Bemisia tabaci sensu lato are amongst the world’s worst agricultural pests and plant-virus vectors. Outbreaks of B. tabaci s.l. and the associated plant-virus diseases continue to contribute to global food insecurity and social instability, particularly in sub-Saharan Africa and Asia. Published B. tabaci s.l. genomes have limited use for studying African cassava B. tabaci SSA1 species, due to the high genetic divergences between them. Genomic annotations presented here were performed using the ‘Ensembl gene annotation system’, to ensure that comparative analyses and conclusions reflect biological differences, as opposed to arising from different methodologies underpinning transcript model identification.The group of > 40 cryptic whitefly species called B. tabaci s.l. genomes from Africa and Asia, and two re-annotated previously published genomes, to provide evolutionary insights into these globally distributed pests. Genome sizes ranged between 616—658 Mb and exhibited some of the highest coverage of transposable elements reported within Arthropoda. Many fewer total protein coding genes (PCG) were recovered compared to the previously published B. tabaci s.l. genomes and structural annotations generated via the uniform methodology strongly supported a repertoire of between 12.8—13.2 × 103 PCG. An integrative systematics approach incorporating phylogenomic analysis of nuclear and mitochondrial markers supported a monophyletic Aleyrodidae and the basal positioning of B. tabaci Uganda-1 to the sub-Saharan group of species. Reciprocal cross-mating data and the co-cladogenesis pattern of the primary obligate endosymbiont ‘Candidatus Portiera aleyrodidarum’ from 11 Bemisia genomes further supported the phylogenetic reconstruction to show that African cassava B. tabaci populations consist of just three biological species. We include comparative analyses of gene families related to detoxification, sugar metabolism, vector competency and evaluate the presence and function of horizontally transferred genes, essential for understanding the evolution and unique biology of constituent B. tabaci. s.l species.We present here six new B. tabaci s.l. biology. They also provide a rich foundation for post-genomic research, including the selection of candidate gene-targets for innovative whitefly and virus-control strategies.These genomic resources have provided new and critical insights into the genetics underlying The online version contains supplementary material available at 10.1186/s12864-023-09474-3. Aleyrodes tabaci was first described by Gennadius in 1889 and due to the lack of taxonomically robust morphological differences [Bemisia tabaci. Recent research, however, has provided unequivocal evidence that “B. tabaci” has been used to refer to more than 40 biological species [Bemisia tabaci sensu lato  sequences [B. tabaci SSA1, with mating compatibility between SSA1-SG1 and SSA1-SG2, but clear incompatibilities between SSA1-SG1 and SSA1-SG3, and between SSA1-SG2 and SSA1-SG3 [B. tabaci s.l. populations [Several cassava-colonizing, phylogenetic species within equences , 10, 26 equences , 27. SNPequences , 29. In equences . BiologiSSA1-SG3 . In addiSSA1-SG3 , 30. As ulations .Bemisia argentifolii, a B. tabaci species also known as MEAM1 (Table B. tabaci species (MED/Q) was published [B. tabaci MED/Q had a higher count of annotated genes  than B. argentifolii. The annotation protocols used for both species were similar, utilizing genomic alignment evidence such as RNA-seq data and ab-initio prediction methods such as AUGUSTUS and GENSCAN. Such prediction methods can incorporate gene models without the requirement of aligned transcriptomic evidence. Furthermore, B. tabaci s.s. and B. argentifolii genomes had c. 80% of all gene models assigned with a functional annotation [In 2016, the first draft genome of ublished , with a notation , 33, butB. tabaci s.l. cassava-colonizing species have increased dramatically [B. tabaci SSA1 populations remains a key factor driving epidemics of African cassava mosaic disease (CMD) and cassava brown streak disease (CBSD) [B. tabaci s.s. and B. argentifolii genomes are available, they have limited use for studying the genetics of the African cassava B. tabaci SSA1 species due to the high genetic divergence between them. More recently, the draft genome of the cassava whitefly B. tabaci Sub-Saharan Africa—East and Central Africa (‘SSA-ECA’) was assembled from short read Illumina data derived from > 10,000 field-collected cassava whiteflies [ab-initio prediction, RNA-Seq aligned evidence and input homology evidence derived from the original B. argentifolii proteome, which may have led to propagating spurious or low-supported gene models. A comparison of published B. tabaci s.l. genomes to ‘SSA-ECA’, shows it has the shortest overall genome length at 513.7 Mb and only slightly improved N50 of 498 kb to that of B. tabaci s.s.Since the mid-1990s in East and Central Africa, the abundances of several atically –17. The e (CBSD) , 18–21. iteflies . The ‘SSB. tabaci s.l. arises from the difficulty of categorizing it within the continuous process of evolution [B. tabaci s.l., we present genomic and associated experimental data sets and propose appropriate biological species names, with the intent of reducing the confusion associated with the mtCO1-phylogenetic naming system. For example, we use the biological species names of Bemisia argentifolii and Bemisia tabaci sensu stricto for B. tabaci MEAM1 and B. tabaci MED, respectively  and considerable genome heterozygosity, we generated full-sib, inbred populations (F5-F7), for each of the  mm and cB. tabaci s.l. genomes was non-uniform, with longer contig N50 values recovered for five of the six genomes (excluding Uganda-1). The mean scaffold count across all six genomes was 2,409. Asia II-5 exhibited the highest scaffold N50 (10.84 Mb) and the lowest L50 value (14) obtained in any B. tabaci s.l. genome published at the time of writing. Genome level contiguity improvements were achieved, with unbroken scaffold lengths considerably longer (22.8 Mb to 53.3 Mb) than that of B. argentifolii (11.1 Mb) and B. tabaci s.s. (2.85 Mb) (Table B. argentifolii (38.7%) and B. tabaci s.s. (38.2%). Notably, the genomic GC content for all cassava-utilizing populations was > 39.4%. Genomic regions with low GC content are associated with heterochromatic DNA, which are much harder to transcribe [n = 1,367), complete BUSCO [n = 1,367) was between 78% (Uganda-1) and 95.1% (B. tabaci SSA2-Ng); with single-copy BUSCO % ranging from 72.1% to 91.7%.Assembly performance for the six te BUSCO ; InsectaBemisia whitefly, Trialeurodes vaporariorum (787 Mb) [B. tabaci s.s. and B. argentifolii genomes and varied between 657.7 Mb—616.1 Mb. Despite the overall improvements gained in genomic assembly of whitefly made in this study, the use of highly accurate longer reads such as PacBio HiFi, Hi-C scaffolding and optical mapping would facilitate generation of more complete and less fragmented chromosome-scale B. tabaci s.l. genomes. All assemblies generated in this study were deposited in the European Nucleotide Archive (ENA) at the European Molecular Biology Laboratory (EMBL-EBI) under project accession numbers PRJEB28507, PRJEB35304, PRJEB39408 (https://www.ebi.ac.uk/ena); genomic PacBio data is described in Additional file Karyotype studies have shown that whitefly genomes are likely to have 10 individual chromosomes . Data ge(787 Mb) , but werB. tabaci s.l. genomes. Early efforts in the genomic annotation of the new whitefly genomes in this work revealed significantly fewer gene models, compared to that of previously published B. tabaci s.l. (~ 12 k vs 15 k). To address this discrepancy, we applied a uniform structural annotation methodology across all Bemisia genomes presented herein. Our approach aimed to maximize the likelihood that the results of comparative analyses and the conclusions drawn would be reflected and underpinned by variance in whitefly biology, as opposed to the methodologies underpinning transcript model identification.Annotation and assembly quality are linked , 45 and,B. tabaci s.l. genomes were performed using the ‘Ensembl gene annotation system’ [ab-initio prediction methods. High quality standards were achieved by ensuring model accuracy that derives solely via integration of alignment of expressed protein, cDNA and other types of biological sequences, such as high-throughput RNA-seq. The Ensembl gene annotation pipeline also facilitates the identification of alternative splice patterns, allowing for multiple alternate splice variants to be captured per transcript model.Genomic annotations of the six new  system’ , which pB. tabaci s.l. genomes were recovered with only small variances in total PCG counts. The number of PCGs captured across the majority of genomes were considerably fewer than previous estimates, decreasing from ~ 15 k (B. argentifolii) by ~ 2.5 k genes. Our analyses of PCGs recovered an average count across B. tabaci s.l. genomes of ~ 13 k PCGs ; see Table B. argentifolii and B. tabaci s.s., the average PCG count increased to 14,316, which was likely due to the uniquely higher gene count recovered in B. tabaci s.s. An average, alternate-spliced transcript count of 2.06 was observed across the six new genomes, which ranged from ~ 24 k (Uganda-1) to ~ 28 K (SSA1-SG1-Ug). Reannotation of B. argentifolii and B. tabaci s.s. genomes recovered a gene-to-transcript ratio shifting away from ~ 1:1 to ~ 2:1; a feature not seen in the previously published annotations. Transcript models recovered had a mean CDS length of 1.7 kb (stdev:234), while coding exon counts averaged 212 K ; see Additional file B. tabaci s.s. and B. argentifolii. The use of a uniform structural annotation methodology resulted in high concordance amongst all eight B. tabaci s.l. gene annotations.Finalized gene sets obtained for all eight B. tabaci s.l. generated as part of this study. Given the close evolutionary relationships of these B. tabaci populations, the transcriptomic evidence readily aligned and was therefore useful for capturing Uganda-1 gene features. The caveat, however, is that the Uganda-1 annotation likely has reduced gene recovery, maximization of complete ORFs, exon–intron boundaries and species-specific features.RNA-Seq evidence specifically for Uganda-1 was lacking in this study, due to the unavailability of biological sample material for sequencing. Examination of aligned evidence in support of protein-coding transcript models, however, showed gene models had RNA-Seq read coverage ranging from 62.6% (Uganda-1) to 83.2% (SSA1-SG1-Ug). Structural annotation was achieved using all combined RNA-Seq data from other B. tabaci s.l. genomes revealed a marked reduction in total gene models recovered, compared to previously published gene sets of B. tabaci s.s. and B. argentifolii. Despite this, we posit that use of a uniform annotation methodology employed across contiguous genome assemblies, and without use of ab-initio methods, provides a well-supported and conservative estimate of B. tabaci s.l. gene space. Furthermore, downstream analyses should benefit from this increased robustness, ensuring conclusions drawn are underpinned by inherent biology and not the result of disparate annotation methodologies.Structural annotation of the B. tabaci s.l. genomes were related to catalytic activity (GO:0003824)  and binding (Parent GO:0005488) ; see Fig. B. tabaci s.l. PCGs were characterized as related to functional binding of: (i) organic cyclic compounds (GO:0097159) or (ii) heterocyclic compounds   variability across TE families. The average genome repeat coverage, summed across all repeat classes of ~ 42% (268.3 Mb), ranged between 38.27% and 46.74%  and B. tabaci s.s.  were intermediate; greater than both SSA1-SG1 genomes and Asia II-5, but less than SSA2-Ng (46.74%), SSA3-Ng (46.25%) and Uganda-1 (43.90%). Apart from SSA1-SG1-Ug, all other new B. tabaci s.l. genomes exhibited higher overall TE coverage than the closely related pea aphid , in agreement with recent reports [B. tabaci s.l. genomes, in comparison to the greenhouse whitefly T. vaporariorum (56.60%) [Transposable elements (TE) are an important source of novel genomic variation and contributor not only to genome structural variation, but they can also influence changes to gene regulation –49. TE c 42% 268. Mb, rang0–39.17% , 50. LevUganda-1 .90%. Apac. 18,164 copies across all six genomes. One of the most striking examples of a species-specific DNA type TE expansion was ‘Tc-Mar-Tc2’ in Uganda-1 . It is unknown why this is so prevalent in Uganda-1, but it may be related to Uganda-1’s phylogenetic position as one of the earliest branching B. tabaci s.l. species to originate in sub-Saharan Africa; a region considered to be the geographical origin of the B. tabaci group of species [B. argentifolii (1.53%), yet more similar to T. vaporariorum (3.40%) [B. tabaci s.l. genomes, showed that DNA superfamily diversity in B. tabaci s.l. is likely richer when methods designed at identifying hitherto uncharacterised TE repeats are applied, see [DNA TEs are part of class II type TEs, which mediate their movements via a DNA ‘cut and paste’ mechanism . The maj species . Estimat (3.40%) . Notablyied, see .B. tabaci s.l. genomes, having an average genomic coverage of 1.30% and between 10 to 12 superfamilies each. Despite LINE family copy count differences between SSA2-Ng and SSA3-Ng, both populations had uniquely higher total LINE TEs with 36.7 k and 40.8 k copies, respectively, compared to a maximum of 23.6 k copies in SSA1-SG1-Ug. Although not a conclusive apomorphic trait; total LINE copy number of between 14.4 K and 18.5 K is a distinguishing feature of SSA2-Ng and SSA3-Ng populations when compared to all other B. tabaci s.l. and lends support for their designation as the same, single, biological species B. tabaci SSA2 ∪ SSA3 (see Table B. tabaci s.l. genomes were similar to those reported for T. vaporariorum (1.07%) and B. argentifolii (0.96%). Our results are supported by Sicat et al. [B. tabaci ‘SSA-ECA’ genome than was initially reported (0.44%) [LINE type retrotransposons (class I type transposons) transpose via an RNA intermediate, in which their transcription and transposition are facilitated via reverse transcriptase (RT) . LINE TEt et al. , which s (0.44%) .c. 11,546 copies. Instances of population specific LTR TE families were also observed, including ‘DIRS’ and ‘ERV4’ located in SSA1-SG1-Ug and Asia II-5, respectively  are a group of autonomous retrotransposons similar to LINEs in that they are also mediated via a RT mechanism to “copy and paste” to new genomic loci . Class Isee Fig. c. DifferSSA-ECA’ . See TabB. tabaci s.l. genomes  showed subtle, yet clearly observable, patterns of more recent acquisition of DNA and LINE type TEs. Peak LTR TE activity for SSA1-SG1-Ug was evident between Kimura score 1 to 6, and for SSA2-Ng between Kimura score 8 to 14. The pattern of LTR activity observed in SSA1-SG1-Ug and SSA2-Ng is not seen in the other B. tabaci s.l. genomes, though Uganda-1 does also show signs of increased LTR TE activity between Kimura score 1 to 3.Kimura-distance-based divergence analysis  was usedmes Fig. b. ‘KimurBemisia TE evolution, we examined B. tabaci s.l. TEs with reference to the Arthropoda TE complement first presented in Petersen et. al. [B. tabaci s.l. genomes exhibit some of the highest proportion of hitherto uncharacterized TE diversity (~ 85%), second only to species of mayfly, such as Ephemera danica or Eurytemora affinis (at + 93% uncharacterized) and comparable to the more closely related species, F. occidentalis (~ 87%) [B. tabaci s.l. genomes have recently been improved whereby DNA and LINE type TE superfamilies were shown to be present at a higher total genomic coverage compared to our analysis [B. tabaci s.l. genomes; but it is likely that it is closer to ~ 80 superfamilies when uncharacterized TEs are identified through clustering and in-depth characterization methods. Importantly, TE diversity described here was close to that reported for pea aphid, A. pisum, another phloem-feeding hemipteran [B. tabaci s.l. genomes have some of the highest proportion of total genomic TE coverage for all major Arthropoda clades examined to date.To gain a greater evolutionary context regarding  et. al. . Notably (~ 87%) . The appanalysis . Here, bmipteran . DespiteB. tabaci s.l. was performed via OrthoFinder (v 2.4.0) analysis [A. pisum (pea aphid), Diaphorina citri (Asian citrus psyllid), Myzus persicae (green peach aphid) and Rhodnius prolixus (kissing bug). To improve overall resolution of the ingroup B. tabaci s.l., the reannotated genomes for B. argentifolii and B. tabaci s.s. produced for this study were included. The non-Bemisia Aleyrodidae greenhouse whitefly T. vaporariorum [Comparative genomics of analysis , 52 and nder v 2..0 analysrariorum  was selerariorum ; see ‘MaB. tabaci s.l. species considered, while on average 7,620 OGCs contained one or more representative genes. We recovered 142 (0.64%) OGCs as B. tabaci s.l. species specific. Excluding all B. tabaci s.l., the average species specific OGC count rose to 470 (2.1%). An explanation for the discrepancy in species specific OGC counts and evidence of concordance of annotation could stem from our application of a uniform methodology across B. tabaci s.l., in contrast to the remaining taxa obtained from a range of community annotations that potentially could have overestimated genes or included poor-quality models.Of the 391,154 input genes that received orthology assignment, 360,756 (92.2%) genes were successfully clustered into 22,225 orthologous gene clusters (OGCs). A total of 2,297 OGCs (10.3%) contained all twenty-three species while 7,205 (32.4%) OGCs were species-specific gene clusters representing 9.1% of all genes analyzed. 2,404 OGCs  included all eight B. tabaci s.l., showed largely similar numbers of OGCs , compared to an average of 41.5 amongst the remaining B. tabaci s.l. Forty-seven OGCs contained unique, single-sequence, one-to-one, single-copy orthologs. This increased to 655 OGCs, when the minimum percentage of ingroup taxa represented by a single sequence ortholog was reduced to 78.3%.The recovery of taxonomically clustered OGCs within: (i) the Hemiptera (excluding whitefly), (ii) whitefly and iii) Aleyrodidae) and monophyletic B. tabaci s.l.  sister to a clade of all non-African whitefly . Of the three non-African whiteflies, B. argentifolii and B. tabaci s.s. clustered as each other's closest relative, to the exclusion of Asia II-5.Phylogenomic analyses on an alignment of 655 OGCs or 131,953 amino acids (average % missing data = 10.34%) were conducted using the best fitting evolutionary model (LG + Γ + I + F) under both maximum likelihood (ML) and Bayesian inference (BI) methods using RAxML and Mr. Bayes, respectively. Only amino acid sequences of the longest canonical protein-coding transcript were considered. Results from both ML and BI phylogenetic analyses recovered near universal support of the exact same topology, providing high support for monophyletic whitefly (n = 100) supported the placement of the non-cassava feeding species Uganda-1 as the earliest branching member of “Africa-only” whitefly clade. The sister group to Uganda-1 was B. tabaci SSA1-SG1-Ug, which was placed in a paraphyletic relationship with SSA1-SG1-Ng. Topological support within this clade only differed regarding the placement of SSA1-SG1-Ng; whose position in the phylogeny was the least stable, highlighted by a fall in BS support (63%).Full posterior probability (PP) and 100% of all bootstrap (BS) replicates (A. pisum + M. persicae) and D. citri, to the exclusion of R. prolixus which was positioned as the earliest branching member of all Hemiptera taxa. Hexapoda was recovered as monophyletic, with full support  of Pancrustacea including Hexapoda sister to D. pulex. Remaining taxa were positioned at the base of Arthropoda, falling in line with the current understanding of major orders and subphyla within Arthropoda [Full support was recovered across both ML and BI analyses for monophyletic Hemiptera, in which whitefly were sister grouped to a clade of aphids  and up to seven secondary, facultative endosymbionts; Cardinium, Arsenophonus, Hamiltonella, Rickettsia, Wolbachia, Fritschea and Hemipteriphilus asiaticus [Portiera, Arsenophonus, Cardinium, Hamiltonella, Rickettsia, and Wolbachia have been reported previously from B. tabaci SSA1 [B. argentifolii used to generate the draft genome, for example, contained Portiera and two secondary endosymbionts, Hamiltonella and Rickettsia, which had assembled genome sizes of 352 kb, 1.74 Mb and 1.38 Mb, respectively. For P. aleyrodidarum, 273 genes were predicted whose functions were essential for basic cellular processes and whitefly nutrition [siaticus , 62. Of utrition .Portiera has been associated with whiteflies since their origin, more than 125 Mya [Portiera genomes of the six new B. tabaci s.l. populations to examine whitefly phylogenetic relationships. The genomic characteristics of the newly obtained Portiera were similar to those from Portiera associated with B. tabaci s.s., Asia II-3 and B. argentifolii  . Indeed, 125 Mya –66. We aslippage , the negPortiera genomes are a complementary source of information to study whitefly evolution. First, we compared the major functions encoded by the different Portiera genomes . Indeed, clustering analysis of ANI values recovered a similar topology as the ones obtained by nuclear and mitochondrial genes,  among odidarum . A clustPortiera from whitefly species belonging to the Aleurolobini tribe, which includes the Bemisia genera, present different genome architectures [Portiera is less conserved among Aleurolobini species that diverged longer ago, such as Singhiella simplex, which separated from the branch leading to B. tabaci s.l. c. 71.34 Mya [Portiera from B. tabaci s.s., B. tabaci s.l., and B. argentifolii suggests a recent divergence, in evolutionary terms, of their hosts  and Asia II-5 (two copies), integrated into the chromosome of B. tabaci SSA2 (two copies) and absent in B. tabaci SSA3 and Uganda 1  on crop and uncultivated plants in Uganda, the most prevalent whiteflies were B. tabaci MED-ASL (30.5% of samples), B. tabaci SSA1 (22.7%) and B. tabaci Uganda-1 (12.1%), which were also the most polyphagous occurring on 33, 40 and 25 different plant species, respectively. Although all three species exhibited a high level of polyphagy, only B. tabaci SSA1-SG1 ∪ SG2 and B. tabaci SSA2 ∪ SSA3 were present on cassava [Host-plant association studies suggest that el hosts . In a st cassava , suggestB. argentifolii genome [B. tabaci s.l. is probably central to their success [Known detoxification gene families, including cytochrome P450s, (UDP)-glucuronosyltransferases, glutathione transferases, ABC transporters and carboxylesterases were reported from the draft i genome . Of thes success  and has B. tabaci s.l. to understand their potential role in adaptations to cassava, as well as to insecticides. Utilizing queries derived from published proteins of B. argentifolii [B. tabaci s.l. that operate in a three-phase process. Phase I includes mainly the activity of cytochrome P450 monooxygenase (P450s) and carboxylesterase enzymes (COEs), which reduce, hydrolyze, or oxidize a variety of endogenous toxic compounds and exogenous substances. Phase II includes enzymes mainly from the glutathione S-transferases (GSTs), uridine diphosphate (UDP)-glucuronosyltransferases (UGTs) and cytosolic sulfotransferases (SULTs) families. These enzymes catalyze the conjugation of glutathione, glucose or sulfonate group to the reactive site of the phase I products, thereby increasing their polarity and facilitating their excretion. Phase III includes mainly ATP-binding cassette transporters (ABCs) that export the conjugated products out of the cells [B. tabaci s.l. have focused so far on their involvement in host-plant adaptation [Here, we characterized detoxification gene families in ntifolii , we condhe cells , 76. Mosaptation , 77 and aptation .B. tabaci Uganda-1 (the genome of the reference B. argentifolii species harbors 268 detoxification genes). The identity level (of the proteins coded by the detoxification genes) between the African species and B. argentifolii was surprisingly high, ranging from 93.1% in the COE family of SSA2-Ng, to 97.2% in the ABC family of SSA1-SG1-Ug  across the seven B. tabaci spp. examined see Additional file Overall, we detected variation in the number of detoxification genes in the five African species, ranging from 207 in SSA3-Ng to 164 in B. tabaci s.l., these enzymes (α/β esterase) play a role in the detoxification of several important groups of insecticides, such as pyrethroids, organophosphates, and carbamates [B. argentifolii orthologs in the three African grouped biological species . Orthologs of carboxylesterase 1E in B. tabaci s.l. (XP_018899849.1 in B. argentifolii), a highly conserved enzyme involved in xenobiotic resistance [T. vaporariorum and Drosophila melanogaster. This gene could not be detected in the genomes of B. tabaci SSA1-SG1 ∪ SG2.COEs catalyze the hydrolysis of an ester bond into the corresponding alcohol and carboxylic acid. Our analysis focused on enzymes previously proposed to display digestive or detoxification functions , 80. In rbamates . Testingsistance , were thB. tabaci s.l., the activity of specific P450 enzymes was shown to confer resistance to chemical insecticides [B. argentifolii orthologs in the three African grouped biological species  [B. argentifolii orthologs in the three African biological species . Most absent genes belonged to only two subfamilies, UGT353 (5/12) and UGT352 (18/33). On the other hand, T. vaporariorum orthologs were found in all subfamilies except UGT355. Expansion of specific UGT subfamilies, UGT352 and UGT353, was detected in B. tabaci s.l. (when compared to T. vaporariorum). Only one gene  showed sufficient conservation to allow its clustering with orthologs from D. melanogaster and T. vaporariorum [B. tabaci SSA2 ∪ SSA3 and Uganda-1.UGTs catalyze the addition of UDP-sugars to small hydrophobic molecules, turning them into more water-soluble metabolites . In inse, UGT50) , 101 , which include an expanded ABC-G subfamily [B. argentifolii orthologs in the three African biological species  family 13 by hydrolyzing sugar to its constituent monosaccharides to facilitate both digestion and osmoregulation within the whitefly gut [B. argentifolii [efly gut –108. Alpefly gut . The α-gefly gut . These mefly gut . Recent ntifolii , 112. ThB. tabaci SSA1-SG1 ∪ SG2 reaches “super-abundant” numbers on cassava, however this phenomenon has not been reported for populations of the same species in West Africa [B. tabaci SSA1-SG1-Ug and B. tabaci SSA1-SG1-Ng genomes and compared them to the non-cassava colonizing species B. argentifolii.In East and Central Africa, t Africa . To inveA. pisum), forming two clusters   protein sequences identified in OGC ‘OG0000016’ resulted in clustering of 26 different clades Fig. . In comped) Fig. . The genB. tabaci SSA1-SG1-Ug and two genes in B. tabaci SSA1-SG1-Ng had orthologs in insect species that do not feed on phloem-sap. For example, three genes from B. tabaci SSA1-SG1-Ug, B. tabaci SSA1-SG1-Ng and B. argentifolii  clustered with RPRC013046-RA (R. prolixus), ACYP1002020-RA (A. pisum), XM020863006.1 (B. terrestris) and RPRCO12963-RA (R. prolixus). This gene encodes for a sucrose hydrolyase with two magnesium ion ligands binding to five amino acid residues; D.46, D.48, D.50, I.52 & D.54 and three protein–ligand interactions forming metal complexes with amino acid residues; D.48, D.50 and I.52. The second set of orthologous genes in B. tabaci, Anopheles gambiae, D. melanogaster, Tribolium castaneum, Bombyx mori and Danaus plexippus encode for heavy chain protein . These are heteromeric amino acids transporter b0, + AT-rBAT complex bound with arginine, and in SSA1-SG1-Ug and SSA1-SG1-Ng are coded by gene ENSSSA1UGT025164 and ENSSSA1NGT004694 respectively. Two SSA1-SG1-Ug genes (ENSSSA1UGT010306 & ENSSSA1UGT014032) were annotated as alpha-amylase, an enzyme that hydrolyze alpha bonds of large polysaccharide such as starch and glycogen. Alpha-amylases have also been previously reported in B. tabaci [T. urticae, R. prolixus and D. melanogaster.A total of four genes in . tabaci . Among tB. tabaci SSA1-SG1-Ug and B. tabaci SSA1-SG1-Ng based on the approximate specificity, selected protein sequences were submitted to the protein structure homology-modeling server (Expasy webserver-SWISSMODEL) [To classify the α-glucosidases in SSMODEL)  to identB. argentifolii contained a protein–ligand interaction of two magnesium ions, interacting with ASP 48, ASP 50 and ILE 52  and 6lga.1.A (sucrose hydrolase) as the two best quaternary structure annotations. The best model built for both ENSSSA1UGT001243 and its ortholog in B. argentifolii ENSMEAMV2T026084 (“Bta03818” [B. tabaci SSA1-Ug to survive on many plant hosts [The ta03818” ) sequencta03818” . The resnt hosts , 112. ThB. tabaci SSA1-SG1 ∪ SG2, the specific relaxation parameter (K) for each gene was determined. Examination of selection pressure In ‘cluster 1’, using the relaxation parameter (K) of both test branches (genes) shifted away from neutrality, with a branch-specific (K) of 1.15 and 1.53 for SSA1-SG1-Ug and SSA1-SG1-Ng, respectively  has experienced episodic diversifying selection with four sites under pervasive diversifying selection  [B. tabaci s.l. and comprise one of the largest genera in the virosphere [vs loss of important viral interaction gene families has occurred within B. tabaci s.l. genomes in order to further elucidate potential whitefly gene targets for interference studies. Our analysis of virus-vector competency gene families centered on sequence-based matching via conditional reciprocal best BLAST (CRBB), as well as manual curation of protein-coding functional annotation information. We expand on results by providing a comparative genomics framework  including specific reference to broader whitefly, and importantly T. vaporariorum. This approach was non-exhaustive, with further examination is warranted to verify these findings and fully elucidate these candidate gene family targets with potential for whitefly control.viridae) . These srosphere , 122. OuD. melanogaster (thirteen genes) contains a rich repertoire while mosquitos (seven genes) and tsetse flies (six genes) genomes contain fewer PGRPs [Evasion of the host immune system is crucial for successful host infection and propagation of virus particles. Here we have examined the presence and diversity of peptidoglycan recognition proteins (PGRP) . PGRPs are a widely distributed, diverse family of proteins conserved between invertebrates and mammals which facilitate maintenance of vector-pathogen homeostasis via innate immune response against invading pathogens –126. PGRer PGRPs –130.BtPGRP’ (AJQ31845.1) has been identified in B. argentifolii [BtPGRP’ [BtPGRP contained an Arg106 which is associated with recognizing meso-diaminopimelic acid (DAP)-type peptidoglycans, as previously shown for D. melanogaster PGRP-LE and PGRP-LC [BtPGRP in whitefly, while further demonstrating its complete sequence conservation of Arg106 between all Aleyrodidae species examined and D. melanogaster PGRP-SB1 (FBtr0075348).To date, only a single PGRP gene ‘ntifolii . In this[BtPGRP’ . Wang et[BtPGRP’  reported PGRP-LC . Our resN-acetylmuramoyl-L-alanine amidase/PGRP domain’ superfamily (IPR002502). ‘OG0003013’ contained B. tabaci s.l. members harboring two gene copies each . However, not all Hemiptera examined  had detectable homologs within either of these two PGRP-like OGCs and highlights a possible PGRP gene loss over time within whitefly and more broadly Hemiptera.The second PGRP-like OGC ‘OG0003013’, contained proteins which exhibited functional domains linked to the ‘B. tabaci s.l. heat shock proteins (HSPs) [B. argentifolii [B. tabaci s.l. utilize HSPs to cope with thermal stress and that thermotolerance differs between cryptic species, possibly conferring ecological advantages [hsp70-related gene families is warranted but falls outside the scope of this investigation. We instead present a targeted examination of hsp70 protein domains  in B. tabaci s.l genomes.The hsp70 family is the largest clade of s (HSPs) –136 and ntifolii , 138. HSntifolii . Severalvantages , 141. Anhsp70-like OGCs identified across all B. tabaci s.l. genomes, were located within a single OGC ‘OG0000087’. Published hsp70 ‘BtHSP70’ (XP_018908958.1) previously identified in B. argentifolii recovered significant CRBB hits to ‘OG0000087’ members. Our results showed hemipteran species exhibit the largest diversity of hsp70-like genes, with OGC ‘OG0000087’ containing up to fourteen HSP70 homologs per species; while B. tabaci s.l. genomes contained between 9–14 compared to 10 in T. vaporariorum.A total of five OGCs  with homology to HSP70 functional protein-domains were identified. Between 58–75% of the total gene members, across all B. tabaci s.l., the vesicle trafficking system of midgut cells plays an important role in the transport of begomoviruses across the midgut basal membrane to the haemolymph [B. argentifolii, which showed an inhibitory role in the transmission of the begomovirus tomato yellow leaf curl virus (TYLCV). Our investigation of VAPB-like proteins in B. tabaci s.l. genomes recovered six OGCs with homology to VAPB protein domains related to “Vesicle-associated membrane-protein-associated protein” (IPR016763), “PapD-like superfamily” (IPR008962) and “Major sperm protein” (MSP) domain (IPR000535). One of the six OGCs ‘OG0002935’ harbored members with high sequence identity to that of B. argentifolii VAPB (XP_018905345.1). All species were represented in ‘OG0002935’ by a single VAPB gene member, to the exclusion of B. tabaci SSA1-SG1-Ng and B. tabaci Asia II-5 which both had two copies. VAPB was not identified in B. tabaci s.s. Notably then, VAPB is potentially not vital for successful TYLCV transmission, given B. tabaci s.s. Is known to be an efficient vector of this virus [Recent studies have demonstrated that for emolymph , 143. Fuemolymph  identifiis virus .B. tabaci midgut proteins (MGP). MGP is crucial for successful begomovirus transmission, where the viral coat protein interacts with the MGP to facilitate transfer into hemolymph and salivary glands [B. tabaci s.l. examined contained evidence of possessing a MBF2 signal peptide as previously reported by Rana et al. [Another crucial protein related to begomovirus transmission is found in y glands . MGP is a et al. .B. argentifolii (XP_018898813.1) and a secreted salivary protein (XP_026481543.1) identified in the cat flea Ctenocephalides felis. ‘OG0009507’ was composed of exclusively whitefly-only gene members, of which only Non-African B. tabaci s.l. members possessed a signal-peptide. A. pisum and M. persicae lacked any identifiable homologs to the MGP-like protein suggesting a this to be a uniquely whitefly-specific gene expansion with a potentially high degree of importance of this protein family during the co-evolution of begomoviruses and B. tabaci s.l. The second OGC ‘OG0000655’, had the highest overall homology to the MGP reported previously for B. argentifolii (AIK97534.1) [OGC ‘OG0009507’, had the most significant BLASTp hits to the previously reported uncharacterized protein in 97534.1) . Both OGB. tabaci s.l. genes with homology to cyclophilins (CyPs). CyPs are a diverse superfamily of proteins found in both prokaryotes and eukaryotes. Cyps are involved in protein folding [B. tabaci s.l. CyP proteins [B. tabaci s.l. genomes (ranging from 16 to 28). In contrast only 17 PCGs were identified with CyP-like functionality in T. vaporariorum, which is unable to transmit TYLCV [Lastly, we examined the diversity of  folding , and fun folding ). CyPs h folding . Whole gproteins  were ideit TYLCV .B. argentifolii [B. tabaci s.l. at ~ 96–99%, which decreased to 75–77% when considering all Aleyrodidae species. Crucial residues involved in CyPB cyclosporin A (CsA) binding and the reported CyPB signature peptidyl-prolyl cis–trans isomerase domain (‘YKGSKFHRVIKDFMIQGG’) had near complete sequence conservation across all whitefly. However, T. vaporariorum uniquely possessed a single substitution to the peptidyl-prolyl cis–trans isomerase domain (Tyr—> Phe) of the CsA binding domain. Of the fourteen CyP-like OGCs identified, only two OGCs ‘OG0009174’ and ‘OG0009922' were identified that appeared to be whitefly-specific, with no discernible sequence homology with any other taxon examined. Proteins in both OGCs had high sequence identity to B. tabaci s.l. PPIase B-like (LOC109043131) and PPIase B1-like (LOC109043200) proteins.Amongst CyP gene families, CyPB was shown to have relatively increased gene expression after TYLCV acquisition by whitefly individuals , while antifolii , 150. GeB. tabaci s.l. genomes, with 142 identified in the B. argentifolii draft genome [B. tabaci SSA1-SG1 , B. argentifolii and B. tabaci s.l. genomes contained between 117 and 164 horizontally transferred genes (HTGs) of bacterial and fungal origins, respectively  and 63  HTGs originally published in the genome of B. argentifolii were used as sequence queries for CRBB analysis to gene transcripts generated as part of this study  of the original fungal HTGs previously reported for this genome. SSA1-SG1-Ng contained the most fungal HTGs (n = 33), but only fractionally more than the average of 26 observed across all eight genomes. Examination of bacterial HTGs showed similar levels of recovery across all B. tabaci s.l., albeit at a slightly higher recovery rate than for fungal HTGs. We found that on average 43 bacterial HTGs were annotated across all Bemisia populations. B. argentifolii had 47 of the 63 bacterial HTGs originally reported, while again SSA1-SG1-Ng showed the highest overall recovery (n = 48).We also analyzed microbial HTGs beyond the comparative functional annotations of the SSA1-SG1 and B. argentifolii. We conclude that previous estimates of HTG diversity in B. argentifolii and B. tabaci s.s. genomes are likely to be overestimated and additional experimental investigation is warranted to characterize the precise diversity of Bemisia endosymbiont related HTG genes.Our re-examination of microbial horizontally transferred genes is consistent with the total number of ‘functionally distinct’ proteins previously published for B. tabaci s.l. genomes applied via a uniform methodology, which excluded any ab-initio prediction component, has resulted in recovery of many fewer total PCG compared to previously published B. tabaci s.l. genomes. Though PCG are observed with only slight variances, to the exclusion of B. tabaci s.s, in the total number of recovered PCG across all eight B. tabaci s.l investigated. The finalized PCG sets of all genomes presented in this study constitute a conservative and well supported evaluation of the B. tabaci s.l. gene space.Structural annotation of B. tabaci s.l. We show that the non-cassava utilizing species B. tabaci Uganda-1 resides as the earliest branching member of all African Bemisia examined in this study. Major relationships and topological branching order of sampled Bemisia species was again supported by independent analysis of 11 protein-coding mitochondrial genes. Reciprocal crossing experiments further corroborate these topological patterning, supporting the hypothesis that three biological species of B. tabaci s.l. colonize cassava in Africa, also adding to the weight of evidence that B. tabaci s.l. is a group of morphologically, highly cryptic biological species. Examination of average nucleotide identity and topological patterns among the primary obligate endosymbiont Portiera from B. argentifolii, B. tabaci s.s. and newly presented B. tabaci s.l. specifically grouped B. tabaci SSA2 and B. tabaci SSA3, as well as B. tabaci SSA1-Ng and B. tabaci SSA1-Ug together, further supporting their biological species’ statuses. Using an integrative systematics approach, we concluded that the seven, sub-Saharan, African cassava populations of B. tabaci s.l. can be reduced to just three biological species, namely B. tabaci SSA1-SG1 ∪ SG2, B. tabaci SSA2 ∪ SSA3 and B. tabaci SSA1-SG3. The former two biological species occur widely in both East and West Africa, so are most usefully identified currently by names that include (molecular) diagnostic information.Nuclear phylogenomics, supported by ML and BI recovered near universal support of both monophyletic whitefly (Aleyrodidae) and Bemisia genomes will make a significant contribution to resources for studies of B. tabaci s.l. comparative evolution and for resolving the number of biological species within this morphologically cryptic group. They shall also assist efforts to unravel the genetics behind species’ differences in their interactions with host plants, invasiveness, and propensity to cause outbreaks. Lastly, this study contributes towards the expansion of valuable genetic information across multiple species of Bemisia further enabling the selection of candidate gene targets for novel whitefly and plant-virus control methods.These new See Additional file correctedErrorRate was decreased when genomic coverage was estimated to be > 60X. A genome refinement protocol was applied to each draft assembly and proceeded by initial generation of a ‘polished’ genome consensus via alignment of input read corrected reads using minimap2 [Prior to assembly, input PacBio read data was screened taxonomically to remove contaminants . Bacteriocyte-specific RNA-seq libraries were applied to ensure bacteriocyte-specific gene expression contributed to structural annotation. Finalized gene builds contain multi-transcript PCG structures, where each transcript can contain overlapping exon boundaries; excluding any redundant transcripts where the splicing pattern is completely redundant when comparing to a longer model [B. tabaci s.l. genomes presented herein were first released in April-2021, via Ensembl Metazoa (Ensembl release version e103) (https://metazoa.ensembl.org/). Re-annotations of both B. argentifolii and B. tabaci s.s. are available as a standalone ‘Figshare’ dataset  [Genomic annotations of pipeline . Ensemblpipeline , 161. Ther model . See AddA, GFF3)  as part https://github.com/ebi-pf-team/interproscan) was used to generate functional annotations of canonical PCG gene models. Data was then exported in TSV format, then parsed to obtain only gene models that were annotated with Gene Ontology GO terms. This data was then imported into the web service WEGO 2.0 [P-value < 0.05) test of independence of expected frequencies of genes with GO terms and their observed frequencies.Interproscan  version WEGO 2.0 , 165. Tan = 23) were identified and clustered via OrthoFinder (v2.4.0) [B. tabaci s. l. presented in this study were included, but also the published whitefly B. argentifolii and B. tabaci s.s [. utilizing PCGs annotated via Ensembl gene annotation pipeline specifically for their inclusion in this study [T. vaporariorum were downloaded from WhiteflyDB (http://www.whiteflygenomics.org). T. vaporariorum [Bemisia genus and was the only other Aleyrodidae family member with a complete genome assembly and annotation available at the time of analysis. See Table Orthologous sequences of twenty-three arthropod species ((v2.4.0) , 52 usinbaci s.s , 33. utiis study . Proteinrariorum  served aProtein sequences of OGCs  were aligned using MAFFT v7.427 . The conPrior to all gene family analyses, a genome-wide screening method was adopted to identify orthologs of gene families of interest. These candidate OGCs were identified via a combination of screening of functional protein/domain annotation obtained from InterProScan v -5.40–77.0 and sequence-based matching using CRBB , 172 forhttps://www.repeatmasker.org/RepeatModeler/) v1.73. To avoid ‘over-masking’ of protein encoding genomic loci, de novo TE repeat libraries were screened with BLASTp and BLASTn MegaBLAST [https://blaxter-lab-documentation.readthedocs.io/en/latest/filter-repeatmodeler-library.html) and was implemented via bash script. Genome-wide copy number variation and overall sequence divergence on TE repeat types was examined using the Perl utility ‘OneCodetofindthemall’ [https://doua.prabi.fr/software/one-code-to-find-them-all), and statistics reported on a per genome scaffold basis. To summarize TE repeat content ‘OneCodetofindthemall’ output was parsed using an in-house Perl script taking ‘.copynumber.csv’ files as input. Kimura divergence scores were extracted from RM generated ‘.out’ output file using the protocol as described [Transposable element (TE) libraries generated RepeatModeler  was used to perform an initial selection of Portiera sequences from the pre-processed PacBio sequences . Kraken-tools v1.2 was used to extract any classified sequence below the Oceanospirillales rank. Primary assemblies of Portiera sequences were produced with flye [Portiera reads with minimap2 v2.17-r941 (-x map-pb) [igshare’ ). Briefligshare’  with theith flye  v2.8.3-b map-pb)  and the Portiera assemblies were annotated with prokka v1.14.6 [Portiera genomes as primary annotation source (–proteins). Obtained annotations were manually inspected in Artemis v1.5 [Polished  v1.14.6   as reference. Genomic synteny among Portiera genomes was plotted with genoPlotR [yidC, mnmE, and mnmG).Orthologous clusters of proteins (OCPs) were computed with OrthoFinder v2.4.0 (-M msa -T iqtree -I 1.5) . EncodinenoPlotR  based onPairwise Average Nucleotide Identity values were calculated with the enveomics toolbox  using USAdditional file 1. Supplementary tables S1-S16.Additional file 2. Supplementary figures S1-S17.Additional file 3. SignalP results for the α-glucosidase (GH13) of selected insect species.Additional file 4. Supplementary text.


Large-scale genotype–phenotype association studies of crop germplasm are important for identifying alleles associated with favorable traits. The limited number of single-nucleotide polymorphisms (SNPs) in most wheat genome-wide association studies (GWASs) restricts their power to detect marker-trait associations. Additionally, only a few genes regulating grain number per spikelet have been reported due to sensitivity of this trait to variable environments.TaSPL17 positively controls grain size and number by regulating spikelet and floret meristem development, which in turn leads to enhanced grain yield per plant. The haplotypes at TaSPL17 indicate geographical differentiation, domestication effects, and breeding selection.We perform a large-scale GWAS using approximately 40 million filtered SNPs for 27 spike morphology traits. We detect 132,086 significant marker-trait associations and the associated SNP markers are located within 590 associated peaks. We detect additional and stronger peaks by dividing spike morphology into sub-traits relative to GWAS results of spike morphology traits. We propose that the genetic dissection of spike morphology is a powerful strategy to detect signals for grain yield traits in wheat. The GWAS results reveal that Our study provides valuable resources for genetic improvement of spike morphology and a fast-forward genetic solution for candidate gene detection and cloning in wheat.The online version contains supplementary material available at 10.1186/s13059-023-03044-2. Triticum aestivum L.) is one of the most important crops worldwide. It supports more than 2.5 billion people > 5.00 according to previous reports  = 7.74. To balance false positives and false negatives, so we conservatively chose - log10 [P value] = 5.00 as a moderate threshold for calling significant association according to previous GWAS work in wheat  formaldehyde). The samples were embedded in Paraplast  after dehydration through a graded ethanol series  and hyalinized using a xylene:ethanol series with v/v ratios of 1:2, 1:1, 2:1, and 1:0. Then, the samples were sectioned into 8-μm sections using a Leica RM2245 rotary microtome. After removing Paraplast using a series of xylene and ethanol solutions, the sections were stained with toluidine blue before images were captured using a Leica ICC50 HD microscope.TaSPL17-specific coding region was subcloned into the pGEM-T Easy vector  and used as a template to generate both antisense and sense RNA probes. Shoot apices of WT wheat seedlings at the third-leaf stage and young inflorescences were fixed using an RNase-free FAA fixative solution and then embedded in Paraplast . Digoxigenin-labeled RNA probes were prepared using a DIG Northern starter kit (Roche) according to the manufacturer’s instructions. Slides were observed under brightfield using a Leica DMR microscope.RNA in situ hybridization was performed as described previously  . A speciTaSPL17-A, TaSPL17-B, and TaSPL17-D were individually cloned in-frame and downstream of the sequence of GFP and placed under the control of the cauliflower mosaic virus (CaMV) 35S promoter in the vector pAN580. Protoplasts were prepared from wheat mesophyll cells, and the TaSPL17-A/B/D-GFP fusion constructs and the nuclear marker OsMADS3-mCherry were co-transfected into wheat leaf protoplasts for transient expression using the polyethylene glycol (PEG4000)-mediated method. A Leica TCS-SP4 confocal microscope was used for fluorescence detection.The coding sequences of TaSPL17-A, TaSPL17-B, and TaSPL17-D , two sgRNA target sequences were designed according to the conserved exon sequences of TaSPL17-A, TaSPL17-B, and TaSPL17-D using the online tool CRISPR-P 2.0 (http://crispr.hzau.edu.cn/CRISPR2/). A segment containing the wheat U3 promoter was amplified from the pCBC-MT1T2 vector using a pair of primers containing the two designed sgRNAs and then cloned into the BsaI site of the clustered regularly interspaced short palindromic repeats CRISPR/Cas9 vector pBUE411, using T4 DNA ligase .To generate mutants in TaSPL17 in all subgenomes , two sgRNAs (sg1 and sg2) were designed targeting the conserved regions within the first and third exons of TaSPL17-A, TaSPL17-B, and TaSPL17-D, and the corresponding sgRNA/Cas9 vector was transformed into the wheat cultivar Fielder via Agrobacterium (EHA105) (Agrobacterium tumefaciens)-mediated transformation . Twenty bialaphos-resistant transgenic events harboring the T-DNA of the sgRNA/Cas9 vector were identified. TaSPL17-A, TaSPL17-B, and TaSPL17-D-specific PCR primers were designed to amplify fragments from both the WT TaSPL17 and edited TaSPL17 loci. DNA sequencing revealed that 50% (10 of 20) T0 events harbored compound heterozygous mutations at the gRNA2 site, whereas no mutations were detected at the gRNA1 site. Seeds of 10 mutant T0 events were collected and planted in the greenhouse, and 10 T1 plants for each T0 event were selected to detect mutation types at the gRNA2 site. A 1-bp deletion and a 1-bp insertion were most frequently identified at the target site. All mutations caused a frameshift leading to the introduction of premature translation termination codons.To simultaneously disrupt TaSPL17 mutants.To avoid the influence of potential off-target sites on phenotypic and functional analyses, the four most likely off-target sites with two or three mismatches were selected for amplification and analysis using specific primers. The results of sequencing did not reveal any mutations in the examined potential off-target sites of all 25 homozygous TaSPL17-A, TaSPL17-B, and TaSPL17-D were individually amplified using DNA polymerase KOD FX  from WT cDNA and cloned into the pMWB110 vector [To generate overexpressing plants, the coding sequences of 0 vector  using an0 vector . All priAdditional file 1: Table S1. Summary of the 306 worldwide wheat accessions. Table S2. Diversity and genetic structure analyses of the 306 wheat accessions. Table S3. The determination of linkage disequilibrium (r2) in this study. Table S4. The heritability of 27 traits in this study. Table S5. The effects of genotype, environment, genotype ✖ environment interaction and heritability for the 27 traits in this study. Table S6. Overview of the 27 spike morphology traits in this study. Table S7. Detailed information of all significant associated SNPs for the investigated traits . Table S8. Detailed information of all genomic intervals for the investigated traits. Table S9. The effects of 392 genomic intervals associated with assimilate partitioning for the grain yield traits. Table S10. The expression of candidate genes (FPKM). Table S11. The distance between lead SNPs and TraesCS7A02G246500. Table S12. Targeted mutagenesis of TaSPL17 in different mutant lines. Table S13. The phenotypic data of knock out lines in this study. Table S14. The phenotypic data of overexpression lines in this study. Table S15. Overview of spike morphology traits among the haplotypes of TaSPL17. Table S16. The differences of spike morphology traits among the haplotypes of TaSPL17. Table S17. Worldwide distribution of the three major haplotypes of TaSPL17-A. Table S18. The percentage of worldwide distribution of the three major haplotypes of TaSPL17-A. Table S19. The Hap-A2 information of TaSPL17-A in 306 wheat accession. Table S20. The percentage of the three major haplotypes of TaSPL17-A in different regions. Table S21. The distribution of the twelve major haplotypes of TaSPL17-A in 799 Chinese wheat accessions. Table S22. The percentage of the three major haplotypes of TaSPL17-A in 799 Chinese wheat accessions. Table S23. The linkage disequilibrium (r2) between lead SNPs and haplotype SNPs. Table S24. The identified SNPs of TaSPL17-A by Sanger sequencing. Table S25. The distribution of the four major haplotypes of TaSPL17-B in different areas. Table S26. The distribution of the four major haplotypes of TaSPL17-B in different areas. Table S27. The results of permutation test. Table S28. Primers used in this study.Additional file 2: Figure S1. Cross-validation (CV) errors of ADMIXTURE runs. Figure S2. Overview of phenotypic data according to the geographical origin of accessions. Figure S3. The distribution of phenotypic values of the 27 spike morphology traits for the 306 wheat accessions. Figure S4. The associations among the 27 spike morphology traits. Figure S5. Manhattan plot displaying the GWAS result of the eight traits. Figure S6. Homozygous targeted mutagenesis of TaSPL17. Figure S7. Phenotypic characterization of the TaSPL17 mutant lines in field. Figure S8. Traits associated with spike assimilate partitioning in wild type (WT), overexpression (OE) and knock out (KO) lines. Figure S9. Early stages of spike development in wild type (WT) and the triple knockout (KO) line. Figure S10. Duration of the terminal spikelet stage (TS), yellow anther (YA), heading stage (HD) and anthesis stage (AN) in wild type (WT), the overexpression (OE) and the triple knockout (KO) lines. Figure S11. The spikes for NILs and RILs of TaSPL17 in field.Additional file 3. Review history.


Lateolabrax maculatus) is an economically important fish species cultured in China. Single nucleotide polymorphisms (SNPs) associated significantly with growth traits were screened to promote culturing conditions for L. maculatus. The insulin-like growth factor 2 gene (igf2) of L. maculatus was isolated. Fourteen SNPs were detected from the genome sequence of igf2. Four SNPs were associated significantly with the growth traits of L. maculatus. The SNPs we identified could be valuable for the genetic breeding of L. maculatus.The spotted sea bass (igf2) is thought to be a key factor that could regulate animal growth. In fish, few researchers have reported on the single nucleotide polymorphisms (SNPs) located in igf2 and their association with growth traits. We screened the SNPs of igf2 from the spotted sea bass (Lateolabrax maculatus) by Sanger sequencing and made an association between these SNPs with growth traits. The full-length complementary (c) DNA of igf2 was 1045 bp, including an open reading frame of 648 bp. The amino acid sequence of Igf2 contained a signal peptide, an IGF domain, and an IGF2_C domain. Multiple sequence alignment showed that the IGF domain and IGF2_C domain were conserved in vertebrates. The genome sequence of igf2 had a length of 6227 bp. Fourteen SNPs (13 in the introns and one in one of the exons) were found in the genome sequence of igf2. Four SNPs located in the intron were significantly associated with growth traits (p < 0.05). These results demonstrated that these SNPs could be candidate molecular markers for breeding programs in L. maculatus.The insulin-like growth factor 2 gene ( In fish, the insulin-like growth factor (Igf) family includes three Igfs, two Igf receptors, and six Igf-binding proteins ,2. TheseOncorhynchus mykiss), the first time that it had been found in fish [Oreochromis mossabicus, Danio rerio, Oryzias latipes, Cyprinus carpio, Ctenopharyngodon idellus, and hybrid yellow catfish (Pelteobagrus fulvidraco) [igf2, but some scholars have found more than two igf2 paralogs in fish. For example, two igf2 genes (igf2a and igf2b) have been detected in zebrafish and grass carp [In 1992, Igf2 was isolated from rainbow trout (vidraco) ,12,13,14ass carp . The exiass carp , followeass carp .Dicentrarchus labrax L.), two SNPs from igf1 are associated with body weight and total length (TL) [Salmo salar), two SNP loci in igf1 are associated with body weight [P. fulvidraco, an SNP of igf2 is associated significantly with nine growth traits [A single nucleotide polymorphism (SNP) is a germline substitution of a single nucleotide at a specific position in the genome . Due to gth (TL) . In Atlay weight . In P. fLateolabrax maculatus) is one of the species from the family Moronidae (Perciformes). It is distributed from the coast of China to the Indo-China Peninsula [L. maculatus has become an economically important fish in China. The annual production in China in 2021 was 199,106 tons . Selection of new varieties of fast-growing L. maculatus requires studies of growth-trait-related genes and associations with molecular markers.The spotted sea bass DNA and DNA sequences of L. maculatus individuals were caught in the East China Sea and reared in net cages for several months. During the breeding season (November–January of the following year), fish with gonadal maturation were transported to a cement pool in a fish farm . Ten males and four females were cross-mated. All embryos were hatched simultaneously. More than 10,000 larvae were transported to the Zhuhai Base of the South China Sea Fisheries Research Institute . All larvae were cultured and managed in the same pond. At the age of 9 months, L. maculatus individuals (average standard length (SL) = 33.34 ± 2.27 cm) were chosen randomly. Body mass (BM) was measured using a fish scale. Five body measurements , body width (BW), head length (HL), and total length (TL)) were measured with vernier calipers  following manufacturer protocols. The quality and concentration of RNA were assessed by electrophoresis on 1% agarose gels and NanoDrop™ 2000 , respectively. One µg of total RNA from each sample was used as a template for cDNA synthesis using the PrimeScript™ II 1st Strand cDNA Synthesis Kit . First-strand cDNA was synthesized from total RNA according to manufacturer instructions.Total RNA from muscle was extracted using TRIzoligf2 was isolated using transcriptome data of L. maculatus noted previously (PRJNA811720). One pair of primers (cs/ca) was used to examine the sequence accuracy , 0.8 μL of each primer (10 mM), 0.2 μL of Ex Taq (5 U/mL), 1.6 μL of a dNTP mixture (2.5-mM each), 0.8 μL of cDNA, and 13.8 μL of double-distilled H2O).The full-length cDNA of accuracy . PolymerPCR conditions were 1 cycle at 94 °C for 3 min, 35 cycles at 94 °C for 30 s, 50 °C for 30 s, 72 °C for 30 s, and a 7 min step at 72 °C. PCR fragments were purified with a SanPrep Column DNA Gel Extraction Kit  and then sequenced by the Sanger’s dideoxy method at Tsingke Biological Technology .Total genomic DNA was isolated using the Marine Animals DNA Kit . The quality and integrity of DNA were examined by electrophoresis on 1% agarose gels and NanoDrop 2000 , respectively. DNA was dissolved in sterile water at a concentration of 50 ng/mL and stored at –20 °C.igf2 was obtained from the L. maculatus genome (CP027267.1). One pair of primers (gs and ga) were used to verify the accuracy of the sequence , 0.8 μL of each primer (10 mM), 1.6 μL dNTP mixture (2.5-mM each), 0.8 μL of DNA, and 0.2 μL of LA Taq (5 U/mL)). The PCR program was 1 cycle at 94 °C for 3 min, 35 cycles at 95 °C for 30 s, 52 °C for 30 s, 72 °C for 3 min, and 1 cycle at 72 °C for 7 min. As described above, PCR products were purified and sent to Tsingke Biological Technology for sequencing.The genome sequence of sequence . PCR waswww.ncbi.nlm.nih.gov/gorf/orfig.cgi/, accessed on 12 October 2022) was used to obtain the open reading frames (ORFs) and predict the coding protein sequence. Primer-BLAST  was employed to assist with primer design. Exons and two introns were analyzed via a comparison between the genomic DNA sequence and cDNA sequence. The signal peptide of the deduced amino acid (aa) sequence was analyzed with SignalP . Functional domains were predicted by the conserved domains detailed in the National Center for Biotechnology Information . The chemical and physical properties of proteins were analyzed by ProtParam . Global Align  was used to analyze the similarities of the aa sequence of Igf2. Multiplex sequence alignment was carried out using Clustal Omega . A phylogenetic tree was constructed using MEGA 7.0 with the maximum-likelihood algorithm [ORF Finder  were used to produce the DNA sequence of igf2 . PCR fraAn association analysis between different genotypes of an SNP and growth traits was carried out using the general linear model with SPSS 19.0 . The model was:om error . The sigigf2. The full-length igf2 cDNA (GenBank accession number: ON462263) was 1045 bp with a 5′-untranslated region (UTR) of 177 bp, 3′-UTR of 220 bp, and ORF of 648 bp  was detected in the N-terminal sequence, an insulin-like growth factor (IGF) domain (51–117 aa), and an IGF2_C domain (147–202 aa)  .igf2 was 6227 bp in length (GenBank accession number: ON462264). It contained four exons and three introns  and 5 were transpositions   . The fird 3 SNPs . Anotherd 3 SNPs , Table 3p < 0.05) (p = 0.044) and BD (p = 0.035). HL and BD in the TC genotype were significantly higher than those of the CC genotype. The length of the SNP g3230A>C locus was associated significantly with TL (p = 0.011), and that of homozygous  was significantly higher than that of heterozygous (AC). SNP g3294C>T genotypes were correlated with BW (p = 0.002), BD (p = 0.044), and HL (p = 0.008). Genotype TT was significantly higher than genotype CC in terms of BD, BW, and HL. SNP g5064C>T genotypes were correlated significantly with individual SL (p = 0.027). SL of genotype CC was significantly higher than that of genotype TT.A general linear model was employed to analyze the relationship between fourteen SNP loci and six growth traits: four SNP loci were correlated significantly with growth traits ( < 0.05) . SNP g29igf2 from L. maculatus. Similar to other vertebrates such as mammals, birds, amphibians, and fish, the protein sequence of Igf2 of L. maculatus also includes an IGF domain and IGF2_C domain . The protein sequence of Igf2 of L. maculatus was found to be highly similar to that of other vertebrates, especially the two domains IIGF and IGF2_C, which were highly conserved , common carp (Cyprinus carpio), salmon (Oncorhynchus keta), tilapia (Oreochromis niloticus), Asian sea bass , and pike perch (Sander lucioperca) [igf2 genome of fish is relatively conserved. In mammals, the number of igf2 exons is greater than that of fish. For example, in mice (Mus musculus), igf2 has eight exons, and in humans and cows (Bos taurus) igf2 has ten exons, respectively [igf2 genome has changed considerably during evolution.We cloned the cDNA of onserved . The gen introns . This stioperca) ,29,30,31ectively ,33. Hencigf2 is a candidate gene for the selection of growth traits [IGF2 of pigs and cattle [igf2 and growth traits in fish. For example, one SNP of igf2 was found to be correlated significantly with nine growth traits of the hybrid yellow catfish (P. fulvidraco (♀) × Pelteobagrus vachelli (♂)) [igf2 of S. lucioperca is closely related to body weight [igf2 in tilapia (O. niloticus) is correlated with growth traits [igf2 of a new strain of Yellow River carp reduced the body weight of fish. Li et al. [igf2 of M. salmoides, and the two haplotypes composed of these loci were closely related to body weight (p < 0.05). In the present study, 14 SNPs were isolated from the igf2 genome sequence of L. maculatus, 13 of which were located in the intron. There are many SNPs in an intron because an intron does not participate in encoding proteins, has little selection pressure, and can accumulate mutations readily [igf2 were associated significantly with growth traits (p < 0.05)  the SNPs associated with growth-related traits in igf2 can be used as markers in studies on L. maculatus.Growth traits are important for fish breeding . Screenih traits . Some SNd cattle ,35,36. Ty weight . One SNPh traits . Xie et h traits  demonstri et al.  detected readily . In addi readily ,40. Simi readily ,36, we f < 0.05) . These rigf2 were isolated. Fourteen SNPs were screened in the genome sequence of igf2. Four SNPs located in the intron were associated significantly with growth traits, which may be useful for genetic improvement in L. maculatus breeding. These results are important markers for the marker-assisted selection in L. maculatus.The cDNA and DNA sequences of


Genome-wide association studies (GWAS) benefit from the increasing availability of genomic data and cross-institution collaborations. However, sharing data across institutional boundaries jeopardizes medical data confidentiality and patient privacy. While modern cryptographic techniques provide formal secure guarantees, the substantial communication and computational overheads hinder the practical application of large-scale collaborative GWAS.This work introduces an efficient framework for conducting collaborative GWAS on distributed datasets, maintaining data privacy without compromising the accuracy of the results. We propose a novel two-step strategy aimed at reducing communication and computational overheads, and we employ iterative and sampling techniques to ensure accurate results. We instantiate our approach using logistic regression, a commonly used statistical method for identifying associations between genetic markers and the phenotype of interest. We evaluate our proposed methods using two real genomic datasets and demonstrate their robustness in the presence of between-study heterogeneity and skewed phenotype distributions using a variety of experimental settings. The empirical results show the efficiency and applicability of the proposed method and the promise for its application for large-scale collaborative GWAS.https://github.com/amioamo/TDS.The source code and data are available at  Over the last few years, genome-wide association studies (GWAS) have allowed a significant understanding of the associations between complex genetic variants and traits in individuals , Secure Multi-Party Computation (SMC), and Federated Learning (FL). Homomorphic encryption is a cryptographic technique that allows specific operations (e.g. addition and/or multiplication) to be conducted directly on encrypted data. While HE offers robust security, it increases the computation and communication burdens for the researchers. Moreover, HE supports only addition and/or multiplication operations. Nonlinear operations necessary for regression analysis (e.g. logistic regression) can only be approximated, impacting result accuracy  with the server. The server amalgamates the parameters from all parties and redistributes them to all researchers, who then iteratively update the model until it converges. FL-based approaches, for instance  GWAS. Our approach unfolds in two phases. In the first phase, local parties collaboratively identify loci in their local data that are not significantly associated. This phase substantially curbs computation and communication costs by removing a large number of non-significant loci from subsequent analysis. In the second phase, all the local parties iteratively share portions of their private datasets with the server. The server performs GWAS on the pooled data and returns the results to the local parties. Our approach, improving existing federated learning methods and meta-analysis, involves sharing partial data, which allows us to achieve the same results as those obtained from aggregated analysis and enhances the performance in identifying significantly associated loci, even with datasets having between-study heterogeneity and imbalanced phenotype distributions. To manage the privacy risk introduced by sharing partial data, we apply the permutation techniques used in  are compared to those lacking that trait (the control group). The GWAS outcome comprises the SNPs most strongly associated with the studied trait. In such study, the phenotype of individual ix is the genotype of a SNP for individual i, and β represents the genetic effect. We utilize the standard Wald test .where rrection  is commoP-values) to the researchers. The proposed framework assures robust results despite between-study heterogeneity through an iterative process to share the partial data, allowing efficient computation while safeguarding the privacy of the participant individuals.In the proposed framework, a group of researchers possesses their private genotype arrays and phenotype vectors. They aim to collaboratively perform a logistic regression test to identify genetic markers (i.e. SNPs) significantly associated with the phenotype by sharing partial data generated from their local dataset with a third party (e.g. a server) equipped with extensive computational resources. Upon receipt of this shared data, the server performs the necessary computations and returns the association results (i.e. TDS) federated GWAS framework. The proposed framework comprises two phases. In Phase 1, both researchers conduct the computation locally and collaboratively identify the insignificant associations. In Phase 1, both researchers iteratively share the partial data, extracted from their local datasets, with the server to detect significant associations for the remaining SNPs. For the simplicity of exposition, we consider only two researchers A and B, but our proposed approach can readily be extended to accommodate multiple researchers. We assume that the researchers are honest and the server is honest-but-curious in the proposed design. An overview of the proposed framework is depicted in This section introduces the Two-step Dynamic Sampling  with the server to simultaneously compute univariate test statistics of all the SNPs. While this approach reduces the communication load, it could raise the privacy risk, particularly when each batch contains a few individuals. We argue that researchers only need to share data of some batches after a few iterations as they acquire high confidence in most SNPs (as demonstrated in the experiment results in Section 4). We also employ the technique in PPKI . Even though it might seem counter-intuitive, using a random subset suffices for good results and concurrently reduces the privacy risk. The researchers shuffle the SNPs (i.e. columns) according to the common seed iR then shuffles the individuals (i.e. rows) and removes the SNP identifier and sample identifier from the genotype arrays and phenotype vector. Notably, the permutation of individuals can be done independently and is unknown to the other researcher(s).Next, both researchers sample K) are system parameters to control the computational load and, more importantly, the computation accuracy and privacy. The effect of K on performance and use m = 300 as the default value [a small number, e.g. 250, is suggested , and obtains the significance condition of all the SNPs in P-value of iSNP is greater than μ, then its significance label, denoted as T iterations, μ can differ from the one used in Phase 1, as researchers can select more stringent values to lower the false negative rates. In Section 4, we empirically demonstrated the impact of μ on the utility. In the next synchronization phase, the researchers need to determine whether to continue. Here, we consider a simple condition where if the significance label for all the SNPs remains constant, both researchers decide to halt the iteration.During the outsourcing stage, both researchers transmit the shuffled genotype arrays and phenotype vector, Phase 1 offloads the computational burden to the server, thereby reducing inter-site communication and ensuring only necessary information is exchanged. The computation cost for the researchers is incurred when performing sampling and shuffling, a task which only needs execution once. On the server side, the computation complexity hinges on the statistical test implemented, the batch size received per iteration, and the overall number of iterations. With T iterations, the computation complexity for the server becomes Phase 1 as well), both the researchers and the server can harness parallel computation techniques to expedite the process .P-values) to both researchers. The objective of each researcher is to discern significant associations between SNPs and specific phenotype vectors across federated datasets, all while maintaining participant privacy.This study encompasses multiple researchers aiming to conduct collaborative logistic regression tests by sharing partial data extracted from their datasets with a server possessing substantial computational resources. Upon receiving this data, the server performs necessary computations and returns the association results  If this leakage is not acceptable, the researchers can employ Private Set Intersection algorithms, such as  the server can match the unordered SNP sequence shared by the researchers with the target’s SNP sequence in each iteration and (ii) the server can discern the linkage among the SNPs shared throughout the iterative process. Hence, privacy risks depend on the number of shared SNPs, the number of iterations, and the correlations between SNPs.Next, we discuss the potential privacy risks in TDS align with those of PPKI . In contrast, TDS assumes that SNP IDs are not revealed to the server. Second, PPKI employs a non-iterative approach, meaning the synchronization and outsourcing stage is performed only once. We then discuss how TDS reduces privacy risks by keeping SNP IDs hidden from the server and mitigates privacy risks introduced by the iterative process.The system and threat models of PPKI. In PPKI, the metadata received by the server includes the set of SNP IDs and shuffled SNPs. Assuming the server has access to a target’s SNP profile, it aims to infer the target’s membership in the federated dataset. The membership inference attack involves two steps: (i) the server attempts to unshuffle the shared SNPs in the metadata and infer the actual IDs, and (ii) it performs a power analysis , it only affects this iteration. With the limited number of SNPs shared in one iteration, the likelihood of a successful membership inference attack is low. Furthermore, TDS can directly adopt the techniques proposed in PPKI, such as using synthetic SNPs to enhance privacy by reducing the power of unshuffling.Below, we review the privacy risks and the membership attack considered in TDS enables efficient collaborative analysis while minimizing the potential privacy risks.In addition, when researchers iteratively share partial data, the server may discover the linkage among the SNPs shared throughout the iterative process and use that linkage to match the unordered SNP sequence. To mitigate the privacy risk due to the iterative process, researchers can select SNPs from different chromosomes for each iteration to eliminate the linkage among SNPs in different iterations. Through these privacy-preserving strategies, We use two real-world genome datasets from 1000 Genome Project : Both the case and control groups in each local dataset comprise 600 individuals.Scenario 2 (mild between-study heterogeneity): Researcher A’s dataset includes 450 individuals in the case group and 750 in the control group, whereas Researcher B’s dataset contains 750 individuals in the case group and 450 in the control group.Scenario 3 : Researcher A’s dataset has a case-to-control ratio of 300:900, while Researcher B’s dataset shows a reversed ratio of 900:300.Scenario 4 (highly skewed phenotype distribution): Each local dataset comprises 300 individuals in the case group and 900 individuals in the control group.Scenario 5 (slight skewness in phenotype distribution and mild heterogeneity): While Researcher A maintains a balanced dataset (case-to-control ratio of 600:600), Researcher B’s dataset is imbalanced, with a case-to-control ratio of 300:900.OpenSNP dataset, we select 28 976 SNPs on chromosome 15 from 800 individuals, using eye color as the phenotype. The case and control datasets are evenly divided between the two researchers.For the TDS comprises two distinct phases. Phase 1 is centered on researchers identifying insignificant associations within their local datasets to enhance efficiency. In contrast, Phase 2 focuses on enhancing utility, i.e. detecting significant associations, by facilitating an iterative sharing of partial data with the server.TDS’s performance and draw comparisons to GWAR  (additive) method. CATT is a prevalent test for GWAS association analyses and does not rely on the assumption of Hardy–Weinberg equilibrium (HWE). The empirical analysis demonstrated that CATT offers potent results and outperforms other tests regarding efficiency. To accommodate between-study heterogeneity, we implemented a random-effects model for the meta-analysis (μ), sample size (K), and the number of iterations T. The ground truth significance labels are derived using the entire dataset, with a chosen significance threshold of 0.005. We executed each experiment five times, reporting the average of the results.We commence by evaluating TDS and GWAR in terms of successful association identification, as measured by sensitivity  and specificity , are depicted in TDS consistently surpasses meta-analysis in performance and can accurately detect true positives. Notably, while the power of GWAR . However, it is not recommended to select an excessively high threshold , a strategy aimed at controlling the false positive rate. TDS achieves high sensitivity and specificity across all five scenarios, particularly in Scenario 5, which closely simulates real-world settings. In Scenario 5, by uniformly selecting 300 and 500 individuals in each iteration, ∼43% and 65% of the entire dataset is employed after two iterations, respectively. Therefore, our method necessitates sharing fewer individuals through the iterative process while achieving high utility. Notably, it is trivial to phrase the proposed method with Bernoulli Sampling, which further amplifies privacy via sampling (We proceed to examine the trade-off between privacy and utility in μ (e.g. 0.3) enhances both efficiency and utility. While a small sample size (K = 300) can achieve high utility, performance can be further amplified by using 500 samples in each iteration. The incurred privacy loss is offset by running fewer iterations. During the experiments, it was noted that TDS attained optimal performance at the fourth iterations in Scenarios 2 and 3, and at the second iteration in Scenarios 4 and 5, respectively, when using a relatively large sample size (e.g. 500) and threshold (e.g. 0.3).In summary, experimental results from simulated scenarios suggest our method surpasses meta-analysis in its robustness for identifying correct associations within heterogeneous data. Although the Bonferroni approximation accounts for the family-wise error rate, it tends to be excessively conservative in identifying significant associations when only a few samples are available. The experiments demonstrate that implementing a relatively large threshold TDS using the OpenSNP dataset with real phenotypes. Phase 1. With only two iterations, we are able to identify the majority of significant associations.Having highlighted the merits of the proposed method, we proceed to evaluate TDS and investigate how the runtime of TDS changes with varying numbers of samples and SNPs (see TDS is around 1.5 times faster (43.5 min versus 65 min for 400K SNPs) without multi-threading and a larger sample size (6000 versus 5823). Moreover, the results show that the runtime increases nearly linearly with the number of SNPs, which can be reduced by using parallel computation techniques. As the sample size grows, runtime does not change much.We then show the efficiency of TDS, a novel two-phase strategy designed to mitigate the computational challenges inherent in existing SMC and HE solutions. Phase 1 facilitates local computations in plaintext, thus minimizing the need for inter-site communication and encryption/decryption operations. On the other hand, Phase 2 adopts an iterative principle to decrease data sharing and consequently reduce privacy risks. The primary objective of our method is to improve the feasibility and efficacy of collaborative GWAS, thereby expediting genomics research while adhering to safety and privacy regulations. We want to emphasize that our intention is not to propose alternative genomic analytics methods. Instead, our goal is to manage privacy risks to ensure they remain within the acceptance boundaries of many institutions, such as those stipulated by the National Institutes of Health (NIH) Genomic Data Sharing (GDS) policy.In this study, we introduce Phase 1 is contingent on the size of the dataset. Larger sample sizes produce more accurate results. Furthermore, the effectiveness also depends on the degree of heterogeneity in the phenotype distribution. In an extreme scenario where only control (or case) samples are present across all researchers, Phase 1 becomes non-applicable. However, Phase 2 can still operate effectively when the pooled datasets contain more than one group of samples. This is possible when some hospitals possess only case samples while others have control samples.In Section 4, we have demonstrated that employing a more relaxed threshold  enables the identification of over half of the insignificant SNPs using only the local dataset. It is critical to emphasize, however, that the effectiveness of TDS focuses on logistic regression tests but can be expanded to accommodate quality control steps used in a GWAS. These steps include the computations of MAF, HWE, Linkage Disequilibrium (LD), and relatedness identification. Federated computation of MAF, HWE, and LD is anticipated to be straightforward, as they are based on allele frequencies and can be attained in Phase 1. Moreover, a recent framework, INK (TDS as it employs a similar system model. Addressing population stratification presents greater challenges in the federated setting, as merging two or more datasets with completely homogeneous populations in isolation can inadvertently introduce bias due to underlying cross-correlations. The application of PCA, a common method for addressing population stratification, is also not straightforward given the privacy requirements. Furthermore, federated PCA algorithms may require more data exchange and numerous iterations to converge on final eigenvectors, giving rise to security and runtime concerns. Developing an efficient federated PCA algorithm to handle both horizontally and vertically partitioned data will be a subject of our future research.Currently, In this article, our proposed framework aims to mitigate general privacy risks without specifying particular attacks or delving into intricate threat models. We prioritize providing a practical and flexible solution that can be applied broadly in scenarios where the threat landscape may vary. Future research endeavors may delve deeper into specific privacy threats, consider more stringent threat models, and explore how to tailor defenses against particular types of attacks while maintaining the overarching privacy principles.btad639_Supplementary_DataClick here for additional data file.


Pythium aristosporum has emerged as one of the dominant causal agents of maize stalk rot. However, a previous study of maize stalk rot disease resistance mechanisms and breeding had mainly focused on other pathogens, neglecting P. aristosporum. To mitigate crop loss, resistance breeding is the most economical and effective strategy against this disease. This study involved characterizing resistance in 295 inbred lines using the drilling inoculation method and genotyping them via sequencing. By combining with population structure, disease resistance phenotype, and genome-wide association study (GWAS), we identified 39 significant single-nucleotide polymorphisms (SNPs) associated with P. aristosporum stalk rot resistance by utilizing six statistical methods. Bioinformatics analysis of these SNPs revealed 69 potential resistance genes, among which Zm00001d051313 was finally evaluated for its roles in host defense response to P. aristosporum infection. Through virus-induced gene silencing (VIGS) verification and physiological index determination, we found that transient silencing of Zm00001d051313 promoted P. aristosporum infection, indicating a positive regulatory role of this gene in maize’s antifungal defense mechanism. Therefore, these findings will help advance our current understanding of the underlying mechanisms of maize defense to Pythium stalk rot.Stalk rot, a severe and widespread soil-borne disease in maize, globally reduces yield and quality. Recent documentation reveals that  Zea mays L.) is a vital crop in China with the highest yields, surpassing wheat and rice in acreage. However, owing to the limited arable land area, continuous cultivation of the same maize variety leads to the accumulation of pathogenic microorganisms in the soil. This results in numerous soil-borne diseases that seriously affect the quality and yield, with stalk rot being a particularly devastating and prevalent disease widespread in maize production  . To dateRpi1 , RpiQI31iQI319-2 , and RpipiX178-2 . HoweverOver the last decade, next-generation sequencing (NGS) technologies have revolutionized crop breeding. NGS enables rapid acquisition of a vast number of SNPs throughout the genome, offering greater DNA variation compared to traditional markers. Now, NGS has been widely used for linkage mapping, genome-wide association study (GWAS), marker-assisted selection (MAS), genomic selection (GS), domestication, and population structure analysis in crops . Using tGWAS has recently gained significant attention due to its ability to analyze the genetic basis of complex traits using existing natural populations, making it more time-efficient than traditional linkage analysis. This approach has been successfully applied to crops like rice, maize, and soybean . GWAS enP. aristosporum stalk rot (PASR). The detected significant loci were subjected to bioinformatic analysis to identify candidate genes for disease resistance. Finally, the expression of the candidate genes was verified using quantitative real-time fluorescence PCR (qRT-PCR), and their functions were verified through virus-induced gene silencing (VIGS). Therefore, our study can deepen the understanding of the genetic mechanisms underlying stalk rot resistance in maize. Additionally, it provides valuable research methods and candidate genes for future molecular breeding strategies aimed at improving maize disease resistance.In this study, we investigated 295 maize inbred lines in two environments for disease severity index (DSI) of stalk rot symptom phenotypes. Genotyping-by-sequencing (GBS) technology was used to genotype these inbred lines. Subsequently, we conducted GWAS analysis to identify SNPs significantly associated with resistance to A total of 295 maize inbred lines were assembled into a panel. The panel included 134 China core germplasms and 161 expired US plant variety protection inbred lines . The test materials of the associated panel were planted in Xinxiang  and Dancheng  experimental stations of Henan Academy of Agricultural Sciences in 2021. The field experiment was arranged in a randomized complete block design with two replicates. Each inbred line was grown in two rows with 15 plants in each row, 0.60 m in row spacing, and 0.25 m in plant spacing. Resistant line Qi319 and susceptible line Y478 served as phenotyping controls, respectively.Pythium aristosporum strain T2 was preserved and propagated in our laboratory and cultured on fresh potato dextrose agar (PDA) plates (ca. 20 ml per plate) at 25°C in the darkness for 5–7 days. The inoculum was achieved by homogenization of five plates of flourish hyphal mats (approximately 125 ml) with kitchen blender, adjusting to a final volume of 200 ml with double-deionized sterile water (ddH2O).Three to four leaf-stage maize seedlings were transferred into 250 ml of 1/2 MS media  poured in a 24 × 24-cm culture dish . Maize roots were placed on the surface of the medium before the infection. Then, the root tips were inoculated with 20 μl of the freshly prepared inoculum, and mock-inoculated maize roots treated with PDA plug served as the control. The root systems were covered with a sheet of germination paper wetted with liquid 1/2 MS media and a piece of aluminum foil to maintain moisture and avoid light exposure and contamination from other potential organisms. Finally, the culture dish was covered with a zip lock bag (BioSharp) and transferred into the growth cabinet for another 48 h at 25/20°C (day/night).Roots of the inoculated seedlings were scored at the disease regions at 24 hours post inoculation (hpi) or 48 hpi with a rating grade of 1–5 as previously described .P. aristosporum inoculum. The wounds were sealed with Vaseline after inoculation. Then, the fields or the planting pots were fully irrigated to mimic growth condition favoring fungal growth and stalk rot disease development.Maize plants at the flowering stage or fifth-leaf stage (for target gene-silenced seedlings) were used for stalk inoculation based on a previously described protocol  with modFor the evaluation of stalk rot symptoms, 48 h (seedlings) or 40 days (adult plant) after inoculation, the inoculated internodes of the individual maize plants were split and symptoms were observed with scores of 0, 1, 3, 5, 7, and 9 according to a previously described classification standard  with adjA  = Two parameters were used to evaluate the symptoms of roots or stalks: the first one was SRS2H) . QTL IciMapping software  was used for variance analysis, best linear unbiased prediction of combined phenotype data, and the evaluation of broad-sense heritability (H2) .https://www.openbioinformatics.org/annovar/)   was emplThe principal component analysis (PCA) was used to evaluate the genetic structure using the software genome-wide complex trait analysis (GCTA) .p< 10−5 was used to declare significant associations based on a previous study with optimization  activity (U/g), catalase (CAT) activity (U/g), and superoxide dismutase (SOD) activity (U/g), were measured with the assay kits of Comin Biotechnology Co., Ltd  following the manufacturer’s protocols.2O2 production in plants was spectrophotometrically measured using xylenol orange assay  were mixed for qRT-PCR to determine the transcriptional levels of the maize genes on the CFX96™ real-time PCR detection system . The expression level of GAPDH mRNA was determined and used as an internal control. The relative expression level of each gene was calculated using the 2–ΔΔCt method  and then treated with RNase-free DNase I . The first-strand cDNA was synthesized using 2.0 μg of total RNA per 20 μl reaction and an oligo (dT) primer. Tenfold diluted cDNA, a set of gene-specific primers . Only the vector harboring foreign gene fragment inserts without changing the stability of BMV RNA3 was chosen for subsequent analysis -derived VIGS vector  was usedeviously .P. aristosporum. Systemically infected maize leaves  from BMV-d051313 or BMV-GFP inoculated plants were harvested from individual plants at 7 and 10 days post inoculation (dpi) and subjected to qRT-PCR to evaluate the efficiency of gene silencing.The third leaves of the three-leaf-stage Va35 plants were rub-inoculated with approximately 20 μg of partially purified BMV virion. More than 20 seedlings were used for each treatment, and the inoculated plants were grown inside a growth chamber at 18/20°C (day/night) for 7–10 days before being challenged with P. aristosporum. P. aristosporum among the 295 inbred lines in the two experimental environments, the overall trend remained consistent. Furthermore, we observed variations in performance between the inbred lines from China and United States  and 141 (47.8%) accessions, respectively, showed moderate resistance, resistance, or high resistance to stalk rot. In Xinxiang, 253 (85.76%) inbred lines were susceptible and highly susceptible, and 52 (17.63%) were highly susceptible. However, in Dancheng, these percentages were 154 (52.2%) and 9 (3.05%), respectively (p< 0.01) among the environment (E), genotype variation (G), and G*E (genotype-by-environment interaction), suggesting that resistance traits to stalk rot are controlled by multiple genes and can be localized for further analysis by GWAS  ranged from 16.67% to 100%, suggesting the abundant phenotypic variation in this panel. In a single environment, the broad-sense heritability of Xinxiang was the highest (0.86), followed by Dancheng (0.84), and the combination of both had the lowest (0.68). Regarding phenotype traits, the heritability of a combination environment was lower than that of a single environment. The experiment revealed significant correlations  > 5 as the screening threshold for the best significantly associated SNPs, we identified a total of 39 significantly associated SNPs in two environments using the six models  for GWAS analysis. p = 9.67E-06), FarmCPU (p = 9.67E-06), GLM (p = 3.61E-06), MLM (p = 3.36E-06), MLMM (p = 1.42E-06), and SUPER (p = 1.60E-06) models. Additionally, we detected another SNP (S6_121809944) under the remaining five models, except the SUPER.Among the 39 significantly associated SNPs, the SUPER model identified the most significant SNPs (28), followed by the GLM model (17), then MLMM, MLM, Blink, and FarmCPU with eight, five, two, and two SNPs, respectively Figure 4Zm00001d051313, Zm00001d051314, and Zm00001d051315) associated with S4_153228905 were jointly detected by six models and the MAF value is 0.095 . We performed functional annotation of the candidate genes using the UniProt database (Zm00001d051313 and Zm00001d051314 were annotated as “leucine-rich repeat (LRR) family protein” and “putative receptor-like protein kinase” in the functional database, respectively. Both genes were potentially associated with plant response to pathogenic microbe infestation. The LRR family encodes LRR-RLKs that were directly related to disease resistance in various crops  and found that the Signal peptide (Sec/SPI) value of this protein is 0.9847. This indicates that the signal peptide is very likely to be present with a cleavage site at position 26–27 and a probability of 80.74%  encodes a potential LRR-RLK protein, which is a major class of PRRs modulating plant defense mechanism positively or negatively depending on the interaction  genes like ZmPR3, ZmPR4, ZmPR10, and ZmPR10 via qRT-PCR. The data showed a ~61%–84% reduction in the expression levels of ZmPR3, ZmPR4, ZmPR5, and ZmPR10 in the Zm00001d051313-silenced seedlings compared to the BMV-GFP controls , POD (peroxidase), and SOD (superoxide dismutase)  to identify significant SNPs associated with stalk rot resistance. We identified four SNPs  associated with maize resistance to inflatum . A previinflatum . In thisZm00001d051313 revealed it as an encoded LRR family protein and localized to the cell membrane with a signal peptide structure , one of the important regulators of jasmonate signaling, was induced significantly in response to Puccinia polysora Underw (ZmJAZ2 (ZIM16) to the promoter region of Zm00001d051313  are deployed to sense conserved molecules derived from microbes, called pattern-associated molecular patterns (PAMPs), and molecules released by plant cells upon pathogen invasion . The rectalk rot . Jasmonia Underw . Analysi1d051313 , suggestZm00001d051313 that is located on chromosome 4 and significantly associated with stalk rot resistance by GWAS analysis. We used VIGS to knock down the expression of Zm00001d051313 in maize, verifying its function as a stalk rot tolerance gene. Our results demonstrated that Zm00001d051313 is an LRR-RK gene that positively regulates stalk rot resistance in maize plants. Although it has important reference value for breeding resistant varieties in the future, the molecular mechanism by which this happens remains obscure; thus, the identification of the direct interaction of Zm00001d051313 with its dimerization partners will help elucidate the underlying mechanism of its function.In summary, we successfully identified the gene 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/YC, CD, and HL conceived and designed the study. MH, JY, SH, and TJ performed the experiments. MH, XZ, and SZ performed bioinformatics analysis. YC, MH, and XZ analyzed the data. LW, JL, LZ, HW, and XW participated in some experiments and provided help for the phenotypic measurement. YC, MH, and XZ wrote the manuscript. YC, CD, and HL revised the manuscript. All authors contributed to the article and approved the submitted version.