The preliminary identification of ROH suggests the presence of selection. However, for the identification of potential candidate genes, ROH detection should be combined with other approaches to improve mapping accuracy.Runs of homozygosity (ROH) are contiguous homozygous segments of the genome where the haplotypes inherited from each parent are identical. The occurrence of ROH is not randomly distributed across the genome, and ROH islands across many animals may be the result of selective pressure. The objective of this study was to demonstrate that the presence of ROH islands may be indicative of selection signatures in French dairy sheep breeds and subpopulations. The data set available included animals  from various breeds and subpopulations: Basco-Béarnaise breed , Manech Tête Noire breed , Manech Tête Rousse breed , Lacaune Confederation subpopulation , and Lacaune Ovitest subpopulation . Animals were genotyped with the Illumina OvineSNP50 BeadChip. After applying filtering criteria, the genomic data included 38,287 autosomal SNP distributed across 26 chromosomes and 8,700 individuals. One island of ROH was detected on OAR6 in the same genomic position across animals (between 30 and 40 Mb). Global Wright's differentiation coefficients for 2 SNP within this ROH island were high (0.67–0.68). The linkage disequilibrium between both SNP was also elevated (0.98). The divergence in allele frequencies in those SNP grouped Basco-Béarnaise, Manech Tête Noire, and Manech Tête Rousse breeds in one cluster and Lacaune Confederation and Lacaune Ovitest subpopulations in another cluster. The closest candidate genes are  Sheep, one of the first domesticated species, have a manageable size and the ability to adapt to different climates and diets with poor nutrition . From thSTF; iHS; Different methods varying in the underlying assumptions have been proposed to detect selection signatures, such as the genome-wide differentiation coefficient FST; , extendeROH) is a potential alternative to detect signatures of selection. The occurrence of ROH is heterogeneous along the genome due to the stochastic nature of the recombination events. Consequently, the detection of ROH islands may be indicative of selective pressure. Runs of homozygosity can provide a better understanding about the population selection history, supporting the discovery of genes or genomic regions under selective pressure (The availability of large numbers of SNP has made these markers appropriate to distinguish old (short runs) from new (long runs) identical by descent segments . Accordipressure .LACCon) and Lacaune Ovitest (LACOvi), which split around 1975 and proceed in parallel selection schemes. In the second region, the Western Pyrenees mountains, there are 3 breeds: Manech Tête Rousse (MTR), Manech Tête Noire (MTN), and Basco-Béarnaise (BB). The third region is Corse, but Corsican sheep were not included in the present study. All of the breeds in this study  have been selected for production (milk yield and composition) and functional  traits with varying intensities. The objective of this study was to demonstrate that the presence of ROH islands may be indicative of selection signatures in French dairy sheep breeds and subpopulations.Selection of French dairy sheep has been implemented for each local breed and subpopulation separately. There are 3 major regions for sheep milk production. The first is around the Roquefort (Aveyron) area, with the Lacaune breed, whose elite population (breeding flocks) consists of 2 subpopulations, Lacaune Confederation (P < 10−6 were discarded). The final data set included 8,700 genotyped individuals and 38,287 autosomal SNP. Markers were positioned using the sheep (Ovis aries) genome assembly 3.1 . See Rams were genotyped with the OvineSNP50 BeadChip (Illumina Inc.). The number of genotyped animals was 321, 329, 1,906, 3,030, and 3,114 for BB, MTN, MTR, LACCon, and LACOvi, respectively. These are AI rams representing several years of birth (10–15) and overlapping generations, and they illustrate the genetic variability of each breed. The SNP quality control included the absence of parent–offspring Mendelian segregation incompatibilities (<3%), SNP call rate >97%, and large deviations from Hardy-Weinberg equilibrium (SNP with ST (Plink software version 1.9  was usedST  between ST  and applThe rehh R package  with theST (or iHS) was calculated for all SNP in each chromosome. The average genetic differentiation coefficient across breeds and subpopulations was 0.0576 ± 0.0003. The genome-wide distribution of global differentiation coefficient for each SNP revealed that the highest selection signal was detected on OAR6  were located at 37,423,374 and 37,542,319 bp, respectively. Three other significant SNP were also detected on OAR6 near the 2 highest ranked SNP at 36,205,101, 36,972,847, and 37,318,895 bp). In addition, 6 isolated significant SNP were detected on OAR3 , OAR11 , OAR13 , and OAR16 . The FST values for the remaining autosomes were not as high as the FST values observed for the 2 highest ranked SNP on OAR6. Results obtained for the iHS derived statistic are shown in ST and iHS was detected only for 1 SNP on OAR13 on 46,841,439 bp. A total of 95 significant SNP for iHS were observed in OAR6. At least 2 significant SNP for iHS on OAR6 (OAR6_39124095.1 and OAR6_42392875.1) were close  to the highest significant SNP for FST.The correlation between the number of times an SNP is included within an ROH and F on OAR6 . The 2 hST and the number of times that an SNP was in an ROH was observed in OAR6 for LACOvi and LACCon subpopulations and MTR breed ranging between 0.36 and 0.42 .The allele frequencies observed for both SNP also diverged for the same allele between Western Pyrenees breeds  and Lacaune subpopulations (LACCon and LACOvi). For SNP OAR6_41583796.1 and OAR6_41709987.1 (the highest ranked SNP for FST). The ROH island detected on OAR6 involves a large interval compared with the reduced interval obtained from the 2 most significant SNP in the FST analysis. This suggests that the FST approach is more precise than ROH methodology in terms of mapping accuracy. However, the gene mapping accuracy of ROH is affected by the criteria used to define an ROH, which in turn depends on the available genotype SNP density. In any case, ROH signals suggest that the hitchhiking effect involves a large region of homozygosity around the potential candidate genes. Some agreement between ROH islands and selection signatures has been previously reported in the literature , NCAPG , and LCORL . ABCG2 has been associated with a strong QTL for milk production in cattle, and NCAPG and LCORL have been associated with fetal growth . These authors indicated that domestic sheep contain a selective sweep in the LCORL promoter region, whereas mouflon contain a sweep downstream of NCAPG.ST analyses agreed that selection signatures exist around markers OAR6_41583796.1 and OAR6_41709987.1 that were located near the NCAPG and LCORL genes. One factor involved in shaping ROH patterns in French Dairy sheep was selection. Accordingly, the preliminary identification of ROH can suggest the presence of selection. However, for the identification of potential candidate genes, ROH detection should be combined with other approaches to improve mapping accuracy.To conclude, ROH and single marker F


Over the last two decades, the use of high-density SNP arrays and DNA sequencing have allowed scientists to uncover the majority of the genotypic space for various crops, including cotton. Genome-wide association study (GWAS) links the dots between a phenotype and its underlying genetics across the genomes of populations. It was first developed and applied in the field of human disease genetics. Many areas of crop research have incorporated GWAS in plants and considerable literature has been published in the recent decade. Here we will provide a comprehensive review of GWAS studies in cotton crop, which includes case studies on biotic resistance, abiotic tolerance, fiber yield and quality traits, current status, prospects, bottlenecks of GWAS and finally, thought-provoking question. This review will serve as a catalog of GWAS in cotton and suggest new frontiers of the cotton crop to be studied with this important tool. Gossypium hirsutum), which has a wide range of adaptability and high yield. Owing to a key component of the global textile industry, cotton fiber traits have gained more attention of researchers as compared to other traits. Cotton is grown in more than 30 countries as a major commercial commodity for food, feed, and renewable fiber  to the New World crossed with a D-genome ancestor (Gossypium raimondii L.), resulting in allotetraploid cotton around 1–2 million years ago  is an experimental design to dissect the association of natural genetic variants and traits in samples representing a big population . GWAS trP-value) Association mapping usually based on single nucleotide polymorphisms (SNPs) markers and phenotype of interest is used to find causative locus as shown in A genome-wide association study’s success and robustness lies in four sound bases: Genetic diversity, trait acquisition veracity, marker density, and statistical methodologies. GWAS has now been successfully applied in several crops, and a considerable number of studies have also been conducted on the cotton crop . In thisGWAS, also known as linkage disequilibrium (LD) mapping, uses the phenotypic and genotypic variations within a species to identify the genetic underpinnings of the traits of interest. hirsutum, 8 in arboreum and 2 in barbadense. GWAS studies for different traits in different species have been shown in GWAS comes up with an opportunity to investigate the relationship between natural variations and major agronomic attributes of the cotton crop. Researchers have focused mainly on fiber, yield and abiotic stress in cotton crop, as shown in Gh_D03G0728 coding for COP1 interactive protein CIP1 regulating the flowering time, GhUCE involved in fiber initiation, GhFL2 a pleiotropic gene-regulating both fiber length and strength and some previously unreported genes associated with fiber length and other FQTs presents an excellent example of the power of GWAS. Most SNPs were found on the D subgenome. COP1 in Arabidopsis is involved in mediating light-dependent growth and development of floral organs. The function of COP1 (designated as GhCIP1) in G.hirutum was unknown, so GWAS was found useful tool to identify genes with previously unknown function. The results of Virus-induced gene silencing (VIGS) for functional validation of Gh_D03G0728 containing different haplotype non-synonymous SNPs further corroborated the GWAS results. Silenced plants (plants with silenced gene under study) had not produced any flowering or squares; however, non-silenced (non-VIGS or CK) plants produced three fruiting branches having squares at the same growth stage, this functional analysis validated the exact effect of gene associated with flowering, suggesting it to be a key lead gene regulating flower development. A team of researchers conducted GWAS study and examined 196 cotton accessions with 41,815 SNPs. Two different GWAS models, a single-locus model [MLM (Q+K)] and five multi-locus models, were employed in six different environments for five fiber-related traits. Both the models commonly found 40 SNPs, 38 quantitative trait loci QTLs and 89 candidate genes for fiber traits. Moreover, 13 QTLs and 5 putative genes were reported with probable pleiotropic effects  particularly fiber length trait (fuzless to extra-long fiber), depicts the genetic complexity of this trait. Low FQTs lowers the market value of cotton lint. Considerable research and breeding efforts have been expended on identifying and utilizing allelic variance that contribute to FQTs improvement. The development of cotton varieties with improved FQTs is highly desirable. More than 30 GWAS studies have been conducted on FQTs till now.  effects . Single  effects . ConcurrGh_D05G0313 lycopene cyclase family gene, located on chromosome D05 coding for phytoene synthase (a key regulator enzyme in the biosynthesis of carotenoids), was found to be associated with lint percentage -like superfamily protein  that increased lint yield were also identified . One non-synonymous SNP significantly associated with a trait of interest located in the gene Gh_A02G1392, a homolog of the AP2/ETHYLENE RESPONSE FACTOR (ERF)-type transcription-factor-encoding gene AINTEGUMENTA-like 6 (AIL6) in Arabidopsis. Comparative RNA-seq and (qRT-PCR) data showed high expression of this gene during seed development from 20 to 25 days post-anthesis, more than tenfold higher in TM-1 (lower lint percentage and number of bolls per plant) than in the corresponding Chinese improved cultivar ZMS12, which has a higher lint percentage and increased number of bolls per plant, in developing ovules at 10 and 20 DPA. The orthologous gene AIL6 has a major role in shoot and flower meristem maintenance, flower initiation, organ size and floral organ identity, and cell proliferation in Arabidopsis. It has been reported that ethylene insensitive 3 like protein (EIL) serves as a key downstream regulator, EIN3, in the ethylene-initiated signaling network and binds to the promoters of ERF genes such as AIL6, stimulating their transcription in an ethylene-dependent manner. When a plant responds to both biotic and abiotic challenges, ethylene is a key phytohormone for orchestrating the integration of environmental signals into a specific phenotype  across three seasons identified the associations of SNPs to a wide range of cotton yield components, including fiber quality, plant architecture, and stomatal conductance. Out of 63,058 markers, only 28,480 SNP markers > 5% MAF were used for GWAS. For fiber length and micronaire, MLM revealed 17 and 50 significant SNP associations, respectively . The posAnother GWAS of 231 recombinant inbred lines RILs genotyped with 122 SSR and 4,729 SNP markers in nine locations was conducted for fiber and yield quality characteristics. The cottonSNP80K array platform, having 77,774 SNPs, was used for genotyping. There were 134 QTLs for fiber traits and 122 QTLs for yield parameters; 57 of these QTLs were sQTLs. Four multi-locus GWAS models identified a total of 209 and 139 (QTNs) linked with fiber yield components, respectively. A total of 51, 50, and 38 QTNs were found to be linked with BW, LP, and SI, respectively, with 82, 83, and 65 candidate genes. KEGG analysis revealed two yield-related candidate genes involved in six pathways. The putative genes found in 57 sQTLs were matched to those found in QTN, 35 common candidate genes were reported with four possibly pleiotropic genes . Yield cTPR gene associated with fiber yield traits using GWAS and functional validation of TPR transgene of Arabidopsis in G. hirsutum in a different study confirms the results of GWAS. So, this study supported by previous studies of molecular characterization of TPR gene, suggests the application of GWAS for the prioritization of candidate genes to develop transgenic plants with increased fiber yield. Previous studies have generally described the role of ethylene in abiotic stress tolerance; however Identification of Plants have evolved intricate molecular pathways to cope with biotic and abiotic stresses. The dynamic climatic conditions have led to exceptional weather patterns resulting in augmented crop losses. Similarly, disease dissemination of pathogens in an era of increased international commerce has resulted in pathogen introduction and adaption to new places, resulting in recurrent outbreaks. A deep apprehension of the molecular processes underpinning plant stress responses to generate climate-smart crop varieties is becoming increasingly important . EnvironVerticillium wilt is a plant disease caused by a soil-borne fungus, Verticillium dahliae. It is a severe disease in cotton ; this gene has been more responsive when treated with verticillium dahilae and salicylic acid (SA). Therefore, it was inferred that CG05 might respond to invasion by V. dahliae via an SA-related signaling pathway. GaGSTF9 was found to be a positive regulator of Verticillium wilt through the use of (VIGS) and overexpression in Arabidopsis. Genes identified in GWAS studies followed by functional validation provides robust and valid results in terms of molecular markers that can be utilized in the development of genetically modified plants with improved characteristics.n cotton . V. dahlery year . To idengstf9 of (GST) was more susceptible to Verticillium wilt than wild-type plants. The endogenous SA and H2O2 expressed a significant effect on Arabidopsis that overexpressed GaGSTF9, depicting that GST may regulate reactive oxygen species (ROS) content via catalytic reduction of the (GSH), subsequently affecting SA content. Plant glutathione S-transferase (GSTs) genes have been classified into several classes, including dehydroascorbate reductase (DHAR), metaxin, phi, tau, zeta, theta, iota, lambda, and others. In cotton, few types of glutathione S-transferase GSTs have been identified. These findings elucidate the success of GWAS in identifying GaGSTF9 gene, a key regulator mediating cotton responses to V. dahliae and a potential candidate gene for cotton genetic improvement  indexed, multi-parent advanced generation inter-cross (MAGIC) population of 550 recombinant inbred lines (RILs) coupled with 11 upland cotton parents with a total of 473,516 polymorphic SNP markers used to identify chromosomal regions for VW resistance, GWAS identified three QTLs on chromosome A01, D02 and D08 in common and three QTL clusters detected on chromosome A01, A13, and D01. Potential candidate genes for VW resistances were found in a narrow region of three common resistance QTLs , pyridoxal (PL), pyridoxamine (PM), and their phosphorylated derivatives. The de novo biosynthesis of VB6 vitamers requires two highly conserved pyridoxal protein families (PDX1 and PDX2) in plants. Vitamin B6 has a crucial role in stress responses as it is involved in pathways of starch metabolism, glucosinolate biosynthesis, ethylene and auxin biosynthesis   for Thrips resistance in two replicated tests. Based on a GWAS using 26,301 polymorphic SNPs Eight QTL were identified for Thrips resistance on five chromosomes, with most SNPs on the and D11) . ExclusiXanthomonas citri pv. malvacearum (Xcm) is a destructive disease of cotton crop in many countries. Breeding BB-resistant cotton cultivars is the most effective strategy for controlling this disease. A current study of GWAS in 335 cotton accessions has been reported. GWAS, based on 26,301 polymorphic (SNP) markers, detected 11 QTLs associated with 79 SNPs, including three QTLs located on each of the three chromosomes, A01, A05 and D02, and one QTL on each of D08 and D10. Once again, these results found more associated SNPs on the D subgenome. Several studies concurrent findings of genomic regions associated with multiple traits on D genome offer some clues to focus on genomic hotspots associated with several traits. These results will assist in breeding cotton for BB resistance and facilitate further genomic studies in fine-mapping resistance genes to enhance the understanding of the genetic basis of BB resistance in cotton , caused by n cotton .Previous studies of abiotic stress have not dealt simultaneously with the genetics and genomics of drought and salt tolerance . A MAGIC population of 550 RILs and their 11 Upland cotton parents used 473,516 SNPs to pinpoint QTLs for (DT) and (ST) at the seedling stage. Transgressive segregation in the MAGIC-RILs indicated tolerant and sensitive alleles recombination during the crossing-over process for the population development. A total of 20 QTL were detected for DT, including 13 and 7 QTL related to plant height (PH) and dry shoot weight (DSW), respectively, whereas 23 QTL were detected for ST, including 12 and 11 QTL for PH and DSW, respectively. Four QTLs were reported on chromosome A13, three QTLs on A01 for DT, four QTLs on D08 and three QTLs on A11 for ST. Nine QTL were shared by DT and ST, showing possible implication that the two stresses have a common genetic base. Salt and drought stress-responsive 53 candidate genes were identified. However, these findings need further validation with functional analysis of putative candidate genes. The QTL discovered for both DT and ST have significantly increased the number of QTL for abiotic stress tolerance that can be used for marker-assisted selection (MAS) to develop DT, ST cultivars and further genomic studies to identify drought and salt responsive genes in cotton . Common RD2 encoding response to desiccation 2 protein, HAT22 encoding a homeobox-leucine zipper protein, PIP2 encoding a plasma membrane intrinsic protein 2, and PP2C encoding a protein phosphatase 2C. PP2C is an important regulator of stress signal transduction pathway and is an excellent candidate gene to decipher complex stress tolerance traits  and peroxidase activity, it depicts positive regulatory role of PIP2;7 in cotton under salt stress  transporter. This shows that GWAS findings are in compliance with functional studies of the same genes in model plants and cotton plants. These results will contribute to a better knowledge of the genetic basis of drought stress tolerance in cotton and prospective markers for breeding drought-tolerant cotton cultivars (319 upland cotton accessions genotyped by more than 55 thousand (SNPs) for nine traits related to drought tolerance, using two datasets to identify QTNs with multi-locus random-SNP-effect MLM (mrMLM) reported a total of 20 QTNs distributed on 16 chromosomes. A compendium of 205 genes was induced after drought stress, combined results of (GWAS), RNA-seq and qRT-PCR verification, proposed four genes to be potential candidate genes for drought tolerance, e traits . Silencit stress . Zheng eultivars .GhRD2, GhNAC4, GhHAT22 and GhDREB2. Moreover, potential candidate genes Gh_A04G0377 and Gh_ A04G0378 were negative regulators of cotton drought response. Comparative analysis of phenomics, GWAS and transcriptomic data provides an exceptional resource to characterize key genetic loci with an unprecedented resolution that can predict future genome-based breeding for improved drought tolerance in cotton  signaling and the calcium transduction pathways , cotyledon wilting score (CWS), and leaf temperature (LT), respectively. Interestingly, combined results of GWAS and RNA seq depicted that DEGs pathways . RNA seqSoil salinity is a serious threat affecting more than 800 million hectares of arable land worldwide. Plant growth and development is significantly hindered under salt stress. GWAS has been successfully utilized to identify SNPs/genes associated with salt stress tolerance mechanisms. A GWAS in 217 cotton accessions for salt tolerance related (ST) traits at the seedling stage performed on 2 years of phenotypic data and more than 50 thousand SNPs identified 27 SNPs scattered over 12 chromosomes associated with three ST traits. Among these, the associations on chromosomes A13 and D08 for relative PH, A07 for relative shoot fresh matter weight (RSFW), A08 and A13 for relative shoot dry matter weight (RSDW) were stable SNPs, indicating that they were likely to be sQTLs. A total of 12 salt-induced candidate genes were identified by GWAS and transcriptome analysis .A genome-wide association conducted in a core collection of 419 accessions of cotton representing a vast genetic background, subjected to GWAS for various agronomic traits. Phenotypic variations depicted significant variation between salt-sensitive and tolerant accessions. 17264 significantly associated SNPs were found distributed on multiple chromosomes. Twenty potential candidate genes discovered around SNPs A10_95330133 and D10_61258588, linked to relative water content under 150mM NaCl salt stress (RWC_150) and leaf fresh weight (FW_150). Fine mapping of these important genomic region can unveil candidate genes for functional validation. Differential expression of candidate genes under normal and drought stress conditions revealed the involvement of the genes in the salinity tolerance mechanism. Further study on the functional validation of candidate genes development of transgenic lines can provide useful knowledge about the genetic control of salt tolerance at the seedling stage .G. hirsutum accessions over two years under four salt conditions. GWAS analysis reported 57,413 SNPs above P-value threshold. A total of 42, 91 and 25 sQTLs were associated with single boll weight SBW, lint percentage LP, and number of bolls per plant NBPP, respectively. Eight sQTLs discovered concurrently in four different salt environments in the case of LP, whereas SBW and BNPP had fewer sQTLs. According to gene ontology (GO) analysis, their regulatory mechanisms were also quite different. The transcriptomic analysis defined 8 genes associated with LP under salt stress; Haplotype analysis showed an MYB gene GhMYB103 with two SNP variants in cis-regulatory and coding areas substantially linked with LP. Moreover, 40 candidate genes from NBPP QTLs were salt stress-responsive  related to 10 salt stress attributes were found, with eight significant SNPs connected with three salt stress variables. Two loci on D5 were significantly associated with chlorophyll content, one on A2 and four on D9 were significantly associated with melondialdehyde content, and one on A12 significantly associated with germination rate  and (Gh) were cultivated independently. A combined result of three GWAS panels (one panel of 229 Gb accessions and two panels of 491 Gh panels) revealed that most functional haplotypes related to agronomic traits were highly divergent, depicting strong divergent improvement between Gb and Gh accessions. According to the interspecific haplotype map, six interspecific introgressions from Gh to Gb were strongly related to Gb phenotypic performance, accounting for 5%–40% of phenotypic diversity in yield and fiber quality. In addition, three introgressions in Gb overlapped with six linked loci, indicating that these introgression sites were under selection and stabilization during the course of improvement. A single interspecific introgression might increase production while lowering fiber quality, or vice versa, making it challenging to raise yield and fiber quality simultaneously  and the elite cultivar G. hirsutum cv. Acala Maxxa, in two environments targeting domesticated cotton phenotypes, found only 22 stable QTLs (sQTLs) associated with phenotypic changes during domestication. Even though around half of the QTL were found in the A-subgenome, numerous critical fiber QTL were found in the D-subgenome, inherited from a species with unspinnable fiber. Many QTLs were environment-specific, with few shared across the two environments, demonstrating that QTLs related to G. hirsutum domestication were genomically clustered yet environmentally mutable. It was concluded that the evolutionary dynamics shaping sympatric speciation divergence and domestication in cotton are complicated and that phenotypic alteration was likely influenced by several interacting and environmentally sensitive variables  is the proportion of the total genetic variance contained in a subpopulation (the S subscript) relative to the total genetic variance (the T subscript). Low STF values within improved subgroups (G3–G6) demonstrated that the genetic divergence was low (0.019–0.067) within improved cultivars, However, the average pairwise STF values were much higher (0.425– 0.552) between improved cultivars and landraces when using G1 (landraces group) as comparison pair than other. Higher Fst values between improved cultivars and landraces depicts noticeable genetic differentiation. Maximum divergence and major haplotypes were traced on chromosome A06 and A08 of G.hirsutum. De novo assembled genome carrying haplotypes of interests showed large scale chromosomal inversions causing haplotype polymorphism. Divergence on chromosomes A06 and A08 is the most notable genomic signature in cultivated G. hirsutum. Population differentiation in animals and plants has been linked to large chromosomal inversions. This evolutionary mechanism allows species to adapt to new environments by repressing recombination to sustain favorable genotypes. These findings support the theory of chromosomal inversion-population differentiation in crops and define the haplotypes associated with geographic differentiation in cotton cultivars. Here we enlist GWAS studies conducted on cotton crop  as many sequences containing domestication signals as At, demonstrating a case of asymmetric domestication for the two coexisting subgenomes. The Dt subgenome also has 2.2 times more regions with selection sweep signatures than the At subgenome, indicating that it has been subjected to more selection pressure than the At subgenome. As demonstrated by the large number of fiber quality-related QTL hotspots discovered in the Dt subgenome. Similarly, findings have reported that there were more fiber-related genes in the Dt subgenome than in the At subgenome  A genome during evolution and domestication, even though the D genome does not produce any spinnable fiber.As it is evident from our previous description of SNPs that, most of the candidate SNPs or loci associated with different yield and FQTs have been found on the GWAS have revolutionized the field of intricate genetic architectures of important agronomic traits over the past decade, providing numerous compelling associations for complex quantitative traits. Despite numerous successes in identifying novel genes and biological pathways and in translating these findings into breeding programs in different crops, GWAS has not been without controversy. Here we provide an overview of the benefits and limitations of GWAS Table 2.Genome-wide association study estimates the statistical association of single nucleotide variants with variables of interest. Although the number of GWASs has exponentially increased in the previous decade, the results have low reproducibility. Different GWASs on the same trait are not in compliance with each other, which means different GWAS results report different genomic regions for the same trait. That might be because of insufficient genetic variation in mapping populations, and different algorithmic and statistical approaches. The potential reason behind this conundrum could be the following factors.Low accuracy of phenotypingPhenotyping of non/low heritable traitsInaccurate sample size in terms of variationWrong statistical analysisPopulation stratificationTechnical biasednesshttp://bioinfo.genyo.es/metagenyo/), AraGWAS (https://aragwas.1001genomes.org/) and GWApp (http://gwas.gmi.oeaw.ac.at) are available for Meta-GWAS analysis. Meta-GWAS have been reported in A. thaliana and soybean crop, but as per our updated knowledge, no Meta-GWAS has been reported in cotton crop. With the rapidly expanding volume of plant-GWAS data , meta-GWAS will be more popular for studying plants.Meta-GWAS is a statistical technique to combine the results across the GWA studies. It has garnered considerable attention from academics to resolve disparities in genome-wide associations. Meta-GWAS has been widely employed in human studies with a large amount of available data. Such work is becoming more readily available for commonly used populations, such as haplotype and regional mapping populations. Online tools such as MetaGenyo  effect. This complexity is not only the result of differences in gene action, but also determined by ontogenic gene networks or even epigenetic effects, and the interaction with surroundings, and greatly changing environmental conditions. It will soon be possible to identify all underlying genes and their activities for major cotton traits, that will significantly speed up molecular breeding strategies. Although GWAS remains a valuable technique for fully using NGS technology breakthroughs, advances in statistical methods and genetic design can help to accomplish this goal. Diverse statistical methods can help us understand the genetic basis of complex features, but their differing assumptions remind us to careful selection of analysis methodology for each study or to combine allied methods. Improved population designs/core collections and new sequencing and statistical approaches may help identify and manipulate genetic elements generating quantitative variation. These novel designs will improve detection precision and accuracy by restructuring allelic spectra and diminishing confounding effects, which will help overcome intrinsic statistical hurdles.Cotton crop has evolved and adapted over millions of years, resulting in the accumulation of genetic based natural variations SNPs from various environments over time, resulting in many phenotypic functional variations. Recent advances in DNA sequencing have allowed for in-depth characterization of plant natural variations. These resources can be exploited with GWAS to link phenotypic variations to relevant genes or functional polymorphisms, providing insight into complex traits. Recent developments in the quantitative omics phenotypes  give rise to veritable alphabets of association studies TWAS, MWAS and EWAS collectively known as OWAS (omics wide association studies) coupled with highthroughput phenotyping platforms are beginning to yield unprecedented insights for the associated genes underlying agronomically important traits, expediting genomics assisted breeding. Consequently, we believe that developing more efficient GWAS computational algorithms would be highly desirable in this context. A better understanding of genetic variability at the SNPs level will help in MY: Writing and idea development; HK: Proof reading and figures; QH: Language and proof reading; MR: Data compilation; MS: Data curation; YJ: Supervision; JR: Supervision. All authors contributed to the article and approved the submitted version.Zhejiang Provincial Natural Science Foundation of China (Grant No. Y21C130006) to YJ.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.


An Fst value of 0.1 has often been taken as indicating that two breeds are indeed genetically distinct, but this concept has not been evaluated critically. Here, Fst values have been collated for the six major livestock species: cattle, sheep, goats, pigs, horses, and chickens. These values are remarkably variable both within and between species, demonstrating that Fst > 0.1 is not a reliable criterion for breed distinctiveness. However, the large body of Fst data accumulated in the last 20–30 years represents an untapped database that could contribute to the development of interdisciplinary research involving livestock breeds.The degree of relationship among livestock breeds can be quantified by the Fst statistic is widely used to characterize between-breed relationships. Fst = 0.1 has frequently been taken as indicating genetic distinctiveness between breeds. This study investigates whether this is justified. (2) Methods: A database was created of 35,080 breed pairs and their corresponding Fst values, deduced from microsatellite and SNP studies covering cattle, sheep, goats, pigs, horses, and chickens. Overall, 6560 (19%) of breed pairs were between breeds located in the same country, 7395 (21%) between breeds of different countries within the same region, 20,563 (59%) between breeds located far apart, and 562 (1%) between a breed and the supposed wild ancestor of the species. (3) Results: General values for between-breed Fst were as follows, cattle: microsatellite 0.06–0.12, SNP 0.08–0.15; sheep: microsatellite 0.06–0.10, SNP 0.06–0.17; horses: microsatellite 0.04–0.11, SNP 0.08–0.12; goats: microsatellite 0.04–0.14, SNP 0.08–0.16; pigs: microsatellite 0.06–0.27, SNP 0.15–0.22; chickens: microsatellite 0.05–0.28, SNP 0.08–0.26. (4) Conclusions: (1) Large amounts of Fst data are available for a substantial proportion of the world’s livestock breeds, (2) the value for between-breed Fst of 0.1 is not appropriate owing to its considerable variability, and (3) accumulated Fst data may have value for interdisciplinary research.(1) Background: The F Much research effort over the last 30 years has been applied to the characterization of livestock breeds by molecular genetics, primarily by microsatellite (MS) and single-nucleotide polymorphism (SNP) technologies. This research has usually aimed to support the conservation and sustainable utilization of livestock biodiversity, and also to elucidate the processes of domestication and the evolution and differentiation of breeds.st statistic. As originally described [st values from 0.05 to 0.15 were taken to indicate moderate differentiation between populations, from 0.15 to 0.25 is high differentiation, and greater than 0.25 is very high differentiation. In principle, Fst could therefore be used to inform discussion relating to particular breeds, for example, that they are sufficiently different from each other to justify support for their conservation, or, conversely, that they are sufficiently similar for them to merge. In practice, Fst measurements are not often used as the main genetic justification for policy decisions regarding breed conservation, but the large number of Fst measurements available represents a data resource that could yield insights into overall patterns of breed differentiation. Indeed, genetic differentiation of breeds has often been placed in a spatial context by investigating how it is paralleled by geographic distance [One of the outputs has been the calculation of the extents to which breeds have diverged from each other, and a very widely used measure for this genetic differentiation has been the Fescribed , Fst valdistance . Furtherdistance ,5 and ecdistance  diversitst > 0.1) …” [st = 0.019)…” [st < 0.1 and suggested clearly differentiated populations …” [st > 0.1.For many years, the literature has included such statements as “… the level often found between related breeds  …” ; “a clos0.019)…” ; “a thre0.019)…” ; “… the 0.019)…” ; “… pairtions …” . The prest calculations were obtained from MS or SNP studies on cattle, sheep, goats, horses, pigs, sheep and chickens. A keyword search was not made because Fst is seldom used as a keyword or included in the title of a paper. The search proceeded initially by studying reference lists and citations of key papers such as [st [R were transformed to Fst [st calculations among herds or flocks were not used except when they related to differentiation between these entities and other, distinct breeds. Breed names and country affiliations were according to [Published data on F such as ,15,16,17 such as . Data prh as [st  were used to Fst . Fst calrding to  when thest for each of the twelve combinations of species and methodology (MS and SNP). Attention was then focused on recent publications, and a further 5080 breed pairs were added from a final total of 166 papers. No claim is made that this is a complete literature survey.Preliminary analysis when 30,000 breed pairs had been obtained showed interpretable patterns of distribution of Fst calculations were classified according to whether the two breeds involved were affiliated to the same country, or to different countries. Those of different countries were coded according to the spatial relationships of the two countries, as defined by their borders. Pairs that included a wild ancestor (as defined in the respective studies) were also considered  were of breed pairs classified as 6-Remote, 21% (7395) were Regional, and 19% (6560) were 1-Same. The proportion of breed pairs defined as 6-Remote was, for most species, higher in studies conducted with SNP methodologies than in MS studies.Species varied in the degree to which different spatial relationships were covered in the literature. Reflecting the relatively small numbers of breed pairs in the four regional classes , in Tablst values for breed pairs also varied according to the spatial relationships of breeds. In all twelve (species × methodology) cases from cattle MS through to chicken SNP, differences in Fst between spatial relationships were highly significant , 1165.5 (5), 186.71 (6), 1117.9 (6), 914.75 (6), 19.39 (5), 249.67 (5), 1605.03 (6), 207.23 (6), 120.45 (6), 196.11 (6), 107.11 (6)).Fst values for each of the spatial relationship categories (excluding 7-Wild_ancestor) were significantly correlated .The rank orders of the median FThese differences are illustrated in It is reported  that thest is 0.1. The ranges of Fst values between pairs of breeds are shown to be so wide that this prediction appears obsolete for practical purposes. It is now very evident that the Fst approach is only one method of visualizing the findings of genomic studies of breeds [st as indicating breed differentiation, for example for interdisciplinary studies or to provide a context for conservation genetics of wild populations. For these purposes, the following benchmarks could be adopted, based on the median values obtained in the present study, cattle: MS 0.06–0.12, SNP 0.08–0.15; sheep: MS 0.06–0.10, SNP 0.06–0.17; horse: MS 0.04–0.11, SNP 0.08–0.12; goat: MS 0.04–0.14, SNP 0.08–0.16; pig: MS 0.06–0.27, SNP 0.15–0.22; chicken: MS 0.05–0.28, SNP 0.08–0.26. However, use of these values as benchmarks must be conditional on acknowledging their stochastic nature and probable dependence on geographical factors.The original aim of this study was, however, to test the general prediction that a realistic threshold value for between-breed Ff breeds , and repf breeds . Neverthst values is novel but not surprising. Fst statistics are well known [st values from earlier studies may itself be an obsolete approach, as raw genotypes from multiple sources could be combined and Fst values reliably calculated from the pooled data.The finding that different spatial relationships of breed pairs may influence Fll known ,17,184 tst data accumulated over the last few decades is still likely to represent a valuable resource. It could be used to audit breed conservation activities, although it will not be a definitive determinant of whether a breed is truly distinctive [The considerable amount of Ftinctive ,186. At st as indicating breed differentiation is not justified, but benchmark values can be proposed for use in specified contexts. Fst data, as obtained from published studies, represent a resource for interdisciplinary research.The use of specific values of F


Pseudomonas aeruginosa (CNPA) strains from intensive care units (ICUs) in a referral hospital in Jakarta, Indonesia (Pseudomonas strains. With whole genome-based Multi Locus Sequence Typing (wgMLST) of the 3 CNPA clones , three to eleven subgroups with up to 200 allelic variants were observed for each of the CNPA clones. Furthermore, we analyzed these CNPA clone clusters for the presence of wgSNP to redefine CNPA transmission events during hospitalization. A maximum number 35350 SNPs (including non-informative wgSNPs) and 398 SNPs (ST-specific_informative-wgSNPs) were found in ST235, 34,570 SNPs (including non-informative wgSNPs) and 111 SNPs (ST-specific_informative-wgSNPs) in ST357 and 26,443 SNPs (including non-informative SNPs) and 61 SNPs (ST-specific_informative-wgSNPs) in ST823. ST-specific_Informative-wgSNPs were commonly noticed in sensor-response regulator genes. However, the majority of non-informative wgSNPs was found in conserved hypothetical proteins or in uncharacterized proteins. Of note, antibiotic resistance and virulence genes segregated according to the wgSNP analyses. A total of 8 transmission chains for ST235 strains followed by 9 and 4 possible transmission chains for ST357 and ST823 were traceable on the basis of pairwise distances of informative-wgSNPs (0 to 4 SNPs) among the strains. The present study demonstrates the value of detailed whole genome sequence analysis for highly refined epidemiological analysis of P. aeruginosa.We have previously studied carbapenem non-susceptible ndonesia . We docu Pseudomonas aeruginosa is a metabolically versatile Gram-negative bacterial species often blooming in soil and aquatic environments. It effectively colonizes the exposed surfaces of plants, animals and humans  and has become one of the most troublesome causes of a wide range of intensive care unit (ICU)-acquired infections  to carbapenems  outbreak investigations (P. aeruginosa wgMLST scheme in BioNumerics™ is as discriminatory as the core genome Single Nucleotide Polymorphism (cgSNP) calling approach and is hence useful for outbreak investigations. Whole genome SNP analysis (wgSNP) is a more advanced method of exploiting variation at the WGS level to help identify bacterial transmission dynamics and to generate useful insights into the sources and routes of infection, again for essentially all bacterial species assuming that there is a sufficient number of diverse genome sequences available  strains from an Indonesian hospital was analyzed at the cgSNP level (P. aeruginosa sequence types (ST) ST235, ST357 and ST823 to reveal exact transmission patterns among patients and between patients, and the environment.In a prior study, relatedness of carbapenem non-susceptible NP level . In the We have used preexisting genomic data of CNPA strains collected in two ICUs of a large referral hospital in Jakarta, Indonesia  with 150-bp paired-end reads or a MiSeq instrument (Illumina Inc.) with 200-bp paired-end reads. Strains from the post-intervention phase were sequenced using a NextSeq 500 instrument (Illumina Inc.), with 150-bp paired-end reads. Reads and assemblies from all sequenced samples are available at the European Nucleotide Archive website under project identifiers (IDs) PRJEB30625 and PRJEB32907, for the clinical and environmental samples, respectively. From most of the patient multiple serial isolates were collected in this study. Each patient was named as a unique ID number such as 6 or 222 etc. Since multiple isolates were collected form single patient, the alphabets were allotted along with the patients unique numeric ID in order to identify each collected isolate independently irrespective of their source of collection. Patients who were colonized by multiple strains were considered while tracing the transmission events. For example isolates from patients ID 6 and 222 isolate ID 6B and 222A, respectively, can be seen in ST235 as well as in ST357 (isolate ID 6A and 222B). For the present study all CNPA genome sequences were assembled and analyzed using BioNumerics™  curve was used to indicate the performances of the different SNP cutoff values. The optimal SNP cutoff value was computed using different Youden indicators where sensitivity, accuracy, specificity and Youden’s Index were found to be at their maximum.Using the BioNumerics™ wgSNP applicationme PAO-1  and NCBIme PAO-1 . Functiome PAO-1 . This fime PAO-1 . MoreoveP. aeruginosa. In contrast, wgMLST also covers highly variable elements such as repetitive genes and pseudogenes, depending upon the microbial species studied  that were shared by both ST357 and ST235 include those in cytochrome C550 (A > G), the oprD porin (C > T), ABC transporters (T > A), MFS transporters (C > T) and a two-component sensors (G > A). The results presented here underscore that wgSNP typing has a higher resolution than wgMLST and that functional information on individual gene variation can be derived from the data. Of note, a large diversity of antimicrobial resistance genes and virulence genes segregated according to wgSNP analyses  under Marie Skłodowska-Curie Grant Agreement no. 765042.During this study MG, MJ and AB were employees of bioMérieux, a company designing, developing, and marketing tests in the domain of infectious diseases. The company was not involved in the design of the current study and the opinions expressed are those of the authors and may be different from formal company opinions and policies.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.


The highest numbers of MEAs were identified for Mg (499), followed by S (399), P (394), Ni (381), Cd (243), Ca (229), Mn (224), Zn (212), Sr (212), Cu (111), Rb (78), Fe (63), Mo (43), K (32) and Co (19). Further, MEAs associated with multiple elements and referred to as pleiotropic SNPs were identified for Mg, P, Cd, Mn, and Zn on chromosomes 1B, 2B, and 6B. Fifty MEAs were subjected to validation using KASIB multilocational trial at six sites in two years using 39 genotypes. Gene annotation of MEAs identified putative candidate genes that potentially encode different types of proteins related to disease, metal transportation, and metabolism. The MEAs identified in the present study could be potential targets for further validation and may be used in marker-assisted breeding to improve nutrient element concentrations in wheat grain.Wheat as a staple food crop is the main source of micro- and macronutrients for most people of the world and is recognized as an attractive crop for biofortification. This study presents a comprehensive investigation of genomic regions governing grain micro- and macroelements concentrations in a panel of 135 diverse wheat accessions through a genome-wide association study. The genetic diversity panel was genotyped using the genotyping-by-sequencing (GBS) method and phenotyped in two environments during 2017–2018. Wide ranges of variation in nutrient element concentrations in grain were detected among the accessions. Based on 33,808 high-quality single nucleotide polymorphisms (SNPs), 2997 marker-element associations (MEAs) with −log10( Therefore, grain quality including health benefits or hazards is important for global food security and safety. Elemental concentration is important in defining the safety and nutrition of wheat grain. Minerals comprising wheat grain can be divided into three main groups: macroelements, calcium (Ca), potassium (K), magnesium (Mg), phosphorus (P), chlorine (Cl), and sulfur (S) which are important for starch and protein formation; toxic heavy metals, arsenic (As), cadmium (Cd), chromium (Cr) and lead (Pb), normally regulated not to exceed certain concentration; and microelements essential for plants and humans, selenium (Se), boron (B), manganese (Mn), copper (Cu), iron (Fe), molybdenum (Mo) and zinc (Zn), that can be also harmful when exceeding certain concentrations. Five microelements  along with Ca and iodine were selected as candidates for biofortification to improve the nutritional value of crops including wheat [Spring wheat is an important crop in Kazakhstan and Western Siberia with an annual area of 17–18 Mha. This is a short-season crop grown from May to August in an extensive rainfed cropping system dominated by cereals and occasionally rotated with oilseed and legume crops. Wheat produced in the region is traded both regionally and internationally. According to FAO  were below maximum quantities as defined by FAO, EU and national regulations . The preA genotype × environment study was based on a multilocational trial of the Kazakhstan-Siberia Network on Spring Wheat Improvement (KASIB) with 49 entries at six sites in two seasons . The effThe third study focused on the spring wheat genetic diversity panel (GDP) assembled to enhance agronomic and quality traits at Omsk State Agrarian University . The panThe objective of this study was to identify the genes contributing to the variation of macro- and microelements, and trace metals in the spring wheat genetic diversity panel using GWAS and validate them using the multilocational KASIB trial mentioned above with the overall aim of developing approaches for marker-assisted breeding for grain elemental concentration.2) followed by USA cultivars (320 g/m2), CIMMYT synthetics (236 g/m2), and Japan synthetics (104 g/m2). Early maturing check Pamyati Azieva demonstrated grain yield of 399 g/m2 and intermediate maturing check Serebristaya 470 g/m2. TKW was in a range of 44.0–45.7 g for all groups though USA cultivars had smaller grain (36.9 g). The highest protein content was recorded for Japan synthetics (20%) followed by USA cultivars (17.9%), CIMMYT synthetics (16.7%), and KASIB groups (16.7%). Overall, the research panel used in the study was highly heterogeneous and contrasting especially for the vegetative period duration and grain yield.Analysis of variance (ANOVA) of the number of days to heading, TKW, grain yield, and protein content data demonstrated the high significance of genotypes, years, and their interaction except for the effect of years on TKW . The relPhosphorus had the highest concentration of all the elements in wheat grain at 5170 and 4693 µg/g in 2017 and 2018, respectively . The othCIMMYT and Japanese synthetics had significantly higher concentrations of K  compared to the KASIB group (3584 µg/g) . Japanes10 > 3.5 and significance of effects with p < 0.003 were initially identified for evaluation and further selection (n = 50); (2) SNPs with MEAs effect on the same element in one year using both original and adjusted data (n = 197); (3) SNPs with pleiotropic effect on several elements using either original or adjusted data (n = 341); and (4) SNPs with effect on one element in one year using either original or adjusted data (n = 1861). The group 1 SNPs with effects in both years were most valuable for use as potential markers while the group 4 SNPs were least promising and excluded from further analysis.GWAS analysis was performed for each element separately for 2017 and 2018 data and separately for original and adjusted values. The exception was data for Ca concentration in 2017, K in 2018, and Co and Rb in both years where only original data was used since adjustment was not justified due to a lack of significant correlations between variables. In total, 55 separate GWAS analyses were conducted for element concentration. Overall, 2997 MEAs with −logelection . These 210 > 3.5 and significant effects for original and adjusted values in one year for all elements with exception of Rb. The largest number of group 2 SNPs was identified for Ni (95) followed by Ca (30) and Cd (23).The number of MEAs varied greatly between the elements, from 19–43 for Co, K, and Mo to 381–499 for Ni, P, S, and Mg . ChromosAll SNPs from groups 1, 2, and 3 were screened for validation suitability using KASIB multilocational trial data. The main suitability criteria were SNP presence in 39 KASIB trial genotypes with the frequency of respective alleles at least 10%. There were 50 SNPs matching this criterion and they were subjected to validation. The remaining SNPs from groups 2 and 3 were left for future validation . The SNPFor Ca SNPs on chromosomes 3B and 6D had a significant effect on concentration varying from 8.4 to 11.0% in 2017 and 7.1–9.6 in 2018. Three SNPs closely located on chromosome 1B affected Mg concentration in a range of 3.2–6.2% depending on the year. Four other SNPs on 2A, 4B, 5B, 6D, and 7B also contributed significantly to Mg concentration in wheat grain. P concentration was affected by two SNPs from the same QTL region on 6B (2.6–4.7%) and one SNP on 6D (4.0–6.2%). Only one SNP on 6D demonstrated a significant effect on Cu concentration in both years. Five SNPs  contributed to Mn concentration in a range of 4.2–8.3% in 2017 and 4.3–8.8% in 2018. Notably, the relative effects of these SNPs were comparable in both years of study. The largest SNP  effects were recorded for Cd concentration: 16.8–35.3% in 2017 and 15.5–39.6%). Again, similar to the case of Mn, the effects of different SNPs were consistent over the two years, demonstrating their stability. Single chromosome 6D SNP affected Sr concentration by 16.2% in 2017 and 13.6% in 2018. Despite the fact that the effects of these SNPs were not validated, they represent good candidates for marker-assisted selection to enhance the elemental composition of wheat grain.There was considerable variation in grain yield across sites and years: trials in Chelyabinsk and Novosibirsk had the highest yields exceeding 3 t/ha in both 2017 and 2018. The lowest yields were in Shortandy at around 2.5 t/ha. Four sites  had high yield variability from 50% to over 100% between years. This is consistent with the large effect of weather and biotic stresses on yield stability in the region. The ANOVA indicated high significance of the three major effects: genotypes, years and locations, and all interactions . The proThe average concentrations of 15 elements in the grain across years at each site are presented in p = 0.05 for all elements. The comparison of elemental concentration between the GWAS genetic diversity panel and KASIB multilocational validation trial demonstrated similarity between the average values but overall variation across 12 sites × years was very large, thus, allowing relevant validation of SNPs effects in different environments.ANOVA revealed significant effects of the three main factors on all element concentrations . The onlThe list of SNPs with validated effects is presented in Five pleiotropic SNPs were validated. Chromosome 1B (S1B_176291121) significantly affected both Mg and P concentrations in two panels. Two SNPs in the same QTL region of chromosome 2B affected Cd and Mn concentration (S2B_780115106), and Cd, Mn, and Zn concentrations (S2B_780665986). Their effect in KASIB trials exceeded 10.7%. Similarly, two closely located SNPs on 6B (S6B_562488824 and S6B_601138481) affected P and Zn in two panels with Zn effects exceeding 20% in two of the KASIB trials. Overall, 20 SNPs identified through GWAS analysis and validated through a multilocational trial can be recommended for use in marker-assisted selection for respective elements.The IWGSC RefSeq annotation V2.1 was used to identify the functional annotation of putative candidate genes underlying significant MEAs. The detailed description of each candidate gene underlying MEAs is listed in The SNPs with significant effects on elemental concentration in both years  and valiAe. squarrosa (369)//Demir , Ukr-Od 1530.94/Ae. squarrosa (392) , Langdon/KU-2075 , Langdon/KU-2093 , Freyr , Lutescens-48-204-03 , Lutescens-1103 , Novosibirskaya-41  and Silach . The genotypes with an optimal concentration of elements were identified across all four groups of germplasm including synthetic wheat, KASIB, and USA cultivars.The optimal grain elemental composition will combine high concentrations of beneficial macro- and microelements, and lower concentrations of trace metals including toxic elements. The genotypes used in the study were ranked for all 15 elements from highest to lowest and 20% of the top entries were marked for macro- and micronutrients and 20% of the lowest entries were marked for trace elements. The genotypes with a high concentration of macro- and microelements and low concentration of trace metals are presented in Selected synthetic wheats and USA cultivars were characterized by lower grain yield of 11.5–73.8% compared to local check and higher protein content by up to 30.5% with variable TKW . The higThe ionome is defined as the mineral nutrient and trace element composition of an organism and represents the inorganic component of cellular and organismal systems . Ionomic2 values and the number of MEAs with significant effects identified in the present study; elements Mg, Ca, Cd and Mn.The key advantage of the present study was a wide range of 15 elements included in the analysis of genetic control of their concentration in grain. The number of significant MEAs and SNPs affecting a particular element is important for designing and applying marker-assisted selection. There was great variation in this respect with the number of MEAs varying from 20–40 for K, Co, and Mo to over 380 for Mg, P, S, and Ni. However, it is important to determine the possible factors making some elements more suitable for selection using genomic tools while others are less suitable. An assumption can be made that macroelements primarily involved in structural grain composition including protein would have less number of MEAs with possibly stronger effects as compared to microelements involved in a number of regulatory fermentative compounds or trace metals that are involved in specific cell processes. However, the reality is that Ni was identified as an element with a number of MEAs and reliable SNPs which can be used for selection. Taking into consideration SNPs with stable significant MEAs expression in both years as well as the SNPs with validated effect, for five elements  there were no SNPs identified with significant reliable effects. The ranking of the remaining elements using the number of reliable MEAs was P  affecting Ca concentration were reported to contribute to herbicide metribuzin tolerance identified in Gulf Atlantic Wheat Nursery in the USA . AlomariThe present study identified SNP S2A_738732586 contributing to Magnesium concentration. The same SNP was identified in CIMMYT synthetics collection contributing to Cu concentration based on both years of study in Turkey . ChromosThe spring wheat breeding framework in northern Kazakhstan and western Siberia is largely based on traditional approaches with limited application of modern tools. The majority of cultivars grown in the region represent tall, day-length sensitive types with limited genetic diversity . The con2 across both years was observed in Tyumen. If breeding efforts targeting elemental composition are to be undertaken, regional cooperation would be essential to design a selection program with an emphasis on elements with high genetic variation to be evaluated at sites with uniform fields. The availability of high throughput and high precision analytical facilities in the region would be an essential component of such a program.The analysis of genotype by environment interactions for the KASIB trials showed that genetic improvement for all elements would be highly affected by the uniformity of experimental fields . Broad sAe. tauschii has been reported as a source of high concentration of microelements including Fe and Zn [Primary synthetic wheat developed from crosses of durum wheat with e and Zn ,42. In tThe crossing strategy to incorporate and combine optimal concentration of a wide range of elements depends on the nature of the germplasm. Synthetic wheat with low yield and a number of undesirable traits like spike threshability requires a top and back crossing scheme to transfer useful traits while maintaining and improving grain yield. Several synthetics from the present study possess resistance to leaf, stem rust, and powdery mildew  making tThe molecular markers identified in the present study for 11 elements may require additional validation within the breeding process considering that the crosses made back in 2017 and 2018 are now in preliminary yield tests and can be traced back to specific parents and SNPs. They provide an essential platform for fast and efficient selection to increase the concentration of beneficial elements and decrease the harmful minerals, thus, contributing to food security and safety of the locally grown wheat grain.Aegilops tauschii from the CIMMYT genebank. The development of the synthetics through targeted selection under abiotic and biotic stresses was described by Morgounov et al. [The GDP panel comprised 135 entries including two checks as listed in v et al. . Eight pv et al.  comprise2 and four replicates in 2017 and 2018. Soil of the experimental field was meadow chernozem with 5% organic matter content and average availability of NPK. Preceding crop was black fallow. Spring soil preparation comprised harrowing in early May followed by shallow cultivation and harrowing in mid-May. Planting took place between 15–20 May in both years. The trials were harvested in the first week of September. No fertilizer or fungicides were applied. Weeds were controlled by application of a common herbicide after tillering stage in mid-June. The field observations included agronomic traits including heading dates, disease evaluations, yield, and yield components. CIMMYT Wheat Physiology Manual [The trial was planted in the experimental field of Omsk State Agrarian University  as a randomized complete block design with plots of 1 my Manual  was usedWeather conditions in 2017 were characterized by 1 °C higher temperature in May-August compared to long-term observations and lower rainfall resulting in drought and yield reduction . In 20182 and two or three replicates. The seeds used for the first year of the trial originated in the institutions which submitted the germplasm. In the second year, all KASIB cooperators used their own seed. The field observations included three adaptation traits  and disease evaluations under natural infection. Grain yield was recorded after combine harvesting. CIMMYT Wheat Physiology Manual [KASIB trial comprised 39 entries including three checks. All of them were included in GWAS GDP and marked in y Manual  was usedSix KASIB testing sites were located between the southernmost Shortandy at 51.6° N and the northernmost Tyumen at 57.1° N or around 800 km. The westernmost site Chelyabinsk (60.7° E) was 1400 km from the easternmost site Novosibirsk (83.6° E). The large distances between the sites are reflected in soil, climate, and weather variation . There iThe soils across this large region were represented by various types of chernozem: ordinary , leached , carbonate (Chelyabinsk) and meadow (Omsk). The soil was generally deep (60–80 cm) and fertile with humus content exceeding 4.5–5.0%. Chelyabinsk soils were characterized by slight salinity. The nitrate nitrogen availability was low in Shortandy, Chelyabinsk, and Omsk. Phosphorus concentration was intermediate in Karabalyk and high at other sites. Potassium availability was low in Novosibirsk, intermediate in Chelyabinsk, and high at other sites.2. Weeds were controlled using common herbicides. The agronomy practices applied in the breeding fields to large extent followed the common local commercial practices. Normally fertilizers are not applied on wheat field following bare fallow and this was the case for KASIB trial.The trials were conducted at experimental fields in Kazakhstan at the Karabalyk Experimental Agricultural Station  and Scientific-Production Center of Grain Farming named after A.I. Barayev , and in Russia at the Chelyabinsk Research Institute of Agriculture, , Omsk State Agrarian University (Omsk), Siberian Research Institute of Plant Production and Breeding (Novosibirsk) and Northern Trans-Ural State Agricultural University, Tyumen. KASIB trial was planted along with the breeding material and followed common agronomy practices for spring wheat at each location. Prior to each trial, the field was under bare (black) fallow. Spring soil preparation was done by cultivation and harrowing. Planting was conducted at optimal dates in mid-May. The seeding rate was 400–450 seeds/mThe ionomics phenotyping platform at the University of Nottingham (UK) conducted the ionomics analysis for both GWAS and validation panels. Kazakh Research Institute of Farming and Crop Production  received and processed all the grain samples, cleaned, and analyzed for protein content using Infratec FOSS 1841 at 14% moisture. Subsamples were sent to the Ionomics Facility at the University of Nottingham and the ionomics analysis was performed using state of the art Perkin Elmer NexION 2000 inductively coupled plasma mass spectrometer (ICP-MS). The samples were prepared for the ICP-MS analysis in the adjoining high throughput preparation laboratory. Wheat grains were transferred into the Pyrex test tubes, weighted, and initially predigested with 1 mL concentrated trace metal grade nitric acid Primar Plus  spiked with 20 μg/L of indium internal standard, for approximately 20 h at room temperature. Indium was added to the nitric acid as an internal standard for assessing errors in dilution, variations in sample introduction, and plasma stability in the ICP-MS instrument. After the pre-digestion step, samples were transferred into DigiPREP MS dry block heaters  and digested for 4 h at 115 °C. After cooling down, 1 mL of trace metal grade hydrogen peroxide  was added to the tubes, and samples were digested in dry block heaters for additional 2 h at 115 °C and then diluted to 10 mL with 18.2 MΩcm Milli-Q Direct water . Five replicate analyses were conducted for each sample from each replication and the mean value represented the sample final readings.The ionomics results were obtained for 23 elements: macroelements, Ca, K, Mg, P, and S; microelements, B, Fe, Cu, Na, Mn, and Zn; toxic trace elements, As, Cd, Co, Cr, Ni, Pb, and Se; and trace elements, Li, Mo, Rb, and Sr. The concentrations were either insignificant or below the limit of quantification for B, Na, As, Cr, Pb, Se, Li, and Ti, therefore, these elements were excluded from the analysis. For the remaining 15 elements, the concentrations were normalized to the weight of the samples and expressed in µg/g of dry weight.Three groups of GWAS GDP varied substantially in grain yield affecting the protein content and elemental concentration. Correlations between individual element concentrations and the other variables, viz., grain yield, 1000 kernel weight (TKW), protein content, and macroelements concentrations, were calculated using Microsoft Excel. The correlation analysis results  were use2) was estimated for each element in individual KASIB trials (for each year separately) based on the ANOVA results.Factorial ANOVA  was used for statistical analysis for all agronomic traits and for each element independently using R software package version 3.4 . For the® 96 Plant Kit (QIAGEN). The GBS libraries were constructed in 96-plex following digestion with the restriction enzymes PstI and MspI at Wheat Genetics Resource Center at Kansas State University . SNP calling was performed using TASSEL v.5.2.40 GBS v2 Pipeline [Genomic DNA was extracted from fresh young leaves (approx. 14 days after sowing) using BioSprintPipeline  with a pPipeline . The idehttps://github.com/xiaolei-lab/rMVP, accessed on 21 June 2022). The top two principal components (PC1 and PC2) and kinship were chosen as covariates to control Type-I error. The principal components were internally calculated, then the package function mvp.data.kin was used to estimate kinship matrix. The multiple tests based on Bonferroni correction are often too conservative, so many MEAs may not pass the stringent criterion of the significant test. To balance the Type-I and Type-II errors, a −log10(p) of 3.5 was adopted as the threshold for significant MEAs.Genome-wide association analyses based on the high-quality SNPs were conducted using the FarmCPU model implemented in R package “rMVP”  using Welch’s software .http://snpeff.sourceforge.net accessed on 15 June 2022). Functional enrichment analysis of candidate genes harboring significant MEAs was conducted at http://wheat.cau.edu.cn/TGT/index.html (accessed 30 June 2022). Gene descriptions as indicated by the IWGSC RefSeq V2.1 database were used in this study.To further understand the genetic control of elemental composition, position-dependent gene search strategy was processed to link significant MEAs with putative candidate genes. Functional annotation and the effect of significant MEAs were performed using SnpEff version 4.3t (


ISF, ROHF). The standardized STF (id) and cross-population extended haplotype homozygosity (XPEHH) methods were used to detect signatures of selection. We observed the lowest heterozygosity (OH = 0.351) and effective population size  for the Balusha breed. The mean allelic richness levels (1.780–1.876) across all analyzed breeds were similar and also comparable with those in worldwide breeds. ROHF estimates (0.023–0.077) were highest for the Balusha population, although evidence of decreased inbreeding was observed in ISF results for the Balusha breed. Two Gene Ontology (GO) TERMs were strongly enriched for Balusha, and involved genes belonging to the melanogenesis and T cell receptor signaling pathways, respectively. This could result from selection for the special coat color pattern of Balusha (black head) and resistance to certain infectious diseases. The analyzed diversity parameters highlight the urgency to preserve the local Kosovar Balusha sheep as it is clearly distinguished from other sheep of Southeastern Europe, has the lowest diversity level and may harbor valuable genetic variants, e.g., for resistance to infectious diseases.There is a growing concern about the loss of animal genetic resources. The aim of this study was to analyze the genetic diversity and potential peculiarity of the endangered Kosovar sheep breed Balusha. For this purpose, a dataset consisting of medium-density SNP chip genotypes  from 45 Balusha sheep was generated and compared with SNP chip genotypes from 29 individuals of a second Kosovar breed, Bardhoka. Publicly available SNP genotypes from 39 individuals of the relatively closely located sheep breeds Istrian Pramenka and Ruda were additionally included in the analyses. Analysis of heterozygosity, allelic richness and effective population size was used to assess the genetic diversity. Inbreeding was evaluated using two different methods ( Since the domestication of sheep at around 10.000 BC ,3,4,5. TThe medium-size Balusha is coarse-wooled with a black head and neck while the fleece and legs are white. Only rams are horned with screw-shaped horns, whereas ewes are polled. In contrast, the medium-sized coarse-wooled Bardhoka is completely white. The majority of rams have spiral horns but some are polled, which is considered untypical. Both breeds are adapted for milk production based on grazing of natural pastures ,12. MediNowadays, especially the black-headed BAL sheep have become rare. It is assumed that 70% of the whole population, which consists of only 300 individuals , is keptThe Illumina Ovine 50K SNP chip  is a powid statistics is an established method used to calculate the locus-specific divergence of a specific breed against other breeds [Regions with high ROH are regions that also carry signatures of selection . Differer breeds . The cror breeds .id statistics as well as on XPEHH were compared. The results should provide a valid basis for the justification and establishment of conservation programs for the endangered BAL breed in a further step.The diversity of some sheep breeds from Eastern Europe was studied and evaluated, mainly by using microsatellite markers, but the BAL breed has not yet been considered ,33,34. Wn = 45) and BAR (n = 29) sheep breeds were collected from two farms in Kosovo (n = 23) and Ruda  were available from a recent study on Balkan sheep breeds . One of a pair of SNPs in high LD (r2  >  0.5) within a window size of 50 SNPs and with a window slide of 5 SNPs was removed. Pairwise identity-by-state (IBS) distances and principal component analysis (PCA)-based multidimensional scaling analysis were performed in PLINK, and a plot showing the first two principal components was constructed with the package “ggplot2” [OH) and expected heterozygosity (EH) were calculated using HIERFSTAT R package [OH and EH are observed and expected heterozygosities, respectively [5) and fifty (Ne50) generations ago were estimated. The ADMIXTURE program -pruned using the ggplot2”  in R [38 package . Additio package  using a ectively . Effectiectively . Defaultectively , and occectively . The Ne n 1.3.0;  was empln 1.3.0; . Clustern 1.3.0; .--homozyg function in PLINK. One missing SNP, one heterozygous SNP, and ROH longer than 1Mb were allowed per run. A scanning window threshold of 0.05, a sliding window of 50 SNPs, a maximum gap between consecutive SNPs set to 250 kb, and a minimum average SNP density of more than one SNP/100 kb were used. The minimum number of SNPs (l) per run was calculated as suggested by [i is the number of individuals, het is the mean heterozygosity across all SNPs. Calculated l was 39 for BAL, 36 for BAR and 34 for IST and RUD. The total and mean ROH length were estimated for each individual and breed. In addition, the total and mean ROH numbers for each individual and breed were determined. To compare ROH frequency between breeds, ROH segments were categorized into 3 length classes .Runs of homozygosity (ROH) were estimated using the QC data and ested by . l=logeαROHF) was determined according to [ROH is the sum of the length of all ROH per animal and LAUTO is the total autosomal SNP coverage (2.648 Gb). The average ROHF per breed was determined for LROH above 1, 5 and 10 Mb, which is indicative of inbreeding up to 50, 10 and 5 generations ago, respectively [ROHF per breed for LROH above 1 Mb.The inbreeding coefficient of ROH  [STF [STF values obtained were standardized (id) according to the method published by [STF between breeds i and j calculated from all 39,214 SNPs. The locus-specific divergence calculated using the id statistics was averaged for SNPs in non-overlapping windows of 1 Mb. A total of 2562 informative windows with an average of 16.07 SNPs per window (SD = 3.6) were left after removal of windows with less than five SNPs. We plotted the window-averaged di values across the chromosome, and the top 1% windows of the empirical distribution were defined as putative selection regions. On the other hand, pairwise comparison between BAL and each of the other breeds for XPEHH scores were computed separately with the R package rehh [fastphase 1.4 [XPEHH denotes the Gaussian cumulative distribution function, which was calculated by the pnorm R function. Since the XPEHH scores were approximately normally distributed, pXPEHH can be interpreted as pXPEHH was the two-sided p-value for testing the null hypothesis that no selection occurred [pXPEHH ≥ 5 equivalent to a p-value of 0.00001. Common candidate selection regions from all three XPEHH analyses with a minimum of three SNPs were chosen as selection signatures specific to BAL. The selected regions were defined with a maximum base-pair length of approximately 1 Mb.To determine selection signatures of BAL, the  (XPEHH)  was emplHH) [FST  values oished by : di=∑j≠iage rehh  using hahase 1.4  by usingoccurred , and we STF (id) and XPEHH methods separately using the Database for Annotation, Visualization and Integrated Discovery (DAVID) software  [p-value threshold of ≤0.05 were considered significant, and correction for multiple testing was carried out using the FDR procedure [Genes located within the candidate selection regions for BAL were identified using the UCSC genome browser  with thery 2022) ,56. The rocedure . Overlaprocedure .ISF for the Balusha (−0.029) and Istrian breeds (−0.009) were observed to be negative with the highest value observed for Ruda (0.054) and the lowest for Istrian. The recent effective population sizes (Ne5) were estimated for all breeds and ranged from 25 (BAL) to 35 (BAR), while the effective population sizes 50 generations ago (Ne50) varied between 228 in BAL and 324 in BAR. The PCA plot  varied between breeds, with the highest found in the BAL breed (0.077), and the lowest in the IST breed (0.023). The within-breed distribution of ROHF (1 Mb) is visualized in ROHF at a minimum ROH length of 5 Mb and 10 Mb were calculated, and the highest ROHF values of 0.039 (5 Mb) and 0.011 (10 Mb) were found in BAL  (The  < 0.05) .pXPEHH values for pairwise comparison of BAL with other breeds in this study are displayed in p-value < 0.05) (The  < 0.05) .Some overlapping signatures of selections were observed. Regions on sheep chromosomes 2 and 13 were jointly identified by both di statistics and XPEHH. A total of 34 genes were identified in these regions .The Kosovar BAL breed is regarded as an endangered breed . Already5 = 35; Ne50 = 324) were observed in the BAR breed. This fits very well with a report that this breed is unlike the others widespread across different countries in eastern Europe [5 = 25; Ne50 = 228). Moreover, Ne50 for the breeds in this study were within the range of ten European and three non-European breeds reported by [The mean expected heterozygosities of the breeds in this study were in concordance with the range reported for other sheep breeds in earlier studies ,21,22,25n Europe , but stiorted by ,59, but orted by .ISF and ROHF whereby the latter is considered the more precise approach [ISF, which suggests increased heterozygosity and decreased inbreeding. It is possible that unsupervised crossbreeding with BAR may be responsible. However, the observed ROHF values and the mean ROH length indicated the presence of inbreeding, which was expected at least for BAL with the highest ROHF (0.077). The distribution of ROH classes  method, while ovine chromosomes 2, 4 and 13 were prominent based on the XPEHH method. Interestingly, there is no concordance between the genes located in these regions with previously published results of common genes within signatures of selection in ruminants  and 13  were also found by Manzari et al. [KIAA2012, SUMO1, Chr. 13: GNAS, Chr. 19: CTNNB1) [In BAL, several signatures of selection were detected on different chromosomes, which may result from selection by humans and/or the adaptation to the environment. Chromosomes 2, 13 and 19 showed the strongest signals based on the rized by ). Howevei et al. . Based o CTNNB1) ,65.WNT4, FZD7, FZD5) are implicated in Wnt signaling [CREB1 gene located within selection signatures on chromosome 2 is involved in the melanin production process by transcriptional activation of the microphthalmia-associated transcription factor (MITF) gene [GNAS and GNAS-AS1 genes, which are involved in the production and regulation, respectively, of G proteins such as the G protein α-subunit (Gs), functioning in melanogenesis as well [EDN3) gene is located, which is an isoform of the endothelin-1 (EDN1) gene [CTNNB1 (ß-catenin) gene observed in a selection signal region on chromosome 19 is important for the enhancement of TCF/LEF (T cell factor/lymphoid enhancer factor) and subsequent transcriptional activation of the MITF gene downstream of the Wnt signaling pathway [ASIP) and MITF genes associated with coat color in animals are located at a distance of 3.1 Mb and 2.2 Mb from the nearest selection signal on chromosome 13 and chromosome 19, respectively. Therefore, due to their participation in the melanogenesis pathway  and Wnt signaling pathway GO:0060071). Potential candidate genes observed within selection signals on chromosome 2  gene . As poss as well ,71,72. A. PotentiN1) gene . StudiesN1) gene ,74,75. E pathway . Our fin pathway , some or pathway .CD28, CTLA4, ICOS) located in a region with selection signals on chromosome 2 [The activation and survival of T cells are associated with three genes , which was associated with tuberculosis (Mycobacterium tuberculosis) susceptibility in humans [Finally, we identified, as a possible candidate gene, the n humans ,84,85. In humans . As it wn humans . The finReliable knowledge of the diversity between and within breeds is, among others, a requirement for an efficient conservation strategy. Therefore, our study on the genetic diversity of the Kosovar BAL breed provides a basic work for the preservation of this rare local breed and indicates options for its future development. The combination of the results from signatures of selection and the ROH analyses provides a better understanding of how the BAL breed is genetically distinguishable from the other considered sheep breeds originating from the Balkan Peninsula. GO TERMS analysis revealed two interesting groups of genes involved in melanogenesis and T cell co-stimulation, which might interfere with BAL’s unique color pattern and the immune system. The latter could point toward a changed disease susceptibility, which would make BAL a valuable genetic resource for breeding for animal health.Since our analyses showed that BAL sheep differ strongly from the other compared Pramenka sheep breeds, and the breed is not known to occur in other countries, conservation of the BAL sheep should be given priority over other regional breeds.