QTL mapping and analysis of the embryo and maternal plant for three limiting amino acids in rapeseed meal

Genome-wide association study reveals the genetic basis of amino acids contents variations in Peanut (Arachis hypogaea L.)

Peanut is a significant source of protein for human consumption. One of the primary objectives in peanut breeding is the development of new cultivars with enhanced nutritional values. To further this goal, a genome-wide association study (GWAS) was conducted to analyze seed amino acids contents in 390 diverse peanut accessions collected worldwide, mainly from China, India, and the United States, in 2017 and 2018. These accessions were assessed for their content of 10 different amino acids. Variations in amino acids contents were observed, and arginine (Arg) was found to have the highest average value among all the amino acids quantified. The geographical distribution of the accessions also revealed variations in amino acids contents. High and positive correlation coefficients were observed among the amino acids, suggesting linked metabolic pathways or genetic regulation. A total of 88 single nucleotide polymorphisms (SNPs) spanning various chromosomes were identified, each associated with different amino acids. By using a combination of GWAS, expression anlaysis, and genomic polymorphisim comparisions, the Ahy_A09g041582 (LAC15) gene located on chromrosome A09 was identified as the key candidate which might be involved in plant growth and regulation and may alter amino acids levels. Expression analysis indicated that Ahy_A09g041582 has higher expressions in the shells and seeds than other genes located in the candidate region. This study may help with marker-based breeding of peanuts with higher nutritional value and offers fresh insights into the genetic basis of the amino acids contents of peanuts.

Enhancing the Nutritional Quality of Major Food Crops Through Conventional and Genomics-Assisted Breeding

Nutritional stress is making over two billion world population malnourished. Either our commercially cultivated varieties of cereals, pulses, and oilseed crops are deficient in essential nutrients or the soils in which these crops grow are becoming devoid of minerals. Unfortunately, our major food crops are poor sources of micronutrients required for normal human growth. To overcome the problem of nutritional deficiency, greater emphasis should be laid on the identification of genes/quantitative trait loci (QTLs) pertaining to essential nutrients and their successful deployment in elite breeding lines through marker-assisted breeding. The manuscript deals with information on identified QTLs for protein content, vitamins, macronutrients, micro-nutrients, minerals, oil content, and essential amino acids in major food crops. These QTLs can be utilized in the development of nutrient-rich crop varieties. Genome editing technologies that can rapidly modify genomes in a precise way and will directly enrich the nutritional status of elite varieties could hold a bright future to address the challenge of malnutrition.

Seed Quality Traits Can Be Predicted with High Accuracy in Brassica napus Using Genomic Data

Improving seed oil yield and quality are central targets in rapeseed (Brassica napus) breeding. The primary goal of our study was to examine and compare the potential and the limits of marker-assisted selection and genome-wide prediction of six important seed quality traits of B. napus. Our study is based on a bi-parental population comprising 202 doubled haploid lines and a diverse validation set including 117 B. napus inbred lines derived from interspecific crosses between B. rapa and B. carinata. We used phenotypic data for seed oil, protein, erucic acid, linolenic acid, stearic acid, and glucosinolate content. All lines were genotyped with a 60k SNP array. We performed five-fold cross-validations in combination with linkage mapping and four genome-wide prediction approaches in the bi-parental population. Quantitative trait loci (QTL) with large effects were detected for erucic acid, stearic acid, and glucosinolate content, blazing the trail for marker-assisted selection. Despite substantial differences in the complexity of the genetic architecture of the six traits, genome-wide prediction models had only minor impacts on the prediction accuracies. We evaluated the effects of training population size, marker density and phenotyping intensity on the prediction accuracy. The prediction accuracy in the independent and genetically very distinct validation set still amounted to 0.14 for protein content and 0.17 for oil content reflecting the utility of the developed calibration models even in very diverse backgrounds.