Malus domestica) is one of the world’s most commercially important perennial crops and its improvement has been the focus of human effort for thousands of years. Here, we genetically characterise over 1000 apple accessions from the United States Department of Agriculture (USDA) germplasm collection using over 30,000 single-nucleotide polymorphisms (SNPs). We confirm the close genetic relationship between modern apple cultivars and their primary progenitor species, Malus sieversii from Central Asia, and find that cider apples derive more of their ancestry from the European crabapple, Malus sylvestris, than do dessert apples. We determine that most of the USDA collection is a large complex pedigree: over half of the collection is interconnected by a series of first-degree relationships. In addition, 15% of the accessions have a first-degree relationship with one of the top 8 cultivars produced in the USA. With the exception of ‘Honeycrisp’, the top 8 cultivars are interconnected to each other via pedigree relationships. The cultivars ‘Golden Delicious’ and ‘Red Delicious’ were found to have over 60 first-degree relatives, consistent with their repeated use by apple breeders. We detected a signature of intense selection for red skin and provide evidence that breeders also selected for increased firmness. Our results suggest that Americans are eating apples largely from a single family tree and that the apple’s future improvement will benefit from increased exploitation of its tremendous natural genetic diversity.The apple ( These changes are often maladaptive to the organism in the wild, but of benefit to humans, leading to genetic differentiation between the wild progenitor species and contemporary commercial cultivars. In many species, domestication involves an intense population bottleneck, which reduces genetic variation. In contrast, long-lived perennial species, including fruit trees, generally experienced mild domestication bottlenecks owing to several factors including the extensive use of clonal propagation, long juvenile phases, and predominantly outcrossing mating habitMalus x domestica Borkh.), is one of the most economically important fruit crops, with a worldwide production value exceeded only by tomatoes (Solanum lycopersicum L.) and grapes (Vitis vinifera L.)2. In the first half of the twentieth century, Nikolai Vavilov suggested that the origin of apple domestication coincided with the centre of phenotypic diversity of the presumed ancestral species Malus sieversii (Ledeb.) Roem., located in the Tien Shan mountains along Kazakhstan’s eastern border with China3. This speculation was largely supported by recent analyses of genetic data, which identified M. sieversii as the primary progenitor species for M. domestica4. In addition, over 20% of the M. domestica genome was likely derived from the wild European crabapple Malus sylvestris (L.) Mill6.The domesticated apple  signatures of positive selection during domestication and improvement, and (3) the improvement history of M. domestica by examining the relationships among modern cultivars in the USDA apple germplasm collection.In this study, we investigated (1) the domestication history of Malus spp.) investigated here are from the USDA apple germplasm repository in Geneva, NY, USA. Leaf tissue was collected from 1949 accessions. The countries of origin of the accessions are indicated in Supplementary Fig. S11. A visual overview of all data processing and analysis steps described below is provided in Supplementary Fig. SThe apples  in separate GBS libraries and were sequenced using Illumina Hi-Seq 2000 technology (96 samples per lane) at Cornell University  across 42 lanes generating 100-bp single-end reads. The DNA sequence data are as NCBI BioProject 16 with the following filters: max missingness of 0.30, minor allele frequency (MAF) of 0.01, minimum depth of 8, and Hardy–Weinberg equilibrium threshold of p = 0.0001. The resulting data set had an imputation accuracy of 0.9778 and a correlation value of 0.8764, with 1598 accessions and 68,392 SNPs remaining.Missing genotypes in the VCF files were imputed using LinkImputeRM. domestica (N = 1154), M. sieversii (N  = 195), Malus (L.) baccata Borkh. (N = 40), Malus floribunda Sieb. ex Van Houtte (N = 17), Malus orientalis Uglitzk. (N = 17), and M. sylvestris (N = 15). Next, the VCF file was converted using PLINK v1.0718 and filtered for MAF 0.01, resulting in 1438 accessions and 47,925 SNPs.The data set was filtered to only include accessions in the USDA apple germplasm collection that were relevant to modern apple development, which includes accessions labelled as 19. We examined heterozygosity by individual, and contrasted these values with labels available in the USDA germplasm database for 2x, 3x and 4x accessions  or 4x (p = 1.385 × 10−4) individuals. There was no significant difference in heterozygosity between 2x and 4x accessions, indicating that the accessions labelled as 4x were likely all autotetraploids and could, therefore, be treated as diploid for the purposes of genotype calling and all downstream analyses. The mean proportion of heterozygous genotypes was 0.191 for accessions labelled as 2x, 0.226 for 3x accessions, and 0.182 for 4x accessions. Based on these results we removed 168 accessions with heterozygosity >0.21 that we inferred to be triploid, including 28 labelled as 2x, 51 labelled as 3x, and 2 labelled as 4x. There were 62 accessions labelled as 3x that were not removed using this filter. The majority of the accessions removed (N = 147) were labelled as M. domestica. After filtering, 1270 accessions remained.Our genotype calling pipeline assumes all accessions are diploid (2x), and we, therefore, aimed to exclude triploid accessions (3x). Previous work has confirmed that triploids can be identified from GBS data due to their excessive heterozygosity20 to evaluate relatedness among samples. First, we included only SNPs anchored to chromosomes 1 to 17, resulting in 46,022 SNPs. Next, we removed accessions that were suspected of being clonally related. To do this, we calculated pairwise identity-by-descent (IBD) using PLINK18 and only retained one accession per clonal group where proportion IBD (pi-hat) > 0.9, which removed a further 207 accessions. Only SNPs with MAF > 0.01 in the remaining accessions were retained, and these were then pruned for linkage disequilibrium (LD) using the PLINK filter (--indep-pairwise 10 3 0.5), resulting in 27,871 SNPs. The data set of 1063 accessions included samples labelled as M. domestica (N = 822), M. sieversii (N = 184), M. baccata (N = 25), M. floribunda (N = 2), M. orientalis (N = 17), and M. sylvestris (N = 13). We examined values of K from 3 to 10 using fastSTRUCTURE and decided on K = 3 based on the choose.py function, which determined that three components were needed to explain structure in the data  were selected as this has been shown to be a crucial factor in accurately inferring genetic relatedness based on PCA22. After establishing the PC axes based on these ancestral populations, the remaining M. sieversii and M. domestica accessions were projected onto the axes and 13 accessions were identified that were likely mislabelled or hybrids, and these were either re-labelled or removed  was conducted with the smartPCA module of the EIGENSOFT packageM. sieversii and M. sylvestris) and domesticated apples differed for cider and dessert apples, we labelled accessions according to primary use based on Migicovsky et al.25. We used the label ‘dessert’ to include cultivars labelled as both dessert and cooking apples in the USDA Germplasm Resources Information Network (GRIN) database and other online sources. A Mann–Whitney U-test was performed to determine if cider (N = 69) and dessert (N = 288) apples significantly differed along PC1 and PC2. Finally, we computed F3 statistics using the qp3Pop test in ADMIXTOOLS26 to test for introgression from wild species into cider or dessert apples.In order to detect if gene flow between wild apples  to M. sieversii (N = 115) using 33,266 SNPs. Genomic regions under positive selection since domestication in M. domestica should be significantly differentiated from its primary progenitor, M. sieversii. We calculated Fst for each SNP according to Weir and Cockerham27 and considered SNPs with Fst values within the top 5% of genome-wide Fst values as potential selection candidates.To identify genomic regions that may have been targets of positive selection in 28 and calculated the cross population extended haplotype homozgosity (XP-EHH) statistic using the selscan software with the --trunc-ok and --max-gap 1,500,000 options29. We contrasted haplotype diversity between M. sieversii (the ‘reference’ population) and M. domestica. Similar to Fst, we considered selection candidates as those SNPs with XP-EHH values within the top 5% of positive values genome-wide, indicating extreme haplotype homozygosity in M. domestica. Genomic regions containing SNPs within the top 5% of both the Fst and the XP-EHH distributions were identified as putatively under selection in M. domestica.Next, we inferred haplotypes using fastPHASE30.Within the genomic regions under putative selection during domestication we conducted a gene ontology enrichment analysis to determine if any GO terms were overrepresented, suggesting possible metabolic or functional changes in the apple genome due to domestication. To conduct the analysis, we obtained the complete gene annotation data set from the Genome Database for Rosaceae that accompanied version GDDH13 v1.1 of the reference genomeM. domestica into the topGO package in R31. We used topGO to test for gene enrichment in biological process ontology using the algorithm = ‘weight01’, which takes GO hierarchy into account, and the statistic = ‘fisher’.We searched for all genes where the entire region fell within +/− 50 kb of a SNP identified as a candidate for selection and reduced the list to unique genes (MD IDs), which resulted in 1738 genes of interest, including 1073 which were associated with at least one GO term. We imported the genome-wide annotations as well as those under putative selection in M. domestica, we filtered the genotype table, following removal of triploid accessions, for only accessions labelled as M. domestica, regardless of hybridisation. The resulting data set included 1005 M. domestica accessions and an additional MAF filter of 0.01 resulted in 31,426 SNPs remaining. We estimated IBD for all pairwise comparisons among the accessions using PLINK18. Groups of clones were defined as having IBD > 0.90 among all members. We visualised clonal relationships using the ‘network’ package in R32.To investigate patterns of relatedness within M. domestica samples. The expected IBD value for first-degree relationships is 0.5. However, observed IBD values for first-degree relatives are expected to vary due to genotyping error, errors in reference genome assembly, and low and uneven SNP density. To calibrate our IBD thresholds for defining first-degree relatives, we identified 55 known parent–offspring pairs from the literature and examined the range of IBD values  among the M. domestica, we examined dessert and cider apples for evidence of selection using both Fst and XP-EHH scans, as performed above for M. domestica and M. sieversii. SNPs in the top 5% of Fst values were separately tested for overlap with the highest (selection in dessert apples) or lowest (selection in cider apples) 5% of XP-EHH values. Once again, we tested unique genes within +/− 50 kb of a SNP for GO enrichment using the topGO package in R31.Within 12 and new imputation method16 were made available following the publication of our previous work and, therefore, our current study includes over three times as many SNPs as the initial GWAS performed in Migicovsky et al.25. We therefore retrieved the phenotype data from Migicovsky et al.25 and repeated the GWAS for fruit colour (red (N = 389) or green (N = 131)), fruit firmness (soft (N = 278) or firm (N = 310)) and fruit size  or large (N = 276)) using Tassel v.5.2.48 with the inclusion of a kinship matrix and PCs 1 to 335. For each of these binary phenotypes, we performed an XP-EHH analysis by comparing accessions with one phenotype  to accessions with the other . To verify that the colour GWAS identified only a single significant locus, we used a multi-locus model in the R package ‘mlmm’ v.0.1.1 to perform GWAS36 again including the kinship matrix and PCs 1 to 3. MLMM incorporates significant SNPs as cofactors using a stepwise regression. The optimal model was selected using the extended Bayesian information criterion (EBIC) and plotted using the plot_opt_GWAS function36. We examined the overlap between the top 5% of XP-EHH SNPs and those passing the Bonferroni-corrected threshold for GWAS. GWAS and XP-EHH results were visualised using the ‘qqman’ R package37, with the location of the D5Y SNP (chr3:30698039) in NAC18.1 (MD03G1222600) indicated on the firmness plot.Next, we conducted genome-wide association study (GWAS) and XP-EHH scans for traits that may have experienced improvement after domestication. A new reference genomeNAC18.1 gene. To determine the likelihood of observing homozygosity for this allele across eight random cultivars, we randomly sampled 8 M. domestica accessions without replacement 10,000 times and counted the frequency with which all eight accessions were homozygous for the firm allele.The eight cultivars we sequenced that are among the top apples sold in the USA were all homozygous for the desirable firm Y allele at the D5Y SNP in the M. domestica accessions within the collection were triploid  and dessert apples (f3 = −0.042006) had negative f3 values, suggesting that both cider and dessert apples included ancestry from both wild relatives, M. sieversii and M. sylvestris.To examine the effects of domestication on apple genetic diversity, we assessed the genetic contributions of the wild relatives, M. sylvestris along PC2: accessions from Germany (PC2 > 0) and Macedonia (PC2 < 0). By projecting both cider and dessert apples onto the PCs, we found that cider apples were significantly differentiated from dessert apples along PC1 , which indicates that cider apples derive more ancestry from M. sylvestris while dessert apples derive more ancestry from M. sieversii , with cider apples appearing more closely related to German M. sylvestris accessions compared to accessions from Macedonia  with another accession in the collection. ‘Golden Delicious’ had the largest number of first-degree relatives (66), while ‘Red Delicious’ had the second largest number of relatives (61). Most accessions 314) had 1 or 2 first-degree relatives, while 44 accessions had >10 . We verified that our GWAS signal captures only a single locus of large effect on chromosome 9 and that the SNPs exceeding the significance threshold on other chromosomes were likely mismapped in the reference genome  did exhibit an extreme XP-EHH value indicating a potential signature of selection for large fruit at this locus. This SNP falls within the LEA gene , which encodes the late embryogenesis abundant (LEA) protein.Following the same procedure, we tested for selection for increased fruit size. We failed to detect significant SNPs in our GWAS for apple size and did not find any convincing regions of selection for fruit size among SNPs with extreme XP-EHH values  to tyrosine (Y) at the fifth amino acid of NAC18.1 and the Y allele has been associated with apple firmness and harvest time in previous studies44. While this putatively causal SNP was not among the 5% most extreme XP-EHH values genome-wide, it was within the top 5.4% of values and thus nearly reached the 5% threshold employed here indicating possible selection for firmer apples , the United Kingdom (33), the United States (9), and Sweden (1). Thus, accessions within the USDA germplasm collection represent broader cider apple diversity, and this may explain the differences in our results.In contrast to our work, Cornille et al.M. domestica to M. sieversii to examine the genome for evidence of selection during domestication using Fst and XP-EHH , most commercially important cultivars are the result of breeding in Florida, and many appear to be closely related to each other59. As was the case for clonal relationships, it is the apple cultivars ‘Golden Delicious’ and ‘Red Delicious’, which have the largest number of first-degree relatives: each has >60 first-degree relatives in the collection. Notably, all of the top 8 apple cultivars grown in the USA, with the exception of ‘Honeycrisp’, were interconnected by a series of first-degree relationships  although cider apples are grown to be turned into juice and fermented into alcoholic cider. Cider apples are often referred to as ‘spitters’ because they are more tannic and acidic than dessert apples, which are desirable attributes when fermenting apple juice into cider. High conentrations of polyphenols, including flavonoids, increase astringency while malic acid contributes to tartness, both characteristics preferred for cider apples65. The long terminal repeat retrotransposon ‘redTE’, upstream of a MYB1 transcription factor, is a known key regulator of apple skin colour42. A homologous MYB gene is also associated with fruit colour in date palms (Phoenix dactylifera)66 and Citrus67. We identified a single locus associated with skin colour at MYB1 using GWAS , 11 cell wall degradation-related genes were found in selective sweep regions, indicating possible selection for crisp fruit flesh in Asian pears71. In apple, the NAC18.1 gene has been associated with apple firmness in several recent studies73. While no SNPs were significantly associated with firmness in our GWAS, the D5Y mutation in NAC18.1 had the lowest p-value of all SNPs tested  is the ancestral allele among angiosperms25. However, the undesirable ‘soft’ allele (D) occurs at a much higher frequency within M. sieversii, the primary progenitor species of M. domestica. Over half (53.9%) of the M. sieversii accessions were homozygous for the soft allele in comparison to only 6.28% of the M. domestica accessions. Thus, if there was selection for the ancestral firm allele, there may have been enough time for that allele to recombine onto numerous different haplotypes. XP-EHH is intended to detect extended homozygosity around a novel allele on a single haplotype that rapidly rises in frequency immediately after it appears in a population. This test is, therefore, underpowered to detect selection at NAC18.1, since the firm allele is ancestral and there have been very few generations of selection. It is worth noting, however, that all of the top 8 cultivars in the USA from the present study are homozygous for the firm allele, which is unlikely to be observed by chance given that the probability of homozygosity for this allele across eight random USDA accessions is 5.7%. Altogether, our results suggest that, by selecting for firmness, apple breeders likely drove the firm NAC18.1 allele to high frequency.The weak signal of selection at M. sylvestris, likely had a greater genomic contribution to cider apples than to dessert apples. We examined clonal and first-degree relationships, finding widespread use of a few commercially successful cultivars during apple breeding. The use of a small number of ‘elite’ cultivars in apple fails to exploit the immense genomic and phenomic diversity available and leaves the apple industry vulnerable to evolving pests and pathogens and a changing climate. Lastly, we identified evidence of selection for red and firm apples during apple improvement. Ultimately, germplasm collections such as the USDA apple collection described in this study will serve as an essential source of diverse accessions and wild relatives, which have enormous potential for future plant improvement.Our genome-wide analysis of the USDA apple germplasm collection allowed us to determine the consequences of domestication and subsequent improvement on patterns of apple genetic diversity. By examining population structure among wild species and the domesticated apple, we found that the European crabapple, Further information on research design is available in the Related Manuscript FileReporting SummarySupplementary Tables - zippedSupplementary figures - zipped


Astragalus membranaceus (AM) provides a rich source of polysaccharides that can act as powerful antioxidants, but their potential as feed ingredients in the lamb industry still rarely exploited. The objective of this study was to investigate the effect of dietary astragalus membranaceus supplementation on oxidative stability of goat muscles. Longissimus dorsi (LD) muscles from two groups of Cashmere goat  were evaluated for lipid oxidation, myoglobin oxidation, activity of antioxidant enzymes, and antioxidant capacity. The results showed that color parameters in Cashmere goat of two feeding conditions were no significant difference (p > .05). In AM group, myoglobin (Mb) content was higher than C, while metmyoglobin (MMb) (p < .05) and malondialdehyde (MDA) (p < .01) were lower. Additionally astragalus membranaceus supplementation had a significant effect on superoxide dismutase (SOD) and catalase (CAT) (p < .001). In whole, the AM group goats presented a relatively higher antioxidant capacity than C. Especially, RSA and CUPRAC values of AM group goats had significantly higher than C (p < .05). Consequently, the AM group goats ingested abundant astragalus membranaceus, which enhanced the antioxidant capacity. Thus, it can eliminate free radicals and effectively inhibit oxidation. Astragalus membranaceus (AM) provides polysaccharides that can act as powerful antioxidants, and AM goats presented a relatively higher antioxidant capacity than the C  goats Astragalus membranaceus (AM) provides polysaccharides that can act as powerful antioxidants.AM group goats presented a relatively lower oxidation degree than the C goats.AM goats presented a relatively higher antioxidant capacity than the C goats.1astragalus membranaceus  can improve its growth performance and antioxidant capacity, working as antioxidants to scavenge free radicals, and enhance the body's immunity and disease resistance. The advantages of Astragalus have no residue and side effects. In the previous studies, as a feed additive, the improvement of antioxidant capacity and meat quality of astragalus membranaceus are more common in pigs, chickens, geese, etc. But their application in goats has rarely been reported.In livestock production, Inner Mongolia Cashmere goats are excellent variety, which can offer cashmere and meat. Now, the research on Cashmere goat is mainly focused on cashmere performance, rumen metabolism, and nutritional metabolism  were equally assigned to the basal diet  and basal diet supplemented with 1% astragalus membranaceus root  (AM group) feed intake. According to Zhang et al.  with a feeder and waterer (access to water was ad libitum). All animals were fed twice daily at 8:00 and 18:00. 2 hr later, and the refusals of each animal were collected and weighed. Lambs were weighted every month before morning feeding. Ingredients and chemical composition of the basal diet are presented in Table Astragalus membranaceus root (diameter of less than 3 mm) was supplied by Shanxi Beiqi Industry Co., Ltd.Twenty‐four ewes (12 months old) with an average live weight of 27.86 ± 1.61 kg  for determination of meat color, Mb, and lipid oxidation. In addition, samples (10g) frozen at −80°C were retained for antioxidant enzyme activity assays.The animal experiments were approved by the Committee of Animal Experimentation and were performed under the institutional guidelines for animal experiments of the College of Animal Science, Inner Mongolian Agricultural University, China. The C group (31.7 ± 1.61 kg) and AM group (33.1 ± 2.05 kg) were humanely harvested according to recommendations proposed by the European Commission  to minim2.4Y = 92.6, x = 0.3162, y = 0.3324) overwrapped with the applicable film, including lightness (L*), redness (a*), and yellowness (b*). Three colorimeter measurements were obtained on each sample. All measurement locations were taken on the skin side surface in an area free of obvious color defects.The meat color was analyzed using a colorimeter  calibrated with a standard white plate  after standing for 5 min. The absorbance of the filtrate was measured at 525 nm (A525) using a spectrophotometer . And sodium phosphate buffer was a blank. The myoglobin concentration was calculated using the following equation: The myoglobin concentration was determined according to method of Canto et al. . Meat sa−1 cm−1 = millimolar absorptivity coefficient of myoglobin at 525 nm; 1 cm = light path length of cuvette; 17,000 Da = average molecular weight of myoglobin; and 10 = dilution factor.where 7.6 mM2.6g, 4°C, 30 min) after standing for 1 hr and the supernatant further clarified by filtration. The absorbance of filtrate was measured at 525, 545, 565, and 572 nm using a spectrophotometer . The percent of oxymyoglobin and metmyoglobin was calculated using the formula  and was measured by thiobarbituric acid reactive substances assay kit  following the description of the manufacturer.2.8g, 10 min, 4°C), and the supernatant was used to determine catalase (CAT), superoxide dismutase (SOD), and glutathione peroxidase (GPx) activity. Assay kits were used to determine CAT, SOD, and GPx activities.Sample (500 mg) was homogenized with ice cold saline . Homogenate was centrifuged . Absorbance at 550 nm was recorded spectrophotometrically , and one unit of CAT activity was defined as 1 mg tissue protein decomposed 1 μmol HSOD activity was determined by measurement of hydroxylamine using a total superoxide dismutase assay kit according to the manufacturer's directions . The absorbance at 550 nm was recorded spectrophotometrically . One unit of SOD activity was defined as the amount of enzyme in 1 ml of the reaction solution at 50% SOD inhibition at 37°C.GPx activity was measured colorimetrically using a glutathione peroxidase assay kit according to the manufacturer's directions . Absorbance was recorded at 412 nm spectrophotometrically . One unit of GPx activity was defined as the amount of the enzyme capable of decomposing 1 μmol/L glutathione per minute at 37°C.2.9The antioxidant capacity was evaluated by three indicators, including total antioxidant capacity (T‐AOC), cupric reducing antioxidant capacity (CUPRAC), and free radical scavenging ability (RSA).T‐AOC was measured by total antioxidant capacity assay kit . Absorbance at 550 nm was recorded spectrophotometrically , and one unit of T‐AOC activity was defined as 1 mg tissue protein increased the absorbance of the reaction system by 0.01 per second at 37°C.RSA of the samples was determined using procedure of Wen et al. . PreparaA0 represents the absorbance of blank and A1 represents the absorbance of experimental group.where Cupric reducing antioxidant capacity (CUPRAC) was determined using procedure of Apak, Güçlü, Özyürek, and Karademir  with mod2.10n = 12) were utilized to data analyses in this study. All data were analyzed using the Statistical Package for the Social Science . Data of LD from goats were analyzed using one‐way analysis of variance (ANOVA) to examine the effect of Astragalus membranaceus on oxidative stability. The ANOVA tables obtained were further analyzed for the comparison of means by least significant difference (LSD) procedures. Mean values and relative standard deviation (RSD) were reported.Twelve replications , but there was no effect of a* and b* value (p > .05). Polysaccharide is one of the main active ingredients of astragalus membranaceus, which can improve the redness of meat  of Cashmere goat is presented in Table 3.2p < .001). It proved astragalus membranaceus supplementation can affect lipid oxidation in meat. Lipid oxidation can produce small molecules of secondary metabolites, such as aldehydes, ketones, and epoxy derivatives which can bind to myoglobin and promoted myoglobin oxidate to metmyoglobin, leading to surface discoloration , metmyoglobin (MMb), and oxymyoglobin (OMb)) oxidation are presented in Table astragalus membranaceus supplementation (p < .05), while the percentage of OMb was not affected. AM group demonstrated higher Mb than C group (p < .05). Some studies found that high Mb concentration in muscle was attributed to high proportion of type I fiber, resulting in greater a* value . Mancini reported that the red meat with more than 20% MMb relative content gradually darkened, reaching 50% changed into reddish brown, more than 70% became brown and lose sales value , which were affected by astragalus membranaceus supplementation. This result aligns with work by Jin et al. . Free radical can cause lipid oxidative chain reaction and make myoglobin in the Fe2+ oxidation of Fe3+, and RSA reflected all the antioxidant substances in muscle tissue free radical scavenging ability. This result confirmed that astragalus membranaceus supplemented in diet can enhance antioxidant ability. T‐AOC is a measure of muscle antioxidant capacity of the main indicators, reflecting the overall antioxidant capacity of the muscle instead of each antioxidant component contribution .The results of antioxidant capacity are presented in Table On the whole, AM group goats have an advantage in suppressing oxidation, which may be related to its relatively rich antioxidant substances , while MMb (p < .05) and lipid oxidation degree lower (p < .01). As the antioxidant enzyme activities, CAT and SOD of AM were higher than C (p < .001). Among antioxidant capacity, the RSA and CUPRAC value of AM goats were significantly higher than C (p < .05). Consequently, the AM group goats ingest abundant astragalus membranaceus, which had an advantage in inhibiting oxidation. Thereby, the antioxidant capacity is higher than C group.The authors have declared that they have no conflicts of interest in this work.


Littorina fabalis and L. obtusata, comprise two sister gastropod species that have an enormous potential to elucidate the mechanisms involved in ecological speciation in the marine realm. However, the molecular resources currently available for these species are still scarce. In order to circumvent this limitation, we used RNA-seq data to characterize the transcriptome of four individuals from each species sampled in different locations across the Iberian Peninsula. Four de novo transcriptome assemblies were generated, as well as a pseudo-reference using the L. saxatilis reference transcriptome as backbone. After transcripts’ annotation, variant calling resulted in the identification of 19,072 to 45,340 putatively species-diagnostic SNPs. The discriminatory power of a subset of these SNPs was validated by implementing an independent genotyping assay to characterize reference populations, resulting in an accurate classification of individuals into each species and in the identification of hybrids between the two. These data comprise valuable genomic resources for a wide range of evolutionary and conservation studies in flat periwinkles and related taxa.The flat periwinkles,  Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11800293 Littorina are among the most interesting models for studying adaptation and speciation3. For example, the recent genomic resources made available for the rough periwinkle (L. saxatilis) have provided important knowledge about the genomic architecture of adaptation and of parallel ecotype evolution6. Although less studied, the flat periwinkles, L. fabalis and L. obtusata, have recently gained recognition as a model system to inform us about the late stages of ecological speciation and how different reproductive barriers accumulate and interact across the speciation continuum9. These two sister-species usually dwell on brown algae , their different ecological requirements result in vertical zonation, with L. obtusata mainly inhabiting the mid to upper intertidal, whereas L. fabalis is more commonly found in the lower part of the shore11. Despite clear morphological and ecological differences, recent studies suggest that they are not fully reproductively isolated12, with a remarkable incidence of hybrids in Cabo do Mundo 8. However, these studies were based on a relatively small number of markers, precluding a more detailed characterization of the type of hybrids. In order to circumvent these limitations, here we use RNA-seq to increase the genomic resources available for this system. We present the first transcriptome for flat periwinkles, identify putative fixed SNP differences between the two species and demonstrate the potential of this information to detect hybrids between them. These resources will facilitate additional evolutionary studies, including the role of adaptive introgression and reinforcement in the diversification of flat periwinkles, producer-herbivore interactions, as well as the identification of genomic regions involved in key traits that diverged between rough and flat periwinkles . Finally, together with the first transcriptome available within the genus Littorina (L. littorea), these data will also allow for comparative immunology studies and contribute to understanding the evolutionary dynamics of host/parasite immune conflicts15.Marine gastropods of the genus gae Fig. , presentA description of the workflow of our approach is represented in a flowchart Fig. .Fig. 2FlN = 12 to 27) were transported alive to the laboratory  within 24 hours after collection. Individuals were sexed under a dissection microscope. Because the two species can be distinguished based on male genitalia morphology10, only males were retained. These were placed in separate aquaria (one per site and species) with running marine water and aeration during 11 to 12 days for acclimation. The maintenance of the samples in similar environmental conditions before RNA extraction was performed to ensure that differences in expression between individuals from the two species were not specifically related to their condition at collection time. The only source of nutrients during this period was the running water, to minimize sequence contamination with DNA from other organisms present in their digestive tract. At the end of the acclimation period, samples were flash frozen in liquid nitrogen and kept at ‐80 °C until further analyses. Four males per species, each from a different location, were then selected for sequencing  in the North to Mindelo  in the South, during 2014. Individuals collected in each sampling site  was performed with the TruSeq RNA Sample Preparation Kit v2 (Illumina) following the manufacturer’s protocol and aiming for insert sizes of ~200 bp. The final libraries were pooled altogether in equimolar ratios before sequencing. Sequencing was performed in two different runs (using the same pool), each consisting of 4/5 of a lane in paired-end mode (first run 2 × 100 bp and second 2 × 125 bp) on an Illumina HiSeq 1500 in CIBIO/InBIO laboratories.16 was assessed with FastQC v0.11.517. Subsequently, reads were clipped to remove Illumina adapters and trimmed for quality with TRIMMOMATIC v0.3618 with the following steps: 1. Remove the adapters; 2. Cut the initial 10 bases at the start of the reads; 3. Trimming when average quality of the nucleotides within a 4 bp window was below 15; 4. Remove bases at the start and end of the reads if quality was below 20; and 5. Discard reads shorter than 25 bp. Read quality was re-evaluated with FastQC at the end of this process19.The quality of the raw readsL. fabalis and L. obtusata, reference transcriptomes were generated using two main approaches. The first one , a member of the sister clade of flat periwinkles closest relatives. It consisted of applying the Pseudo-it pipeline developed by Sarver et al.20 to obtain a pseudo-reference minimizing mapping biases towards any of the species. More specifically, this pipeline allows the generation of a pseudo-reference by replacing the original nucleotides in the reference (L. saxatilis) transcript sequences (e.g. A) by those that represent a fixed difference among the eight flat periwinkle individuals (e.g. T) or by using the IUPAC ambiguity code (e.g. R) when an alternative allele is fixed among the four L. fabalis (T) and the four L. obtusata (C) sequenced in this study. This was done through an iterative process with multiple steps, including: mapping reads to the reference using bwa mem v0.7.1521, removing duplicates with Picard v2.8.2 (http://broadinstitute.github.io/picard), identifying insertions and deletions following realignment and calling haplotypes with Genome Analysis Toolkit - GATK v3.722, and injecting the filtered variants in the original reference making use of the FastaAlternateReferenceMaker within GATK. In line with the authors’ recommendations, five iterations were implemented to guarantee a full incorporation of species variability. Further processing  was done with SAMtools v1.3.123.In order to identify highly discriminant SNPs between one Fig.  was basede novo transcriptome assemblies. Four de novo assemblies were generated: two using the reads from the four individuals sequenced for each species (Fab4ind and Obt4ind), and the other two using only the reads from the individual with the highest coverage for each species . All these assemblies were performed using TRINITY v2.2.024 with default parameters, using the cleaned paired reads. The quality and completeness of the produced assemblies was evaluated with Transrate v1.0.325. This tool defines an optimal cut-off for each assembly, enabling the removal of possible chimeras and poorly supported contigs by mapping back the cleaned reads against the generated reference. Only the best-supported contigs (“good transcripts”) were retained for further analyses. In order to reduce redundancy, an extra step was performed with CD-HIT-EST v4.6.426 to keep only one transcript among those with more than 95% similarity. CD-HIT-EST was used after Transrate for all de novo transcriptomes, except for L. fabalis based on multiple individuals probably due to its high complexity. Finally, TransDecoder v3.0.027 was employed to predict candidate coding regions, discarding possible non-coding RNA and DNA contamination.The second approach consisted of reconstructing species-specific Littorina saxatilis transcriptome reference plus those showing homology with PFAM common protein domains28. Raw and curated versions of each transcriptome are available in Figshare19. Annotation of transcripts was carried out using Conditional Reciprocal Best BLAST (crb-blast) v0.6.929 against the L. saxatilis reference transcriptome and the Swiss-Prot database30 and is available in Figshare19.The final transcriptome comprises those transcripts with predicted open reading frames and with homologs in the L. fabalis and L. obtusata16. First, reads from all individuals were aligned to the pseudo-reference and to the respective species-specific de novo references with BWA-MEM v0.7.1521 using default parameters. Read group information was added to each sequencing lane-sample pair. SAMtools v1.3.123 was used for converting the resulting alignments (SAM format) into binary files (BAM format), sorting and fixing mate-pair information. Duplicate reads were removed with Picard v1.140 using MarkDuplicates. Read realignment was performed with RealignerTargetCreator and IndelRealigner tools within GATK v3.722. The pseudo-reference was chosen for downstream analyses based on mapping statistics  obtained with VCFtools34. Furthermore, only one random biallelic SNP per transcript was retained, reducing the effect of linkage disequilibrium. Population structure analysis was performed with fastStructure v1.035 based on the same SNP subset, with 10 cross-validation tests for each of three replicate runs, and considering the number of genetic clusters (K) between 2 and 8. The best number of clusters was inferred with the chooseK python script of fastStructure.Genetic variability across individuals and species was first assessed by a Principal Component Analysis (PCA) using PLINK v1.90b3.45https://github.com/simonhmartin/genomics_general), after filtering the raw VCF file for minimum SNP quality of 30 , minimum coverage of 5 per sample and minimum number of calls of 8 . A more conservative set of putatively fixed SNP differences was also created by applying a more stringent filter of a minimum coverage of 20 per sample19.Putative fixed single nucleotide differences between species were identified using the python tools created by Simon Martin . Sequences containing at least one polymorphism across 50 bp to each side of the target SNP were discarded to reduce the probability of missing genotypes or null alleles. Finally, to ensure that the 101 nucleotides were located in the same exon avoiding fragments that span introns, all the selected fragments were blasted against the www.genotyping.se) at Uppsala University, Sweden. This consisted of using multiplexed primer extension (iPLEX) and a MassARRAY analyzer39 from Agena Bioscience for allele detection by mass spectrometry, and subsequent conversion to a genotype using Typer (Agena Bioscience). Forty-eight samples from the Iberian Peninsula were genotyped  was inferred using STRUCTURE v2.3.444, running 10 replicates for K = 2, each consisting of 1,000,000 iterations after a 100,000 burn-in. Since all replicates resulted in a similar outcome, the one with the highest likelihood was plotted with DISTRUCT v1.145.A final set of 42 putatively diagnostic SNPs was tested with an independent genotyping assay at the National Genomics Infrastructure (NGI), SNP&SEQ Technology Platform  and biosample-specimen . For each biosample, four files were submitted, corresponding to two different Illumina HiSeq lanes and one for each of the paired reads. All intermediate and final files produced during the previous steps are available on Figshare19, including: pre- and post-cleaning FASTQC quality reports , the raw trinity assembly results , curated versions of the transcriptome assemblies , annotated pseudoreference transcriptome based on Swiss-Prot and the L. saxatilis transcriptome (LfabLobt_5it.pseudoref.annotated.fa) and combined crb-blast annotation file based on Swiss-Prot and the L. saxatilis transcriptome (Annotation_PseudoRef_SP_and_LsaxTranscriptome.txt), transcriptomes crb-blast annotation files based on Swiss-Prot and the L. saxatilis transcriptome, separately (Annotation.7z), raw SNP call VCF files , filtered VCF file , list of putatively diagnostic SNPs (FixedSNPs_PseudoRef_min5.vcf and FixedSNPs_PseudoRef_min20.vcf), and genotypes of the samples used for SNP validation analysed with Structure . The L. saxatilis transcriptome and genome sequence data used as reference in this study are publically available at GenBank (GHPE00000000.1)46.The core dataset of this work comprises the transcriptomic data of flat periwinkles. The raw data (eight SRA experiments) were deposited in RNA quality was evaluated after electrophoresis on the Bioanalyser 2,100 (Agilent Technologies) with the RNA 6000 Nano Chip Kit (Agilent Technologies). RNA quantity was measured with Qubit using the RNA BR Assay Kit (Life Technologies).17.A total of 736 million reads were generated , with an average of ~92,000 reads per sample  with a mean contig length and N50 of 2,139 bp and 2,963 bp, respectively Table . AnnotatL. fabalis assembly based on the “cleaned” reads from 4 individuals of this species initially resulted in 396,047 contigs with a mean length of 612 bp, a N50 of 832 bp and a total size of 242,362,528 bp; whereas the L. obtusata transcriptome is 251,985,325 bp long, distributed across 349,459 contigs with a mean length of 721 bp and N50 of 1,133 bp , resulted in the retention of 48.5%  to 75.3%  of the initial number of contigs. Final curated flat periwinkle assemblies based on four individuals are composed of 53,214 and 32,433 predicted open reading frame (ORF) transcripts with mean lengths of 818 bp and 707 bp, and a N50 length of 1,080 and 861, for L. fabalis and L. obtusata respectively. The single individual assemblies are composed of 31,279 (fSAM) and 24,047 (oMIN) ORF, with mean lengths of 685 bp and 740 bp, and a N50 length of 801 and 912, respectively , of which 10,592 and 6,275 were uniquely annotated to the Swiss-Prot database, while 20,246 and 15,690 had homologs in the L. saxatilis reference transcriptome. The annotation of the assemblies based on a single individual resulted in 23,947 annotated transcripts for L. fabalis (fSAM) and 19,228 for L. obtusata (oMIN)19.Regarding the annotation of the assemblies based on data from four individuals, a total of 38,439 transcripts in the L. fabalis individuals according to geographical distance: two from Ria de Vigo (fSAM-G03R12 and fTIR-G10R14) and two from Ria de Muros (fABE-G04R13 and fMUR-G11R15)  were mapped to the pseudo-reference transcriptome (Table 19).Putative species-diagnostic SNPs were identified from the raw SNP call using the python tools created by Simon Martin and two different filtering criteria. In the more stringent filter (minimum coverage of 20), 19,072 SNPs were inferred as putatively species-diagnostic  whereas in the less stringent filter (minimum coverage of 5), 45,340 were identified as putatively species-diagnostic SNPs  and many of the transcripts may not be complete, this result should be interpreted with caution.L. fabalis and L. obtusata, i.e. fixed differences between the putatively pure populations. The non-diagnostic set consisted of two (5.7%) nearly diagnostic SNPs , five (14.2%) informative SNPs , and two others (5.7%) with very little or no discriminatory power between the two species defined morphologically according to male genitalia.From the 42 putatively diagnostic SNPs selected for validation, two failed the quality control tests for primer design. Among the remaining SNPs, one failed during genotyping, one revealed no variability and three others revealed more than two alleles. All these SNPS were excluded resulting in a final set of 35 SNPs. All loci were genotyped for at least 91.7% of the individuals, whereas all individuals were genotyped for at least 94.3% of the loci. No conflicts were found among genotyping replicates (215), resulting in 100% reproducibility. Among these 35 loci, 26 (74.3%) SNPs are potentially diagnostic between K = 2 based on these 29 SNPs confirmed the pure genetic ancestry of the reference L. fabalis and L. obtusata individuals and the occurrence of admixture in Cabo do Mundo, demonstrating the usefulness of these markers for species discrimination and detection of hybridization between flat periwinkles (Fig. No significant Hardy-Weinberg deviations for any of the 35 loci were detected in the reference samples. However, LD remained significant between eight pairs of loci across the three populations. This resulted in the exclusion of six additional SNPs to avoid using non-independent information in subsequent analysis of ancestry. The STRUCTURE analysis for les Fig. .Fig. 6StL. fabalis and L. obtusata should first be tested in other parts of the species distribution range before they are applied outside Iberia.The two species-specific transcriptomes and the SNPs made available in this study are the first genomic/transcriptomic resources made publicly available for flat periwinkles. This information will be useful for a wide range of studies, including evolutionarily-oriented projects concerning adaptation, parallel evolution, comparative immunology, producer-herbivore interactions, hybridization and speciation; as well as for characterization of population genetic diversity and differentiation across the species’ distribution ranges to help implementing appropriate management and conservation measures. However, it is important to emphasise that the discriminatory power of the SNPs here developed was based on individuals from the Iberian Peninsula. Although we expect that most of these SNPs will also be diagnostic in other regions, their power to discriminate between


The confidence intervals of these QTLs ranged between 11 and 127 centimorgan (cM), which contained hundreds of genes and hampered the identification of QTLs. A whole-genome sequence imputation of the population was used for fine mapping in this study.Muscle fiber types, numbers and area are crucial aspects associated with meat production and quality. However, there are few studies of pig muscle fibre traits in terms of the detection power, false discovery rate and confidence interval precision of whole-genome quantitative trait loci (QTL). We had previously performed genome scanning for muscle fibre traits using 183 microsatellites and detected 8 significant QTLs in a White Duroc× Erhualian F2 population. Genotyping was performed for 1,020 individuals . The whole-genome variants were imputed and 21,624,800 single nucleotide polymorphisms (SNPs) were identified and examined for associations to 11 longissimus dorsi muscle fiber traits.A whole-genome sequences association study was performed in the FKNDC1]), TFN (KNDC1), and I_RA  were identified.A total of 3,201 significant SNPs comprising 7 novel QTLs showing associations with the relative area of fiber type I (I_RA), the fiber number per square centimeter (FN) and the total fiber number (TFN). Moreover, one QTL on pig chromosome 14 was found to affect both FN and TFN. Furthermore, four plausible candidate genes associated with FN (kinase non-catalytic C-lobe domain containing [An efficient and powerful imputation-based association approach was utilized to identify genes potentially associated with muscle fiber traits. These identified genes and SNPs could be explored to improve meat production and quality via marker-assisted selection in pigs. Pork serves as one of the main animal protein sources in human diets, with its basic composition unit being the muscle fiber. With the rapid increases in living standards, consumers’ demand for higher quality meat has been increasing. One of the most important factors that influence meat/muscle quality is its histological characteristics. There are three main muscle fiber types in swine: slow-twitch type I, fast-twitch type IIA, and IIB . The are2 pig population. We have previously evidenced 8 genome-wide significant QTLs for muscle fiber traits in a Chinese Erhualian×White Duroc F2 resource population [Genetic studies of muscle fiber traits are limited. A total of 113 quantitative trait loci (QTLs) for swine muscle fiber traits have been reported in the AnimalQTLdb database up to now . Fifteenpulation . The con2 resource population. Twenty-four single nucleotide polymorphisms (SNPs) were identified to be associated with 6 muscle fiber traits, including 1 significant SNP associated with the red muscle fiber rate located on pig chromosome  6; 4 significant SNPs associated with the white fiber rate on SSC7 and SSC11; 2 significant SNPs associated with the intermediate fiber rate on SSC14; 7 significant SNPs associated with muscle fiber area on SSC3, SSC4, and SSC9; 5 significant SNPs associated with muscle fiber density on SSC6; and 5 significant SNPs associated with muscle fiber diameter on SSC3 and SSC9 [2 resource population using a mixed linear model and detected 37 significant SNPs to be associated with muscle fiber diameter, muscle fiber area, and muscle fiber density, which were located on SSC3, SSC4, SSC5, SSC6, SSC7, and SSC14. GWAS partly overcomes the inadequacy of QTL mapping strategy, but repeatability is poor and confidence intervals are still too large to search for candidate genes.With the development of high-throughput genotyping technology, genome-wide association studies (GWAS) have been extensively conducted for traits of interest in farm animals. To our knowledge, few GWASs for swine muscle fiber traits have been reported. Guo et al  performeand SSC9  performe2 resource population and 98 unrelated individuals  and reconstructed their haplotypes as a reference haplotypes library. The whole-genome sequence variants of the F2 individuals were then imputed from the reference haplotype library, and were used to test their associations with 11 muscle fiber traits, including numerical percentage; area and relative area of type I, IIA, and IIB myofibers; fiber number per square centimeter (FN); and total fiber number (TFN). These identified significant loci could aid in the genetic improvements of meat quality via marker assisted selection in swine breeding programs.To fine map and overcome the weakness of detecting small-effect QTLs using a low-density BeadChip, we sequenced 19 founders of the FAll procedures involving animals followed the guidelines for the care and use of experimental animals  approved by the National Standard of the People’s Republic of China. The ethics committee of Jiangxi Agriculture University specially approved this study.2 resource population was established as described previously [1 boars and 59 F1 sows were then randomly chosen and mated to produce a total of 1,912 F2 individuals in six batches. All F2 piglets were raised under the same conditions at a pig farm in Jiangxi Agricultural University (China). Animals were slaughtered at the age of 240±3 days at a slaughter facility following industry procedures. Herein, 11 fiber traits within the longissimus dorsi muscle were measured on 120 F2 animals that were divided into two batches. These 11 traits included area (A), relative area (RA), and numerical percentage (NP) of the three types of myofibers  and FN and FTN. This approach was based on previous study that measured muscle fiber traits [A three-generation White Duroc×Erhualian F eviously . In shorr traits .0, 68 F1, and 933 F2) were genotyped using the Illumina PorcineSNP60 BeadChip  according to the manufacturer’s protocols. The genotyping data was then filtered by a call rate of no less than 0.95 and a Mendel error of no more than 0.05.Genomic DNA was extracted from swine ear tissues using a standard phenol–chloroform method. The DNA samples were then quantified using a Nanodrop 1,000 spectrophotometer  and diluted to a concentration of 50 ng/μL in a 96-well plate as previously described . A total2 population was carried out using IMPUTE2 tools with a basic scenario of a one-phased reference panel [0, 98 unrelated individuals were added into the reference panel. Briefly, 117 pigs  were re-sequenced using an Illumina Hiseq 2000, with an average depth of ~25 coverages being regarded as a reference panel. SNP calling was performed using Genome Analysis Toolkit  tools, with individual genotypes being generated using the GATK Haplotypecaller as previously described [2 individuals were imputed using IMPUTE2 with default options (version 2.3.2) and the imputation accuracy was estimated using an internal cross-validation strategy. A cutoff of 0.3 was set for post-imputation SNP filtering and the clean data was converted to a BIMBAM file using a bash script in the GEMMA software manually [In this study, a whole-genome sequence imputation of the Fce panel . In ordeescribed . The rawescribed . Only SNmanually . FinallyWhole genome sequence associations were built based on a linear mixed model and implemented in the GEMMA software (version 0.94), which is described in the following equation:y is the vector of phenotypic observation; W is a matrix including covariates and a column of 1s; α is a vector of fixed effects ; x is a vector of genotypes; β is the effect size of the marker; u is a vector of random effects following the multivariate normal distribution; τ−1 represent the variance of the residual errors and λτ−1 represent the variance of the random effect, λ is the ratio between the two variance of the residual errors; K is a kinship matrix that is estimated from the whole genome sequence variants and is calculated following the formula:where In being an identity matrix. With high density markers throughout the whole genome, a naïve Bonferroni correction of 0.05 divided by the examined SNP number was used and generated an overly conservative threshold due to the SNPs being highly correlated with each other; thus deviating from the assumption of independent tests when performing Bonferroni corrections. We herein took the empirical distribution of p values of whole genome markers and calculated the genome-wide false discover rate (FDR) following Storey [ɛ is a vector of errors following the multivariate normal distribution, with g Storey  and Benjg Storey . The mati is p value of tested SNP and order(pi) is the smallest p value. Herein, we assumed that independent haplotype segments between pigs and humans are equal, thus the same genome-wide threshold was utilized. The 95% confidence interval for each QTL was determined by finding the regions on both sides of a given candidate SNP that correspond to a decrease of −2 log  units according to the study of Rebai et al [where m is the largest p value, Pai et al .http://ensembl.org/Sus_scrofa/Info/Index) and Gene Cards (http://www.genecards.org) were used to determine the functions of the genes.Candidate genes were carefully inspected within one Mb area around candidate SNPs using the Ensembl browser  located on SSC4, SSC7, SSC9, SSC15, and SSC18; and the rests are associated with TFN (3) and FN (6) located on SSC14 and SSC16. The substitution effects and the frequency of top SNP for each QTL was listed in For each QTL, in order to verify whether other detected significant SNPs are the same QTL as the top SNP, we took the genotype of the top SNP as covariant variable contained in the mix model frame and carried out conditional GWAS analysis again. The result showed that the original QTL signal disappeared and no new QTL loci appeared, indicating that these SNPs are controlled by the same QTL.KNDC1) gene. The other one is located on SSC16 at position 2,353,144 bp (p = 3.04E-09), the confidence interval is 1.36 kb, with only two SNPs exceeding the significance threshold, and a substitution effect of 2.10E+04. The most closely associated gene for this QTL was ENSSSSCG000000 29792 and its function is currently unknown. Furthermore, for TFN, the same results for FN on SSC14 were observed. For I_RA, 5 QTLs located on SSC4, SSC7, SSC9, SSC15, and SSC18 were identified at position of 109,585,219, 3,489,379, 28,986,217, 31,876,859, and 23,539,146 bp, respectively. The most closely associated genes for these QTLs are ENSSSSCG 00000024957, ENSSSCG00000001016, solute carrier family 36 member 4 (SLC36A4), the contactin associated protein-like 5 (CNTNAP5), and glutamate metabotropic receptor 8 (GRM8). For the remaining muscle fiber traits, no significant signals were observed (FDR>0.05).Two significant QTLs associated with FN were identified. One QTL is located on SSC14 at position 153,137,973 bp (p = 7.52E-11) with a confidence interval of 154.64 kb and substitution effect of 2.72E+04 . This QTL was found to be located within the intron of the kinase non-catalytic C-lobe domain containing , KCNA2 (one significant SNP is located in this gene), SLC36A4, GRM8 CNTNAP5, ENSSSCG00000024957, ENSSS CG00000001016 (one significant SNP is located in this gene) and ENSSSSCG00000029792. However, in terms of functional annotation, these genes are not directly related to muscle fiber traits. We herein extended gene searching to ~ 1-Mb region near to these SNPs, just a disintegrin and metalloproteinase domain-containing protein 8 (ADAM8) gene and bridging integrator-1 (BIN1) gene stand out for QTLs on SSC14 and SSC15 respectively.In the present study, if two significant SNPs were identified on the same chromosome and the distance between these two SNPs is less than 5 Mb, they are treated as identical QTL. Based on this simple rules, 3,201 significant SNPs comprising 7 QTLs were identified. Moreover, one QTL on SSC14 was found to be associated with both FN and TFN, thus indicating a multi-effect QTL. Genes closest to these significant SNPs included ADAM8 is a member of the disintegrin and metalloprotease (ADAM) family and is located on SSC14 with a confidence interval of 154.64 kb. ADAM8 inhibition is characterized by increased muscle degeneration and increased numbers of necrotic and calcified muscle fibers. ADAM8 enhances neutrophils invasiveness into injured muscle fibers by reducing their adhesiveness to blood vessels after infiltration into interstitial tissues [ADAM8 is also the primary protease for α-cleavage of cellular prion protein and appears to be self-regulated in muscle tissue [ tissues . ADAM8 ie tissue .BIN1 gene was found to be located near the QTL on SSC15, and its expression, structure and localization have been reported to be tightly regulated during muscle differentiation [BIN1 plays a critical role in cardiac muscle development [BIN1) was a common pathomechanism for centronuclear myopathy, myotonic dystrophy, and inherited myopathy in Great Danes. Furthermore, reproductive BIN1 splicing alterations has been shown to promote T-tubule alterations and muscle weakness [The ntiation . BIN1 plelopment . Johann elopment  demonstrweakness .FAM105A) gene, which is located 1.977 Mb from the identified SNP on SSC16, has been shown to play a role in apoptosis, but its overall biological function is still unclear. Overexpression of FAM105A was found to increase levels of the pro-apoptotic factor Bax, while decreasing levels of the anti-apoptotic factor Bcl-2 [FAM105A promotes myofibroblast apoptosis, thereby affecting muscle fiber numbers.The family with sequence similarity 105, member A (or Bcl-2 . It is p2 resource population. Imputed whole genome sequence data was obtained and 3,201 significant SNPs showed associations with I_RA, FN and TFN, corresponding to 7 novel QTLs for these traits on SSC4, SSC7, SSC9, SSC14, SSC15, SSC16, and SSC18. The most plausible candidate genes at the identified loci included ADAM8, BIN1, and FAM105A.In this study, GWAS was performed to detect QTLs for muscle fiber traits in a F


Kernel morphology is one of the major yield traits of wheat, the genetic architecture of which is always important in crop breeding. In this study, we performed a genome-wide association study (GWAS) to appraise the genetic architecture of the kernel traits of 319 wheat accessions using 22,905 single nucleotide polymorphism (SNP) markers from a wheat 90K SNP array. As a result, 111 and 104 significant SNPs for Kernel traits were detected using four multi-locus GWAS models  and three single-locus models , respectively. Among the 111 SNPs detected by the multi-locus models, 24 SNPs were simultaneously detected across multiple models, including seven for kernel length, six for kernel width, six for kernels per spike, and five for thousand kernel weight. Interestingly, the five most stable SNPs  were simultaneously detected by at least three multi-locus models. Integrating these newly developed multi-locus GWAS models to unravel the genetic architecture of kernel traits, the mrMLM approach detected the maximum number of SNPs. Furthermore, a total of 41 putative candidate genes were predicted to likely be involved in the genetic architecture underlining kernel traits. These findings can facilitate a better understanding of the complex genetic mechanisms of kernel traits and may lead to the genetic improvement of grain yield in wheat. Triticum aestivum L.) is a staple food crop worldwide, providing 20% of the caloric intake of humans [Bread wheat , which dissects the genetic basis of complex traits. Unlike QTL mapping, GWAS can explore wider genetic variations with higher resolution, in order to phenotype several traits with one cycle of genotyping . It is aTherefore, the present study was designed to conduct GWAS in a set of 319 wheat accessions in central China and the Yangzi River region, using the high-throughput wheat 90K iSelect SNP array . The objPhenotypic variations of kernel traits among the 319 wheat accessions were evaluated at the experimental field in Wuhan during the winter seasons of 2017 and 2018. Phenotypic evaluation revealed significant variations for all traits  in the two years . Kernel p < 0.01) of thousand kernel weight with both kernel width (r = 0.49–0.81) and length (r = 0.38–0.64), indicating that the kernel weight was influenced by kernel size. By contrast, kernel length and width showed significant negative correlations with the kernels per spike (p < 0.01) among the genotypes for all traits , kernel width (r = 0.73), kernels per spike (r = 0.63), and thousand kernel weight (r = 0.73), indicating the consistency of these traits across the years. Furthermore, the correlation coefficients showed a significant positive association . To obtain more reliable results, the SNPs which were simultaneously detected by at least two methods or in two years were regarded as consensus SNPs. By not considering the repeated SNPs by different models, we obtained a total of 79 and 75 loci for the kernel traits detected on all 21 chromosomes using the ML-GWAS and SL-GWAS methods, respectively —mrMLM, FASTmrMLM, FASTmrEMMA, and pLARmEB—and three single-locus GWAS (SL-GWAS) methods—FarmCPU, MLM, and MLMM—were used to detect significant loci associated with these traits. In ML-GWAS, loci with critical logarithm of odds (LOD) values equal to 3 or greater than 3 were considered significant. For SL-GWAS, the selection criteria were set to ectively a.Four multi-locus GWAS methods in the mrMLM software were used in this study. A total of 34, 25, 21, and 31 significant SNPs were detected by mrMLM, FASTmrMLM, FASTmrEMMA, and pLARmEB, respectively, for KL (29), KW (27), KPS (23), and TKW (32) across two environments b. Among For further validation, we compared the results across the different ML-GWAS models and both years. As a result, we found 24 significant SNPs co-detected by at least two ML-GWAS methods, whereas three SNPs were consistently repeated in both years. Interestingly, most of these SNPs were located on chromosome 7A and 7B, while chromosome 1B harbored only one SNP. Among the 24 stably expressed SNPs across different ML-GWAS models, five SNPs  were simultaneously detected by at least three ML-GWAS methods. Furthermore, to observe the association of these consistent SNPs, seven loci were associated with KL with phenotypic variance (PVE) explained by the SNPs ranging from 2.58% to 10.51%. The remaining consistent SNPs associated with the rest of kernel traits are shown in Single-locus GWAS (SL-GWAS) models, including FarmCPU, MLM, and MLMM, were implemented using the Genomic association and prediction integrated tool (GAPIT) package in R, in order to further evaluate the above kernel traits. A total of 75 significant SNPs was detected by integrating the three SL-GWAS methods in two environments . Among tSNPs controlling more than one trait are prominent in marker-assisted selection. In the present study, we screened some candidate SNPs through the ML-GWAS methods and detected a total of five pleiotropic SNPs  associated with different kernel traits . Among tCandidate genes were detected around the peak SNPs, based on the annotated wheat reference genome. A total of 41 putative candidate genes were associated with kernel traits . Among tTriticum aestivum L.) is one of the major staple food crops, which has an important role in achieving food security [Wheat  associated with the different traits under study. Wheat strip rust is one of the most serious treats to wheat yield. Therefore, genetic resistance against this disease is the most effective strategy to control the disease. Cheng et al. [The advent of next-generation sequencing technologies has enabled the detection of many SNPs, demonstrating high-density genotyping compared to conventional marker analysis . SNP chig et al.  conducteg et al.  conducteg et al.  conducteHkps/sn-7B loci for increased grains and spikelet number per spike [TaSus1-7B genomic region for TKW [TaCYP78A3 candidate gene for grain size [Recently, several studies based on 90K SNP arrays can be found which have used GWAS for kernel yield ,64,65, fer spike , the TaS for TKW , and theain size . Accordiain size  and in QBy further reviewing the results of commonly identified SNPs in the ML-GWAS methods, a stable SNP (Tdurum_contig15734_221) was consistently repeated by three ML-GWAS methods, which we referred to as a pleiotropic SNP, having association signals with multiple traits identified for kernel length and width, on chromosome 7B. The same SNP has been identified in a bi-parental QTL mapping for plant height- and yield-contributing traits in hexaploid wheat . AnotherKernel number per spike is another important trait for kernel yield in wheat. For KPS, most of the stable SNPs were located on chromosome 7A, as revealed by the ML-GWAS results. Earlier studies conducted on GWAS for yield-contributing traits reported significant loci linked to KPS on chromosome 7A ,77. A stThe plant material was comprised of 319 wheat accessions obtained from the Hubei Academy of Agricultural Sciences in Hubei Province, China, which represent a wheat gene pool adapted to the central China and the Yangzi River region. Most of the accessions comprised of released varieties and landraces (266), some of which were genetic stocks (41) and improved genotypes (12). These accessions were evaluated in randomized complete blocks with three replicates at the experimental farm of Huazhong Agricultural University (HZAU), Wuhan, China, for two consecutive winter seasons (2017–2018 and 2018–2019). Twenty individual plants from each accession were selected and grown in two rows, with distances of 15 cm between the plants and 20 cm between the rows. Field management essentially followed normal local wheat cropping practices. The plants were harvested individually at maturity for phenotype evaluation.SmartGrain ver. 1.2, developed for the high-throughput phenotyping of rice grains [Four traits, including kernel length (KL), kernel width (KW), kernels per spike (KPS), and thousand kernel weight (TKW), were evaluated at the experimental farm of HZAU, Wuhan, China, during the winter seasons of 2017 and 2018. The measurements of these kernel traits were performed by selecting the main spikes of five random individual plants in the middle of the row for each accession. Kernels per spike were estimated by hand-threshing the mature spike. Thousand kernel weight (TKW) of each plant was recorded by weighing all the seeds from a sample, dividing it by the total seed number measured, and multiplying the result by 1000 . In the e grains , and whie grains . Measurehttps://zzz.bwh.harvard.edu/plink/) [The 319 wheat accessions were genotyped using the Illumina 90K wheat chip  in the g/plink/)  for the H2 = VG/(VG + VE) where VG and VE represent the estimates of genetic and environmental variance, respectively [Statistical analyses, including ANOVA, heritability, correlation, and descriptive statistics, were conducted using the R statistical package . The broectively .k = 1 to 7) was assessed using 100,000 burn-in iterations followed by 500,000 recorded Markov chain iterations. In order to reveal the sampling variance (robustness) of the inferred population structure, 10 independent runs were carried out for each K value. K was estimated, according to the method of Evanno et al. [Population structure analysis was carried out by the STRUCTURE 2.3.4 software , using Bo et al. , using ao et al. . Linkageo et al. .p-value was calculated based on the number of markers , according to the method of [p-value distributions  are shown with a quantile–quantile plot.To appraise the genetic architecture of kernel-related traits, we used mrMLM software for four multi-locus GWAS (ML-GWAS), including FASTmrMLM, FASTmrEMMA, mrMLM, and pLARmEB, as well as three single-locus GWAS (SL-GWAS) models, including FarmCPU, MLM, and MLMM, as implemented by the Genomic Association and Prediction Integrated Tool (GAPIT) in R . Previouethod of . Signifiethod of . Importahttps://urgi.versailles.inra.fr/blast/docs/aboutviroblast.html). To obtain the putative related candidate genes of SNP-flanking regions, a BLASTx search was conducted for significant marker-trait associations (MTAs) against the recently released genome sequence i.e. international wheat genome sequencing consortium (IWGSC) RefSeq v1.0 [The R Package Pathway Association Study Tool (PAST) version 1.0.1  was usedSeq v1.0 .The present study utilized four multi-locus GWAS (ML-GWAS) and three single-locus GWAS (SL-GWAS) models, resulting in the detection of the consensus SNPs and genes responsible for the determination of kernel traits. Comparatively, 111 and 104 significant SNPs were found to be significant using the ML-GWAS and SL-GWAS methods, respectively. By integrating these GWAS models, the mrMLM and MLM approaches outperformed in SNP detection compared to the other ML-GWAS and SL-GWAS models, respectively. Overall, the ML-GWAS results revealed five stable SNPs , which were simultaneously detected by at least three of the ML-GWAS methods. Furthermore, 41 putative candidate genes were assessed as controlling kernel width and thousand kernel weight, thus providing new insights into the genetic architecture of these kernel traits.


Wheat is a major food crop worldwide. The plant architecture is a complex trait mostly influenced by plant height, tiller number, and leaf morphology. Plant height plays a crucial role in lodging and thus affects yield and grain quality. In this study, a wheat population was genotyped by using Illumina iSelect 90K single nucleotide polymorphism (SNP) assay and finally 22,905 high-quality SNPs were used to perform a genome-wide association study (GWAS) for plant architectural traits employing four multi-locus GWAS (ML-GWAS) and three single-locus GWAS (SL-GWAS) models. As a result, 174 and 97 significant SNPs controlling plant architectural traits were detected by ML-GWAS and SL-GWAS methods, respectively. Among these SNP makers, 43 SNPs were consistently detected, including seven across multiple environments and 36 across multiple methods. Interestingly, five SNPs  consistently detected across multiple environments and methods, played a role in modulating both plant height and flag leaf length. Furthermore, candidate SNPs  constantly repeated in different years and methods associated with flag leaf width and number of tillers. We also detected several SNPs  having common associations with more than one trait across multiple environments. By further appraising these GWAS methods, the pLARmEB and FarmCPU models outperformed in SNP detection compared to the other ML-GWAS and SL-GWAS methods, respectively. Totally, 152 candidate genes were found to be likely involved in plant growth and development. These finding will be helpful for better understanding of the genetic mechanism of architectural traits in wheat. Triticum aestivum L.) is a staple crop worldwide, providing 20% of total food needs of the world population2. Plant architecture is a complex trait mainly depends on the three dimensional structure of the plant stature including branching pattern, morphology of leaves and flower organs3. Plant height directly indicates the ability of plant to compete for light, influencing plant growth and development4. It is of prime importance, strongly influencing plant defense against environmental stress, potential grain yield, and plant adaptability for better cultivation and harvesting6. Leaf morphology can regulate many important aspects related to plant growth and development7. In cereals, flag leaves have prominent role in photosynthesis and contribute about 43% of the total carbohydrates required for gain filling8. To date, several loci have been identified associated with flag leaf related traits in cereals10. Productive tillers in wheat are of great importance which may directly affect spike number and thus influence the final yield. The plant stature and the number of tillers influence many factors, including the process of photosynthesis, the flowering and grain set in plant11. It is thus understandable that the genetic elucidation of tillers at various plant growth stages is an important component in wheat breeding research programs11.Wheat  dwarfing genes. Among them, the Rht-B1 and Rht-D1 loci ensured short stature by limiting the response to the growth-promoting hormone gibberellin (GA). In addition, a newly discovered gene for reduced height Rht24 belongs to GA-sensitive type and was predicated to be commercial importance in worldwide wheat breeding17. Till now, more than 50 loci have been detected for plant height17. For instance, Wei et al.18, detected several stable SNPs on chromosomes 2A, 2B, 2D, 3B, 4B, 5A, 5D, 7B, and 7D associated with plant height. Griffiths et al.16, identified several height related genes on all chromosomes except for 3D, 4A, and 5D. Further studies are required to discover, to fine map and to clone more new semi-dwarf genes thus expanding the portfolio of Rht genes for the breeding of the favorable plant architecture.Considerable work has been done to dissect the genetic background of plant height in wheat. To date, 25 height reducing genes have been identified across different chromosomes in wheat9, maize19, rice20, and cotton21. Comparatively hexaploid wheat has larger genome (≈ 17.9 Gb) than rice (≈ 400 Mb) and maize (≈ 2.32 Gb)9. Over the past decade, the absence of fully sequenced reference genome has limited the gene discovery of wheat. Recent advances in functional genomics have provided breeders with a new impetus to achieve their goals22. However, substantially more work is required because of the experimental bottleneck emerging from the absence of inconsistency among studies, and the utilization of low-density marker platforms in gene mapping studies. Despite the current research on plant height in wheat, the effects of important loci and several other candidate loci responsible for fine-tuning of plant height in hexaploid wheat is still an intrinsic part in wheat breeding17. The availability of high quality reference genome allows for previously impossible follow-up analysis. The application of SNPs as molecular markers provides better understanding of variation in an organism or individual part and further provide high-throughput maps for detecting candidate loci and genes for target traits. Molecular markers are mostly used in segregation analysis, forensic examination, genetic mapping and diagnosis, and numerous biological applications25. In the present study we were interested to gain a comprehensive picture about the candidate loci responsible for modulating plant height and related traits in wheat through a series of different GWAS models.Currently research has been accomplished through GWAS in wheatThus the present study was designed to conduct GWAS in a set of 319 wheat accessions employing high-density wheat iSelect 90K SNP array. The objectives of this study included: (1) the investigation of marker-trait associations (MTAs) for plant architectural traits (2) appraising the correlation among these traits and further highlighting SNPs common to more than one trait (3) detecting candidate genes responsible for corresponding morphological traits. Overall, this study will provide insights by integrating three single-locus and four newly developed multi-locus GWAS methods, and will be helpful to establish a regulatory network in the genetic improvement of wheat architectural traits.P < 0.001) for all the studied traits , flag leaf length (FLL), flag leaf width (FLW) and the number of tillers per plant. The phenotypic characteristics for plant height across four environments and flag leaf length, flag leaf width and number of tillers per plant across two environments as shown in Supplementary Table Broad sense heritability was also estimated for PH, FLL, FLW and number of tillers with values ranged from 0.79 (FLL) to 0.91 (PH), suggesting the stability of these traits. The correlation analysis revealed significant correlation between different environments for each of the four traits, indicating the consistency of these traits across various environments Fig. . FurtherK-values. ΔK was calculated for increasing the number of K-value determined by STRUCTURE analysis according to the procedure of Evanno26. At K = 2, a break in the slope was observed followed by flattening of the curve   across four environments and for FLL (24), FLW (15) and Tillers (18) across two environments . Among these, one SNP was associated with FLL and the rest of four were identified for PH across multiple environments and methods Fig. . ComparaThree single-locus GWAS (SL-GWAS) methods i.e. FarmCPU, MLM, and MLMM were used to further analyzed the results of the same plant architectural traits. A total of 97 significant SNPs were detected by the three SL-GWAS methods for the above mentioned traits across multiple environments Fig. . Among tSNPs associated with more than one trait are very useful for marker-assisted selection. A total of five SNPs  were detected associated with more than one trait across multiple environments  which was reported to be involved in cell elongation and plant development34. Another gene (TraesCS6A01G174700) corresponds to Cytochrome P450, which is a part of ent-kaurenoic acid oxidase, an enzyme of the gibberellin acid (GA) metabolism35. Additionally, six putative candidate genes surrounding significant SNPs associated with number of tillers have annotations as F-box family protein, involved in plant vegetative and reproductive growth36. Further examples are given in Fig. To further understand the genetic basis of plant architectural traits, we predicted a total of 152 candidate genes that were surrounding the peak SNPs. Interestingly, several major candidate genes that were directly associated with the consensus SNPs had exact same annotations Fig. . For ins37. The real success of green revolution was the use of semi dwarf wheat cultivars. We identified some significant SNPs across multiple years by different ML-GWAS approaches  was located on chromosome 2B with LOD ranging from 3.2–6.4, which was also detected as one of the environment-stable SNPs and was revealed the positive effects on harvest index50. Two consensus SNPs (RAC875_c8121_1490 on chromosome 3A and Ku_c5191_340 on chromosome 6B), were also reported for plant height across different wheat populations51. The stable SNP (tplb0049a09_1302) located on chromosome 5A, was also reported by Ain et al.38, they used 90K array to identify several genomic regions associated with yield related traits in historical wheat genotypes of Pakistan. Another SNP (BobWhite_c5694_1201) located on chromosome 4B is likely same to the QTL identified in a spring wheat population by Zou et al.52.Five SNPs were detected across both multiple environments and methods for plant height and flag leaf length, of which two were located on chromosome 6B Table . These r53, both at the physical location 20.8 MB with a dwarf gene (Rht8-19.6 Mb). The cultivars that harbors the reduced height gene Rht8, short stature tended to get more spike number in unit area9. In addition, the Rht8 has the ability to improve the early vigor of semi-dwarf wheat53. Thus, the SNP D_contig10675_778 identified in this study is of interest for further genetic studies and molecular breeding.In addition to the stable SNPs, if the other SNPs in this study were considered, a wider co-localization was found between the SNPs in this study and in previous studies. For example, the SNP, D_contig10675_778 (located in chromosome 2D at 12.3 cM) was found to be co-localized with the QPht/Sl.cau-2D.1 (BobWhite_rep_c63957_1472 located in chromosome 2D at 12.24 cM) reported in a previous study1. It should be mentioned, except the Rht8, the SNPs in this study were not well co-mapped with some semi-dwarf genes such as the Rht1, 2, 14, 16 and 18. Similarly, in a previous study, where a total of 14 SNPs for plant height was mapped on chromosomes 1A, 1B, 2A, 3A, 3B, 4D, 5A, 5B, 6B and 7A, however, only the AX_108916749 on chromosome 4D is at the same position as Rht-D154. The reasons might be (i) that a relatively small population from a limited region was used in studies, (ii) that the 90K array in which the lower density of makers is not reliable for detected the SNPs with small effects and for the comparison of the SNPs across different studies and (iii) the inconsistency of SNPs with previous studies possibly indicated the identification of potentially new genes.The SNPs BS00023152_51 and tplb0049a09_1302 (located on chromosome 5A) detected in two environments in this study falls in the region of the SNP  reported by a previous study8. In present study, we detected several consensus SNPs associated with flag leaf length and width. For flag leaf length (FLL), three consensus SNPs were detected on chromosomes 4B, 5A, and 6D  associated with FLL simultaneously detected via two ML-GWAS approaches i.e. FASTmrMLM and pLARmEB was reported earlier in QTL mapping55. Number of tillers have been considered the primary trait for increasing cereal yield no matter in favorable or unfavorable environments11. In the present study, we highlighted several prominent SNPs controlling number of tillers. Two stable SNPs (BS00022127_51 and wsnp_BE499835B_Ta_2_5) associated with the number of tillers per plant corresponded to the previously reported SNPs in wheat57. Among the stably detected SNPs for number of tillers, BS00022127_51 located on chromosome 7B, consistently detected by all four ML-GWAS methods , which reveal the setting of very high threshold, due to which many small-effect loci are missed59. To make up for the limitations of these methods, some multi-locus approaches such as FASTmrMLM60, FASTmr EMMA61, mrMLM59, and pLARmEB62 have been used in this study. These models can improve the accuracy of SNPs with high detection power and less stringent criteria, and no Bonferroni multiple test correction is needed61. In our results, the number of significant SNPs by ML-GWAS were comparatively higher than SL-GWAS models, which suggest the significance of ML-GWAS models. Jaiswal et al.63 verified that ML-GWAS has more detection power than SL-GWAS by revealing ten MTAs through SL-GWAS while, 22 MTAs through multi locus mixed model (MLMM) and 58 MTAs through multi-trait mixed model (MTMM). Furthermore, we detected a total of 19 SNPs co-detected by using ML-GWAS and SL-GWAS methods together  in hexaploid wheat. A total of 271 significant SNP was detected across multiple environments and in different methods. Comparatively, 174 and 97 significant association signals were detected by ML-GWAS and SL-GWAS models, respectively which signifies the importance of ML-GWAS over SL-GWAS approaches. By further appraising these GWAS methods, the pLARmEB and FarmCPU models outperformed in SNP detection compared to the other ML-GWAS and SL-GWAS methods, respectively. Taken together, the results of ML-GWAS, revealed five most stable SNPs i.e. Kukri_c34553_89, RAC875_c8121_1490, wsnp_Ex_rep_c66315_64480362, Ku_c5191_340, and tplb0049a09_1302 which were consistently detected across multiple environments and methods.69 and to convert the SNPs of interest into kompetitive allele-specific PCR (KASP) markers, and to further verify in bi-parental populations and (iii) to jointly investigate the SNPs for the other agronomic important traits including grain yield and grain qualities across multiple genetic mapping populations thus to gain a comprehensive picture for breeding design.Our study will provide new insights to the genetic basis of plant architectural traits and can serve as a basis for further functional investigation. The loci and significant SNP markers identified in this study can be used for pyramiding favorable alleles in developing varieties with desirable plant architecture and potentiality in the genetic improvement of grain yield. Among them, the stable SNPs identified across years in this study are of great importance. Secondly, based on the correlation between traits and the direction of SNP effects, we can design the combinations or find the accessions with a high percentage of favorable alleles. For example, the plant height and flag leaf length generally positive correlated with each other, but both have less correlation with flag leaf width and tiller numbers. These information, together with the SNPs identified, will be beneficial for breeding design. However, to achieve these goal, the larger populations and higher density genetic maps are required. It is necessary (i) to narrow down the SNP confidence interval thus that the markers tightly linked to the genes of interest should be much reliable for marker-assisted selection and for fine mapping and subsequent cloning of the candidate genes, (ii) to estimate the effects of number of alleles for desirable phenotypic values for each traits as reported by some previous studiesA total of 319 wheat germplasm accessions from the collection at the Hubei Academy of Agricultural Science in Hubei Province, China, which represent a wheat gene pool adapted to central China and the Yangzi River regions. The plant materials were grown in randomized complete blocks with three replicates at the experimental farm of Huazhong Agricultural University, Wuhan, China for four consecutive winter seasons (2015–2018). Twenty individuals from each variety (line) were grown in two rows with a distance of 15 cm between plants in each row and 20 cm between rows. Field management essentially followed normal local wheat cropping practices. The lines were harvested individually at maturity to prevent seed contamination among lines. Four phenotypic traits were evaluated, including plant height across four environments (2015–2018) and the rest of three traits i.e. flag leaf length, flag leaf width, and the number of tillers per plant across two environments (2017–2018). The measurements of these traits were performed by selecting five random individual plants in the middle of the row for each accession. Plant height was measured after physiological maturity by measuring the distance between the stem base and the top of the spike excluding awns. Flag leaf length was measured as the distance from the base to the tip of the leaf. Flag leaf width as the width of the widest section of the leaf. Number of tillers were recorded by counting the total number of fertile tillers per plant.70 in the genotyping Laboratory of North Dakota State University in Fargo as described in our previously published study27. A quality preprocessing of genotyping data was done for sample call rate, SNP call rate, minor allele frequency (MAF) and Hardy–Weinberg equilibrium (HWE). This preprocessing was implemented in PLINK software (https://zzz.bwh.harvard.edu/plink/)71.A total of 319 wheat accessions were genotyped using the Illumina iSelect 90K SNP array 72. The broad sense heritability for the traits was estimated by the formula H2 = VG/(VG + VE) where VG and VE represent estimates of genetic and environmental variance, respectively73. Variance components for the studied traits were analyzed according to our previous study27, using general linear model to detect the effect of genotypes, environment, replication and genotype × environment interaction. All sources of variation were considered as random effects.Descriptive analysis, ANOVA, correlation analysis and heritability estimates were conducted in the R statistical package27. Population structure using a Bayesian cluster analysis was estimated by STRUCTURE 2.3.4 software74, and the obtained results were visualized with the STRUCTURE HARVESTER software75. A putative number of subpopulations ranging from K = 1 to 7 was assessed using 100,000 burn-in iterations followed by 500,000 recorded Markov-Chain iterations. To estimate the sampling variance (robustness) of inferred population structure, 10 independent runs were carried out for each K. K was estimated using an ad-hoc statistic ∆K based on the rate of change in log probability of data between successive values26. Principle component analysis (PCA) was calculated by R software for evaluating the population structure and compared to the result of STRUCTURE9. LD among markers was calculated using observed vs. expected allele frequencies of the markers in TASSEL v.5.038.The SNP markers and estimated methods for population structure and linkage disequilibrium (LD) were the same as in Muhammad et al.76. Previously, SL-GWAS methods were mostly applied such as GLM and MLM. However, single-locus approaches have some limitations such as GLM leads to high false-positive rates (FPRs), while MLM utilizes Bonferroni corrections for loci detection to reduce the FPRs77. Though, this procedure is so stringent that results in missing significant SNPs59. Therefore, multi-locus GWAS approaches are the best alternatives. The stringent Bonferroni multiple test correction in the SL-GWAS analysis is substituted by a flexible selection criterion in multi-locus GWAS analysis, that reduces the possibility of missing out significant loci61. The four ML-GWAS methods were performed with default parameters, and the screening criteria for significance were set with LOD scores 3 or > 362. However, for SL-GWAS models, the threshold for P-value was calculated based on the number of the markers  according to the method of78. Significant markers were visualized with a Manhattan plot using Haploview 4.2 software79. Important p-value distributions  were shown with a quantile–quantile plot.In this study, we used mrMLM software for four ML-GWAS  and three SL-GWAS  implemented by Genomic Association and Prediction Integrated Tool (GAPIT) in Rhttps://urgi.versailles.inra.fr/blast/docs/aboutviroblast.html). The R Package Pathway Association Study Tool (PAST) version 1.0.1 was used to identify genes around the peak SNPs with a window size of 200 kb. To find candidate genes or putative related proteins of SNP flanking-regions, BLASTx search was conducted for significant marker-trait associations (MTAs) against recently released genome sequence IWGSC RefSeq v1.080.Candidate gene sites were aligned and downloaded from the ViroBLAST database (Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.