Identifying Anti-Oxidant Biosynthesis Genes in Pearl Millet [Pennisetum glaucum (L.) R. Br.] Using Genome—Wide Association Analysis

Abstract

Pearl millet [Pennisetum glaucum (L.) R Br.] is an important staple food crop in the semi-arid tropics of Asia and Africa. It is a cereal grain that has the prospect to be used as a substitute for wheat flour for celiac patients. It is an important antioxidant food resource present with a wide range of phenolic compounds that are good sources of natural antioxidants. The present study aimed to identify the total antioxidant content of pearl millet flour and apply it to evaluate the antioxidant activity of its 222 genotypes drawn randomly from the pearl millet inbred germplasm association panel (PMiGAP), a world diversity panel of this crop. The total phenolic content (TPC) significantly correlated with DPPH (1,1-diphenyl-2-picrylhydrazyl) radical scavenging activity (% inhibition), which ranged from 2.32 to 112.45% and ferric-reducing antioxidant power (FRAP) activity ranging from 21.68 to 179.66 (mg ascorbic acid eq./100 g). Genome-wide association studies (GWAS) were conducted using 222 diverse accessions and 67 K SNPs distributed across all the seven pearl millet chromosomes. Approximately, 218 SNPs were found to be strongly associated with DPPH and FRAP activity at high confidence [–log (p) > 3.0–7.4]. Furthermore, flanking regions of significantly associated SNPs were explored for candidate gene harvesting. This identified 18 candidate genes related to antioxidant pathway genes (flavanone 7-O-beta-glycosyltransferase, GDSL esterase/lipase, glutathione S-transferase) residing within or near the association signal that can be selected for further functional characterization. Patterns of genetic variability and the associated genes reported in this study are useful findings, which would need further validation before their utilization in molecular breeding for high antioxidant-containing pearl millet cultivars.

Metabolite Diversity and Metabolic Genome-Wide Marker Association Studies (Mgwas) for Health Benefiting Nutritional Traits in Pearl Millet Grains

“…A total of 197 genotypes were picked randomly from the pearl millet inbred germplasm association panel (PMiGAP), as described in Sehgal et al [21] and Yadav et al [22], were used in this study. These inbred lines represent the diverse pearl millet germplasm originating from 22 different countries including 40 accessions from ICRISAT, 15 accessions from India, 22 from Niger, 10 from Nigeria, 9 each from Namibia, Zimbabwe, Togo, and the remaining 52 from other pearl millet growing regions (Supplementary Table S1).…”

“…These inbred lines represent the diverse pearl millet germplasm originating from 22 different countries including 40 accessions from ICRISAT, 15 accessions from India, 22 from Niger, 10 from Nigeria, 9 each from Namibia, Zimbabwe, Togo, and the remaining 52 from other pearl millet growing regions (Supplementary Table S1). The seeds of each of the 197 accessions were multiplied in the uniform field conditions at ICRISAT, Patancheru, India, following standard agronomic practices as described by Upadhyaya et al [23], Ramya et al [24], and Yadav et al [22]. The seeds were collected after selfing under strict pollination conditions to obtain pure seeds of each line.…”

“…The PMiGAP lines showed significant variation in the following: starch and sucrose metabolism; cutin, suberin and wax biosynthesis, antioxidants biosynthesis pathways (such as folate biosynthesis, inositol phosphate metabolism, flavonoid biosynthesis, thiamine metabolism, carotenoid biosynthesis, flavone and flavonol biosynthesis, zeatin biosynthesis, stilbenoid, diarylheptanoid, and gingerol biosynthesis); lipid biosynthesis (fatty acid degradation, fatty acid biosynthesis, biosynthesis of unsaturated fatty acids), vitamins (one carbon pool by folate, riboflavin metabolism, nicotinate and nicotinamide metabolism, linoleic acid metabolism, biotin metabolism, vitamin b6 metabolism) and nitrogen metabolism. Broad sense heritability (H 2 ) was analyzed as described by Yadav et al [22], and the best linear unbiased prediction (BLUP) value was estimated as defined in the user manual of HAPPI GWAS (Holistic Analysis with Pre-and Post-Integration GWAS) [25]. The BLUP values were used as the input data for metabolic genome-wide marker association studies.…”

“…Metabolic genome-wide marker association studies (mGWAS) were performed using a mixed linear model (MLM) on an R platform implemented in GAPIT (Genomic Association and Prediction Integrated Tool) for each of the metabolites contributing to the identified nutritional traits [26]. MLM is a robust model for rectifying the fixed and random genetic effects and controlling the bias of population stratification while identifying associations between SNP polymorphisms and the traits [22]. Additionally, the model also reduces any confounding effect between testing markers using the kinship matrix (K) and Q matrix as random effects.…”

“…mGWAS analyses were performed for 333 mass features using 76648 polymorphic SNPs, which were filtered following Yadav et al [22] with minor allele frequencies (MAFs) < 0.05, or missing data > 20% from the peal millet 28 M SNP described in Varshney et al [4]. A total of 6714 SNP showed significant association with 333 metabolites at the p-value of ≤ 0.001 (Supplementary Table S4).…”