Oryza sativa ssp. Indica and ssp. Japonica. Under HNT stress, the Japonica ssp. exhibited lower reductions in PL and NSP and a higher level of genetic variation compared with the other subpopulations. Whole genome sequencing identified 6.5 million single nucleotide polymorphisms (SNPs) that were used for the genome-wide association studies (GWASs) of the PL and NSP traits. The GWAS analysis in the Combined, Indica, and Japonica populations under HNT stress identified 83, 60, and 803 highly significant SNPs associated with PL, compared to the 30, 30, and 11 highly significant SNPs associated with NSP. Among these trait-associated SNPs, 140 were coincident with genomic regions previously reported for major GY component quantitative trait loci (QTLs) under heat stress. Using extents of linkage disequilibrium in the rice populations, Venn diagram analysis showed that the highest number of putative candidate genes were identified in the Japonica population, with 20 putative candidate genes being common in the Combined, Indica and Japonica populations. Network analysis of the genes linked to significant SNPs associated with PL and NSP identified modules that were involved in primary and secondary metabolisms. The findings in this study could be useful to understand the pathways/mechanisms involved in rice GY and its components under HNT stress for the acceleration of rice-breeding programs and further functional analysis by molecular geneticists.To dissect the genetic complexity of rice grain yield (GY) and quality in response to heat stress at the reproductive stage, a diverse panel of 190 rice accessions in the United States Department of Agriculture (USDA) rice mini-core collection (URMC) diversity panel were treated with high nighttime temperature (HNT) stress at the reproductive stage of panicle initiation. The quantifiable yield component response traits were then measured. The traits, panicle length (PL), and number of spikelets per panicle (NSP) were evaluated in subsets of the panel comprising the rice subspecies  Oryza sativa L.) is the main food source for more than half the world population and one of the most important cereal crops after wheat, supplying 35–60% of the dietary calorie intake for an estimated three billion people worldwide (Indica and Japonica) and two subclasses  of rice growing around the world  (size), number of spikelets per panicle (NSP), seed set [number of filled grains per panicle (FGP)], and individual seed/grain size and weight , 2019. ATo understand the basis of elevating the GY and enhancing the heat tolerance of rice under HNT stress, dissecting the natural genetic variation widely distributed among the diverse rice accessions, where the identification of favorable alleles for GY components such as PL/size and NSP are the easiest phenotypes to quantify, could be a useful approach , 2019. SConventionally, genetic variation has been characterized using bi-allelic mapping populations in previous studies . HoweverO. sativa, where we gain power by directly accounting for significantly associated alleles/SNPs from the mapping model. The results from the present study will aid and strengthen rice-breeding programs for high temperature tolerance using an SNP-based marker assisted selection and the pyramiding of the QTNs/SNPs related to the GY components and heat tolerance in elite rice cultivars of rice growing areas, especially the United States.In this study, to dissect and quantify the natural genetic variation in diverse populations, we reported the genetic dissection of the United States Department of Agriculture (USDA) rice mini-core collection (URMC) with the genotyping data set generated by whole genome sequencing that detected the best quality SNPs. The SNPs are most densely populated based on the number of SNPs/kb interrogated across the genomes. We explored the value of these resources for GWAS using GY components under HNT stress, such as PL and NSP, as the phenotypes. We also used several analytical techniques in plant genetics to identify the significant associations revealing QTNs/SNPs. Using these significant SNPs, we identified the putative potential QTNs/genes involved directly or indirectly in the development of panicles and spikelets expressing heat tolerance in rice. Furthermore, we showed the different advantages of populations of Indica (Indica and aus), 81 Japonica , and 7 Admixture (mixed populations of Indica and Japonica) accessions, further comprising 53.68% Indica, 42.63% Japonica, and 3.68% Admixture accessions, was used in this study , was obtained from the USDA ARS Dale Bumpers National Rice Research Center, Stuttgart, AR, United States . This cois study .–2 s–1) light and 60–65% relative humidity (RH) for the growth of the rice plants. The experimental design was a completely randomized design (CRD) with three replications (each replication is one plant in the pot). The plants were watered every day and fertilized with the Peter Professional soluble fertilizer  containing chelated iron once a week for full vegetative growth. Plant protection methods were applied to prevent insect pests and diseases, which followed the Rosen Center greenhouse standard procedures.The seeds obtained from the USDA were used for multiplication and purification using the single seed decent (SSD) method for a season before this experiment was initiated. Staggered planting of this diverse panel was done to deal with the variation in heading days (HD) of the panel . This wa® data logger, Cape Cod, MA, United States) were installed in both greenhouses (control and HNT stress treatments) for the continuous monitoring and recording of the day and nighttime temperatures until physiological maturity  for each accession of the panel were harvested separately in individual brown bags, air-dried (12–14% grain moisture content), and used for the phenotyping of GY components such as PL (cm), NSP, and many others (other components not presented here). The PL of each accession with both (control and HNT stress) treatments was measured with a plastic ruler (60 cm), while NSP of each accession was counted manually by skilled personnel.At panicle initiation stage, as described by maturity . The datp < 0.05) using the hsd function in R packages and JMP version 12, as Tukey’s HSD can determine slight differences between the means.The statistical analysis of the phenotype data from the panel was performed using R statistical packages v3.6.2 and John’s Macintosh Project (JMP) Genomics Pro version 12.0 for descriptive statistics. The test for normal distribution and homogeneity of variances was done using the Shapiro–Wilk test and Brown–Forsythe test, respectively. An ANOVA was carried out with a statistical model that included the effects of accession, treatment (control and HNT stress), and the interaction between accession and treatment. The Tukey’s Honest Significant Difference (HSD) test was used to compare the means of treatments among all the accessions for significant effects  populations of the panel, the Pearson’s correlation between PL and NSP for both treatments was calculated and displayed in scatter plots using the package ggplot2 in R v3.6.2. The significance of the results was tested by the function cor.test at the 95% confidence level. The means of PL and NSP in the Combined population of Indica and Japonica under HNT stress were used for the GWAS.In the individual and Combined genotype  was calculated as:To quantitatively estimate the genetic variation in PL and NSP in the Combined, Indica, Japonica, and Admixture populations, respectively (ectively .2) was estimated to describe how the environment affected PL and NSP in the Combined population and the Indica, Japonica, and Admixture subpopulations using the lmer4 function in R v3.6.2  for SNP detection. To generate the SNP dataset for the genetic dissection of the panel, 6.5 million of the most densely distributed (SNP/Kb) and high-quality SNPs, with a less than 2% missing rate and more than 5% minor allele frequency (MAF), were detected and annotated  according to the instructions of the manufacturer and used for whole genome sequencing. All the rice genomes of the panel were sequenced by Novogene,nnotated .K) ranging from 1 to 5, and with three independent runs each. A python script “chooseK.py” was used to identify the model complexity that maximized marginal likelihood, choosing the most likely K values based on the rate of change in LnP between successive K values. The python script “ditstrcut.py” was also used to construct the population structure plot using the most likely K values. The marginal likelihood plot was plotted with the marginal likelihood K values and number of populations (K = 5) using the package ggplot2 in R v3.6.2.Principle component analysis (PCA) was performed using the Genome-wide Complex Trait Analysis (GCTA) v1.92.1 software to estimate the number of subpopulations  in the pIndica, and Japonica populations  using the correlation coefficient (r2) between pairs of SNPs by using the – −r2 – -ld-win 1000 – -ld-score – -ld-score-cut-off 0.01 command in GCTA v1.92.4 beta2  was plotted against the physical distance (Kb) using the package ggplot2 in R v3.6.2.The genome-wide pairwise linkage disequilibrium (LD) was calculated in the Combined, .4 beta2 . A set oIndica and Japonica populations, while the Admixture population was removed as the population size was too small (less than 4% of the panel). For the GWAS, the Genome-wide Efficient Mixed Model Association (GEMMA) was the software that implemented the GEMMA algorithm  were used as covariates. Genome-wide critical values were determined by permutations: each studied phenotype was permutated, and the genome-wide lowest p-values were recorded in the GWAS. To declare the significant associations, a threshold of −log10p (1e−05) was set using Wald test criteria. To visualize the association results, the quantile–quantile (Q–Q) plots of observed p-values were constructed against expected p-values, and Manhattan plots were constructed with the chromosome position on the X-axis against −log10  of all SNPs using the package qqman in R v3.6.2  dataset was converted to BED format using PLINK v1.9 . The GWAlgorithm , which uR v3.6.2 . After tIndica, and Japonica populations, GEMMA, an LMM model, was used to estimate SNP effects as the allelic effect. The SNP effect was expressed as a positive value if the allelic effect increased PL and NSP in the Combined, Indica, and Japonica populations, otherwise showing a negative value when the allelic effect decreased.Using the differences in means of PL and NSP between accessions under HNT stress in the Combined, Using several variance components of the GWAS results, we estimated the proportion of variance in the phenotype explained by each SNP (PVE) using the information described by Where, N represents the sample size of the panel, beta is the effect for the genetic variant (SNP) of interest, SE (beta) is the standard error of effect for the genetic variant (SNP) of interest, MAF is the minor allele frequency for the genetic variant (SNP) of interest.p < 0.001) from the PL and NSP GWAS datasets were mapped to the rice genome (MSU v7). The SNPs that mapped within the defined genic regions for the reference genome were selected, with the rest being filtered. Note that we did not use any LD to associate genes with SNPs. This allowed us to keep the gene-list to a minimum size for downstream network analysis. Both of the trait-associated gene-lists were then used as two different queries to probe the rice regulatory network we recently created , minimum values, maximum values, and coefficients of variation (CV) of PL and NSP in all the rice populations are summarized in Japonica population showed the highest PGV, which contained 102.42% genetic variation under HNT stress and 97.02% genetic variation under the control treatment, compared to the Indica population, which exhibited 86.42% genetic variation under HNT stress and 63.48% genetic variation under the control treatment. The Admixture population exhibited the lowest PGV, expressing 73.28% genetic variation under HNT stress and 58.21% genetic variation under control treatment  analysis in PL, the Combined population showed an H2 of 0.689 under HNT stress and 0.8 under the control treatment, while the Japonica population exhibited the highest H2 of 0.823 under HNT stress and 0.701 under control when compared with the Indica population under both treatments  in PL, followed by the Combined (11.8%), Indica (13.72%), and Admixture (15.33%) populations (Japonica population showed the least reduction in NSP (26.36%), followed by the Combined (32.97%), Indica (36.59%), and Admixture (42.83%) populations  between PL and NSP, followed by the Combined (r = 0.54 for HNT and r = 0.51 for control), Japonica (r = 0.39 for HNT and r = 0.4 for control), and Admixture  populations. The results revealed that PL showed strong positive correlation with NSP under the HNT stress and control treatments in all the populations. As expected, there was enormous natural genetic variation in the panel , suggesting a strong potential impact on PL and NSP for the improvement of rice cultivars in rice-growing regions under climate change conditions.The Pearson’s correlation coefficient was determined to evaluate the relationship between PL and NSP in each of the subpopulations under the HNT stress and control treatments . As showIndica and Japonica populations in groups explaining 26.74% of the genetic variation. The second PC separated the aus and Indica subpopulations explaining 11.19% of the genetic variation , temperate Japonica (TEJ), and aromatic (ARO) subpopulations with 4.04% of the genetic variation in the panel  with O. sativa ssp. Japonica  populations  based on the SNPs across the genome. Genome-wide LD decay patterns, along with the distance in all the populations , are shown in Indica population exhibited the most rapid LD decay to 0.4 at 30 kb, followed by the Combined population LD decay to 0.4 at 120 kb, while the Japonica population showed the longest or most extended LD decay to 0.4 at 460 kb because of the smaller size of the Japonica population compared to the other populations.Linkage disequilibrium, the non-random association of alleles at different loci, indicated that genetic forces structure the genome . InvestiIndica and Japonica populations under HNT stress. To cope with false positive associations, we used a LMM, estimated the relatedness matrix as a covariate, and further controlled population structure using the first four principal components as covariates .To identify genomic regions associated with the mapped PL and NSP traits, GWAS was carried out for the Combined population and, then, for the variates  in all tvariates  and NSP variates  in the CIndica population and 804 significant SNPs  rice genome2 using LD intervals of ±30, ±120, and ±460 kb of each significant SNP for PL and NSP in the Combined, Indica, and Japonica populations, respectively. For PL, we identified 945, 443, and 9,945 putative candidate genes in the Combined, Indica, and Japonica populations, respectively. On the other hand, for NSP, 384, 185, and 1,326 putative candidate genes were identified in the Combined, Indica, and Japonica populations, respectively. A Venn diagram analysis was conducted for the identification of common putative candidate genes for PL and NSP in the Combined, Indica, and Japonica populations. For PL, three sets in Indica, and Japonica populations. The Combined and Indica population sets shared 12 common putative candidate genes, the Indica and Japonica populations sets shared 173 genes, the Japonica and Combined population sets shared 502 genes, and the three sets together  shared 12 common putative candidate genes. For NSP, the three sets shown in Indica, and Japonica populations, with no genes common to all three populations. There were, however, 83 genes shared between the Combined and Japonica populations and 20 genes shared between the Combined and Indica populations.Based on the LD analysis, the extents of LD were 30 kb in the Combined, 120 kb in the Indica, and Japonica populations associated with PL and NSP were found coincident with the genomic regions of the previously reported 32 QTLs of DFT, FGP, FLL, GY, HD, NSP, PH, PL, PNL, PS, SF, and SS GY components under HDT stress  and extracted 5,265 putative candidate genes for the PL loci  and 3,13p-values) from both GWAS datasets. These gene-lists were then used as inputs to the GRAiN web-application3 for the identification of modules containing co-regulated and potentially functionally related genes. For the PL gene-list, GRAiN reported 11 modules of co-regulated genes significantly enriched within our query . Co-regulated modules in the GRAiN platform indicated groups of functionally related genes that were potentially regulated by the same sets of transcription factors (n = 200), we found six NSP modules consisting of genes involved mainly in different categories of the carbohydrate metabolism, signaling, and DNA repair pathways. The GRAiN also reported that the promoters of the genes in the PL and NSP modules were enriched with several stress- and development-related plant cis-regulatory , 81 Japonica , and 7 Admixture (a mixture of Indica and Japonica) accessions were phenotyped for the GY components PL and NSP under HNT stress. The effects of HNT stress compared to the control treatment in PL and NSP were observed and quantified in the panel  and contained mixed genetic backgrounds representative of different ecosystems. Strong positive correlations between PL and NSP and a higher broad sense heritability for PL and NSP were found in the Japonica population when compared to the other rice populations in the panel, showing trait stability under HNT stress environments. This could be a good prospect for selecting the Japonica population to develop pure-line material for testing in multiple replications with a high frequency and the magnitude of heat stress environments. Furthermore, the identified putative candidate SNPs/loci and insights derived from the genetic dissection of the URMC panel for GY components in this study may also facilitate rapid progress in rice breeding compared to traditional approaches.To investigate and quantify the natural genetic variation in rice, a panel of 190 diverse accessions consisting of 102 he panel , where treatment . Along wIndica population. However, the Japonica population with a highly extended LD decay showed the longest extent of LD in the genome, which was possible because of the small size of the Japonica population in the panel. Furthermore, to contribute to the emergence and maintenance of the extents of LDs and patterns, there are several forces that can be utilized, including mutation drift, population bottlenecks, population structure and Admixture, population size, and levels of inbreeding and selection  of highly significant SNPs associated with PL in Japonica when compared to the Combined (83 SNPs) and Indica (60 SNPs) populations , suggesting that the Japonica population contained a larger repertoire of favorable variant alleles contributing to panicle development and stress tolerance under HNT stress conditions. Along with PL, the variation for NSP in the Japonica population showed the largest allelic effect, which was governed by highly significant SNPs that may, in turn, increase the number of spikelets in the population compared to other populations, as the development of NSP in rice directly or indirectly depends on the PL/size  compared to other populations , 173 putative candidate genes were common in the Indica and Japonica populations, and 83 putative candidate genes identified by GWAS SNPs associated with NSP were common in the Indica and Japonica populations. These common genes could be potential candidate genes for further analysis and validation in rice accessions with extreme phenotypes for high and low scores for GY components under HNT stress using transcriptome analysis and followed up by genome editing in elite rice cultivars.In order to understand the origin and distribution of natural genetic variation in the rice subspecies, GWAS was performed in the Combined population and individually in the ulations , which e PL/size . Using tulations . InteresqHTT8 on chromosome 8 related to heat tolerance leading to spikelet fertility in rice, and they mapped the genomic region from 3,555,000 to 4,520,00 bp . In summary, the data from the comparison of 140 highly significant SNPs for the effect of HNT on rice GY and quality components and previously reported QTLs have revealed very useful information, which can be implemented in rice breeding using SNP-based marker-assisted selection and introgression of the co-localized genomic regions with these QTLs of GY into elite rice cultivars for the development of high-yielding rice cultivars under heat stress environments. In addition, a follow-up search for the SNPs we found in other germplasms might enable the selection of the trait phenotypes for other novel elite alleles.The identification of highly significant SNPs co-localized with the genomic regions of earlier reported/published QTLs for GY components in rice under heat stress provided strength and evidence for true marker trait associations in rice. All the highly significant SNPs associated with PL and NSP under HNT stress were found to overlap with genomic regions of previously reported QTLs for GY components under HDT stress. Out of these, 140 highly significant SNPs associated with PL and NSP were coincident with the genomic regions of 32 previously reported QTLs related to potential GY components across the rice genome . The sigp < 0.001, identified two modules (M028 and M023) in the rice gene network. The potential functions of these modules  and related CREs are all well-known stress response mechanisms of plants. The recovery of these modules by our GWAS testified for the accuracy of our study and led to new functional insights on the potential functions of the candidate genes that were selected. For instance, some of the putative candidate genes are yet to be unannotated but lie within the network modules annotated as cellular metabolic, response to stress, and carbohydrate derivative metabolism. These processes are relevant for spikelet/grain development and carbohydrate metabolism. Within the CREs, abre3hva22 is a well-known ABA-response element involved in abiotic stress responses and sure2stpat21 is a sucrose-dependent element involved in plant growth and development. Insights from GO enrichment and network analysis, thus, show the potential to enhance our understanding of the basis of mechanisms involved in heat tolerance, subsequently contributing to increases GY in rice under heat stress conditions.By using the global genetic variation (approximately 42% genetic variation) and validation of GWAS results of the Combined population in the panel, GO enrichment and network analysis, with the 5,265 associated genes identified by all the GWAS SNPs associated with PL and 3,136 genes identified by all GWAS SNPs associated with NSP at Indica, Japonica, Admixture, and Combined subpopulations at the phenotypic and genetic level. Phenotypic screens indicated that the Japonica population showed the least reduction in PL and NSP under HNT stress compared to the Combined and Indica populations, while the Admixture population exhibited the highest reduction. In addition, the highest percentage of genetic variation and higher broad sense heritability in the Japonica subpopulation suggested that the Japonica population, as an exotic gene pool, could be used in rice breeding programs, especially since the United States generally grows Japonica rice. Trait correlation analysis determined that PL was positively correlated with NSP in the Combined, Indica, and Japonica populations under HNT stress, and, like other important GY components, panicle size and NSP were crucial components contributing to GY enhancement in rice. Using the global natural genetic variation in the populations, we found highly significant SNPs associated with PL in the Combined (83 SNPs), Indica (60 SNPs), and Japonica (804 SNPs) populations. For NSP, there were 31 highly significant SNPs in the Combined, 31 highly significant SNPs in the Indica, and 11 highly significant SNPs in the Japonica populations. Using these SNPs, we confirmed that 140 significant SNPs associated with PL and NSP were coincident with previously reported genomic regions of the QTLs of major GY components in rice under heat stress. The SNPs that were coincident with previously reported QTLs and all significant GWAS SNPs, in all the populations, could be important resources for introgression or the pyramiding of favorable alleles to improve rice cultivars for heat tolerance and GY. Enrichment and network analyses provided additional insights into the biological processes involved in pathways and CREs, which are involved in responses to abiotic stress and plant growth and development. Therefore, all the findings from this study are noteworthy, with the phenotypic and genetic dissection of the panel providing deep insights into the phenotypic and genetic variations in different subpopulations, which are also accessible to rice breeders and geneticists, to understand the mechanisms related to heat stress tolerance contributing to the stability of GY in rice.This is the first integrated study to screen a panel of diverse rice accessions at the panicle initiation stage for GY components such as PL and NSP under HNT stress and dissect the panel in subsets of The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/AK and AP conceived the idea and designed the study. AK and JT performed phenotypic screening in the greenhouse. AK and CG performed the bioinformatics analysis. AK performed the phenotypic and genotypic data analysis and drafted the manuscript. AP acquired the funding and supervised the research. All authors approved the content of this manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Millennia of directional human selection has reshaped the genomic architecture of cultivated cotton relative to wild counterparts, but we have limited understanding of the selective retention and fractionation of genomic components.Gossypium hirsutum and Gossypium barbadense reference genomes respectively, of which 38.2%  and 14.2%  of genes exhibit presence/absence variation (PAV). We document the landscape of PAV selection accompanied by asymmetric gene gain and loss and identify 124 PAVs linked to favorable fiber quality and yield loci.We construct a comprehensive genomic variome based on 1961 cottons and identify 456 Mb and 357 Mb of sequence with domestication and improvement selection signals and 162 loci, 84 of which are novel, including 47 loci associated with 16 agronomic traits. Using pan-genome analyses, we identify 32,569 and 8851 non-reference genes lost from This variation repertoire points to genomic divergence during cotton domestication and improvement, which informs the characterization of favorable gene alleles for improved breeding practice using a pan-genome-based approach. Gossypium hirsutum and Gossypium barbadense) originated from interspecies hybridization between putative diploid A genome (Gossypium herbaceum or Gossypium arboretum) and D genome (Gossypium raimondii) ancestors approximately 1–1.5 million years ago (MYA) ) by sharking overnight at 4 °C with three biological replicates. Indole-3-acetic-2,2-d2 acid , eviously .Additional file 1: Table S1. Summary of genomic sequencing data of 1961 cottons in this study. Table S2. Summary of SNP filtering in 1913 G. hirsutum, G. barbadense and other Gossypium species accessions for each chromosome. Table S3. Summary of InDels filtering in 1913 accessions for each chromosome. Table S4. Summary of SNP filtering in 1623 G. hirsutum accessions for each chromosome. Table S5. Summary of SNP and InDel filtering in 261 G. barbadense accessions for each chromosome. Table S6. The SNP and InDel annotation of subpopulation. Table S7. The number of structural variations filtered by four steps in each subpopulation. Table S8. Summary of copy number variations for each chromosome in different subpopulation. Table S9. The SNP-based domestication sweeps and covering genes. Table S10. The SNP-based improvement signals and covering genes. Table S11. Continuously selected signals and genes during domestication and improvement. Table S12. The CNV-based domestication signals and overlapped SNP-based signals. Table S13. The CNV-based improvement signals and overlapped SNP-based signals. Table S14. Significant SNP-based GWAS signals for 15 agronomic traits. Table S15. Summary of snpQTLs that overlapped with domestication and improvement signals. Table S16. Summary of pleiotropic snpQTLs in multiple panels. Table S17. Summary of pleiotropic cnvQTLs in 419 panel accessions. Table S18. Summary of cnvQTLs that overlap with domestication and improvement signals. Table S19. Summary of assembly non-reference contigs between G. hirsutum and G. barbadense. Table S20. Filtering non-reference sequences following several filtering steps. Table S21. Protein-coding genes predicted in the G. hirsutum non-reference genome. Table S22. Protein-coding genes predicted in the G. barbadense non-reference genome. Table S23. Summary of PacBio reads and Illumina reads for 10 representative G. hirsutum cotton accessions. Table S24. Summary of polished contigs for 10 representative G. hirsutum accessions and draft genomes mapped to the GhPangenome. Table S25. Meta-genome likely assembly for landrace, American and modern cotton variety groups. Table S26. Selected PAVs during cotton domestication. Table S27. Selected PAVs during cotton improvement. Table S28. Selected PAVs during both domestication and improvement. Table S29. Summary of selective regions and covering genes during domestication and improvement. Table S30. Detail information of functional SNP variation in landrace and two geographic groups. Table S31. Summary of PAVs associated with QTLs.Additional file 2. Figures S1-S30.Additional file 3. Supplementary Notes.Additional file 4. Review history.


Trifolium pratense) is one of the most important fodder crops worldwide. The knowledge of genetic diversity among red clover populations, however, is under development. This study provides insights into its genetic diversity, using single nucleotide polymorphism (SNP) markers to define population structure in wild and cultivated red clover. Twenty-nine accessions representing the genetic resources available at NordGen (the Nordic gene bank) and Lantmännen  were used for this study. Genotyping was performed via SeqSNP, a targeted genotype by sequencing method that offers the capability to target specific SNP loci and enables de novo discovery of new SNPs. The SNPs were identified through a SNP mining approach based on coding sequences of red clover genes known for their involvement in development and stress responses. After filtering the genotypic data using various criteria, 623 bi-allelic SNPs, including 327 originally targeted and 296 de novo discovered SNPs were used for population genetics analyses. Seventy-one of the SNP loci were under selection considering both Hardy-Weinberg equilibrium and pairwise FST distributions. The average observed heterozygosity (HO), within population diversity (HS) and overall diversity (HT) were 0.22, 0.21 and 0.22, respectively. The tetraploids had higher average HO (0.35) than diploids (0.21). The analysis of molecular variance (AMOVA) showed low but significant variation among accessions , and among diploids and tetraploids . This study revealed a low mean inbreeding coefficient (FIS = −0.04) exhibiting the strict outcrossing nature of red clover. As per cluster, principal coordinate and discriminant analyses, most wild populations were grouped together and were clearly differentiated from the cultivated types. The cultivated types of red clover had a similar level of genetic diversity, suggesting that modern red clover breeding programs did not negatively affect genetic diversity or population structure. Hence, the breeding material used by Lantmännen represents the major genetic resources in Scandinavia. This knowledge of how different types of red clover accessions relate to each other and the level of outcrossing and heterozygosity will be useful for future red clover breeding.Red clover ( Trifolium pratense) is an important crop to secure sustainable cattle farming and thus meat and dairy production, as it is one of the most important forage legumes worldwide  . The annotation of the assemblies can be used to locate gene sequences and thus identify SNPs targeting desired genes.DNA markers have been widely used to conduct population genetics research in crops and their wild and weedy relatives, both for understanding their genetic makeup as well as to establish their genetic and phylogenetic relationships. SeqSNP is a targeted genotype by sequencing method that can be designed for genotyping of known highly polymorphic SNPs. If desirable SNPs are unavailable, novel SNP candidates can be identified through allele mining of genomic resources of a target crop from the GenBank. The significance of these novel SNPs can be evaluated by using them for population genetic studies. Based on the results, a core set of SNPs can be developed for various applications including marker-aided breeding. 92005.1) . The late family . A codinAs explained by 3 whereas the remaining eight were obtained from Lantmännen Seed,4 a plant breeding and agricultural seed company based in Sweden. The NordGen accessions were selected based on their passport data and represent cultivars, landraces and wild forms from the Nordic countries, as well as one Russian accession collected near the Finnish border. The accessions from Lantmännen represent cultivars and synthetic populations and include both diploids and tetraploids. Hereafter, cultivars from NordGen and Lantmännen will be referred for distinction as cultivarN and cultivarL, respectively.Twenty-nine red clover accessions were used for this study . Twenty-NGB11586, which was represented by eight individual seedlings. Hence, 288 individuals originating from different seeds were separately sampled in total. For sampling the leaf tissue, BioArk Leaf collection kit provided by LGC, Biosearch Technologies5 was used. From each plant, ten 6 mm leaf discs were sampled using a punch and were put in a sampling plate. The collected leaf tissue was then sent to LGC, Biosearch Technologies  for DNA extraction and subsequent genotyping. Using the Sbeadex plant kit,6 high quality genomic DNA was extracted for SeqSNP genotyping.A minimum of 20 seeds were planted for each accession in a greenhouse at the Swedish University of Agricultural Sciences  in May 2020. For each accession, two 2 L plastic pots filled with soil were used for planting. Poorly germinating seeds were treated by manual scarification according to 2 where the CDS of genes of interest were aligned against the red clover reference genome  using the Basic Local Alignment Search Tool (BLAST) of the National Center for Biotechnology Information (NCBI). As only CDS sequences with a single hit in the red clover genome were used for the SNP identification, all SNPs used in this study were considered to be from single-copy genes. If a CDS could not be clearly associated with the list by its associated protein name, the UniProt database7 was used to identify the related pathway. If the related pathway was in accordance with the selection criteria, the sequence was chosen for the SNP mining. Alignments were accepted at a threshold of 95% sequence identity between the query and subject sequences. Whenever more than one SNP was selected per target sequence, the SNPs were at least 55 bp apart. In total, 641 target SNPs in 324 CDS sequences were used for SeqSNP assay design. Of the 641 target SNPs, 571 SNPs were fully covered (two oligo probes per target) and passed high specificity assay design , whereas the remaining 70 SNPs failed. Among the 571 SNPs that passed the high specificity assay design, 400 target SNPs in 247 sequences were then selected for SeqSNP analysis by taking into account the distribution of the SNPs across the genome, the function of the genes as well as the distance between SNPs within a gene  or growth and development were targeted for single nucleotide polymorphism (SNP) mining. The SNP mining was performed using coding sequences (CDS) of red clover genes downloaded from the legume information system (LIS) databasen a gene . In thisN were also considered diploids based on observed phenotypic characteristics. On the other hand, the four accessions from Lantmännen are known tetraploids. A preliminary genetic diversity analyses of these tetraploid accessions conducted using their allele frequency data, calculated from their allele counts, provided similar results with the results obtained by treating them as diploids. Hence, the genotype of each sample at each locus was determined based on the allele-count, as diploid for all accessions in the final data analyses. Hence, individuals having only one allele with an allele-count of above eight were recorded as homozygotes whereas those with an allele-count of above eight for two alleles were scored as heterozygotes. The bi-allelic SNP data were then filtered to retain only loci with missing data of less than 5%. Among the 352 bi-allelic SNPs, 21 had a missing data of over 5%, thus only 331 were retained for further analysis. In addition to the target SNPs, 292 bi-allelic SNPs that fulfilled all aforementioned filtering criteria were identified de novo within 75 bp range on both sides of the target SNPs.After sequencing, the raw reads were adapter-clipped and quality-trimmed . For genotype calling, the SNP genotyping pipeline was set to diploid genotyping with a minimum coverage of eight reads per sample per locus, as described below. The alignment of quality trimmed reads against the reference genome using Bowtie2 v2.2.3  and variST  assumptions, at 0.05 level of significance. For this analysis, 1000,000 steps in Markov-chain and 100,000 dememorization steps were used. Similarly, Arlequin was used to detect loci under selection (at 0.05 level of significance) through the examination of the joint distribution of Fulations . This leulations  was usedde novo SNP calling by mapping the reads from the 288 individuals to the reference genome led to the discovery of an additional 296 bi-allelic SNPs  of known diploids ranged from −0.05 to 0.03 whereas that of known tetraploids ranged from -0.33 to -0.29. Those with unknown ploidy had FIS ranging from −0.08 to 0.06 (O) across all loci and accessions was 0.22 with individual values ranging from 0.16 (accession NGB1009 and NGB24176) to 0.36  ranged from 0.16 (accession NGB1009 and NGB24176) to 0.26 (SW RK1166) with an overall average of 0.21 (T) mean estimate was 0.22 and share similar range of values with that of HS (ST) was 0.05, whereas the mean inbreeding coefficient (FIS) was −0.04  of the single Russian accession (NGB24176) had the lowest mean value but the range of the individuals was corresponding to the other groups. The range of estimated values of population differentiation (FST) was larger for accessions from Sweden and Norway compared with those from other sources  differentiation among the accessions accounting for 5.4% of the total variation. AMOVA was then conducted by grouping the 29 accessions or their subsets into different groups according to origin, population type, maturity type and ploidy. In all cases, a highly significant differentiation was obtained among accessions within groups (P < 0.001) explaining from 0.5% to 1.8% of the total variation (P = 0.012) was revealed when the eight Lantmännen accessions were compared with the 21 NordGen accessions, accounting for 0.5% of the total variation. Similarly, significant differentiation (P = 0.018) was obtained among groups of NordGen accessions from different countries, accounting for 0.9% of the total variation. The differentiation between different population types of the NordGen accessions was highly significant (P < 0.001) with 1.8% of the total variation residing among them. The eight Lantmännen accessions were grouped based on three different criteria . A low but significant differentiation was obtained among diploids and tetraploids, accounting for 1.1% of the total variation (P = 0.02). However, no differentiation was observed between cultivars and synthetic populations as well as between different maturity groups. Even though there was significant structure distinguishing the accessions, the variation explained by each grouping criteria was very low. The highest variation explained by grouping the accessions was between the NordGen population types, accounting for 1.8 % of the total variation.The AMOVA analysis on the 29 accessions  prior toariation . A low bST based pairwise differentiation matrix between accessions is illustrated in P < 0.001) pairwise differentiation with average FST values ranging from 0.07 to 0.11. These are Swedish (NGB1420 and NGB1009), Russian (NGB24176) and Norwegian (NBG15558 and NGB15623) wild accessions, as well as the Danish cultivars (NGB11605 and NGB11608), all of which were obtained from NordGen. The pairwise FST between two Lantmännen accessions, SW RK1166 (a synthetic population) and ‘Vicky’ (a cultivar) was 0, thus suggesting that they are closely related and might have been developed based on the same source. Additionally, the differentiation between SW RK1166 and SW RK1158 (synthetic populations from Lantmännen) was also 0, thereby suggesting that they too might have been developed from the same source. Interestingly, ‘Vicky’, SW RK1166 and SWRK1158 are three of the four known tetraploids included in this study. The fourth tetraploid accession (SWA 1675209) showed slightly higher differentiation from these three tetraploids.The FST values between groups of accessions according to their origin . The differentiation between Finland and Denmark was also low (FST = 0.010), which is interestingly half the value of the differentiation between Denmark and Norway or Sweden . Furthermore, the differentiation between Denmark and Sweden or Norway are almost equal to the differentiation between Lantmännen and Sweden or Norway . Overall, the wild populations are the most differentiated among these groups with average FST value of 0.023 (A matrix illustrating the pairwise Fr origin  shows a on types  showed sof 0.023 .NGB14448) was clustered with a landrace accession from Finland (NGB1142) and two Lantmännen diploid accessions (SW YNGV and SWÅ RK0700). The NJ tree also clearly depicted a close clustering of the Lantmännen tetraploid accessions while the four diploids were spread across three clusters. With regard to country of origin, the Danish accessions  were closely clustered together with the exception that two accessions (a cultivar and a breeding population) were placed in a separate but closely related cluster. Interestingly, the Lantmännen tetraploid accessions showed higher genetic similarity with the Danish accessions than with those from other countries, and were closely clustered with the NGB11608 (a breeding population). The diversity among the landrace accessions was also displayed in the NJ tree, where even those from the same country were placed far apart in different clusters.A neighbor joining (NJ) tree  construcNGB14448 (from Finland) that had members in clusters-1, -6 and -8  from HWE. Eighteen of these loci showed excess heterozygosity whereas 33 loci were heterozygote deficient , and were therefore considered to be under selection. The translation of the coding sequences of genes containing these loci using the alternative SNPs resulted in 42 and two missense and nonsense mutations, respectively . However, it should be noted that our approach might have excluded the possibility of discovering SNPs in previously uncharacterized genes of potential significance in red clover improvement. The 623 bi-allelic polymorphic markers genotyped across 288 individuals representing the 29 red clover accessions using SeqSNP revealed significant population structure differentiating accessions due to origin and cultivar type. Four wild accessions, two Swedish (NGB1420 and NGB1009), one Russian (NGB24176) and one Norwegian (NGB15558) were consistently grouped together while separated from the other accessions. Additionally, the known tetraploids  grouped together in the NJ trees while the known diploids were dispersed across different clusters together with NordGen accessions. Furthermore, the tetraploids showed similar patterns in zygosity in markers associated with growth and development  of the tetraploids were significantly lower than that of the diploids (−0.01) as expected, because there is higher probability of heterozygosity in tetraploids than in diploids under an outcrossing reproductive system. The FIS values for the known diploids ranged from 0.05 to 0.03 whereas that of the 21 NordGen accessions with unknown ploidy ranged from 0.08 to 0.06 (mean = 0.00). IS values for red clover ecotypes (diploids) from United Kingdom in their GBS based study. Whereas the ecotypes from central Europe recorded a slightly higher FIS, suggesting the Scandinavian red clover may be more similar to that of United Kingdom than to those of central Europe. In a recent bi-allelic SNP marker-based study by Guizotia abyssinica  for tetraploids (0.35) was higher than that of the diploids (0.21) and accessions with unknown ploidy (0.20). The average observed heterozygosity for European red clover ecotypes in IS and HO values suggest that all 21 accessions with unknown ploidy obtained from NordGen are most likely diploids. Overall, the heterozygosity and FIS values obtained in this study (S = 0.25) when compared to all other accessions (mean HS = 0.20). According to The mean values for observed heterozygosity  in the present study was slightly lower than those reported in It is interesting that the wild accessions had lower average within population diversity than the other groups with four of the six accessions recording the lowest HS values . This isST values was larger for accessions from Sweden and Norway , and thus contain a higher level of within population genetic diversity and have higher FST values compared to the modern cultivated types that include breeding and synthetic populations and cultivars. However, the present study revealed that genetic diversity and population differentiation of all cultivated types are generally similar indicating that modern red clover breeding programs did not lead to loss of genetic diversity and affect population structure. As suggested by The Norwegian and Swedish populations, on the other hand, included only wild and landrace accessions explaining the higher FG. abyssinica  in their GBS based study that included European and Asian ecotypes and cultivars.As previously discussed, red clover is a strictly outcrossing species, and outcrossing species have a greater gene flow than self-pollinating species due to the role of their pollinators. This increased gene flow between subpopulations reduces the difference between them. From the AMOVA , 5.4% ofyssinica . Comparethaliana  and bothZingiber , which gAMOVA analysis was further performed after six separate criteria were applied to group the 29 accessions. Four of the groupings revealed a very low but significant differentiation between the groups. Overall, the variation explained by the groupings was very low with only NordGen accessions grouped by population types and the Lantmännen accessions grouped by ploidy explaining over 1% of the total variance .The cluster analysis at both accession and individual genotype levels demonstrated the genetic relationship between the accessions used in the present study. The eight Lantmännen accessions displayed interesting pattern at both levels. The four tetraploid accessions are closely related and all accessions and their individual members were placed in the same cluster. The result suggests a narrow genetic base of the tetraploid red clover and hence crossbreeding among them may not lead to a significant improvement of desirable traits. Since tetraploid red clover is a result of successful plant breeding, based on limited tetraploid genotypes developed through chromosome doubling , the obtNGB14448 is interesting. Unlike all other wild accessions, it was closely grouped with cultivated accessions including a Lantmännen cultivar (at accession level), as shown by cluster analysis, PCoA and DAPC. At individual level, its genotypes were placed in different clusters, of which three individuals clustered with the other wild genotypes. This may suggest that the accession is a result of mixture of seeds  from cultivated and wild accessions. Hence, it is interesting to investigate this accession further and other similar accessions at NordGen.Similar to the cluster analyses, the relatively close relationship between the cultivated accessions was also shown in the PCoA, where they were clustered close to the center of the PCoA bi-plot with a slight separation of the three Danish cultivars  and one Finnish cultivar (NGB14444). The relatively higher divergence between the wild accessions as compared to the case of cultivated pool is expected due to a limited gene flow among the wild populations. In agreement with the cluster analyses, five of the six wild accessions were separated from the other groups along the principal coordinate 1, which accounted for 54% of the total variation whereas the differentiation between them is clearly observable along the principal coordinate 2 . The cleUnlike the PCoA bi-plot that was the result of the first two principal coordinates, the DAPC, which described 100 principal components , led to The 623 SNP loci used in the present study are located within the coding sequences of 231 genes, of which 51 loci (8.2%) showed significant deviation from HWE. Because, red clover is an outcrossing species with a gametophytic self-incompatibility system , excess O  family proteins and proteins related to disease resistance are among the most frequent groups. The PPR protein family is one of the largest protein families in land plants and are known to regulate RNA expression and have an effect on respiration, photosynthesis, growth and development, and responses to environmental stresses . The zygO  where LaST could be due to association with another loci; an effect referred to as “hitch-hiking” by ST assumptions. The level of heterozygosity was higher in the PPR protein markers than in the disease resistance proteins and the SNP loci in the disease resistance genes were more often homozygous. Interestingly, the nonsense mutation  with the cultivated ones showed a relatively lower within population diversity in the wild populations reflecting differences in allele frequency between the two groups, likely due to the dominance of wild type alleles across loci in the ecotypes. Most, but not all, wild red clover populations were distinctly separated from the cultivated gene pool. In relation to this, the case of wild accession PRJNA765476.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/MG secured the funding with RO and CH as co-applicants. JO and MG selected and ordered the seed material from NordGen and Lantmännen Seed, analyzed the data, and compiled the results with input from RO and CH. JO was responsible for the planting and taking care of the plant material in the greenhouse, together with MG and CH sampled the plant material, performed the SNP mining with the assistance of MG, and wrote the manuscript. All authors contributed to the design of the study, provided inputs, revised the manuscript, and approved its final version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Glycine max var. Peking and Glycine soja IT182936. Whole genome sequencing was carried out, and the DNA variations in the whole genome were compared between the normal and dwarf samples. We found a large number of DNA variations in both the dwarf and semi-dwarf lines, with one single nucleotide polymorphism (SNP) per at least 3.68 kb in the dwarf lines and 1 SNP per 11.13 kb of the whole genome. This value is 2.18 times higher than the expected DNA variation in the F6 population. A total of 186 SNPs and 241 SNPs were discovered in the coding regions of the dwarf lines 1282 and 1303, respectively, and we discovered 33 homogeneous nonsynonymous SNPs that occurred at the same loci in each set of dwarf and normal soybean. Of them, five SNPs were in the same positions between lines 1282 and 1303. Our results provide important information for improving our understanding of the genetics of soybean plant height and crop breeding. These polymorphisms could be useful genetic resources for plant breeders, geneticists, and biologists for future molecular biology and breeding projects.Plant height is an important component of plant architecture and significantly affects crop breeding practices and yield. We studied DNA variations derived from F5 recombinant inbred lines (RILs) with 96.8% homozygous genotypes. Here, we report DNA variations between the normal and dwarf members of four lines harvested from a single seed parent in an F6 RIL population derived from a cross between  Soybean is one of the most important leguminous crops worldwide due to its use in human food and oil production. Currently, the United States, Brazil, and Argentina account for more than 80% of the worldwide production of soybean . In SoutThe rapid development of next-generation sequencing (NGS) technology and instruments has supported quick and efficient genomics research ,10. The Glycine soja was sequenced and compared to the Glycine max reference, found 2.5 megabases (Mb) of substituted sequences, 4.6 kilobases (kb) of indels, 32.4 Mb of deletions and 8.3 Mb of new sequences in a total of 915.5 Mb of genome sequence  class) family (35%) and auxin-like 1 protein (27%) . The disThere were three nonsense SNPs found in both genotypes: one was on Chr 2, another was on Chr 13, and the last was on Chr 20; the SNP from Chr 20 was homogeneous in the dwarf lines . The thrWhen we considered the SNP among normal and dwarf lines and not with the reference, 426 SNPs were obtained, among which five were common to both samples (1282 and 1303) . The SNPG. max and G. soja and none of the parent exhibited dwarf phenotype. From F3 generation we obtained few lines that exhibited dwarf phenotype which continued to produce dwarf lines in subsequent generation. We chose two lines from F6 that had both phenotypes  to survey the variance among them. Genome variant analysis was performed in such lines from F6 generation exhibiting dwarf and semi-dwarf phenotype. We observed that the dwarf lines in this study had a higher number of SNPs and indels than the semi-dwarf lines  were in vital genes but may or may not have impacted plant growth. We were unable to derive any particular conclusion about the dwarf phenotype based on our results, but this study will provide a basis to analyze further and evaluate which SNPs affect plant growth type. The identification of functional SNPs in genes and analysis of their effects on phenotype may lead to a better understanding of their impacts on gene function and thus support varietal improvement.


EPHA2 variant in LUCC03 and a known homozygous c.2353G>A;p.(Ala785Thr) EPHA2 variant in the other two recessive families. EPHA2 encodes a transmembrane tyrosine kinase receptor, which is primarily involved in membrane-transport, cell-cell adhesion, and repulsion signaling processes. Computational structural modeling predicts that substitution of a threonine for an alanine p.(Ala785Thr) results in the formation of three new hydrogen bonds with the neighboring residues, which causes misfolding of EPHA2 in both scenarios. Insights from our study will facilitate counseling regarding the molecular and phenotypic landscape of EPHA2-related HCC.Hereditary congenital cataract (HCC) is clinically and genetically heterogeneous. We investigated HCC that segregates in three inbred families . Ophthalmological examinations revealed cataracts with variability related to the age of onset segregating in a recessive manner in these families. Exome sequencing of probands identified a novel homozygous c.2710delG;p.  EPHA2 (OMIM:176946), have been associated with the nonsyndromic HCC with a variable prevalence rate worldwide  of Pakistan , indicatPakistan . Our finAfter obtaining informed consent from all the participants, three consanguineous families were enrolled from different areas of Sindh, Pakistan. Detailed family history was recorded, and pedigrees were drawn to ascertain the mode of inheritance. All the affected individuals underwent detailed ophthalmologic examinations, and the clinical history of cataract was noted. The clinical findings were recorded by slit-lamp microscopy. Peripheral blood samples were collected for DNA extraction.Exome sequencing was performed on the proband of each family by adopting the previously described method . The Nimhttps://www.ebi.ac.uk/Tools/msa/clustalo/, (accessed on 9 September 2021)) was used to evaluate the evolutionary conservation of the identified mutations. Varsome ) was used for pathogenicity prediction of EPHA2 variants, as in accordance with the American College of Medical Genetics and Genomics (ACMG) guidelines. Polyphen-2 ), SIFT, and CADD scores ) were used to appraise the impact of the identified variants on the encoded proteins. 3D protein models were generated using the I-Tasser server ) and analyzed through PYMOL ). Their stereochemistry was assessed through PDBsum and Ramachandran plots. Finally, the protein tolerance map was plotted using MetaDome ).Different bioinformatics tools were used to access the pathogenic effects of the variants and the potentially deleterious effects of the amino acid changes on the structure and the function of EPHA2. Clustal Omega multiple sequence alignment (


Bos taurus autosome (BTA) 7 and 14), adjusted 305-d fat yield , adjusted 305-d protein yield (BTA 8), and somatic cell score (BTA 8 and 23) of milk production traits. Furthermore, we discovered three main candidate genes (diacylglycerol O-acyltransferase 1 (DGAT1), phosphodiesterase 4B (PDE4B), and anoctamin 2 (ANO2)) through bioinformatics analysis. These genes may help to understand better the underlying genetic and molecular mechanisms for milk production phenotypes in the Korean Holstein population.Milk production traits that are economically important in the dairy industry have been considered the main selection criteria for breeding. The present genome-wide association study was performed to identify chromosomal loci and candidate genes with potential effects on milk production phenotypes in a Korean Holstein population. A total of eight significant quantitative trait locus regions were identified for milk yield (n = 2780). We identified four significant SNPs for each phenotype in the single marker regression model: AX-311625843 and AX-115099068 on Bos taurus autosome (BTA) 14 for milk yield (MY) and adjusted 305-d fat yield (FY), respectively, AX-428357234 on BTA 18 for adjusted 305-d protein yield (PY), and AX-185120896 on BTA 5 for somatic cell score (SCS). Using the BayesC model, we discovered significant 1-Mb window regions that harbored over 0.5% of the additive genetic variance effects for four milk production phenotypes. The concordant significant SNPs and 1-Mb window regions were characterized into quantitative trait loci (QTL). Among the QTL regions, we focused on a well-known gene (diacylglycerol O-acyltransferase 1 (DGAT1)) and newly identified genes (phosphodiesterase 4B (PDE4B), and anoctamin 2 (ANO2)) for MY and FY, and observed that DGAT1 is involved in glycerolipid metabolism, fat digestion and absorption, metabolic pathways, and retinol metabolism, and PDE4B is involved in cAMP signaling. Our findings suggest that the candidate genes in QTL are strongly related to physiological mechanisms related to the fat production and consequent total MY in Korean Holstein cattle.We performed a genome-wide association study and fine mapping using two methods (single marker regression: frequentist approach and Bayesian C (BayesC): fitting selected single nucleotide polymorphisms (SNPs) in a Bayesian framework) through three high-density SNP chip platforms to analyze milk production phenotypes in Korean Holstein cattle ( Milk production is an economically important trait affecting profitability in Holstein dairy cattle. Holstein selection for genetic improvement with economic characteristics in the past was based on a traditional best linear unbiased prediction together with an estimated breeding value (EBV), a traditional animal breeding program that depends only on phenotypic and pedigree information . Recent https://www.animalgenome.org/cgi-bin/QTLdb/BT/index accessed on 1 October 2020). The specific genes or mechanisms involved in the detection of QTL, however, have not been sufficiently characterized because of the low resolution of the traditional positional cloning approach using microsatellite markers [For dairy cattle, since Georges et al.  reported markers  and the  markers .Advances in DNA analysis technology led to the emergence of genome-wide panels containing hundreds of thousands of single nucleotide polymorphisms (SNPs), which have been widely used for detecting QTL for complex traits in many species , and itsBos taurus autosome 4 (BTA 4), two SNPs related to fat yield on BTA 14/2, and one SNP related to fat percentage on BTA 1 were identified using the Bovine SNP50K BeadChip platform  in the Braunvieh cattle breed [DGAT1), transcriptional repressor GATA binding 1 (TRPS1), stearoyl-CoA desaturase (SCD), and growth hormone receptor (GHR) were found in GWAS using a high-density SNP chip platform [DGAT1 related to milk production, fat, and protein [Previous GWAS were performed using various SNP chip platforms in cattle breeds. Two SNPs related to milk yield on le breed . Dozens le breed . In addile breed , and manplatform ,13,14,15 protein ,17.The ultimate goal of GWAS is to elucidate the causative alteration rather than the locus, and high-density SNPs are required to identify such causative alterations closely. Furthermore, higher-density fine mapping was performed recently using a customized SNP array in which SNPs were included in the existing commercialized SNP chip platform through whole-genome sequencing methods or other genotyping techniques ,19,20. ABioinformatics analysis provides useful information for understanding the biological and biochemical mechanisms of genes at the molecular level . Recent c is the genetic variance ratio explained by the SNP marker information (assumed as 0.4) [The multi-lactation single-trait model considering parity, provided by the National Institute of Animal Science, was applied to MY, FY, PY, and SCS traits. The estimates for genetic, residual variances and heritability for each trait are shown in  as 0.4) . After cn = 1095), Illumina Bovine SNP50K v3 (n = 1013), and Affymetrix Axiom Bovine Custom300K (n = 672) (DNA samples (200 ng adjusted to 50 ng/µL) from 2780 Korean Holsteins (926 bulls and 1854 cows) were prepared from sampled hair according to standard protocols. DNA concentration and quality were measured using a NanoDrop ND-1000 spectrophotometer . Genotypic data were obtained using three SNP panels: Illumina Bovine SNP50K v2 (n = 672) .Bos taurus UMD3.1 (http://oct2018.archive.ensembl.org/Bos_taurus/Info/Index accessed on 1 October 2020) was performed using either the Axiom Bovine Custom SNP300K array  containing 297,424 SNPs or the Illumina Bovine SNP50K v2 and v3 BeadChip  containing 53,218 and 54,609 SNPs, respectively. To construct the customized Axiom array, SNPs were collected from previous commercial SNP genotyping platforms  and ranges of QTL regions , EvC ciliary complex subunit 2 (EVC2), annexin A10 (ANXA10), GDNF family receptor alpha 2 (GFRA2), neurotrophin 3 (NTF3), argininosuccinate synthase 1 (ASS1), LDL receptor related protein 4 (LRP4), integrin subunit beta 2 (ITGB2), uridine monophosphate synthetase (UMPS), COPI coat complex subunit alpha (COPA), coagulation factor XI (F11), enoyl-CoA hydratase, short chain 1 (ECHS1), DNA polymerase lambda (POLL), myostatin (MSTN), zinc finger imprinted 2 (ZIM2), and anoctamin 2 (ANO2)) associated with various phenotypes in cattle including Holstein. Affymetrix Axiom Bovine Custom SNP300K Information of total SNPs in Affymetrix Axiom Bovine Custom SNP300K is described in Genome-wide SNP genotyping based on Ensembl p-value < 0.0001 were removed. Secondly, 57 individuals did not pass the call rate < 0.95 criterion, and additionally, 109 individuals whose genomic information differed from their pedigree information were also excluded based on a paternity test. After that, the imputation was performed based on the Affymetrix Axiom Bovine Custom SNP300K using FImputeV3 [QC was performed with the SNPs and animals in each genotyping platform. First, for SNPs, the unmapped SNPs and sex chromosome SNPs were excluded, and then SNPs with a call rate < 0.95, minor allele frequency (MAF) < 0.01, and Hardy–Weinberg equilibrium (HWE) with ImputeV3 . Finallyp-values < 0.0001, and SNPs which are less than 250 kb away from an index variant and have r2 larger than 0.5 with it were removed). The significance threshold of the −log10p-value ≥ 6.61 was determined based on Bonferroni correction. In addition, Quantile-Quantile (Q-Q) plots for each phenotype were performed to identify population stratification or cryptic relatedness.PLINK 1.9  was usedy is an The general statistical model for the BayesC method was used with a π value of 0.997 and was fitted as follows:software . The BayBos taurus database and STRING v11.0 [The PPI analysis was performed based on each candidate gene with the 20 most interactive genes using the NG v11.0 . To inveNG v11.0  pathway NG v11.0 .n = 15,260), and BTA 27 had the smallest number of SNPs (n = 2959). As for the average interval between SNPs, the longest and shortest were found on BTA 15  and BTA 23 (3881.9 bp), respectively. In the MDS plot, high similarities between individuals were identified  in Korean Holstein is indicated in entified .10p-value ≥ 6.61)  with MAF of 0.015 located in the intron of paired related homeobox 1 (PRRX1). For the FY phenotype, the most significant SNP was AX-115099068  with MAF of 0.284 located in the upstream region of forkhead box H1 (FOXH1), and the highest SNP effect was observed in AX-320911501  with MAF of 0.016 located in the intergenic region. For the PY phenotype, the most significant SNP was AX-429486957  with MAF of 0.068 located in the intron of transmembrane protein 143 (TMEM143), and the highest SNP effect was observed in AX-429404258  with MAF of 0.010 located in the exon of the meiosis specific with coiled-coil domain (MEIOC). For the SCS phenotype, the most significant SNP was the AX-429899067  with MAF of 0.399 located in the intergenic region, and the highest SNP effect was observed in AX-310577007  with MAF of 0.053 located in the intergenic region. The abovementioned SNPs are marked in red in For the four DEBV phenotypes  related to milk production, significant SNPs were identified based on the SMR determined by the Bonferroni correction (−log ≥ 6.61) A–D. Q-Q  ≥ 6.61) . The numHSF1) and DGAT1. In addition, two SNPs (AX-185121607 and AX-106735408) were identified in the window on BTA 6, which were located in the intergenic regions. The remaining five windows presented one informative SNP each, indicating relatively low genetic effects. For the FY phenotype, five significant windows were identified on BTA 14 (1 Mb), 5 (104 Mb), 8 (0 Mb), 23 (24 Mb), and 3 (79 Mb) (SPATC1), DGAT1, and HSF1. In addition, two SNPs (AX-106724308 and AX-169413290) were identified in the window on BTA 3, which were located in phosphodiesterase 4B (PDE4B). The remaining three windows presented one informative SNP each, indicating relatively low genetic effects. For the PY phenotype, four significant windows were identified on BTA 23 (24 Mb), 8 (69 Mb), 8 (0 Mb), and 1 (69 Mb)  based on BayesC. We found 20 window regions including 40 informative SNPs based on their genetic effects E–H. For  (79 Mb) F. Thirte (69 Mb) G, and fo (69 Mb) H. Those DGAT1 on BTA 14 showed significant effects in both methods (ANO2 on BTA 5 and AX-106724308 (rs42314807) in PDE4B on BTA 3 were shown to have significant effects in both methods. Although no common SNPs between SMR and BayesC were found on BTA 14, many significant and informative SNPs were found in each method. The other significant region was the 69–70 Mb on BTA 8 for the PY . For the MY phenotype, the common significant regions were the 1–2 Mb on BTA 14 and 73–74 Mb on BTA 7. It was noted that AX-311625843 (rs211223469) in  methods . For the methods . AX-4196r the PY . That rer the PY .DGAT1, ANO2, and PDE4B) were selected based on GWAS using the two methods (SMR and BayesC) and were subjected to PPI identification : glycerolipid metabolism, fat digestion and absorption, metabolic pathways, and retinol metabolism  in a Korean Holstein population. The associations between SNPs and phenotypes were analyzed based on two methods (SMR and BayesC). Many previous studies reported comparisons between these two methods ,39,40,41DGAT1 that MDS indicated high similarities in this population  . The BTADGAT1 on BTA 14, in particular, is one of the most important SNPs, as it affects both MY and FY phenotypes. It was suggested that BTA 14 is an important QTL region [DGAT1 gene is a causal gene related to the QTL region [DGAT1 gene produces an enzyme that catalyzes the final step in synthesizing triglyceride, which makes up about 98% of milk fat [DGAT1 gene encodes a key metabolic enzyme catalyzing the biosynthesis of triacylglycerols and participates in glycerolipid metabolism, retinol metabolism, and fat digestion and absorption as indicated by the KEGG pathway analysis and as reported previously [DGAT1 gene is considered to affect FY as well as MY in dairy cattle through physiological mechanisms.AX-311625843 (rs211223469) at the 1–2 Mb in L region ,50,51 anL region ,53,54. Tmilk fat ,56, and milk fat ,58. In aeviously . The DGAPDE4B on BTA 3 was identified to have a significant effect on FY in both methods. Previous studies observed a significantly strong relationship between the PDE4B gene and milk-related traits  [PDE4A, PDE4B, PDE4C, and PDE4D), which has been reported to be involved in various functional activities such as cell desensitization or adaptation, signaling cross-talk and cAMP signal compartmentalization. Although the correlation between the level of PDE4 and cAMP concentration remains unclear, this family is considered to be an important cAMP homeostatic regulator [PDE4B with 20 interactor genes revealed by the KEGG enrichment analysis. Among the significant KEGG pathways, the cAMP signaling pathway was related to FY through the regulation of AMP-activated protein kinases by cAMP in adipocytes. This physiological mechanism was reported in previous studies [AX-106724308 (rs42314807) at the 79–80 Mb in  and PY) ,61,62. P and PY) ,64, and egulator . PDE4 tregulator . There w studies ,67,68.ANO2 on BTA 5 was identified to have a significant effect on FY in both methods. In previous studies, this region of BTA 5 was shown to have many QTL related to milk fat content, a characteristic of milk production [ANO2 gene in this region, however, were not reported in relation to milk content.AX-419663582 rs43454033) at the 104–105 Mb in 033 at thoduction ,69. The The region identified by both methods to have a significant effect on PY was the 69–70 Mb region on BTA 8, which was also shown to be a QTL region related to PY in previous studies ,70. The As milk is produced through mammary glands, the health status of these glands is especially important, and the SCS is used as an index of health status. The somatic cells refer to the leukocytes of milk in cows. For example, as the leukocytes around the mammary glands of a dairy cow suffering from mastitis increase rapidly, and these leukocytes are reflected in the SCS of milk, this score indicates the presence of infection and the health status of the cow. A lower SCS represents a healthier cow as well as a higher quality of milk ,73,74,75DGAT1, ANO2, and PDE4B were subjected to PPI network and KEGG enrichment analyses, and the results showed that the DGAT1 gene is involved in glycerolipid metabolism, fat digestion and absorption, metabolic pathways, and retinol metabolism pathways, affecting the MY and FY phenotypes. The PDE4B gene was found to be closely involved in metabolic activity through the cAMP signaling pathway, affecting the FY phenotype. No strong candidate genes, however, were selected for the PY and SCS phenotypes, though significant regions were identified, suggesting the necessity of further studies. Our findings are expected to provide important information for the genomic selection of those phenotypes to improve milk production in Korean Holstein cattle.A GWAS was performed to analyze milk production phenotypes  in a Korean Holstein population, and six QTL regions  were detected. In addition,


Amyema gibberula var. tatei (n = 154) and 10 populations of its associated host Hakea recurva subsp. recurva (n = 160). Reduced‐representation sequencing was used to obtain genome‐wide markers and putatively adaptive variation detected using genome scan methods. Climate associations were identified using generalized dissimilarity modelling, and these were mapped geographically to visualize the spatial patterns of genetic composition. Our results supported the hypothesis of parasites and host species responding differently to climatic variables. Temperature was relatively more important in predicting allelic turnover in the specialist mistletoe while precipitation was more important for the host. This suggests that parasitic plants and host species may respond differently to selective pressures, potentially as a result of differing nutrient acquisition strategies. Specifically, mistletoes acquire water from hosts (rather than the abiotic environment), which may provide a buffer to precipitation as a selective pressure. This work deepens and complements the physiological and other ecological studies of adaptation and provides a window into the evolutionary processes that underlie previously observed phenomena. Applying these methods to a comparative study in a host–parasite system has also highlighted factors that affect the study of selection pressure on nonmodel organisms, such as differing adaptation rates and lack of reference genomes.Parasitism is a pervasive phenomenon in nature with the relationship between species driving evolution in both parasite and host. Due to their host‐dependent lifestyle, parasites may adapt to the abiotic environment in ways that differ from their hosts or from free‐living relatives; yet rarely has this been assessed. Here, we test two competing hypotheses related to whether putatively adaptive genetic variation in a specialist mistletoe associates with the same, or different, climatic variables as its host species. We sampled 11 populations of the specialist mistletoe  We aimed to develop our understanding of the association of putatively adaptive genetic variation along climatic gradients between parasites and their associated host species. Specifically, we examined (a) the association of putatively adaptive variation with climatic variables and the relative importance of temperature, precipitation and geographic distance with allelic turnover and (b) the predicted spatial pattern of putatively adaptive variation.Here, we applied a genotyping‐by‐sequencing approach to test these hypotheses by examining the patterns of genome‐wide variation between a mistletoe 22.1Amyema gibberula var. tatei and its host Hakea recurva subsp. recurva have a widespread distribution in south‐western Australia, spanning c. 500 km north‐south and c. 300 km east‐west  and three precipitation variables . Variables in the final data set were plotted in the R package raster v3.0‐12 . Sequences were filtered to remove those with a Phred score <30, and the remainder were collapsed into identical sequences. SNP marker calling was performed with Diversity Array Technology's propriety DArTsoft14 pipeline. Approximately 25% of samples were regenotyped as technical replicates to allow a measure of DArTseq reproducibility at each locus to be calculated. NCBI BLAST  test and two environment association (EA) analyses to detect loci under selection. Specifically, we used pcadapt uses a robust Mahalanobis test statistic to identify SNPs in which ɀ‐scores do not follow the same distribution as those of the larger data set, and these are considered as outliers  following the package vignette, performing principal component analysis (PCA) on the data set using the R function prcomp  that was used to adjust p‐values based on a chi‐squared (χ2) distribution  or above 97% , which were considered outliers. To identify association with environmental variables, outlier SNPs  were removed to create a neutral data set that was run in BayPass with the same settings as the core model. The average of four runs was used to create a neutral covariance matrix. Finally, the auxiliary model was run in BayPass using the neutral covariance matrix and the seven climatic variables with the same settings as the core model. Bayes factors were obtained from the mean of four runs and were transformed into deciban units (dB) using the 10log10(BF) transformation. Values of 20 deciban units or more were considered as strong evidence for significant SNP–environment associations  were used as the biological response variable, and predictor data sets were assembled with geographic coordinates of each population along with the seven climatic variables.Generalized dissimilarity modelling  of each climatic variable  and 15,187 SNPs across 10,415 loci for specialist mistletoe Amyema gibberula var. tatei (n = 154). All replicates had greater than 97% genetic similarity. Following further quality control filtering, the working data sets comprised 15,422 SNPs for the host and 2055 SNPs for the mistletoe  and 225 SNPs as outliers for the mistletoe . LFMM identified 59 significant SNP–environment associations for the host involving 47 SNPs. In contrast, LFMM identified 272 significant SNP–environment associations for the mistletoe involving 135 SNPs. Similarly, BayPass identified 88 SNP–environment associations involving 81 SNPs for the host and 107 SNP–environment associations for the mistletoe involving 105 SNPs. For both EA approaches, the number of significant SNP–environment associations varied between climatic variables and no SNPs for either species were significantly associated across all variables .Overall, 574 SNPs were identified by at least one analytical method for the host Figure  and thosPCoA identified differing genetic structure between species and data sets Figure . For the3.3α = 0.05) for the putatively neutral data set of the host, although we opted to use mean temperature of the wettest quarter (p = 0.06) in the final GDM model , with patterns varying by both predictor variable and species Figure . GeograpGeographic distance explained approximately 43% of the GDM model deviance of putatively adaptive data sets for both species Figure . In contSpatial patterns of predicted genetic composition were similar for putatively neutral data sets of both species, but not for putatively adaptive data sets Figure . Specifi4Our investigation of genome‐wide variation along climatic gradients in a specialist mistletoe and its host found that putatively adaptive genetic variation was associated with different climatic variables for each species. These differences between host and parasite in the turnover of putatively adaptive SNPs contrasted with the similarity in turnover of putatively neutral SNPs and support our alternative hypothesis that these species respond differently to climatic variables. Specifically, temperature was relatively more important in predicting allelic turnover for the mistletoe, while precipitation was more important for the host. This could reflect a parasitic life history as parasitic plants have different water acquisition strategies and transpiration rates to their hosts. While genome‐wide markers have been used to examine climate adaptation in numerous taxa, applying these methods to a comparative study of nonmodel organisms in a host–parasite system has presented some distinct challenges, which we discuss below.4.1Mistletoes and their hosts have been previously recorded as having similar relationships between physiological parameters and climate (Scalon & Wright, GDM analysis showed putatively adaptive variation in the host to be associated with precipitation, which is consistent with findings of other studies on autotrophic plants (e.g. Manel et al., Similar observations on associations of genome‐wide variation with climatic variables have also been found for a generalist parasite and sympatric autotroph (Walters et al., In this study, we found putatively adaptive variation to positively correlate with geographic distance, similar to that in other plant species (e.g. Shryock et al., 4.2Genome‐wide markers have been used to identify signals of selection across many taxonomic groups (reviewed in Ahrens et al., Another complicating factor for our study is that both nonmodel species lack reference genomes or transcriptomes, and consequently, we have not been able to verify the gene function of putatively adaptive SNPs. While signals of selection can still be obtained for species that lack prior genomic knowledge (Savolainen et al., per se. For instance, polygenic adaptation of many loci with small effect that result in phenotypic changes may be difficult to detect with genome scans in comparison to loci with a single, large effect (Le Corre & Kremer, a priori. Furthermore, we also found stronger population differentiation in the mistletoe and this is known to influence the detection of loci under selection (Flanagan et al., Lastly, while our study has enabled association of putatively adaptive variation with different climatic gradients, it does not provide insight into the genetic architecture of climate adaptation in these species 4.3Our findings on the association of putatively adaptive variation with climatic variables in a mistletoe–host system were similar to those of a previous study on a generalist parasite and co‐occurring autotroph (Walters et al., None declared.All authors conceptualized the study design. S.J.W. collected field samples, extracted the DNA and analysed the data with input from co‐authors. S.J.W., P.N., T.P.R. and M.B. interpreted the results. S.J.W. wrote the manuscript. All authors read, edited and approved the final manuscript.Supplementary MaterialClick here for additional data file.