A 238-plant F2 population and 69 recombinant inbred lines for this region from the same parents delimited globe to approximately 392-kilobases. A germplasm survey representing materials from multiple breeding programs demonstrated that the locus explains the flat/globe shape broadly. A single base insertion in an exon of Solyc12g006860, a gene annotated as a brassinosteroid hydroxylase, segregated completely with shape in all populations tested. CRISPR/Cas9 knock out plants confirmed this gene as underlying the globe locus. In silico analysis of the mutant allele of GLOBE among 595 wild and domesticated accessions suggested that the allele arose very late in the domestication process. Fruit measurements in three genetic backgrounds evidenced that globe impacts fruit size and several fruit shape attributes, pedicel length/width, and susceptibility of fruit to weather check. The mutant allele of GLOBE appears mostly recessive for all traits except fruit size where it acts additively.Within large-fruited germplasm, fruit size is influenced by flat and globe shapes. Whereas flat fruits are smaller and retain better marketability, globe fruits are larger and more prone to cuticle disorders. Commercial hybrids are often developed from crosses between flat and globe shaped parents because flat shape is thought to be dominant and fruit size intermediate. The objectives of this study were to determine the genetic basis of flat/globe fruit shape in large-fruited fresh-market tomato germplasm and to characterize its effects on several fruit traits. Twenty-three advanced single plant selections from the Fla. 8000 × Fla. 8111B cross were selectively genotyped using a genome-wide SNP array, and inclusive composite interval mapping identified a single locus on the upper arm of chromosome 12 associated with shape, which we termed  Fresh-market tomato has a production value of more than 1.2 billion dollars in the US, with the state of Florida accounting for more than 36% of that value2. Fruit shape and size are important traits in fresh market tomato, influencing consumer preference, packing demands, and market value4.Tomato is a major vegetable crop worldwide, valued at more than $95 billion annually5 and was domesticated in Central America, possibly in Mexico6. The size of cultivated tomato fruit has dramatically increased relative to wild species. Fruit of wild tomatoes are two-locular and weigh a few grams. In contrast, a modern tomato fruit may be multilocular and weigh up to 1 kg7. There is also tremendous variation in fruit shape: whereas fruit of wild tomatoes are simply round, fruit of cultivated tomatoes can be flat, round, fasciated, elongated, pear-shaped, plum, bell pepper-shaped, etc.8. It is thought that such variation emerged early in the domestication process by selecting alleles with larger fruits and variable shapes, and that these alleles accumulated over time, giving rise to the present-day tomatoes7.Tomato originated in the Andean region of South America7. However, most of the variation is explained by mutations in four genes: SUN, OVATE, LC, and FAS9. While SUN and OVATE are involved in fruit elongation, LC and FAS regulate the number of locules in the fruit, which in turn impacts both fruit shape and size. On the other hand, mutations at two loci, FW2.2 and FW3.2, are implicated in the increase in fruit size to a great extent.As many as 30 QTLs likely explain the overall morphological variation in the fruit of tomatosun, where a 24.7 kb insertion is linked to elongated fruit shape11. It was determined that this segment was copied from chromosome 10 by Rider, a Copia-like retrotransposon, present at both loci11. This rearrangement put SUN under the control of a defensin (DEFL1) promoter, leading to its increased expression in the fruit12. This mutation results in increased number of cells in the longitudinal area and reduced in the transversal area of the fruit12, thus not impacting fruit weight.The short arm of chromosome 7 harbors OVATE encodes a member of the Ovate Family Proteins (OFP), which are believed to be a class of regulatory genes important for plant development13. A SNP in the second exon of the OVATE gene leads to a premature stop codon13, resulting in fruit that can be pear-shaped, round, elongated, or ellipsoid—depending on the genetic background14. OVATE interacts with the sov1 and sov2 loci on chromosomes 10 and 11, respectively, the first influencing the degree of pear shape and the second fruit elongation15.FAS and LC, located on chromosomes 2 and 11, respectively, regulate the number of locules in the fruit, which in turn impacts both fruit shape and size19. The fas mutation results from a 294-kb inversion that impairs the expression of SlCLV320. It is thought that FAS impacts meristem organization and boundary formation within the floral meristem, resulting in changes in the number of locules in the fruit21. On the other hand, SlWUS is the gene underlying the lc mutant, which controls the inflorescence and floral meristem formation phase ultimately affecting the final size of the fruit19. Two SNPs 1080 bp downstream of this gene are believed to cause the phenotype by disrupting the regulatory region of this gene, resulting in increased expression19. Potentially involved in the SlWUS-SlCLV3 network is ENO, a recently identified transcription factor of the AP2/ERF superfamily which may act by regulating SlWUS expression22. A natural 85-bp deletion in the promoter of ENO leads to decreased expression of this gene and increased locule number, and evidence suggests that this mutation occurred prior to tomato domestication and is now fixed in cultivated tomato22.fw2.2 is responsible for as much as 30% of the difference in fruit size between modern and wild tomatoes23. fw2.2 increases fruit weight by causing the expansion of the placenta and columella of the fruit15. FW2.2 is a cell number regulator (CNR) located on the bottom of chromosome 2, and it is believed that a mutation in the promoter region increases fruit size by influencing the rate of cell division24.FW3.2 is located on chromosome 3 and is as a member of the Cytochrome P450 gene family (formerly identified as SlKLUH) whose members are known to control organ and plant size27. The increase in fruit weight is the result of pericarp and septum expansion. SlKLUH likely effects these changes by influencing the duration of cell division, thereby increasing the number of cells27. Although, it was initially believed that a SNP in the promoter region of the SlKLUH gene was responsible for fw3.2, it was recently discovered that a ~50-Kbp tandem duplication at this locus resulted in two copies of SlKLUH, thus affecting fruit size through increased gene expression28. The expression of SlKLUH is exceptionally high in the growing seeds27, suggesting that a seed-produced compound may be involved in the increase in fruit mass27. Other important fruit size and shape QTLs are fs8.1, controlling fruit elongation, and fw11.3 which explained from 8 to 13% of fruit weight variation in populations between domesticated and wild tomato accessions30.sun, lc, ovate, fas, fw2.2, and fw3.2) and found that there was no variation among sampled fresh market large-fruited tomato inbred lines from the UF/IFAS and NCSU breeding programs31. Specifically, the set of breeding lines genotyped from both programs are fixed for the domesticated allele at FW2.2, FW3.2, FW11.3, and LC, and fixed for the wild allele at FAS, OVATE, and SUN31. Hence variation in shape/size within this market class is explained by another, as yet unknown, locus(loci).Whereas the above-mentioned loci account for major fruit shape and size variation between wild and cultivated germplasm and among some market classes of cultivated tomato, the genetics of shape and size variation within specific market classes is less well understood. A study evaluated the variation for six major fruit shape and size loci  is 74VF18, an inbred line from the University of California at Davis . 74VF18 is likewise in the pedigree of Fla. 7060, the globe parent in the hybrid ‘Solar Set’ and the source of globe fruit shape in the North Carolina State University (NCSU) tomato breeding program . Previous observations suggest that flat/globe fruit shape is simply inherited . Moreover, globe-fruited germplasm from the UF/IFAS and the NCSU tomato breeding programs has been distributed to many public and private breeding programs over the years, and it is possible that the genetic control of this fruit shape characteristic is common among various tomato breeding programs.globe using CRISPR/Cas9 knock outs and to inquire its origin in the domestication process; and (4) to quantitatively characterize the locus(loci) for its effect on fruit size, fruit shape, fruit disorders, and other plant traits.The objectives of this research were: (1) to characterize the genetic basis of flat and globe fruit shapes in large-fruited fresh-market tomato, and to identify and fine-map the underlying locus(loci); (2) as genetic control is determined, to explore the extent to which this locus(loci) may explain fruit shape variation among a diverse panel of germplasm from the UF/IFAS and NCSU public breeding programs, and from several private breeding programs; (3) to identify the gene(s) underlying 6 RILs identified 39 with flat and 44 with globe fruit shape, and a chi-square test for a 1:1 suggested that fruit shape was controlled by a single locus . From this group, 11 flat and 12 globe-shaped RILs were selected and individually genotyped with a 7720 SNP array, identifying 751 polymorphic loci. Inclusive composite interval mapping (ICIM) analysis with this information detected a single locus in the upper arm of chromosome 12 that was significantly associated with fruit shape  and indicating dominant gene action by the flat shape. ICIM analysis on this population, using eight new sequence-based markers spanning the chromosome 12 interval from 390,069 to 2,877,860 bp confirmed the presence of a major QTL explaining 99.58% of the phenotypic variation for fruit shape in this region  were developed, phenotyped for fruit shape, and genotyped with the seven markers. Genotypes and phenotypes of the 69 FM-RILs are presented in Fig. globe locus to an approximately 392 Kbp region between 1,031,490 (marker M2.1) and 1,423,513 bp (marker 16EP133). Marker M3 demonstrated complete co-segregation with the phenotype throughout the entire FM-RIL population.F2 population from the cross between Fla. 7946 (flat) and Fla. 8442 (globe). One hundred and three plants were phenotyped for fruit shape and genotyped with the M3 marker, among which, 81 were flat, and 22 were globe, fitting a 3:1 ratio and supporting a single dominant locus . All plants with globe fruit shape were homozygous for the globe allele at marker M3; and among the plants with flat fruit shape, 20 were homozygous for the flat allele, and 61 were heterozygous. These genotypes also fit a 1:2:1 ratio , indicating no distortion in Mendelian segregation.Results were validated using a separate Fglobe locus were determined for three globe-shaped inbred lines , for three flat near isogenic lines (NILs) that corresponded to each globe inbred, and for F1s resulting from the cross between each globe recurrent parent and its flat NIL. Each NIL was developed by a modified backcrossing approach, without the use of molecular markers, to introgress the flat fruit shape phenotype into the recurrent parent. Notably, whereas each of the globe parents were homozygous for the globe allele, all three of the flat NILs were homozygous for the flat allele, and each F1 was heterozygous and had flat fruit shape. These results further support the ability of the globe locus in explaining variation of flat and globe fruit shape in multiple backgrounds.To investigate the efficacy of this QTL across backgrounds, fruit shape phenotype and genotype at the globe locus explains fruit shape variation across a broader set of germplasm, a panel of 204 inbred lines from multiple breeding programs were genotyped and surveyed for fruit shape. Of 176 large-fruited samples surveyed, 129 had flat fruit shape, and 47 had globe fruit shape , and 2 INDELs  and fully domesticated tomato varieties (S. lycopersicum L. var. lycopersicum). Among these accessions, only three cultivated accessions harbored the mutant allele: Fla. 7060, TS-103 (EA00389), and TS-306 , the homozygous globe genotype had the highest yield in two of the three backgrounds. On the other hand, in spring 2018, homozygous globe genotypes consistently had the lowest marketable yield Table .With respect to culls, fall 2017 results indicated no significant differences among genotypes except for the Fla. 7776 background, where the homozygous flat genotypes had the highest percentage of culls. In contrast, the homozygous globe genotype had the highest percentage of culls in all backgrounds in spring 2018 and fall 2018. These differences were significant in all backgrounds except for Fla. 8022 in spring 2018 Table .The two fruit disorders that contributed the most to culls were radial cracking and weather check. With respect to radial cracking, there were no significant differences among genotypes in any background in fall 2017. In spring and fall 2018, the homozygous globe genotype typically had the highest percentage of radial cracking, and the heterozygous genotype tended to have the smallest percentage or was not significantly different from the homozygous flat genotype Table . With reThe relationship between weather (rain and temperature) and percentage of fruit with weather check damage was assessed by collecting weather data for each season and plotting it against the percentage of culled fruit with weather check damage by genotype Fig. S. Fruit wglobe locus, with the homozygous globe genotype consistently having the highest values, and the homozygous flat genotype having the lowest  were significantly affected by genotype at the st Figs.  and S4. The analysis of the fruit shape index (FSI) resulted in significant differences among genotypes and interaction between the background and the genotype Fig. . Still, globe locus significantly affected fruit shape at the proximal (stem) end of fruit. Although the analysis suggested a significant interaction between genotype and background for all proximal end traits , compared to the heterozygous and homozygous flat genotypes. In those two backgrounds, the plant biomass was reduced by 12–16% in the globe genotypes compared to the flat. Although the mean of the globe genotype was the lowest in the Fla. 8022 background, the analysis showed no significant differences among genotypes in this background Table .Fruit pedicel size was significantly different among genotypes. For pedicel length, although the analysis suggested that the interaction between genotypes and backgrounds was significant, the pedicel was consistently longest in the homozygous globe genotype (~19 mm), and intermediate in the heterozygous (~10–12 mm) but closer in length to the homozygous flat genotype (~9–11 mm). Similarly, the homozygous globe genotype consistently had the thinnest pedicel among backgrounds (~3.3 mm). The pedicels of the homozygous flat and heterozygous genotypes had similar values in all backgrounds (~4–4.5 mm) except for the Fla. 7776 background, where the heterozygous was significantly different from both homozygous genotypes Table .sun, lc, ovate, fas, fw2.2, and fw3.2) in fresh market large-fruited germplasm from the UF/IFAS breeding program31. In fact, the study by Blanca et al.31 included the parents of the mapping population (Fla. 8000 and Fla. 8111B) and found that all materials were fixed for the domesticated allele at FW2.2, FW3.2, FW11.3 and fixed for the wild-type allele at SUN, FAS, and OVATE, suggesting that other locus(loci) explain variation in fruit shape and size in these materials.Previous studies have identified many major and minor QTLs controlling fruit size and/or shape. Such studies were mainly carried out by crossing domesticated tomato with wild accessions. Thus, the QTLs represent critical fruit shape/size loci involved in the transition from wild to modern tomato. In contrast, the present study focused on fruit shape and size variation existing within large-fruited cultivated tomato. Evidence suggests that there is no allelic variation at known major fruit shape and size loci  and belongs to the Cytochrome P450 family of genes. Brassinosteroids are steroidal hormones distributed ubiquitously in plants and are involved in the regulation of important processes like cell development growth and multiplicationFW3.2 (SlKLUH) is also a member of the Cytochrome P450 gene family. It is presently understood that increased expression of SlKLUH originated by the tandem duplication at the FW3.2 locus is what drives increased fruit mass28. In contrast to SlKLUH, Solyc12g006860 contains a mutation within the coding region resulting in a loss of function rather than in a change of gene expression.Interestingly, SIKLUH on fruit size is observed in the pericarp of fruit, its expression is particularly high in developing seeds, and it is therefore speculated that SlKLUH regulates fruit weight through a seed-produced compound, similar to KLUH in Arabidopsis39. Interestingly, a transcriptome mapping study of tomato fruit development and ripening determined that expression of Solyc12g006860 is likewise highest in developing seeds40. Besides providing circumstantial evidence supporting Solyc12g006860 as the GLOBE gene, these findings suggest that SlKLUH and Solyc12g006860 may function similarly in the regulation of fruit size and shape. Further research is needed to determine the physiological basis of globe on fruit morphology.Although the effect of globe mutation provides a useful molecular resource for breeders and geneticists who may desire to distinguish flat and globe genotypes. The survey of commercial hybrids indicates that although globe parents are being used in some crosses, other hybrids are being developed by crossing two flat parents. Hybrid cultivar releases from UF/IFAS and NCSU alike remark on the advantage of intercrossing flat and globe parents to boost fruit size in the hybrid.The globe has a relatively additive effect on fruit size, it acts in a recessive manner on many fruit shape attributes. However, this should be interpreted with care, as illustrated by the more additive gene action in the Fla. 7776 background for several fruit shape descriptors. Other genetic factors in this background may modify the effects of the locus on fruit shape. Results also indicate that although the globe fruit shape is more prone to imperfections that reduce marketability , heterozygosity for the QTL results in similar marketability rates as flat fruit shape. Thus, compared to the flat genotype, the heterozygote has increased size and similar shape and quality, with no penalty in yield. Furthermore, in light of the findings that a large number of current commercial cultivars do not involve crosses between flat and globe parents . sun does not affect fruit size because the increase in height is compensated by a reduction in width12; fs8.1 causes an increase in fruit height with no reduction in width29; ovate results in different elongated shapes depending on the genetic background with a slight reduction in fruit weight13. Thus, globe has an apparently unique effect, as it impacts both fruit shape and size, and it seems to have a greater proximal end impact than fw3.2.FW3.2 were affected for plant height, leaf size, and seed number, implying a pleiotropic effect on development27. globe also has an effect on plant biomass and pedicel length, where homozygosity for the mutant allele results in a reduced plant biomass and longer and thinner fruit pedicels. Furthermore, differences in seedling height were observed between the globe breeding lines Fla. 7776, Fla. 8022 and Fla. 8735, and their respective flat NIL and F1, where seedlings homozygous for the globe allele were taller (7–34%) and appeared thinner and leggier at 27 days after sowing than either homozygous flat or heterozygous seedlings . Thus, similar to FW3.2, GLOBE may also have pleiotropic effects on plant growth. More work is necessary to elucidate the effect of this gene on seedling and young plant growth, and cell size and cell number in the fruit.Besides the effect on fruit size, it was found that transgenic lines for 6 RILs were grown in the field and phenotyped for fruit shape. From those, 23 RILs  were sampled for initial genotyping using the Illumina Infinium SolCAP tomato SNP array (described below). A 238-plant F2 population from the same breeding lines was likewise grown in the field and phenotyped for fruit shape for confirmation of the mapping results from the RILs. Fine mapping was conducted in F4 and F5 populations from the same cross following a map-based approach.Several populations developed from the cross between the breeding lines Fla. 8000 and Fla. 8111B were used for mapping and fine mapping the locus controlling flat and globe fruit shape. Fla. 8000 and Fla. 8111B have flat and globe fruit shape, respectively. Eighty-three F2 population from the cross between Fla. 7946 (flat) and Fla. 8442 (globe), as well as three near isogenic backgrounds. Recurrent parents, NILs, and F1 progeny resulting from their cross were then genotyped with markers specific to the globe locus.Validation of the identified locus was conducted using additional populations. These included a 103-plant Fglobe locus explains fruit shape across a broader collection of cultivated germplasm, a survey was conducted including large-fruited inbred lines from the UF/IFAS and NCSU breeding programs, and inbred lines and hybrids from multiple private sector breeding programs.To determine the extent to which the Further details about the development of the populations used for mapping and germplasm surveyed are provided in 42 or a NaOH rapid DNA extraction method43. Samples were stored at 4 °C until PCR was performed. For either extraction method, tissue was collected from young leaves using a 3 mm diameter hole punch .DNA was extracted using either a modified Cetyl Trimethylammonium Bromide (CTAB) method44 was used to identify polymorphic loci for confirmation and fine mapping of the globe locus. In addition to SNPs, insertions/deletions (indels) were predicted from aligned read data using FreeBayes (version 0.9.16-1-gf46d24f) 45. Data were visualized using JBrowse 1.11.5 (ref. 46). Loci that were polymorphic between Fla. 8000 and Fla. 8111B were considered for marker development (described below). Haplotypes at additional polymorphic loci within the fine mapped interval were compared among other breeding lines for further sequence characterization. Sequences were compared for six flat-fruited breeding lines, Fla. 8000, Fla. 8624, Fla. 8820, Fla. 8916, Fla. 8924, and Fla. 8932; and for four globe-fruited breeding lines, Fla. 8111B, Fla. 8297, Fla. 8872B, and Fla. 8925. Gene annotations were based on version 4.1 of the International Tomato Annotation Group (ITAG4.1). Genome physical locations were based on the version SL4.0 of the Tomato Genome Assembly and the sequences were accessed through the Sol Genomics Network database (https://solgenomics.net/)47. The wild-type and mutant sequences of the candidate gene were translated into peptide sequences using the ExPASy Translate Tool (https://web.expasy.org/translate/).Whole-genome resequencing data previously described48. DNA samples were extracted using the CTAB method described above, and SNP chip processing was performed by the Michigan State University Potato Breeding and Genetics SNP genotyping facility. An Illumina iScan system was used for SNP chip processing, and SNP analysis was done using GenomeStudio 2.0 software .Selective genotyping for initial mapping was performed using the Illumina Infinium SolCAP tomato SNP array , which encompasses 7720 Single Nucleotide Polymorphism (SNP) sites distributed across the genome2 plants from the Fla. 8000 × Fla. 8111B cross were used for confirmation of the mapped flat/globe locus. Kompetitive allele-specific PCR , Cleaved Amplified Polymorphic Sequences (CAPS), derived Cleaved Amplified Polymorphic Sequences (dCAPS) markers, and an indel-based marker, were designed from polymorphisms spanning the mapped globe locus and used to genotype this population. Subsequent mapping and fine mapping relied upon High resolution Melting Analysis (HRM) and additional dCAPS markers. Marker protocols are described further in Two hundred and thirty-eight F6 RIL population using a chi-square test for goodness of fit to 1:1 ratio of flat:globe phenotypes. Genotype data generated by the SolCAP SNP chip was used for initial mapping of the flat/globe fruit shape locus. Data were filtered, and SNPs identified as polymorphic between Fla. 8000 and Fla. 8111B and segregating among the 23 RILs were utilized for genetic map construction with JoinMap® 4.1 using the Kosambi mapping function50. RIL phenotypes, along with SNP genotypes and calculated genetic positions were then used to perform inclusive composite interval mapping analysis with QTL IciMapping 4.1 (refs. 52). The ICIM-ADD mapping method was used with a scanning step of 1 cM and a probability in stepwise regression of 0.001 as implemented in the software.Monogenic inheritance of fruit shape was tested in the Fla. 8000 × Fla. 8111B F2 population, the frequency of flat:globe phenotypes was tested for goodness of fit to a 3:1 ratio using a chi-square. Phenotypes of these F2 individuals along with their genotypes at eight markers were used to construct a genetic map and perform inclusive composite interval mapping as described above.To confirm monogenic inheritance of fruit shape in the Fla. 8000 × Fla. 8111B FFla. 8059 seeds were sown directly in SunGro Germination mix soil in 72 count 6 pack trays and grown in a growth chamber under 16-h light/8-h dark photoperiod for 4 weeks under lithonia ibz 632 wd with ProLume eco-shield F32T8-850-ECO bulbs . Four weeks-old seedlings were transplanted into 6.06 L pots and were cultivated in an air-conditioned greenhouse under 16-h light/8-h dark photoperiod with daytime temperatures 75–80F (8am–6pm) and nighttime temperatures 60/65F (9pm–6am) in Athens, GA, USA.http://cbi.hzau.edu.cn/cgi-bin/CRISPR)53. The gRNAs were selected to have no fewer than four mismatches within the coding region of potential off target genes. The CRISPR/Cas9 constructs were assembled using the NEBuilder Assembly of CRISPR vectors using ssDNA oligos as previously described54. Electroporation was used to introduce the final binary vectors into Agrobacterium tumefaciens strain LBA4404, which was kindly provided by Dr. Joyce Van Eck, Cornell University. The LBA4404 harboring the binary vector was used for the transformations of Fla. 8059. The genetic transformations were performed as described55. The gRNAs were cloned and transgenic plants were genotyped using primers listed in Table The gRNAs targeting Solyc12g006860 were designed using the CRISPR-P tool  measurements were performed for each of five WT plants and five mutant plants. The average of the shape index and pedicel length per plant was determined and used to perform a t-test to look into statistically significant differences.Ripe fruits were halved and scanned at 200dpi using an HP Scanjet G4050. These images were analyzed using Tomato Analyzer to determine shape index (Length/width)in silico using BLAST search57 against publicly available tomato sequencing projects60. Data was retrieved from the Sequencing Read Archive (https://www.ncbi.nlm.nih.gov/sra). A 61-nucleotide sequence containing the single base pair insertion in the globe allele (consisting of 30 bp upstream and downstream of the insertion) was used as a query. Blastn program was chosen, leaving all other parameters to default values.The distribution of the globe allele across a broad collection of wild and domesticated tomato germplasm was determined 1s were used for the characterization trials. The trials were conducted during three seasons  at the UF/IFAS Gulf Coast Research and Education Center in Balm, FL. The plants were established in the field in 12-plant plots arranged in a randomized complete block design with four replicates. Eight plants were harvested from each plot. Due to pollination problems, the backcross F1 for the Fla. 8022 background was not evaluated in spring 2018.The three flat NILs described previously along with the globe recurrent parents and corresponding backcross FFruit weight was assessed from four to five weekly harvests of vine-ripened fruit in each of the three trials. The fruit from each experimental unit were graded for marketability and size according to the US Standards for Grades of Fresh Tomatoes . The fruit weight was obtained by averaging the total weight of marketable fruit over the total number of fruits per plot. Culled fruit included, but were not limited to, fruit with radial cracking, weather check, zippering, zippering with windows, and rough scars. Radial cracking and weather check are illustrated in Fig. S56. The default settings of the software were used, and boundaries and end points were corrected as needed. The attributes assessed in this study are listed in Table For fruit shape, 24 mature fruit per plot, from the second to fourth harvest, were randomly selected and cut in half longitudinally. One half of each fruit was scanned into a 200-dpi (dots per square inch) image. Several fruit shape attributes were measured from images with the Tomato Analyzer 2.2 softwareFurther details on data collection for plant biomass and fruit pedicel are described in the globe locus with each of the traits measured. The model was defined as:εpqij represent the variance due to season, block (nested within season), and the residual, respectively, and conform the design structure effects, hence were set as random. On the other hand, μij is the mean structure for the fixed effects given by:μ is the overall mean, αi is the main effect for the ith genotype at the fruit shape locus , βj is the main effect for the jth genetic background , and αβij is the interaction between genotype and background. The design structure effects have nothing to do with the mean structure and interactions between season and treatments were not included for this reason61.A linear mixed effect analysis was performed to assess the association of the genotype at the Lp, and bpq(L) terms in the design structure of the model were dropped and bq, representing the block effect, was added instead.Since it is known that, in tomato, fruit yield and marketability usually show a high variation across seasons, and fruit disorders like weather check are known to be highly dependent on weather, the analysis of the marketability traits was made individually for each season, hence the 62. The lmer function of the lme4 package63 was used to fit the model. The model assumptions were checked by visually inspecting the QQ plot, histogram, and distribution of the standardized residuals. Weather check data in both fall seasons was log-transformed in order to meet the model assumptions.The R programing language (R Core Team 2017) was used to perform the analysislsmeans function to calculate the fixed-effects estimates and p-values of the pairwise differences adjusting with the Tukey HSD method (P ≤ 0.05). The significance letters were generated with the cld function. Both functions belong to the lsmeans package64. The pairwise differences were calculated within and among backgrounds.The model results were later entered into the Supplementary file 1 - marked upSupplementary file 1Supplementary file 2


Streptococcus is a globally significant human pathogen. The extensive variability of the GAS genome, virulence phenotypes and clinical outcomes, render it an excellent candidate for the application of genotype-phenotype association studies in the era of whole-genome sequencing. We have catalogued the distribution and diversity of the transcription regulators of GAS, and employed phylogenetics, concordance metrics and machine learning (ML) to test for associations. In this review, we communicate the lessons learnt in the context of the recent bacteria genotype-phenotype association studies of others that have utilised both genome-wide association studies (GWAS) and ML. We envisage a promising future for the application GWAS in bacteria genotype-phenotype association studies and foresee the increasing use of ML. However, progress in this field is hindered by several outstanding bottlenecks. These include the shortcomings that are observed when GWAS techniques that have been fine-tuned on human genomes, are applied to bacterial genomes. Furthermore, there is a deficit of easy-to-use end-to-end workflows, and a lag in the collection of detailed phenotype and clinical genomic metadata. We propose a novel quality control protocol for the collection of high-quality GAS virulence phenotype coupled to clinical outcome data. Finally, we incorporate this protocol into a workflow for testing genotype-phenotype associations using ML and ‘linked’ patient-microbe genome sets that better represent the infection event.Group A  Streptococcus pyogenes (group A Streptococcus: GAS) is a globally important, strictly human bacterial pathogen. Diseases caused by GAS are diverse in both severity and clinical outcome, and GAS infection impacts a range of tissues  and machine learning (ML), has recently been successfully applied to large GAS genome datasets. Kachroo et al. have reported on a study of a population of the invasive  disease . The int disease .Streptococcus pneumoniae, a human pathogen capable of causing life-threatening invasive diseases (CCDC33) and nine pneumococcal genes . Studies of a similar design have used the nomenclature ‘joint’ , and were able to predict emm type using ML with 97% accuracy. However, no strong phylogeny-based associations were observed between the individual TR loci and infectious manifestation. Additionally, ML was used to predict emm subtype, country and invasiveness, but we were unable to usefully predict tissue tropism and clinical outcome. Significantly, three biological models were developed explaining rare recombination in the important genes of the mga regulon, that are detailed below.We recently characterised the distribution and diversity of the nucleotide sequences of the two-component systems (TCSs) and stand-alone transcription regulators (TRs) in 944 GAS genomes, and then explored phenotype associations using phylogeny and concordance metrics . Subsequin silico techniques that exploit the increasing tractability and cost-efficiency, and decreasing lead-time of whole-genome sequencing (WGS). However, the field currently lacks easy-to-use, universally-applicable, end-to-end workflows  correlates with the resultant phenotype , and compiled catalogues describing the allelic variability of 14 TCSs and 53 TRs. We also developed a novel allele-typing tool that was based on the ‘sort’ and ‘find unique’ algorithms. Consequently, we were able to identify many different recombination events, and that the most prevalent form of variation in these loci was the single-nucleotide polymorphism (SNP). Our curated database serves as a template for the use of GAS WGS-derived techniques in the selection of a candidate set of genes for comparative genomics studies.emm as a GAS virulence factor, the quintessence of the emm type as the strain-defining, gold standard of GAS genotyping is under increasing scrutiny  with 97% accuracy and establish that mga2 and lrp were the most mathematically powerful predictors of strain   , likely ne (ska) . Accordine (ska) .mga regulon including mga2-switching, emm-switching, and chimeric emm-enn events and were also able to develop evolutionary models to explain them (maeP) that stands as a biomarker for the invasive emm subtype 89.0 (We discovered examples of rare recombination of the ain them . Furtherype 89.0 . Collectype 89.0  that an ype 89.0 .emm-based systems for interpreting GAS phylogenetic delineation. The main advantages are: i) the absence of surface-exposure with the resulting lack of immunogenicity and positive selection pressure from host immunity; ii) multiple constituent genes that offer genomically-dispersed loci and an inherent redundancy, compared to the single ‘point of failure’ of the emm locus; iii) a range of recombinogenicities from which to select  backwards compatibility to emm-based knowledge, and vi) the ability to detect rare mga anomalies, all whilst sharing the genotype-dependency and WGS-amenability of the emm-based systems. Placing this in context, we can see that the M protein was chosen as the original basis of phylogenetic delineation partly because it availed itself to the technology of the day, i.e. serotyping. We contend that the WGS era calls for the exploration of novel WGS-amenable typing systems, of which our TR-based system is one. Moreover, we contend that one typing system should not necessarily be assumed to be appropriate for both functions of phylogenetic delineation and interpretation of the biology of epidemiology.Throughout our studies, we have come to appreciate the virtues of the TR-based typing system over the o select ; iv) a gThe global burden of infectious bacterial disease is significant. Whilst the terms ‘virulence’ and ‘pathogen’ have a foundational and pragmatic utility that endures, there is mounting evidence suggesting that these terms have fallen short as tools in the lofty ambition of fully elucidating microbial pathogenesis. Historical methods in this field used a framework that was pathogen-centric, reductionist, not dynamic, discretely binary in its classification, and limited by contemporary technology. Accordingly, there has been a shift of focus and an expansion of the scope of attention, so that in the WGS era we are advancing our approach to contextualise the host-microbe interactions into a dynamic continuum that accommodates a shift of an individual microbe from a harmful pathogen, to an opportunist, and even a commensal . Where ck-mers refers to the sets of complete and overlapping subsequences (k nucleotides in length) that are extractable from biological sequence  features, and the output of which is a majority vote of the trees that reduces the risk of an inaccurate prediction caused by any individual trees. In a supervised method, the rules for attaining the correct answer (label) are ‘learnt by example’, therein converting data into information. The RF is a robust and scalable method whose advantages include the ability to determine the importance of the predictor variable at predicting the correct answer. This is important because it allows for the elimination of statistically-dependent variables, reducing the dimensions of the input dataset and the computational resource usage. All of which is completed with highly interpretability.mga regulon , they are not necessarily causal. Conversely, an excluded variable could be causal or partially causal.GWAS is a comparative genomics technique that uses a suite of statistical models to test for associations or ‘statistical dependencies’ between variations in the DNA (genotype) of many genomes and the corresponding phenotype metadata, that may indicate causal relationships . It is aBecause they reproduce clonally, there are several considerations that hinder the application of GWAS techniques to bacteria that are not as relevant in human GWAS studies. The first of these is strong population structure or population stratification. Care must be taken to control for population structure to avoid identifying non-causal (spurious) relationships generated by: linkage disequilibrium with legitimately causal variants, environmental variables that are not controlled, and sampling errors induced by stratification . CurrentThe recently published review by  articulaWe propose a quality control protocol for the collection of the virulence phenotype and clinical outcome data of GAS infection, with the benefit of qualifying a reportable metric. Our system allows for the continued analysis of existing data, while simultaneously incentivising the progressive production and consumption of higher- quality genomic metadata. At the time of sampling of the bacterial isolate for subsequent sequencing, we recommend that clinicians assign a classification for all of the following categories  to v) above has a non-null entry using The future application of comparative genomics in GAS genotype-virulence phenotype association studies is highly promising, and a key tool is the unbiased GWAS. Furthermore, we recommend the synergistic utility of ML with GWAS as a tool for dimension reduction and appraisal of ‘candidate genes’, all with high interpretability. We have suggested that the quality and abundance of bacterial phenotype data lags behind that of the accompanying genome data, and proposed a quality control protocol that incentivises the eventual improvement of the quality of GAS virulence phenotype data collection. Finally, we envisage the inevitable widespread use of ‘linked’ genome sets in eukaryotic host-microbe interaction studies and have developed a work flow for the application of ML to these sets.SB and RH conceived the manuscript and selected the candidate journal. SB drafted and formatted the manuscript. RH revised the manuscript. SB is the intended first and corresponding author. All authors contributed to the article and approved the submitted version.SB and RH wish to acknowledge the generous support of the University of the Sunshine Coast.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


This result highlighted an established dilemma for strawberry breeders and a need to uncouple the relationship, particularly under June-bearing, protected production systems comparable to this study. A large effect of quantitative trait nucleotide was associated with perceived acidity and pH whereas multiple loci were associated with firmness. Therefore, we recommended the implementation of both marker assisted selection (MAS) and genomic prediction to capture the observed variation respectively. Furthermore, we identified a large effect locus associated with a 10% increase in the number of class one fruit and a further 10 QTN which, when combined, are associated with a 27% increase in the number of marketable strawberries. Ultimately, our results suggested that the best method to improve strawberry yield is through selecting parental lines based upon the number of marketable fruits produced per plant. Not only were strawberry number metrics less influenced by environmental fluctuations, but they had a larger additive genetic component when compared with mass traits. As such, selecting using “number” traits should lead to faster genetic gain.Over the last two centuries, breeders have drastically modified the fruit quality of strawberries through artificial selection. However, there remains significant variation in quality across germplasm with scope for further improvements to be made. We reported extensive phenotyping of fruit quality and yield traits in a multi-parental strawberry population to allow genomic prediction and quantitative trait nucleotide (QTN) identification, thereby enabling the description of genetic architecture to inform the efficacy of implementing advanced breeding strategies. A negative relationship ( Wild strawberry fruits have evolved to attract frugivorous animals. The sweet flesh provides nutrition in return for endozoochory or the dispersal of seeds Beal, . Achenes“activation” of germination potential and, therefore, completion of the natural strawberry life cycle McAtee, . The mutFragaria × ananassa became the dominant cultivated strawberry species in the eighteenth century and systematic breeding was subsequently implemented to improve the fruit size and vigour of strawberry plants  can improve traits that are controlled by a small number of major effect genes  associated with traits and determine the potential efficacy of genomic selection breeding approaches. In this manuscript we present a comprehensive analysis of the genetic components influencing fruit quality and yield traits in strawberries and discuss how our findings may help to optimise strawberry breeding through the implementation of genomic approaches. In this study, we asked (1) to what extent can we parameterize and standardise sensory fruit quality assessment, (2) can robust measures truly act as a surrogate for a human scoring system, and (3) can we implement advanced breeding strategies using subjective data sets in a fashion able to assist breeding for fruit quality?−1 (10 s−1 45 m). Replicated blocks represented both planting date and tunnel position. The picking date varied for each berry as strawberries were picked when ripe between 11 July 2018 and 8 November 2018. The fruits were picked every weekday and assessed on the day of picking. Fruit quality traits were assessed using three berries where possible for each replicate plant across the five blocks. Yield metrics were assessed on every pick and later summed to provide a total end-of-season value for assessment.The multi-parental strawberry population used in this study was designed to segregate for multiple fruit quality traits. Interrelated crosses between 26 parental lines were made, to produce 26 families of between 6 and 15 individuals (average 10.8). The number of genotypes per family is denoted in The phenotyping process is detailed in Fragaria × ananassa chromosome number is denoted by 1–7 and the sub-genome number is represented by A–D as specified in the study by van Dijk et al. . The Axiom® IStraw35 384HT array (i35k)  was used for genotyping  was calculated based on analysis of variance statistics, where r is replicate number, G represents genotypic variance, and E represents residual error. Narrow-sense heritability was calculated by h2 = σA2/ (σA2 +σE2), where A represents additive genetic variance, where the relationship matrix was calculated using the R package “snpReady”  were calculated for each genotype and trait using a linear mixed effect model that included the cofactors of the assessor, individual, picking date, and block. The model type fitted was specified individually for each trait as detailed in p-value of 0.001 was used to identify significant QTN. The values for the R2 of QTN effect size were calculated using a linear model comparing BLUE calculated values vs. predicted values assuming an additive relationship between focal SNPs. A genomic best linear unbiased prediction (GBLUP) was calculated using the software ASReml-R  and neck position (0.49), whereas the lowest values were observed for aromatics (−0.02) and glossiness (0.13) . Furtherp < 0.00001; p < 0.05, r = −0.22).There was a strong positive correlation between the total number of fruits, number of marketable fruits, and number of class one fruits with marketable fruit mass , and to a lesser extent flavour , were negatively correlated with acidity perception, indicating acidity may impair good flavour. Total soluble sugar values varied between 3.8 and 17.9 °Bx with an average of 9.37 °Bx . A total of 24 and 15 QTN were found to represent flesh firmness and skin firmness, respectively. The R2 values of 0.33 and 0.31 were associated with the presence of the QTN for flesh firmness and skin firmness respectively, these illustrate that a moderate proportion of the variation observed can be explained by the identified QTN. These QTN are particularly notable since both firmness traits are rated as 8 out of 9 for importance. Many of the skin and flesh firmness QTN co-localise, with four of the shared QTN improving both traits simultaneously whereas two QTN impact upon the traits antagonistically  to very strong 9;  and flesstically . The GBLstically . The R2 fied QTN . By contfied QTN .Since the shape is an ordinal trait, a quantitative measure of strawberry shape was adopted by measuring the H:W of each berry. H:W ratio is a continuous trait that allows data from across the population to be used in genetic analysis. Three QTN were associated with the H:W ratio on chromosomes 3A, 5B, and 6A, the alleles combined explained a total of 16% of the variation , 5. The Through plotting the importance of a trait as defined through breeding priorities against heritability, predictive accuracy, and number of QTN on a 3D scatter plot, it was possible to visualise the relative ability vs. the desire to improve yield and fruit quality traits within the study population . The figr= −0.22) was observed between total soluble sugars and class one plant yield metrics in June-bearing plants grown under a protected production system. Physiological or genetically linked trade-offs fundamentally limit the possibility that some combinations of phenotypes can occur  inhibiting overall expansion  but at different locations to those identified in bi-parental populations  indicating that it is a highly heritable trait and yet a lower narrow-sense heritability (26%) with no QTN detected. These results indicated that the trait may have a highly polygenic nature or potentially involves complex epigenetic interactions. Truss number was associated with a prediction ability of 0.33 as calculated via breeding is through selecting upon the variation that is caused by genetic components. Therefore, the selection of variation that is largely influenced by environmental conditions (such as mass) will lead to lower genetic gain. It should be acknowledged that mass traits were more influenced by environmental components and had lower narrow sense heritability scores, compared with fruit number traits. As such, using mass traits for yield selection is associated with lower accuracy. Therefore, we suggest that selecting based upon the number of marketable strawberries could improve the accuracy of selection and, thus, lead to greater genetic gain. However, to prevent selection for smaller and yet marketable berries, it is recommended that breeders increase the threshold for acceptable berries.More importantly, our findings suggested that the number of marketable fruits per plant may be the best trait to select upon when breeding for high cropping strawberry varieties, particularly when using genomic prediction approaches. Enhancing the accuracy of selection is a critical component for enhancing genetic gain  BB/M01200X/2 and Innovate UK project 101914.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


During the past decade, polygenic scores have become a fast-growing area of research in the behavioural sciences. The ability to directly assess people’s genetic propensities has transformed research by making it possible to add genetic predictors of traits to any study. The value of polygenic scores in the behavioural sciences rests on using inherited DNA differences to predict, from birth, common disorders and complex traits in unrelated individuals in the population. This predictive power of polygenic scores does not require knowing anything about the processes that lie between genes and behaviour. It also does not mandate disentangling the extent to which the prediction is due to assortative mating, genotype–environment correlation, or even population stratification. Although bottom-up explanation from genes to brain to behaviour will remain the long-term goal of the behavioural sciences, prediction is also a worthy achievement because it has immediate practical utility for identifying individuals at risk and is the necessary first step towards explanation. A high priority for research must be to increase the predictive power of polygenic scores to be able to use them as an early warning system to prevent problems. Research using polygenic scores emerged as a fast-growing area in the behavioural sciences during the past decade. Polygenic scores consist of sums of thousands of single-nucleotide polymorphisms (SNPs) each weighted by the effect size of its association with a target trait derived from genome-wide association studies .N = 1271) of the total publications. Figure In 2009, the first paper was published reporting a polygenic score that predicted up to 3% of the liability to schizophrenia in independent case–control samples [The predictive power of polygenic scores has increased steadily during the past decade for dozens of common disorders and complex traits. For example, the polygenic score for schizophrenia, which predicted up to 3% of the liability variance in 2009, can now predict 6% , 5% for Explain is the word used in statistical parlance to refer to effect sizes, but the word predict is more appropriate because polygenic scores do not explain how inherited DNA differences become associated with behavioural traits. Polygenic score predictions of behavioural traits are correlations and correlations do not imply causation. Causation is a complicated concept that generally refers to mechanisms that precede effects, often identified by experimental manipulation. Here, however, we refer to explanation in the more limited sense of statistical models of nonexperimental data that attempt to infer causation from correlational data , 13.The purpose of this perspective is to contrast prediction and explanation. Prediction and explanation offer different scientific perspectives, and neither is right nor wrong, just more or less useful to achieve different research goals. The goal of prediction is to account for as much variance as possible, without regard for explanation. The goal of explanation is to deduce causality, without regard for prediction , 15. TheThe value of explanation without prediction is seldom questioned but we argue here that prediction without explanation is also valuable. This point is widely acknowledged in some scientific disciplines, for example, in artificial intelligence where machine learning is an increasingly popular tool for prediction that explicitly eschews explanation. However, in the behavioural sciences, evidence for prediction has often been downplayed and devalued if it was devoid of explanation. This attitude seems especially paradoxical in the context of genomic research because success in identifying DNA differences came only after the search for candidate genes selected for their possible causal connection to a trait was superseded by a hypothesis-free approach that is agnostic about the specific function of DNA variants .The predictive power of polygenic scores is groundbreaking. Predicting 10% of the variance marks an important milestone because effect sizes of this magnitude are large enough to be ‘perceptible to the naked eye of a reasonably sensitive observer’ . NonethePolygenic score prediction compares favourably with other predictors in the behavioural sciences, which are rarely subjected to the same harsh spotlight of effect size. For example, in contrast to polygenic scores that predict 15% of the variance in tested school performance in the UK at age 16, ratings of school quality obtained by an independent body of evaluators (Ofsted) only predict 4% of the variance in the same tests of school performance . DespitePolygenic scores will never predict complex traits with perfect precision because heritabilities are about 50% for most behavioural traits . Other lp = 0.05, one-tailed).The ability to directly assess people’s genetic propensities has transformed research by enhancing the power and precision of genetic research on diagnoses and dimensions, heterogeneity and co-morbidity, developmental change and continuity and gene-environment correlation and interaction . PolygenPolygenic score predictions are correlations, and correlations do not necessarily imply causation. However, polygenic scores have a unique causal status among predictors in one important sense: correlations between polygenic scores and traits can only be interpreted in one direction causally. That is, there can be no backward causation in the sense that the brain, behaviour or the environment cannot change inherited DNA variation. The unchanging nature of inherited DNA variation from the moment of conception also makes polygenic score predictions unique in that they are just as predictive of adult traits early in life as they are in adulthood.Causal models using genomic data are burgeoning . Much ofQuantitative genetic research that uses family, twin and adoption designs to disentangle nature and nurture provides a backdrop for genomic studies of these processes. For example, a clever combination of twin and partner data indicated that assortative mating is caused by social homogamy rather than genetic influence on choice or environmental convergence of spouses over time . HoweverForty years of quantitative genetic research on GE correlation has revealed that most environmental measures widely used in the behavioural sciences show substantial genetic influence, about 25% heritability on average –30 and cTwin studies commingle GE correlation in their estimates of heritability, but adoption designs  and combQuantitative genetic research has had much less to say about population stratification. Because ancestral and regional groups are usually included in twin analyses, their effects, which are solely between-family effects, are read as shared environmental influence.Genomic methods have created many new opportunities to investigate assortative mating , 37, popAll these methods indicate that assortative mating, passive GE correlation and population stratification can contribute to polygenic score predictions. The most notable finding is that they contribute much more to polygenic score predictions of cognitive traits than other behavioural domains. This seems likely to be part of the reason why polygenic scores are more predictive for cognitive traits.Assortative mating, GE correlation and population stratification are interesting in their own right, and it is also reasonable to investigate the extent to which they contribute to polygenic score predictions. However, proclaiming that these processes make polygenic scores confounded, biased or inflated as predictors confuses explanation and prediction.From the perspective of predicting individual differences in a particular population, that population’s assortative mating, GE correlation and population stratification are legitimate sources of genetic variance for polygenic score prediction. If our goal is prediction, we would not want to ‘correct’ the polygenic score to remove genetic variance that can be ascribed to assortative mating, GE correlation or population stratification. In contrast, in causal models such as Mendelian randomisation , these pMost controversial is population stratification, which is so assumed to be a confounder that its genetic variance is removed in the first step of genome-wide association studies by covarying principal component scores for groups that differ in SNP resemblance. Polygenic scores are corrected again for group principal components in analyses of their association with a phenotype. The chopsticks example  illustraThe long-term goal of the behavioural sciences is to map the explanatory pathways from DNA through the brain to behaviour . Yet, prFrom the prediction perspective, anything that improves the predictive power of polygenic scores is welcome, such as improved methodologies for creating polygenic scores from current genome-wide association data  or using


Carcass traits are crucial characteristics of broilers. However, the underlying genetic mechanisms are not well understood. In the current study, significant loci and major-effect candidate genes affecting nine carcass traits related to meat production were analyzed in 873 purebred broilers using an imputation-based genome-wide association study.DRD2). The most significant SNP, rs15226023, accounted for 4.85–7.71% of the estimated genetic variance of the six weight traits. The remaining SNPs for carcass composition traits (whole thigh percentage and thigh percentage) were clustered around the 42.52 Kb region (GGA3: 53.03–53.08 Mb) and contained only one candidate gene (ADGRG6). The most significant SNP in this region, rs13571431, accounted for 11.89–13.56% of the estimated genetic variance of two carcass composition traits. Some degree of genetic differentiation in ADGRG6 between large and small breeds was observed.The heritability estimates of nine carcass traits, including carcass weight, thigh muscle weight, and thigh muscle percentage, were moderate to high and ranged from 0.21 to 0.39. Twelve genome-wide significant SNPs and 118 suggestively significant SNPs of 546,656 autosomal variants were associated with carcass traits. All SNPs for six weight traits  were clustered around the 24.08 Kb region (GGA24: 5.73–5.75 Mb) and contained only one candidate gene (DRD2 was the major-effect candidate gene for weight traits, and ADGRG6 was the major-effect candidate gene for carcass composition traits. Our results supply essential information for causative mutation identification of carcass traits in broilers.We identified one 24.08 Kb region for weight traits and one 42.52 Kb region for thigh-related carcass traits.  Improvements in the weight and carcass traits are major goals in modern broiler breeding programs. In particular, thigh development and meat production are closely related to the efficiency of the broiler industry. These traits have moderate to high heritability and are controlled by multiple genes , genes, and/or causative mutation. In pigs, a causative mutation of e growth . In largody mass . In miceody mass . A non-sk cattle . Many QTand LDB2 , 2015. Rand LDB2 . HoweverAn imputation-based genome-wide association study was conducted to identify significant loci and candidate genes affecting multiple weight and carcass composition traits in fast-growing white-feathered broilers.Chickens were obtained from a fast-growing white-feathered broiler line B. The chickens were produced and raised by Foshan Gaoming Xinguang Agricultural and Animal Industrials Co., Ltd. . In generation 5 (G5), 873 breeders  were randomly selected and slaughtered at 42 day of age. All birds were raised in stair-step cages under the same recommended environmental  and nutrIn addition, thirty Chahua and 24 Daweishan mini chickens were used for the phylogenetic and selective sweep analysis. Chahua chickens are similar to red junglefowl. The blood samples were supplied by the Aquatic Animal Husbandry Association of Yulin City in the Guangxi Zhuang Autonomous Region. The individuals are small, and the body weight rarely exceeds 1 kg. The blood samples of the Daweishan mini chickens were obtained from the Yunnan Agricultural University. The Daweishan mini chickens are a small-sized Chinese indigenous breed similar to junglefowl distributing in the tropical and subtropical zone in Yunnan Province. The typical characteristics of Daweishan Mini chickens are a slow growth rate and low body weight compared with Chinese indigenous chickens of the same age .The weight traits and carcass composition traits were measured in line B as follows: body weight at 42 day of age (BW42), carcass weight (CW), eviscerated weight (EW), whole thigh weight , thigh weight , thigh muscle weight , and the WThW, ThW, and ThMW as percentages of BW42 . In addition, the same method  was usedF-statistics showed that the fixed effect of sex was statistically significant (P < 0.05). The variance components were estimated using a univariate mixed liner animal model. The animal model was expressed as follows:A linear mixed model (LMM) was fitted using the residual maximum likelihood (REML). The ASReml software  was usedY is the vector of observations; b is the vector of the fixed effects; u is the vector of the additive genetic effects, with u A is the additive genetic relationship matrix (GRM), and e is the vector of the random residual effects; and X and Z are the incidence matrices assigning observations to effects. The fixed effect in the model was sex.where The phenotypic variance is the sum of all variance components and is defined as follows:The heritability is the ratio of the additive genetic variance to the phenotypic variance and is defined as follows:where A bivariate animal model was fitted to estimate the phenotypic and genetic correlations between carcass traits using the ASReml software package. The description of the model terms was the same as that for the univariate mixed linear animal model.A likelihood ratio test (LRT) was used to determine the significance of the heritability and the genetic correlations. The LRT compares the likelihood of a full model with that of a nested model (without the additive genetic component) and was used to test for nonzero additive genetic variance.Genomic DNA was extracted from blood samples using the phenol-chloroform method. The 873 broilers were genotyped with the customized chicken 55 K SNP array obtained from Beijing Compass Biotechnology Co., Ltd.  of 230 broilers  from the same line in G7 were used for the reference panel . Brieflyftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/002/315/GCF_000002315.6_GRCg6a/) with the Burrows-Wheeler Aligner (BWA)-MEM algorithm . Variant calling was performed according to a standardized bioinformatics pipeline . Specifilgorithm . Then, Psoftware  and the Genotype imputation of the 55 K genotypes of the broilers to the imputed WGS level was performed with Beagle 5.0 . The effNT(t) is the effective population size estimated t generations ago, f(ct) is the mapping function used to estimate the recombination rate (ct) t generations ago, N is the population sample size), and “a” is a constant accounting for mutation. The results showed that the Ne was 432 before the 13th generation.where R2 values for each variant and the genotype concordance rate for a random 2% SNPs were determined. The allelic R2 was calculated as the estimated squared correlation of the imputed sequence genotype on the true sequence, which was given by Beagle 5.0. The genotype concordance rate was calculated by comparing the imputed and real genotypes for a randomly masked 2% of the SNPs analyzed with both panels. We applied the following post-imputation filtering criteria for each SNP: allelic R2 ≥ 0.8 and MAF ≥ 0.05. Finally, 6,546,656 autosomal variants and 873 samples remained for the subsequent genome−wide association study  is an n × c matrix of covariates, including a column of 1 s; A is a c × d matrix of the corresponding coefficients, including the intercept; x is an n-vector of the marker genotypes; β is a d vector of the marker effect sizes for the d phenotypes; U is an n × d matrix of the random effects; E is an n × d matrix of the errors; K is the centered relatedness matrix estimated from 6,546,656 variants, In  ×n is a n × n identity matrix, Vg is a d × d symmetric matrix of the genetic variance components, Ve is a d × d symmetric matrix of the environmental variance components, and MNn×d  denotes the normal distribution of the n × d matrix with mean 0, a row covariance matrix V1 (n by n), and a column covariance matrix V2 (d by d). The Wald test was used as a criterion to select the SNPs associated with the carcass traits.where P-value of the 5% Bonferroni genome-wide significance was 7.63e-9 , and that of the significance of the “suggestive association” that allows a one-time false positive effect in the GWAS test was 1.52e-7 . It was calculated using the same method. Manhattan and quantile-quantile (Q-Q) plots were constructed for each trait using the CMplot package4 in R (version 4.0.0). LD blocks of the target regions were identified using the Haploview version 4.2 software (5. Boxplots were produced with the ggplot2 package in R (version 4.0.0).The threshold software . The SNPCπ . The Bayesian analyses were performed using the hibayes package6 in R (version 4.0.0). The number of iterations after the burn-in phase was 20,000, and that of the burn-in period was 10,000. Sex was fitted as a fixed covariate for all traits.A Bayesian approach called Bayes Cπ  was used7. The approach is based on fitting the equation:The estimation of SNP effect size was performed using MLMM analysis with the genotype data used to compute the GRM in DISSECT 7. The apy is the vector of the phenotypes, β is the vector of fixed effects that included sex, u is the vector of the SNP effects distributed as I is the identity matrix, and ϵ is the vector of the residual effects distributed as ∼N. W is a genotype matrix defined by the equation:where sik is the number of copies of the reference allele for the SNP k of the individual i, and pk is the frequency of the reference allele for the SNP k. In this model, the variance of y is:where 2 is the residual variance. The variance components were estimated using the REML.where A is the GRM, Using data from 80 white-feathered broilers with different-parents, 30 Chahua chickens, and 24 Daweishan mini chickens, the pair-wise genetic distance matrices were calculated based on whole-genome SNPs, two candidate genes, and five randomly selected 100 Kb regions, respectively. A total of eight neighbor-joining trees were then constructed using MEGA X . The defFST) was calculated as described by FST values were plotted in 20-kb overlapping genomic bins (for more than 10 SNPs) with a 10-kb step-size.Selective sweep analysis was performed on 80 white-feathered broilers with different parents, 30 Chahua chickens, and 24 Daweishan mini chickens. The population differentiation index (ChahuaandMINI/πlineB) was computed. The log2(πratio) was defined as The nucleotide diversity (π) of each population was estimated using a 20-kb sliding window (for more than 10 SNPs) with a 10-kb step across the whole genome, and the ratio , and the unique gene DRD2 was located in the region (ADGRG6 was located in the region (Based on e region . Anothere region .DRD2 and had a positive effect (β < 0) on all weight traits  in the significant region was detected by LD analysis . This LDt traits . Interest traits .ADGRG6 and had a negative effect (β < 0) on all carcass composition traits  was detected by LD analysis and contained 69 significant SNPs . This LDn traits .The effects of the significant LD block resulted in significant differences in the carcass traits, as shown in The Manhattan plots of the genetic variance and phenotypic variance explained by each 1-Mb window on different chromosomes for all traits are shown in The Manhattan plots of the SNP effect size are shown in DRD2, or the SNPs within ADGRG6, respectively  of the whole genome. The log2(πratio) values in DRD2 were between 0.07 and 0.20, and those in ADGRG6 were between -0.04 and 0.27, reaching the top 10% threshold (-0.04) of the whole genome. The zHp values in DRD2 were between 0.18 and 0.45, and those in ADGRG6 were between -1.37 and 0.19, reaching the top 10% threshold (-1.37) of the whole genome. The XP-EHH values in DRD2 were between -0.12 and 0.30, and those in ADGRG6 were between 0.64 and 1.02. Overall, only the ADGRG6 region showed some degree of genetic and nucleotide differentiation between large and small breeds.A genome-wide selective sweep analysis was performed with the fast-growing Line B chickens, the Chahua chickens, and the Daweishan mini chickens . The FSTR2 value and the genotype concordance rate between the imputed and true genotypes were 0.89 and 0.91, respectively.Genotype imputation has been widely used in GWAS to boost power . This meThe heritability estimates for the six weight traits were moderate to high (0.30–0.39), showing high consistency with previous reports . However8, this region has been recorded as a QTL (GGA24: 4.3–6.0 Mb) for BW08 in an F2 population , which encodes the D2 subtype of the dopamine receptor. The gene is highly expressed in the basal ganglia . The Galbase data shows that DRD2 has high mRNA expression in chickens’ brain tissue9. Therefore, brain tissue should be obtained and tested in a future study.A 24.08 Kb QTL on GGA24 (GGA24: 5.73–5.75 Mb) was identified for the six weight traits. According to the Animal QTL Database ganglia , which i studies . The deny intake . Down-reght gain . In the ADGRG6), which encodes the G-protein-coupled receptor 126. ADGRG6 is critical for myelination of peripheral nerves in humans and mutations of ADGRG6 are responsible for severe arthrogryposis multiplex congenita. ADGRG6 are related to trunk length, hip axis length, and height. Numerous studies have shown that ADGRG6 is associated with adult height and pediatric stature  was identified for WThP and ThP. This QTL was not reported previously. This region contained only one candidate gene  values and zHp values reached the top 10% threshold of the whole genome. ADGRG6 showed some degree of genetic and nucleotide differentiation between the fast-growing Line B chickens and the Chahua and Daweishan mini chickens. However, the DRD2 showed no related signals. Since the significant SNPs were located in the introns and the upstream and intergenic regions of the candidate genes, it is challenging to investigate causative mutations, which will be performed in a future study.In the current study, the phylogenetic tree indicated genetic differentiation in  regions . The FSTDRD2 was the only major-effect candidate gene for weight traits, and ADGRG6 was the only major-effect candidate gene for carcass composition traits. Some degree of genetic differentiation in ADGRG6 between large-sized and small-sized breeds was observed. Our results supply essential information for causative mutation identification of carcass traits in broilers.In summary, the genomic heritability estimates of nine chicken carcass traits ranged from moderate to high (0.21 to 0.39). Twelve genome-wide significant SNPs and 118 suggestively significant SNPs were detected. One 24.08 Kb region (GGA24: 5.73–5.75 Mb) for six weight traits and one 42.52 Kb region (GGA3: 53.03–53.08 Mb) for three thigh-related carcass traits were identified. The significant SNPs could be used in genomic selection programs to improve the weight traits and thigh composition traits. In the QTL regions, https://bigd.big.ac.cn/gsa, CRA002454 and CRA004023.The datasets presented in this study can be found in online repositories. The names of the repository and accession numbers can be found below: All experimental procedures with broilers were performed according to the Guidelines for Experimental Animals established by the Ministry of Science and Technology . Ethical approval on animal survival was given by the animal welfare and ethics committee of the Institute of Animal Sciences (IAS) and the Chinese Academy of Agricultural Sciences  with the following reference number: IAS2019–44.XY contributed to data collection, data analysis and interpretation, and wrote the manuscript. JS contributed to data collection and manuscript revision. GZ, MZ, and JW designed the research and revised manuscript. WL, XT, FF, and DL contributed to the chicken raising, sampling, and data collection. RL designed the research and contributed to data collection, data analysis and interpretation, and wrote the manuscript. All authors submitted comments on the draft and approved the final manuscript.FF and DL were employed by the Foshan Gaoming Xinguang Agricultural and Animal Industrials Corporation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


There has been a growing interest in the genetic improvement of carcass traits as an important and primary breeding goal in the beef cattle industry over the last few decades. The use of correlated traits and molecular information can aid in obtaining more accurate estimates of breeding values. This study aimed to assess the improvement in the accuracy of genetic predictions for carcass traits by using ultrasound measurements and yearling weight along with genomic information in Hanwoo beef cattle by comparing four evaluation models using the estimators of the recently developed linear regression method. We compared the performance of single-trait pedigree best linear unbiased prediction [ST-BLUP and single-step genomic (ST-ssGBLUP)], as well as multi-trait (MT-BLUP and MT-ssGBLUP) models for the studied traits at birth and yearling date of steers. The data comprised of 15,796 phenotypic records for yearling weight and ultrasound traits as well as 5,622 records for carcass traits , resulting in 43,949 single-nucleotide polymorphisms from 4,284 steers and 2,332 bulls. Our results indicated that averaged across all traits, the accuracy of ssGBLUP models (0.52) was higher than that of pedigree-based BLUP (0.34), regardless of the use of single- or multi-trait models. On average, the accuracy of prediction can be further improved by implementing yearling weight and ultrasound data in the MT-ssGBLUP model (0.56) for the corresponding carcass traits compared to the ST-ssGBLUP model (0.49). Moreover, this study has shown the impact of genomic information and correlated traits on predictions at the yearling date (0.61) using MT-ssGBLUP models, which was advantageous compared to predictions at birth date (0.51) in terms of accuracy. Thus, using genomic information and high genetically correlated traits in the multi-trait model is a promising approach for practical genomic selection in Hanwoo cattle, especially for traits that are difficult to measure. There is a growing interest in improving economically important traits in beef cattle breeding programs worldwide because it has a substantial effect on the overall profitability of the meat production system. Carcass traits are a comprehensive term that constitutes a major set of target traits for genetic evaluation and selection in beef cattle. In the Korean beef industry, Hanwoo cattle has been intensively bred for meat over the last four decades, and its beef is well known for its marbled fat, tenderness, juiciness, flavor , and is H matrix that combines the pedigree-based numerator relationship matrix (A) and a genomic relationship matrix (G)  genotyping technologies, genomic selection has become an appealing tool for accelerating genetic improvement in many breeding programs throughout the world . Genomictrix (G) . Severaltrix (G)  and othetrix (G) . Most oftrix (G)  and realtrix (G) . In our trix (G) , the strtrix (G) . Nonethetrix (G) .The phenotypic measurements used in this study were derived from 10,114 bulls and 5,682 Hanwoo steers born between 1997 and 2017 at Hanwoo Experiment Station .The records of body weight at 12 months of age for 15,796 animals, yearling ultrasound traits of 8,945 animals, carcass traits of 5,622 steers, and a pedigree consisting of 54,284 animals were used.th rib and the first lumbar vertebrae after chilling for approximately 24 h postmortem. The MS was graded using a 9-point scale following the Korean Beef Marbling Standard . The ultrasound carcass traits were collected by an experienced technician using a B-mode real-time ultrasound device  with an 18 cm, 3.5 MHz linear probe. The animals were scanned for the longissimus muscle area between the 13th rib and the first lumbar UBFT over the longissimus muscle at point three-fourths the length ventrally of the longissimus muscle area, and UIMF was the percentage intramuscular fat. Phenotypic data for UBFT, UEMA, and UIMF were obtained by analyzing the ultrasonic images with scanning software developed by HIC based on the CUP Lab at Iowa State University1. The yearling weight for each animal was determined from the weight at the termination of the test (body weight at ∼12 months) and the previous weight recorded at a time point before the termination (body weight at ∼6 months), according to the equation described by The eight traits analyzed were yearling body weight (YW), ultrasound backfat thickness (UBFT), ultrasound eye muscle area (UEMA), ultrasound intramuscular fat (UIMF), BFT, CW, EMA, and MS. Briefly, carcass traits were measured according to the Korean carcass grading system in steers at approximately 24 months of age, ribbed between the 13A total of 6,616 animals were genotyped  with the Illumina Bovine SNP50 BeadChip . These genotyping data were from animals  with at least one observation for the interest of traits or as a sire in the pedigree. Missing genotypes of SNPs were imputed using FImpute V3 software , and 52,The Bayesian multi-trait pedigree-based animal model was applied to obtain variance-covariance components for YW, ultrasound, and carcass traits using the gibbsf90test software  as folloy is the vector of observations for the trait of interest; b is the vector of fixed effects, including batch-sex-technician (108 levels), birth place (103 levels), and age of recording as a covariate for ultrasound traits; batch-sex (87 levels) and birth place (109 levels) for YW, slaughter date (274 levels), and slaughter age (days from birth to slaughter) was considered as a covariate for carcass traits; u is the vector of random genetic additive effects; e is the vector of random residual effects; X and Z are incidence matrices related to fixed and random genetic additive effects, respectively. Var(u) = G⊗A and Var(e) = R⊗I were assumed, where A is the numerator relationship matrix, I is the identity matrix, and G and R are additive genetic and residual covariance, respectively, for the eight traits.where The Markov Chain Monte Carlo (MCMC) method was used to estimate variance components and heritabilities in 550,000 cycles with a thinning interval of 50 and 50,000 iterations as burn-in. The convergence of the chain was verified by visual inspection of trace plots. The variance components and correlations were estimated as the posterior means of the corresponding sampled values. Furthermore, the estimated variance components from the multi-trait model were compared with those obtained using a single-trait animal model, which assumes zero for genetic and residual covariance among the traits.Pedigree-based evaluations were performed to estimate breeding values for each trait based on ST-BLUP and MT-BLUP models. The model is defined in equation (1). Furthermore, the genetic and residual covariances for the eight traits were assumed to be zero for the ST-BLUP model.A– 1, which was replaced by matrix H– 1. This matrix was obtained using the following equation  and MT-ssGBLUP (ST-ssGBLUP) were estimated using the pedigree-based multi-trait (single-trait) animal model (Equation 1). Breeding values in all models were obtained using BLUPF90 software .A22  relative to the latter (0.31) model. The estimated heritabilities were moderate for the ultrasound and YW ranged from 0.19 to 0.33, while the heritabilities were high for BFT, EMA, and MS traits ranged from 0.48 to 0.59 . The lowFavorable and high genetic correlations were estimated between UIMF and MS (0.78), UBFT and BFT (0.63), and UEMA and EMA (0.65). In addition, the estimated genetic correlation between YW and CW was positive and high (0.84) and relatively moderate between YW and EMA (0.43) . The phePredictive accuracies for carcass traits at birth and yearling dates of steers obtained with the four models  are presented in The results showed that the accuracies of estimated breeding values from single-step genomic BLUP methods were substantially higher than those of conventional BLUP for all traits, regardless of the single or multi-trait models. When information on the correlated traits was included in the prediction of breeding values in the multi-trait analyses, the accuracy increased for BFT, CW, and EMA, whereas a trivial gain in accuracy was observed for MS by changing from a single-trait to a multi-trait model . Our resThe results of evaluations at birth date were comparable with those obtained from the yearling date in terms of accuracy, bias, and dispersion of predictions. When additional information of the correlated traits at the yearling date was used, prediction accuracies were higher than those of birth date for all traits , 2. The Based on the observed results, the yearling ultrasound and carcass traits were moderate to high heritable traits, which were comparable to those reported in Brangus , Angus , SimmentThe results of this study showed that the accuracies of breeding values obtained from ssGBLUP were substantially higher than those of the traditional BLUP model for all traits at birth and yearling date, regardless of using single- or multi-trait genomic models. The observed gain in accuracy is due to additional variation in genomic information capture in Mendelian sampling by a realized relationship matrix . Hence, The accuracies of ST-BLUP models for carcass traits were similar to those previously reported for Hanwoo beef cattle ; howeverOne effective strategy to improve the accuracy of evaluations for carcass traits that are difficult or expensive to measure is the use of a multi-trait model with indicator traits that are easier or cheaper to measure. One of the main benefits of the multi-trait model is the simultaneous use of information from relatives and genetically correlated traits, mostly for the candidate animals and their offspring without any phenotypes .The results demonstrated that multi-trait models generally yielded higher accuracies than single-trait models in both pedigree and genomic evaluations. Based on our results, the average improvement of the accuracies for carcass traits was +4 percentage points (from 0.28 to 0.32%) using MT-BLUP instead of ST-BLUP, and +2 percentage points (from 0.49 to 0.51) by changing the model from ST-ssGBLUP to MT-ssGBLUP when using the information of correlated traits from relatives at the birth date of steers. Similar to predictions at the birth date, accuracies using multi-trait models were higher than single-trait models for all traits at the yearling date because of the additional information of correlated traits (yearling weight and ultrasound traits) recorded from steers. In other words, when the data for yearling ultrasound of steers as correlated traits were available at yearling age, the average gain in accuracy for carcass traits obtained +21 percentage points (from 0.28 to 0.49) by changing the model from ST-BLUP to MT-BLUP, and +12 percentage points (from 0.49 to 0.61) by moving the model from ST-ssGBLUP to MT-ssGBLUP. Consistent with our results, several previous studies have shown that multi-trait genomic prediction is superior to single-trait genomic analyses using simulated  and realIn general, the advantage of using multi-trait models for improving the prediction accuracies at the yearling date was more obvious than those obtained at the birth date for all the considered traits. These results can be attributed to the use of phenotypic records from YW and ultrasound traits of steers  in addition to their relatives’ information at the yearling date, whereas information from the correlated traits (YW and ultrasound traits) is not available for focal animals at birth, and only information from their relatives was included in the multi-trait models. In this line,  revealedThe findings revealed that using genomic information along with including high genetically correlated traits, such as yearling weight and ultrasound traits, in the multi-trait model can be useful in ongoing Hanwoo cattle breeding programs, especially for traits that are difficult to measure, such as carcass traits.The data analyzed in this study is subject to the following licenses/restrictions: This dataset was generated to select proven bulls by Hanwoo Improvement center under the national government rule of Hanwoo breeding schemes. Requests to access these datasets should be directed to DL.All animals used in this study were cared for slaughtered according to animal health and welfare guidelines approved by the Animal Care and Use Committee of the National Institute of Animal Science (NIAS), Rural Development Administration of South Korea. Genotype and phenotype data were collected by Hanwoo Improvement Center (HIC) of National Agricultural Cooperative Federation.HM and DL conceived and designed the study. HM conducted statistical analyses. HM and MN interpreted and discussed the results and drafted the manuscript. DL was responsible for phenotypic and genotyping data collection. NI-E conceived the study, evaluated the experiments, interpreted, and discussed the results, and edited the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.