Performance and Stability of Pearl Millet Varieties for Grain Yield and Micronutrients in Arid and Semi-Arid Regions of India.

Pearl millet [Pennisetum glaucum (L.) R. Br.] is grown under both arid and semi-arid conditions in India, where other cereals are hard to grow. Pearl millet cultivars, hybrids, and OPVs (open pollinated varieties) are tested and released by the All India Coordinated Research Project on Pearl Millet (AICRP-PM) across three zones (A1, A, and B) that are classified based on rainfall pattern. Except in locations with extreme weather conditions, hybrids dominate pearl millet growing areas, which can be attributed to hybrid vigor and the active role of the private sector. The importance of OPVs cannot be ruled out, owing to wider adaptation, lower input cost, and timely seed availability to subsidiary farmers cultivating this crop. This study was conducted to scrutinize the presently used test locations for evaluation of pearl millet OPVs across India, identify the best OPVs across locations, and determine the variation in grain Fe and Zn contents across locations in these regions. Six varieties were evaluated across 20 locations in A1 and A (pooled as A) and B zones along with three common checks and additional three zonal adapted checks in the respective zones during the 2019 rainy season. Recorded data on yield and quality traits were analyzed using genotype main effects and genotype × environment interaction biplot method. The genotype × environment (G × E) interaction was found to be highly significant for all the grain yield and agronomic traits and for both micronutrients (iron and zinc). However, genotypic effect (G) was four (productive tillers) to 49 (grain Fe content) times that of G × E interaction effect for various traits across zones that show the flexibility of OPVs. Ananthapuramu is the ideal test site for selecting pearl millet cultivars effectively for adaptation across India, while Ananthapuramu, Perumallapalle, and Gurugram can also be used as initial testing locations. OPVs MP 599 and MP 600 are identified as ideal genotypes, because they showed higher grain and fodder yields and stability compared with other cultivars. Iron and zinc concentration showed highly significant positive correlation (across environment = 0.83; p < 0.01), indicating possibility of simultaneous effective selection for both traits. Three common checks were found to be significantly low yielders than the test entries or zonal checks in individual zones and across India, indicating the potential of genetic improvement through OPVs.

Identification of stable reference gene for transcript normalization in black pepper-Phytophthora capsici pathosystem.

A systematic validation of reference genes is a pre-requisite for the proper normalization of gene transcripts. In the present study, the annotated sequences from black pepper (Piper nigrum L.) leaf transcriptome were used as reference genes namely actin (PnACT), glyceraldehyde phosphate dehydrogenase (PnGAPDH), ß-tubulin (PnTUB), ubiquitin conjugating enzyme (PnUBCE), 18srRNA and elongation factor-1-α (PnElF) to identify the stable reference gene. We focused the selection of stable reference gene on important biotic stress (Phytophthora) with different algorithms (geNorm, NormFinder and BestKeeper) along with Reffinder which resulted in identification of PnGAPDH and PnUBCE as stable genes. Norm qPCR (R package) was also used to estimate the stability of the selected genes. We elucidated the expression patterns of a target gene PnBGLU which codes for 1,3 beta glucanase with most stable as well as least stable reference genes by which the importance of selecting the stable gene for gene expression studies in this system was emphasized. The mean expression levels of PnBGLU was significantly overestimated and misinterpreted when least stable reference gene was used as normalizer. The selected reference genes on further analysis of the expression dynamics of PnBGLU among resistant and susceptible genotypes showed PnGAPDH as the suitable reference gene for P. nigrum-P. capsici pathosystem.

Deciphering Genomic Regions for High Grain Iron and Zinc Content Using Association Mapping in Pearl Millet.

Micronutrient malnutrition, especially deficiency of two mineral elements, iron [Fe] and zinc [Zn] in the developing world needs urgent attention. Pearl millet is one of the best crops with many nutritional properties and is accessible to the poor. We report findings of the first attempt to mine favorable alleles for grain iron and zinc content through association mapping in pearl millet. An association mapping panel of 130 diverse lines was evaluated at Delhi, Jodhpur and Dharwad, representing all the three pearl millet growing agro-climatic zones of India, during 2014 and 2015. Wide range of variation was observed for grain iron (32.3-111.9 ppm) and zinc (26.6-73.7 ppm) content. Genotyping with 114 representative polymorphic SSRs revealed 0.35 mean gene diversity. STRUCTURE analysis revealed presence of three sub-populations which was further supported by Neighbor-Joining method of clustering and principal coordinate analysis (PCoA). Marker-trait associations (MTAs) were analyzed with 267 markers (250 SSRs and 17 genic markers) in both general linear model (GLM) and mixed linear model (MLM), however, MTAs resulting from MLM were considered for more robustness of the associations. After appropriate Bonferroni correction, Xpsmp 2261 (13.34% R2-value), Xipes 0180 (R2-value of 11.40%) and Xipes 0096 (R2-value of 11.38%) were consistently associated with grain iron and zinc content for all the three locations. Favorable alleles and promising lines were identified for across and specific environments. PPMI 1102 had highest number (7) of favorable alleles, followed by four each for PPMFeZMP 199 and PPMI 708 for across the environment performance for both grain Fe and Zn content, while PPMI 1104 had alleles specific to Dharwad for grain Fe and Zn content. When compared with the reference genome Tift 23D2B1-P1-P5, Xpsmp 2261 amplicon was identified in intergenic region on pseudomolecule 5, while the other marker, Xipes 0810 was observed to be overlapping with aspartic proteinase (Asp) gene on pseudomolecule 3. Thus, this study can help in breeding new lines with enhanced micronutrient content using marker-assisted selection (MAS) in pearl millet leading to improved well-being especially for women and children.

Mapping QTLs Controlling Flowering Time and Important Agronomic Traits in Pearl Millet.

Pearl millet [Pennisetum glaucum (L.) R. Br.] is a staple crop for the people of arid and semi-arid regions of the world. It is fast gaining importance as a climate resilient nutricereal. Exploiting the bold seeded, semi-dwarf, and early flowering genotypes in pearl millet is a key breeding strategy to enhance yield, adaptability, and for adequate food in resource-poor zones. Genetic variation for agronomic traits of pearl millet inbreds can be used to dissect complex traits through quantitative trait locus (QTL) mapping. This study was undertaken to map a set of agronomically important traits like flowering time (FT), plant height (PH), panicle length (PL), and grain weight (self and open-pollinated seeds) in the recombinant inbred line (RIL) population of ICMB 841-P3 × 863B-P2 cross. Excluding grain weight (open pollinated), heritabilities for FT, PH, PL, grain weight (selfed) were in high to medium range. A total of six QTLs for FT were detected on five chromosomes, 13 QTLs for PH on six chromosomes, 11 QTLs for PL on five chromosomes, and 14 QTLs for 1,000-grain weight (TGW) spanning five chromosomes. One major QTL on LG3 was common for FT and PH. Three major QTLs for PL, one each on LG1, LG2, and LG6B were detected. The large effect QTL for TGW (self) on LG6B had a phenotypic variance (R2) of 62.1%. The R2 for FT, TGW (self), and PL ranged from 22.3 to 59.4%. A total of 21 digenic interactions were discovered for FT (R2 = 18-40%) and PL (R2 = 13-19%). The epistatic effects did not reveal any significant QTL × QTL × environment (QQE) interactions. The mapped QTLs for flowering time and other agronomic traits in present experiment can be used for marker-assisted selection (MAS) and genomic selection (GS) breeding programs.

Evaluation of random forest regression for prediction of breeding value from genomewide SNPs.

Genomic prediction is meant for estimating the breeding value using molecular marker data which has turned out to be a powerful tool for efficient utilization of germplasm resources and rapid improvement of cultivars. Model-based techniques have been widely used for prediction of breeding values of genotypes from genomewide association studies. However, application of the random forest (RF), a model-free ensemble learning method, is not widely used for prediction. In this study, the optimum values of tuning parameters of RF have been identified and applied to predict the breeding value of genotypes based on genomewide single-nucleotide polymorphisms (SNPs), where the number of SNPs (P variables) is much higher than the number of genotypes (n observations) (P ¼ n). Further, a comparison was made with the model-based genomic prediction methods, namely, least absolute shrinkage and selection operator (LASSO), ridge regression (RR) and elastic net (EN) under P ¼ n. It was found that the correlations between the predicted and observed trait response were 0.591, 0.539, 0.431 and 0.587 for RF, LASSO, RR and EN, respectively, which implies superiority of the RF over the model-based techniques in genomic prediction. Hence, we suggest that the RF methodology can be used as an alternative to the model-based techniques for the prediction of breeding value at genome level with higher accuracy.

Quantitative trait loci associated with constitutive traits control water use in pearl millet [Pennisetum glaucum (L.) R. Br].

There is substantial genetic variation for drought adaption in pearl millet in terms of traits controlling plant water use. It is important to understand genomic regions responsible for these traits. Here, F7 recombinant inbred lines were used to identify quantitative trait loci (QTL) and allelic interactions for traits affecting plant water use, and their relevance is discussed for crop productivity in water-limited environments. Four QTL contributed to increased transpiration rate under high vapour pressure deficit (VPD) conditions, all with alleles from drought-sensitive parent ICMB 841. Of these four QTL, a major QTL (35.7%) was mapped on linkage group (LG) 6. The alleles for 863B at this QTL decreased transpiration rate and this QTL co-mapped to a previously detected LG 6 QTL, with alleles from 863B for grain weight and panicle harvest index across severe terminal drought stress environments. This provided additional support for a link between water saving from a lower transpiration rate under high VPD and drought tolerance. 863B alleles in this same genomic region also increased shoot weight, leaf area and total transpiration under well-watered conditions. One unexpected outcome was reduced transpiration under high VPD (15%) from the interaction of two alleles for high VPD transpiration (LG 6 (B), 40.7) and specific leaf mass and biomass (LG 7 (A), 35.3), (A, allele from ICMB 841, B, allele from 863B, marker position). The LG 6 QTL appears to combine alleles for growth potential, beneficial for non-stress conditions, and for saving water under high evaporative demand, beneficial under stressful conditions. Mapping QTL for water-use traits, and assessing their interactions offers considerable potential for improving pearl millet adaptation to specific stress conditions through physiology-informed marker-assisted selection.

Development of a molecular linkage map of pearl millet integrating DArT and SSR markers.

Pearl millet is an important component of food security in the semi-arid tropics and is assuming greater importance in the context of changing climate and increasing demand for highly nutritious food and feed. Molecular tools have been developed and applied for pearl millet on a limited scale. However, the existing tool kit needs to be strengthened further for its routine use in applied breeding programs. Here, we report enrichment of the pearl millet molecular linkage map by exploiting low-cost and high-throughput Diversity Arrays Technology (DArT) markers. Genomic representation from 95 diverse genotypes was used to develop a DArT array with circa 7,000 clones following PstI/BanII complexity reduction. This array was used to genotype a set of 24 diverse pearl millet inbreds and 574 polymorphic DArT markers were identified. The genetic relationships among the inbred lines as revealed by DArT genotyping were in complete agreement with the available pedigree data. Further, a mapping population of 140 F(7) Recombinant Inbred Lines (RILs) from cross H 77/833-2 × PRLT 2/89-33 was genotyped and an improved linkage map was constructed by integrating DArT and SSR marker data. This map contains 321 loci (258 DArTs and 63 SSRs) and spans 1148 cM with an average adjacent-marker interval length of 3.7 cM. The length of individual linkage groups (LGs) ranged from 78 cM (LG 3) to 370 cM (LG 2). This better-saturated map provides improved genome coverage and will be useful for genetic analyses of important quantitative traits. This DArT platform will also permit cost-effective background selection in marker-assisted backcrossing programs as well as facilitate comparative genomics and genome organization studies once DNA sequences of polymorphic DArT clones are available.

Development and mapping of simple sequence repeat markers for pearl millet from data mining of expressed sequence tags.

BACKGROUND: Pearl millet [Pennisetum glaucum (L.) R. Br.] is a staple food and fodder crop of marginal agricultural lands of sub-Saharan Africa and the Indian subcontinent. It is also a summer forage crop in the southern USA, Australia and Latin America, and is the preferred mulch in Brazilian no-till soybean production systems. Use of molecular marker technology for pearl millet genetic improvement has been limited. Progress is hampered by insufficient numbers of PCR-compatible co-dominant markers that can be used readily in applied breeding programmes. Therefore, we sought to develop additional SSR markers for the pearl millet research community. RESULTS: A set of new pearl millet SSR markers were developed using available sequence information from 3520 expressed sequence tags (ESTs). After clustering, unigene sequences (2175 singlets and 317 contigs) were searched for the presence of SSRs. We detected 164 sequences containing SSRs (at least 14 bases in length), with a density of one per 1.75 kb of EST sequence. Di-nucleotide repeats were the most abundant followed by tri-nucleotide repeats. Ninety primer pairs were designed and tested for their ability to detect polymorphism across a panel of 11 pairs of pearl millet mapping population parental lines. Clear amplification products were obtained for 58 primer pairs. Of these, 15 were monomorphic across the panel. A subset of 21 polymorphic EST-SSRs and 6 recently developed genomic SSR markers were mapped using existing mapping populations. Linkage map positions of these EST-SSR were compared by homology search with mapped rice genomic sequences on the basis of pearl millet-rice synteny. Most new EST-SSR markers mapped to distal regions of linkage groups, often to previous gaps in these linkage maps. These new EST-SSRs are now are used by ICRISAT in pearl millet diversity assessment and marker-aided breeding programs. CONCLUSION: This study has demonstrated the potential of EST-derived SSR primer pairs in pearl millet. As reported for other crops, EST-derived SSRs provide a cost-saving marker development option in pearl millet. Resources developed in this study have added a sizeable number of useful SSRs to the existing repertoire of circa 100 genomic SSRs that were previously available to pearl millet researchers.