Santosh Deshpande

Population genomic and genome-wide association studies of agroclimatic traits in sorghum

Accelerating crop improvement in sorghum, a staple food for people in semiarid regions across the developing world, is key to ensuring global food security in the context of climate change. To facilitate gene discovery and molecular breeding in sorghum, we have characterized ∼265,000 single nucleotide polymorphisms (SNPs) in 971 worldwide accessions that have adapted to diverse agroclimatic conditions. Using this genome-wide SNP map, we have characterized population structure with respect to geographic origin and morphological type and identified patterns of ancient crop diffusion to diverse agroclimatic regions across Africa and Asia. To better understand the genomic patterns of diversification in sorghum, we quantified variation in nucleotide diversity, linkage disequilibrium, and recombination rates across the genome. Analyzing nucleotide diversity in landraces, we find evidence of selective sweeps around starch metabolism genes, whereas in landrace-derived introgression lines, we find introgressions around known height and maturity loci. To identify additional loci underlying variation in major agroclimatic traits, we performed genome-wide association studies (GWAS) on plant height components and inflorescence architecture. GWAS maps several classical loci for plant height, candidate genes for inflorescence architecture. Finally, we trace the independent spread of multiple haplotypes carrying alleles for short stature or long inflorescence branches. This genome-wide map of SNP variation in sorghum provides a basis for crop improvement through marker-assisted breeding and genomic selection.

Genome-environment associations in sorghum landraces predict adaptive traits

Genome-environment associations and phenotypic analyses may reveal the basis of environmental adaptation. Improving environmental adaptation in crops is essential for food security under global change, but phenotyping adaptive traits remains a major bottleneck. If associations between single-nucleotide polymorphism (SNP) alleles and environment of origin in crop landraces reflect adaptation, then these could be used to predict phenotypic variation for adaptive traits. We tested this proposition in the global food crop Sorghum bicolor, characterizing 1943 georeferenced landraces at 404,627 SNPs and quantifying allelic associations with bioclimatic and soil gradients. Environment explained a substantial portion of SNP variation, independent of geographical distance, and genic SNPs were enriched for environmental associations. Further, environment-associated SNPs predicted genotype-by-environment interactions under experimental drought stress and aluminum toxicity. Our results suggest that genomic signatures of environmental adaptation may be useful for crop improvement, enhancing germplasm identification and marker-assisted selection. Together, genome-environment associations and phenotypic analyses may reveal the basis of environmental adaptation.

Stay-green quantitative trait loci’s effects on water extraction, transpiration efficiency and seed yield depend on recipient parent background

A stay-green phenotype enhances the adaptation of sorghum (Sorghum bicolor (L.) Moench) to terminal drought, although the mechanisms leading to its expression remain unclear. Differences in tillering and leaf area at anthesis, transpiration efficiency (TE), water extraction, harvest index (HI) and yield under terminal drought and fully irrigated conditions were assessed in 29 introgression lines (IL) targeting stay-green quantitative trait loci (QTLs) Stg1, Stg2, Stg3, Stg4, StgA and StgB in an S35 background, and 16 IL targeting Stg1, Stg3, Stg4 and StgB in an R16 background. TE was increased by StgB in the R16 background, whereas there was no effect in the S35 background. Water extraction was increased by Stg1 in the S35 background but not in R16. StgB modified the proportion of water extracted before and after anthesis in the S35 background. While tillering and leaf area at anthesis were decreased by Stg1 and Stg3 in S35, there was no effect in R16. Yield data under fully irrigated conditions showed higher tiller grain yield in Stg1, Stg2 and Stg3 ILs. Although yield differences were mostly explained by HI variation, the yield variation unexplained by HI was closely related to TE in S35 (R2=0.29) and R16 (R2=0.72), and was closely related to total water extracted in S35 (R2=0.41) but not in R16. These data indicate the potential for several stay-green QTLs to affect traits related to plant water use. However, these effects depend on the interaction between the genetic background and individual QTLs.

Dissecting genome-wide association signals for loss-of-function phenotypes in sorghum flavonoid pigmentation traits

Genome-wide association studies are a powerful method to dissect the genetic basis of traits, although in practice the effects of complex genetic architecture and population structure remain poorly understood. To compare mapping strategies we dissected the genetic control of flavonoid pigmentation traits in the cereal grass sorghum by using high-resolution genotyping-by-sequencing single-nucleotide polymorphism markers. Studying the grain tannin trait, we find that general linear models (GLMs) are not able to precisely map tan1-a, a known loss-of-function allele of the Tannin1 gene, with either a small panel (n = 142) or large association panel (n = 336), and that indirect associations limit the mapping of the Tannin1 locus to Mb-resolution. A GLM that accounts for population structure (Q) or standard mixed linear model that accounts for kinship (K) can identify tan1-a, whereas a compressed mixed linear model performs worse than the naive GLM. Interestingly, a simple loss-of-function genome scan, for genotype-phenotype covariation only in the putative loss-of-function allele, is able to precisely identify the Tannin1 gene without considering relatedness. We also find that the tan1-a allele can be mapped with gene resolution in a biparental recombinant inbred line family (n = 263) using genotyping-by-sequencing markers but lower precision in the mapping of vegetative pigmentation traits suggest that consistent gene-level resolution will likely require larger families or multiple recombinant inbred lines. These findings highlight that complex association signals can emerge from even the simplest traits given epistasis and structured alleles, but that gene-resolution mapping of these traits is possible with high marker density and appropriate models.

In silico mapping of important genes and markers available in the public domain for efficient sorghum breeding.

Crop genome sequencing projects generate massive amounts of genomic sequence information, and the utilization of this information in applied crop improvement programs has been augmented by the availability of sophisticated bioinformatics tools. Here, we present the possible direct utilization of sequence data from a sorghum genome sequencing project in applied crop breeding programs. Based on sequence homology, we aligned all publicly available simple sequence repeat markers on a sequence-based physical map for sorghum. Linking this physical map with already existing linkage map(s) provides better options for applied molecular breeding programs. When a new set of markers is made available, the new markers can be first aligned on a sequence-based physical map, and those located near the quantitative trait locus (QTL) can be identified from this map, thereby reducing the number of markers to be tested in order to identify polymorphic flanking markers for the QTL for any given donor × recurrent parent combination. Polymorphic markers that are expected (on the basis of their position on the sequence-based physical map) to be closely linked to the target can be used for foreground selection in marker-assisted breeding. This map facilitates the identification of a set of markers representing the entire genome, which would provide better resolution in diversity analyses and further linkage disequilibrium mapping. Filling the gaps in existing linkage maps and fine mapping can be achieved more efficiently by targeting the specific genomic regions of interest. It also opens up new exciting opportunities for comparative mapping and for the development of new genomic resources in related crops, both of which are lagging behind in the current genomic revolution. This paper also presents a number of examples of potential applications of sequence-based physical map for sorghum.

Population Genetics and Structure of a Global Foxtail Millet Germplasm Collection

Foxtail millet [Setaria italica (L.) P. Beauv.] is one among the most ancient crops of dryland agriculture. It is the second most important crop among millets grown for grains or forage. Foxtail millet germplasm resources provide reservoirs of novel alleles and genes for crop improvement that have remained mostly unexplored. We genotyped a set of 190 foxtail millet germplasm accessions (including 155 accessions of the foxtail millet core collection) using genotyping‐by‐sequencing (GBS) for rapid single nucleotide polymorphisms (SNP) characterization to study population genetics and structure, which enable allele mining through association mapping approaches. After filtering a total 350,000 raw SNPs identified across 190 germplasm accessions for minor allele frequency (MAF), coverage for samples and coverage for sites, we retained 181 accessions with 17,714 high‐quality SNPs with ≥5% MAF. Genetic structure analyses revealed that foxtail millet germplasm accessions are structured along both on the basis of races and geographic origin, and the maximum proportion of variation was due to among individuals within populations. Accessions of race indica were less diverse and are highly differentiated from those of maxima and moharia. Genome‐wide linkage disequilibrium (LD) analysis showed on an average LD extends up to ∼150 kbp and varied with individual chromosomes. The utility of the data for performing genome‐wide association studies (GWASs) was tested with plant pigmentation and days to flowering and identified significant marker–trait associations. This SNP data provides a foundation for exploration of foxtail millet diversity and for mining novel alleles and mapping genes for economically important traits.

Genetic Variability, Genotype × Environment Interaction, Correlation, and GGE Biplot Analysis for Grain Iron and Zinc Concentration and Other Agronomic Traits in RIL Population of Sorghum (Sorghum bicolor L. Moench)

The low grain iron and zinc densities are well documented problems in food crops, affecting crop nutritional quality especially in cereals. Sorghum is a major source of energy and micronutrients for majority of population in Africa and central India. Understanding genetic variation, genotype × environment interaction and association between these traits is critical for development of improved cultivars with high iron and zinc. A total of 336 sorghum RILs (Recombinant Inbred Lines) were evaluated for grain iron and zinc concentration along with other agronomic traits for 2 years at three locations. The results showed that large variability exists in RIL population for both micronutrients (Iron = 10.8 to 76.4 mg kg−1 and Zinc = 10.2 to 58.7 mg kg−1, across environments) and agronomic traits. Genotype × environment interaction for both micronutrients (iron and zinc) was highly significant. GGE biplots comparison for grain iron and zinc showed greater variation across environments. The results also showed that G × E was substantial for grain iron and zinc, hence wider testing needed for taking care of G × E interaction to breed micronutrient rich sorghum lines. Iron and zinc concentration showed high significant positive correlation (across environment = 0.79; p < 0.01) indicating possibility of simultaneous effective selection for both the traits. The RIL population showed good variability and high heritabilities (>0.60, in individual environments) for Fe and Zn and other traits studied indicating its suitability to map QTL for iron and zinc.

Accumulation of stem sugar and its remobilisation in response to drought stress in a sweet sorghum genotype and its near‐isogenic lines carrying different stay‐green loci

Near isogenic lines (NILs) of sweet sorghum genotype S35 into which individual stay green loci were introgressed, were used to understand the contribution of Stay green loci to stem sugar accumulation and its remobilization under drought stress exposure. Sugar and starch content, activities of sugar metabolism enzymes and levels of their expression were studied in the 3rd (source) leaf from panicle and the 5th (sugar storing) internode of the three lines, in irrigated plants and in plants exposed to a brief drought exposure at the panicle emergence stage. Annotation of genes in the respective Stay green loci introgressed in the NILs was carried out using bioinformatics tools. The leaves of NILs accumulated more photoassimilates and the internodes accumulated more sugar, as compared to the parent S35 line. Drought stress exposure led to a decrease in the starch and sugar levels in leaves of all three lines, while an increase in sugar levels was observed in internodes of the NILs. Sugar fluxes were accompanied by alterations in the activities of sugar metabolizing enzymes as well as the expression of genes related to sugar metabolism and transport. Remobilization of sugars from the stem internodes was apparent in the NILs when subjected to drought stress, since the peduncle, which supports the panicle, showed an increase in the sugar content, even when photoassimation in source leaves was reduced. Several genes related to carbohydrate metabolism were located in the Stay green loci, which probably contributed to variation in the parameters studied.

Genomic Approaches for Abiotic Stress Tolerance in Sorghum

Although sorghum is a crop grown under harsh environments, its productivity is adversely affected by various abiotic stresses including drought, temperature extremes, low fertility, and mineral toxicity among others. In recent years a large number of genetic and genomic resources have become available in sorghum, which provide researchers opportunities to relate sequence variations with phenotypic traits of interest and their utilization in sorghum improvement programs. The application of the molecular marker and genomic technologies has shown promise for efficient breeding. However, very few successful examples are available in the public domain of research in this direction. Some of these successes specifically related to application of molecular marker technologies for improving abiotic stresses are explained in this chapter. With recent advances in next-generation sequencing technologies and high-throughput phenotyping platforms/technologies, utilizing the new/advanced mapping populations such as nested-association mapping (NAM), backcross-derived NAM has shown great potential. These recent advancements will be the drivers for integration of genomics technologies in routine breeding programs in the immediate future.

Genomic diversity among sorghum genotypes with resistance to sorghum shoot fly, atherigona soccata

Host plant resistance is one of the important components for management of sorghum shoot fly, Atherigona soccata. The levels of resistance in cultivated germplasm are low to moderate, and therefore, it is important to identify sorghum genotypes with diverse mechanisms of resistance based on physico-chemical and or molecular markers. We assessed the genetic diversity of 15 sorghum genotypes with different levels of resistance/susceptibility to shoot fly, A. soccata using 93 sorghum simple sequence repeat (SSR) primer pairs and simultaneously characterized for 15 morpho-biochemical traits associated with shoot fly resistance. Of these 93 SSR primer pairs, amplification products from 79, thought to correspond to single-copy loci distributed across all ten sorghum chromosome pairs, showed good polymorphism across the 15 sorghum genotypes. The polymorphic information content (PIC) values of these 79 SSR markers ranged from 0.06 to 0.86. The Principal Coordinate Analyses (PCoA) and cluster analyses based on dissimilarity matrices derived from SSR based allelic variation (Neighbor-Joining distance) and variation in 15 morpho-biochemical traits (based on Gower’s distance), revealed grouping of most susceptible genotypes in single cluster. The improved breeding lines grouped with resistant or susceptible genotypes, based on shared pedigree. Based on these results, three resistant accessions viz., IS 1054, IS 1057 and IS 4664 were found diverse to IS 18551, which is widely used as shoot fly resistance donor. These diverse sources, after further characterization for resistance mechanisms, can be used in breeding programs for improving shoot fly resistance.