Most ant species have two distinct female castes, queens and workers, yet the developmental and genetic mechanisms that produce these alternative phenotypes remain poorly understood. Working with a clonal ant, we discovered a variant strain that expresses queen-like traits in individuals that would normally become workers. The variants show changes in morphology, behavior, and fitness that cause them to rely on workers in wild-type colonies for survival. Overall, they resemble the queens of many obligately parasitic ants that have evolutionarily lost the worker caste and live inside colonies of closely related hosts. The prevailing theory for the evolution of these workerless social parasites is that they evolve from reproductively isolated populations of facultative intermediates that acquire parasitic phenotypes in a stepwise fashion. However, empirical evidence for such facultative ancestors remains weak, and it is unclear how reproductive isolation could gradually arise in sympatry. In contrast, we isolated these variants just a few generations after they arose within their wild-type parent colony, implying that the complex phenotype reported here was induced in a single genetic step. This suggests that a single genetic module can decouple the coordinated mechanisms of caste development, allowing an obligately parasitic variant to arise directly from a free-living ancestor. Consistent with this hypothesis, the variants have lost one of the two alleles of a putative supergene that is heterozygous in wild types. These findings provide a plausible explanation for the evolution of ant social parasites and implicate new candidate molecular mechanisms for ant caste differentiation. et al. report a variant strain of the clonal raider ant that expresses queen-like traits in individuals that would normally become workers. These obligately parasitic variants have a mutated supergene and arose directly from their free-living parent strain, suggesting a genetic mechanism for the evolution of ant workerless social parasites.Trible  The dev5. All workerless social parasites are obligate inquilines: they live inside host colonies that they exploit for food, shelter, and brood care, and they possess a suite of derived phenotypes, known as the inquiline syndrome, that facilitate this lifestyle9. Workerless social parasites exploit the colonies of closely related ants, which they resemble chemically and morphologically. In fact, the initial host of many workerless social parasites may be the free-living forms of their parental populations12. If we assume that complex parasitic phenotypes evolve gradually via genome-wide selection, the close phylogenetic relationship between parasites and their hosts implies that a newly emerging parasite must quickly develop reproductive isolation to evolve into a highly specialized, obligate workerless social parasite16.One of the most extreme forms of caste evolution occurs in the workerless social parasites that feature a modified and miniaturized queen morphology and loss of the worker caste , A and B10. He argued that facultative parasitism always evolves before obligate parasitism and the loss of the worker caste. Under this scenario, nascent parasites must be able to survive independently from their host/ancestor so that they can speciate in allopatry and then re-encounter the host and evolve into an obligate parasite. Wilson incorporated Ernst Mayr’s views of speciation into his framework, but earlier researchers, including William Morton Wheeler and Charles Darwin, also proposed that obligate ant social parasites evolve from facultative intermediates11. This facultative hypothesis predicts that any obligate parasites should be phylogenetically nested within a clade of facultative parasites, rather than being sister to a free-living ant. However, recent molecular phylogenetic evidence implies that most workerless social parasites arise directly from free-living ants, rather than evolving from facultative parasites as Wilson’s theory proposes4.In 1971, E.O. Wilson proposed a mechanism for the evolution of workerless social parasites based on the assumption that parasites cannot sympatrically speciate from their host25. These discoveries raise the alternative possibility that the suite of traits necessary for workerless social parasitism can be conferred by the inheritance or transformation of a single genetic locus, or module26. Under this scenario, a workerless social parasite could emerge as an intraspecific genetic variant within a free-living population due to the action of a single locus, while gene flow across the rest of the genome gets disrupted only secondarily. Therefore, the discovery of a pleiotropic genetic module that confers a viable parasitic phenotype could provide a solution to the mystery of the rapid, parallel, and sympatric evolution of ant workerless social parasites.In recent years, researchers have discovered that many complex intraspecific polymorphisms are conferred by discrete genetic modules, including sex chromosomes, social chromosomes, and other supergenesOoceraea biroi, is a queenless, asexual ant species in which wild-type (WT) individuals have attributes typical of wingless worker ants  variant.To test whether this phenotype arises via genetic variation or phenotypic plasticity, we isolated winged individuals and wild-type controls for rearing experiments that featured cross-fostering of the two types. Eggs from winged individuals reared by WTs developed into winged individuals with 100% penetrance. In contrast, WT eggs reared by winged individuals invariably developed into WT adults . Further5. Some ant lineages display reduced phenotypic plasticity and exhibit a genetic bias for queen development31. For instance, a ‘cheater’ genotype with queen morphology has been reported from a different queenless clonal ant species , but do not alter the association between overall body size and adult caste morphology31. This mechanism thus results in phenotypically normal queens that are larger than workers.In principle, there are two developmental genetic mechanisms by which a queen-like morphology could re-appear in a queenless ant species  for two QLMs and 15 WT Line A adults and used this dataset to create a whole-genome molecular phylogeny . QLMs arO. biroi is parthenogenetic, and we are therefore not able to perform crosses for a traditional association study. However, we can assume that all QLMs have the same genotype at any loci that are causal to the phenotype, and that this causal genotype is not found in any WT Clonal Line A genomes. Of 168,819 single-nucleotide polymorphisms (SNPs) in our short-read dataset, only 400 SNPs differed consistently between QLMs and WTs. Interestingly, 364 of these SNPs (91%) mapped to the second-smallest chromosome in O. biroi, Chromosome 13 . The 9 de novo SNPs that distinguish QLMs from colony C1 WTs therefore imply that the QLMs originated in our lab culture only a few generations ago. In light of this recent origin and the very small number of mutations that distinguish the QLM lineage from WTs  and harbors at least one large inversion  genes   genes . The CYP groups) . CYPs plP9 genes . The los of CYPs . In ants56. In other organisms, genetic polymorphisms that reduce or eliminate novel developmental programs  are evolutionarily common58. Early population geneticists assumed that highly pleiotropic genetic variants are generally harmful, but more recent theory suggests that pleiotropic variants, including alleles introgressed via hybridization, can accelerate phenotypic evolution60. Our research adds to the evidence that even complex phenotypic syndromes can have a modular genetic and developmental organization that enables rapid evolution63.Ant queens have fully formed reproductive, visual, and flight systems that are similar to those of their wasp ancestors, while ant workers develop according to a modified program that leads to reduced growth of these tissues during metamorphosis64, may follow the evolution of obligate parasitism, rather than precede it. However, there are many different paths a free-living species can take to become an obligate parasite4. Our conclusions apply specifically to the evolution of workerless social parasites because QLMs and workerless social parasites share two characteristics that are not found in any other parasitic forms: the loss of the worker caste, and a major shift in allometric scaling that produces queens roughly the size of workers in the ancestral population. Other forms of social parasitism, such as brood raiding, nest usurpation, and xenobiosis, are not comparable to workerless social parasites or QLMs.The QLMs arose from WTs just a few generations before we discovered them in our laboratory colonies. We did not observe any intermediate phenotypes in the source colony, further supporting the conclusion that the QLM phenotype was not gradually assembled across multiple successive generations of clonal evolution. Instead, it seems that the phenotype was induced in a single genetic step: a WT parent produced a daughter with a phenotype resembling that of workerless social parasites. In a sexual ant species, an analogous event could immediately give rise to obligate workerless parasitism without the prior emergence of genome-wide genetic isolation. This raises the possibility that further divergence, including sympatric speciation and morphological, physiological, and behavioral aspects of the inquiline syndrome that are not present in QLMs62. This suggests the possibility that Chromosome 13 contains a supergene that confers social parasitism. Relative to the recent origin of the QLMs, this putative supergene must be ancient, and likely segregated in the ancestral sexual population in the form of a recessive Mendelian polymorphism for free-living and workerless social parasite variants. For example, this population could have possessed a recessive workerless allele, such that W/W and W/w are free-living wild-types, while w/w are QLMs. Line A WTs would then be heterozygous for this supergene (W/w), and the loss of heterozygosity produced a variant strain that is homozygous recessive (w/w) along some or all of the supergene.The QLMs possess a 2.25Mbp loss of heterozygosity that could have produced the complex QLM phenotype, either by containing a single causal allele or a group of causal alleles in linkage. This loss of heterozygosity is located on Chromosome 13, which is structurally similar to the non-recombining social chromosomes of other ant speciesO. biroi is parthenogenetic, so we cannot test for two key features of supergenes: Mendelian inheritance of the QLM phenotype and a suppression of recombination between alternative haplotypes. However, several pieces of evidence support a supergene origin for the QLM phenotype. First, Chromosome 13, unlike any other chromosome in the O. biroi genome, has characteristics that suggest recombination was suppressed in the ancestral sexual population: a heteromorphic karyotype, at least one large inversion, an enrichment of structural variants and TEs, and a proportional reduction of exons .Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Waring Trible  are available at https://github.com/triblelab/qlm.https://github.com/triblelab/qlm.Original code is available at Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.Ooceraea biroi colonies were reared as reported previously65. Briefly, colonies were maintained at 25°C in circular Petri dishes  with a plaster of Paris floor ca. 4 mm thick. Colonies were fed 3 times weekly with fire ant (Solenopsis invicta) brood and cleaned and watered at least once per week. In the context of this manuscript, wild-type (WT) always refers to phenotypically normal ants derived from Clonal Line A of O. biroi28. For penetrance and larval survival, colonies of 100% WT or 100% QLM adults (assessed by phenotype) were established without brood. Eggs were removed, allowed to hatch into larvae on glass slides, and then fostered into units to be reared by 16 WT or 16 QLM adults .cytochrome oxidase I (COI) was performed using primers as reported previously28. Amplification was performed in a final volume of 12 μL using Applied Biosystems’ AmpliTaq Gold Kit following manufacturers’ recommendations. Cycling profiles started with a denaturation at 94°C for 10 min, and then proceeded with 40 cycles of 94°C for 30 s, 55°C annealing for 30s, 72°C extension for 30 s, finally followed by an extension of 72°C for 10 min. Microsatellite reagents and cycling profiles followed a previous report28. Fragment analysis was performed by GeneWiz . Microsatellite loci were scored using Peak Scanner (Applied Biosystems).PCR amplification of 66. Fifteen measures were used for morphometric analysis, extracted from side view, top view, and head view images. Side view measures were: eye length, head length, head height, Weber’s length, mesosoma height, and length of 1st gastral segment  while controlling for age and rearing environment. Colonies used for experimental treatments were always matched to control for cohort variation among the chaperone ants.39. Briefly, ants were maintained in a two-part dish with a 2cm diameter nest connected by a narrow opening to a 6.5cm diameter foraging arena. Ants were placed in the nest  without brood and allowed to lay eggs. Once these eggs hatched into larvae (approximately 14 days later), the seal was removed to allow foragers to leave the nest, and food was placed in the foraging arena. Videos of foraging behavior and subsequent food retrieval were recorded and manually annotated as reported previously39. Colonies were fed, watered, and used for behavioral recordings every day until the larvae entered the prepupal stage (approximately 14 days after initiating recordings). This yielded approximately 10–15 foraging raids per colony. Small numbers of ants that died during the course of the experiment were removed the following day.Foraging experiments were conducted as reported previouslyAll behavioral experiments consisted of colonies established using 16 ants that were painted with color tags to make it easier to identify their genotype in the video recordings. Ant colonies were established using a strict paired design, such that every group of QLM ants was paired to a group of WT ants that was produced by the same rearing unit and subsequently reared in an identical manner. Ants ranged from 14 days to 2 months old at the start of experiments, and ages were matched within each pair. Pure colonies were all QLM (n = 4) or WT (n = 4). Mixed colonies (n = 8) were established with 8 QLMs and 8 WTs.Egg-laying rates were measured from five replicate QLM and WT colonies that were matched for age, colony size, and rearing environment. Egg to pupa survival and eclosion survival were measured by establishing colonies with WT chaperones and varying proportions of QLM and WT brood.st instar, 2nd instar, 3rd instar, 4th instar, prepupal stage, pupal stage, and eclosion). Overall survival from larva to adult was scored at the end of the experiment.Efficiency of larval rearing was measured using colonies with 20 WT or QLM chaperones established with <24h old eggs. When these eggs hatched into larvae, colonies were reduced to 16 first-instar larvae. Developmental transitions were scored as the date that the majority of surviving individuals had entered the respective developmental stage  were removed from the dataset. Because read depth differs among samples due to minor differences in library concentration during loading onto the Illumina flow cell, the normalized read depth was calculated by dividing the read depth at each SNP by the median read depth across all SNPs. Next, the mean normalized read depth was calculated for SNPs within each contig.Read depth distributions can contain excessively high and low values due to errors in genome assembly. These values can augment or diminish estimates of mean read depth across genomic regions. To avoid these effects, read depth was calculated using samtools depthhttps://github.com/edgardomortiz/vcf2phylip), such that heterozygous sites were represented using IUPAC uncertainty codes. These files were then used to create a diploid-aware phylogeny with the -GENOTYPE setting in RAxML-NG72. RAxML72 was then used to estimate bootstrap support using the model -GTGTR4+G with 100 replicates.For molecular phylogenetics, whole-genome SNP calls (excluding sites with missing data in any library) were converted from vcf to fasta using vcf2phylip (https://github.com/triblelab/qlm).Sites that distinguished the QLMs were defined as sites where both QLM libraries had the same genotype and differed from all 15 WT libraries. These sites were identified using a custom R script  and alternate  assemblies. These assemblies were phased with published Hi-C data45 using Falcon-phase74, and then scaffolded using the Obir v5.4 genome assembly using RaGOO75. Finally, this primary assembly, which we refer to as the Obir v5.6 assembly, was polished and masked as described previously45.To improve the assembly of Chromosome 13, we re-analyzed published data using the Falcon pipeline. First, we ran Falcon-unzipO. biroi following a published protocol76 and sequenced two barcoded libraries using the Oxford Nanopore PromethION platform. Raw reads were assembled de novo using Flye (version 2.8.2)77 and aligned to the Obir v5.6 contigs using Mauve (snapshot 2015–02-25)78. This allowed us to manually re-order and re-orient the contigs of v5.6 by aligning them with the contigs of the two de novo assemblies as well as the Obir v5.4 assembly, yielding an improved Obir v5.7 assembly. Assemblies v5.6 and v5.7 do not differ in sequence content, but only in the position and orientation of Chromosome 13 contigs  to identify TEs belonging to known transposon classes from published genome assemblies . To quantify gene content, we used the entire set of exons from the NCBI annotation of Obir v5.4 as a proxy (https://www.ncbi.nlm.nih.gov/genome/annotation_euk/Ooceraea_biroi/100/). We excluded predicted TE sequences that overlapped with annotated exons and exonic sequences that overlapped with predicted TEs from the analyses. We calculated the proportion of each scaffold comprised of each feature as the number of base pairs occupied by predicted TEs or annotated exons divided by the ungapped length of each scaffold in base pairs.To document transposable element (TE) content in O. biroi annotation v5.445 to identify genes within those contigs. Genes in the loss of heterozygosity region were then tested for GO and KEGG enrichment using a custom R script (https://github.com/triblelab/qlm).To identify genes in the loss of heterozygosity region, we used the Mauve alignments (above) to identify contigs in the v5.4 genome assembly that aligned to the loss of heterozygosity region. We then used the published NCBI O. biroi CYP9 genes were initially identified using a BLAST search using an exemplar protein (O. biroi CYP9_1_1). These results were then curated using the NCBI genome browser and amino acid sequence alignments to ensure that each gene name refers to a single enzyme. Every gene we report here is transcribed and contains intact CYP functional domains, implying that they are not pseudogenes. However, some CYP9 genes lack start codons and may be transcribed and translated with their 5’ neighbor. The spatial array of CYP9 genes was created in Adobe Illustrator using start and stop coordinates of each exon. The phylogeny of CYP9 genes was generated using RAxML. This phylogeny was also used to identify and remove proteins annotated as CYP9s that actually belong to the CYP6 subfamily.CYP9 conservation and expansion, we obtained published CYP9 amino acid sequences from Apis mellifera and Nasonia vitripennis52, and the procedure described above was repeated for CYP9 proteins in three additional ant species that possess high-quality genomes and span the ant phylogeny . Ant CYP9 enzymes were identified using a BLAST search with O. biroi CYP9_1_1 against the genome of each of the four ant species. All identified sequences were downloaded, and duplicates and sequences that fall phylogenetically outside of the insect CYP9 clade (as defined previously52) were removed. Note that 12 CYP9 genes occur in the loss of heterozygosity region according to the Obir v5.4 genome assembly and annotation, but manual re-annotation of this assembly identified 24 CYP9 genes in this region (https://github.com/triblelab/qlm.To quantify s region . Amino aDATA AND SOFTWARE AVAILABILITY and METHOD DETAILS.Statistical analyses were performed using Graphpad’s Prism 7, unless otherwise stated, and the details of statistical tests can be found in the figure legends. Code and packages used for genomics analyses are reported in 1Data S1: Definitions of caste terminology and ant caste evolution, related to 2Data S2: Raw morphometric dataset and principal component values, related to3Data S3: Supporting information for genomic analyses, related to45Video S1: Exemplar WT foraging raid, related to6Video S2: Exemplar QLM foraging raid, related to


There is a long-standing debate about the magnitude of the contribution of gene-environment interactions to phenotypic variations of complex traits owing to the low statistical power and few reported interactions to date. To address this issue, the CHARGE Gene-Lifestyle Interactions Working Group has been spearheading efforts to investigate  While it is commonly agreed that complex traits are influenced by genetic and environmental factors and their interactions 9, there is a long-standing disagreement about the magnitude of the 11. As pointed out in 12, arbitrarily defined parameterizations of genetic effects with non-additive gene actions may explain the same degree of genetic variation as the currently prevailing additive model. Thus, while using additive genetic models such as polygenic risk scores to predict individual quantitative or qualitative phenotypes has become standard 5, these models may not be fully informative in understanding genetic architecture.Current genome-wide association studies (GWAS) focus on detecting genetic variants that lead to different phenotype means across genotype groups 13. Theoretical studies have demonstrated that a significant portion of variance can be explained by an additive model even when the genetic contribution to a phenotype is purely through 14. This limitation is one of the key factors explaining the low power of approaches modeling interactions conditional on additive variance. As a result, studies focusing on detecting 15, where only a modest number of genetic loci have been identified through testing for 19. The joint test limits our ability to determine to what degree the currently identified loci reflect evidence for Interactions are often studied secondarily in comparison to additive variance, whose advantage is to explain most of the correlations among relatives and fit natural selection models well 21. Although MR approaches have been used to explain 22 and assess risk factor interactions23, the underlying connection between testing pleiotropic variants in the MR framework and the detection of 25 can be used for detecting 3 and the summary statistics in the interaction analysis with cigarette smoking and alcohol drinking in the CHARGE GLI working group 17, with replication using direct interaction tests performed in the UK Biobank (UKBB) data. Although the UKBB data accounted for about one third of sample in the GLGC consortium, theoretical work suggests that such replication is statistically independent  has been developed and widely applied to study causal relationships between risk exposures and outcomes in the post-GWAS era ependent .Y is modeled through a linear regression:Traditionally a genome wide interaction study (GWIS) with an environmental exposure on a quantitative trait direct test statistic 14. However, the power of the direct test is usually low because of the collinearity between 14.where In practice, a GWAS is routinely conducted first when studying the genetic contribution to a trait, which is typically done through a linear regression model without including environmental factors, i.e.,G × E and mediation, and further reduces to testing for the 17, from which the summary statistics of the marginal, main, and interaction effects are available and the marginal effect was obtained after adjusting for 27. Therefore, directly using where we refer to ng for E . HoweverTo overcome this bias, we note that the marginal effect estimate 24. In doing so, we first select a set of independent variants associated with trait 28. The statistic where ronments . The genhe trait . We thenhrough E .Note that ependent , and theWe first performed a series of simulations to investigate the type-I error rate and power of ediction . The intunbiased , althougntrolled ). The pois lower . AdditioWe next investigated the performance of s absent . When meerlapped . This in present .-7 reported in the early GWAS of cigarette smoking status or alcohol drinking 29, which represent relatively large effect sizes of variants on cigarette smoking and alcohol drinking. We observed that We applied the two-step procedure to search for genetic variants interacting with cigarette smoking and alcohol drinking for serum lipids, using the summary statistics of high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG) from the GLGC (n=1.65M) and the CHARGE GLI (n=134K). To mitigate the effects of mediation through cigarette smoking or alcohol drinking, we excluded all loci with P-value <5×10ectively . The dep-8, r2 < 0.1), including 4 and 5 loci for LDL-C, 7 and 5 loci for HDL-C, and 5 and 6 loci for TG, interacting with cigarette smoking and alcohol drinking or mediating through them, respectively . Tissue enrichment analyses using GWAS-based pathway analysis tools, MAGMA31 and FUMA 32, suggest that these loci are enriched in liver, hippocampus, small intestine, and stomach tissues , 5 of which were genome wide significant by the LPL, which is not significant in UKBB  with a variant which has causal effect on cigarette smoking or alcohol drinking. To examine this, we searched if there exists a variant(s) at each of the loci in We next aimed to understand if the interaction evidence observed in this study had an alternative explanation 35. Note that this heritability is a lower bound of the phenotype variance contributed by the -3) with cigarette smoking and alcohol consumption to lipids traits, ranging from 1.76%  to 14.05%  of SNP heritability estimated by the marginal effects (Since  effects .3 after excluding the variants in 3. We compared the correlations and observed a median of 24.4% drop of the cross-population correlation coefficient (As noted in equation (3), the marginal effect estimate of a genetic variant in GWAS consists of the fficient , strongl36, but the new two-stage procedure is robust because To the best of our knowledge, this study is the first to utilize marginal effects from GWAS to search for 29 in our analysis, which mitigates the potential mediation contribution in the 37 that is defined by additive genetic actions without the inclusion of Our study demonstrated that the current heritability estimates based on marginal effects also include significant contributions from  factors . We excl38. Among them, the interaction between alcohol consumption and cholesteryl ester transfer protein (CETP) has been reported for HDL-C and coronary artery disease 41. The interaction between alcohol consumption and APOE on LDL-C has also been reported in a Mediterranean Spanish population 42, while the interactions between APOA5 and cigarette smoking and alcohol drinking status associated with elevated TG and reduced HDL-C were observed in the Chinese and Korean populations 44. However, our study is the only well powered study demonstrating significant evidence at the genome wide level and the interaction loci are replicable. SMARCA4 was reported to be associated with LDL-C in the lipids GWAS in Africans 45 but not in the recent largest lipids GWAS which is predominantly European ancestry 3. Overall, the marginal effect sizes of the variants are less correlated across populations for the variants with 46. We expect that including The 5 replicated loci  interacting with cigarette smoking or alcohol consumption contain genes that are enriched in liver tissue, possibly reflecting the effect of alcohol drinking on aspartate amino transferase, alanine aminotransferase and γ-glutamyl transferase activities via the actions of numerous ingredients that alter the activities of enzymes found in the liver  without , empiric24 and, in particular, it is expected that most of the IVs do not interacted with the environmental factor E.It is well known that causal effect estimates in the MR framework can be biased when any of the three IV assumptions are violated. However, the MR-based In summary, our novel 3 were downloaded at http://csg.sph.umich.edu/willer/public/glgc-lipids2021. GLGC consists of GWAS results from 1.65M subjects representing five genetic ancestry groups: European (N = 1.32M); African or admixed African (N = 99K); East Asian (N = 146K); Hispanic (N = 48K); and South Asian (N = 41K). We did not perform South Asian specific analysis because there was no corresponding GWIS in the CHARGE consortium. The GWIS summary statistics from CHARGE GLI working group in this study are available via dbGaP (accession number phs000930).The marginal summary statistics of high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG) from the Global Lipids Genetics Consortium study  29.To perform MR analysis, we merged the GWAS summary statistics for HDL-C, LDL-C, and TG from the GLGC with the corresponding GWIS summary statistics from the CHARGE GLI consortium. We flipped the effect size if the corresponding reference allele did not match. We dropped the genetic variant if its two alleles were either {A, T} or {C, G}. We also excluded any variants with minor allele frequency (MAF) difference larger than 0.15 between GLGC and CHARGE GLI studies. If multiple variants fell on the same chromosome position, we required the matched variants with MAF difference less than 0.01. We further excluded any variants with the effective sample size in GLGC trans-ethics or European less than 100K and the other populations  less than 30K. To reduce the effect by mediations through the smoking and alcohol drinking, we excluded all loci with P-value < 5E-7 identified by the GWAS of smoking status or alcohol drinking24 to estimate the causal effect by considering the main effect sizes from the GWIS of the CHARGE gene-lifestyle consortium as the exposure effects, and the marginal effects from the GLGC as the outcome effects, respectively. To identify instrumental variables, we first selected all the variants with the P-value < 5E-8 after GC-correction in the GLGC, and then pruned them using the window size 500 KB and r2 value 0.1 by the Plink software 47. We standardized the effect sizes as in 28. IMRP requires the input of the correlation coefficient to account for the effect of sample overlapping between GWAS and GWIS cohorts and this correlation was calculated based on the variants with P-value > 0.05) across the genome48. After estimating the causal effect, we performed To perform r2 < 0.1. The 1000G data was used as the reference genetic data for LD calculation.Independent loci were defined as the regions within 1Mb of the most significant variants by the By applying ectively . At a lo35 to estimate heritability contributed by 49 to estimate LD scores with the 1000G Phase 3 reference data and then estimated the chromosome-specific heritability with default settings. We observed that some of the chromosome specific heritability estimates were negative. We only summed the non-negative chromosome specific heritability estimates.We applied the LD score regression31  and DEPICT 50 (Data-driven Expression Prioritized Integration for Complex Traits) to identify tissues and cells that are highly expressed at genes within the We performed overall enrichment tests using the residual 51. We chose the public genotype-tissue expression (GTEx) v7 with eQTL 33 as the QTL data and chose the public European reference panels for calculating the LD data. We performed colocalization analysis between GWIS and QTL results within a locus using eCAVIAR  52, where the Colocalization Posterior Probability (CLPP) was used to describe the significance level of colocalization. We only recorded colocalization with CLPP > 0.01, as suggested by the authors of eCAVIAR.We performed colocalization analysis by using the software ezQTL53 individual-level data used for replications were available through Application ID: 81097. Quality Controls Participants in the UKBB were genotyped using a custom Affymetrix UK Biobank Axiom array 54. Genotypes were imputed by the UKBB using the Haplotype Reference Consortium reference panel 55 with imputation r2 value greater than 0.3. Related individuals with pairwise kinship coefficient greater than 0.0884 (suggested by UKBB) were removed from analysis, resulting in N = 445,424 individuals of European, African, and East Asian ancestries. For a pair of related individuals, one was randomly excluded. The principal components were calculated by UKBB with genotype data within each ancestry to account for population structure. These data were independent of GLI cohorts and consisted of European, African, and Asian individuals (race determined using UKBB field ID 21000–0.0). Linear regression model (1) in the main text was performed. Covariates included age at assessment (21003–0.0), age2, sex (31–0.0), the first 10 PCs (22009–0.1 to 22009–0.10), the environmental exposure, a genetic variant and their interaction. Environmental exposures included ever/never smoking status (20116–0.0), current/non-current smoking status (20116–0.0), and alcohol intake frequency (1558–0.0).The UK Biobank (UKBB) 17, HDL-C (30760–0.0) and TG (30870–0.0) measurements were natural log transformed and LDL-C measurements (30780–0.0) were converted from mmol/L to mg/dl then multiplied by a factor of 0.7 if there was a history of lipid-lowering medication (6177–0.0) present. LDL-C measurements therefore did not consider medication if there were missing values for medication history. This introduced missing values in LDL-C for 248,419 individuals.Analogous to the In MR analysis, the instrumental variables are independent and are genome wide significant variants selected from GWAS. Let The causal effect It is much simpler to work on Since only the variants without either By applying Slutsky’s theorem, and let Because which converges to 1 when We propose E can be performed by the IMRP method in MR analysis24.for testing the combined effect of Our procedure to test for the For the 𝑖 th individual, we generated 𝑚 = 102 independent variants for The main effect size of the 𝑗th variant was generate by where We generated 20 independent variants by No mediation and no interaction: Mediation but no interaction: Mediation and interaction: We let mean 0.5 . We firsSupplement 1


Legumes are extremely valuable because of their high protein content and several other nutritional components. The major challenge lies in maintaining the quantity and quality of protein and other nutritional compounds in view of climate change conditions. The global need for plant-based proteins has increased the demand for seeds with a high protein content that includes essential amino acids. Genome-wide association studies (GWAS) have evolved as a standard approach in agricultural genetics for examining such intricate characters. Recent development in machine learning methods shows promising applications for dimensionality reduction, which is a major challenge in GWAS. With the advancement in biotechnology, sequencing, and bioinformatics tools, estimation of linkage disequilibrium (LD) based associations between a genome-wide collection of single-nucleotide polymorphisms (SNPs) and desired phenotypic traits has become accessible. The markers from GWAS could be utilized for genomic selection (GS) to predict superior lines by calculating genomic estimated breeding values (GEBVs). For prediction accuracy, an assortment of statistical models could be utilized, such as ridge regression best linear unbiased prediction (rrBLUP), genomic best linear unbiased predictor (gBLUP), Bayesian, and random forest (RF). Both naturally diverse germplasm panels and family-based breeding populations can be used for association mapping based on the nature of the breeding system (inbred or outbred) in the plant species. MAGIC, MCILs, RIAILs, NAM, and ROAM are being used for association mapping in several crops. Several modifications of NAM, such as doubled haploid NAM (DH-NAM), backcross NAM (BC-NAM), and advanced backcross NAM (AB-NAM), have also been used in crops like rice, wheat, maize, barley mustard, etc. for reliable marker-trait associations (MTAs), phenotyping accuracy is equally important as genotyping. Highthroughput genotyping, phenomics, and computational techniques have advanced during the past few years, making it possible to explore such enormous datasets. Each population has unique virtues and flaws at the genomics and phenomics levels, which will be covered in more detail in this review study. The current investigation includes utilizing elite breeding lines as association mapping population, optimizing the choice of GWAS selection, population size, and hurdles in phenotyping, and statistical methods which will analyze competitive traits in legume breeding. The term legume originated from the Latin word “legumen”, which denotes “seeds harvested in pods”. During the Neolithic Revolution, which marked the beginning of human farming methods, farmers were accompanied by legumes that belong to the family Fabaceae. It is acknowledged that inadequate protein-energy intake and micronutrient deficits are two of the primary causes of undernutrition. Legumes play a minor but significant role in our food system. They are the superior economical dietary solutions due to their rich protein content (17%–30%) and relevant nutritional richness compared to expensive food sources containing animal-based protein and dairy products that may be difficult to obtain in situations where there is food insecurity .2O) and carbon dioxide (CO2). In addition, they can help to reduce the utilization of fossil-based energy inputs in the chain of agriculture and food production by infusing high-quality organic matter, facilitating nutrient circulation, and promoting water retention in the soil  is an effective technique for determining the genes underlying a particular trait. To accomplish this, it is ideal to assess the genomic regions where genotypic and phenotypic variations are correlated with each other. In comparison to standard biparental populations, GWAS offers greater mapping precision for detecting interactions among molecular markers and desirable characteristics in a variety of crops . It has Furthermore, improvements can be applied in fields like omic-wide association studies, which utilize GWAS techniques to analyze relationships among desirable morphological traits and other kinds of omics data that include transcriptomic or metabolomic. GWAS requires structuring the population of diverse panels to estimate genetic distinction and minimize the detection of spurious connections . BreederAssociation mapping (AM), an alternative to QTL mapping, is dependent on linkage disequilibrium (LD) and uses collections of genotypes with known or unknown ancestry that have a significant degree of genetic variation due to hundreds of recombination cycles. The ultimate goal of association studies is to find a strong correlation between a genome-wide DNA marker and an interesting attribute that can be highly useful in marker-assisted selection for crop development. GWAS and candidate gene (CG)–based analysis are two important approaches to AM.The creation of a mapping population that will be tested for the marker–trait relationship is a prerequisite for the GWAS. Both broad-based natural populations and narrow-based breeding populations can be utilized as the mapping population for GWAS Figure 1Any naturally occurring panmictic population with a significant history of recombination events can undergo AM. Utilizing hundreds of recombination events makes it simple to do an LD analysis of the target characteristic. These populations, however, are not appropriate for QTL mapping. When a germplasm accession collection represents the natural population, it may be a core collection or a sample that is more resilient to environmental changes. The population is excellent for assessing the QTLs for rare alleles that can help develop elite breeding lines or highly heritable domestic features. QTLs for some agronomically key characteristics have been uncovered in germplasms of several crops using GWAS, such as in 135 pea accessions , 366 sesThe cultivars and lines created by a deliberate breeding program are known as elite inbred lines. These lines are unbreakable and can be maintained by numerous researchers in various places to identify QTLs using an AM panel. For instance, two AM panels of maize having 306 dent corn and 292 European flint corn inbred lines were individually assessed using single-nucleotide polymorphism (SNP) markers in the cold and control growth chamber conditions to identify genes related to cold tolerance . For GWASalix sp.) identified several major QTLs along with QTL hotspots (Recombinant inbred lines (RILs) and Near Isogenic Lines (NILs) are the most used biparental population, usually used for linkage mapping or Quantitative Trait Locus (QTL) or QTL mapping. Whereas the power of QTL identification is higher in linkage mapping as compared to AM, the resolution has a reverse relationship with both mapping schemes. The concept of joint linkage AM (JLAM) was introduced to fully exploit the capabilities of both mapping methods. JLAM uses either a biparental population set or one or more multiparental AM panels, or two sets of genotypes consisting of germplasm and biparental mapping populations, which are genotyped utilizing the same set of markers . Hence, hotspots . Severalhotspots , pleiotrhotspots , and thehotspots . Recent hotspots .The multiparent populations include several founder parents, which reflect wider genetic diversity. Hence, in AM studies, the use of multiparent mapping populations helps limit the demerit of recombination frequency in biparental populations. Multiparent mapping populations provides tools to control population structure and balance allele frequencies. The historical and artificial recombinational events of the multiparent mapping populations such as NAM and MAGIC populations and their derivatives increase the efficiency of QTL identification in AM. Because of the controlled crosses, NAM population has higher power because of maximized population structure and minimal familial relatedness and accumulated frequency of rare alleles. The population facilitates cost-effective GWAS and allows the perpetual sharing of the NAM panel with global researchers.2F4:6 lines, which were derived from 25 wild barley accessions by backcrossing to the cultivar Rasmusson , eight-way cross, and single/double round robin. NAM population was first developed in maize using RILs developed from a diverse set of parents. Twenty-five diverse families in maize were used to develop 5,000 RILs that were evaluated for southern leaf blight disease resistance , and theasmusson . Using 3asmusson .Vigna unguiculata) , highly homozygous lines will be developed to establish the MAGIC population. To develop MAGIC populations for wheat and rice that can be deployed for QTL mapping, indica and japonica lines have been adopted. Seven cycles of SSD selfing resulted in 305 F8 lines in cowpea (iculata) . In the iculata) .The molecular markers are being progressively used to expedite breeding efforts in the post-genome sequencing era. Modern plant breeding is shifting from classical breeding to molecular breeding, where various genotyping technologies are being used for the discovery of molecular markers. In the last decade, a huge number of molecular markers were used for structural analysis of large germplasm populations to understand the diversity and use in GWAS. The whole-genome sequencing for most of leguminous crops has already been completed. Chromosome-level genomes are completed for most of the leguminous crops . In the Molecular markers have become crucial components in molecular breeding over the past 2 years . MoleculHowever, continuous improvement of next-generation sequencing (NGS) technologies in recent years has made it cost-effective and accessible for any crop, including legumes . ReferenAmplification of DNA segments with PCR leads to the development of multiple genotyping methods. If the primers in a PCR reaction include the variation of interest, then it is called as allele-specific PCR. Allele-specific markers are generally used during foreground selection during marker assisted selection. PCR-Restriction Fragment Length Polymorphism (RFLP) is another method of PCR-based genotyping , where tAlthough the SSRs are a potent marker system because of high reproducibility, co-dominance, and polymorphism, it is time, therefore, to generate the thousands of genome-wide SNP markers, restriction-sites associated with DNA sequencing (RADSeq) for large populations to study population genetics and genetic dissection of complex traits . HoweverQTL-hotspot” was discovered with 743 SNP loci  was screened using 554 DArT markers. Cicer species, including 94 genotypes. Aldemir and coworkers (2017) used an advanced version of DArT technology, i.e., DArt sequencing (DArTseq), for the identification of QTL for iron content in lentil seeds. DArTseq is also a hybridization-based technology but combines with NGS and provides a much simpler form of sequencing than DArT , PacBio (10–25Kb), and NanoPore (read size of 500 bp to 2.3 Mb). Large LD blocks  in plants, specially self-pollinating. Large LD blocks include several CGs. Therefore, with dense genotyping, we can have SNP variants in each of the CGs in the block and individual CGs can be identified using GWAS carried out on WGRS genotyping data. A gene for salinity tolerance  soybean . Recentl soybean .In the era of different omics like genomics, transcriptomics, and proteomics with the help of NGS technologies, genotyping of large germplasm at multiple locations has become feasible for plant scientists. Thus, phenotyping these large germplasms/populations with higher accuracy have become difficult. Thus, high-throughput genotyping technologies have shifted the bottleneck of plant science from genotyping to phenotyping . Thus, iGWAS has continuously expanded in the last few decades due to advancements in sequencing technologies and the collective effort of the research community. In addition, HTP technologies have allowed us to measure many plant traits that are now frequently analyzed through GWAS tools. Recent years have seen GWAS methods solving issues of computation complexity or enhancing statistical power. It is utilized to detect new associations with traits of interest and to replicate loci detected by other different approaches. A diverse set of researchers is involved in rare-variant detection, statistical model optimization, synthetic associations, and using GWAS findings to better our knowledge of disease etiology. These methods can detect genetic variants associated with biochemical or agronomic and molecular phenotypes. In the future, this will enhance the utility of GWAS methods and their implications for plant science.In the GWAS, linear or logistic regression models are used to test for associations. The linear model is used for continuous traits such as plant height, whereas logistic regression models are used for binary traits indicating that the disease is present or absent. In addition, some covariates are included to account for confounding effects from demographic factors. However, naïve approaches often suffer from inflated false-positive rates that might be induced due to genetic relatedness among study participants . In GWASThe MLM frameworks used in GWAS have drastically decreased the false-positive rates in comparison with conventional naïve approaches. Among these, the fast GWA tool is an ultra-efficient tool for MLM-based GWAS analysis of biobank-scale data . MLM appRecent years have seen tremendous growth in the machine learning methods targeted for GWAS. The approaches used by these methods include classification, regression, ensemble-based learning, and neural networks.Logistic regression coupled with the least absolute shrinkage and selection operator (LASSO) regularization approach is a famous method for GWAS. The penalized logistic regression method was used for the classification of patients with Crohn’s disease using genotyping data at the genome-wide level. The LASSO and ridge regression are among the most frequently utilized penalized regression algorithms . RecentlSupport vector machine (SVM)–based classification methods such as COMBI have been developed for unknown phenotype prediction for a given unseen genotype . In thisThese methods comprise an ensemble of decision trees. For example, random forest is an example of the ensemble learning algorithm. A bootstrapped subsample of the initial training dataset is used to create each decision tree in this instance. Some authors used gradient-boosting and random forest approaches to identify potent SNPs . Nguyen Transcriptome-wide association study (TWAS) methods perform association analysis for gene expression variations and quantitative traits. TWAS is an approach based on genes with the ability to expand GWAS for a better understanding of functional relationships in complex traits. These methods are alternatives to variant-based association methods representing a subgroup of multi-marker association or locus-based methods. The locus-based methods have been so popular due to the larger apprehension and acceptability of the polygenic framework of the complex traits. In principle, locus-based approaches rely on multiple genetic variants to estimate the contribution of a gene or loci. TWAS uses GWAS results and transcriptome-level information to perform association testing at the gene level . The abiPheWAS methods perform unique associations in addition to utilizing known genotype–phenotype associations acquired through GWAS. These established relationships might serve as “positive controls” for additional high-throughput analysis. PheWAS methods suffer from high false-positive rates due to thousands of genotype–phenotype associations being tested in such studies . In addiGS has been utilized as a practical genomic approach for upgrading complex traits in various crops . In segrZhang and coworkers (2016) showed a GWAS-assisted GS with 309 soybean lines and 31,405 SNPs for seed weight using the rrBLUP approach. They showed GS prediction accuracies of 0.75–0.87, outperforming marker-assisted selection with prediction accuracies of 0.62–0.75. In alfalfa, In chickpeas, In common bean, cooking time (CKT), seed weight, and water absorption capacity were evaluated using 922 lines consisting of four populations  panel . Six modIn peanut or groundnut, 281 Kersting’s groundnut lines were used for GWAS-assisted GSs for several traits, including seed traits, 100 seed weight, leaf length, days to 50% flowering, and days to maturity using 493 SNPs and rrBLUP model . They reRecently, genomic resources have been made available in some minor legume crops . In peasSeveral GS methods were used for predicting GEBVs in legume crops Figure 2Since the last decade, GWAS has been successfully used in major legume crops to dissect or identify the genetic bases for various agronomic traits , qualityRevolutionization and rapid development in genomic techniques in the recent past have accelerated molecular studies not only in model crops but also in other crops like legumes . SequencLegumes are an essential component of human nutrition and play a vital role in sustainable agriculture due to their protein-rich content, soil quality improvement, and reduced environmental impact. With the increasing global population and changing climatic conditions, there is a pressing need to develop high-yielding, disease-resistant legume cultivars that can meet the nutritional needs of the growing population. Improvement in the nutritional and production quality of legume crops with the use of conventional breeding methods is not at the required rate. Whole-genome sequencing is available only for a few major crops. As per the availability, low (RFLP) to high-throughput (SNP) markers have been used in various crops for AM, QTL mapping, or GWAS. NGS has become feasible in the model and even in non-model crops with improved efficiency and affordable sequencing methods. GWAS has been used in major legume crops to identify the genomic region linked with desired characteristics of the plant. It is yet to be exploited in minor legumes with sufficient germplasm/population. The availability of reference genomes and rapid development in genomic techniques has made it feasible for researchers to identify the alleles/QTL association with the desired trait in any germplasm. The use of suitable models, methods, and bioinformatic tools determines the accuracy of GWAS. The development of model tools for legume crops can trigger GWAS in major and minor legume crops. Authentication or precision of identified marker–trait association is required for their utilization in plant breeding programs or MAS/BAC programs. Using NGS and other high-throughput techniques for sequencing will make it possible to develop a genomic-assisted crop improvement program in legumes. Rapid development can be gained concerning agronomic traits, biotic/abiotic stress tolerance, and after-use quality improvement. Legume yield potential is meager compared to other major crops; this yield plateau can be broken in legumes for climate change problems using GWAS with multi-location phenotyping. The integration of these new and improved technologies with traditional breeding methods will help to accelerate the development of new legume cultivars with improved yield and nutritional qualities.PS, PK conceptualized the review study and edited the manuscript. PY contributed to advanced methods and tools for GWAS. SS put forth experimental populations for association mapping studies in various crops. GK was associated with GWAS-assisted genomic selection. SSG and MP contributed to new high throughput-genotyping technologies in plants. VS and TT assisted in collecting data for applications of GWAS in plant breeding and drafting the manuscript. All authors contributed to the article and approved the submitted version.


Actinidia arguta (kiwiberries), some of the main challenges when breeding for fruit characteristics are the selection of potential male parents and the long juvenile period. Currently, breeding values of male parents are estimated through progeny tests, which makes the breeding of new kiwiberry cultivars time-consuming and costly. The application of best linear unbiased prediction (BLUP) would allow direct estimation of sex-related traits and speed up kiwiberry breeding. In this study, we used a linear mixed model approach to estimate narrow sense heritability for one vine-related trait and five fruit-related traits for two incomplete factorial crossing designs. We obtained BLUPs for all genotypes, taking into consideration whether the relationship was pedigree-based or marker-based. Owing to the high cost of genome sequencing, it is important to understand the effects of different sources of relationship matrices on estimating breeding values across a breeding population. Because of the increasing implementation of genomic selection in crop breeding, we compared the effects of incorporating different sources of information in building relationship matrices and ploidy levels on the accuracy of BLUPs’ heritability and predictive ability. As kiwiberries are autotetraploids, multivalent chromosome formation and occasionally double reduction can occur during meiosis, and this can affect the accuracy of prediction. This study innovates the breeding programme of autotetraploid kiwiberries. We demonstrate that the accuracy of BLUPs of male siblings, without phenotypic observations, strongly improved when a tetraploid marker-based relationship matrix was used rather than parental BLUPs and female siblings with phenotypic observations.In dioecious crops such as The online version contains supplementary material available at 10.1007/s11032-023-01419-8. Successful plant breeding is the art of identifying and selecting potential parents with desirable traits and exceptional performance for the next round of crossing from within a variable population. Modern plant breeding can utilise further information, such as genomics and new statistical analysis tools, to improve parental selection.A) or numerator relationship matrix (NRM). Rules for calculating A have been developed for diploid animal species such as livestock, where gametes carry only one of the two alleles (Henderson A) and its inverse (A−1) in diploid species, mainly for animal breeding. In diploid species, it is assumed that gametes cannot carry two or more alleles that are identical by descent (IBD) because of meiotic reduction division. In autopolyploids, where non-preferential pairing of chromosome occurs, such an assumption cannot be made. The A matrix is the probability that an allele is identical by descent (kinship coefficient) among individuals, multiplied by two for diploids, by four for tetraploids, by six for hexaploids and so forth . Selection on a trait requires that much of the variation in the trait be due to segregating heritable genetic factors rather than due to the environment. To estimate the genetic effects, statistical methodologies that estimate best linear unbiased predictions (BLUPs) have been developed to estimate variance components and predict breeding values . Several species and hybrids of Actinidia have been introduced into cultivation, the main two being Actinidia chinensis (Planch.) var. chinensis and Actinidia chinensis var. deliciosa (A. Chev.) A. Chev.  Planch. ex Miq. var. arguta (2n = 4x = 116) (kiwiberry) is one of the species recently introduced into cultivation. Kiwiberries produce small fruit with a soft, hairless, edible skin. Like other Actinidia species, kiwiberries are usually dioecious, with staminate flowers on male vines and pistillate flowers on female vines. Female flowers fully develop ovaries, styles, stigmata and stamens but produce non-viable, empty pollen. Male flowers produce viable pollen but rudimentary female organs lacking ovules that are able to develop into fruit  and varying the complexity of assumptions of chromosome inheritance. The effects of these modifications on the breeding value estimates for parental generations, female progenies with trait records and male progenies with no records, are compared.A. arguta, consisting of two incomplete factorial crossing designs (Supplementary Table A seedling population of tetraploid One vine characteristic (fruit load) and five fruit characteristics  were assessed for this study. During the 5-year trial, fruit load was recorded in 2017 and 2018. Fruit load was scored from 0 to 9  assumption as 0 (AAAA), 1 (AAAB), 2 (AABB), 3 (ABBB) and 4(BBBB). For pseudo-diploid (2x) genotyping, all heterozygote genotypes were assumed to be one class and therefore recoded as 0 (AAAA = AA), 1  and 2 (BBBB = BB).Young leaf tissue was collected in spring, and DNA was extracted by Slipstream Automation . Final dsDNA concentration was standardised to a quantity of ~ 500 ng per sample and vacuum dried to the requirement of the high throughput targeted resequencing platform Flex-Seq® Ex-L of RAPiD Genomics . Resulting sequence reads were mapped against the A linear mixed model (LMM) was used to predict breeding values for a segregating population comprising two incomplete crossing designs:Variance components and their standard errors were estimated using ASReml-R software in R  was used to compute the accuracy of breeding value prediction for all tested variance–covariance matrices. Female progeny with phenotypic observations were used to train the model, and BLUPs were estimated for all individuals included in the crossing scheme. Results were grouped into parental generation (with distant ancestors), individuals with observation  and no-observations .i. The calculation of accuracy requires the diagonal elements of the mixed model equation (LHS = left-hand-side), when calculating standard error , and fruit traits were fairly to moderately right-skewed  Table .Table 2OA. arguta progeny of 50 crosses were planted and managed under commercial breeding programme conditions for 5 years. Pedigree information across the population and 7259 (G4) or 2660 (G2) genome-wide distributed bi-allelic markers were available for analysing the effects of incorporating different relationship matrices.A total of 1752 x and 4 × is shown in Supplementary Fig. For the G2 model, genotypes were classified in two homozygote classes and one heterozygote class under the assumption of re-calling genotypes from tetraploid dosage call to pseudo-diploid. Distribution of allele dosage classes under the assumption of 2The genetic parameters of the full model, which includes all progeny with phenotypic information, and the mean over 10 iterations of the tenfold cross-validation model, are summarised in Supplementary Table The expected genetic gain (EGG) for each trait and year and overall for the average of multiple years is shown in Supplementary Table We investigated the accuracy of predicted BLUPs for all six traits. BLUPs and the corresponding accuracy were estimated for all individuals with or without observations, incorporating different relationship matrix approaches into the LMM equation. The standard error of BLUP estimation, and therefore the accuracy of predicted breeding values of an individual, relies on the available information. Parental BLUPs, and therefore the accuracy of breeding value prediction, depend heavily on phenotypic records of progeny and relatives as well as the number of relatives. However, the accuracy of individuals within the progeny generation depends on the individual performance of those with phenotypic records, or on the family mean for individuals without phenotypic observations.In this study, female progeny with observations and parents without phenotypic observations showed similar high accuracy of breeding value predictions. Within the parental generation, no significant differences were observed when using different pedigree-based relationship matrices. For all traits, the accuracy of prediction was significantly lower under the assumption of pseudo-diploidy of the marker-based relationship, whereas tetraploid genetic marker methodology showed no differences from pedigree-based relationship methodologies Fig. a. Includβ) of 1.0 (threshold line) indicates no differences in variance between observed phenotypes and predicted breeding values. In comparison with the pedigree-based LMM approach, A2, A4 and A4w showed a mean regression coefficient close to β = 1.0, indicating a similar variance among predicted breeding values and mean phenotypic observation . An indicator of inflation/deflation of predicted breeding value variance was explored. A regression coefficient (PA) for all studied traits. PA of the tetraploid-realised relationship methodology (G4) varied depending on the trait. A significant difference between G4 and the pedigree-based approach was observed for fruit load, whereas no difference between G2 and G4 was observed for dry matter content, ripe soluble solids content, or fruit circularity  for all tested methodologies of calculating relationship matrices was obtained by computing the overall Pearson’s correlation of each validation set. No difference in predictive ability was observed between pedigree-based and pseudo-diploid realised relationships based on the predicted abilities  for each model approach and trait. In general, the MSE was higher under the G4 model assumption compared with other models. The only significant difference between G4 and the other studied models was observed for fruit weight. No significant difference was observed between all three pedigree-based models and between pedigree-based and G2 models  were first developed in animal breeding to estimate additive random individual effects and are now used in plant breeding. An improvement for predicted breeding values and accelerating genetic gain of economically important quantitative traits can be achieved by including the genetic information of individuals  were estimated under the different relationship matrices  and accounted for ploidy (2 = diploid and 4 = tetraploid). Studies of double reduction during meiosis in natural and induced tetraploid A. arguta, a second A4 model with a double reduction coefficient of 10% (w = 0.1) was proposed in this study.Evidence of marker segregation in autotetraploids showed that the rate of double reduction increases towards the telomere because multivalent and chromosome formations cross-over events are more likely. Nevertheless, double reduction is often ignored and this, therefore, may be one reason for the low rate of genetic improvement in polyploids compared with their diploid counterparts , the proportion of residual variance to additive genetic variance. The factor lambda (λ) is shrinking the distribution of phenotypic observations towards the population mean, which results in a reduced variation in breeding values. A low shrinkage factor results in high variance of predicted breeding values compared with observed variance. Probabilistic relationship matrix-based models tend to have a bias value of around one, indicating similar variance of predicted breeding values and observation. Therefore, the model prediction is more robust for pedigree-based models. Our marker-based relationship matrix model showed significant differences from the pedigree-based models as well as between allele dosages  within the validation set did not contribute phenotypic information for model development because their observations were masked. Therefore, BLUPs of individuals within the validation set were the predicted family mean, accounted for by phenotyped family members. This can lead to overestimation of BLUPs, whereas models considering realised relationships lead to more precise prediction. Our tested models showed a slightly lower predictive ability for marker-based models, suggesting improvement of the genotyping approach will improve the predictive ability and the mean squared error  for pedigree-based models, which suggests no significant difference in variance estimation. Considering allele dosage in the marker-based relationship, the variance comparison between phenotypic observations and predicted breeding values was significant. The G2 model showed a higher variability in observations than the predicted. On the other hand, G4 predicted a higher variability in BLUPs than observations . A consequence of grouping these small populations made the comparison of model validation less reliable, as suggested by Gurka and Edwards . FurtherA. arguta breeding programmes. Overall, the results of six traits considered in a marker-based relationship matrix showed a positive correlation of predictions to mean observations, indicating a better representative genetic architecture of genome-wide marker coverage using a multiplex PCR and new generation sequencing combination approach, compared with previous studies of other Actinidia species , parental individuals as well as distant ancestors and male progeny without records (blue). Female progeny with observed records were divided randomly into training and validation sets using a 10-fold cross-validation approach. In the validation set, the observations were masked. Progeny with observations were randomly grouped into 10 groups; each was used once as a validation set (light red), whereas nine groups were used to train the model . Individuals with no phenotypic information were explored using the full model (PNG 335 KB)10-fold cross-validation methodology. The base population contained High Resolution (TIF 22.7 MB)Supplementary Fig. 2Actinidia arguta kiwiberry allele dosage classes shown under re-classification for pseudo-diploid (a) and tetraploid dosage classification (b) (PNG 107 KB)Heterozygosity, distribution of High Resolution (TIF 50.5 MB)Supplementary Fig. 3a) regression coefficient of the mean observed Actinidia arguta kiwiberry phenotype (multiple years) and predicted breeding values is described as Bias, with a threshold of 1.0 (grey dashed line), when equal variance is observed, (b) the correlation of mean observation over multiple years and predicted breeding values (Predictive Ability), and (c) the mean squared error (MSE) of the predicted breeding value and mean observation. A Tukey’s HSD test, conducted at a significance level of 0.05, indicates significant differences by the different letter (PNG 440 KB)Validation variables of the 10 x 10-fold cross-validation approach. Supplementary file2 (DOCX 26.8 KB)


ST levels (p < 0.00001) than African populations, although studies using ancestry markers have shown an important similarity between Brazilian and African populations (12–24%). U20_BFDSC were genetically similar to professional athletes, showing the intense genetic selection pressure likely to occur before this maturation stage. Our study highlighted that performance-related genes might undergo selective pressure due to physical performance and environmental, cognitive, and sociocultural factors. This replicative study suggests that molecular variance and Wright’s statistics can yield novel conclusions in exercise science.Classically, genetic association studies have attempted to assess genetic polymorphisms related to human physiology and physical performance. However, the heterogeneity of some findings drives the research to replicate, validate, and confirmation as essential aspects for ensuring their applicability in sports sciences. Genetic distance matrix and molecular variance analyses may offer an alternative approach to comparing athletes’ genomes with those from public databases. Thus, we performed a complete sequencing of 44 genomes from male Brazilian first-division soccer players under 20 years of age (U20_BFDSC). The performance-related SNP genotypes were obtained from players and from the “1000 Genomes” database . Surprisingly, U20_BFDSC performance-related genotypes had significantly larger F Physical performance is a multifactorial trait to which genetic data can potentially be applied for precision medicine approaches . ClassicACTN3 gene, have had their mechanisms described and related to sports performance. In this case, the absence of α-actinin-3 releases more calcineurin favoring endurance adaptation [AMPD1 rs1760272, a key enzyme for purine metabolism, was previously related to ATP recovery and fatigue during exercise, a core valence for soccer players. In addition, peroxisome proliferator-activated receptor (PPAR)-related genes and their SNPs likely exert a complex, modulatory influence on fatty acid and carbohydrate oxidation pathways. However, their specific mechanisms remain to be understood [FTO rs9939609 appeared in more than one GAS related to fitness and body fat but without real validation [Thus, important single-nucleotide polymorphisms (SNPs), like the non-sense rs17602729 in the lidation .Alleles that enhance essential traits for specific physical activities likely experience selective pressures in elite athletes without consistently affecting unrelated genes . HoweverThe recent results in 25 male Brazilian soccer players  showed a®, San Diego, CA, USA). The libraries were sequenced on the NovaSeq6000 platform  with an average depth of 44×. Read mapping and genome assembly were performed based on the GRCh38 version of the human genome using DRAGEN Germline App v3.7.5 available in the BaseSpace cloud file deposit . Variant calling was performed using the DRAGEN tool™. The SNP chosen genotypes were obtained from VCF files and checked manually from BAM files using the Integrative Genomics Viewer (IGV) tool  [We selected an independent study group of soccer players for replication. We performed a complete sequencing of 44 genomes from male Brazilian first-division soccer players under 20 years of age (U20_BFDSC) to compare them with a group of 25 probands (BFDSC). DNA was obtained from buccal cells using a MagMAX™ DNA Multi-Sample kit . Genomic libraries were constructed from 1 μg of DNA using a TruSeq DNA PCR Free kit  . Hardy–Wwww.ensembl.org, accessed on 1 October 2022). The genetic distances based on molecular variance (FST) were calculated using Arlequin software v3.5  [https://itol.embl.de, accessed on 21 January 2023). This study was approved by the Local Research Ethical Committee (76189817.0.0000.5235 and 69253417.1.0000.5149) according to the ethical standards of the Helsinki Declaration. All participants signed a written consent form to participate.Genotypes from European, African, American, East Asian, and South Asian populations were obtained from the 1000 Genomes database using the Ensembl home browser (zerland) . The prizerland)    . FST valST values and the statistical robustness of the model.In the multidimensional PCA analysis, the U20_BFDSC group clustered closely and in the same quadrant as the European and the previous BFDSC group population, near and in different quadrants from the American population, but far from the African or East Asian populations . The PCAST) were observed between the U20_BFDSC and BFDSC groups, and they remained very close on the phylogenetic tree , Africans (12–24%), Native Americans 10–15%), and Asians (10%) ,31. Surp%, and AsThe clustering ability of molecular variance may be used even in populations with different physical phenotypes to evaluate performance. Although Wright’s statistics-based studies have a powerful tool for clustering, they are sometimes less informative because they show the global molecular variance and do not point to which specific gene plays a vital role in that phenotype. It is possible to cluster genetically related subjects or distinguish them from others, but further studies are necessary to validate a potential variant related to the phenotype. In this case, good science is mandatory in selecting homogeneous populations to identify the genetic trait. Furthermore, clustering related subjects from the same ethnicity and similar phenotypes in a multifactorial trait could be harder to distinguish and may demand more genes and DNA sequencing costs.The study of performance-related variants could allow for the design of a supposedly optimal profile. However, selecting an athlete based on molecular variance closer to some overrepresented clusters raises ethical concerns. As a multifactorial trait, soccer performance depends on genetics and environmental aspects. Without significant validation, there is no evidence to support a genetics-based selection of subjects, which poses a real risk of data misinterpretation that could compromise the integrity of the sport.Finally, our replication results highlighted the possibility that, in male elite soccer players, performance-related genes might undergo selective pressure due to physical performance and environmental, cognitive, and sociocultural factors, justifying the slightly different results concerning the African or Asian population in studies with markers of ancestry that are not related to sports performance. In addition, the U20 athletes may resemble professional athletes, although even younger players could show genetic differences between them. The possibility of using Wright’s statistics in larger samples, based on variability data from athletes’ whole genomes (consortia), could generate conclusions never seen before in exercise science.


Nested ANOVAs revealed highly significant differences between DAPC clusters within environments for all traits except kernel roundness. Ten UPOV traits showed significant diversity for their frequencies in the two environments. By PCAmix multivariate analysis, plant height, heading time, spike length, weight of kernels per spike, thousand kernel weight, and the seed related traits had heavy weight on the differentiation of the groups, while UPOV traits discriminated moderately or to a little extent. The data collected in this study provide useful resources to facilitate management and use of wheat genetic diversity that has been lost due to selection in the last decades.Durum wheat is a worldwide staple crop cultivated mainly in the Mediterranean basin. Progress in durum wheat breeding requires the exploitation of genetic variation among the gene pool enclosed in landraces, old cultivars and modern cultivars. The aim of this study was to provide a more comprehensive view of the genetic architecture evolution among 123 durum wheat accessions , grown in replicated randomized complete block in two areas, Metaponto (Basilicata) and Foggia (Apulia), using the Illumina iSelect 15K wheat SNP array and 33 plant and kernel traits including the International Union for the Protection of new Varieties of Plants (UPOV) descriptors. Through DAPC and Bayesian population structure five groups were identified according to type of material data and reflecting the genetic basis and breeding strategies involved in their development. Phenotypic and genotypic coefficient of variation were low for kernel width (6.43%) and for grain protein content (1.03%). Highly significant differences between environments, genotypes and GEI (Genotype x Environment Interaction) were detected by mixed ANOVAs for agro-morphological-quality traits. Number of kernels per spike (h Larger kernels not only impact on grain yield but also could have favorable effects on seedling vigor and early growth; moreover, the market and industry requirements are for almost spherical grains .A lot of agro-morphological traits are quantitative with polygenic variation  and highThe objectives of this study were to provide a more comprehensive view of the genetic architecture evolution of durum wheat by integrating SNP genotyping, phenotyping and pedigreed varieties in order to: a) detect pattern of diversity of 123 durum wheat accessions labeled as landraces, old and modern cultivars; b) investigate the genotype x environment interactions (GEI) and traits heritability for identifying characters useful for genetic improvement; c) compare the durum wheat genetic resources with pedigreed varieties for knowing how much genetic diversity is still keep and available for breeding works.22.1Triticum durum Desf.) collection included a set of one-hundred-twenty-three accessions, originated in different geographical areas (Triticum turgidum subsp. turanicum Jakubz (KAMUT_CREA and KAMUT_MOL_DV). The accessions were grown during the growing season 2018/2019 in South Italy in two areas: Metaponto  and Foggia . The data of maximum and minimum temperature and rainfall for the growing season (November to July) were obtained from the nearest weather station located, as shown in The analysed durum wheat , and were reported in The agro-morphological characterization was performed using 33 morphological traits. 18 out of 33 traits were detected according to descriptors for wheat defined by IBPGR . As suggested in Roundness = (Width/Length + Thickness/Length + Thickness/Width)/3The wheat kernels that had been individually measured were compared to their respective Digital Image Analysis (DIA) values and a small SeedCount Roundness adjustment equation was generated.Semolina technological traits, namely sedimentation-SDS, grain protein content and total carotenoid content were evaluated on 5 g of 0.5 mm semolina for each sample obtained by a laboratory mill (CT193 Cyclotec ™ - FOSS), using near-infrared spectroscopy Rapid Content ™ - FOSS XDS .https://www.upov.int/educes/tgdocs/en/tg120.pdf), as reported in The remaining 15 morphological traits were detected according to descriptors for durum wheat defined by . The degree of kernels’ color reaction was evaluated after 4 h at room temperature and in daylight (out of direct sunshine) and classified according to the level of expression shown in 2.42.4.1adegenet package v. 2.1.6  of groups. Kmeans runs sequentially with increasing values of k, and different clustering solutions are compared using Bayesian Information Criterion (BIC). The optimal number of clusters was indicated as the value of k above which the BIC value decreased or increased. The dapc function describes the relationships between the identified clusters. The optimal number of PCs was determined with optim.a.score function. The results obtained were plotted in a scatterplot of the first and second linear discriminants of DAPC.The population structure within the collection, based on SNPs data, was examined by Discriminant Analysis of Principal Components DAPC,  implemenv. 2.1.6 . This mupairwise.WCfst function of R hierfstat package  based on different sources of variation: between environments, between DAPC clusters within the environments and between accessions within DAPC clusters and environments. Since the ANOVA showed significant differences between environments, it was decided to proceed by analyzing the two environments separately. The variability between the two environments and among the three groups of accessions in each environment was evaluated considering both quantitative agro-morphological and UPOV-traits.Univariate statistics including means, minimum, maximum and coefficient of variation, computed by MEANS procedure of SAS OnDemand for Academics , were used to describe the variability of the 18 quantitative agro-morphological traits among the five clusters identified by previous analysis for both the environments.2.4.3The frequencies for the 15 UPOV-traits were calculated by SAS FREQ procedure and tested with Pearson chi-squared test (χ2).The Shannon’s diversity index  was estiip is the proportion of traits belonging to the ith type of class and n is the number of the phenotypic classes for a trait. Since different numbers of phenotypic classes were recognized among the traits, this index was standardized by converting it to the relative index, H’ estimated, by dividing it with maxH = ln(n) as in formula (2):wre which ensured all H’ values to be in the range of 0 – 1. According to this index, diversity level is defined as high (H’ > 0.60), intermediate (0.40< H’< 0.60) or low (0.10< H’<0.40) . The ind2.4.4PCAmixdata  was carried out to determine the overall morphological traits distinctiveness for all the 33 morphological traits using the PCAmix function of R package Amixdata . PCAmix Amixdata .33.1syryacum typicum  lines of Near east and Morocco, four obtained from Triticum aestivum crosses and Vera obtained from Eiti-6 x Russello (Triticum turgidum subsp. turanicum Jakubz) landraces clustered in this group too, near to the North Africa landraces Jean Rhetifah and Jenah Kottifa.The population structure within the durum wheat collection was examined through DAPC analysis based on 1907 SNP data to describe how the accessions were related to each other. The optimal number of clusters individuated by BIC analysis was five genetic clusters. The scatter plot of the two principal components of DAPC is presented in C2 was related also with C3 and C4. C3 was composed of all the accessions of Dauno (n=5 of Dauno III and n=2 of Dauno). C4 consisted of 36 accessions  and related, in addition to C2, also to C5. The seven modern cultivars grouped in C4 were all Italian genotypes: Antas, Castello, Ciccio, Ciclope, Fortore, Lesina and Platani, characterized by having Capeiti or Capeiti8 in their pedigree. Antas was related with Capeiti8 by Ichnusa  cultivar parent. Strampelli selections as Senatore Cappelli (or Cappelli), Aziziah and Tripolino clustered in this group together with OC that were Cappelli related. Cappelli was selected from the North Africa population “Jeanh Rhetifah” and is still considered one of the most relevant ancestors of the modern durum wheat cultivars.C5 consisted of 34 out of 41 MC and was related to C4. All the France, Spain and USA MC and OC clustered in C5 together with 20.6% of the Italian durum wheat accessions.In order to estimate the divergence between the clusters identified by DAPC analysis, pairwise FSTs were calculated . A total of 33 agro-morphological traits were scored: 18 quantitative and 15 qualitative (UPOV-related) traits.3.2.12p) for all the 18 quantitative traits was always higher than genotypic variance (σ2g) as well as CVp when compared to CVg, indicating the influence of the environment and requiring multi-environmental selection to maximize genetic values. The CVg and CVp discrepancies were higher for blackstain-blackpoints, yellow berry, number of kernels per spike and weight of kernel for spike, showing their larger environmental plasticity than the other traits. Notwithstanding, the estimates of CVg were closer in magnitude to phenotypic ones for kernel length, plant height, kernel width, total carotenoid content and kernel area.The estimates of genetic parameters, obtained by mixed ANOVAs, for 18 quantitative agro-morphological traits are presented in 2) for the studied characters could be divided into four categories as The broad sense heritability  based on different sources of variation: between environments (Foggia and Metaponto), between DAPC clusters among the environments, between accessions or genotypes among the clusters and environments, Differences between environments were highly significant (p<0.001) in 16 out of 18 traits except for number of kernels per spike and blackstain-blackpoints. Kernel roundness was not significant for the other sources of variation.Since the analysis of the ANOVA showed significant differences between environments, it was decided to proceed by analyzing the two environments separately. The means for each of the 18 quantitative agro-morphological traits in both the environments were computed and then compared Figure 3In addition, descriptive statistics between the group averages obtained by SNP DAPC analysis were reported Figure 43.2.2The 15 morphological traits detected according to descriptors for durum wheat defined by UPOV consisted of ten quantitative (QN), four pseudo-qualitative (PQ) and one qualitative (QL) parameters, expressed in discontinuous states and not influenced by the environment. Their frequencies and differences were analysed Table 6 All levels of expression for each UPOV descriptor were observed in the whole durum wheat collection, except for the glaucosity of sheath and the glaucosity of blade (lower side) of flag leaf, and the shape of the spike. Ten traits showed significant diversity for their frequencies in the two environments  was then determined for all UPOV descriptors in both the environments. Overall, high levels of polymorphism were displayed in each environment, except for the glaucosity of sheath of flag leaf, and the kernels coloration with phenol (H’ = 0.38 and 0.49 respectively) in Metaponto, as shown in With regard to the frequencies of the 15 UPOV descriptors compared between the SNP DAPC clusters, the traits of the awns and the spike, such as color and density, and the kernels coloration with phenol were highly significant diverse (p<0.0001), 3.2.3To analyze the morphological traits distinctiveness for all the 33 morphological traits, a PCAmix multivariate analysis was carried out.The results of the PCAmix revealed that the durum wheat collection grown in the two environments of Foggia and Metaponto expressed a similar phenotype. In detail, the distribution of the accessions S6, 7.The analysis also revealed that in Foggia the first two dimensions explained the 17.53% of total variance: the first dimension accounted for 9.91%  (It should be noted that genotypes clustering in C1 (Timilia and Marzellina Saccone accessions) according to the DAPC analysis, showed late heading, medium-short spike length, low 1000 kernels weight and low kernel area; C1 kernel size was low and grains showed very strong coloration with phenol. On the contrary, genotypes grouped in C3  and most accessions of C2 (such as Russello accessions) were characterized by tall plants, high 1000 kernels weight and high kernel area and size. Most of the accessions in the C5 group, predominantly MC, confirmed the progress of the ion SDS) S5.In the case of C4 group, some accessions as Capeiti8 and its related cultivars shared their phenotype predominantly with C5 accessions; some other C4 genotypes, as Senatore Cappelli and Cappelli, Margherito, Grifoni, Ciclope, with C2 and C3 accessions.4The durum wheat breeding programs over the years were influenced by consumer’s demands and focused on grain quality and productivity . The selGenetic diversity is imperative to provide a robust food security system capable of adapting to recurrent stresses. It is a crucial step in noticing alleles that could be used as a source of novel traits with high yielding, resilience for biotic and/or abiotic stresses, satisfactory productivity or in meeting the end-user demands in plant breeding .Germplasm banks and research institutions keep, multiply and rejuvenate collections of genetic resources of different genepools collected over time in different areas. There is still little information on these materials, passport data are incomplete and still few accessions are included in pre-breeding and breeding programs.In the present study we compared SNP genotyping and plant phenotyping by agro-morphological traits, UPOV descriptors and kernel-related traits in order to characterize a wheat collection, consisting of three groups , under two different environmental conditions and into five clusters as suggested by molecular analysis.All the obtained results will contribute to knowing the amount of variation of the ex situ durum wheat collection, the genetic relationships among groups, the response of the collection to climate changes, and to facilitate both management and use of wheat genetic diversity that has been lost due to selection in the last decades.4.1The durum wheat collection analysed by DAPC based on SNP markers revealed a clear lowest Bayesian information criterion subdividing the accessions into five groups in accordance with type of material and pedigree data. The distribution of accessions in the clusters was in agreement with Moreover, the landrace accessions in C2 showed genetic differentiation (Fst=0.16-0.19) lower than accessions in C1 and C3. Instead, the OC accessions released from 1914 (Dauno group) to 1973  were part of C4 but were also dispersed in all DAPC groups. snmf analysis, at K=5 identified five subpopulations that in part reflected the groups shown by DAPC: both divisions reflected the history of Italian durum wheat breeding with modern cultivars differentiated by old cultivars and landraces.The structure of the collection, analyzed by T. turgidum were concentrated in Sicily, while a few botanical forms of T. durum, characterized by a very large number of cultivars, originated from the Sirio-Palestinian area and were concentrated in Sardinia. Breeders started to utilize this variation at the end of the last century by selecting and putting into cultivation the best lines from the original landraces, and since the very beginning of the present century by crossing good lines with introduced germplasm.As All accessions of Timilia and Marzellina Saccone were again separate from the other Sicilian landraces such as Russello that clustered all together in K3. K3 also included all the accessions of Dauno, Dauno III and Jeanh Rhetifha, a landrace that even if considered the progenitor of Cappelli, resulted genetically distant from Cappelli and Senatore Cappelli accessions which, indeed, were grouped in K1 in agreement with DAPC and Bayesian analysis based on SNP data confirmed how not all available resources have been utilized to the full for the development of improved varieties. Most probably, the Timilia and Marzellina Saccone accessions were not used due to their low productivity characteristics , and have been reintroduced only in recent years thanks to some characteristics such as the especially high content of grain protein. Timilia accessions had also a strong coloration with phenol but a low content of carotenoids in the semolina in both the environments. 4.2Gene banks could be considered complex “libraries” of materials to be properly cataloged with the aim to recover and use them in research and plant breeding . Our stuThe whole durum wheat collection revealed a large amount of variation for morphological (CV from 12% to 36%) and kernel-related traits  as reported in 2 = 0.02) and the grain protein content (h2 = 0.03) were not a heritability character and highly influenced by the environment . Significant differences among durum wheat genotypes and their environmental interaction were reported in literature  in agree4.3T. durum, are not a highly desirable trait in the milling industry. T. monococcum, T. dicoccum, T. durum, T. polonicum, T. aestivum ssp. aestivum and T. spelta) by seed image, observed that the ancestral taxa had more elongated and less round kernels than the modern bread wheat varieties, due to the selection performed in favor of rounded kernels associated with more yield in the milling process. T. durum accessions from the National Plant Germplasm System (USA) and compared them with 12 durum wheat cultivars and Kamut® wheat taking into account dimension and shape descriptors. They assumed that the grains would be more elongated in T. durum accessions than in the modern intensively-farmed cultivars, but they did not demonstrate that. They evidenced greater differences in color than in shape descriptors between the investigated accessions and cultivars. In our study we considered five kernel size IPGRI descriptors  and in addition UPOV kernel shape descriptor. We compared all them between environments and between SNP DAPC clusters within environments. In Foggia environment, the C2 and C4 displayed seeds significantly longer, thicker and with larger area, than C5  that were different from T. turgidum ssp. durum ones, especially for high length and area of kernel and low kernel roundness. On the contrary, the Timilia accessions (C1) confirmed the lower 1000 kernels weight and lower mean values for all the seed morphological parameters detected in comparison to the other LR (C2) considered in the study, according to the results from Kernel dimensions and shape are important factors affecting 1000 kernel weight and grain yield in crops  and are s 7.1 mm Figure 4s 7.1 mm . The var4.4https://www.upov.int/edocs/tgpdocs/en/tgp_10.pdf). All UPOV traits, except for pubescence of external surface of lower glume, qualitative trait, were quantitative or pseudo-qualitative ones and for them the level of variation due to the environment can differ from genotype to genotype, especially in heterogeneous landraces, and from characteristic to characteristic. In conclusion, UPOV protocols provided basic information of the accessions and it would be desirable to use traits able to distinguish the varieties and stable over repeated propagations of such a variety. But in our study the only UPOV traits did not show a clear discrimination among the collection and, also, some traits were difficult to detect so it would be useful to review the parameters to be used for the description in agreement with those reported by The UPOV protocols were important to protect and distinguish crop varieties. Generally, no single morphological trait could be used to distinguish cultivar so these protocols studied the appropriate combination of traits useful to better characterize the varieties. In this study 15 UPOV-defined traits were detected to describe the durum wheat collection. Their frequencies underlined the significant differences between environments and DAPC groups within them. Five out 15 UPOV traits were stable between the environments Figure 54.5Thagouty et al. (2017) studied a set of 29 durum wheat genotypes grown in Morocco, released in different periods during the 20th and the beginning of 21st centuries, in order to quantify the achievement of past and present Moroccan breeding efforts. They showed that the sowing-heading phase decreased in modern cultivars allowing them to escape to higher air temperature and drought stresses accentuated by climate change and confirmed previous reported evidences on Spanish and Italian genotypes as in The breeders’ work has driven the development of cultivars that are highly stable across diverse environments and nowadays more resilient than the past. 5A huge variability was detected at morphological level, with landraces being more diversified than modern varieties. The data collected in this study provide useful resources to facilitate both management and use of wheat genetic diversity that has been lost due to selection in the last decades.The analysed ex situ durum wheat collection provides an opportunity for supporting the protocol for official examination of Distinctness, Uniformity and Stability (DUS) for durum wheat varieties entered for National Variety List (NVL) and Plant Breeders’ Rights. Furthermore, the phenotypic and genotypic data acquired for the genetic materials under threat of genetic erosion (i.e. landraces and old varieties) could be used to start the administrative procedure for the registration to the NVL as “conservation variety”.The original contributions presented in the study are included in the article/Conceptualization: TG, GL, SM, SE, FF, FT, and PV. Methodology:SM, GL, TG, and PV. Supply of materials: PV. Analyzed data: SR, SM, and GL. Writing—original draft preparation: SR and GL. Writing—review and editing: SM, GL, TG, and PV. Supervision: TG, GL, and PV. Contributed to the article and approved the submitted version: all authors.