These SNPs were located in close proximity to or within the sequence of the thirteen candidate genes, such as Galanin And GMAP Prepropeptide (GAL), Centromere Protein (CENPF), Glypican 2 (GPC2), Phosphatidylethanolamine N-Methyltransferase (PEMT), Transcription Factor AP-2 Delta (TFAP2D), and Carboxypeptidase Q (CPQ) gene related to egg-laying and Solute Carrier Family 5 Member 7 (SLC5A7), Neurocalcin Delta (NCALD), Proteasome 20S Subunit Beta 2 (PSMB2), Slit Guidance Ligand 3 (SLIT3), and Tubulin Tyrosine Ligase Like 7 (TTLL7) genes related to egg quality. Interestingly, one of the genes involved in bone formation (SLIT3) was identified as a candidate gene for ESS. Our candidate genes and SNPs associated with egg-laying traits were significant for molecular breeding of egg-laying traits and egg quality in LingKun chickens.Egg production is the most important trait of laying hens. To identify molecular markers and candidate genes associated with egg production and quality, such as body weight at first oviposition (BWF), the number of eggs produced in 500 days (EN500), egg weight (EW), egg shell thickness (EST), egg shell strength (ESS), and Haugh unit (HU), a genome-wide analysis was performed in 266 LingKun Chickens. The results showed that thirty-seven single nucleotide polymorphisms (SNPs) were associated with all traits ( However, despite a range of studies in this area, wide confidence intervals (CIs) for the positions of QTL remain that have rarely been replicated. A new research era was initiated with advances in sequencing technology and single nucleotide polymorphism (SNP) chip, and genome-wide association study (GWAS) has become one of the most effective methods to detect genetic variation in livestock. In previous studies, two SNPs related to body weight at first oviposition (BWF) were located at 78.8 and 78.9 Mb, respectively, on the GGA4 gene by GWAS of Jinghai Yellow chicken, and both were located in FAM184B gene  contains 16,271 QTLs from 367 publications, which represent 442 different traits , and ESS and EST are significantly correlated (p = 0.555). Meanwhile, EST and ESS were negatively correlated with EN500. Meanwhile, Manhattan diagram (A) and QQ diagram (B) of correlation analysis results are shown in The basic statistics for egg-laying traits and egg quality in the Lingkun chicken for the GWAS are shown in p-value = 3.84E-09) was located on chromosome 3 at position 21,147,253 bp, which was found within the Centromere Protein (CENPF) gene, an associated SNP G16754641A  located on chromosome 5 at position 16,754,641 bp was found to be within the upstream of the Galanin And GMAP Prepropeptide (GAL) gene, two significant SNPs, G3321921T  and G3321930A , were located at 3,321,921 and 3,321,921 bp on chromosome 9, respectively, and both were within the Glypican 2 (GPC2) gene, and two significant SNPs, T4287370A  and C5246144G , were located at 4,287,370 and 5,246,144 bp on chromosome 14, respectively, which were found to be within the upstream of the Cytochrome P450  and Phosphatidylethanolamine N-Methyltransferase (PEMT) gene. An associated SNP G2854459T  was located on chromosome 23 at position 2,854,459 bp, which was found to be within the downstream of the Transmembrane Protein 200B (TMEM200B) gene.A total of eight significant loci distributed on chromosomes 3, 5, 6, 9, 14, and 23 were associated with BWF ; Table 3EN500. The most associated SNP C108380147G  was located on chromosome 3 at position 108,380,147 bp was found 62 kb downstream of the Transcription Factor AP-2 Delta (TFAP2D) gene. Seven significant SNPs were located within the 69.78 Mb segment (between 57.21 and 126.99 Mb) on chromosome 2. In between, two significant SNPs, A57924869T  and A57925378C , were located at 57,924,869 and 57,925,378 bp, respectively, and both were within the GPC2 gene, an associated SNP A57945258T  was located at position 57,945,258 bp, which was found within the Neurensin 1 (NRSN1) gene, two significant SNPs, C126998745A  and T126998774C  were located at 12,699,8745 and 126,998,774 bp and both were within the Carboxypeptidase Q (CPQ) gene.A total of seven significant loci distributed on chromosomes 2 and 23 were associated with p-value = 1.63E-08) and T137349076C , located at positions 137,349,071 and 137,349,076 bp, were found to be within the Solute Carrier Family 5 Member 7 (SLC5A7) gene. Four significant SNPs were located within 48.93 Mb segment (between 79.95 and 128.89 Mb) on chromosome 2. In between, 79958113G , C83033381T , and 128890264G  were located at 79,958,113, 83,033,381, and 128,890,264 bp, respectively, which were found to be within the downstream of Uridine Phosphorylase 1 (UPP1) gene, Formin Homology 2 Domain Containing 3 (FHOD3) gene, and Neurocalcin Delta (NCALD) gene. The associated SNP C17452066T  was located on chromosome 3 at position 17,452,066 bp, which was found within the Actin Regulator (ENAH) gene, an associated SNP T6405164C  located on chromosome 8 at position 6,405,164 bp was found to be within the Centrosomal Protein 350 (CEP350) gene, In addition, an associated SNP C8498988T  was located on chromosome 17 at position 8,498,988 bp, which was found within the Ring Finger Protein 2 (RNF2) gene.A total of fourteen significant loci distributed on chromosomes 1, 2, 3, 8, and 17 were associated with EW. Seven significant SNPs were located within 28.08 Mb segment (between 137.34 and 165.43 Mb) on chromosome 1, in between, two associated SNPs, C137349071T  was located on chromosome 23 at position 4,415,750 bp, which was found within the upstream of the Proteasome 20S Subunit Beta 2 (PSMB2) gene. A total of four SNPs  were significantly associated with ESS, which were located within the 3,769 kb segment  on chromosome 13, which were found within the Slit Guidance Ligand 3 (SLIT3) gene.Two significant SNPs were associated with EST, the most associated SNP C4415750T  was located on chromosome 13 at position 17,272,906 bp, which was found within the downstream of Tubulin Tyrosine Ligase Like 7 (TTLL7) gene.The associated SNP A17272906G . All phenotypic values of traits in genotyped individuals were tested for normality, and some abnormal values that were extremely deviating from normal distribution were deleted before conducting association tests.Blood samples were collected from the wing vein of 266 female offspring using heparin sodium as anticoagulants. The genomic DNA of each individual was extracted from Lingkun chicken using the E.Z.N.A. Tissue DNA Kit (Omega Bio-Tek) following the Tissue DNA – Spin Protocol. After the DNA samples were delivered, a quality control test was carried out on the specimens, and the qualified DNA  was used to do further study. All the samples in this experiment were tested using genomic DNA gelose gel electrophoresis and a total of 266 have reached the qualified requirements. Electrophoresis point holes were required to be clean and pollution-free, the main band was clear, and there was no trailing.ftp://ftp.ensembl.org/pub/release-76/fasta/.We chose Illumina pair-end sequencing (PE150) to do the resequencing project. For Illumina pair-end sequencing, at least 1 μg of genomic DNA was used for sequencing library construction for each sample. Paired-end libraries with insert sizes of ≈450 bp were prepared following Illumina's standard genomic DNA library preparation procedure. Purified genomic DNA is sheared into smaller fragments with the desired size by Covaris, and T4 DNA polymerase was applied to generate blunt ends. After adding an “A” base to the 3' end of the blunt-phosphorylated DNA fragments, adapters are ligated to the ends of the DNA fragments. The desired fragments can be purified through gel-electrophoresis, then selectively enriched and amplified by PCR. The index tag could be introduced into the adapter at the PCR stage as appropriate followed by a library quality test. After being quantified by TBS380, paired-end libraries were sequenced by Shanghai Biozeron Biotechnology Co., Ltd.  with the Illumina HiSeq platform according to the manufacturer's standard protocols. In this experiment, clean reads (72788874.44) were generated and 99.18% of these were mapped to the chicken genome −8 were rejected. Finally, 266 individuals and 9,327,106 SNPs were identified in the present study. The experiment fast structure (https://github.com/rajanil/fastStructure) was used to perform population structure analysis. It was based on the maximum likelihood method (MLM). In addition, a principal component analysis (PCA) for the independent 9,327,106 SNPs was performed in Genome-wide Complex Trait Analysis (GCTA) (version 1.24). In order to reduce the influence of population stratification, the first and second components of the PCA were used in the model.A total of 1,509,826,877 SNPs were obtained. Quality control of the genotyping data was performed in PLINK (v1.9). During this process, markers were selected based on three conditions: The SNPs that exhibited minor allele frequencies (MAFs) lower than 5%, low minor allele frequency lower than 2%, and a Hardy–Weinberg equilibrium tests lower than 1.0 × 10The general linear regression model (GLM) in PLINK was used in this study. The model is as follows:b is a vector matrix of fixed effects that includes population structure, g is a vector of SNP effects with p-value of the 5% Bonferroni genome-wide significance was calculated based on the estimated number of independent SNPs and LD blocks for autosome SNPs. The threshold p-value of the 5% Bonferroni genome-wide significance was set at 9.47E-08 , and the threshold p-value for significance was 1.89E-06 .where Y is the vector of observations; X is a design matrix of covariates containing all other fixed effects; The gene closest to the SNP with an extremely significant correlation is considered as a candidate gene. Sequence alignment was conducted through the National Center for Biotechnology Information (NCBI) and Ensembl databases to identify genes within 100 KB upstream and downstream of significant SNPs.https://bigd.big.ac.cn/gsa/browse/CRA006206).The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: National Genomics Data Center, China National Center for Bioinformation .LL, HC, SZ, YZ, SL, CW, YT, and TZ contributed to conception and design of the study. XL organized the database. WX performed the statistical analysis. JG wrote the first draft of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.The authors are grateful for the financial support provided by Zhejiang Major Scientific and Technological Project on Agricultural (Livestock's) Breeding (2021C02068-10) and Wenzhou New Poultry Variety Breeding Cooperation Group Project (2019ZX005).XL was employed by the company Wenzhou Golden Land Agricultural Development Co., Ltd. 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.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.


HUNK, ARHGDIB, ERP27, RERG, NEDD9, TMEM170B, SCAF4, SOD1, TIAM1, ENSSSCG00000048838, ENSSSCG00000047227, EDN1, HIVEP1, ENSSSCG00000043944, LRATD1, ENSSSCG00000048577, ENSSSCG00000042932, ENSSSCG00000041405, ENSSSCG00000045589, and ADTRP. This study provided effective molecular information for the genetic improvement of GL in pigs.Gestation length (GL) plays an important role in piglet maturation of major organs and development of body, while the genetic molecular markers of GL have not been extensively identified. In this study, according to the 5,662 effective records of 3,072 sows, the heritability and repeatability of GL were estimated through the dmuai of DMU Version 6.5.1 with a repeatability model, namely,  The GL of pigs is well documented as 3 months, 3 weeks, and 3 days (114 days). GL was influenced by the genotypes of piglets and sow . Within 9 pounds  and indi9 pounds . The gai9 pounds . Selecti9 pounds . Hence, GL is not measurable until sexual maturity, and traditional breeding technologies are limited. Genomic selection improving the efficiency and accuracy of breeding is a form of marker-assisted selection in which genetic markers covering the entire genome are used . Some ofIn this study, we obtained 5,662 valid GL records from 3,097 pigs, and adopted a strict repeatability model to estimate the heritability of GL. To identify the genetic SNPs and genes associated with GL, we conducted the GWAS analyses according to the GGP Porcine 50K Chip, imputed SNPs and performed the Fst analysis. Further, we carried out the functional enrichments to annotate these candidate genes. This research will provide valuable genetic resources for the future understanding of GL regulation mechanisms and breeding work.A total of 3,072 pigs were used in the present study, and they were from two farms in Liaoning and Shanxi Provinces, China. The GL, breed, farm, parity, and total litter birth weight were collected for these sows, and 5,662 records were shown in By the dmuai of DMU Version 6.5.1, we estimated the variance and covariance components of GL based on a repeatability model, which was described as follows:Further, we calculated the heritability and repeatability of GL by the following formulas:p < 0.000001). Finally, we obtained 34,609 SNPs and 906 animals for GWAS, in which, all the individuals had a high SNP detection rate (>90%). All SNP positions were annotated according to the pig genome assembly Sscrofa 11.1.Of all 3,072 sows with GL phenotypes, 906 individuals were used for GWAS. The ear samples of these 906 sows were collected and the DNA was isolated from them by a commercially available kit . Then these pigs were genotyped with the GeneSeek Genomic Profiler (GGP) Porcine 50K Chip , which contained 50,697 SNPs. With the PLINK, we performed the quality control by removing the SNPs with Minor Allele Frequencies (MAF) < 0.05, call rate <95%, and a deviation from Hardy-Weinberg equilibrium , which included 2,012 haplotypes, 34 million SNPs of autosomes, and 71 pig breeds (R2 ≥ 0.8 and MAF = 0.05), and obtained 9,212,179 SNPs, in which, the average R2 was 0.9248. Further, we conducted the quality control and GWAS according to the filter and model used in the front GGP Porcine 50K Chip, where 9,043,409 SNPs and 906 individuals were utilized.To improve the identification of QTL, the whole-genome sequence data is expected to use , while sg breeds . After iWe conducted the GWAS by GCTA software with the model as follows:p-value <0.05/n, in which, n was the number of SNPs. Further, the threshold of suggestive significant association was calculated according to 1/n.The results of GWAS were shown by Manhattan and quantile-quantile (Q-Q) plots. A significant SNP was determined if the raw In population genetics, Fst is an index to measure whether the actual frequency of genotypes in a population deviates from the theoretical proportion of genetic balance. In recent years, the number of litter sizes has increased, and the GL has been obvious for a long. To evaluate the degree of genetic differentiation of individuals with the different GL, Fst was carried out through the vcftools software. We calculated the Fst value of each SNP between GL 114 days and 115/116/117/118 days based on the following formulas:In which, The SNPs involved in the top 1%  were selected as the core SNPs associated with the GL trait.http://kobas.cbi.pku.edu.cn/kobas3/genelist/), and the significance threshold was corrected p-value < 0.05.By BioMart of Ensembl database, the candidate genes within 500 kb of significant SNPs were screened based on the pig reference genome (Sscrofa11.1). Further, we investigated the functions of these genes through the KOBAS , Sus scrofa Chromosome (SSC) 3, and ALGA0073634 and WU_10.2_13_204954425 located on SSC13). According to the suggestive significant threshold p < 2.89E-05 , additional five SNPs were detected ] on pig chromosomes SSC13 were identified , 112 SNPs were detected to be associated with GL, containing 111 located on SSC13 and one located on SSC7  were the core SNPs in the region where the signal occurred , a totalHUNK, ARHGDIB, ERP27, RERG, NEDD9, TMEM170B, SCAF4, SOD1, TIAM1, ENSSSCG00000048838, ENSSSCG00000047227, EDN1, HIVEP1, ENSSSCG00000043944, LRATD1, ENSSSCG00000048577, ENSSSCG00000042932, ENSSSCG00000041405, ENSSSCG00000045589, and ADTRP.The genes located within a 500 kb distance on either side of the significant SNPs were considered candidates for GL. In total, 4,588 candidate genes were detected, of which, 27 were identified by GWAS of GGP Porcine 50K Chip data , 12 wereTo research the functions of candidate genes, we performed functional annotation by KOBAS. 12 genes from GWAS of GGP Porcine 50K Chip data were significantly involved in four KEGG pathways and 84 GO terms, including protein and lipid metabolism, regulation of multicellular organism growth, embryo implantation, and ovarian follicle development . Seven cGL is closely related to sow reproductive traits, such as litter size, herein deciphering the genetic mechanism of GL has important implications for genomic selective breeding. In the present study, we obtained the GL trait of pigs from a separate farm and estimated the heritability based on the 5,662 records of 3,072 sows. A total of 906 individuals were genotyped by 50K SNP Chip, and used for the GWAS by GCTA. Also, the genotypes were imputed to 9,212,179 SNPs for a further GWAS. In addition, we conducted the Fst analysis to detect more genetic markers for the GL trait. For these SNPs and candidate genes, we searched their enrichment annotation to provide preliminary functional analysis. While, the biological mechanism of these genetic associations between the SNPs or genes with GL still needs to be verified in future research.The heritability of GL was estimated by a repeatability model with the pooling data of three breeds and was shown with pulation , 0.29 fopulation , around pulation , 0.16 inpulation , 0.18 fopulation , and 0.2pulation . CompariKRTAP11-1 was a hair keratin-associated protein, as a possible crucial element for the physical properties of hair shafts, and was related to the embryonic hair development of Foxn1nu mice (SCAF4 encoded the member of the serine/arginine-rich splicing factor family, which was an antiterminator during transcription (SCAF4 was important for correct usage of polyA sites for mRNA termination, and might be associated with the variable neurodevelopmental disorder (SOD1 was involved in fetal loss of women (TIAM1 was part of the AMPK-Tiam1-Rac1 signaling pathway, which mediates contraction-stimulated glucose uptake in skeletal muscle cells and tissues (By the GWAS, nine and 112 SNPs significantly associated with GL were identified by GGP Porcine 50K Chip and imputed SNPs, respectively, and 34 candidate genes were screened. Among them, five genes located on SSC13 were identified at the same time by two association analyses. ENSSSCG00000039409 was a novel gene, and its function study was insufficient until now. 1nu mice . SCAF4 ecription . SCAF4 wdisorder . SOD1 waof women  and muscof women . TIAM1 w tissues . The lac tissues .SCAF4, SOD1, and TIAM1 were detected in two GWASs, and ENSSSCG00000048838, ENSSSCG00000047227, ENSSSCG00000043944, ENSSSCG00000048577, ENSSSCG00000042932, ENSSSCG00000041405, and ENSSSCG00000045589 were the novel genes. HUNK was involved in transferase activity, transfer phosphorus-containing groups, and protein tyrosine kinase activity, and was thought to play a role in breast cancer metastasis (ARHGDIB, RERG, and TMEM170B were also related to breast cancer (ERP27 was a redox-inactive member of the PDI family, and enhanced receptor stability and downstream signaling maintain the stability of the organism (NEDD9 was found to regulate the developing embryonic nervous system (HIVEP1 was involved in the obesity of newborns (ADTRP played a critical role in vascular development and vessel integrity and function (EDN1 was linked to the local hemodynamic response of uteroplacental, and was a key player in the pathophysiology of hypoxia-induced fetal growth restriction (Fst is a measure of population divergence, and it is widespread to quantify the genetic distance between populations and to assess differentiation at individual SNPs by Fst . In thistastasis . ARHGDIBt cancer , implyinorganism . NEDD9 ws system , and HIVnewborns . ADTRP pfunction , and alsfunction . LRATD1 function  and invotriction , and itstriction .In the present study, the heritability 0.1594 of GL was shown, and a total of 1,002 SNPs were detected to impact GL phenotype by GWAS analyses of GGP Porcine 50K Chip and imputed SNPs, and Fst analysis. Further, 4,588 genes were considered candidates for GL, and their functions were confirmed, in which, 20 genes simultaneously confirmed by GWAS and Fst analysis were the most suggestive candidates.


Agaricus bisporus var. bisporus, the button mushroom, has a predominantly pseudohomothallic life cycle. Most of its spores are heterokaryotic and give rise to fertile heterokaryons. However, previous studies have suggested that outcrossing should not be rare in wild populations. In order to discover how outcrossing occurs, we experimentally favored it between aerial propagules of a fruiting donor mycelium and a delayed receiver mycelium that only invaded culture trays. Two donor/receiver pairs were studied, and potentially hybrid basidiomata collected on the receiver trays were analyzed with a mitochondrial marker, two unlinked nuclear CAPS markers, then haplotype markers based on DNA sequences obtained after PCR cloning of the rDNA ITS region and the fruk gene. For one of the two pairs, most basidiomata were hybrids between the donor and the receiver. Genotyping of the hybrids revealed only two genotypes consistent with outcrossing involving airborne mycelium fragments rather than basidiospores. The resident receiver heterokaryon that provided its mitochondria to the hybrid basidiomata is suspected to have had a trophic contribution to their growth and successful fruiting. The high level of heterozygosity and the cultivar introgression previously revealed in wild populations of this pseudohomothallic species may result from outcrossing involving airborne pieces of mycelium. Agaricus bisporus (J.E.Lange) Imbach, is a secondary decomposer ) [fruk gene are haplotypes characterized by their nucleotides at all the SNPs found in these sequences.Different patterns of DNA fragments result from SNP in the recognition site of (Tetr)]) , using t(Tetr)]) . The difiAbi11 intron of the cox1 gene and electrophoresis as indicated above, the lengths of amplified fragments for strains Bs243, Bs177, and Bs256 were 0 (lacking intron), 987 , and 2230 nt, respectively. In the latter case, the resident heg of the iAbi11 intron would itself be invaded by a mobile ORF.The design of the mitochondrial marker is based on previous data of Jalalzadeh et al.  for the Basidiomata of the three strains Bs177, Bs256 and Bs243 used in this study exhibited different cap colors in cultivation. They are dark brown, medium brown, and cream, respectively A.fruk DNA segments were 721 and 840 bp in length, respectively. As reported in a previous study [fruk gene revealed 7 and 22 single nucleotide polymorphism (SNP) positions, respectively.The sequenced ITS and us study  a high lfruk and detailed in fruk sequence, respectively (SNPs were numbered according to their position in the 5′3′sequenced fragments of ITS and ectively . These hits:541) and the other is at position 655 of the fruk sequence (locus fruk:655). As explained in detail in HaeIII is only available when nucleotide C is present at these loci. Therefore, after digestion by HaeIII and electrophoresis different patterns of DNA fragments can be observed. These patterns and the genotypes are reported in detail in its:541 and fruk:655 are its:541-2/2 fruk:655-1/1, its:541-1/2 fruk:655-1/2, and its:541-1/1 fruk:655-1/2, respectively. Once developed for parental strains, CAPS markers can be used to quickly screen genotypes among a large number of samples, offspring or hybrids by simply digesting PCR products and running electrophoreses.CAPS makers were based on two SNPs (C/T). One is at positions 541 of the ITS sequence  were found and reported in fruk haplotype profiles are useful markers for our purpose because, following a single meiotic generation, they should remain intact in the descendants of the three strains or the hybrids occurring between them. Indeed, crossovers between SNPs in ITS or fruk sequenced DNA segments is unlikely not only because of the short genetic distance between them but also because intrachromosomal recombination (crossovers) is suppressed in A. bisporus var. bisporus [fruk markers which are found in Chromosome IX [MAT-linked on Chromosome I [its and fruk haplotype markers are its-1/2 fruk-1/1, its-3/4 fruk-2/3, and its-5/5 fruk-4/5, respectively.Haplotype nuclear markers were based on the haploid DNA sequences obtained after PCR cloning of ITS, which varied at 7 SNPs, or those of  strains . Throughbisporus . Howeverosome IX  and are mosome I , respectits and fruk gene of the three wild strains have been previously deposited in GenBank [fruk gene have been deposited in GenBank and their accession number are also in Sequences of  GenBank  are recaiAbi11 intron of cox1 gene as previously reported by Jalalzadeh et al. [iAbi11-S , iAbi11-0 (0 = no intron), and iAbi11-L (L = large intron), to strains Bs177, Bs243, and Bs256, respectively.The mitochondrial marker is based on the length of the PCR products of the h et al.  and in tIn total, 50 basidiomata (25 per tray) were collected for the receiving strain Bs177. Basidiomata were harvested every day for one month and as much as possible over the entire compost surface. All the basidiomata were dark brown, as was the receiving strain Bs177.its:541-2/2 fruk:655-1/1, the same mitochondrial haplotype iAbi11-S, and the same dark brown color of the cap. Despite the fact that we did not observe any hybridization events, after PCR amplification we sequenced 6 of these 26 samples and confirmed that their genotypes at the two haplotype markers were its:541-1/2 and fruk:655-1/1 as in Bs177. We conclude that hybridization did not spontaneously occur in this experiment. In this experiment the donor was Bs243 and 26 of the 50 basidiomata collected on the trays of the receiver Bs177 were analyzed. For the CAPS markers, all had the same nuclear genotype as the receiver Bs177 In this experiment the donor was also Bs243. A total of 50 basidiomata (25 per tray) were collected for receiving strain Bs256. Trays were divided in small plots as shown in iAbi11-L of the receiver Bs256. The CAPS markers revealed two genotypic classes. One class includes 18 basidiomata with the genotypes its:541-1/2 fruk:655-2/2 and a cream cap color. The two constituent nuclei of these fruiting bodies had the genotypes its:541-1 fruk:655-2 and its:541-2 fruk:655-2, respectively. its:541-2 fruk:655-2 came from spores or mycelium of the donor Bs243 because allele ITS:541-2 is not present in Bs256. In contrast, nucleus its:541-1 fruk:655-2, which could theoretically come from both parents likely came from Bs256 because fruk is tightly linked to MAT and these heterokaryons should not have received two times the same fruk allele and thus the same mating type allele from the same parent Bs243. In conclusion, these 18 basidiomata are hybrids bearing the mitochondria and one nucleus of the receiver, whereas the second nucleus came from the mycelium or the spores of the donor. In the other genotypic class, the 14 remaining basidiomata had the genotype its:541-1/1 fruk:655-1/2 with the nuclear constituent genotypes its:541-1 fruk:655-2 and its:541-1 fruk:655-1. Despite they had a cap color similar to the receiver and also its mitochondrial haplotype iAbi11-L, All 32 basidiomata inherited the mitochondrial haplotype its-4/5 fruk-3/4. In its4 fruk3 and another nucleus bearing Bs256 receiver-specific haplotypes its5 fruk4. One of these hybrid basidiomata is shown in its-3/5 fruk-2/4. One of these hybrid basidiomata is shown in its3 fruk2 and nuclei bearing Bs256 receiver-specific haplotypes its-5 fruk-4. We note that the latter nucleus was present in all hybrids of both groups. The two remaining medium brown samples had the genotype of the receiver its-5/5 fruk-4/5 (nuclei its-5 fruk-4 + its-5 fruk-5). Since both nuclear and mitochondrial haplotypes as well as the cap color were consistent with the receiver, these two basidiomata were likely fruiting bodies of the receiver Bs256. One of them is shown in iAbi11-L and the same nuclear haplotype its-5 fruk-4 of the receiver Bs256. However, 6 of the 10 hybrids received the haplotypes its-4 fruk-3 from Bs243, whereas the remaining 4 hybrids received the haplotypes its-3 fruk-2 from Bs243. As indicated in its-4 fruk-3-and its-3 fruk-2 could simply be the genotypes of the two constituent nuclei of the donor Bs243. In this scenario hybridizations occurred between pieces of mycelium from Bs243 and the mycelium of the receiver Bs256.Twelve basidiomata, six of each two genotypic classes described above using the CAPS markers, were analyzed with haplotype nuclear markers using sequencing after PCR amplification. The six samples which had hybrid genotypes with CAPS markers and a cream cap all had the same haplotypes fruk sequences of two hybrid basidiomata H10 and H17 belonging to the two genotypic classes with the haplotypes its4/5 fruk3/4 and its3/5 fruk 2/4, respectively, were deposited in GenBank. The accession numbers are in ITS and Using haplotype markers, we concluded above that hybrid basidiomata collected on the Bs256 trays likely resulted from crosses between Bs256 and airborne pieces of mycelia from Bs243. In an alternative interpretation, hybridizations would have occurred between Bs256 mycelium and spores or mycelia from spores of Bs243. However, this latter scenario is unlikely for the four following reasons.fruk gene and the MAT locus are tightly linked, the fact that all hybrids possessed the fruk-4 nuclear haplotype of the Bs256 receiver and received either fruk-2 or fruk-3 from the Bs243 donor implies that fruk-2, fruk-3, and fruk-4 are linked to three different mating type alleles. Since there is no sexual incompatibility, if the crosses had occurred via basidiospores, we should have observed four genotypic classes of hybrids that would have received nuclei with genotypes its-3 fruk-2 or its-4 fruk-3  as well as genotypes its-3 fruk-3, or its-4 fruk-2 (presumably recombinant types) from donor Bs243  in each of two plastic tunnels. The length of the pieces of mycelium varied from 7µm, which is also the mean length of the spores, up to 45 µm.In conclusion, under the conditions of our experiment, among the airborne propagules dispersed on the receiver trays, mycelium fragments were surprisingly as numerous as spores. We tested outcrossing for two pairs of donor and receiver strains. For one pair, all basidiomata growing on the receiver trays were fruiting bodies of the receiver. For the other pair, most basidiomata were hybrids with only two types of genotypes likely resulting from outcrossing involving airborne mycelium fragments. All the analyzed hybrids had the mitochondrion and one nucleus  from the receiver, whereas its other nucleus was one of the two presumed constituent nuclei of the donor. All data can be simply explained by crossing with mycelium fragments without any evidence of spore involvement.However, half of the spores could have potentially produced hybrids with similar genotypes as those produced by mycelium fragments. Therefore, we cannot exclude that some of the evidenced hybrids resulted from outcrossing between spores of the donor and mycelium of receiver.A. bisporus var. bisporus, knowing that the preferred life cycle of this variety is mainly pseudohomothallic and thus theoretically does not favor outcrossing. To do so, we simulated a situation in which crosses were promoted between airborne propagules from a fruiting donor mycelium and a receiver mycelium that invaded the culture substrate but had not yet fruited. Indeed, when the temperature and humidity remain relatively high after substrate invasion, there is a phase during which an aerial mycelium covers its surface. This aerial vegetative phase that is known in culture but that we have also observed in the wild has no nutritional interest for a secondary decomposer species such as A. bisporus. Thus, we suspected that it could be a phase naturally favoring the emission of mycelium fragments or the reception of propagules and consequently outcrossing. It was the challenge of our experiment to use this vegetative phase and the time lag between the fruiting of two strains to promote spontaneous hybridization between them. At the time of this phase, a cloud of propagules consisting of spores and mycelium fragments of the donor mycelium was aerially disseminated onto the receiver. The conditions for mycelium growth, fruiting, propagule dissemination and isolation were appropriate since we successfully collected hybrid fruiting bodies and they did not possess any unexpected alleles not belonging to the parental strains. This indicates that our experiment was somewhat secure, without crosses with unexpected propagules and without interaction between the two couples of parents which were isolated in two plastic tunnels. Similar and simultaneous processes were applied for both donor/receiver pairs Bs243/Bs177 and Bs243/Bs256 but hybrids were detected only among basidiomata collected on the receiver Bs256 and they only had two different genotypes. These experiments prove that outcrossing spontaneously occurs between strains growing in close proximity to each other. Given the predominantly pseudohomothallic life cycle of A. bisporus var. bisporus, outcrossing could be considered infrequent, but studies of local populations have shown a high level of diversity and heterozygosity with infrequent clonal or pseudoclonal lineages, suggesting that outcrossing may not be so rare [A. bisporus generally extend to a small area in nature. However, in light of the present study strains isolated from specimens collected in the wild could have themselves resulted from hybridization between strains of the same collection site and thus the area of the resident mycelium could be larger than estimated from the genotypes of collected specimens. In addition, different genotypes and even different varieties or even species of Agaricus are frequently found close to each other probably because a resident mycelium can stimulate spore germination [Our general goal was to understand how outcrossing naturally does occur in  so rare . In thismination  even betmination . In addimination . These ciAbi11-L) and one of its two constituent nuclei that possesses the its-5 and fruk-4 haplotypes. The remaining constituent nucleus of the hybrids was one of the two constituent nuclei of the donor Bs243 that likely possess either the haplotypes its-4 and fruk-3 or its-3 and fruk-2. In addition, hybrids with the former haplotypes exhibited a cream cap like the donor parent, whereas those receiving the latter haplotypes exhibited a medium brown cap like the receiver parent Bs256. Therefore, the cap color of the later hybrids was not distinguishable from those of the basidiomata of their parent Bs256 fructifying on the same culture tray at the same time. Although data remained insufficient to hypothesize the genetics of cap color, this trait did identify one of the two classes of hybrids. The two hybrid genotypes and the absence of recombinants completely agree with the hypothesis that hybrid basidiomata resulted from crosses between the receiver BS 256 and only mycelium fragments of the donor Bs243 and not its spores.Genotype analysis using a mitochondrial marker based on intron presence/absence and two nuclear haplotypes markers based on multiple SNPs revealed that most basidiomata collected on the receiver trays invaded by Bs256 were hybrids that all inherited both its mitochondrial haplotype . Outcrossing was evidenced by analyzing the basidiomata. How this occurred remains unknown but we can propose some hypotheses. The fact that both mycelia of the donor and the receiver were involved simply suggests that crosses may have occurred between heterokaryons. It is the first time that outcrossing between heterokaryons is reported in situ. Crosses between heterokaryons of A. bisporus were first evidenced by Raper et al. [The question arises as to why no hybrid or only hybrids with two genotypes were found among the basidiomata collected on the receiver trays. In first, the absence of hybrid basidiomata in the case of the couple Bs243/Bs177 suggests either a total incompatibility between these two strains and that outcrossing never occurred or hybridization occurred but the hybrid was not able to fruit when the receiver Bs177 was installed in the substrate. In this case, a problem of incompatibility might exist between the new hybrids and the resident mycelium. Genetics of vegetative incompatibility remain unknown in r et al.  who obseIt could be hypothesized that deheterokaryotisation would have occurred through mycelium fragmentation and that, in this case, crosses might occur between homokaryotic and heterokaryotic mycelia following a process called the Buller phenomenon . We noteA. bisporus) may be favorable. Therefore, it could exist different seasons favorable for outcrossing or for fruiting.Another question is when does outcrossing occur? If mycelium fragments are involved, it should not necessarily occur during the fruiting process as in our experiment, but at any time when the aerial mycelium rises to the soil surface in warm, humid weather as indicated above in the conditions for outcrossing. In temperate north hemisphere, the period following the summer thunderstorms  and proceeding the fruiting season , and this for seven days A,B. PiecIn the future, the respective role of spore and mycelium fragments should be evaluated. Based on the new information provided by this first approach to understanding outcrossing processes, it may make sense to use parents with better known constituent nuclei for their mating types, as well as additional independent markers to analyze outcrossing events. Vegetative incompatibility between parental heterokaryons should be assessed in vitro.We did not observe recombination between the two markers located on different chromosomes in agreement with the fact that meiotic spores were not involved in the crosses. However, this is not a strict rule since Xu et al.  evidenceOur objective was to experiment outcrossing as it presumably occurs in the wild. If the objective would have simply been to obtain hybrid basidiomata, our data suggest that a simple inoculation of the donor mycelium in the casing layer of the receiver trays could produce same hybrids as in our experiment. This could be a simpler but less efficient method than the one proposed by Callac et al.  who obtaA. bisporus, nuclear migration rarely occurs through the septa of either homokaryotic or heterokaryotic hyphae, which are both multinucleate and without clamp connection [In nnection ,41,42. Innection  with itsnnection  such as nnection , or in cnnection . The bioOur experiment can be compared to a previous study in which a parental homokaryotic strain was co-inoculated as grain spawn in the culture substrate simultaneously with spores (mainly heterokaryotic) from another parental heterokaryotic strain. All the fruiting hybrids possessed the nucleus and the mitochondria of the inoculated homokaryotic strain and resulted from crosses with heterokaryotic spores or mycelium from the latter ,40. AlthWhen we designed the experiments, we expected to collect basidiomata on the receiver trays which were produced either by the receiver mycelium or by hybrid mycelia between the receiver and propagules from the donor. We tested two pairs of parents in the same conditions. In both cases, about half of the propagules were mycelium fragments and half were spores but in one case none of the basidiomata was hybrid, whereas in the other case, most basidiomata were hybrids. Among the hybrids only two genotypes were found. All these hybrids shared the mitochondia and one nucleus of the receiver and one of the two presumed constituent nuclei of the donor. The absence of recombinant nuclei from the donor revealed that airborne mycelium fragments rather than spores were involved in outcrossing. Therefore, outcrossing can easily occur in the wild between heterokaryotic mycelia growing in close proximity to each other. The success of the mycelium fragments is possibly due to their resistance to dryness and their activation probably faster than that of the spores when these propagules are disseminated on a resident mycelium. The comparison with previous experiments suggests that the inheritance of the mitochondria of the receiver resident strain should favor its biotrophic contribution to the growth of the new hybrid mycelium and its fruiting.A. bisporus var. bisporus was considered as a pseudohomothallic species in which outcrossing should unfrequently occur. Our data show that outcrossing involving mycelium fragments could easily occur and thus contribute to the high level of heterozygosity and the cultivar introgression previously reported in wild populations. We do not claim to have elucidated all the allogamous processes that occur in A. bisporus var. bisporus, but we believe we have uncovered an effective and unexpected process that could impact population dynamics and structure.


Performing a sample size calculation for a randomized controlled trial requires specifying an assumed benefit  and a target power. There is a widespread belief that judgments about the minimum important difference should be used when setting the assumed benefit and thus the sample size. This belief is misguided — when the purpose of the trial is to test the null hypothesis of no treatment benefit, the only role that the minimum important difference should be given is in determining whether the sample size should be zero, that is, whether the trial should be conducted at all.The true power of the trial depends on the true benefit, so the calculated sample size will result in a true power close to the target power used in the calculation only if the assumed benefit is close to the true benefit. Hence, the assumed benefit should be set to a value that is considered a realistic estimate of the true benefit. If a trial designed using a realistic value for the assumed benefit is unlikely to demonstrate that a meaningful benefit exists, the trial should not be conducted. Any attempt to reconcile discrepancies between the realistic estimate of benefit and the minimum important difference when setting the assumed benefit merely conflates a valid sample size calculation with one based on faulty inputs and leads to a true power that fails to match the target power.When calculating sample size, trial designers should focus efforts on determining reasonable estimates of the true benefit, not on what magnitude of benefit is judged important. When calculating the required sample size for a definitive randomized controlled trial, an assumed benefit  must be specified. In broad terms, the main approaches to setting the assumed benefit include (1) select a value for Cohen’s (standardized) effect size, (2) use an estimate obtained from previous studies (possibly pilot studies), (3) elicit a value from experts, (4) use a value that is considered a realistic benefit, and (5) use a value that represents the minimum important difference (MID). What constitutes a MID often is not well-defined, but criteria typically are anchored to the notions of the smallest benefit that would be of interest to stakeholders or the benefit that would need to be observed to justify a change in practice. The minimum clinically important difference (MCID) , when the connection with a minimum value is implied) often is used synonymously with the MID. This commentary does not summarize the extensive literature discussing the advantages and shortcomings of the various approaches, but instead focuses on the (mis-)use of the MID and the central role of the realistic benefit. Unless noted otherwise, it is assumed throughout that the null hypothesis being tested is that the true benefit is zero.2 study [2, commissioned by the UK’s Medical Research Council, comprised literature reviews, a Delphi process engaging stakeholders, a 2-day workshop, and a finalization of core guidance. DELTA2 concluded that “The target difference between treatments that is considered realistic or important by one or more key stakeholder groups plays a critical part in the sample size calculation.” However, no specific guidance was given on how one should reconcile potential discrepancies between “realistic” and “important” values, beyond acknowledging (1) that “The target difference does not necessarily have to be the minimum value that would be considered important if a larger difference is considered a realistic possibility or would be necessary to alter practice” and (2) that others have argued that the target difference should satisfy both criteria [Trials leading up to July 18, 2022, we found that five protocols (17%) reported setting the assumed benefit to a MID value (referred to as either a MCID or a CID). Acceptance of the use of MCID or CID is so widespread that literature and software illustrating sample size calculation commonly instruct users to enter a MCID or CID for the assumed benefit with little or no discussion of the appropriateness of doing so. The manner in which MID currently is used in determining sample size is misguided and leads to incorrect justifications of the required sample size.The DELTA2 study   a positive trial result only guarantees that the point estimate of the true benefit is at least 0.7 × MID; (2) the probability that the point estimate achieves the MID is 50%; (3) the probability of “proving” the true benefit is at least the MID  is 2.5%! Thus, setting the assumed benefit to the MID clearly is inadequate for generating strong evidence for a MID benefit. When the outcome is a continuous variable, an additional challenge is connecting mean improvement to the proportion of individuals who experience a benefit greater than the MID. Resources such as the PROMID repository (www.promid.org) are available to assist researchers with this challenge.The primary motivation for considering the MID when setting the assumed benefit is the notion that this will help to ensure that the trial can determine whether a meaningfully important benefit exists. Chuang-Stein et al.  discussevalid. That is, adopting the calculated sample size would result in a trial whose true power (nearly) equals the target power used in the calculation. Assuming other inputs are accurate, this occurs when the value for the assumed benefit is close to the true benefit; when the assumed benefit is far from the true benefit, the calculated sample size simply is meaningless. Thus, the appropriate choice for the assumed benefit is a value that is judged to be a realistic estimate for the true benefit. What magnitude of benefit we consider to be important or hope for has no impact on whether the true power achieves the target power.However, there is a more fundamental problem than choosing the wrong numerical value for the assumed benefit based on the MID. Although the primary aim of the sample size calculation is to ensure that the trial will generate sufficient information, a fundamental requirement is that the calculation must be For example, suppose we set the sample size based on an assumed benefit to 4 units, reflecting our best estimate for the true benefit. If indeed, the true benefit is near 4 units, then the true power of the trial will match the target power. Now suppose that a 1-unit benefit is considered important. We should be satisfied that our trial likely will yield reasonably strong evidence that the intervention provides an important benefit and proceed using this sample size. We should not change the assumed effect to 1 unit; that would increase the sample size by a factor of 16 and result in an overpowered  trial. Conversely, suppose that the minimum important benefit is 8 units. We should acknowledge that the trial is highly unlikely to show that the intervention produces a meaningful benefit and simply abandon the trial. We should not change the assumed effect size to 8 units; this would decrease the calculated sample size by a factor of 4 and result in an underpowered trial that even more surely will not show a meaningful benefit. Any attempt to reconcile discrepancies in judgments between what is important and what is realistic  to obtain a value for the assumed benefit conflates the goal of ensuring sufficient information with the requirement that the calculation be valid and leads to a sample size that addresses neither. This argument does not imply that only one value for the realistic benefit should be considered, given the uncertainty about the true benefit, but judgments about what is an important benefit should not be used to inform judgments about what is a realistic benefit.2 guidance, methods that could be used to inform what is a realistic benefit include pilot studies, opinion seeking, and review of the evidence base. Cautions have been raised regarding the uncritical acceptance of each of these methods. Because the estimated treatment effect in a pilot study is highly uncertain, multiple authors [As summarized in the DELTA authors , 6 have  authors  have exp2 guidance) to facilitate assessing the degree of support for the chosen assumed benefit. Recent practice falls far short of this goal as evidenced in the 30 Trials protocols referenced above. Sixteen protocols stated the choice of the assumed benefit was obtained from/based on previous studies/literature, yet only two provided any discussion of why or the extent to which the results from previous studies ought to be applicable to the planned trial, despite differences in study populations, intervention, design, or outcome definitions. More disappointing, eight protocols stated the assumed benefit without providing any indication of how the value was chosen. One protocol did not perform a sample size calculation on the grounds that there was no existing data to inform the assumed benefit. The poor adherence to the DELTA2 guidance may be due to some of these trials having been designed prior to the publication of the guidance or to lack of awareness of its publication.All of the foregoing concerns are valid, but ultimately, a value for the realistic effect must be chosen. This judgment should be made through thoughtful consideration of the trial context and all available evidence—information should not be discarded simply because it is not “ideal.” For example, the risks of adopting a treatment effect estimate from a pilot study because it has large uncertainty notwithstanding, it would be counterproductive to ignore this information altogether. Because different sources, types, and amounts of information may need to be considered, each with uncertain levels of relevance, developing a universally applicable synthesis process may not be practical. However, identifying such a process is not as important as ensuring that whatever process is undertaken, it considers the task thoroughly and is reported transparently  greater than the minimum important difference.


Grain yield is a complex trait that results from interaction between underlying phenotypic traits and climatic, edaphic, and biotic variables. In rice, main culm panicle node number  and maximum node production rate  are primary phenotypic plant traits that have significant positive direct effects on yield-related traits. Degree-days to heading (DDTH), which has a significant positive effect on grain yield, is influenced by the interaction between MCPNN and MNPR. The objective of this research is to assess the phenotypic variation of MCPNN, MNPR, and DDTH in a panel of diverse rice accessions, determine regions in the rice genome associated with these traits using genome-wide association studies (GWAS), and identify putative candidate genes that control these traits.Considerable variation was observed for the three traits in a 220-genotype diverse rice population. MCPNN ranged from 8.1 to 20.9 nodes in 2018 and from 9.9 to 21.0 nodes in 2019. MNPR ranged from 0.0097 to 0.0214 nodes/degree day > 10°C in 2018 and from 0.0108 to 0.0193 nodes/degree-day > 10°C in 2019. DDTH ranged from 713 to 2,345 degree-days > 10°C in 2018 and from 778 to 2,404 degree-days > 10°C in 2019.P < 2.91 x 10-7) trait-single nucleotide polymorphism (SNP) associations were identified using the multilocus mixed linear model for GWAS. Significant associations between MCPNN and three SNPs in chromosome 2  were detected with both the 2018 and best linear unbiased prediction (BLUP) datasets. Nine SNPs in chromosome 6  were significantly associated with MNPR in the 2019 dataset. One SNP in chromosome 11 (S11_29358169) was significantly associated with the DDTH in the BLUP dataset.Thirteen significant (This study identifies SNP markers that are putatively associated with MCPNN, MNPR, and DDTH. Some of these SNPs were located within or near gene models, which identify possible candidate genes involved in these traits. Validation of the putative candidate genes through expression and gene editing analyses are necessary to confirm their roles in regulating MCPNN, MNPR, and DDTH. Identifying the underlying genetic basis for primary phenotypic traits MCPNN and MNPR could lead to the development of fast and efficient approaches for their estimation, such as marker-assisted selection and gene editing, which is essential in increasing breeding efficiency and enhancing grain yield in rice. On the other hand, DDTH is a resultant variable that is highly affected by nitrogen and water management, plant density, and several other factors.The online version contains supplementary material available at 10.1186/s12864-022-08629-y. IPA1, MOC1, and FC1 for tiller number , explaining 77.54% of the variation  Fig. . While f97) Fig. . MCPNN aindicas and japonicas into separate groups, while the second PC (12.0% of the variation) further separated the japonica group into temperate japonicas and tropical japonicas. The admixed accessions can be found in between these three major subgroups  decay was estimated to be at ~150,000 bp. The results are within the range estimated in previous findings, ranged from close to 100,000 bp to over 200,000 bp –34.Thirteen significant trait-SNP associations were identified Table . Using tP < 2.91 x 10-7, after multiple testing) between MCPNN and three SNPs in chromosome 2  were detected in the 2018 and BLUP datasets. The SNP S02_12030176 is within the Oryza sativa nicotianamine aminotransferase 1 (OsNAAT1) gene [S02_11971745 is Os02g0305600, described as a spectrin repeat-containing protein. Nine SNPs in chromosome 6  were significantly associated with MNPR in the 2019 dataset. The SNP S06_2296852 is within the SULTR-like phosphorus distribution transporter (SPDT) gene, also known as Oryza sativa sulphate transporter 3;4  [S06_1970442, S06_1970597, and S06_1970602, are close to the candidate gene Os06g0137100 in the RAP-DP genome browser. The counterpart of Os06g0137100 in the MSU-RGAP genome browser [S11_29358169) was significantly associated with DDTH in the BLUP dataset. The closest candidate genes are Os11g0672300, which is described as being similar to a kinase domain-containing protein, and Os11g0672400, a calponin homology domain containing protein in chromosome 11.Significant association  , 37. Thr browser , LOC_Os0Phenotyping MCPNN and MNPR is relatively time-consuming, compared to the typical measuring of resultant variables such as plant height, days to heading, days to peak flowering, and other secondary traits measured in most rice breeding programs. However, to state measuring a new trait is “time-consuming” is a value judgement based on a paradigm that assumes shallow phenotyping of large numbers of genotypes is the most efficient way to eliminate lines because they are suspected to result in poor yield performance. It might in fact be more cost-effective to spend more time measuring traits that have a greater influence on yield performance and possibly even evaluate fewer genotypes and thereby decrease the average amount of effort required to release a higher yielding genotype. A major goal of the current research is to identify relationships between the measured traits, and to develop markers to more efficiently select these traits.There is wide variation in MCPNN, MNPR, and DDTH across the rice accessions. The standard deviations for MCPNN and MNPR are comparable to those of Samonte et al. , while t2) estimates for the three traits ranged from 0.20 for MNPR (2018) to 0.99 for DDTH (2019). Modifying the planting method in 2019  may have reduced plant stress, which may have improved estimation of the genotype effects, and thereby improved the H2 estimates for MNPR in 2019. However, because plant density impacts plant to plant competition, not thinning the plants could have also contributed to the observed differences comparing the years. The timing of N fertilization may have also affected the heritability estimated for MNPR, where two splits were done in 2018 (at planting and six weeks after planting), and three splits were done in 2019 . Khing et al. [Broad-sense heritability  as wellr = 0.86 ) were ve2 = 0.78), and follow the same trend as reported by Samonte et al. [Samonte et al. reportede et al. . The higS02_12032235 is significantly associated with MCPNN. In the 2019 dataset, while no significantly associated SNPs were detected, these three SNPs were in the top 20, with the peak at S02_12032235. The OsNAAT1 gene harbors the SNP S02_12030176 and is in high linkage disequilibrium (r2 = 0.91) with S02_12032235. OsNAAT1 plays a role in iron (Fe) homeostasis in rice, where it is involved in Fe acquisition from the soil and transport within the plant [OsNAAT1 also flanks a QTL for grain yield in chromosome 2 in rice [S02_11971745. Spectrin-like proteins have been detected in the plant nuclei and plasma membranes and are suggested to perform multiple roles, such as stability and ion transport [SNP-trait associations for MCPNN, MNPR, and DDTH were determined using the multi-locus mixed model approach, which included PCA for population structure and kinship matrix to minimize false associations and stepwise linear mixed-model regression with significantly-associated markers as cofactors. MCPNN was significantly associated with three SNPs in chromosome 2, which were detected in the BLUP dataset, which uses the data from both 2018 and 2019. In the individual experiments, only he plant . OsNAAT1 in rice . The genransport –46.S06_2296852, which lies within the SPDT  gene. SPDT is found to be highly expressed in node 1, which connects the panicle to the flag leaf, and serves as a switch to distribute phosphorus to the grains [S06_1970442, S06_1970597, and S06_1970602 are in high linkage disequilibrium (r2 = 0.73) with the gene model Os06g0137100. This gene model is similar to a predicted kinesin-like protein. Kinesins are microtubule motor proteins involved in cell division and growth. In rice, genes encoding kinesins confer traits related to plant height, grain length, and shape, and pollen partial sterility [MNPR is significantly associated with e grains . Three oterility –53. The terility .Some of the candidate genes within 100 kb of SNPs  with the gene model Os11g0672300, which is similar to a protein kinase domain-containing protein but needs to be characterized further to determine its specific function. In plants, the protein kinase superfamily is very broad and diverse, with roles in metabolic signaling, stress response, cell division regulation, and plant-specific functions, such as flowering [lowering .Hd17, which is located at a 2.2 Mb region in chromosome 6 [qHD11.3, located in a 28.7 Mb region in chromosome 11 [ZmMADS3 in maize is involved in node number. In this study, there is no significant SNP-trait association in the region where the closest homolog of ZmMADS3 in rice, OsMADS15 in chromosome 7, is located. For MNPR, two regions in Chromosome 7 are significantly associated with node development rate [Genes, QTLs, and chromosomal regions identified to be associated with MCPNN, MNPR, and DDTH in the literature were checked to determine if they overlap with the SNPs identified in this study. While none of the SNPs are co-located with reported or identified genes, some are close to previously reported QTL associated with heading date, such as mosome 6  and qHD1osome 11 . Heuer eosome 11  reportedent rate , while Qent rate , 60. HowSNP markers significantly associated with MCPNN, MNPR, and DDTH were detected by association analyses using the multilocus linear mixed model. The identified SNPs are located within or in LD with gene models, which could be potential candidate genes for these traits. Further validation of the candidate genes through expression and gene editing analyses are needed to confirm if they are the causal agents for these traits. Identifying the genes regulating MCPNN and MNPR could lead to the development of fast and efficient approaches for their estimation, such as marker-assisted breeding or gene editing. Functional markers can be designed and could be used to select for early-generation breeding lines possessing higher MCPNN and faster MNPR. Gene editing can also be applied to alter the MCPNN and MNPR of a rice variety in order to achieve optimum yields. Understanding the underlying genetic basis for MCPNN, MNPR is an essential step in increasing breeding efficiency for enhancing grain yield in rice.indica and japonica cultivars, landraces, inbred lines, and hybrids  each with one seed placed in the center of each row. The Jiffy pots were marked with flags, while the ends of the middle rows were seeded with the designated entry using a manual seeder. This was done to eliminate thinning the plants before data collection. The field was flash-flooded on April 17, after the drill-seeding and manual planting were completed for all accessions. Fifteen of the 18 plants were used for data collection, with the remaining three plants as backup. Urea was applied in 3 splits for a more efficient distribution of N: 59.4 kg N ha-1 at planting (17 April 2019), 128.9 kg N ha-1 at two weeks after planting (23 May 2019), and 47.1 kg N ha-1 at 11 weeks after planting (3 July 2019).In 2019, the entries were sown on April 16-17 in three-row plots that were 2.44 m long, with rows spaced 0.28 m apart. The slight change in row spacing for the two years is due to the different planters used in each year. The side rows of each plot were mechanically drill-seeded, and the middle rows were manually planted at the same time as the drill-seeding. The middle row of each plot was created by digging a 2.44-m long furrow (approximately 6 cm deep), and a strip of eighteen 5.7 cm square x 5.7 cm deep biodegradable Jiffy pots  for Windows. Fixed and random effects were estimated using PROC MIXED in SAS software, and these were used to estimate best linear unbiased predictions (BLUPs) in R version 3.6.1 . AnalyseOryza sativa ssp. japonica cultivar Nipponbare, International Rice Genome Sequencing Project (IRGSP) Build 5 [th quartile or less than the 25th quartile by more than 1.5 x (75th quartile - 25th quartile) [DNA was extracted from leaf tissues of 220 rice accessions  .2 was then computed every 10,000 base pairs (bp), with linkage disequilibrium decay determined as the distance in bp wherein the average r2 decreased to half its maximum value.GWAS for MCPNN, MNPR, and DDTH were conducted using the phenotype data for 2018 and 2019 separately, as well as the BLUPs estimated from data collected in both years. Factors that may cause false trait-SNP associations  and genetic relatedness (K)) were controlled using principal component analysis (PCA) and kinship matrix, respectively. The most probable number of subpopulations was determined by plotting the number of principal components (PC) against the variance explained by the PCs, and the optimum number of PCs was selected when the decrease in variance has reached a plateau. In this study, the total number of PCs used to account for population structure was four. The VanRaden kinship algorithm  was usedeff_G). The Meff_G was used to compute for the multiple testing threshold in a similar way as the Bonferroni correction method in which the significant threshold (α= 0.05) was divided by the Meff_G or P = α/ Meff_G. For this study, the multiple testing threshold to declare significant association was set to P = 2.91 x 10-7. Manhattan and quantile-quantile (Q-Q) plots were constructed using the R package 'qqman' [https://rapdb.dna.affrc.go.jp/viewer/gbrowse/build5/) in the Rice Annotation Project Database (RAP-DB) [http://rice.plantbiology.msu.edu/cgi-bin/gbrowse/rice/) [Association analyses were conducted using the multilocus mixed model (MLMM)  implemen 'qqman' . Identif(RAP-DB)  and Miche/rice/) .Additional File 1. Supplementary FiguresAdditional File 2. Supplementary TablesAdditional File 3. Appendix 1. Phenotyping for Maximum Node Production Rate (MNPR) and Main Culm Panicle Node Number (MCPNN)


Haplotype and outlier‐based tests revealed a putative chromosomal inversion that contained three expressed genes and was nearly fixed in walleye assigned to Lake Winnipeg. These patterns exist despite the potential for high gene flow between these proximate Canadian lakes, suggesting that the inversion may be important for facilitating adaptive divergence between the two lakes despite gene flow. However, a specific adaptive role for the putative inversion could not be tested with the present data. Our study illuminates the importance of genomic architecture consistent with local adaptation in freshwater fishes. Furthermore, our results provide additional evidence that inversions may facilitate local adaptation in many organisms that inhabit connected but heterogenous environments.Differences in genomic architecture between populations, such as chromosomal inversions, may play an important role in facilitating adaptation despite opportunities for gene flow. One system where chromosomal inversions may be important for eco‐evolutionary dynamics is in freshwater fishes, which often live in heterogenous environments characterized by varying levels of connectivity and varying opportunities for gene flow. In the present study, reduced representation sequencing was used to study possible adaptation in  Sander vitreus) from three North American waterbodies, and found evidence of selection and a chromosomal inversion between two connected Canadian lakes. This inversion may underlie divergence between the Canadian lakes, local adaptation in an especially important fishery of one lake, and contributes to evidence that inversions may facilitate local adaptation in many organisms that inhabit connected but heterogeneous environments.Chromosomal inversions between populations may facilitate adaptation despite opportunities for gene flow. We used reduced representation sequencing on n=345 walleye ( Walleyen = 345 individuals from the three waterbodies described above. Population structure and demographic history were explored to provide context for signatures of selection. Simulations with EASYPOP were used to estimate historical migration rates between Lake Manitoba and Lake Winnipeg , a reduced representation approach, to genotype Balloux, . CandidaBalloux, , and out22.1n = 129, 51, and 55 respectively). Prior to sampling, a Portable Electroanesthesia System (Smith Root) was used to anesthetize fish following approved animal use protocols of Fisheries and Oceans Canada , the University of Manitoba (F2018‐019), and the University of Nebraska‐Lincoln (Project ID: 1208). Fin clips were stored in 95% ethanol. For Lake Manitoba fish, n = 50 fry from the 2019‐year class were sampled at Swan Creek Hatchery and stored in 95% ethanol. These fry were the offspring of wild fish from Lake Manitoba. From Cedar Bluff Reservoir, anal fin clips were taken from n = 60 sexually mature walleye . Fin clips were collected after gametes were harvested. Procedures for sample collection at Cedar Bluff Reservoir were approved by the Fort Hays State University Institutional Animal Care and Use Committee (Protocol #19‐0023). Fish were captured using seven 25‐mm‐mesh trap nets and four 76‐mm‐mesh gill nets (91.44 × 1.83 m panel per net). In total, n = 345 walleye were genotyped for the present study. See Figure Walleye anal fin clips from Lake Winnipeg individuals were collected by boat electrofishing in Spring 2017 and 2018 from spawning sites in the Red River and possible spawning sites near Matheson Island and in the Dauphin River, representing the south basin, channel, and north basin, respectively  to efficiently sequence a large number of individuals by separating RAD tag isolation and sequencing library preparation. BestRAD libraries were baited using protocols outlined in Euclide et al.  designed by Arbor Biosciences . Target loci for RAPTURE were selected based on minor allele frequency (>0.01) and alignment position along a draft walleye genome to retain one SNP approximately every 5,000 bp along every contig greater than 0.1 megabases. A total of 99,636 baits were thus selected. Read length was 150 base pairs with paired end sequencing and one capture reaction per library, where approximately 3.6 libraries were used for the present study . Bait‐captured libraries were then sequenced on a S4 NovaSeq 6000  at NovoGene .DNA was extracted using the Qiagen DNeasy Blood & Tissue Kit  following manufacturer protocols. Restriction site associated DNA sequencing (RAD‐seq) library preparation was performed with the NEBNext Ultra DNA Library Prep Kit for Illumina  with the e et al.  using a Perca flavescens) genome with bwa v0.7.17 using bwa mem , gene diversity (HS), and inbreeding coefficients  for each population were calculated.Hierfstat v0.5–7 was used to find  Goudet, . Ninety‐NeEstimator v2.1 was used to estimate effective population size in 95% confidence intervals for each population using the linkage disequilibrium method and only comparing SNPs on different chromosomes . In EASYPOP, 1,000 simulated loci with two allelic states were used with free recombination allowed, using a KAM mutation model with mutation rate µ of 3.28x10−9 based on divergence between the blackfin icefish  and dragonfish (Parachaenichthys charcoti)  among walleye populations was analyzed via LD decay with PopLDdecay v3.40 . Significant candidate regions of selection were identified by analyzing 100 kilobase (kb) windows overlapping by 10 kb, in which at least three significant SNPs (q < 0.05) showing XP‐EHH were found. For visualization, only significant SNPs within candidate regions were highlighted (thus a significant SNP outside a candidate region would be de‐emphasized), using the R package ggman v0.99.0 with relative SNP positions to reflect distances between points in the reduced representation data (https://rdrr.io/github/veera‐dr/ggman/). Results for XP‐EHH were divided into three pairwise tests between each assigned population , and significant results for each pairwise test were further categorized into which population showed elevated XP‐EHH scores .Signatures of selection, where one genomic region has approached or reached fixation in one population compared to another, were explored with XP‐EHH using the R package rehh v3.2.1, with the markers previously described as phased and imputed after filtering for 90% present data  and measured in pairwise comparisons between each of three populations defined by Admixture. In addition, absolute allele frequency differences were analyzed between populations. VCFtools was used to calculate allele frequencies for each population. Minor alleles in Lake Winnipeg‐assigned individuals were used for comparisons across populations.Hierfstat v0.5‐7 was used to provide supporting evidence for selection at individual SNPs using 2.6r2), we calculated r2 using PLINK v1.9 for SNPs on chromosome 8 (‐‐r2 ‐‐ld‐window‐r2 0.05 ‐‐ld‐window 999999999 ‐‐ld‐window‐kb 30000) and generated a LD heatmap  was a putative inversion, we conducted the following analyses as suggested by Huang et al.  and Shi Jombart, . Third, Jombart,  but see Jombart, . Individn = 48 walleye in three sites in Lake Winnipeg, using previously published reads (NCBI SRA database accession #PRJNA596986)  values from the walleye transcriptome‐aligned data . 96,955 SNPs were called in STACKS, of which 63,882 were accepted as non‐paralogous SNPs after using HDPlot. Pruning non‐paralogous SNPs for linkage with PLINK left 46,342 SNPs for use for population structure and demographic reconstruction. The dataset of 63,882 SNPs was filtered for a maximum of 10% missing data and imputed and phased into a dataset of 18,601 SNPs for use with XP‐EHH. With the 46,342 pruned non‐paralogous SNPs and a method of moments estimation of relatedness, kinship estimates were 0.34 at the highest between any two individuals . No individuals were thus removed for relatedness. Synteny between the walleye and yellow perch genomes was high, indicating a large degree of concordance between the two genomes  Figure .3.2Admixture identified K = 2 populations based on the lowest cross validation error between K = 1 through 6, separating Canadian  and Kansas  populations of walleye   were each highest for the Cedar Bluff Reservoir population, with FIS approximately two times higher for Cedar Bluff walleye than the walleye in the northern lakes  was at approximately 2.8 kb. For Lake Manitoba, half‐decay was at approximately 4.2 kb, while moderate LD was at approximately 5.7 kb. For Cedar Bluff Reservoir, half‐decay was at about 1.9 kb, while moderate LD was at approximately 2.6 kb. And for unassigned individuals, half‐decay was at approximately 5.6 kb, while moderate LD was at about 8.0 kb. Individual pairwise r2, distance, and nonlinear regressions were visualized for each population in Figures For Lake Winnipeg, half‐decay was at approximately 2.4 kb, while moderate LD  were significant between Lake Winnipeg and Lake Manitoba Table . There wWith 63,882 SNPs accepted as non‐paralogous, two principal components (PCs) showed population structure in pcadapt, with PC1 showing 5.76% variance explained between northern and southern sites and PC2 showing 2.28% variance explained by Lake Winnipeg and Cedar Bluff Reservoir versus Lake Manitoba Figure . 345 SNPF’ST outlier SNPs , ETS homologous factor (EHF), and leucine‐rich repeat‐containing protein 4C (LRRC4C). Each of these genes had transcripts expressed in Lake Winnipeg walleye, with counts per million between 0.4 and 20 . Historic gene flow likely occurred in sporadic pulses, as we observed evidence of limited but present contemporary gene flow possibly facilitated by flooding in 2011 and 2014. These potential pulses of gene flow were consistent with population differentiation (FST) between the Canadian lakes that was lower in magnitude than each Canadian lake compared to a waterbody in Kansas, USA. Lake Winnipeg walleye showed unexpectedly strong signatures of selection on chromosome 8, with concurrent evidence for a chromosomal inversion between approximately 15.26 and 15.90 Mb. An inversion may have played a role in adaptive divergence between Lake Winnipeg and Lake Manitoba walleye  from individuals originating from each lake, based on admixture analyses  between two large lakes in Canada (Lake Winnipeg and Lake Manitoba), and comparatively stronger differentiation between the two lakes in Canada and the entirely stocked population in Cedar Bluff Reservoir, Kansas, USA of unknown origin. This relatively stronger differentiation between the Canadian lakes used here and the waterbody in Kansas was similar in magnitude to differentiation observed between walleye in Minnesota and Wisconsin (USA), differences which were attributed to Pleistocene lineages  out of 119 significant outliers (q < 0.05) along PC 2 in a pcadapt analysis are all from this small outlier region , the putative inversion was nearly fixed for the Lake Winnipeg genotype. Therefore, this putative inversion may play a role in walleye adaptation to Lake Winnipeg, but a specific adaptive role for the inversion could not be tested with the present data.PDHX, EHF, and LRRC4C. All three were expressed in Lake Winnipeg walleye, indicating at least some gene transcription activity within the putative inversion. While functional information from model species exists for each of these genes, their specific functional significance in Lake Winnipeg walleye is unknown. The specific life stage or environmental context in which the three genes within the putative inversion may have an effect on walleye biology is similarly unknown. Moreover, genes adjacent to the inversion may have functional consequences, and possibly in another group of walleye instead . That this putative inversion may have played a role in recent divergence between fish of two connected lakes, and that its frequency among likely admixed fish is consistent with a possible adaptive role in Lake Winnipeg walleye, indicates a potential importance for chromosomal inversions in freshwater fishes more generally. Inversions have been previously related to environmental adaptation, such as in freshwater emerald shiner (Notropis atherinoides) with high gene flow ; Data curation ; Formal analysis ; Investigation ; Methodology ; Visualization ; Writing – original draft (lead). Peter T. Euclide: Data curation ; Formal analysis ; Investigation ; Methodology ; Resources (supporting); Software (supporting); Validation ; Writing – review & editing . Jennifer D. Jeffrey: Conceptualization ; Formal analysis (supporting); Investigation ; Methodology ; Writing – review & editing . Yue Shi: Investigation ; Methodology ; Writing – review & editing . Jason R. Treberg: Conceptualization ; Funding acquisition ; Investigation ; Project administration ; Resources ; Supervision ; Writing – review & editing . Douglas A. Watkinson: Conceptualization ; Data curation ; Investigation ; Resources ; Supervision ; Writing – review & editing . Eva C. Enders: Conceptualization ; Data curation ; Funding acquisition ; Methodology ; Project administration ; Resources ; Supervision ; Validation ; Writing – review & editing . Wesley A. Larson: Conceptualization ; Data curation ; Formal analysis ; Investigation ; Methodology ; Resources ; Supervision ; Validation ; Visualization ; Writing – review & editing . Yasuhiro Kobayashi: Conceptualization ; Investigation ; Methodology ; Supervision ; Writing – review & editing . Ken M Jeffries: Conceptualization ; Data curation ; Formal analysis ; Funding acquisition ; Investigation ; Methodology ; Project administration (lead); Resources ; Supervision (lead); Validation ; Visualization ; Writing – review & editing .https://doi.org/10.6084/m9.figshare.19610082.This article has been awarded Open Data, Open Materials Badges. All materials and data are publicly accessible via the Open Science Framework at Supplementary MaterialClick here for additional data file.


Solanum lycopersicum) is bred for direct consumption and is selected for a high yield of large fruits. To understand the genetic variations (distinct types of DNA sequence polymorphism) that influence the yield, we collected the phenotypic variations in the yields of total fruit, extra-large-sized fruit, small-sized fruit, or red-colored fruit from 68 core inbred contemporary U.S. fresh-market tomatoes for three consecutive years and the genomic information in 8,289,741 single nucleotide polymorphism (SNP) positions from the whole-genome resequencing of these tomatoes.The fresh-market tomato  mapping using the SNP data with or without SNP filtering steps using the regularization methods, validated with quantitative trait loci (QTL) linkage mapping, identified 18 significant association signals for traits evaluated. Among them, 10 of which were not located within genomic regions previously identified as being associated with fruit size/shape. When mapping-driven association signals [558 SNPs associated with 28 yield (component) traits] were used to calculate genomic estimated breeding values (GEBVs) of evaluated traits, the prediction accuracies of the extra-large-sized fruit and small-sized fruit yields were higher than those of the total and red-colored fruit yields, as we tested the generated breeding values in inbred tomatoes and FTogether, these results provide an understanding of the genetic variations underlying the heritable phenotypic variability in yield in contemporary tomato breeding and the information necessary for improving such economically important and complex quantitative trait through breeding.The online version contains supplementary material available at 10.1186/s12870-022-04018-5. Solanum lycopersicum) is the most valuable fruit crop worldwide , or red-colored fruit regardless of size (RY), in a group of 68 core inbred contemporary U.S. fresh-market tomatoes, for elucidating the genetic basis of these traits. GWA mapping using the SNP data with or without SNP filtering steps using the regularization methods, validated with QTL linkage mapping, was used to identify association signals for the evaluated traits. In fresh-market tomato breeding programs, emphasis was on a high yield of large fruit (XY); phenotyping of small fruits (SY) was ignored. Therefore, the genetic mechanism by which SY contributes to Y and/or XY remains unknown. Fruit coloring (changing color from green to red) is an important trait in fresh-market tomato. The fruit markets and the fruit color at harvest are tightly linked, which directs breeding aims . Fruit coloring is also an essential phenotype for postharvest handling of fruits including shipping and long-term storage. This study reports the first genomic estimated breeding values (GEBVs) generated for the yield of fresh-market tomato. GS  was not correlated with five distinct clines. Further, this chromosome 1 Y SNP  had a significant positive association with XY  fell within the known fruit size locus FW8.1 . Togan indel . In addian indel ).1, a fresh-market tomato cultivar currently in the U.S. market   is not often preferable for breeding and improvement programs because it inevitably compromises existing superior marketable traits . The general negative relationship observed between the fruit size and important horticultural traits including disease resistance .The collected 68 core contemporary fresh-market tomatoes (hereafter referred to as inbred tomato set) met four requirements: i) a pool of released elite inbred cultivars or inbred breeding lines that have been selected for marketable traits including yield and genetic diversity , ii) tht et al. ), B , C  evaluations were performed, as described in our previous study  XY regardless of color, (iii) SY regardless of color, (iv) RY regardless of size]. A normality test was performed for each trait data set from each year using Anderson-Darling at a significance level of 0.05. Phenotypic datasets from three seasons were used to calculate the best linear unbiased prediction (BLUP) values of each tomato using a model, lmer(phenotype ~ (1|genotype) + (1|Year) + (1|genotype:Year)), implemented in the R package ‘lme4’ (version 1.1–28) [Field trials of the inbred tomato set were conducted during three consecutive years, 2019, 2020, and 2021 at the UF Gulf Coast Research and Education Center , where these tomatoes were originally bred, as described previously . For the et al. ). Briefle et al. ; and by  ‘Green’ ) and rede et al. ; USDA coe et al. ). For ea 1.1–28)  (Additio 1.1–28) .2 population set, respectively, following the same basic procedures used for Y, XY, SY, and RY traits and our previous study [FN, the total number of fruits per plant; MLXFN, the number of medium-sized or larger fruits; MLXRFN, the number of medium-sized or larger red fruits; MLXRY, the yield of medium-sized or larger red fruits; MLXY, the yield of medium-sized or larger fruits; RFN, the number of red fruit; SFN, the number of any fruit smaller than medium size; SRY, the yield of any red fruit smaller than medium size; and XFN, the number of extra-large fruit. Six traits evaluated for the inbred tomato set only are as follows: LFN, the number of large-size fruit; LY, the yield of large-size fruit; MFN, the number of medium-size fruit; MY, the yield of medium-size fruit; SW, the average weight of any fruit smaller than medium size; and W, the average fruit weight per plant. Eleven traits evaluated for the F2 population set only are as follows: GFN, the number of green fruit; GY, the yield of green fruit per plant; MLXGFN, the number of medium-sized or larger green fruits; MLXGY, the yield of medium-sized or larger green fruits; SGFN, the number of any green fruit smaller than medium size; SGY, the yield of any green fruit smaller than medium size; SRFN, the number of any red fruit smaller than medium size; XGFN, the number of extra-large-sized green fruit; XGY, the yield of extra-large-sized green fruit; XRFN, the number of extra-large-sized red fruit; XRY, the yield of extra-large-sized red fruit. The BLUP values were calculated for each of the traits in the inbred tomato set as described above.To create a phenotypic dataset for the prediction of GEBVs, 15 and 20 additional traits  were evaluated for the inbred tomato set and Fus study  (Additio2 plants of F2 population set). All sequence reads were created using the same technical conditions as follows: i) a single plant of each tomato line used for DNA extraction, ii) PCR-free 350-bp library preparation, iii) paired-end (2 × 150-bp) sequencing using Illumina NovaSeq technology, and iv) average sequence data of 23 Gb per core tomato (approximately 30 × genome coverage of each sample) or 5 Gb per F2 plant. For the raw reads, the quality controls were as follows: i) all positions having average Phred quality scores 30 or higher, ii) read without adapter sequence(s), and iii) read with ambiguous nucleotides  < 10%. In addition to the sequence reads we generated in this study, 63 raw read datasets of contemporary U.S. fresh-market tomatoes, which were generated by using the same technical conditions and quality control as described here, were obtained from our previous studies [We performed WGS for 425 plants  [SNP detection was performed as described in our previous study . Once SN 1.90b3) , resultihttps://blast.ncbi.nlm.nih.gov/Blast.cgi; query, 325 bp sequence of SL4.0 reference genome assembly, which flanks a SNP position; subject, SL4.0 reference genome assembly). We examined sequence variant(s) that were on the top two Blast hits, with respect to Identity (%). For eight out of the 16 SNP sites, the top two Blast hits carry each of two alleles at each SNP site . First,π)  and inbreeding coefficient (F) on the filtered SNP set. Principal component analysis (PCA) was performed using PLINK (version 1.90b3) [VCFtools (version 0.1.15)  was used 1.90b3)  on the sY, XY, SY, and RY) were estimated using both LASSO and MCP regularization methods as described in our previous study [Y, XY, SY, and RY) and the GWA mappings-derived individual significant association signals, at a significance of p < 0.05. The genetic position (cM) of association signals were estimated based on a genetic map for the U.S. fresh-market tomato [In addition to the filtered SNP set described above, another SNP set (referred to as regularization SNP set), was prepared; two different SNP sets, filtered SNP set and regularization SNP set, were used for the GWA mapping. To develop the regularization SNP set, the effect of SNPs for each of four traits . A confidence interval length was calculated using the R function qtl.length in the R package ‘qtldesign’ (version 0.941) [After calculating the genotype probabilities using the R function calc.genoprob, a QTL mapping function stepwiseqtl  of the Rn 0.941) ; the lin2 population set . The whole 68 tomatoes in the inbred tomato set was used as a training set.Four different SNP datasets [i) a set of combined significant SNP sites, which were identified by either the GWA mapping of 28 traits using the filtered SNP set or QTL linkage mapping (hereafter, mapped SNP set; 558 SNP sites; Additional file Additional file 1.Additional file 2.Additional file 3.Additional file 4.Additional file 5.Additional file 6.Additional file 7.Additional file 8.Additional file 9.Additional file 10.Additional file 11.Additional file 12.Additional file 13.Additional file 14.Additional file 15.Additional file 16.Additional file 17.Additional file 18.Additional file 19.