TraesCS3B02G024900 within the Fhb1 region on chromosome 3B and is ~3 Mb from a cloned Fhb1 candidate gene TaHRC. While the PLHT QTN on chromosome 6B is putatively novel, the 1B DTA QTN co-locates with a disease resistance protein located ~10 Mb from a Flowering Locus T1-like gene TaFT3-B1, and the 7A DTA QTN is ~5 Mb away from a maturity QTL QMat.dms-7A.3 of another study. GWAS and QTN candidate genes enabled the characterization of FHB resistance in relation to DTA and PLHT. This approach should eventually generate additional and reliable trait-specific markers for breeding selection, in addition to providing useful information for FHB trait discovery.Fusarium head blight (FHB) is a highly destructive fungal disease of wheat to which host resistance is quantitatively inherited and largely influenced by the environment. Resistance to FHB has been associated with taller height and later maturity; however, a further understanding of these relationships is needed. An association mapping panel (AMP) of 192 predominantly Canadian spring wheat was genotyped with the wheat 90K single-nucleotide polymorphism (SNP) array. The AMP was assessed for FHB incidence (INC), severity (SEV) and index (IND), days to anthesis (DTA), and plant height (PLHT) between 2015 and 2017 at three Canadian FHB-inoculated nurseries. Seven multi-environment trial (MET) datasets were deployed in a genome-wide association study (GWAS) using a single-locus mixed linear model (MLM) and a multi-locus random SNP-effect mixed linear model (mrMLM). MLM detected four quantitative trait nucleotides (QTNs) for INC on chromosomes 2D and 3D and for SEV and IND on chromosome 3B. Further, mrMLM identified 291 QTNs: 50 (INC), 72 (SEV), 90 (IND), 41 (DTA), and 38 (PLHT). At two or more environments, 17 QTNs for FHB, DTA, and PLHT were detected. Of these 17, 12 QTNs were pleiotropic for FHB traits, DTA, and PLHT on chromosomes 1A, 1D, 2D, 3B, 5A, 6B, 7A, and 7B; two QTNs for DTA were detected on chromosomes 1B and 7A; and three PLHT QTNs were located on chromosomes 4B and 6B. The 1B DTA QTN and the three pleiotropic QTNs on chromosomes 1A, 3B, and 6B are potentially identical to corresponding quantitative trait loci (QTLs) in durum wheat. Further, the 3B pleiotropic QTN for FHB INC, SEV, and IND co-locates with  Triticum aestivum L.) is a hexaploid species  with a 17-gigabase-pair genome (Fusarium graminearum Schwabe [teleomorph Gibberella zeae (Schwein.) Petch]. Globally, FHB ranks second only to leaf rust as a disease responsible for the largest yield losses in wheat  gene (TaHRC) protein gene , Type II (resistance to spread within the spike), and Type III resistance to the accumulation of DON in infected kernels . FHB sevFhb1  and the b1 (Fhb2  have beeb2 (Fhb4  and Sumasome 5AS  are two ng wheat . Howevere to FHB . Thus faFT) gene  and a haein gene .Rht1 (Rht-B1) on chromosome 4B (Rht2 (Rht-D1) on chromosome 4D (Rht8 on chromosome 2D (Inverse correlations of FHB Type I and II resistances with plant height (PLHT) have been reported . Furtherosome 4B , Rht2  is widely used . Genome-An AMP comprising 192 predominantly Canadian spring wheat cultivars was assessed for FHB INC and SEV in multi-environment trials (METs) conducted between 2015 and 2017 at the three Agriculture and Agri-Food Canada (AAFC) FHB-inoculated nurseries near Morden (MDN) and Brandon (BDN) in Manitoba, and Ottawa (OWA) in Ontario, Canada. The panel comprised cultivar Sumai 3 with superior FHB resistance and its Canadian derivatives , derived from parents Alsen and ND744 . HoweverF. graminearum isolates was carried out at the MET nursery sites, as described in F. graminearum isolates  from Dr. Henriquez’s FHB culture collection was applied at AAFC’s Morden and Brandon FHB nurseries; the inoculum mixture was spread (8 g/m row) twice, 1 week apart. Plots were irrigated thrice a week using Cadman Irrigation travellers with Briggs booms at Morden and with an overhead irrigator system at Brandon. At the Ottawa nursery, F. graminearum inoculum was prepared at a 1:1 ratio mixture of corn and barley kernels inoculated with three isolates  obtained from the Canadian Collection of Fungal Cultures at the Ottawa Research and Development Centre. The three isolates collected locally were chosen for their high DON-producing capacity. Inoculation with 12 g of fresh inoculum was performed twice, with the first application occurring when the earliest lines started stem elongation, before flag leaf emergence (approx. Zadoks stage 31–36), and again 2 weeks later. Plots were irrigated daily applying approximately 1.5 cm of rain equivalent with wedge drive impact sprinklers. At approximately 21 days post-anthesis, the proportion of infected spikes per row (INC) and the average infected spikelets per head (SEV) were assessed visually and recorded as a percentage on a 0–100 scale  j, ijGE is the G × E interaction between genotype i and environment j, and ije is the residual effect associated with genotype i in environment j. The restricted maximum likelihood (REML) method was used to determine variance components (VCs). VCs were used to obtain broad sense heritability (H2) estimates for individual traits in a given environment, where replication is nested within the environment, using the formula H2 = σ2G/(σ2G + σ2GY/y + σ2GL/l + σ2GYL/yl + σ2e/ply), where σ2G is the genetic variance; σ2GY is the variance of the Genotype × Year interaction; σ2GL and σ2GYL are the interaction variances of the Genotype × Location, and Genotype × Year × Location, respectively; and σ2e is the error variance. Further, y is the number of years in which trials were conducted, l is the number of trial locations, and p is the total number of replications per location. Three-year (2015–2017) data on PLHT and DTA from MDN were averaged separately and deployed in a correlation analysis with FHB traits for BDN and OWA environments. An analysis of variance (ANOVA) for FHB traits across environments was performed using the aov function of the dplyr package in R to determine the effects of genotype (G), environment (E), and genotype-by-environment (G × E) variances on FHB INC, SEV, and IND.Statistical analyses of FHB datasets were performed separately for each of the three trial locations at BDN, MDN, and OWA. With the use of the  package  in R ver package , best li® Beadchip (Genomic DNA (gDNA) extraction from freeze-dried seedling leaves of the 192 cultivars was performed using a cetyltrimethylammonium bromide (CTAB)-based method. An automated AutoGen DNA isolation system  and a Quant-iT™ PicoGreen dsDNA Assay Kit  were deployed to obtain final gDNA sample concentrations of 50 ng/μl for genotyping. The AMP was genotyped with the wheat 90K single-nucleotide polymorphism (SNP) InfiniumBeadchip . SNP cal® software (K) or sub-populations within the AMP, using the Evanno method (K. Output from Structure was processed with the Structure Harvester® (https://taylor0.biology.ucla.edu/structureHarvester/). Structure Harvester uses an algorithm to execute the Evanno method, with a minimum of three sequential values of K and three replicates, and produces three plots, one of which uses a Delta K statistic to determine the number of K groups. To visualize relatedness or similarity among genotypes, a dimensionless Constellation plot depicting the hierarchical clustering patterns of the AMP was generated in JMP® Version 17 . In TASSEL  5  5 , a genotnkage) 5  to replaggplot2 function. The scatter plot helped visualize and determine LD decay on a whole-genome basis. To estimate kinship, the HapMap genotype data file was uploaded to TASSEL 5 and processed to output a.csv kinship (K-matrix) file. For PCA, the HapMap, phenotypic data file, and a population structure Q-matrix file (from the Structure run) were uploaded to TASSEL 5 to generate a.csv PCA output file. Both PCA and kinship.csv files were used as input for the GWAS analyses.LD decay, principal component analyses (PCAs), and relatedness or kinship analyses were estimated in TASSEL 5. An LD statistics output file from TASSEL 5 was imported to R to generate a scatter plot using the csv files, along with HapMap and phenotypic data files, were used as input for the GWAS analyses. In the mrMLM method, QTNs with a limit of detection (LOD) score greater than 3 and appearing in two or more environments were considered statistically significant. However, in the single-locus MLM method, QTNs were considered statistically significant if they exceeded a more stringent threshold of −log10(p) = 5, which is based on a critical p-value (α = 0.05) subject to a Bonferroni correction of 0.05/n, where n is the number of SNP markers  and multi-locus random SNP effect-mixed linear model (mrMLM) were deployed to compare and validate marker–trait associations. Further, to assess the consistency or reproducibility of QTNs detected from a multi-locus GWAS of individual environments, a combined multi-locus GWAS analysis using entry trait means based on all six environments was performed. With the use of the GAPIT (Genome Association and Prediction Integrated Tool) 3 package  in R, da markers .QTN4B_144-226 is the collective QTN name, which represents two PLHT QTNs (144_Tdurum_contig42229_113 and 226_Tdurum_contig42229_113) detected on chromosome 4B.Statistically significant QTNs detected across all environments by single-locus MLM and multi-locus mrMLM methods were numbered from 1 to 291. In most cases, the same SNP was associated with one or more traits per environment, or across environments. Hence, to refer to all QTNs associated with a given trait or traits, as a single entity, the chromosomal location followed by QTN numbers is given in the QTN name. For example, −4) on the online IWGSC RefSeq v2.1 database (https://urgi.versailles.inrae.fr/blast_iwgsc). High-confidence Traes gene IDs (as per RefSeq v1.0) were each searched in Ensemble Plants (http://plants.ensembl.org/index.html) and UniProt (https://www.uniprot.org/) databases to obtain their corresponding gene functional descriptions.The IWGSC reference genome RefSeq v1.0 was used for the physical positioning of QTNs and candidate genes detected from the GWAS analyses. The RefSeq v1.0 was preferred over RefSeq v2.0 and the current RefSeq v2.1 since it provided all QTN-associated candidate gene annotations. However, for better context, the current/new physical locations of updated candidate gene IDs (as per RefSeq v2.1) have also been provided in all MET dataset tables. For candidate gene detection, 90K SNP nucleotide sequences were subject to a nucleotide-BLAST (BLASTn) basic search with an expected threshold of 0.0001  observed for PLHT was 92% and 87% for DTA , positive and highly significant correlations ranging from 0.24 to 0.97 were observed among FHB INC, SEV, and IND traits. Overall weak, inverse correlations were observed between INC and PLHT (−0.14 to −0.36), SEV and PLHT (−0.24), and IND and PLHT (−0.30). DTA had overall weak inverse correlations with INC and with IND (−0.23 to −0.43), as well as weak positive correlations (0.18 to 0.2) with SEV . The largest number of SNP markers (2422) was distributed across the B genome, followed by the A genome with 1,978 and the D genome with 1,041. On a per-chromosome basis, the most number of SNP markers (551) was mapped to 2B, while 4D had the least number (43) of SNPs. The SNP marker distribution across 21 bread wheat chromosomes is given in an SNP density plot depicting the number of SNP markers within a 1-Mb window  (2 of marker pairs versus the genetic distance in base pairs (bp) across the genome. In the association panel, LD decay below a threshold of 0.2 r2 was observed to occur at a physical distance of ~7.08 Mb  > 5) for FHB INC on chromosomes 2D and 3D, and for SEV and IND on chromosome 3B, only from two MDN 2015 environment datasets (MDN15-1 and MDN15-2). The SEV and IND QTNs detected on chromosomes 3B in both datasets are identical and represented by SNP marker CAP7_c1576_371, which co-locates with TraesCS3B02G024900, which encodes a DEAD/DEAH box RNA helicase domain or TraesCS3B03G0056500 (3B:15817353..15827081) in RefSeq v2.1 (p < 0.05) SNP markers explained 17%–26% of the phenotypic variance for INC, 264 SNPs explained 19%–35% of the variance for SEV, and 270 statistically significant SNP markers explained 23%–36% phenotypic variance for IND  for INC, SEV, DTA, and PLHT, across nearly all chromosomes. In the environment-wise distribution of the 291 QTNs, 50 were for INC, 72 for SEV, 90 for IND, 41 for DTA, and 38 for PLHT, as given in 2) for FHB traits of the seven pleiotropic QTNs ranged from 1.6% to 22.4% for INC, up to 49.1% for SEV, and from 3.4% to 34.1% for IND. The three pleiotropic QTNs for INC and DTA had R2 values ranging between 11.7% and 20.1% for DTA and up to 19% for INC. For the two QTNs pleiotropic for FHB traits and PLHT, R2 values ranged from 5.0% to 13.2% for INC, 5.6% to 22.7% for SEV, 2.0% to 30.6% for IND, and 6.1% to 11.7% for PLHT , and co-locates with TraesCS3B02G024900, which encodes a DEAD/DEAH box RNA helicase domain (3B:15817353..15827081) in RefSeq v2.1 (QTN2D_7-31-162-184 is located at 62.96 Mb on chromosome 2D and co-locates with a ribosome biogenesis protein NSA2 homolog gene (2D:65431554..65434070) in RefSeq v2.1  in RefSeq v2.1  of chromosome 6B  in RefSeq v2.1  in RefSeq v2.1. The second, QTN1D_49-255, on chromosome 1D co-locates with a pentatricopeptide repeat-containing protein (1D:240672206..240673769) in RefSeq v2.1. The third FHB INC and DTA QTN on chromosome 6B, QTN6B_59-262, co-locates with an ATP synthase mitochondrial F1 complex assembly factor 1 (6B:567042315..567046184) in RefSeq v2.1  and co-locates with a protein-coding gene (6B:653923276..653926026) in RefSeq v2.0  in RefSeq v2.0 , explained 8.28%–46.28% of the phenotypic variance in DTA and co-locates with a disease resistance protein (1B:570333804..570337336) in RefSeq v2.1. The second QTN, QTN7A_61-219, on chromosome 7A (located at 675.23 Mb) explained 3.41%–13.96% of the phenotypic variance for DTA and co-locates with an uncharacterized protein (7A:679827145..679827600) in RefSeq v2.1  in RefSeq v2.1. The second 4B QTN, QTN4B_65-145-227, (located at 578.04 Mb), explained 3.94%–34.1% of the phenotypic variance and co-locates with a N-acetyltransferase domain-containing protein (4B:577090863..577094213) in RefSeq v2.1. The third PLHT QTN on chromosome 6B, QTN6B_151-229 (located at 8.78 Mb), accounted for 7.66%–43.58% of the phenotypic variance for PLHT and co-locates with an uncharacterized protein (6B:10687959..10689500) in RefSeq v2.1 , two of which were located on chromosome 4B and one on chromosome 6B. The first PLHT QTN on chromosome 4B, H2) values were lower for FHB traits when compared to PLHT and DTA. Multi-locus mrMLM detected a total of 291 statistically significant QTNs, 17 of which were detected at two or more environments of MDN and BDN. Twelve of these 17 QTNs were pleiotropic for INC, SEV, IND, DTA, or PLHT; two QTNs corresponded to DTA and three to PLHT. Consistency of the above results was backed by a separate combined six-environment multi-locus GWAS analyses. Among notable findings of this study are the detection of a pleiotropic QTN within the Fhb1 region on chromosome 3B, which is ~3 Mb from a cloned Fhb1 candidate gene TaHRC; a putatively novel PLHT QTN on chromosome 6B; a 1B DTA QTN located ~10 Mb from a Flowering Locus T1-like gene TaFT3-B1; and a DTA QTN on chromosome 7A, which is ~5 Mb from a maturity QTL QMat.dms-7A.3 of another study. Further, four of the 12 pleiotropic QTNs on chromosomes 1A, 1B, 3B, and 6B are potentially identical to corresponding QTLs in durum wheat. Upon validation of the above QTNs, their suitability for the downstream development of trait-specific markers for breeding selection will be assessed.This study detected pleiotropic QTNs for FHB resistance traits, DTA, and PLHT, as well as single-trait QTN (DTA and PLHT), for the characterization of FHB traits in relation to DTA and PLHT. Overall, highly significant and moderate-to-strong correlations were observed among FHB traits, which however shared weak inverse correlations with PLHT and with DTA. Broad-sense heritability . Further, in a protein–protein interaction study, the DEAD/DEAH box RNA helicase protein PRH75, also located on chromosome 5BL, was identified as a key hub protein that is induced in response to the powdery mildew fungus Blumeria graminis f. sp. tritici (Bgt). Given its co-location with FHB trait QTN3B_9-16-32-77-89-96-164-185 and proximity to TaHRC gene  on chromosome 2D, co-locates with a ribosome biogenesis protein NSA2 homolog (TraesCS2D02G113600). In a consensus genetic map by wsnp_Ex_c6400_11123059 representing the above QTN is located ~6 cM from SSR marker Xgwm484, which, along with marker Xgwm261, flanks the photoperiod sensitive gene Ppd-D1 on chromosome 2D (QAnth.crc-2D mapped at ~37.1 cM in a Kenyon/86ISMN recombinant inbred line (RIL) population, which is likely the effect of Ppd-D1  synthase protein located (224.1–233.3 Mb) within the Fhb2 region on chromosome 6B  carrying the Fhb1 locus and derived from an HC374/98B69*L47 cross (Fhb1 (The third pleiotropic anthesis . Its expt grains . The fouosome 6B . In resp47 cross . Furtherss (Fhb1 .QTN7A_40-274-288-173-197) pleiotropic FHB trait QTN on chromosome 7A corresponds to a multiple organellar RNA editing factor (MORF) 2 gene. MORF family proteins are involved in RNA editing  pleiotropic FHB trait QTN, also on chromosome 7A, co-locates with an actin–fragmin kinase (AFK) catalytic domain-containing protein. AFK, an actin-binding protein kinase  stability/assembly factor. PSII is bound by oxygen-evolving enhancer proteins  on chromosome 7B, ns OEEs; , which ans OEEs;  during ans OEEs; .QTN1A_46-252 (508.5 Mb), co-locates with an uncharacterized protein; in addition, it coincides with a durum wheat INC and SEV 1A.3 QTL interval (503–580 Mb) identified by QTN1D_49-255, on chromosome 1D co-locates with a pentatricopeptide repeat (PPR)-containing protein. PPR proteins  encoding genes catalyze the oxidative phosphorylation of ADP to ATP  on chromosome 7B, coincides with an MMS19 nucleotide excision repair protein. MMS proteins, among other genes, are involved in the excision repair of DNA damaged by ultraviolet (UV) light in plants , co-locates with a disease resistance protein (570.33–570.337 Mb). It is ~8 Mb from a wheat FLOWERING LOCUS T 1-like gene , co-locates with an uncharacterized protein located ~0.85 Mb (as per RefSeq v2.1) away from a maturity QTL QMat.dms-7A.3 (680.67–717.91 Mb in RefSeq v2.1) identified in a RIL population of a ‘Peace’ × ‘CDC Stanley’ cross  genes Rht-B1, Rht-D1, and Rht-8. Of the three QTN detected for PLHT, two were located on chromosome 4B, and one was located on chromosome 6B. The first on chromosome 4B, QTN4B_144-226 (located at 38.28 Mb), is associated with a protein kinase domain-containing protein, located at 10.15 Mb from the Rht-B1 the gene TraesCS4B02G043100 (4B:30861268..30863723 bp in RefSeq v2.1) represented by SNP Tdurum_contig27834_923 (30.86 Mb). Plant protein kinases catalyze the phosphorylation of proteins, some of which are associated with plant growth and development, disease resistance, and abiotic stresses , is associated with a N-acetyltransferase (NAT) domain-containing protein located within a ~30 Mb genomic region (569.2–599.6 Mb in RefSeq v2.1), which harbors genes regulating maturity and flower development , is associated with an uncharacterized protein. High R2 values and LOD scores observed for QTN4B_65-145-227  and QTN6B_151-229  suggest that both QTNs could have a significant influence on PLHT. Several GWAS and QTL studies have reported physical locations of various PLHT QTLs and SNP markers on chromosome 6B (QTN6B_151-229 (8.78 Mb) on chromosome 6B of our study does not coincide with the physical locations of 6B QTL loci reported in the above studies suggests the novelty of the QTN6B_151-229 locus. Therefore, similar to FHB and DTA, breeders might consider recombining the above PLHT QTN and FHB QTN loci in elite lines that lack desirable FHB and PLHT traits and select for FHB-resistant lines with desired plant height and maturity suited to their respective geographical regions. For example, in Canada, recombining FHB resistance with PLHT and DTA would be most desirable in terms of developing FHB-resistant cultivars that are early maturing and semi-dwarf/short stature, making them less prone to lodging and hence beneficial from a yield standpoint. Taller lines have the added advantage of some level of escape from soil residue-borne FHB infection or Type I resistance  and PLHT QTN are the results of true FHB resistance or disease escape/passive resistance of the taller lines , all originating from crosses of bridging parents Alsen and/or ND744 (2 up to 36%) in INC, SEV, IND, DTA, or PLHT traits were detected. For example, 18 statistically significant QTNs for FHB traits were detected only in the OWA 2017 environment and not in the other two environments of MDN or BDN (The detection of or ND744 . In addiQTN1A_46-252 (508.5 Mb) for INC and DTA on chromosome 1A, whose location coincides with an INC and SEV 1A.3 QTL interval (503–580 Mb) on chromosome 1A of durum wheat. The second, QTN1B_48-206 (563.67 Mb), for DTA falls within a 1B.1 QTL interval (544–581 Mb) influencing DTA, PLHT, and FHB traits in durum wheat and is located ~8 Mb from a wheat FLOWERING LOCUS T 1-like gene (TaFT3-B1). The third, QTN3B_9-16-32-77-89-96-164-185 (10.71 Mb), for FHB , which was detected within the Fhb1 region on chromosome 3B, is located in close proximity to a 3B.2 (9.8 Mb) QTL of durum wheat. Finally, the fourth pleiotropic QTN6B_70-169-194 for SEV, IND, and PLHT on chromosome 6B of this study is located within a 585–707-Mb interval of a 6B.1 QTL for FHB resistance in durum wheat.Another interesting finding of this study might suggest the presence of potentially similar or identical QTLs for FHB, DTA, and/or other traits, in bread and durum wheat, given their proximal or near-identical genomic locations. Based on the results of a durum GWAS by Fhb1 region, a 1B DTA QTN close to a Flowering Locus T1-like gene TaFT3-B1, a putatively novel 6B PLHT QTN, a 7A DTA QTN close to a maturity QTL QMat.dms-7A.3 of another study, and four pleiotropic QTNs, all potentially identical to their counterparts in durum wheat. Further assessment and validation of the identified QTNs are merited, for example, in bi-parental segregating or doubled-haploid populations tested across multiple environments. Once validated, the above QTN-derived KASP markers would provide breeders with options to deploy pleiotropic loci for FHB resistance and PLHT or recombine FHB resistance with PLHT and/or with DTA loci and select for FHB-resistant lines with desired maturity and height.Given that FHB is a yield and quality-limiting disease of wheat, there is a constant need for reliable SNP markers to identify FHB-resistant genotypes. The 17 QTNs detected in this study are potentially significant from a breeding and cultivar improvement perspective. Among notable ones are the 3B QTN within the The original contributions presented in the study are included in the article/RK, YR, and RC conceived, designed, and provided oversight on the study. RC and RK constructed the bread association mapping (AM) population. RC, RK, and YR contributed to the bread germplasm development and seed increase in the AM population. MH, SK, AB, SB, FB, IP, YR, RC, and RK performed field trials and disease evaluation at FHB nurseries located in Morden and Brandon, Manitoba, and Ottawa, Ontario. WZ, KB, and PF contributed to the genotyping. BM, WZ, and LL generated an initial genetic map. AC performed all statistical analyses as part of the GWAS for QTNs and candidate detection and prepared the original draft. AC, RK, YR, and RC analyzed the data and interpreted the results. AC, RK, and YR edited the original draft. All authors contributed to the article and approved the submitted version.


Identifying individual characteristics based on trace evidence left at a crime scene is crucial in forensic identification. Microbial communities found in fecal traces have high individual specificity and could serve as potential markers for forensic characterization. Previous research has established that predicting body type based on the relative abundance of the gut microbiome is relatively accurate. However, the long-term stability and high individual specificity of the gut microbiome are closely linked to changes at the genome level of the microbiome. No studies have been conducted to deduce body shape from genetic traits. Therefore, in this study, the vital role of gut bacterial community characteristics and genetic traits in predicting body mass index (BMI) was investigated using gut metagenomic data from a healthy Chinese population.2) of 0.56 and a mean absolute error (MAE) of 2.09 kg/m2. The overweight group exhibited significantly higher α-diversity than the other BMI groups at the level of gut microbial genes. Furthermore, there were significant variations observed in the single-nucleotide polymorphism (SNP) density of 732 contigs between these three BMI groups. The BMI prediction model, reliant on the 62 most contributing contigs, exhibited a model R2 of 0.72 and an MAE of 1.56 kg/m2. The model predicting body type from 44 contigs correctly identified the body type of 93.55% of the study participants.Regarding the gut microbial community, the underweight group displayed increased α-diversity in comparison to the other BMI groups. There were significant differences in the relative abundances of 19 species among these three BMI groups. The BMI prediction model, based on the 31 most significant species, showed a goodness of fit (RBased on metagenomic data from a healthy Chinese population, we demonstrated the potential of genetic traits of gut bacteria to predict an individual’s BMI. The findings of this study suggest the effectiveness of a novel method for determining the body type of suspects in forensic applications using the genetic traits of the gut microbiome and holds great promise for forensic individual identification.The online version contains supplementary material available at 10.1186/s12934-023-02255-3. Because it visually represents an individual’s body shape, it is sometimes used to describe the appearance of criminal suspects. Body type is related to height, weight, bone structure, and other factors. Forensic anthropology and forensic genetic approaches can be used to indirectly indicate a person’s body type by estimating their height and weight. Forensic anthropology methods determine height through regression equations derived from direct measurements or CT scans of the long bones of the upper and lower extremities, sternum, and vertebrae . Following this, we included the SNP density of individual samples as a biomarker to the model, which led to better predictive performance compared to the model based only on species abundance. Specifically, the R2 value increased while the MAE decreased, and the SNP density index of individual samples played a significant role in improving model performance, suggesting that genetic characteristics of gut bacteria may be more effective in predicting individual BMI. To investigate this, we measured the SNP densities of all sample contigs and used the Spearman test to determine the correlation between species abundance and contig SNP density with BMI. Our findings indicate that there is a low and almost statistically nonsignificant correlation between species abundance and BMI. On the other hand, the correlation between SNP densities of the 767 contigs is relatively high, which may suggest that genetic traits are more influential than community traits. Based on these findings, the SNP densities of 62 contigs were chosen as indicators to create the prediction model, and we achieved the best fit and accuracy with an R2 value of 0.72 and an MAE of 1.56 kg/m2. These results highlighted the effectiveness of the BMI prediction model based on contig SNP density, which demonstrated the highest accuracy and the lowest mean absolute error. In conclusion, the findings indicate that the genetic specificity of gut bacteria is a more reliable predictor of individual characteristics than community specificity, and using genetic characteristics of gut bacteria to predict individual BMI is an innovative and effective approach that requires further research.Advances in high-throughput sequencing technology have increased the amount of microbial information available to forensic scientists. This information can be used to differentiate between individuals or groups based on the abundance of species present in the gut microbial community or to infer various traits to aid in investigation and trial evidence. Wang et al.  developeTo further validate the utilization of gut bacterial genetic specificity for the prediction of individual traits over community specificity, we established a model for the prediction of body shape based on two markers, species abundance and contig SNP density. The SNP density model showed 94% accuracy in predicting body shape, which was significantly higher than that of the species abundance model. However, all the underweight samples were inaccurately predicted by both models. This might be due to the limited number of underweight volunteers who participated in our study, and their BMI was close to normal weight, which averaged 17.60 (± 0.47), making it impossible to correctly characterize the samples in the underweight group. The comparable gut microbial composition in the underweight and normal weight groups may also be a plausible explanation. Our study showed a significant difference in the abundance of just one species between the two groups, but none of the contigs’ SNP densities varied significantly. Furthermore, Wang et al.  used a lThis study presents an initial investigation into gut metagenomes and genetic characterization of microorganisms as they relate to forensic practice. Examination of gut metagenomes allows for the investigation of potential correlations between bacteria and hosts, such as specific species and gene-level associations. The genetic characterization of gut microbes can predict the individual’s BMI and body type with higher accuracy than the community characterization of species. This implies that the genetic attributes of bacteria can be utilized to infer individual characteristics with precision. In this study, the correlation between the gut microbiome and human BMI were examined in a healthy Chinese population. It must be noted that an individual’s smoking status, alcohol consumption, and medication history are typically unknown in forensic cases, as is their health status and whether they suffer from any diseases. Future studies should take into account a broader range of factors that affect the gut microbiome. In conclusion, the applicability of the gut microbiome in forensic personal characterization requires further research.2 of 0.72 and an MAE of 1.56 kg/m2, and the accuracy of predicting body type was 94%. This corroborates the potential of forensic BMI or body type trait inference at the genome level, offering novel ideas and methods for practical forensic applications to deduce external features of criminal suspects.In this study, the association between changes in the microbial community and gene levels and host BMI characteristics was investigated using gut metagenomic data. We utilized the RF machine learning algorithm to develop two prediction models, and these models exhibited high accuracy on both the training and test sets, indicating a significant correlation between the gut microbiome and personal BMI characteristics. In particular, the prediction model based on species SNP density delivered the best performance: the model accurately predicted BMI with an RBelow is the link to the electronic supplementary material.Supplementary Material 1


Polygenic risk score (PRS) analyses are now routinely applied across biomedical research. However, as PRS studies grow in size, there is an increased risk of sample overlap between the genome-wide association study (GWAS) from which the PRS is derived and the “target sample,” in which PRSs are computed and hypotheses are tested. Despite the wide recognition of the sample overlap problem, its potential impact on the results from PRS studies has not yet been quantified, and no analytical solution has been provided.Here, we first conduct a comprehensive investigation into the scale of the sample overlap problem, finding that PRS results can be substantially inflated even in the presence of minimal overlap. Next, we introduce a method and software, EraSOR (Erase Sample Overlap and Relatedness), which eliminates the inflation caused by sample overlap (and close relatedness) in almost all settings tested here.EraSOR could be useful in PRS studies  similar to those investigated here, either (i) to mitigate the potential effects of known or unknown intercohort overlap and close relatedness or (ii) as a sensitivity tool to highlight the possible presence of sample overlap before its direct removal, when possible, or else to provide a lower bound on PRS analysis results after accounting for potential sample overlap. Polygenic risk scores (PRSs) are proxies of individuals’ genetic liability to a trait or disease  that havR2) and association P values, leading to false-positive and exaggerated findings [However, expansion in data sizes comes with a cost in this setting: as sample sizes increase, it is more likely that samples are recruited into multiple cohorts or that entire cohorts are included in multiple consortia. For PRS analyses, which typically test for association between PRS and a trait(s) or outcome of interest, overlapping samples between the GWAS and target data samples can result in spurious inflation of the coefficient of determination , a software that adjusts GWAS summary statistics  to correk = {1, 2} performed on the same continuous outcome kY. The effect size of the gth single-nucleotide polymorphism (SNP) in study k  would equal 1). Assuming sample overlap does not affect the standard error estimates, LeBlanc et al. [c) is known, one can adjust the joint distribution of the summary statistics (z-scores) of the 2 GWASs aswhich captures correlations due only to sample overlap, independent of the true causal effect  score regression [where ffective , it requgression , 19 to eSTF here measures the genetic differences between 2 subpopulations that make up each study population, and the STF within each study should be the same. Yengo et al. further introduced an environmental stratification term, Bivariate LD score regression  is typicj and all other SNPs within the same LD block; where ectively . Since wc and hence apply Equation We wish to solve for Njl corresponding to a scaled estimate of heritability  of the genotype data, and those individuals in the (largest) cluster corresponding to European ancestry were retained for the primary analyses because polygenic risk scores have been shown to have low portability between ancestries [The UK Biobank is a prospective cohort study of around 500,000 individuals recruited across the United Kingdom during 2006–2010. The genetic data from the UK Biobank comprises 488,377 samples and 805,426 SNPs. Standard quality control (QC) procedures were performed, removing any SNPs with minor allele frequency <0.01, genotype missingness >0.02, and a Hardy–Weinberg equilibrium test cestries , motivatY) with heritability (2h) of 0, 0.1, and 0.5 were simulated using the UK Biobank genotype data  as input. Quantitative traits were simulated asQuantitative phenotypes  to generate 2 different sizes of base GWAS data. Next, we randomly sampled 1,000, 5,000, 10,000, or 50,000 individuals from the remaining sample to act as 3 different sizes of target data, of which 0%, 5%, 10%, 50%, or 100% were randomly selected from the base data sample so that there was a known degree of sample overlap between the base and target data. In addition, we generated an “overlap-free” base cohort in which the overlapping samples were removed from the base cohort so that we could compare the result of applying EraSOR against results of physically removing overlapped samples from the base cohort.In order to search a feasible parameter space in sufficient depth, we only simulate phenotype with a heritability of 0.5, with a base cohort of 250,000 and a target cohort of 5,000; only simulate a base cohort with 120,000 samples when the phenotypic heritability is ≤0.1 and target cohort has 5,000 samples; and only simulate a target cohort with 1,000 and 10,000 samples when the base cohort contains 250,000 samples and the phenotypic heritability is ≤0.1. The entire set of simulations was repeated 100 times.Binary traits were simulated under the liability threshold model , simulatIn the binary trait setting, overlap can be ascertained such that the overlap is among cases, among controls, or among both. To investigate the effect of case-only or control-only overlap, we randomly selected 120,000 effective samples (effective samples defined as ocument} ) as the In order to search a feasible parameter space in sufficient depth, we only varied the trait heritability when the population prevalence was 0.1 and only varied the population prevalence when the trait heritability was ≤0.1. These simulations were repeated 100 times.Spurious inflation in PRS analysis test statistics may also be observed when there are closely related individuals between the base and target cohorts. To investigate the effects of relatedness on PRS results, we repeated the quantitative trait simulations with a modified Equation ε represents a combination of nonshared environment and random error, which follows where An assumption of the EraSOR algorithm is that the environmental stratification (UK Biobank samples were divided into European and non-European ancestries based on 4-mean clustering on PC1 and PC2 (see above). Quantitative traits with environmental stratification were then simulated aswith the environmental stratification term (S) defined aswhere To investigate the effect of sample overlap in real phenotypic data, we extracted body mass index , height (field ID 50), and low-density lipoprotein  from the UK Biobank. We residualized the phenotypes against age , sex (field ID 31), genotyping batch, UK Biobank assessment center (field ID 54), and 40 PCs. For LDL, we additionally removed individuals who were on statin medication see  and addiTo model polygenic risk score analyses with sample overlap, we randomly sampled two-thirds of the individuals with phenotypic information to generate the base GWAS data. Next, we randomly sampled 5,000, 10,000, 5,000, and one-third of the individuals with phenotypic information from the remaining sample to act as the target data, of which 0%, 5%, 10%, 50%, 60%, 70%, 80%, 90%, or 100% were randomly selected from the base data sample so that there was a known degree of sample overlap between the base and target data. In addition, we generated an “overlap-free” base cohort in which the overlapping samples were removed from the base cohort so that we could compare the results of applying EraSOR with the results of direct removal of overlapping samples from the base cohort. The entire set of simulations was repeated 50 times.In addition, we utilized the latest GWAS summary statistics on LDL from the Global Lipids Genetics Consortium (GLGC)  to evalu–glm function. As binary traits were simulated only for the European ancestry–only analyses, where population structure was not simulated, and considering the computational cost of including covariates in the logistic regression, we did not include PCs in our binary trait analysis. On the other hand, quantitative traits were simulated in all scenarios, some of which were population stratified. Thus, we included 15 PCs as a covariate for our quantitative trait analyses. The resulting summary statistics were then provided to EraSOR to generate the adjusted summary statistics using European LD scores [R2 and P value of association of the PRS–trait tests were reported.GWASs were performed on the base and target cohorts using PLINK 2.0 (version 2021–08-04)  with theD scores  calculatD scores . PRS anaD scores  with theR2, calculated using base cohorts without overlapped samples. The bias could then be measured as the observed PRS R2 minuses the baseline PRS R2  using summary statistics adjusted by EraSOR (“adjusted PRS”) and (ii) using summary statistics generated from a base cohort with all overlapping and/or related samples removed (“overlap-free PRS”). We present the performance of EraSOR as the PRS–trait association clumping+thresholding (C+T) PRS calculation method [PRSice [The presence of overlapping samples between the base and target datasets is known to cause inflated association between PRSs and phenotypes, but the extent and characteristics of the problem have not been described. Here, we performed extensive simulations using the UK Biobank  genotypementclass2pt{minim [PRSice , 28.P value of association) was highly positively correlated with the degree of overlap (Pearson correlation coefficient P < 2.2 × 10−16). For example, when there was a base cohort of 250,000 samples, target cohort of 5,000 samples, and 250 overlapping samples , the false-positive rate was 16%, while this increased to 90% when there were 500 overlapping samples   Fig. .P = 1.18 × 10−9). The reverse relationship between inflation and population prevalence was observed for control-only overlap (P = 1.18 × 10−19). For a population prevalence of 0.5, case-only and control-only overlap had the same impact on the inflation , where intercohort relatedness is defined as h2SNP <5%.The analyses in this section were performed only to highlight the potential impact of sample overlap, since highly significant PRS–trait associations were observed with overlap even for nonheritable traits, for which there should be no PRS–trait association. EraSOR should not be applied to data on traits for which there is little evidence of heritability, since PRS analyses performed on underpowered GWAS are more likely to generate misleading results and conclusions based on them. Therefore, as for PRS analyses in general , we do nIn the next section, we extend these investigations to consider the effects of sample overlap on PRS–trait associations on heritable traits, but we present these findings in conjunction with results based on the application of our method EraSOR, which is designed to resolve the problem.To tackle the problem of inflation caused by inter-cohort overlap and relatedness, we developed the EraSOR method. Using GWAS summary statistics generated from the base and target cohorts, EraSOR implements univariate and bivariate LD score regression , 19 to eIn order to evaluate the performance of EraSOR, we conducted an extensive set of simulations covering a range of scenarios of intercohort sample overlap and relatedness (see below and Methods).−5 (standard error: 3.83 × 10−4) when there are 10,000 causal variants and 2.21 × 10−4 (standard error: 4.77 × 10−4) when there are 100,000 causal variants. Left unadjusted, the mean −4), which is still far closer to the truth than the unadjusted PRS  data Fig. . These sted Fig. , with th(v0.4.5)  instead −5 (standard error = 2.76 × 10−4)   in relation to control-only overlap.EraSOR also performs extremely well for binary traits in our simulations Fig. –D. In bi−4) Fig. , and the−4) Fig. . On the −4 (standard error = 1.03 × 10−4). This is likely caused by the fact that a key component of the mathematics underlying the EraSOR algorithm . For nonheritable quantitative traits with base cohorts of 120,000 samples and target cohorts of 10,000 samples, if all target samples are also present in the base cohort, then we observe a false-positive rate of 20%, with a mean . For nonOne of the main assumptions of EraSOR is that there is no environmental stratification (STF between the European and non-European samples (see Methods) in UK Biobank (STF = 0.018), coupled with a high environmental stratification  removed was 0.145, and the PRS R2 using the GWAS that included overlapping samples was 0.228, representing a relative increase of 57%. Although we did not observe the same inflation with the EraSOR-adjusted summary statistics, which yielded a PRS R2 of 0.0962, its performance was less accurate compared to the case where base and target ancestries were relatively matched, with a relative PRS R2 decrease of 33.5%, suggesting that EraSOR might be overly conservative in such settings.We note that EraSOR relies on the intercept term from LD score regression, the estimate of which assumes that the level of genetic and environmental stratification is similar between the base and target cohorts . When weOverall, our real data analyses confirmed the performance of EraSOR as predicted by our simulations, and we anticipated that EraSOR will be an appropriate tool for sensitivity analyses in real-world settings.The recent advent of large-scale national and regional biobank projects, such as the UK Biobank , Japan BR2), and false-positive rate. This inflation can be high even when the absolute number of overlapping individuals is small, if this makes up a notable fraction of the target samples. The inflation is noticeably more severe for binary traits with a small population prevalence when all the overlapping samples are cases. Therefore, PRS results will likely be misinterpreted unless intercohort sample overlap and close relatedness are properly accounted for.We demonstrated that intercohort overlap results in a significant and often substantial inflation in the observed PRS–trait association, coefficient of determination , and thus, we do not recommend applying EraSOR to data for which the STF within or between base and target data is greater than 0.02. In practical terms, this means that until further testing or development of EraSOR has been performed, EraSOR should only be applied to single-ancestry base and target datasets that are closely matched by ancestry to each other. As it happens, cohorts of highly different ancestry are less likely to involve sample overlap. Additionally, EraSOR has only been tested using the C+T and lassosum PRS calculation method and so is subject to their limitations, such as potential overfitting of PRS–trait associations. However, given the similar performance of different PRS methods [Our algorithm is not without limitation. First, as EraSOR relies on LD score intercept estimates for adjustment, all assumptions of LD score regression also apply to EraSOR. For instance, if the level of genetic and environmental stratification differs between the 2 cohorts, the bivariate LD score equation may not hold , which c methods , we do nIt is important to note that, due to reliance on LD score regression estimates, EraSOR only produces sufficiently accurate adjustments for application when both base and target cohorts have sample sizes greater than 1,000 and is only consistently accurate when both cohorts have greater than 5,000 samples. In addition, when there is a complete overlap between 2 equal-sized cohort, EraSOR can fail to fully adjust for the inflation caused by sample overlap. Despite this, EraSOR-adjusted results are still far closer to the empirical truth than the unadjusted PRS results. In general, EraSOR tends to overcorrect, meaning that PRS–trait associations are underestimated after EraSOR adjustment. One possible explanation is that the equations by LeBlanc et al.  were basNevertheless, we argue that a large discrepancy between the unadjusted and EraSOR-adjusted results should act as a warning as to the possible presence of intercohort overlap or close relatedness. In such cases, it is recommended to investigate the potential source of sample overlap or relatedness and take appropriate measures to address them, such as removing the related or overlapped individuals, or adjusted for such bias using EraSOR to obtain more reliable PRS analysis results.Project Name: EraSORhttps://choishingwan.gitlab.io/EraSOR/Project homepage: Programming language: Python (version 3.0+)License: GNU General Public License version 3.0 (GPLv3)Any restrictions to use by nonacademics: Nonehttps://gitlab.com/choishingwan/sample_overlap_paperSimulation scripts: biotools ID: erasorRRID: SCR_023542GigaScience repository, GigaDB [All code used for this article is available in the GitLab repository  and impl, GigaDB .Supplementary Table S1. Simulation results.Supplementary Table S2. Results for real data analyses.Supplementary Figure S1 False positive rate corresponding to different level of sample overlap.Supplementary Figure S2 Effect of sample relatedness on performance of PRS for non-heritability phenotypesSupplementary Figure S3 Comparing the performance of the PRS using the EraSOR adjusted summary statistics and unadjusted summary statisticsSupplementary Figure S4 Comparing the performance of PRS using the EraSOR adjusted summary statistics and the unadjusted summary statistics for quantitative trait when there are population stratifications.Supplementary Figure S5 Comparing the performance of PRS using the EraSOR adjusted summary statistics and the unadjusted summary statistics for binary trait analyseSupplementary Figure S6 Performance of EraSOR when adjusting for related samples between the target cohort and the base cohortgiad043_GIGA-D-22-00019_Original_SubmissionClick here for additional data file.giad043_GIGA-D-22-00019_Revision_1Click here for additional data file.giad043_GIGA-D-22-00019_Revision_2Click here for additional data file.giad043_GIGA-D-22-00019_Revision_3Click here for additional data file.giad043_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giad043_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giad043_Response_to_Reviewer_Comments_Revision_2Click here for additional data file.giad043_Reviewer_1_Report_Original_SubmissionChristopher C. Chang, Ph.D. -- 2/14/2022 ReviewedClick here for additional data file.giad043_Reviewer_2_Report_Original_SubmissionJack Pattee, Ph.D. -- 2/21/2022 ReviewedClick here for additional data file.giad043_Reviewer_2_Report_Revision_1Jack Pattee, Ph.D. -- 10/19/2022 ReviewedClick here for additional data file.giad043_Reviewer_3_Report_Original_SubmissionSamuel A. Lambert -- 2/28/2022 ReviewedClick here for additional data file.giad043_Reviewer_3_Report_Revision_1Samuel A. Lambert -- 11/3/2022 ReviewedClick here for additional data file.giad043_Reviewer_3_Report_Revision_2Samuel A. Lambert -- 4/27/2023 ReviewedClick here for additional data file.giad043_Supplemental_FileClick here for additional data file.BMI: body mass index; EraSOR: Erase Sample Overlap and Relatedness; GLGC: Global Lipids Genetics Consortium; GWAS: genome-wide association study; LDL: low-density lipoprotein; PC: principal component; PRS: polygenic risk score; QC: quality control; SNP: single-nucleotide polymorphism.The authors declare that they have no competing interests.http://dx.doi.org/10.13039/501100000265 MR/N015746/1 and the National Institutes of Health (R01MH122866) to P.F.O. This report represents independent research partially funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. Research reported in this article was supported by the Office of Research Infrastructure of the National Institutes of Health under award number S10OD026880. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, NHS, the NIHR, or the Department of Health.Medical Research Council FundRef identification ID: Conceptualization: S.W.C. and P.F.O. Methodology: S.W.C., T.S.H.M., C.H., and P.F.O. Investigation: S.W.C. Software: S.W.C. Supervision: P.F.O. Funding acquisition: P.F.O. Writing—original draft: S.W.C. Writing—review and editing: S.W.C., C.H., and P.F.O.


Information on the nature and extent of genetic and genotype × environment (GE) interaction is extremely rare in wheat varieties under different sowing dates. In the present study, the GGE biplot method was conducted to investigate genotype × environment interaction effects and evaluate the adaptability and yield stability of 13 wheat varieties across eight sowing dates, in order to facilitate comparison among varieties and sowing dates and identify suitable varieties for the future breeding studies.Considerable genotypic variation was observed among genotypes for all of the evaluated traits, demonstrating that selection for these traits would be successful. Low broad sense heritability obtained for grain yield showed that, both genetic and non-genetic gene actions played a role in the control of this trait, and suggested that indirect selection based on its components which had high heritability and high correlation with yield, would be more effective to improve grain yield in this germplasm. Hence, selection based on an index may be more useful for improvement of this trait in recurrent selection programs. The results of the stability analysis showed that the environmental effect was a major source of variation, which captured 72.21% of total variation, whereas G and GE explained 6.94% and 18.33%, respectively. The partitioning of GGE through GGE biplot analysis showed that, the first two PCs accounted for 54.64% and 35.15% of the GGE sum of squares respectively, capturing a total of 89.79% variation. According to the GGE biplot, among the studied varieties, the performance of Gascogen was the least stable, whereas Sirvan, Roshan, and Pishtaz had superior performance under all sowing dates, suggesting that they have a broad adaptation to the diverse sowing dates. These varieties may be recommended for genetic improvement of wheat with a high degree of adaptation.The results obtained in this study demonstrated the efficiency of the GGE biplot technique for selecting high yielding and stable varieties across sowing dates. Wheat is one of the most important staple-foods with global production of over 700 million tones and supplies about 20% of the total calories and daily proteins to 4.5 billion people worldwide , 2. Due Sowing date is an important management factor in the production of any crop , becauseThe genotype × environment (GE) interaction results from genotypic rank fluctuation or fluctuations in the absolute differences between genotypes without rank change , 11. GEIUnivariate linear regression models  and multThe efficiency of a selection program is mainly dependent on the nature and extent of genetic variability and heritability of the traits . At thisThe GE interaction reduces the association of phenotypic and genotypic values and selection progress, and results in bias in the estimations of gene effects and combining ability of different traits that are sensitive to environmental changes . KnowledP ˂ 0.01) between the genotypes in terms of all traits; indicating considerable genotypic variation among the selected varieties  had the highest portion of phenotypic variance; while, for GY and TGW, the genotype × year (σ2gy) effect had the highest components of phenotypic variance. Based on the combined data over three years and eight sowing dates, broad-sense heritability estimates ranged from 31.39% for GY to 76.17% for BY. Moreover, the heritability of yield components  were higher than that of grain yield (Table Phenotypic and genotypic coefficients of variation (PCV and GCV) are presented in Table R2 = 89–97%); while, under late sowing dates, the number of grains per spike (NGSP) was the most important component . Moreover, under all of the sowing dates, NSP, NGSP, TGW, and BY were identified as effective yield components and explained over 90% of the observed variation for grain yield  method Fig. . This meA unique feature of the GGE biplot is that it allows comparison of genotypes with the ideal genotype. In this regard, the genotypes which are located close to the ideal genotype in the biplot, are desirable ones. Thus, using the ideal variety as the center, concentric circles were drawn to help visualize the distance between each variety and the ideal variety. According to the ideal genotype view Fig. , the varThe discriminating power of the genotypes and representativeness of other environments, are two important features of an environment Fig. . These fIn this study, 6th November had the longest vector and therefore was the most discriminating sowing date, followed in order by 6th September, 6th October, 6th February, and 6th March Fig. ; howeverThe GGE biplot can also be used for determining the relative performance of all genotypes in a specific environment. As an example, we consider the sowing date of 6th November, which had the highest grain yield among the other sowing dates. A line was drawn that passed through the biplot origin and the 6th November position to make an axis for this sowing date, and then a dashed line was perpendicularly drawn from each variety towards the 6th November axis Fig. . The varKnowledge on the relative extent of genetic, environment, and their interaction effects is essential in the breeding of crop plants. The GGE biplot is a very powerful tool for the analysis and interpretation of GE; which effectively detects the interaction pattern graphically, identifies ‘which-won-where’ pattern, and delineates mega-environments among the test locations . CombineWide genetic variation observed for all of the studied traits is promising for genetic progress through selection in the studied germplasm. More gain will result through selection when the difference between PCV and GCV is small, since it implies the more negligible effect of the environment and therefore higher heritability. In this study, smaller differences were observed between these coefficients for HI, BY, and TGW, indicating that more gain will result through selection for these traits. Jalata et al.  in barleEstimation of heritability is an important objective in breeding programs, because it determines the influence of environmental and genetic factors on the expression of the traits of interest, and provides early information for designing an effective breeding program to maximize genetic improvement . In the As the effect of yield components on yield is not explained by correlation coefficients , correlaThe GE interaction has been an important and challenging issue among plant breeders and agronomists involved in performance testing. This complicates the selection process, because it reduces the usefulness of genotypes by confounding their yield performance through minimizing the association of genotypic and phenotypic values . TherefoAs the AEC abscissa approximates the genotype contributions to G, the AEC ordinate must approximate the genotype contributions to GE, which is a quantification of stability or instability . In thisTwo groups of varieties were identified in terms of average performance. The first group, with above-average performance, included four varieties of Pishtaz, Mihan, Parsi, and Oroom, which were highly stable. The remaining varieties, i.e. Pishgam, Chamran, Falat, and Baz, showed moderate to low stability. The four varieties of Pishtaz, Mihan, Parsi, and Oroom, with high yield and highest stability, can be considered as the most desirable for different sowing dates, and using of them by farmers would result in stable performance across different sowing dates. However, Pishgam, Chamran, Falat, and Baz, which had high yield and low stability, are desirable for specific sowing dates. Overall, the varieties of the first group could also be used in the breeding programs for the development of new varieties with consistent performance. The second group, which had varieties with low yield but moderate to high stability, was considered as the more undesirable group.According to the GGE biplot, an ideal genotype should have the highest mean performance of all genotypes and be absolutely stable to show broad adaptability in a wide range of environments . In thisIn conclusion, high genetic variation for agronomical studied traits showed the high potential of the studied germplasm for genetic improvement through targeted selection in breeding programs. The moderate to high broad-sense heritability for yield components  showed that these traits are mainly under genetic effects; therefore, recurrent selection would be useful in achieving genetic advance for the above mentioned traits. However, the order of priority of these components and their direct and indirect effects was different for diverse sowing dates. So, under early sowing dates, NSP had the highest positive direct effect on the grain yield, and therefore, was the most important and effective component of grain yield; while, under late ones, NGSP showed the highest positive direct effect on it and was the most important component of grain yield. This suggests that indirect selection for the development of high yielding varieties should be done with a specific model. Moreover, because of the moderately low broad-sense heritability for grain yield, both genetic and non-genetic effects played a role in the genetic control of this trait. Therefore, selection based on an index may be more useful for the improvement of grain yield in recurrent selection programs. On the other hand, as the interaction of G × E is significant, selection of superior varieties for the improvement of wheat should be done based on the multi-environmental trails. From analysis of wheat varieties through the GGE biplot method, two groups of varieties were identified in terms of average performance. The first group, with above-average performance, included four varieties of Pishtaz, Mihan, Parsi, and Oroom, which were highly stable. The remaining varieties i.e. Pishgam, Chamran, Falat, and Baz showed moderate to low stability. Within the first group, three varieties of Pishtaz, Mihan, and Oroom had superior performance in all of the sowing dates, suggesting that they have broad adaptation to the diverse sowing dates. These varieties may be recommended for the genetic improvement of wheat with a high degree of adaptation. However, the varieties of the second group , which had high yield and low stability, are desirable for specific sowing dates.◦C, respectively (www.havairan.com). According to the classification of Koppen, this region has a tropical and subtropical steppe cool climate. The monthly mean climatic variables  of this region over the years of this study are given in Table This research was conducted for three years (2012—2014) at the research farm of the Mashhad Agricultural and Natural Resources Research Station, located in Torqabeh, Mashhad, Iran  on a silty loam soil, with pH 7.8. The mean annual precipitation and temperature of the region were 230 mm and 14.7T. turgidum) variety were used as the genetic materials of this study  was measured by counting three sub-sets of 100 grains randomly chosen from each variety and weighing them. Number of spikes (NSP), number of grains per spike (NGSP), and grain weight per spike (GWSP), were measured based on five randomly selected samples of each variety per replication, and the mean values of five plants were calculated and used for the analysis. Furthermore, assessments of grain yield  and biological yield  were done for each plot as a whole. The harvest index was calculated as the ratio of grain yield (GY) by the biological yield (BY), according to the following formula:p ˂ 0.05. Components of variance were estimated by evaluating traits from mean squares of the ANOVA after being equated to their expected variance components [2b) was estimated on a phenotypic mean basis averaged over replications, years, and sowing dates (environments), according to Hallauer and Miranda [2b is the broad-sense heritability, p is the square root of the phenotypic variance, σg is the square root of the genotypic variance, and µ is the phenotypic mean. Stepwise multiple linear regression analysis was used to determine the variables accounting for the majority of grain yield variability. Path coefficient analysis was conducted on the basis of phenotypic correlation coefficients, taking grain yield as an effect and the characters that were added to the multiple linear regression models as a cause.Prior to analysis of variance (ANOVA), the normality distribution of data was initially checked by the Kolmogorov– Smirnov test, and the homogeneity of residual variance was examined by the Bartlett test, before combining the data of the three years. Subsequently, combined analysis of variance, proposed by Steel and Torrie , was permponents . Broad-s Miranda :\documenFor the stability analysis, each sowing date was considered as the test environment, creating eight test environments. After verifying the existence of significant GE interaction, analysis of adaptability and grain yield stability was performed by the GGE biplot analysis, using the model described by Yan . To iden


AMPD1 rs17602729 C, CDKN1A rs236448 A, HFE rs1799945 G, MYBPC3 rs1052373 G, NFIA-AS2 rs1572312 C, PPARA rs4253778 G, and PPARGC1A rs8192678 G alleles for endurance; ACTN3 rs1815739 C, AMPD1 rs17602729 C, CDKN1A rs236448 C, CPNE5 rs3213537 G, GALNTL6 rs558129 T, IGF2 rs680 G, IGSF3 rs699785 A, NOS3 rs2070744 T, and TRHR rs7832552 T alleles for power; and ACTN3 rs1815739 C, AR ≥21 CAG repeats, LRPPRC rs10186876 A, MMS22L rs9320823 T, PHACTR1 rs6905419 C, and PPARG rs1801282 G alleles for strength. It should be appreciated, however, that elite performance still cannot be predicted well using only genetic testing.Phenotypes of athletic performance and exercise capacity are complex traits influenced by both genetic and environmental factors. This update on the panel of genetic markers (DNA polymorphisms) associated with athlete status summarises recent advances in sports genomics research, including findings from candidate gene and genome-wide association (GWAS) studies, meta-analyses, and findings involving larger-scale initiatives such as the UK Biobank. As of the end of May 2023, a total of 251 DNA polymorphisms have been associated with athlete status, of which 128 genetic markers were positively associated with athlete status in at least two studies . The most promising genetic markers include the  Athletic success is influenced by many genetically determined factors, including transcriptomic, biochemical, histological, anthropometric, physiological, and psychological traits, as well as general health status ,5,6,7,8.ACE, ACTN3, AMPD1, PPARA, PPARD, and PPARGC1A genes [Starting in the late 1990s, research began to identify DNA polymorphisms associated with predisposition to certain types of sports and exercise-related phenotypes, with initial focus on variants of the 1A genes ,21,22,231A genes ,27,28,291A genes . In addi1A genes , testost1A genes , handgri1A genes ,35,36, s1A genes , and bri1A genes .p < 5 × 10−8) significant SNPs associated with height (which in combination account for 40% of phenotypic variance) required the study of 5.4 million individuals [CDKN1A rs236448 A, GALNTL6 rs558129 C, NFIA-AS2 rs1572312 C, MYBPC3 rs1052373 G, SIRT1 rs41299232 G, and TRPM2 rs1785440 G alleles) [AGRN rs4074992 C, CDKN1A rs236448 C, CPNE5 rs3213537 G, NUP210 rs2280084 A, and GALNTL6 rs558129 T alleles) [APC rs518013 A and LRRN3 rs80054135 T alleles, respectively) [It is important to consider that the discovery of 12,111 independent genome-wide  ,46, fivealleles) ,47,48, ap < 5 × 10−8) markers of exercise-related phenotypes (correlated with sport-related traits) discovered in large cohorts of untrained subjects  was proposed [p < 0.05 .To overcome the issue of small samples of elite athletes, the use of genome-wide significant  gene was associated with strength in athletes and non-athletes from the Japanese population [Another approach that has proven effective in addressing the possibility of false positive results in sports genomics literature is to perform replication studies in two or more independent athletic cohorts , followed by a meta-analysis to quantify the overall effect of a polymorphism on athlete status and/or a sport- and exercise-related trait ,63,64,65pulation ,67,68. ICOMT rs4680 GG) genotype [KIBRA rs17070145 T) [As well as the phenotypes of athlete status or competitive performance, several recent studies have investigated a broader range of traits which may relate directly or indirectly to athletic capability. These include flexibility, coordination, cardiorespiratory fitness, spatial ability, stress resilience, mental toughness, fat loss efficiency, and cardiovascular and metabolic responses to training, amongst others ,82,83,84genotype , whilst 70145 T) . Such di70145 T) ,99,100 a70145 T) ,113,114.Technological advancement has lowered the cost of conducting genomic studies, increasing accessibility to researchers who wish to investigate the genetic underpinnings of sport and exercise phenotypes. Consequently, sports genomics is a dynamic and continually developing field, making it important to regularly appraise the contribution of recent advances to the field. Therefore, the aim of the current review was to summarise recent progress in understanding the genetic determinants of athlete status, and to detail novel DNA polymorphisms that may underpin differences between individuals in their athletic potential.At the time of writing (end of May 2023), the total number of DNA polymorphisms associated with athletic performance since the first discovery in 1998 is 251 . Our seaAMPD1 rs17602729 C, CDKN1A rs236448 A, HFE rs1799945 G, MYBPC3 rs1052373 G, NFIA-AS2 rs1572312 C, PPARA rs4253778 G, and PPARGC1A rs8192678 G alleles for endurance; ACTN3 rs1815739 C, AMPD1 rs17602729 C, CDKN1A rs236448 C, CPNE5 rs3213537 G, GALNTL6 rs558129 T, IGF2 rs680 G, IGSF3 rs699785 A, NOS3 rs2070744 T, and TRHR rs7832552 T alleles for power; and ACTN3 rs1815739 C, AR ≥ 21 CAG repeats, LRPPRC rs10186876 A, MMS22L rs9320823 T, PHACTR1 rs6905419 C, and PPARG rs1801282 G alleles for strength. This update on the panel of genetic markers associated with athlete status covers advances in research reported in the past two years  from the original 251 identified in our literature search. The most promising genetic markers  include  in 2021 ). The cu in 2021 ,116,117.2max), among others [An individual’s endurance capacity is determined by many factors, including their muscle fibre typology, haemoglobin mass, mitochondrial biogenesis, maximal cardiac output, and maximal rate of oxygen consumption , proportion of slow-twitch muscle fibres, recovery speed, long-distance running performance, running economy, lactate threshold, erythropoietin and haemoglobin levels, number of erythrocytes, capillary density, mitochondrial density, fat metabolism, and fatigue resistance.To support the observed findings from endurance-related case-control studies, researchers subsequently perform functional, lab-based studies to determine the relationship between genotypes and physiological measures. Examples of measurements used to complement genomic studies include (but are not limited to) VOAMPD1 rs17602729 C, CDKN1A rs236448 A, HFE rs1799945 G, MYBPC3 rs1052373 G, NFIA-AS2 rs1572312 C, PPARA rs4253778 G, and PPARGC1A rs8192678 G alleles. In contrast, the other 73 markers  have not passed our strict criteria: ACOXL rs13027870 G, ADRA2A 6.7kb, ADRA2A rs1800544 G, ADRB1 rs1801252 G, AGT rs699 A, BDKRB2 rs1799722 T, CAMK1D rs11257754 A, CHRNB3 rs4950 G, CLSTN2 rs2194938 A, CNDP2 rs6566810 A, COL5A1 rs71746744 AGGG, COL6A1 rs35796750 T, CPQ rs6468527 A, CYP2D6 rs3892097 G, DMT1 258 bp, EPAS1 (HIF2A) rs1867785 G, EPAS1 (HIF2A) rs11689011 T, GABPB1 rs8031031 T, GALM rs3821023 A, GNB3 rs5443 T, GRM3 rs724225 G, GSTT rs17856199 (+), IGF1R rs1464430 A, IL6 rs1800795 C, IL15RA rs2228059 A, ITPR1 rs1038639 T, ITPR1 rs2131458 T, FMNL2 rs12693407 G, KCNJ11 rs5219 C, L3MBTL4 rs17483463 T, MSTN rs11333758 D, MtDNA loci  ≤ 4, poly(C ≥ 7) stretch at m.568–573, m.16080G, m.5178C, N9, V, unfavourable: B, J2, T, L3*), NALCN-AS1 rs4772341 A, NACC2 rs4409473 C, NATD1 rs732928 G, NOS3 rs1799983 G, NOS3 (CA)n 164bp, NOS3 27bp 4B, PPARD rs2016520 C, PPARD rs1053049 T, PPARGC1A rs4697425 A, PPARGC1B rs11959820 A, PPP3CA rs3804358 C, PPP3CB rs3763679 C, SGMS1 rs884880 A, SLC2A4 rs5418 A, SOD2 rs4880 C, SPOCK1 rs1051854 T, TPK1 rs10275875 T, TTN rs10497520 T, Y-chromosome haplogroups ; unfavourable: E3b1), and ZNF429 rs1984771 G. Most of these markers have been described in previous reviews [Our literature search revealed that at least 41 of the 114 reported markers could be associated with endurance athlete status based on our criteria . The mos reviews  but cannACTN3 rs1815739 C, AMPD1 rs17602729 C, CDKN1A rs236448 C, CPNE5 rs3213537 G, GALNTL6 rs558129 T, IGF2 rs680 G, IGSF3 rs699785 A, NOS3 rs2070744 T, and TRHR rs7832552 T alleles. In contrast, the remaining 50 genetic markers  did not meet our strict criteria: ARHGEF28 rs17664695 G, CACNG1 rs1799938 A, CALCR rs17734766 G, CLSTN2 rs2194938 C, CNDP1 rs2887 A, CNDP1 rs2346061 C, CNDP2 rs3764509 G, COTL1 rs7458 T, CREM rs1531550 A, DMD rs939787 T, EPAS1 (HIF2A) rs1867785 G, EPAS1 (HIF2A) rs11689011 C, FOCAD rs17759424 C, GABRR1 rs282114 A, GALNT13 rs10196189 G, GPC5 rs852918 T, IGF1R rs1464430 C, IL1RN rs2234663 *2, IP6K3 rs6942022 C, MCT1 rs1049434 A, MED4 rs7337521 T, MPRIP rs6502557 A, MtDNA loci , MTR rs1805087 G, MTRR rs1801394 G, NOS3 rs1799983 G, NRG1 rs17721043 A, PPARGC1A rs8192678 A, PPARGC1B rs10060424 C, RC3H1 rs767053 G, SLC6A2 rs1805065 C, SUCLA2 rs10397 A, TPK1 rs10275875 C, UCP2 rs660339 C, VEGFR2 rs1870377 T, WAPL rs4934207 C, and ZNF423 rs11865138 C. The majority of these markers are reported in previous reviews [Several characteristics are positively associated with power performance, including circulating levels of testosterone, percentage and cross-sectional area of fast-twitch muscle fibres, muscle mass and strength, body and calcaneus height, muscle fascicle length, and reaction time, among others ,243,244. reviews ,127 and Performance in strength-based sports is based on multiple factors. However, the factors considered to contribute substantially to strength phenotypes include skeletal muscle hypertrophy (muscle fibre size), hyperplasia, the predominance of fast-twitch muscle fibres, a greater muscle fascicle pennation angle, improved neurological adaptation, high glycolytic capacity, and increased circulatory testosterone . ImportaLRPPRC rs10186876, MMS22L rs9320823, and PHACTR1 rs6905419 polymorphisms were also associated with superior competitive weightlifting performance [Previously, 170 DNA polymorphisms were reported to be associated with handgrip strength in three large GWASs ,35,36. Iformance .ACTN3 rs1815739 C, AR ≥ 21 CAG repeats, LRPPRC rs10186876 A, MMS22L rs9320823 T, PHACTR1 rs6905419 C, and PPARG rs1801282 G alleles.Our literature search based on our new inclusion criteria revealed at that least 42 genetic markers could be associated with strength athlete status . The mosThe current review demonstrates that at least 251 genetic markers are reportedly linked to sport-related traits. However, only 128 (51%) of these markers  have been associated with athlete status in two or more studies. On the other hand, of these 128 genetic markers, the significance of 29 (22.7%) DNA polymorphisms was not replicated in at least one study, raising the possibility that a number of findings may represent false positives. It is important to consider that there may be one of several reasons why the findings of a study may not be replicated by another, including disparity of sample sizes, small sample sizes in one or more of the studies, different study designs, inconsistent classification of sporting groups or types of sport , variability in how researchers or research groups define the term “elite athletes” , and the ethnicity/geographical ancestry of the cohorts studied, amongst others.As discussed previously, height remains not only the most studied exercise-related phenotype at the genetic level, but also the most studied human trait, with 12,111 associated SNPs . It is eWhilst genomics is the among the most established molecular sub-disciplines of sport and exercise research, sport- and exercise-related DNA polymorphisms do not fully explain the heritability of athlete status. Consequently, other forms of variation, such as rare mutations ,329 and p-values etc.) given word limit. Third, some genetic markers (out of the 128 most significant) were selected based on data obtained in case-control studies only, without confirmation of functional significance (genotype–phenotype studies are therefore warranted).Our review does have limitations. First, we have not provided information regarding genetic markers associated with team (game) and combat sports, flexibility, coordination, personality, cognitive abilities, muscle fibre composition, skeletal muscle hypertrophy, injuries, and responses to training/supplements. These markers are well described elsewhere ,333,334.In conclusion, our literature search revealed at least 251 DNA polymorphisms that could be associated with endurance, power, and strength athlete statuses. Most of these genetic markers have been discovered in studies involving Australian, Brazilian, British, Canadian, Chinese, Croatian, Czech, Ethiopian, Finnish, French, German, Greek, Hungarian, Indian, Iranian, Israeli, Italian, Jamaican, Japanese, Kenyan, Korean, Lithuanian, Polish, Qatari, Russian, Slovenian, South African, Spanish, Taiwanese, Tatar, Tunisian, Turkish, Ukrainian, and US athletes.