SNP discovery in proso millet (\u3ci\u3ePanicum miliaceum\u3c/i\u3e L.) using lowpass genome sequencing

Domesticated ~10,000 years ago in northern China, Proso millet (Panicum miliaceum L.) is a climate-resilient and human health-promoting cereal crop. The genome size of this self-pollinated allotetraploid is 923 Mb. Proso millet seeds are an important part of the human diet in many countries. In the USA, its use is restricted to the birdseed and pet food market. Proso millet is witnessing gradual demand in the global human health and wellness food market owing to its health-promoting properties such as low glycemic index and gluten-free. The breeding efforts for developing improved proso millet cultivars are hindered by the dearth of genomic resources available to researchers. The publication of the reference genome and availability of costeffective NGS methodologies could lead to the identification of high-quality genetic variants, which can be incorporated into breeding pipelines. Here, we report the identification of single-nucleotide polymorphisms (SNPs) by low-pass (1x) genome sequencing of 85 diverse proso millet accessions from 23 different countries. The 2 x 150 bp Illumina paired-end reads generated after sequencing were aligned to the proso millet reference genome. The resulting sequence alignment information was used to call SNPs. We obtained 972,863 bi-allelic SNPs after quality filtering of the raw SNPs. These SNPs were used to assess the population structure and phylogenetic relationships among the accessions. Most of the accessions were found to be highly inbred with heterozygosity ranging between .05 and .20. Principal component analysis (PCA) showed that PC1 (principal component) and PC2 explained 19% of the variability in the population. PCA also clustered all the genotypes into three groups. A neighbor-joining tree clustered the genotypes into four distinct groups exhibiting diverse representation within the population. The SNPs identified in our study could be used for molecular breeding and genetics research (e.g., genetic and association mapping, and population genetics) in proso millet after proper validation

A role for heritable transcriptomic variation in maize adaptation to temperate environments

Background: Transcription bridges genetic information and phenotypes. Here, we evaluated how changes in transcriptional regulation enable maize (Zea mays), a crop originally domesticated in the tropics, to adapt to temperate environments. Result: We generated 572 unique RNA-seq datasets from the roots of 340 maize genotypes. Genes involved in core processes such as cell division, chromosome organization and cytoskeleton organization showed lower heritability of gene expression, while genes involved in anti-oxidation activity exhibited higher expression heritability. An expression genome-wide association study (eGWAS) identified 19,602 expression quantitative trait loci (eQTLs) associated with the expression of 11,444 genes. A GWAS for alternative splicing identified 49,897 splicing QTLs (sQTLs) for 7614 genes. Genes harboring both cis-eQTLs and cis-sQTLs in linkage disequilibrium were disproportionately likely to encode transcription factors or were annotated as responding to one or more stresses. Independent component analysis of gene expression data identified loci regulating co-expression modules involved in oxidation reduction, response to water deprivation, plastid biogenesis, protein biogenesis, and plant-pathogen interaction. Several genes involved in cell proliferation, flower development, DNA replication, and gene silencing showed lower gene expression variation explained by genetic factors between temperate and tropical maize lines. A GWAS of 27 previously published phenotypes identified several candidate genes overlapping with genomic intervals showing signatures of selection during adaptation to temperate environments. Conclusion: Our results illustrate how maize transcriptional regulatory networks enable changes in transcriptional regulation to adapt to temperate regions

Meta-analysis identifies pleiotropic loci controlling phenotypic trade-offs in sorghum

Community association populations are composed of phenotypically and genetically diverse accessions. Once these populations are genotyped, the resulting marker data can be reused by different groups investigating the genetic basis of different traits. Because the same genotypes are observed and scored for a wide range of traits in different environments, these populations represent a unique resource to investigate pleiotropy. Here, we assembled a set of 234 separate trait datasets for the Sorghum Association Panel, a group of 406 sorghum genotypes widely employed by the sorghum genetics community. Comparison of genome-wide association studies (GWAS) conducted with two independently generated marker sets for this population demonstrate that existing genetic marker sets do not saturate the genome and likely capture only 35–43% of potentially detectable loci controlling variation for traits scored in this population. While limited evidence for pleiotropy was apparent in cross-GWAS comparisons, a multivariate adaptive shrinkage approach recovered both known pleiotropic effects of existing loci and new pleiotropic effects, particularly significant impacts of known dwarfing genes on root architecture. In addition, we identified new loci with pleiotropic effects consistent with known trade-offs in sorghum development. These results demonstrate the potential for mining existing trait datasets from widely used community association populations to enable new discoveries from existing trait datasets as new, denser genetic marker datasets are generated for existing community association populations

A role for heritable transcriptomic variation in maize adaptation to temperate environments

Background: Transcription bridges genetic information and phenotypes. Here, we evaluated how changes in transcriptional regulation enable maize (Zea mays), a crop originally domesticated in the tropics, to adapt to temperate environments. Result: We generated 572 unique RNA-seq datasets from the roots of 340 maize genotypes. Genes involved in core processes such as cell division, chromosome organization and cytoskeleton organization showed lower heritability of gene expression, while genes involved in anti-oxidation activity exhibited higher expression heritability. An expression genome-wide association study (eGWAS) identified 19,602 expression quantitative trait loci (eQTLs) associated with the expression of 11,444 genes. A GWAS for alternative splicing identified 49,897 splicing QTLs (sQTLs) for 7614 genes. Genes harboring both cis-eQTLs and cis-sQTLs in linkage disequilibrium were disproportionately likely to encode transcription factors or were annotated as responding to one or more stresses. Independent component analysis of gene expression data identified loci regulating co-expression modules involved in oxidation reduction, response to water deprivation, plastid biogenesis, protein biogenesis, and plant-pathogen interaction. Several genes involved in cell proliferation, flower development, DNA replication, and gene silencing showed lower gene expression variation explained by genetic factors between temperate and tropical maize lines. A GWAS of 27 previously published phenotypes identified several candidate genes overlapping with genomic intervals showing signatures of selection during adaptation to temperate environments. Conclusion: Our results illustrate how maize transcriptional regulatory networks enable changes in transcriptional regulation to adapt to temperate regions

Association mapping across a multitude of traits collected in diverse environments in maize

Classical genetic studies have identified many cases of pleiotropy where mutations in individual genes alter many different phenotypes. Quantitative genetic studies of natural genetic variants frequently examine one or a few traits, limiting their potential to identify pleiotropic effects of natural genetic variants. Widely adopted community association panels have been employed by plant genetics communities to study the genetic basis of naturally occurring phenotypic variation in a wide range of traits. High-density genetic marker data—18M markers—from 2 partially overlapping maize association panels comprising 1,014 unique genotypes grown in field trials across at least 7 US states and scored for 162 distinct trait data sets enabled the identification of of 2,154 suggestive marker-trait associations and 697 confident associations in the maize genome using a resampling-based genome-wide association strategy. The precision of individual marker-trait associations was estimated to be 3 genes based on a reference set of genes with known phenotypes. Examples were observed of both genetic loci associated with variation in diverse traits (e.g., above-ground and below-ground traits), as well as individual loci associated with the same or similar traits across diverse environments. Many significant signals are located near genes whose functions were previously entirely unknown or estimated purely via functional data on homologs. This study demonstrates the potential of mining community association panel data using new higher-density genetic marker sets combined with resampling-based genome-wide association tests to develop testable hypotheses about gene functions, identify potential pleiotropic effects of natural genetic variants, and study genotype-by-environment interaction

Quantitative Trait Loci Associated with Protein, Oil and Carbohydrates in Soybean [Glycine max (L.) Merr.] Seeds

Soybean is mainly cultivated for its oil and high quality protein meal for feed, fuel and food uses. Achieving an improved balance of protein and oil in the seed, along with yield will enhance crop value. In practice, this has been difficult to achieve due to significant negative correlations of oil and protein, and the mostly negative relationship reported between seed protein concentration and yield. Most previous studies have focused on increasing seed oil concentration (SOC) or seed protein concentration (SPC) individually, and a few focused on decreasing raffinosacharides to improve digestibility and metabolizable energy of the feed for monogastric animals. None of the studies to date have considered improving the balance of SOC and SPC by also considering variation in total soluble sugars, which comprise the third largest component in soybean seed. Three related bi-parental recombinant inbred line (RIL) populations were developed by crossing two plant introduction lines that have lower total sugar concentration with two high-yielding soybean lines having higher SOC resulting in two pairs of half-sib populations. The objectives of this study were to identify genomic regions that influence oil, protein and carbohydrate concentrations in the seed in three uniquely structured bi-parental RIL populations using Molecular Inversion Probes (MIPs) markers, and evaluate relationships among seed composition traits and seed yield, seed weight and plant maturity from multiple environments. In total, 51 QTLs for seed, seed composition and plant traits were mapped on 17 chromosomes. All populations showed transgressive segregation for the sum of seed oil+protein concentration (SUM) in both directions but showed little transgressive segregation for SOC or SPC in two populations. There was a positive correlation of SOC and SPC with the SUM in two populations and a near to zero relationship of SUM with plot yield. Over the three populations, about 85% of the lines met processor targets of 10-12 pounds of oil per bushel and would produce 48% protein meal. The selected lines from this study could be further evaluated for yield and desirable agronomic traits in multi-location trials, which could lead to higher yielding soybean lines with improved seed composition. This work will ultimately lead to higher profitability for both the processors and farmers

Quantitative Trait Loci Associated with Protein, Oil and Carbohydrates in Soybean [Glycine max (L.) Merr.] Seeds

Soybean is mainly cultivated for its oil and high quality protein meal for feed, fuel and food uses. Achieving an improved balance of protein and oil in the seed, along with yield will enhance crop value. In practice, this has been difficult to achieve due to significant negative correlations of oil and protein, and the mostly negative relationship reported between seed protein concentration and yield. Most previous studies have focused on increasing seed oil concentration (SOC) or seed protein concentration (SPC) individually, and a few focused on decreasing raffinosacharides to improve digestibility and metabolizable energy of the feed for monogastric animals. None of the studies to date have considered improving the balance of SOC and SPC by also considering variation in total soluble sugars, which comprise the third largest component in soybean seed. Three related bi-parental recombinant inbred line (RIL) populations were developed by crossing two plant introduction lines that have lower total sugar concentration with two high-yielding soybean lines having higher SOC resulting in two pairs of half-sib populations. The objectives of this study were to identify genomic regions that influence oil, protein and carbohydrate concentrations in the seed in three uniquely structured bi-parental RIL populations using Molecular Inversion Probes (MIPs) markers, and evaluate relationships among seed composition traits and seed yield, seed weight and plant maturity from multiple environments. In total, 51 QTLs for seed, seed composition and plant traits were mapped on 17 chromosomes. All populations showed transgressive segregation for the sum of seed oil+protein concentration (SUM) in both directions but showed little transgressive segregation for SOC or SPC in two populations. There was a positive correlation of SOC and SPC with the SUM in two populations and a near to zero relationship of SUM with plot yield. Over the three populations, about 85% of the lines met processor targets of 10-12 pounds of oil per bushel and would produce 48% protein meal. The selected lines from this study could be further evaluated for yield and desirable agronomic traits in multi-location trials, which could lead to higher yielding soybean lines with improved seed composition. This work will ultimately lead to higher profitability for both the processors and farmers

Correlation study for Protein Content, Grain yield and Yield Contributing Traits in Quality Protein Maize (QPM) (Zea mays L.)

Seventy Quality Protein Maize (QPM) (Zea mays L.) hybrids were grown in duplicate randomized complete block design forcharacter association study to assess the relationship among total grain protein content, grain yield and its components. Totalgrain protein showed significant correlation with plant height and ear height. Character association analysis revealed strongpositive association of Grain yield per plant with plant height, ear height, ear length, ear diameter, kernel rows per cob, kernelsper row, test weight and shelling per cent. Total grain protein showed strong negative association with days to 50 % flowering,days to anthesis and days to 50% silking. Hence, simultaneous selection of plant height and ear height would contribute for theimprovement of the grain yield per plant and total protein content in the grains at the same time

SNP discovery in proso millet (Panicum miliaceum L.) using low‐pass genome sequencing

Abstract Domesticated ~10,000 years ago in northern China, Proso millet (Panicum miliaceum L.) is a climate‐resilient and human health‐promoting cereal crop. The genome size of this self‐pollinated allotetraploid is 923 Mb. Proso millet seeds are an important part of the human diet in many countries. In the USA, its use is restricted to the birdseed and pet food market. Proso millet is witnessing gradual demand in the global human health and wellness food market owing to its health‐promoting properties such as low glycemic index and gluten‐free. The breeding efforts for developing improved proso millet cultivars are hindered by the dearth of genomic resources available to researchers. The publication of the reference genome and availability of cost‐effective NGS methodologies could lead to the identification of high‐quality genetic variants, which can be incorporated into breeding pipelines. Here, we report the identification of single‐nucleotide polymorphisms (SNPs) by low‐pass (1×) genome sequencing of 85 diverse proso millet accessions from 23 different countries. The 2 × 150 bp Illumina paired‐end reads generated after sequencing were aligned to the proso millet reference genome. The resulting sequence alignment information was used to call SNPs. We obtained 972,863 bi‐allelic SNPs after quality filtering of the raw SNPs. These SNPs were used to assess the population structure and phylogenetic relationships among the accessions. Most of the accessions were found to be highly inbred with heterozygosity ranging between .05 and .20. Principal component analysis (PCA) showed that PC1 (principal component) and PC2 explained 19% of the variability in the population. PCA also clustered all the genotypes into three groups. A neighbor‐joining tree clustered the genotypes into four distinct groups exhibiting diverse representation within the population. The SNPs identified in our study could be used for molecular breeding and genetics research (e.g., genetic and association mapping, and population genetics) in proso millet after proper validation