Identifying loci with breeding potential across temperate and tropical adaptation via EigenGWAS and EnvGWAS

Natural variation of domestication-related genes contributed to latitudinal expansion and adaptation in soybean

Soybean is a major source of protein and edible oil worldwide. Originating from the Huang-Huai-Hai region, which has a temperate climate, soybean has adapted to a wide latitudinal gradient across China. However, the genetic mechanisms responsible for the widespread latitudinal adaptation in soybean, as well as the genetic basis, adaptive differentiation, and evolutionary implications of theses natural alleles, are currently lacking in comprehensive understanding. In this study, we examined the genetic variations of fourteen major gene loci controlling flowering and maturity in 103 wild species, 1048 landraces, and 1747 cultivated species. We found that E1, E3, FT2a, J, Tof11, Tof16, and Tof18 were favoured during soybean improvement and selection, which explained 75.5% of the flowering time phenotypic variation. These genetic variation was significantly associated with differences in latitude via the LFMM algorithm. Haplotype network and geographic distribution analysis suggested that gene combinations were associated with flowering time diversity contributed to the expansion of soybean, with more HapA clustering together when soybean moved to latitudes beyond 35°N. The geographical evolution model was developed to accurately predict the suitable planting zone for soybean varieties. Collectively, by integrating knowledge from genomics and haplotype classification, it was revealed that distinct gene combinations improve the adaptation of cultivated soybeans to different latitudes. This study provides insight into the genetic basis underlying the environmental adaptation of soybean accessions, which could contribute to a better understanding of the domestication history of soybean and facilitate soybean climate-smart molecular breeding for various environments.

Genotype-by-environment interactions and local adaptation shape selection in the US National Chip Processing Trial

KEY MESSAGE

We find evidence of selection for local adaptation and extensive genotype-by-environment interaction in the potato National Chip Processing Trial (NCPT). We present a novel method for dissecting the interplay between selection, local adaptation and environmental response in plant breeding schemes. Balancing local adaptation and the desire for widely adapted cultivars is challenging for plant breeders and makes genotype-by-environment interactions (GxE) an important target of selection. Selecting for GxE requires plant breeders to evaluate plants across multiple environments. One way breeders have accomplished this is to test advanced materials across many locations. Public potato breeders test advanced breeding material in the National Chip Processing Trial (NCPT), a public-private partnership where breeders from ten institutions submit advanced chip lines to be evaluated in up to ten locations across the country. These clones are genotyped and phenotyped for important agronomic traits. We used these data to interrogate the NCPT for GxE. Further, because breeders submitting clones to the NCPT select in a relatively small geographic range for the first 3 years of selection, we examined these data for evidence of incidental selection for local adaptation, and the alleles underlying it, using an environmental genome-wide association study (envGWAS). We found genomic regions associated with continuous environmental variables and discrete breeding programs, as well as regions of the genome potentially underlying GxE for yield.

Landscape genomics reveals adaptive genetic differentiation driven by multiple environmental variables in naked barley on the Qinghai-Tibetan Plateau

Understanding the local adaptation of crops has long been a concern of evolutionary biologists and molecular ecologists. Identifying the adaptive genetic variability in the genome is crucial not only to provide insights into the genetic mechanism of local adaptation but also to explore the adaptation potential of crops. This study aimed to identify the climatic drivers of naked barley landraces and putative adaptive loci driving local adaptation on the Qinghai-Tibetan Plateau (QTP). To this end, a total of 157 diverse naked barley accessions were genotyped using the genotyping-by-sequencing approach, which yielded 3123 high-quality SNPs for population structure analysis and partial redundancy analysis, and 37,636 SNPs for outlier analysis. The population structure analysis indicated that naked barley landraces could be divided into four groups. We found that the genomic diversity of naked barley landraces could be partly traced back to the geographical and environmental diversity of the landscape. In total, 136 signatures associated with temperature, precipitation, and ultraviolet radiation were identified, of which 13 had pleiotropic effects. We mapped 447 genes, including a known gene HvSs1. Some genes involved in cold stress and regulation of flowering time were detected near eight signatures. Taken together, these results highlight the existence of putative adaptive loci in naked barley on QTP and thus improve our current understanding of the genetic basis of local adaptation.

Heritage genetics for adaptation to marginal soils in barley

Future crops need to be sustainable in the face of climate change. Modern barley varieties have been bred for high productivity and quality; however, they have suffered considerable genetic erosion, losing crucial genetic diversity. This renders modern cultivars vulnerable to climate change and stressful environments. We highlight the potential to tailor crops to a specific environment by utilising diversity inherent in an adapted landrace population. Tapping into natural biodiversity, while incorporating information about local environmental and climatic conditions, allows targeting of key traits and genotypes, enabling crop production in marginal soils. We outline future directions for the utilisation of genetic resources maintained in landrace collections to support sustainable agriculture through germplasm development via the use of genomics technologies and big data.

Discovering Loci for Breeding Prospective and Phenology in Wheat Mediterranean Landraces by Environmental and eigenGWAS

Knowledge of the genetic basis of traits controlling phenology, differentiation patterns, and environmental adaptation is essential to develop new cultivars under climate change conditions. Landrace collections are an appropriate platform to study the hidden variation caused by crop breeding. The use of genome-wide association analysis for phenology, climatic data and differentiation among Mediterranean landraces led to the identification of 651 marker-trait associations that could be grouped in 46 QTL hotspots. A candidate gene analysis using the annotation of the genome sequence of the wheat cultivar 'Chinese Spring' detected 1097 gene models within 33 selected QTL hotspots. From all the gene models, 42 were shown to be differentially expressed (upregulated) under abiotic stress conditions, and 9 were selected based on their levels of expression. Different gene families previously reported for their involvement in different stress responses were found (protein kinases, ras-like GTP binding proteins and ethylene-responsive transcription factors). Finally, the synteny analysis in the QTL hotspots regions among the genomes of wheat and other cereal species identified 23, 21 and 7 ortho-QTLs for Brachypodium, rice and maize, respectively, confirming the importance of these loci.

Genetic networks underlying salinity tolerance in wheat uncovered with genome-wide analyses and selective sweeps

A genetic framework underpinning salinity tolerance at reproductive stage was revealed by genome-wide SNP markers and major adaptability genes in synthetic-derived wheats, and trait-associated loci were used to predict phenotypes. Using wild relatives of crops to identify genes related to improved productivity and resilience to climate extremes is a prioritized area of crop genetic improvement. High salinity is a widespread crop production constraint, and development of salt-tolerant cultivars is a sustainable solution. We evaluated a panel of 294 wheat accessions comprising synthetic-derived wheat lines (SYN-DERs) and modern bread wheat advanced lines under control and high salinity conditions at two locations. The GWAS analysis revealed a quantitative genetic framework of more than 200 loci with minor effect underlying salinity tolerance at reproductive stage. The significant trait-associated SNPs were used to predict phenotypes using a GBLUP model, and the prediction accuracy (r²) ranged between 0.57 and 0.74. The r² values for flag leaf weight, days to flowering, biomass, and number of spikes per plant were all above 0.70, validating the phenotypic effects of the loci discovered in this study. Furthermore, the germplasm sets were compared to identify selection sweeps associated with salt tolerance loci in SYN-DERs. Six loci associated with salinity tolerance were found to be differentially selected in the SYN-DERs (12.4 Mb on chromosome (chr)1B, 7.1 Mb on chr2A, 11.2 Mb on chr2D, 200 Mb on chr3D, 600 Mb on chr6B, and 700.9 Mb on chr7B). A total of 228 reported markers and genes, including 17 well-characterized genes, were uncovered using GWAS and EigenGWAS. A linkage disequilibrium (LD) block on chr5A, including the Vrn-A1 gene at 575 Mb and its homeologs on chr5D, were strongly associated with multiple yield-related traits and flowering time under salinity stress conditions. The diversity panel was screened with more than 68 kompetitive allele-specific PCR (KASP) markers of functional genes in wheat, and the pleiotropic effects of superior alleles of Rht-1, TaGASR-A1, and TaCwi-A1 were revealed under salinity stress. To effectively utilize the extensive genetic information obtained from the GWAS analysis, a genetic interaction network was constructed to reveal correlations among the investigated traits. The genetic network data combined with GWAS, selective sweeps, and the functional gene survey provided a quantitative genetic framework for identifying differentially retained loci associated with salinity tolerance in wheat.

Analysis of historical selection in winter wheat

KEY MESSAGE

Modeling of the distribution of allele frequency over year of variety release identifies major loci involved in historical breeding of winter wheat. Winter wheat is a major crop with a rich selection history in the modern era of crop breeding. Genetic gains across economically important traits like yield have been well characterized and are the major force driving its production. Winter wheat is also an excellent model for analyzing historical genetic selection. As a proof of concept, we analyze two major collections of winter wheat varieties that were bred in Western Europe from 1916 to 2010, namely the Triticeae Genome (TG) and WAGTAIL panels, which include 333 and 403 varieties, respectively. We develop and apply a selection mapping approach, Regression of Alleles on Years (RALLY), in these panels, as well as in simulated populations. RALLY maps loci under sustained historical selection by using a simple logistic model to regress allele counts on years of variety release. To control for drift-induced allele frequency change, we develop a hybrid approach of genomic control and delta control. Within the TG panel, we identify 22 significant RALLY quantitative selection loci (QSLs) and estimate the local heritabilities for 12 traits across these QSLs. By correlating predicted marker effects with RALLY regression estimates, we show that alleles whose frequencies have increased over time are heavily biased toward conferring positive yield effect, but negative effects in flowering time, lodging, plant height and grain protein content. Altogether, our results (1) demonstrate the use of RALLY to identify selected genomic regions while controlling for drift, and (2) reveal key patterns in the historical selection in winter wheat and guide its future breeding.

Genome-Environment Associations, an Innovative Tool for Studying Heritable Evolutionary Adaptation in Orphan Crops and Wild Relatives

Leveraging innovative tools to speed up prebreeding and discovery of genotypic sources of adaptation from landraces, crop wild relatives, and orphan crops is a key prerequisite to accelerate genetic gain of abiotic stress tolerance in annual crops such as legumes and cereals, many of which are still orphan species despite advances in major row crops. Here, we review a novel, interdisciplinary approach to combine ecological climate data with evolutionary genomics under the paradigm of a new field of study: genome-environment associations (GEAs). We first exemplify how GEA utilizes in situ georeferencing from genotypically characterized, gene bank accessions to pinpoint genomic signatures of natural selection. We later discuss the necessity to update the current GEA models to predict both regional- and local- or micro-habitat-based adaptation with mechanistic ecophysiological climate indices and cutting-edge GWAS-type genetic association models. Furthermore, to account for polygenic evolutionary adaptation, we encourage the community to start gathering genomic estimated adaptive values (GEAVs) for genomic prediction (GP) and multi-dimensional machine learning (ML) models. The latter two should ideally be weighted by de novo GWAS-based GEA estimates and optimized for a scalable marker subset. We end the review by envisioning avenues to make adaptation inferences more robust through the merging of high-resolution data sources, such as environmental remote sensing and summary statistics of the genomic site frequency spectrum, with the epigenetic molecular functionality responsible for plastic inheritance in the wild. Ultimately, we believe that coupling evolutionary adaptive predictions with innovations in ecological genomics such as GEA will help capture hidden genetic adaptations to abiotic stresses based on crop germplasm resources to assist responses to climate change. "I shall endeavor to find out how nature's forces act upon one another, and in what manner the geographic environment exerts its influence on animals and plants. In short, I must find out about the harmony in nature" Alexander von Humboldt-Letter to Karl Freiesleben, June 1799.

Genetic Diversity and Selection Signatures in Synthetic-Derived Wheats and Modern Spring Wheat

Synthetic hexaploid wheats and their derived advanced lines were subject to empirical selection in developing genetically superior cultivars. To investigate genetic diversity, patterns of nucleotide diversity, population structure, and selection signatures during wheat breeding, we tested 422 wheat accessions, including 145 synthetic-derived wheats, 128 spring wheat cultivars, and 149 advanced breeding lines from Pakistan. A total of 18,589 high-quality GBS-SNPs were identified that were distributed across the A (40%), B (49%), and D (11%) genomes. Values of population diversity parameters were estimated across chromosomes and genomes. Genome-wide average values of genetic diversity and polymorphic information content were estimated to be 0.30 and 0.25, respectively. Neighbor-joining (NJ) tree, principal component analysis (PCA), and kinship analyses revealed that synthetic-derived wheats and advanced breeding lines were genetically diverse. The 422 accessions were not separated into distinct groups by NJ analysis and confirmed using the PCA. This conclusion was validated with both relative kinship and Rogers' genetic distance analyses. EigenGWAS analysis revealed that 32 unique genome regions had undergone selection. We found that 50% of the selected regions were located in the B-genome, 29% in the D-genome, and 21% in the A-genome. Previously known functional genes or QTL were found within the selection regions associated with phenology-related traits such as vernalization, adaptability, disease resistance, and yield-related traits. The selection signatures identified in the present investigation will be useful for understanding the targets of modern wheat breeding in Pakistan.

Genome-wide association study of eigenvectors provides genetic insights into selective breeding for tomato metabolites

Background

Long-term domestication and intensive breeding of crop plants aim to establish traits desirable for human needs, and characteristics related to yield, disease resistance, and postharvest storage have traditionally received considerable attention. These processes have led also to negative consequences, as is the case of loss of variants controlling fruit quality, for instance in tomato. Tomato fruit quality is directly associated to metabolite content profiles; however, a full understanding of the genetics affecting metabolite content during tomato domestication and improvement has not been reached due to limitations of the single detection methods previously employed. Here, we aim to reach a broad understanding of changes in metabolite content using a genome-wide association study (GWAS) with eigenvector decomposition (EigenGWAS) on tomato accessions.

Results

An EigenGWAS was performed on 331 tomato accessions using the first eigenvector generated from the genomic data as a “phenotype” to understand the changes in fruit metabolite content during breeding. Two independent gene sets were identified that affected fruit metabolites during domestication and improvement in consumer-preferred tomatoes. Furthermore, 57 candidate genes related to polyphenol and polyamine biosynthesis were discovered, and a major candidate gene chlorogenate: glucarate caffeoyltransferase (SlCGT) was identified, which affected the quality and diseases resistance of tomato fruit, revealing the domestication mechanism of polyphenols.

Conclusions

We identified gene sets that contributed to consumer liking during domestication and improvement of tomato. Our study reports novel evidence of selective sweeps and key metabolites controlled by multiple genes, increasing our understanding of the mechanisms of metabolites variation during those processes. It also supports a polygenic selection model for the application of tomato breeding.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01327-x.

Trends of genetic changes uncovered by Env- and Eigen-GWAS in wheat and barley

Variety age and population structure detect novel QTL for yield and adaptation in wheat and barley without the need to phenotype. The process of crop breeding over the last century has delivered new varieties with increased genetic gains, resulting in higher crop performance and yield. However, in many cases, the alleles and genomic regions underpinning this success remain unknown. This is partly due to the difficulty of generating sufficient phenotypic data on large numbers of historical varieties to enable such analyses. Here we demonstrate the ability to circumvent such bottlenecks by identifying genomic regions selected over 100 years of crop breeding using age of a variety as a surrogate for yield. Rather than collecting phenotype data, we deployed 'environmental genome-wide association scans' (EnvGWAS) based on variety age in two of the world's most important crops, wheat and barley, and detected strong signals of selection across both genomes. EnvGWAS identified 16 genomic regions in barley and 10 in wheat with contrasting patterns between spring and winter types of the two crops. To further examine changes in genome structure, we used the genomic relationship matrix of the genotypic data to derive eigenvectors for analysis in EigenGWAS. This detected seven major chromosomal introgressions that contributed to adaptation in wheat. EigenGWAS and EnvGWAS based on variety age avoid costly phenotyping and facilitate the identification of genomic tracts that have been under selection during breeding. Our results demonstrate the potential of using historical cultivar collections coupled with genomic data to identify chromosomal regions under selection and may help guide future plant breeding strategies to maximise the rate of genetic gain and adaptation.

Crop Quality Improvement Through Genome Editing Strategy

Good quality of crops has always been the most concerning aspect for breeders and consumers. However, crop quality is a complex trait affected by both the genetic systems and environmental factors, thus, it is difficult to improve through traditional breeding strategies. Recently, the CRISPR/Cas9 genome editing system, enabling efficiently targeted modification, has revolutionized the field of quality improvement in most crops. In this review, we briefly review the various genome editing ability of the CRISPR/Cas9 system, such as gene knockout, knock-in or replacement, base editing, prime editing, and gene expression regulation. In addition, we highlight the advances in crop quality improvement applying the CRISPR/Cas9 system in four main aspects: macronutrients, micronutrients, anti-nutritional factors and others. Finally, the potential challenges and future perspectives of genome editing in crop quality improvement is also discussed.

Characterization of the genetic basis of local adaptation of wheat landraces from Iran and Pakistan using genome-wide association study

Characterization of genomic regions underlying adaptation of landraces can reveal a quantitative genetics framework for local wheat (Triticum aestivum L.) adaptability. A collection of 512 wheat landraces from the eastern edge of the Fertile Crescent in Iran and Pakistan were genotyped using genome-wide single nucleotide polymorphism markers generated by genotyping-by-sequencing. The minor allele frequency (MAF) and the heterozygosity (H) of Pakistani wheat landraces (MAF = 0.19, H = 0.008) were slightly higher than the Iranian wheat landraces (MAF = 0.17, H = 0.005), indicating that Pakistani landraces were slightly more genetically diverse. Population structure analysis clearly separated the Pakistani landraces from Iranian landraces, which indicates two separate adaptability trajectories. The large-scale agro-climatic data of seven variables were quite dissimilar between Iran and Pakistan as revealed by the correlation coefficients. Genome-wide association study identified 91 and 58 loci using agroclimatic data, which likely underpin local adaptability of the wheat landraces from Iran and Pakistan, respectively. Selective sweep analysis identified significant hits on chromosomes 4A, 4B, 6B, 7B, 2D, and 6D, which were colocalized with the loci associated with local adaptability and with some known genes related to flowering time and grain size. This study provides insight into the genetic diversity with emphasis on the genetic architecture of loci involved in adaptation to local environments, which has breeding implications.

Powerful detection of polygenic selection and evidence of environmental adaptation in US beef cattle

Selection on complex traits can rapidly drive evolution, especially in stressful environments. This polygenic selection does not leave intense sweep signatures on the genome, rather many loci experience small allele frequency shifts, resulting in large cumulative phenotypic changes. Directional selection and local adaptation are changing populations; but, identifying loci underlying polygenic or environmental selection has been difficult. We use genomic data on tens of thousands of cattle from three populations, distributed over time and landscapes, in linear mixed models with novel dependent variables to map signatures of selection on complex traits and local adaptation. We identify 207 genomic loci associated with an animal's birth date, representing ongoing selection for monogenic and polygenic traits. Additionally, hundreds of additional loci are associated with continuous and discrete environments, providing evidence for historical local adaptation. These candidate loci highlight the nervous system's possible role in local adaptation. While advanced technologies have increased the rate of directional selection in cattle, it has likely been at the expense of historically generated local adaptation, which is especially problematic in changing climates. When applied to large, diverse cattle datasets, these selection mapping methods provide an insight into how selection on complex traits continually shapes the genome. Further, understanding the genomic loci implicated in adaptation may help us breed more adapted and efficient cattle, and begin to understand the basis for mammalian adaptation, especially in changing climates. These selection mapping approaches help clarify selective forces and loci in evolutionary, model, and agricultural contexts.

Natural population re-sequencing detects the genetic basis of local adaptation to low temperature in a woody plant

Total of 14 SNPs associated with overwintering-related traits and 75 selective regions were detected. Important candidate genes were identified and a possible network of cold-stress responses in woody plants was proposed. Local adaptation to low temperature is essential for woody plants to against changeable climate and safely survive the winter. To uncover the specific molecular mechanism of low temperature adaptation in woody plants, we sequenced 134 core individuals selected from 494 paper mulberry (Broussonetia papyrifera), which naturally distributed in different climate zones and latitudes. The population structure analysis, PCA analysis and neighbor-joining tree analysis indicated that the individuals were classified into three clusters, which showed forceful geographic distribution patterns because of the adaptation to local climate. Using two overwintering phenotypic data collected at high latitudes of 40°N and one bioclimatic variable, genome-phenotype and genome-environment associations, and genome-wide scans were performed. We detected 75 selective regions which possibly undergone temperature selection and identified 14 trait-associated SNPs that corresponded to 16 candidate genes (including LRR-RLK, PP2A, BCS1, etc.). Meanwhile, low temperature adaptation was also supported by other three trait-associated SNPs which exhibiting significant differences in overwintering traits between alleles within three geographic groups. To sum up, a possible network of cold signal perception and responses in woody plants were proposed, including important genes that have been confirmed in previous studies while others could be key potential candidates of woody plants. Overall, our results highlighted the specific and complex molecular mechanism of low temperature adaptation and overwintering of woody plants.

Labelling Selective Sweeps Used in Durum Wheat Breeding from a Diverse and Structured Panel of Landraces and Cultivars

Simple Summary

Evaluation of the genetic diversity of a crop species is a critical step for breeding. Landraces are essential to avoid genetic erosion, and Mediterranean landraces are an important group of genetic resources due to their high genetic variability, adaptation to local conditions in rainfed environments, and their resilience to pests and pathogens. This study uses a genome-wide association approach employing eigenvectors to identify selective sweeps among Mediterranean durum wheat landraces and a world panel of modern cultivars.

Abstract

A panel of 387 durum wheat genotypes including Mediterranean landraces and modern cultivars was characterized with 46,161 diversity arrays technology (DArTseq) markers. Analysis of population structure uncovered the existence of five subpopulations (SP) related to the pattern of migration of durum wheat from the domestication area to the west of the Mediterranean basin (SPs 1, 2, and 3) and further improved germplasm (SPs 4 and 5). The total genetic diversity (H_T) was 0.40 with a genetic differentiation (G_ST) of 0.08 and a mean gene flow among SPs of 6.02. The lowest gene flow was detected between SP 1 (presumably the ancient genetic pool of the panel) and SPs 4 and 5. However, gene flow from SP 2 to modern cultivars was much higher. The highest gene flow was detected between SP 3 (western Mediterranean germplasm) and SP 5 (North American and European cultivars). A genome wide association study (GWAS) approach using the top ten eigenvectors as phenotypic data revealed the presence of 89 selective sweeps, represented as quantitative trait loci (QTL) hotspots, widely distributed across the durum wheat genome. A principal component analysis (PCoA) using 147 markers with −log₁₀p > 5 identified three regions located on chromosomes 2A, 2B and 3A as the main drivers for differentiation of Mediterranean landraces. Gene flow between SPs offers clues regarding the putative use of Mediterranean old durum germplasm by the breeding programs represented in the structure analysis. EigenGWAS identified selective sweeps among landraces and modern cultivars. The analysis of the corresponding genomic regions in the ‘Zavitan’, ‘Svevo’ and ‘Chinese Spring’ genomes discovered the presence of important functional genes including Ppd, Vrn, Rht, and gene models involved in important biological processes including LRR-RLK, MADS-box, NAC, and F-box.

EigenGWAS: An online visualizing and interactive application for detecting genomic signatures of natural selection

Detecting genetic regions under selection in structured populations is of great importance in ecology, evolutionary biology and breeding programmes. We recently proposed EigenGWAS, an unsupervised genomic scanning approach that is similar to F ST but does not require grouping information of the population, for detection of genomic regions under selection. The original EigenGWAS is designed for the random mating population, and here we extend its use to inbred populations. We also show in theory and simulation that eigenvalues, the previous corrector for genetic drift in EigenGWAS, are overcorrected for genetic drift, and the genomic inflation factor is a better option for this adjustment. Applying the updated algorithm, we introduce the new EigenGWAS online platform with highly efficient core implementation. Our online computational tool accepts plink data in a standard binary format that can be easily converted from the original sequencing data, provides the users with graphical results via the R-Shiny user-friendly interface. We applied the proposed method and tool to various data sets, and biologically interpretable results as well as caveats that may lead to an unsatisfactory outcome are given. The EigenGWAS online platform is available at www.eigengwas.com, and can be localized and scaled up via R (recommended) or docker.

Genome-wide analyses reveal footprints of divergent selection and popping-related traits in CIMMYT's maize inbred lines

Popcorn (Zea mays L. var. Everta) is the most ancient type of cultivated maize. However, there is little known about the genetics of popping-related traits based on genotyping-by-sequencing (GBS) technology. Here, we characterized the phenotypic variation for seven popping-related traits in maize kernels among 526 CIMMYT inbred lines (CMLs). In total, 155 083 high-quality single nucleotide polymorphism (SNP) markers were identified by a GBS approach. Several trait-associated loci were detected by genome-wide association study for color, popping expansion volume, shape, pericarp, flotation index, floury/vitreous, and protein content, explaining a majority of the observed phenotypic variance, and these were validated by a diverse panel comprising 764 tropical landrace accessions. Sixty two of the identified loci were recognized to have undergone selection. On average, there was a 55.27% frequency for alleles that promote popping in CMLs. Our work not only pinpoints previously unknown loci for popping-related traits, but also reveals that many of these loci have undergone selection. Beyond establishing a new benchmark for the genetics of popcorn, our study provides a foundation for gene discovery and breeding. It also presents evidence to investigate the role of a gradual loss of popping ability as a by-product of diversification of culinary uses throughout the evolution of teosinte-to-modern maize.

Extensive haplotypes are associated with population differentiation and environmental adaptability in Upland cotton (Gossypium hirsutum)

Three extensive eco-haplotypes associated with population differentiation and environmental adaptability in Upland cotton were identified, with A06_85658585, A08_43734499 and A06_113104285 considered the eco-loci for environmental adaptability. Population divergence is suggested to be the primary force driving the evolution of environmental adaptability in various species. Chromosome inversion increases reproductive isolation between subspecies and accelerates population divergence to adapt to new environments. Although modern cultivated Upland cotton (Gossypium hirsutum L.) has spread worldwide, the noticeable phenotypic differences still existed among cultivars grown in different areas. In recent years, the long-distance migration of cotton cultivation areas throughout China has demanded that breeders better understand the genetic basis of environmental adaptability in Upland cotton. Here, we integrated the genotypes of 419 diverse accessions, long-term environment-associated variables (EAVs) and environment-associated traits (EATs) to evaluate subgroup differentiation and identify adaptive loci in Upland cotton. Two highly divergent genomic regions were found on chromosomes A06 and A08, which likely caused by extensive chromosome inversions. The subgroups could be geographically classified based on distinct haplotypes in the divergent regions. A genome-wide association study (GWAS) also confirmed that loci located in these regions were significantly associated with environmental adaptability in Upland cotton. Our study first revealed the cause of population divergence in Upland cotton, as well as the consequences of variation in its environmental adaptability. These findings provide new insights into the genetic basis of environmental adaptability in Upland cotton, which could accelerate the development of molecular markers for adaptation to climate change in future cotton breeding.

A Deep Learning Approach to Population Structure Inference in Inbred Lines of Maize

Analysis of population genetic variation and structure is a common practice for genome-wide studies, including association mapping, ecology, and evolution studies in several crop species. In this study, machine learning (ML) clustering methods, K-means (KM), and hierarchical clustering (HC), in combination with non-linear and linear dimensionality reduction techniques, deep autoencoder (DeepAE) and principal component analysis (PCA), were used to infer population structure and individual assignment of maize inbred lines, i.e., dent field corn (n = 97) and popcorn (n = 86). The results revealed that the HC method in combination with DeepAE-based data preprocessing (DeepAE-HC) was the most effective method to assign individuals to clusters (with 96% of correct individual assignments), whereas DeepAE-KM, PCA-HC, and PCA-KM were assigned correctly 92, 89, and 81% of the lines, respectively. These findings were consistent with both Silhouette Coefficient (SC) and Davies-Bouldin validation indexes. Notably, DeepAE-HC also had better accuracy than the Bayesian clustering method implemented in InStruct. The results of this study showed that deep learning (DL)-based dimensional reduction combined with ML clustering methods is a useful tool to determine genetically differentiated groups and to assign individuals into subpopulations in genome-wide studies without having to consider previous genetic assumptions.

Characterization of Genetic Diversity and Genome-Wide Association Mapping of Three Agronomic Traits in Qingke Barley (Hordeum Vulgare L.) in the Qinghai-Tibet Plateau

Barley (Hordeum vulgare L.) is one of the most important cereal crops worldwide. In the Qinghai-Tibet Plateau, six-rowed hulless (or naked) barley, called “qingke” in Chinese or “nas” in Tibetan, is produced mainly in Tibet. The complexity of the environment in the Qinghai-Tibet Plateau has provided unique opportunities for research on the breeding and adaptability of qingke barley. However, the genetic architecture of many important agronomic traits for qingke barley remains elusive. Heading date (HD), plant height (PH), and spike length (SL) are three prominent agronomic traits in barley. Here, we used genome-wide association (GWAS) mapping and GWAS with eigenvector decomposition (EigenGWAS) to detect quantitative trait loci (QTL) and selective signatures for HD, PH, and SL in a collection of 308 qingke barley accessions. The accessions were genotyped using a newly-developed, proprietary genotyping-by-sequencing (tGBS) technology, that yielded 14,970 high quality single nucleotide polymorphisms (SNPs). We found that the number of SNPs was higher in the varieties than in the landraces, which suggested that Tibetan varieties and varieties in the Tibetan area may have originated from different landraces in different areas. We have identified 62 QTLs associated with three important traits, and the observed phenotypic variation is well-explained by the identified QTLs. We mapped 114 known genes that include, but are not limited to, vernalization, and photoperiod genes. We found that 83.87% of the identified QTLs are located in the non-coding regulatory regions of annotated barley genes. Forty-eight of the QTLs are first reported here, 28 QTLs have pleotropic effects, and three QTL are located in the regions of the well-characterized genes HvVRN1, HvVRN3, and PpD-H2. EigenGWAS analysis revealed that multiple heading-date-related loci bear signatures of selection. Our results confirm that the barley panel used in this study is highly diverse, and showed a great promise for identifying the genetic basis of adaptive traits. This study should increase our understanding of complex traits in qingke barley, and should facilitate genome-assisted breeding for qingke barley improvement.

Enhancing Hybrid Prediction in Pearl Millet Using Genomic and/or Multi-Environment Phenotypic Information of Inbreds

Genomic selection (GS) is an emerging methodology that helps select superior lines among experimental cultivars in plant breeding programs. It offers the opportunity to increase the productivity of cultivars by delivering increased genetic gains and reducing the breeding cycles. This methodology requires inexpensive and sufficiently dense marker information to be successful, and with whole genome sequencing, it has become an important tool in many crops. The recent assembly of the pearl millet genome has made it possible to employ GS models to improve the selection procedure in pearl millet breeding programs. Here, three GS models were implemented and compared using grain yield and dense molecular marker information of pearl millet obtained from two different genotyping platforms (C [conventional GBS RAD-seq] and T [tunable GBS tGBS]). The models were evaluated using three different cross-validation (CV) schemes mimicking real situations that breeders face in breeding programs: CV2 resembles an incomplete field trial, CV1 predicts the performance of untested hybrids, and CV0 predicts the performance of hybrids in unobserved environments. We found that (i) adding phenotypic information of parental inbreds to the calibration sets improved predictive ability, (ii) accounting for genotype-by-environment interaction also increased the performance of the models, and (iii) superior strategies should consider the use of the molecular markers derived from the T platform (tGBS).

Identifying loci with breeding potential across temperate and tropical adaptation via EigenGWAS and EnvGWAS

Understanding the genomic basis of adaptation in maize is important for gene discovery and the improvement of breeding germplasm, but much remains a mystery in spite of significant population genetics and archaeological research. Identifying the signals underpinning adaptation are challenging as adaptation often coincided with genetic drift, and the base genomic diversity of the species in massive. In this study, tGBS technology was used to genotype 1,143 diverse maize accessions including landraces collected from 20 countries and elite breeding lines of tropical lowland, highland, subtropical/midaltitude and temperate ecological zones. Based on 355,442 high‐quality single nucleotide polymorphisms, 13 genomic regions were detected as being under selection using the bottom‐up searching strategy, EigenGWAS. Of the 13 selection regions, 10 were first reported, two were associated with environmental parameters via EnvGWAS, and 146 genes were enriched. Combining large‐scale genomic and ecological data in this diverse maize panel, our study supports a polygenic adaptation model of maize and offers a framework to enhance our understanding of both the mechanistic basis and the evolutionary consequences of maize domestication and adaptation. The regions identified here are promising candidates for further, targeted exploration to identify beneficial alleles and haplotypes for deployment in maize breeding.