selection of new varieties should be oriented towards high-yielding varieties with high dry matter content, deployed stems, and roots of round, oblong, or elliptical shape with good culinary characteristics (dry matter, sweet taste, dry texture, absence of fiber). Plants must be drought resistant, be tolerant to poor soil, diseases, and pests, and have a good yield. The color of skin and flesh of the sweetpotato, although constituting criteria of choice, are not essential for the acceptance or the rejection of a variety by users. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Assessing Drought Tolerance in a Large Number of Upland Cotton Plants (Gossypium hirsutum L.) under Different Irrigation Regimes at the Seedling Stage.

The cotton plant is important since it provides raw materials for various industry branches. Even though cotton is generally drought-tolerant, it is affected negatively by long-term drought stress. The trial was conducted according to the applied experimental design as a completely randomized design (CRD) with three replications to determine a panel of 93 cotton genotypes' genotypic responses against drought under controlled conditions in 2022. All genotypes were watered with 80 mL-1 of water (100% irrigation, field capacity) until three true leaves appeared, and then water stress was applied at a limited irrigation of 75% (60 mL-1), 50% (40 mL-1), and 25% (20 mL-1) of the field capacity. After the trial terminated at 52 days, the cv. G56, G44, G5, and G86 in RL; G1, G56, G44, G86, G51, and G88 in RFW; advanced line G5, followed by the cv. G56, advanced line G44, G75, and the cv. G90 in RDW; G44, followed by G86, the cv. G56, and elite lines G13 and G5 in NLRs were observed as drought-tolerant genotypes, respectively, while G35, G15, G26, G67, and G56 in SL; G15, G52, G60, G31, and G68 in SFW; G35, G52, G57, G41, and G60 in SDW show the highest drought tolerance means, respectively. In conclusion, the commercial varieties with high means in roots, namely G86, G56, G88, and G90, and the genotypes G67, G20, G60, and G57 showing tolerance in shoots, are suggested to be potential parent plants for developing cotton varieties resistant to drought. Using the cultivars found tolerant in the current study as parents in a drought-tolerant variety development marker-assisted selection (MAS) plant breeding program will increase the chance of success in reaching the target after genetic diversity analyses are performed. On the other hand, it is highly recommended to continue the plant breeding program with the G44, G30, G19, G1, G5, G75, G35, G15, G52, G29, and G76 genotypes, which show high tolerance in both root and shoot systems.

Evaluasi Pertumbuhan Klon Tanaman Tebu (Saccharum officinarum L.) Ratoon I Di Desa Watesari, Kecamatan Balongbendo, Kabupaten Sidoarjo

In the last five years, sugar cane production in Indonesia has fallen by 4%. Sugar self-sufficiency can be achieved through plant breeding programs to create high-yielding sugarcane varieties. The research objective was to determine the correlation between growth variables, genetic diversity, and sugarcane (Saccharum officinarum L.) growth in test clones. The research was conducted at the Watesari Village, Balongbendo District, Sidoarjo Regency in November 2022 - August 2023. The test materials were clones SB01 UMG NX 22, SB03 UMG NX 22, SB04 UMG NX 22, SB11 UMG NX 22, SB12 UMG NX 22, SB19 UMG NX 22, and SB20 UMG NX 22, BL variety and PS881 variety. The variables observed included stem height, stem diameter, number of stems, number of leaves, and brix. Data analysis used ANOVA, DMRT test, correlation test, genetic diversity, and heritability. The SB12 UMG NX 22 clone had the best growth, including stem height of 293.89 cm (39 MSK); total of 8.22 units (31 MSK), 8.44 units (33 MSK), 8.44 units (35 MSK), 8.78 units (37 MSK), and 9.11 units (39 MSK). There is a correlation between growth variables. There are genetic variations that affect the growth of sugarcane plants including moderate KKG values ​​(stem diameter, number of stems, and brix). Low KKG (stem height and number of leaves). Low GFC (stem height, stem diameter, stem, number of leaves and brix). H2 height (rod height, stem diameter, number of rods, and brix). Moderate H2 (number of leaves).

Genetic similarity among Dendrobium species from Indonesia using RAPD markers

Abstract. Hartati S, Samanhudi, Cahyono O. 2023. Genetic similarity among Dendrobium species from Indonesia using RAPD markers. Biodiversitas 24: 5015-5021. Dendrobium genus orchids are one of the most popular commodities in the world due to their diverse range of flower sizes, shapes, and colors. To enhance plant breeding programs and genetic resources, it is necessary to obtain information on genetic similarity between orchids of the Dendrobium genus through molecular analysis techniques. Therefore, this study aimed to assess the genetic similarity among five Dendrobium species using molecular markers, specifically RAPD. The plant material used was obtained from five species of the Dendrobium spp, namely (i) Dendrobium mirbelianum; (ii) Dendrobium lamellatum, (iii) Dendrobium secundum, (iv) Dendrobium bracteosum, and (v) Dendrobium purpureum. The analysis was carried out to determine the genetic diversity of the Dendrobium orchids using RAPD markers. A total of five RAPD primers were used for amplification, namely OPD 8, OPA 7, OPA 13, OPB 12, and OPB 18. The scoring data were analyzed using NTSYS-pc (Numerical Taxonomy and Multivariate Analysis System) version 2.02 which produced data in the form of cluster dendrograms. The dendrogram that was constructed by Unweighted Pair Group Method Using Arithmetic Average (UPGMA) classified the five Dendrobium species into two main clusters. The results showed that there were two clusters, namely Cluster A consisting of D. mirbelianum, D. lamellatum, and D. secundum while Cluster B consisted of D. bracteosum and D. purpureum. Furthermore, the polymorphism of the five RAPD primers was very high, ranging from 91.6 to 100%, measuring 250-1900 bp. The coefficient of genetic similarity analyzed using the five RAPD primers ranged from 0.24 to 0.77. The species D. mirbelianum and D. lamellatum had a high coefficient of genetic similarity. Which can be discovered through parental selection in breeding program.

Impact of Low Light Stress on Physiological Characters, Yield and Yield Attributes of Rice (Oryza sativa L.)

Background: Keeping in view of low light effect on rice yield and quality, identification of low light tolerant genotypes as donor for plant breeding program is needed. Methods: Field experiments were conducted for low light tolerance during kharif 2021 at Regional Agricultural Research Station, Maruteru with an aim to screen and identify low light tolerant cultivars. The impact of low light was investigated on physiological, yield and yield attributes of eighteen rice genotypes. Result: Low light treatment resulted and increase in mean days to 50% flowering and days to maturity. An increment of both plant height and leaf area was noted under low light stress in all the tested genotypes. Yield and yield attributes such as panicle number, grain number per panicle and test weight were also affected. The reduction in total dry matter and harvest index was recorded on exposure to low light condition. Under low light grain yield was maximum in IET 29032 followed by IET 30408 and Gayatri and these were identified as low light tolerant genotypes. These genotypes had a higher panicle number. Genotype Gayatri showed maximum 1000 grain weight and minimum reduction in harvest index was evident in IET 29032 and IET 30408. The chlorophyll a pigment content reduced under low light while majority of the genotypes showed an increase in chlorophyll b pigment. Gayatri followed by IET 29032 and IET 30408 retained higher chlorophyll content under low light stress. Lesser reduction in chlorophyll content (less than 13%) was evident in IET 30408, Gayatri, Swarnaprabha, IET 29031 and IET 29032.

ShinyFruit: interactive fruit phenotyping software and its application in blackberry.

Horticultural plant breeding programs often demand large volumes of phenotypic data to capture visual variation in quality of harvested products. Increasing the throughput potential of phenomic pipelines enables breeders to consider data-hungry molecular breeding strategies such as genome-wide association studies and genomic selection. We present an R-based web application called ShinyFruit for image-based phenotyping of size, shape, and color-related qualities in fruits and vegetables. Here, we have demonstrated one potential application for ShinyFruit by comparing its estimates of fruit length, width, and red drupelet reversion (RDR) with ImageJ and analogous manual phenotyping techniques in a population of blackberry cultivars and breeding selections from the University of Arkansas System Division of Agriculture Fruit Breeding Program. ShinyFruit results shared a strong positive correlation with manual measurements for blackberry length (r = 0.96) and ImageJ estimates of RDR (r = 0.96) and significant, albeit weaker, correlations with manual RDR estimation methods (r = 0.62 - 0.70). Neither phenotyping method detected genotypic differences in blackberry fruit width, suggesting that this trait is unlikely to be heritable in the population observed. It is likely that implementing a treatment to promote RDR expression in future studies might strengthen the documented correlation between phenotyping methods by maximizing genotypic variance. Even so, our analysis has suggested that ShinyFruit provides a viable, open-source solution to efficient phenotyping of size and color in blackberry fruit. The ability for users to adjust analysis settings should also extend its utility to a wide range of fruits and vegetables.

A Method of Constructing p-rep Designs

Background: Early generation breeding trials (EGBTs) are very important in plant breeding programmes. In most cases, a large number of breeding lines are to be tested, often with very few available resources and it is also required to repeat these trials in a number of environments. For such trials, an alternative is to use partially replicated designs, where a proportion of the test lines are replicated at each environment. Methods: Here, a general method of constructing a series of efficient partially replicated designs for EGBTs in equal block sizes, through initial blocks is developed. Result: Taking all environments together, the designs obtained are equi-replicate and are partially balanced. They are cost effective in terms of resources as they require lesser replications.

The Development of Thematic Core Collections in Cassava Based on Yield, Disease Resistance, and Root Quality Traits.

Thematic collections (TCs), which are composed of genotypes with superior agronomic traits and reduced size, offer valuable opportunities for parental selection in plant breeding programs. Three TCs were created to focus on crucial attributes: root yield (CC_Yield), pest and disease resistance (CC_Disease), and root quality traits (CC_Root_quality). The genotypes were ranked using the best linear unbiased predictors (BLUP) method, and a truncated selection was implemented for each collection based on specific traits. The TCs exhibited minimal overlap, with each collection comprising 72 genotypes (CC_Disease), 63 genotypes (CC_Root_quality), and 64 genotypes (CC_Yield), representing 4%, 3.5%, and 3.5% of the total individuals in the entire collection, respectively. The Shannon-Weaver Diversity Index values generally varied but remained below 10% when compared to the entire collection. Most TCs exhibited observed heterozygosity, genetic diversity, and the inbreeding coefficient that closely resembled those of the entire collection, effectively retaining 90.76%, 88.10%, and 88.99% of the alleles present in the entire collection (CC_Disease, CC_Root_quality, and CC_Disease, respectively). A PCA of molecular and agro-morphological data revealed well-distributed and dispersed genotypes, while a discriminant analysis of principal components (DAPC) displayed a high discrimination capacity among the accessions within each collection. The strategies employed in this study hold significant potential for advancing crop improvement efforts.

How Can Pairing Quantitative With Qualitative Data Collection Methods Better Elicit Rice Varietal Selection? Evidence From Burundi

Participatory Varietal Selection (PVS) is the selection by stakeholders of varieties in advanced testing stages by plant breeding programs. With Burundi as a case example, this study incorporated qualitative Focus Group Discussions (FGDs) into the quantitative PVS structure so as to elicit deeper insights into rice trait preferences and illuminate broader issues affecting rice farmers. During two consecutive years, this study surveyed 174 participants across six stakeholder groups (administrators, farmers, custom millers, researchers, seed producers, and traders) in three locations. There were statistically significant associations in rice trait preferences across locations, participating stakeholders, and genders, highlighting preference alignment. Moreover, multiple traits were desired simultaneously, beyond productivity-related traits, and sometimes contradicting researchers’ preferences, especially in rainfed systems. By moving beyond quantitative PVS preference scores as being the only way of gathering trait preference data, this study has shown how the incorporation of qualitative FGDs into the PVS structure can elicit deeper insights on trait preferences and illuminate broader issues affecting rice farmers, which when solved can accelerate the momentum in widespread adoption of new rice varieties.

Application of combination of auxins in callus induction and proliferation of shallot cv Trisula and Sumenep through in vitro technique

Shallot (Allium cepa L. var. aggregatum) is one of a strategic commodity in Indonesia. To increase of production of shallot seed, there needs a good tuber with high productivity tolerant to abiotic stress. One of the obstacles to increase of shallot production with tuber in good quality and tolerant to abiotic stress is not available. Tissue culture technique is one of the methods proved can propagate plant in large amounts and in a shorter time compared to the conventional method. The objectives of this research are to get the best media formulation for callus induction and proliferation. The explant used in this research are shoot tip of shallot from cultivar Trisula and Sumenep. The Media used were MS. The medium was enriched with (0,5 – 3 mg/l) 2,4-D + 2 mg/l picloram (0,5 – 3 mg/l) or NAA + with or without 3 gr/l Casein hydrolysate; and MS + 3 mg/l Picloram + 2 or 3 mg/l NAA + 3 g/l Casein Hydrolysate. The best media formulation for callus induction of Trisula variety was MS + 3 mg/l 2,4D + 2 mg/l picloram + 3 g/l Casein Hydrolysate, whereas the best media formulation for callus induction of Sumenep variety was MS + 0.5 mg/l 2,4-D + 2 mg/l picloram. The best media formulation for callusproliferation was MS + 0.5 mg/l 2,4-D + 2 mg/l picloram both Trisula and Sumenep varieties. These results could be used for plant propagation and breeding program of shallot in the future.

Selection of drought-tolerant rice genotypes under cajuput (Melaleuca cajuputi subsp. cajuputi) agroforestry system

Abstract. Taryono, Supriyanta, Wulandari RA, Nurmansyah, Ambarwati E, Arsana IGKD, Aristya VE, Purba AE, Aisya AW, Alam T. 2023. Selection of drought-tolerant rice genotypes under cajuput (Melaleuca cajuputi subsp. cajuputi) agroforestry system. Biodiversitas 24: 4791-4802. Rice production can be increased by expanding rice cultivation area on agroforestry land. However, planting rice in agroforestry system, which is generally rainfed, might cause drought stress and eventually can reduce rice yields. The study aimed to select rice genotypes under drought stress conditions in a rainfed agroforestry system with cajuput or kayu putih (Melaleuca cajuputi subsp. cajuputi Powell). Ten promising rice lines and four control cultivars were grown during the dry season from March-July 2022 at the Menggoran Forest Management Resort, Playen Forest Section, Yogyakarta Forest Management, Indonesia. The observation was conducted on drought stress tolerance (sensitivity and recovery), molecular identification, and yield of rice cultivars. The results showed that G5 was the genotype with drought tolerance and had the best recovery compared to the other promising rice lines and control cultivars. The molecular identification of the 14 rice genotypes using 20 SSR markers detected 108 alleles with a PIC value of 0.707. The G5, G4, and G9 had higher yields than the control cultivars. The BLUE showed that the yield of G5, G4, and G9 were 6.59, 5.93 and 5.50 tons ha-1, respectively, while for BLUP by 6.45, 5.87 and 5.43 tons ha-1, respectively. Furthermore, there were four clusters consisting of cluster 1 (G9), cluster 2 (G7), cluster 3 (G5 and Inpago 12), and cluster 4 (Inpari 42, G6, Inpari 30 Ciherang Sub 1, Situ Bagendit, G3, G4, G10, G2, G1, and G8). The findings of this study recommend G4, G5, and G9 as promising rice lines which tolerant to drought stress under M. cajuputi agroforestry system and can be utilized in future plant breeding programs.

3D reconstruction of plants using probabilistic voxel carving

We propose a probabilistic voxel carving algorithm to efficiently reconstruct 3D models of maize plants and extract leaf traits for phenotyping. Traditional voxel carving algorithm is restricted to a limited number of views and usually requires multiple coordinated cameras in the imaging setup. They are also not robust small movements of the object, which introduce noise into the data. These imperfections in data collection can lead to large regions of the object being carved away during the voxel carving process, leading to incomplete and disjoint objects. We have developed a probabilistic voxel carving algorithm to overcome these challenges. In this approach, instead of carving out or keeping a voxel in a binary manner, we associate a probability of a voxel corresponding to it being part of the plant. We then use a user-defined probability cutoff to obtain the final voxelized plant geometry. We optimize the data collection procedure by adopting a rotating base to hold the plant and then capturing videos of the rotating plants, thereby obtaining an arbitrary number of views by extracting the image frames. Additionally, we leverage GPU computing to implement our voxel carving and trait extraction pipeline for a large dataset with over 1000 maize plants with high voxel resolutions (such as 10243). Our results demonstrate that our algorithm is robust and can handle an arbitrary number of views, and can automatically extract plant traits such as the number of leaves and leaf angles. Our approach shows that 3D reconstructions of plants from multi-view images can accurately extract multiple phenotypic traits, enabling better plant breeding programs.

Genomic selection for salinity tolerance in japonica rice.

Improving plant performance in salinity-prone conditions is a significant challenge in breeding programs. Genomic selection is currently integrated into many plant breeding programs as a tool for increasing selection intensity and precision for complex traits and for reducing breeding cycle length. A rice reference panel (RP) of 241 Oryza sativa L. japonica accessions genotyped with 20,255 SNPs grown in control and mild salinity stress conditions was evaluated at the vegetative stage for eight morphological traits and ion mass fractions (Na and K). Weak to strong genotype-by-condition interactions were found for the traits considered. Cross-validation showed that the predictive ability of genomic prediction methods ranged from 0.25 to 0.64 for multi-environment models with morphological traits and from 0.05 to 0.40 for indices of stress response and ion mass fractions. The performances of a breeding population (BP) comprising 393 japonica accessions were predicted with models trained on the RP. For validation of the predictive performances of the models, a subset of 41 accessions was selected from the BP and phenotyped under the same experimental conditions as the RP. The predictive abilities estimated on this subset ranged from 0.00 to 0.66 for the multi-environment models, depending on the traits, and were strongly correlated with the predictive abilities on cross-validation in the RP in salt condition (r = 0.69). We show here that genomic selection is efficient for predicting the salt stress tolerance of breeding lines. Genomic selection could improve the efficiency of rice breeding strategies for salinity-prone environments.

Deciphering the morpho-physiological and biochemical responses in Lablab purpureus (L.), Sweet, seedlings to water stress

Drought is a major crop productivity-limiting factor, which adversely affects growth and yield worldwide. Lablab (Lablab pupureus [L.] Sweet), is better adapted to withstand drought stress, however, characterization of lablab in Kenya has not been undertaken and there is limited knowledge on plant response to drought stress. Ten lablab genotypes were evaluated under greenhouse to determine the morpho-physiological and biochemical response at the seedling stage. The study revealed significant differential responses among lablab genotypes to water stress. The interaction between the water stress and the genotypes significantly affected the evaluated traits. Genotypes D1 and D6 showed significantly increased and superior morphological adaptation, respectively, indicating enhanced adaptation for tolerance to water stress. Significant variations of ascorbate peroxidase, guaiacol peroxidase and catalase activities were detected during the experimental period. The plants exhibited varying non-enzymatic antioxidants and osmolytes with an increase in proline, total phenol and water-soluble carbohydrates and a decline in free amino acids and water-soluble proteins under severe water stress. Genotype D10 recorded high antioxidant enzymatic activity and water-soluble carbohydrates, whereas D1 accumulated more proline and proteins, indicating better adaptation in scavenging reactive oxygen species to prevent membrane damage. The higher photosynthetic efficiency of seedlings of genotype D1 and superior morphological adaptation of D6 indicated better agronomic adaptation to severe water stress and could be utilized in plant breeding programmes for tolerance to drought.

Multi-Traits Selection of Maize Hybrids under Sole-Crop and Multiple-Crops with Soybean

Multi-trait selection is very helpful in plant breeding programs. This technique can select genotypes for specific traits. Yield performance of maize hybrids is influenced by genotype-by-environment interactions (GEIs). This requires multi-environmental testing to identify superior hybrids. The aims of this study were to: (1) select maize hybrids within favorable traits such as grain yield and yield attributes; (2) identify genotype and environment interactions (GEIs) within maize yield; (3) evaluate maize hybrids adaptability to four different locations in West Java; and (4) identify a representative environment for testing and developing new maize hybrids. Twenty-two maize hybrids were evaluated using a randomized block design repeated three times in each environment. The genotype by traits (GT) biplot showed that G3 maize hybrid was superior to ear weight (EW), ear weight without corn husk (EwoH), ear weight per plot (EWP), plant height (PH), ear diameter (ED), and 1000 seed weight (OSW) traits. The G22 maize hybrid was superior to ear weight without corn husk per plot (EwoP), ratio seed weight and ear weight without corn husk (RSEW), grain yield per ha (Y), seed weight (SW), and seed weight per plot (SWP) traits. Combined analysis of variance showed that genotype (G), environment (E), and their interactions (GEIs) had a significant effect on yield (p &lt; 0.01) with contributions of 85.63%, 2.24%, and 12.13%, respectively. The combined stability measurement results and the GGE biplot reveal that G2 is a stable high-yield hybrid, while G4 and G16 are unstable high-yield hybrids. The most representative environment is Arjasari, with the sole-cropping system (ARJ M). This study reveals that the resulting maize hybrid can be useful as a substitute for commercial hybrids currently used. The stable high yield of maize hybrids can be recommended as new varieties with wide adaptation, while unstable high-yield hybrids can be recommended as varieties adapted to specific environments.

The Role of Genetics and Plant Breeding for Crop Improvement: Current Progress and Future Prospects

Genetics and plant breeding play crucial roles in driving crop improvement efforts, ensuring sustainable food production and addressing global challenges such as population growth and climate change. This review article provides a comprehensive overview of the role of genetics and plant breeding in crop improvement, The article explores the fundamental principles of genetics, including inheritance patterns and genetic variation, and their implications for trait expression in crops. It discusses the application of genetic markers and quantitative trait loci (QTL) mapping, along with the emerging field of genomic selection, in facilitating the selection of desirable traits for plant breeding programs. Traditional breeding methods, hybridization, and the integration of advanced molecular breeding tools are explored as means to enhance crop performance. Additionally, the potential of genome editing technologies, such as CRISPR-Cas9, in accelerating the breeding process and enabling precise modifications in plant genomes is discussed. The article also addresses important considerations in crop improvement, such as balancing yield improvement with agronomic traits, enhancing resistance against biotic and abiotic stresses, and incorporating socio-economic and environmental factors. Germplasm conservation and utilization for future breeding endeavors are emphasized. Overall, the review highlights the pivotal role of genetics and plant breeding in achieving crop improvement and underscores the need for ongoing research and innovation to meet the challenges of the future.

Image-based phenotyping of seed architectural traits and prediction of seed weight using machine learning models in soybean.

Among seed attributes, weight is one of the main factors determining the soybean harvest index. Recently, the focus of soybean breeding has shifted to improving seed size and weight for crop optimization in terms of seed and oil yield. With recent technological advancements, there is an increasing application of imaging sensors that provide simple, real-time, non-destructive, and inexpensive image data for rapid image-based prediction of seed traits in plant breeding programs. The present work is related to digital image analysis of seed traits for the prediction of hundred-seed weight (HSW) in soybean. The image-based seed architectural traits (i-traits) measured were area size (AS), perimeter length (PL), length (L), width (W), length-to-width ratio (LWR), intersection of length and width (IS), seed circularity (CS), and distance between IS and CG (DS). The phenotypic investigation revealed significant genetic variability among 164 soybean genotypes for both i-traits and manually measured seed weight. Seven popular machine learning (ML) algorithms, namely Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Random Forest (RF), Support Vector Regression (SVR), LASSO Regression (LR), Ridge Regression (RR), and Elastic Net Regression (EN), were used to create models that can predict the weight of soybean seeds based on the image-based novel features derived from the Red-Green-Blue (RGB)/visual image. Among the models, random forest and multiple linear regression models that use multiple explanatory variables related to seed size traits (AS, L, W, and DS) were identified as the best models for predicting seed weight with the highest prediction accuracy (coefficient of determination, R2=0.98 and 0.94, respectively) and the lowest prediction error, i.e., root mean square error (RMSE) and mean absolute error (MAE). Finally, principal components analysis (PCA) and a hierarchical clustering approach were used to identify IC538070 as a superior genotype with a larger seed size and weight. The identified donors/traits can potentially be used in soybean improvement programs.

Genome-wide association studies: an intuitive solution for SNP identification and gene mapping in trees.

Analysis of natural diversity in wild/cultivated plants can be used to understand the genetic basis for plant breeding programs. Recent advancements in DNA sequencing have expanded the possibilities for genetically altering essential features. There have been several recently disclosed statistical genetic methods for discovering the genes impacting target qualities. One of these useful methods is the genome-wide association study (GWAS), which effectively identifies candidate genes for a variety of plant properties by examining the relationship between a molecular marker (such as SNP) and a target trait. Conventional QTL mapping with highly structured populations has major limitations. The limited number of recombination events results in poor resolution for quantitative traits. Only two alleles at any given locus can be studied simultaneously. Conventional mapping approach fails to work in perennial plants and vegetatively propagated crops. These limitations are sidestepped by association mapping or GWAS. The flexibility of GWAS comes from the fact that the individuals being examined need not be linked to one another, allowing for the use of all meiotic and recombination events to increase resolution. Phenotyping, genotyping, population structure analysis, kinship analysis, and marker-trait association analysis are the fundamental phases of GWAS. With the rapid development of sequencing technologies and computational methods, GWAS is becoming a potent tool for identifying the natural variations that underlie complex characteristics in crops. The use of high-throughput sequencing technologies along with genotyping approaches like genotyping-by-sequencing (GBS) and restriction site associated DNA (RAD) sequencing may be highly useful in fast-forward mapping approach like GWAS. Breeders may use GWAS to quickly unravel the genomes through QTL and association mapping by taking advantage of natural variances. The drawbacks of conventional linkage mapping can be successfully overcome with the use of high-resolution mapping and the inclusion of multiple alleles in GWAS.

Genetic variation for grain nutritional profile and yield potential in sorghum and the possibility of selection for drought tolerance under irrigated conditions

BackgroundIncreasing grain nutritional value in sorghum (Sorghum bicolor) is a paramount breeding objective, as is increasing drought resistance (DR), because sorghum is grown mainly in drought-prone areas. The genetic basis of grain nutritional traits remains largely unknown. Marker-assisted selection using significant loci identified through genome-wide association study (GWAS) shows potential for selecting desirable traits in crops. This study assessed natural variation available in sorghum accessions from around the globe to identify novel genes or genomic regions with potential for improving grain nutritional value, and to study associations between DR traits and grain weight and nutritional composition.ResultsWe dissected the genetic architecture of grain nutritional composition, protein content, thousand-kernel weight (TKW), and plant height (PH) in sorghum through GWAS of 163 unique African and Asian accessions under irrigated and post-flowering drought conditions. Several QTLs were detected. Some were significantly associated with DR, TKW, PH, protein, and Zn, Mn, and Ca contents. Genomic regions on chromosomes 1, 2, 4, 8, 9, and 10 were associated with TKW, nutritional, and DR traits; colocalization patterns of these markers indicate potential for simultaneous improvement of these traits. In African accessions, markers associated with TKW were mapped to six regions also associated with protein, Zn, Ca, Mn, Na, and DR, suggesting the potential for simultaneous selection for higher grain nutrition and TKW. Our results indicate that it may be possible to select for increased DR on the basis of grain nutrition and weight potential.ConclusionsThis study provides a valuable resource for selecting landraces for use in plant breeding programs and for identifying loci that may contribute to grain nutrition and weight with the hope of producing cultivars that combine improved yield traits, nutrition, and DR.

Improved genomic prediction using machine learning with Variational Bayesian sparsity

BackgroundGenomic prediction has become a powerful modelling tool for assessing line performance in plant and livestock breeding programmes. Among the genomic prediction modelling approaches, linear based models have proven to provide accurate predictions even when the number of genetic markers exceeds the number of data samples. However, breeding programmes are now compiling data from large numbers of lines and test environments for analyses, rendering these approaches computationally prohibitive. Machine learning (ML) now offers a solution to this problem through the construction of fully connected deep learning architectures and high parallelisation of the predictive task. However, the fully connected nature of these architectures immediately generates an over-parameterisation of the network that needs addressing for efficient and accurate predictions.ResultsIn this research we explore the use of an ML architecture governed by variational Bayesian sparsity in its initial layers that we have called VBS-ML. The use of VBS-ML provides a mechanism for feature selection of important markers linked to the trait, immediately reducing the network over-parameterisation. Selected markers then propagate to the remaining fully connected feed-forward components of the ML network to form the final genomic prediction. We illustrated the approach with four large Australian wheat breeding data sets that range from 2665 lines to 10375 lines genotyped across a large set of markers. For all data sets, the use of the VBS-ML architecture improved genomic prediction accuracy over legacy linear based modelling approaches.ConclusionsAn ML architecture governed under a variational Bayesian paradigm was shown to improve genomic prediction accuracy over legacy modelling approaches. This VBS-ML approach can be used to dramatically decrease the parameter burden on the network and provide a computationally feasible approach for improving genomic prediction conducted with large breeding population numbers and genetic markers.