Haplotype analysis was conducted on the QTLs to identify the functional SNP haplotypes and estimate their phenotypic effects, and the haplotypes were tracked through the pedigree. Four QTLs (two per trait) were consistent across the years and/or both analysis methods and validated the previously reported QTLs. qCrack-LG1.1m (the label given to a consistent QTL for cracking incidence on chromosome 1) explained 2–15.1% of the phenotypic variance, while qCrack-LG5.1m, qFirm-LG1.2m, and qFirm-LG3.2m explained 7.6–13.8, 8.8–21.8, and 1.7–10.1% of the phenotypic variance, respectively. At each QTL, at least two SNP haplotypes had significant effects and were considered putative functional SNP haplotypes. Putative low-cracking SNP haplotypes were tracked to an unnamed parent of ‘Emperor Francis’ and ‘Schmidt’ and unnamed parents of ‘Napoleon’ and ‘Hedelfingen,’ among others, and putative high-firmness haplotypes were tracked to an unnamed parent of ‘Emperor Francis’ and ‘Schmidt,’ an unnamed grandparent of ‘Black Republican,’ ‘Rube,’ and an unknown parent of ‘Napoleon.’ These four stable QTLs can now be targeted for DNA test development, with the goal of translating information discovered here into usable tools to aid in breeding decisions.Breeding for decreased fruit cracking incidence and increased fruit firmness in sweet cherry creates an attractive alternative to variable results from cultural management practices. DNA-informed breeding increases its efficiency, yet upstream research is needed to identify the genomic regions associated with the trait variation of a breeding-relevant magnitude, as well as to identify the parental sources of favorable alleles. The objectives of this research were to identify the quantitative trait loci (QTLs) associated with fruit cracking incidence and firmness, estimate the effects of single nucleotide polymorphism (SNP) haplotypes at the detected QTLs, and identify the ancestral source(s) of functional haplotypes. Fruit cracking incidence and firmness were evaluated for multiple years on 259 unselected seedlings representing 22 important breeding parents. Phenotypic data, in conjunction with genome-wide genotypic data from the RosBREED cherry 6K SNP array, were used in the QTL analysis performed  Prunus avium L.) breeding programs are tolerance to rain-induced fruit cracking (hereafter referred to as “cracking incidence” or simply “cracking”) and high fruit firmness  analysis is an important step in uncovering the genetic factors contributing to a given trait. However, from a breeding perspective, understanding the genetic contribution of a trait is a means to an end rather than an end itself. Recognizing the ultimate goal of crop improvement does not change the QTL analysis  process . Ultimat process . Various process . Highly  process . A pedig process , has bee process  in such  process , as well process . The dis process . Recentl process  and BLIN process  were dev process .H2) for three types of cracking—pistillar-end, stem-end, and side. For pistillar-end cracking, often the most prevalent form, the H2 estimates ranged from 0.608 to 0.905, along with estimates from 0.575 to 0.742 and 0.354 to 0.557 for stem-end and side cracking, respectively. Using multiple bi-parental populations, various methodologies for determining cracking propensity, and recording this phenotypic data over an extended period of time (7–8 years), several cracking QTLs have also been reported, thus, establishing the genetic contribution to this phenomenon  for fruit firmness was estimated as 0.27  from the PNWSCBP at the Washington State University were used for this work, all of which were located at the Roza Research Farm in Prosser, Washington, and managed according to commercial cultural practices. The seedlings were from two populations. RosBREED seedlings (n = 149) were a subset of the RosBREED Crop Reference set, representing 23 important breeding parents (IBPs) and was developed specifically for use in QTL discovery via a PBA approach (n = 157) were a subset of the available PNWSCBP seedlings planted from 2012 to 2014 on their own roots, which increased the representation of the 22 IBPs. The program seedlings were chosen through a simple random sample. Seedlings with at least one undomesticated parent were excluded from the analyses to reduce confounding effects due to population structure, as well as focus on QTL discovery in more immediately breeding-relevant germplasm. This reduced the final number of seedlings used in the analyses to a total of 259  representing 22 IBPs with an average allelic representation  were collected as insurance for any necessarily discarded fruits. The fruits were stored on ice in a cooler during transport from the Roza Research Farm to the Washington State University Irrigated Agriculture Research and Extension Center in Prosser, Washington (∼3 miles), where they were refrigerated at 1°C until phenotypic evaluation . Upon evaluation, the fruits were removed from the cooler and allowed to warm to room temperature (∼22°C). The fruits were then counted and inspected for defects and blemished fruits were discarded, while retained fruits were photographed. The same fruits were used for both firmness and cracking assays (in years when measured together). Because the method for measuring fruit firmness was non-destructive, it is unlikely that the firmness measurements taken on the fruit influenced cracking assays. Firmness measurements (g/mm) were taken using the FirmTech2 , on one cheek  of the 50 fruits, with the average firmness used as an estimate for each seedling. Cracking incidence was measured (% cracking incidence) using a modified, high-throughput version of Christensen’s classic method , in whicPrunus persica L. (Batsch)] genome v2  and metal ball bearings in place of silica. The program seedling DNA samples were diluted to a concentration between 30 and 100 ng/μl and sent to GeneSeek  for genotyping using the 6+9K cherry SNP array (n = 259) and RosBREED seedlings (n = 119). For the program seedlings that were scanned with the 6+9K array, a total of 3,302 robust SNPs  were used when analyzing the program seedlings alone (n = 140).RosBREED seedlings were previously genotyped using the RosBREED cherry 6K SNP array  and curaenome v2 . DNA wasNP array . The rawNP array  and thusvia PBA was implemented through the FlexQTL™ software (Quantitative trait locus (QTL) detection software , 2014 thsoftware  as well software . Additiosoftware , in ordep2 was the total phenotypic variance. Estimates of PVE by QTLs discovered using the reduced genetic map were reported as the average estimates from the two additive-only model runs.The maximum number of QTLs was set at 10 for all models, and the Markov chain length was set between 200,000 and 500,000  depending on the run to ensure adequate model convergence. Effective chain sample sizes were ensured to be greater than 100 as outlined by variance . The meap < 0.05 (Bonferroni adjustment applied for multiple test correction) were considered significantly associated with the traits. Narrow-sense heritability was estimated as the proportion of additive genetic variance to the total phenotypic variance estimated by the GAPIT package, using the multiyear data for each trait. The PVE by each significant SNP was estimated as the adjusted r2 value when using the associated SNP as an independent variable in a simple linear regression model carried out in the base stats package of R  ABABBAB , and a seedling inherited the following haplotype from the parent: BAAABAB, this seedling haplotype would be classified as H1 due to the B allele present at the PS. Where the output phasing from FlexQTL™ was uncertain, manual phasing was attempted using parental haplotypes as guides. Uncertain SNP haplotypes after manual phasing were left unassigned. Only SNP haplotypes that were represented at least five times among the seedlings were used for comparative analyses.Guided by the QTL detection results from both FlexQTL™ and BLINK, haploblocks were chosen for haplotype analysis. Haploblocks were chosen based on the location of the “primary SNP” (PS) from the QTL detection. PSs were designated as such by (1) being significantly associated with trait variation as reported by BLINK in either the Combined population or subpopulation (the latter case only occurring for the stable cracking QTL on LG 5), (2) being within a haploblock that was less than 10 cM from the QTL peak as determined by FlexQTL™, and (3) explaining the most phenotypic variance compared to other significant SNPs in the region. The haploblocks were previously established based on the absence of known historic recombination . Despitep = 0.05, Bonferroni correction applied). The SNP haplotypes that were significantly different from at least one other SNP haplotype were considered “functional” SNP haplotypes, while those not significantly different from any other haplotype were not assigned an effect. A maximum of three levels of functional SNPs could be assigned: high, moderate, and low (for cracking or firmness). The ranking of functional SNPs was relational, meaning that the assignment of a given SNP haplotype was somewhat dependent on the assignment of other haplotype assignments. High or low assignments were given to haplotypes with the greatest number of significantly different contrasts between all other SNP haplotypes (whether the assignment was high or low depended on whether the effect was high or low). For example, if the average trait level associated with haplotype H1 was significantly higher than H2, H3, and H4 trait levels, H1 would be assigned as a high (cracking/firmness) haplotype. A moderate effect was assigned in two ways (continuing the above example): (a) to H5 if its associated average trait level was significantly higher than H2 but not H3 or H4, or (b) to H3 if its associated average trait level was significantly higher than H6. Boxplots were created using ggplot2  test  or a Pairwise Wilcoxon Rank Sum test  to determine significantly different pairwise contrasts  at QTL haploblocks for all seedlings and their known ancestors were imported into an Excel worksheet along with pedigree information , as was cracking incidence (2019 vs. 2020 r = 0.58) (r = −0.1)  . Multiye = −0.1) ; howevervia FlexQTL™ was 24.4% (0.244) and 36.7% (0.367) for all QTLs detected via BLINK , and 34.1% (0.341) for all QTLs detected via BLINK , while BLINK did not detect any QTLs ; thus, no PCs were used. Exploratory tests incorporating the first two PCs using BLINK still detected the four stable QTLs using multiyear cracking and firmness phenotypic data (results not shown). Although population structure was minimal  was located at 41–72 cm, peaked at 44–51 cm (∼13.5–21 Mbp), and was estimated to explain just 2% of the 2019 cracking phenotypic variance, 13–15% of the 2020 phenotypic variance, and 11–13% of the multiyear cracking incidence variance . The othThe firmness QTL on LG 1 (qFirm-LG1.2m) coincided with qCrack-LG1.1m (45–70 cM), peaked at 45–48 cM (∼15.3–18 Mbp), and was estimated to explain 9–16 and 19–22% of the 2020 and multiyear firmness phenotypic variance, respectively . The firBLINK showed some variability when manually incorporating covariates into the model demarcating offspring of ‘Ambrunes’ and ‘Sweetheart’ × ‘Sweetheart’ . For exaThree segments (haploblocks) within or overlapping the intervals of the four QTLs of interest were used for SNP haplotype analysis . qCrack-n = 7) and H7 (−50 g/mm). At both multiyear firmness QTLs, there were three functional SNP haplotype groups; high, moderate, and low firmness  using the known pedigree , 14. In Out of the four possible firmness SNP haplotypes (across the two firmness QTLs), ‘Bing,’ ‘Lapins,’ ‘Regina,’ ‘Sweetheart,’ and ‘Van’ had two putative high-firmness haplotypes, while ‘Benton,’ CC, ‘Chelan,’ ‘Cowiche,’ GG, ‘Glacier,’ ‘Kiona,’ and PMR-1 each had one putative low-firmness haplotype . No IBP Four stable QTLs, two for fruit cracking and two for fruit firmness, were successfully detected and characterized using a PBA (via FlexQTL™) and a GWAS (via BLINK) in the PNWSCBP germplasm representing 22 IBPs. Putative effect estimates were successfully assigned to SNP haplotypes at each QTL, and these haplotypes were able to be tracked through known pedigree records.This work attempted to increase the number of individuals studied compared with previous reports, but this strategy came with its own drawbacks, especially in terms of cracking incidence estimates . Quero-Gh2) for both cracking incidence and firmness by GAPIT compared to FlexQTL™ were possibly because GAPIT incorporated all markers to derive its estimate, while FlexQTL™ used only the QTL variance to derive its estimate. A recently reported long-term study estimated the pistillar-end, stem-end, and side cracking H2 at 0.608–0.905, 0.575–0.742, and 0.354–0.557, respectively (h2 estimates presented here (0.34–0.54). This discrepancy is to be expected, as broad-sense heritability includes all components of genetic variance. In addition, differences in phenotyping could have influenced differences reported in heritability estimates. The firmness h2 GAPIT estimate presented here was 0.70, which is similar to previous estimates of broad-sense heritability (H2) for firmness that has ranged from 0.77 to 0.85 (h2 for firmness was inflated when non-additive variance sources were not included in the model (h2 jumping from 0.27 in the full model to ∼0.75 in the additive-only model), and this could explain the high h2 presented here for firmness by GAPIT (a purely additive model). Assuming the genetic model (additive only) is accurate, any missing heritability could be due to many minor-effect QTLs and/or rare alleles that were not detected (The higher narrow-sense heritability estimates (ectively . Most of to 0.85 . Piaskowdetected .r = 0.58) and firmness  were comparable to those reported by r = 0.49 for stylar cracking from tunnel data and r = 0.58 for firmness. Significantly, moderately high between-year correlations for cracking incidence  close to qFirm-LG1.2m, ranging from 0 to 7 cm (1–4 Mbp) apart, were reported over 2 years by The between-year variation in PVE by each stable QTL  could haThe stable BF value of “NA” for qFirm-LG1.2m derived from the 2020 firmness data using the reduced genetic map  was unexResults from the QTL detection in the RosBREED and Program seedling populations separately indicated that some of the QTLs detected in the Combined seedling population were driven disproportionately by one subpopulation. This unequal contribution of subpopulations to the results in the Combined seedling population could be due to the large number of ‘Sweetheart’ × ‘Sweetheart’ seedlings in the Program seedling population. This hypothesis that ‘Sweetheart’ × ‘Sweetheart’ seedlings heavily influence the detection of QTLs was supported by the result that ‘Sweetheart’ was heterozygous at qCrack-LG1.1m, with one putative low-cracking haplotype and one putative high-cracking haplotype . The larvia SNP haplotype analysis  with accession number tfGDR1053.The original contributions presented in the study are included in the article/WC designed and carried out the experiments and analyses, as well as wrote the manuscript. PM designed and oversaw the experiments and analyses and revised the manuscript. CP and ZZ guided the analysis methods and revised the manuscript. All authors contributed to the article and approved the submitted version.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.


Zea mays L.) germplasm in China Summer maize ecological region (CSM) or central corn-belt of China is diverse but has not been systematically characterized at molecular level. In this study, genetic variation, genome diversity, linkage disequilibrium patterns, population structure, and characteristics of different heterotic groups were studied using 525,141 SNPs obtained by Genotyping-By-Sequencing (GBS) for 490 inbred lines collected from researchers at CSM region. The SNP density is lower near centromere, but higher near telomere region of maize chromosome, the degree of linkage disequilibrium (r2) vary at different chromosome regions. Majority of the inbred lines (66.05%) show pairwise relative kinship near zero, indicating a large genetic diversity in the CSM breeding germplasm. Using 4849 tagSNPs derived from 3618 haplotype blocks, the 490 inbred lines were delineated into 3 supergroups, 6 groups, and 10 subgroups using ADMIXTURE software. A procedure of assigning inbred lines into heterotic groups using genomic data and tag-SNPs was developed and validated. Genome differentiation among different subgroups measured by Fst, and the genetic diversity within each subgroup measured by GD are both large. The share of heterotic groups that have significant North American germplasm contribution: P, SS, IDT, and X, accounts about 54% of the CSM breeding germplasm collection and has increased significantly in the last two decades. Two predominant types of heterotic pattern in CSM region are: M-Reid group × TSPT group, and X subgroup × Local subgroups.Maize ( Zea mays L.) is the largest crop by acreage in China and it is an important source of food, feed, and industrial material in China. China Summer Maize (CSM) ecological region, also called China central corn belt, or Huanghuai Corn belt, including almost entire area of Henan, Shandong, Shaanxi Province, Southern part of Hebei, Shanxi Province, and Northern part of Jiangsu and Anhui Province, is the second largest among 6 ecological regions in China. The CSM region with more than 14 million hectare accounts for 35% of the national corn planting acreages and more than 40% of grain corn output in China.Maize  timely in later September or early October. Because inbred lines and hybrids from North America and Europe do poorly at CSM region and are difficult to be directly used in hybrid creation and corn production, researchers have long faced the challenges of broadening the germplasm basis of breeding population.2.Maize was introduced into China nearly 500 years ago. The local germplasm widely used at present days, such as, Tang SiPingTou (referred to as TSPT), and LvDa Red Cob (Referred to as LRC) originated from a government-sponsored national collection of open pollinated populations from local farmers in early 1950s and have accumulated a large number of unique genetic mutations well adapted to China spring corn region and summer corn region (CSM) as well3.Starting from 1950s, maize germplasms from all parts of world, mostly from North America have been brought into CSM region multiple times for new traits and yield heterosis enhancement4, a large number of inbreds and commercial hybrids with better grain yield, low kernel moisture, fast dry-down, and suitability to mechanical harvesting have been created in the last two decades.Three big germplasm introductions to China in the last four decades that have significant impact on the formation of modern maize germplasm at CSM region are: (1) introduction of Pioneer hybrids P78599, P3147, and P3382 as breeding germplasm in the later 1980s to bring in disease resistance genes, that leads to the formation of P heterotic subgroup and M-Reid_PA subgroup (2) the introduction of over 300 ex-PVP inbreds into China in the last decade and (3) the successful commercialization of hybrid Xianyu 335 and other hybrids by Pioneer Hybrid International in China’s Spring corn region at early 2000s and their expansion to CSM region at 2010s. Both (2) and (3) have enhanced the fusion and integration of North American germplasm into Chinese germplasm and explain the existence of the heterotic subgroup IDT and subgroup X in CSM germplasm. Using these new germplasm and the breeding methodology of pyramiding breeding of favorite traits1 hybrids, researchers assign inbred parents into different germplasm groups, also different gene pools, called heterotic groups, and create F1 hybrids by inter-heterotic group hybridization in order to obtain superior F1 heterosis in yield and many other traits; they improve the traits of a parental inbred line itself by intragroup hybridization to avoid heterosis or non-additive effect and to obtain large genetic gain in trait selection. The heterotic groups in US breeding germplasm were well studied6, and the heterotic groups in Chinese breeding germplasm were also studied in detail9.Maize is one of the most successful crops in heterosis utilization through commercial hybrids, in particular, single-cross hybrids. To increase the probability of obtain high hybrid vigor in F11. In recent years, researchers start to assign an inbred line to a heterotic group using genome data, in particular, a large set of SNP marker data. Several studies on germplasm diversity and heterotic group pattern using large scale diversity panels of inbreds and SNP data have been reported on world-wide maize inbred collection15 and North American germplasm18. Several studies also have been reported on Chinese germplasm in general15, and germplasm with focus to a specific ecological region, such as Northern China Spring Maize19, Southwestern China Spring Maize21. However germplasm at China Summer Maize (CSM) region has not been studied systematically at molecular level, and many important aspects of CSM germplasm are poorly understood. In this study, 490 maize inbred lines collected by CSM region researchers were studied using 525,141 SNPs obtained by Genotyping-By-Sequencing (GBS), to address the following questions from breeders and geneticists as well: (1) the degree of genetic diversity and genome differentiation (2) the population structure and heterotic group identity (3) major heterotic groups and predominant heterotic pattern of commercial hybrid creation (4) historical changes in germplasm composition and heterotic group in CSM region .Traditionally a maize inbred line is assigned to a particular heterotic group based on the field experiment data (the Combining Ability Test) from crossing the inbred line with a set of testers (a set of representative inbred lines from one or more heterotic groups)22. Genomic DNA was digested with the restriction enzyme ApeK1. GBS libraries were constructed in 96-plex and sequenced on Illumina HiSeq 2000. SNP calling was performed using TASSEL-GBS pipeline23 with Maize B73 RefGen_v3 as the reference genome. Initially, 877,631 SNP loci went through a quality control procedure that filters out SNP loci with high missing rate and spurious heterzygocity arising from sequencing error and the artifactual SNPs originating from paralogous tags24; 876,305 of them were assigned to chromosomes 1 to10, and 1326 of them could not be anchored to any of the 10 maize chromosomes and were excluded. Then data was loaded into TASSEL 5.225(https://www.maizegenetics.net/tassel), and SNP loci with minor allele frequency (MAF) < 5% / missing rate > 20% / residual heterozygocity > 5% were further filtered out, and data for 525,141 high-quality SNP loci was kept for all analyses involving the entire data set. For all subpopulation-specific analysis, tagSNP data instead of single locus SNP were used, loci with  missing rate > 10% or with residual heterozygocity were treated as missing and were excluded from calculation.A panel of 490 maize inbred lines was collected from researchers at China summer corn-belt, with origin from China Summer Corn ,North East China , North West China , South West China (SW China 32), and USA . Leaf sample from each line was used for DNA extraction with a CTAB procedure. DNA sequencing follows a protocol of Elshire et al.26 and Genetic Diversity (GD) were calculated ether using PowerMarker V 3.2527 or manually28. MAF and Heterozygosity (H) were calculated using TASSEL5.2. The pairwise relative kinship between two inbreds was estimated using 525,141 SNP loci by TASSEL 5.2.Allele frequency analysis of both unfiltered and filtered data was carried out with TASSEL5.2 software. The Polymorphic Information Content (PIC)https://ggplot2.tidyverse.org/) . For the LD pattern of entire inbred collection (Table https://www.broadinstitute.org/haploview/haploview) from the filtered SNP data were used.A chromosome was divided into 50 kb segment and the pair-wise LD was calculated using Pearson correlation coefficient  from Wright30 was calculated using a software module of ADMIXTURE 1.3.0 .ADMIXTURE 1.3.0 was used to detect the population structure among all 490 maize inbred lines using 4849 tagSNPs. Principal component analysis (PCA) based on 525,141 SNPs was performed using the R package SNPRelate. Pairwise Fixation Indexes , SNP density, and four genetic diversity parameters: MAF, GD, PIC, and H (Heterozygosity)) are plotted by chromosome for the un-filtered data  and filtered data  in Fig. For the data of 525,141 SNP loci, the pattern of SNP density along each chromosome is similar for all 10 chromosomes: low near centromere region and high near telomere region  to 245 kb (Chr. 9). Chr. 8 and Chr. 2 have the shortest LD decay distance , smallest mean value of Linkage disequilibrium (LD) analysis was carried out for 525,141 SNP loci using TASSEL5.2 software. The segment average LD vary along a chromosome, with high and low LD segments or regions alternate as red (high LD) and blue (low LD) colored bands or stripes of different length  are equal or close to 0, only 9% of them are above 0.05  PCA plot visualization at 2-D space and (2) comparing the group identity or affiliation assigned by ADMIXTURE with that based on other independent prior knowledges for a set of well-known core or indicator inbreds.The ADMIXTURE output was visualized on PCA plots at K = 2, 3, 6, and 10 Fig. . The cleThe ADMIXTURE output was further validated by comparing the group affiliation assigned by ADMIXTURE and that established by other independent prior knowledge for a set of inbred lines, which we call the core inbreds or indicator inbreds of a heterotic group or subgroup. These set of inbreds are called indicator inbreds because their heterotic group affiliation are known and were established based on independent prior knowledge, including genetic pedigree information , results of field combing ability tests, and the consensus of a majority of corn breeders.The results show that ADMIXTURE did assign these inbreds correct group affiliations Fig. . Here onTable PCA plots at Fig. Its two subgroups TSPT_C72 and TSPT_HZS takes up 9% and 13% respectively Fig. b, they a87 inbreds . Many inbreds in the X subgroup are female and male parents of big commercial hybrids at CSM region, such as, Jing724, M03, and NH60 as female parent of Hybrid Jingke968, Liangyu99, and Nonghua101 respectively, and DH382 as male parent of Hybrid Denghai605. IDT and X subgroup have the closest genetic affiliation with each other but are far from all other 8 subgroups revealed at the PCA plot and Fst tree  , including all inbreds in subgroup IDT, X, P, Lan, and SS, have major contribution from North American germplasm and 28% of them (74/265) are Ex-PVP inbreds , SS (22), and Lan (23) , and TSPT_C72 subgroup and the TSPT_HZS subgroup have the smallest genomic differentiation or genomic distance (Fst = 0.209). IDT has the largest mean Fst value (0.275) and Lan has the smallest mean Fst (0.228), indicating that IDT subgroup has the largest genomic distance from all other subgroups and Lan subgroup has the smallest genomic distance. These findings are consistent with the PCA results shown in Fig. LD block distribution along chromosomes Different subgroups have very different LD block distribution pattern  genome differentiation with Wright’s Fst (2) linkage disequilibrium pattern (3) SNP Loci Polymorphism measured by GD and PIC (4) Heterotic group-specific SNP allele polymorphism.14 33. LD decay very rapidly on all 10 chromosomes and reached r2 = 0.11 at about 20 Kb on average  of SNP loci reside very closely physically on chromosome  is MAF = 0.22, Wu et al.35 reported an average MAF = 0.22 for the 362,008 SNP loci data set collected using GBS sequencing of 538 CIMMYT maize inbred lines. Since SNP loci is bi-allelic, the above result indicate that the frequency of two alleles of a SNP locus is p = 0.22 and q = 0.78 respectively on average, far from the allele frequency p = q = 0.5 expected from a randomly mating Mendelian population with two neutral alleles; the high percentage of rare allele loci and the lower average MAF (far from MAF = 0.5) are all strong indications of natural and artificial selection that drive the formation of population structure in the CSM germplasm. Further analysis of allele frequency difference between subgroups show the existence of the subgroup-specific SNP loci, these loci are neutral or have frequency near 0.5 in one subgroup but were fixed or nearly fixed  in another group . Ramoy et al.15 and Zhao et al.19.The Genetic Diversity, or GD, measures difference in frequency between two alleles in SNP loci. The entire inbred panel of 490 inbreds has average GD = 0.344 and the 10 subgroups have GD ranging from 0.277 to 0.333 Table . The GD 39. In this study, GBS technology developed by Elshire et al.22 was adopted to produce high quality SNP data, and Haploview v4.2 was used to identify 4849 tagSNPs sites from 525,141 SNP markers loci of GBS data, the combination of these two technologies make development of a set of high quality SNP markers without significant loss of information possible. Wu et al.15 shows that 700–1000 SNPs were necessary to robustly estimate the genetic difference among subpopulations, our result shows that about 5000 tagSNPs and haplotype loci derived from them are suitable for population structure analysis and germplasm study. In maize and many other organisms, it has also been reported that haplotype loci derived from tagSNPs are more informative than binary SNPs41.To reduce the cost of genome DNA sequencing in SNP loci discovery and the cost of genotyping in applications to germplasm study and molecular breeding, several molecular marker technologies have been developed, including but not limited to: GBS, GBTS, SLAF-seq, SNP Chip, et al.st value between any two subgroups ranges from 0.209 to 0.346. According to Wright’s guide29, two populations with Fst > 0.25 have very large genetic differentiation and with 0.15 < Fst < 0.25 have large genetic differentiation. Therefore, our Fst results indicate that there is very large genomic differentiation between different heterotic groups at the CSM collection. Zhao et al.19 find the heterotic groups in Northern China have Fst value: 0.325–0.457 using SNP chip data and 344 inbreds.The ADMIXTURE output and PCA representation all show clearly the existence of population structure in the CSM collection and the 490 inbreds can be grouped into 3 supergroups, 6 groups and 10 subgroups with clear boundary and very little mixing. Fixation Index (Fst) was calculated to measure the subgroup differentiation and the F42. Based on pedigree information, B73 has contributed 3/4 or 75% of genome to both LH132 [(H93xB73)/B73] and LH1[(B73xH644)/B73] , but based on genomic DNA sequence data  contribution is unknown, from ADMIXTURE output, the IDT contribution to PHG83  and PHG71  are 0.69 and 0.58 respectively. For many inbred lines, pedigree data are either missing or misreported, genomic data can provide important information on their heterotic group affiliation.Traditionally, breeding pedigree information is used to determine the parental contribution and predict the heterotic group affiliation of a particular inbred line, but it is not possible to know the exact proportion of genome contribution from a parent or a progenitor to the inbred line solely based on pedigree information because trait selection applied by breeders at every step of the breeding process could change the proportion. Here we show that genome data can do a better job. For example, Inbred LH132 and LH1 are taken as SS germplasm because they both are derived from SS core inbred B73st analysis  , and X subgroup × Local subgroups (Type II). The Type I is exemplified by Zhengdan 958 [Zheng 58 (M-Reid) × Chang 7–2 (TSPT)] and a number of other hybrids with large commercial utilization: XunDan 20 (Xun9058 × Xun92-8), YeDan 2 (Ye107 × HZS), YeDan 12 (Ye478 × Ye515), and Zhongke 11 (CT03 × Chang7-2)44. The dominant presence of hybrid Zhengdan 958 at CSM commercial corn production (took up to 30% of corn planting area at its peak years of 2000s) leads to the increasing use of M-Reid_Z58 and TSPT_C72 breeding germplasm which are mostly derived from two inbred parents of Zhengdan 958: Zheng58 (Z58 for short) and Chang 7-2 (C72 for short) in inbred breeding programs in CSM region. Subgroups M-Reid_Z58 and TSPT_C72 count about 6% and 9% respectively in CSM breeding germplasm. The Type II, that is, X subgroup × Local subgroups, is exemplified by corn hybrid DH605 , Jingke 968 (Jing724 × Jing 92), and Liangyu 99 (Mo3 × M5972), many of them also have large presence in China Spring maize region.The Predominant heterotic pattern in the last two decades in CSM region is Introduced × Local, which is also true in Chinese spring maize regions9 have studied the CSM germplasm during later 1980s and early 1990s and has identified four major heterotic groups at CSM region and their relative proportion: M-Reid (25.6%), Lancaster (25.6%), TSPT (16.2%), LRC (10.7%), counting about 78% of the CSM germplasm9, whereas the four groups M-Reid (15.1%), TSPT (15.1%), LRC + Lan (24%), count about 54% of total germplasm now (Fig. In the past two decades, corn hybrid grain yield per unit area has increased more than two folds in CSM region and corn germplasm basis and heterotic pattern at CSM region has changed significantly and North American germplasm become more important at CSM region. Wang et al.Supplementary information 1.Supplementary information 2.Supplementary information 3.


BTLA, PD-1, LAG3, and CTLA4, and two SNPs in methylase genes, DNMT1 and EZH2, in 999 uBMT pairs. Although no correlations were observed between these SNPs and post-uBMT outcomes, recipient EZH2 SNP exhibited a low p-value in the analysis of grade 2–4 acute GVHD (p = 0.010). This SNP may be useful for outcome predictions and needs to be confirmed in a larger-scale study.Hematopoietic stem-cell transplantation (HSCT) is a curative therapy for blood disorders. Unrelated bone marrow transplantation (uBMT) is a type of allogeneic HSCT that uses the bone marrow of an unrelated donor. While HLA mismatch is a risk factor for poor outcomes in HSCT, such as graft-versus-host disease (GVHD), the importance of non-HLA single-nucleotide polymorphisms (SNPs) remains unclear. The clinical application of immune checkpoint and chromatin methylation inhibitors to cancer has been attracting attention. In the present study, we retrospectively genotyped five SNPs in four immune checkpoint genes, BTLA, PD-1, LAG3, and CTLA4, and two SNPs in the methylase genes, DNMT1 and EZH2, in 999 uBMT donor–recipient pairs coordinated through the Japan Marrow Donor Program matched at least at HLA-A, -B, and -DRB1. No correlations were observed between these SNPs and post-uBMT outcomes (p > 0.005). This result questions the usefulness of these immune checkpoint gene polymorphisms for predicting post-BMT outcomes. However, the recipient EZH2 histone methyltransferase gene SNP, which encodes the D185H substitution, exhibited a low p-value in regression analysis of grade 2–4 acute graft-versus-host disease (p = 0.010). Due to a low minor allele frequency, this SNP warrants further investigation in a larger-scale study.Unrelated bone marrow transplantation (uBMT) is performed to treat blood disorders, and it uses bone marrow from an unrelated donor as the transplant source. Although the importance of HLA matching in uBMT has been established, that of other genetic factors, such as single-nucleotide polymorphisms (SNPs), remains unclear. The application of immunoinhibitory receptors as anticancer drugs has recently been attracting attention. This prompted us to examine the importance of immunoinhibitory receptor SNPs in uBMT. We retrospectively genotyped five single-nucleotide polymorphisms (SNPs) in the immune checkpoint genes,  Hematopoietic stem-cell transplantation (HSCT) is a curative therapy for blood disorders, such as leukemia. Unrelated bone marrow transplantation (uBMT) is an allogeneic HSCT that uses bone marrow from an unrelated donor as the transplant source. Due to advances in treatment, overall survival (OS) after uBMT, similar to other types of allogeneic HSCTs, has been prolonged; however, further improvements are needed. Acute and chronic graft-versus-host disease (GVHD) pose serious risks to post-transplant recipients. HLA mismatches between a recipient and donor are well-established risk factors for poor HSCT outcomes ,2,3. MorInhibitory receptors on T cells, such as cytotoxic T-lymphocyte associated-4 (CTLA4) , programEnhancer of Zeste 2 (EZH2) gene encodes histone H3 lysine 27 methyltransferase and is a component of polycomb repressive complex-2 (PRC-2) .,40.BTLA,Genomic DNA preparation, TaqMan SNP Genotyping Assays , and direct Sanger sequencing following PCR were performed as previously described . In TaqMThe primary outcome of the present study was grade 2–4 aGVHD within 100 days of transplantation. Secondary outcomes were grade 3–4 aGVHD within 100 days, chronic GVHD (cGVHD), extensive cGVHD (ecGVHD), OS, non-relapse mortality (NRM), and relapse. The detailed definitions and competing events of these outcomes used in the present study were identical to those previously described .Recipient characteristics used as covariates were sex, age, disease stage, body mass index, the cytomegalovirus (CMV) serostatus, and performance status. The donor characteristics used were sex, age, and the CMV serostatus. Transplantation characteristics used as covariates were myeloablative conditioning, cyclosporine A, arabinofuranosyl cytidine (Ara-C), cyclophosphamide, the number of nucleated cells infused, and days from diagnosis to BMT. Mismatches between a recipient and the corresponding donor of the ABO blood type, HLA-C, HLA-DPB1, and HLA-DQB1 were also used as covariates. Total HLA mismatches denote the sum of the numbers of mismatches at HLA-C, -DPB1, and -DQB1 because HLA-A, -B, and -DRB1 were matched. These covariates are shown in p-values in the regression analysis. All p-values are two-tailed unadjusted values, and p < 0.005 was considered to be significant. The R software (ver. 3.2), the EZR software [Statistical analyses were conducted as previously described . The addsoftware , VCFtoolWe genotyped all SNPs in 999 subjects, except for the LAG3 SNP, rs870849. It was not possible to genotype one recipient for rs870849. The TaqMan assay produced a very weak signal and three independent trials of PCR amplification for the Sanger sequencing failed for an unknown reason; therefore, the rs870849 genotype of this recipient was finalized as undetermined . Howeverp > 0.005; Allele frequencies were similar among the donors, recipients, and 104 Japanese individuals in Tokyo (JPT104) from the 1000 Genomes Project . The nulp > 0.005; p > 0.005; EZH2 SNP in the additive model exhibited low p-values .We conducted a univariable regression of grade 2–4 aGVHD, the primary outcome, on these seven SNPs under additive, dominant, and recessive genetic models. None of these SNPs correlated with grade 2–4 aGVHD ). There was no SNP within the extracellular domain affecting ligand binding that met our SNP selection criteria. Collectively, the present results do not necessarily indicate the true lack of a relationship between the BTLA gene and aGVHD.In a murine model, BTLA played distinct roles in GVHD; the administration of an anti-BTLA monoclonal antibody inhibited donor anti-host T-cell responses, whereas BTLA also served as a ligand that sent a prosurvival signal in donor T cells . The twose women  and has se women . AlthougPD-1 SNP, rs2227982, was not associated with any post-uBMT outcomes. Although rs2227982 may not be a functional PD-1 SNP, previous studies on east Asians reported correlations with the risk of type 1 diabetes, with glucose and insulin levels after the oral glucose tolerance test [PD-1 is associated with BMT outcomes, this relationship may depend on specific clinical characteristics. A similar explanation may be applied to the LAG3 SNP used in the present study, rs870849, which was previously shown to be associated with multiple sclerosis [PD-1 is another immune checkpoint protein, and its blockade is of widespread interest as a treatment for cancer, including Hodgkin’s lymphoma ,44. Blocnce test , and witnce test . Therefoclerosis .CTLA4 SNPs correlated with transplant outcomes in some [CTLA4 SNP used in the present study, rs231775, encodes an Ala-to-Thr substitution at the 17th amino-acid position; the CTLA4 protein with threonine 17 more strongly inhibits T-cell activation [CTLA4 may not be associated with BMT outcomes, or, even if it is, the relationship may depend on some factors, such as patient characteristics. in some ,30,31,32 in some ,49, and  in some ,29. The tivation . DespiteDNMT1 and EZH2, and BMT outcomes. The DNMT1 SNP used, rs2228612, was identified as a cancer risk factor in a meta-analysis [DNMT1 SNP and post-BMT outcomes. DNMT3a and DNMT3b SNPs were not included in the present study because we did not detect any SNPs in these genes with sufficiently high MAFs in the Japanese population.We also examined the relationships between polymorphisms in the methylase genes, analysis . Howeveranalysis . This isEZH2 SNP, rs2302427, encodes the Ezh2 D185H substitution, which is located in the domain that interacts with DNA methyltransferases  in the additive model (As already discussed, EZH2 is involved in the silencing of MHC-I , as wellsferases . This SNve model  despite ve model . Therefove model . The rs2ve model .The present study had a number of limitations. The study design was retrospective, and clinical decisions should be based on prospective studies. Furthermore, SNP–SNP interactions were not considered. Several of the genes analyzed may be redundant for HSCT outcomes in humans. Therefore, future studies on a larger sample size are needed. In addition, the molecular functions of five of the SNPs are unknown .EZH2 SNP, rs2302427, which encodes the D185H substitution, exhibited a low p-value in analysis of grade 2–4 aGVHD despite a low MAF and, thus, warrants further investigation in a large-scale study.Although we expected immune checkpoint gene polymorphisms to be useful for predicting BMT outcomes, no correlations were observed. The recipient


CONSTANS-like genes probably regulate fruit maturation differences among lychee cultivars. These genomic resources provide insights into the natural history of lychee domestication and will accelerate the improvement of lychee and related crops.Lychee is an exotic tropical fruit with a distinct flavor. The genome of cultivar ‘Feizixiao’ was assembled into 15 pseudochromosomes, totaling ~470 Mb. High heterozygosity (2.27%) resulted in two complete haplotypic assemblies. A total of 13,517 allelic genes (42.4%) were differentially expressed in diverse tissues. Analyses of 72 resequenced lychee accessions revealed two independent domestication events. The extremely early maturing cultivars preferentially aligned to one haplotype were domesticated from a wild population in Yunnan, whereas the late-maturing cultivars that mapped mostly to the second haplotype were domesticated independently from a wild population in Hainan. Early maturing cultivars were probably developed in Guangdong via hybridization between extremely early maturing cultivar and late-maturing cultivar individuals. Variable deletions of a 3.7 kb region encompassed by a pair of  Two divergent haplotypes from a highly heterozygous lychee genome of the cultivar ‘Feizixiao’ and resequencing of 72 lychee accessions provide insights into the genome evolution and domestication history of lychee. Litchi chinensis Sonn., Sapindaceae) is an important tropical fruit tree species worldwide, and a valuable fruit for which the edible portion is an aril , early-to-intermediate-maturing cultivars (EMC) and late-maturing cultivars (LMC)n = 2× = 30) and resequenced 72 wild or cultivated accessions to explore the structure and evolution of the lychee genome as well as its origin and domestication history. We also investigated expression patterns of allelic genes and potential regulatory mechanisms involved in timing of flowering and fruit maturation. These results will improve our understanding of the lychee genome, accelerating genetic improvement of lychee and its relatives in Sapindaceae.Lychee originated in broad, yet isolated and distant, regions of southern China, where numerous wild lychees exist in the Hainan and Yunnan rainforests, and hilly areas of western Guangdong and eastern Guangxi. However, the exact center(s) of origin and the history of lychee domestication remains unknown. To address these questions and provide a genome-enabled breeding platform, we generated a high-quality reference genome of the highly heterozygous cultivar ‘Feizixiao’ , the initial assembly was almost twice as large (Supplementary Tables 5) (Supplementary Tables We generated 58.6 Gb PacBio long reads (124× coverage) from the lychee cultivar ‘Feizixiao’ and 86.25 Gb (184× coverage) clean Illumina short reads from libraries with different insert sizes (Supplementary Table Xanthoceras sorbifolium) and citrus (Citrus sinensis) around 39.4 (34.3–44.7) and 67.6 (64.5–72.2) million years ago (Mya), respectively  were similar to that of lychee (Ks = 1.43), indicating absence of further whole-genome duplications (WGDs) in Sapindaceae since the ancient gamma triplication event6  .A detailed analysis of relatedness among individuals revealed close affinities among Yunnan, Daxin and Vietnam wild populations, with several individuals showing homozygous allelic patterns corresponding to first- to third-degree relatives. In fact, two GDXWs were closely related to three YNWs and all VNWs , showing a clear division between EEMC/YNW and LMC/HNW populations, with EMC cultivars admixed. Hybridity was also suggested by chloroplast genome phylogenetic relationships, wherein ‘Feizixiao’ grouped with a LMC/HNW clade and other EMC accessions  and high impact (πn) nucleotide diversities among them ) combined with single-cell 10x Genomics sequencing (100× coverage) to obtain 15 pairs of homologous chromosomes each  of ‘Feizixiao’ lychee also enabled us to generate two haplotypes using SNP phasing (with HapCUT2 ref. 2) combin% of ‘Fein values. Average heterozygosity (2.38%) from total SNPs between HY and HH was similar to the k-mer estimate (2.27%), supporting the accuracy of haplotype assignment  and gene copy number variants (CNVs), were found between these two haplotypes  increased log-linearly with sample quantity, plateauing at ~14,000 DEAs with over 35 samples  for most alleles, indicating strong identity between allelic genes  , indicating that DEAs were under greater purifying selection pressure , while 3.7%  of allelic pairs showed possible positive selection (Ka/Ks >1)  of DEAs had much higher SNP densities, suggesting that their differential expression may reflect different binding affinities of transcription factors to promoter regions Fig. . SNP denSHORT VEGETATIVE PHASE (SVP) gene of Arabidopsis, which controls flowering time by negatively regulating the expression of FT via direct binding to the CArG motifs in its promoter15. Eight out of ten lychee SVP homologs are arranged in tandem , but is absent in yellowhorn (X. sorbifolium) , a member of this class, maintains vernalization via repressing FLOWERING LOCUS C (FLC) to promote flowering17. In the lychee genome, we identified a tandem cluster of 24 B3-domain-containing genes located within a ~170 (169.6) kb region of chromosome 5, each containing 1–3 B3 domains —a species from the same order, Sapindales, wherein the 129.25 kb region contains many fewer VRN1-like genes (only 7) and B3 domains (only 14)  Fig. .Fig. 5ThVRN1 were found within these syntenic blocks, as expected  during lychee domestication  encodes a CONSTANS-like (COL) protein—a transcription factor whose Arabidopsis homolog mediates the circadian clock and flowering control18. Allelic variants of this gene (subsequently referred to as COL307) were differentially expressed, with the HH allele showing greater expression. This pattern correlates well with the fact that maturation time of ‘Feizixiao’ lychee is intermediate between EEMC (HY) and LMC (HH) accessions. Among HY/HH COL307 variants, a heterozygous deletion of 3,781 bp exists in the 3′ region of HY only  ~100 kb away from COL307  using our 72 accessions  versus that from Hainan and Bobai County  reflected two ancestral populations, albeit with geographic linkage. Conceivably, YNW lychee spread downstream along the Xijiang River, resulting in the GXW and HNW populations. Second, Bobai County and the Leizhou Peninsula of Guangdong are adjacent. The Leizhou Peninsula and Hainan Island were once connected, but were separated during the Pleistocene21. A vast region of modern Bobai, Leizhou and Hainan was affected by explosive volcanic activity22, leading to large-scale environmental changes in lychee habitat that might have helped drive differential evolution of HY and HH after the YNW population spread to those areas.We speculate that lychee might have originated in Yunnan, then spreading to Guangxi and Guangdong before finally arriving in Hainan, since lychee seeds are often too large to be animal-dispersed in the wild; instead, they are distributed mainly by river. The Xijiang River is the main channel of the Pearl River, which originates in the Maxiong Mountains in Zhanyi County in northeast Yunnan, whereafter it runs through Yunnan, Guangxi and Guangdong. This river is known as the ‘Golden Waterway’ that connects South China to Southwest China23, but further methods development will be necessary to formally incorporate reproductive strategy differences into modeling.Population structure and phylogenetic analyses of resequenced lychee genomes indicated independent domestication events for cultivated lychee, wherein EEMC was domesticated from YNW, while LMC was domesticated from HNW in Guangdong and Guangxi, and further refined by subsequent crosses with local inbred populations. Given admixture statistics, evidence from inbreeding, relatedness and comparative mapped reads coverage between the HY and HH genomes, EMC probably originated from hybridization between EEMC and LMC parents, with LMC probably the female and EEMC the male parent. In wild populations, we found evidence for long-lasting inbreeding in YNW, resulting in demographies inconsistent with the more outbred Hainan population. We developed an ad hoc approach to compensate for thisFLC (ref. 24). Although this chilling requirement in perennial fruit trees might be different from the vernalization process in crucifers, the well-maintained synteny of VRN1-like genes in core eudicots implies a potentially conserved role in flowering control. Furthermore, the expansion of the VRN1-like gene cluster in Sapindaceae points toward more diversified functions of these genes in flowering (or other processes) within this family. The Arabidopsis CO protein is a key player in the photoperiod pathway, which controls flowering in response to seasonal day length changes25. A polymorphic 3.7 kb deletion near a lychee CO-like gene (COL307) is strongly associated with maturation times of different lychee varieties, providing an ideal target for molecular breeding and genomic selection for more diversified fruit maturity.Floral induction of lychee requires a period of relatively low temperature. In the Arabidopsis vernalization response, VRN1 cooperates with other proteins to promote flowering via the induction of epigenetic silencing of the repressor gene The lychee cultivar ‘Feizixiao’ is a prevalent lychee cultivar in China and the main cultivar traded on the world market. Therefore, we chose ‘Feizixiao’ for full-genome sequencing.30 (v.0.36) with default parameters.DNA was extracted from leaf tissue using a Qiagen DNeasy Plant Mini Kit, and a 270-bp paired-end library was generated using an NEBNext Ultra DNA Library Prep Kit. Sequencing was performed using Illumina HiSeq2500 and Hiseq4000 platforms. In total, 30.65 Gb (~66×) of data were obtained, and the reads were trimmed using TrimmomaticMore than 6 µg of sheared DNA was subjected to size-selection by the BluePippin system and ~20 kb Sequel SMRT bell libraries were prepared according to the protocol provided by the Pacific Biosciences Company (PacBio). Four single-molecule real-time (SMRT) cells were run on a PacBio RSII system using P6-C4 chemistry, with ~58.6 Gb (~124×) long-read data generated.31. A total of 232 million (~144×) 150 bp paired-end reads were produced on the Illumina HiSeq ×10 platform.A Hi-C library was constructed from young leaves of ‘Feizixiao’ by the BioMarker Technologies Company  as described elsewhere32 (v.0.7) with short reads. Subsequently, corrected long reads were set as input for Canu33 (v.1.6) with the parameter ‘correctedErrorRate=0.035’. The initial assembly obtained was twice the size of the anticipated genome size, suggesting that it contained two haplotypes. We next used HaploMerger2 (ref. 34) (release 20180603) to extract two haplotypes with parameters ‘minOverlap=99999999’ in B3 stages, otherwise using default settings. The one haplotypic assembly (HM_ctg2) closer to the expected genome size was anchored into 15 pesudochromosomes  using Juicer35 (v.1.6.2) and 3D-DNA36 in combination with the Hi-C reads.A flow chart of our genome assembly approach is shown in Extended Data Fig. 37 mem (v.0.7.17), and the Hi-C contact matrix was calculated and plotted with HiCExplorer38 (v.2.1.1).Right and left reads of Hi-C data were mapped separately to the reference genome with parameters ‘-A1 -B4 -E50 -L0’ using BWA37 mem, and the resulting alignment file was duplication-marked and sorted using Samtools39. The sorted alignment file was then used for variant calling using the Genome Analysis ToolKit (GATK) (v.4.1.4.1) pipeline40. The variants passing the hard-filter were kept with criteria ‘QD < 2.0 || MQ < 26.0 || FS > 100.0 || SOR > 5.0 || MQRankSum < −7.5 || ReadPosRankSum < −8.0’, giving rise to the ‘Feizixiao’ VCF file. Only biallelic SNPs were selected for haplotype phasing using HapCUT2 (ref. 41) (v.1.1), combined with Hi-C and corrected PacBio reads. We extracted the MVP blocks and filtered out both unphased SNPs and unknown genotypes. Eventually, blocks of haplotypic SNPs were retained, covering 11.2 million phased SNPs.To obtain accurate haplotypes, short reads were mapped against the reference genome using BWA42. We used phased haplotypic SNPs from 10x Genomics to correct those SNPs from HapCUT2 (v.1.1)41 for blocks containing >1,000 phased SNPs identified in 10x Genomics. The corrected haplotypic SNPs were used for PacBio reads phasing using a method first described for sex chromosome phasing43. Phased reads were assembled de novo using Canu (v.1.6)33. Contigs were linked with the help of reference genome sequences using RaGOO (v.1.1)44 software.Meanwhile, 10x Genomics reads were mapped to reference genome sequences, and phased SNPs blocks were calling using Long Ranger (v.2.2.2)Finally, 15 pairs of homologous pseudochromosomes were obtained. Afterwards, Illumina population resequencing reads from different lychee accessions were aligned to the 15 pseudochromosome pairs, with those dominated by reads from the YNW/EEMC/GXW cultivar group assigned to the HY haplotype, with the remaining haplotype being termed HH.45 genome annotation pipeline (v.2.31.8), which integrates both ab initio gene predictions generated by AUGUSTUS46 (v.3.3.2) and SNAP47 (v.2013-11-29) and homology evidence including plant protein sequences in the SwissProt database48 (release 2018_12) as well as a de novo transcriptome assembly generated from lychee RNA-seq data using Trinity49 (v.2.8.3). For improved results, we ran the MAKER pipeline iteratively  as recommended45. The gene models were further refined using PASA50 (v.2.3.3). For the gene models from the two haplotypes, those models corresponding to the longest transcripts in the reference were converted to their two haplotypic sequences using GMAP (v.2017-11-15)51 with parameter ‘n = 2’. Haplotypic gene models were considered redundant if the overlap between two gene models was over 60%, and these were subsequently removed from the final gene model list.Gene models were annotated using the MAKER52 (v.3.0.1) was used for evaluation of annotation completeness with the ‘eudicotyledons_odb10’ database.BUSCO53 (v.1.07) with parameters ‘-D 15000 -d 1000 -L 7000 -l 100 -p 20 -C -M 0.9’ and with LTRharvest54 (v.1.5.10) using parameters ‘-similar 90 -vic 10 -seed 20 -seqids yes -minlenltr 100 -maxlenltr 7000 -mintsd 4 -maxtsd 6’. Results from the above LTR annotation steps were integrated using LTR_retriever55 (v.2.6), and LAI56 was also evaluated.LTRs were predicted using LTR_FINDERD. longan57, X. sorbifolium58, C. sinensis59, Arabidopsis thaliana60, Carica papaya61, Fragaria vesca62, Malus domestica63, Cucumis sativus64, Medicago sativa65, Vitis vinifera6, Solanum lycopersicum66 and Acer yangbiense67, were downloaded from public databases  (v.2.3.3).Genome assemblies and GFF3 files for 12 species, including 70 (v.4.9i) with two calibration points . We adopted the Bayesian relaxed molecular clock approach to estimate species divergence time using the independent molecular clock and Empirical+F models with default ‘wag.dat’ amino acid substitution rate in the program MCMCtree70, with other parameters set to defaults.Twenty single-copy orthologous protein sequences were used to construct the phylogenetic tree using MCMCtree71 software, while Ka and Ks were calculated in TBtools68 using coding and protein sequences. We then filtered out zero values and plotted the data with the ggplot2 (ref. 72) package.Paralogous and orthologous gene pairs were identified in syntenic blocks using the MCScanX73 and LTR annotation files, respectively. All the above ‘Feizixiao’ genome features were viewed in Circos plotted using TBtools68.Gene density was calculated directly from the ‘Feizixiao’ GFF3 file in 100-kb windows. SNP density and nucleotide diversity were calculated only for SNPs extracted from the ‘Feizixiao’ VCF file. Similarly, sRNA and LTR densities were calculated from small RNA annotationL. chinensis, D. longan57, X. sorbifolium58, C. sinensis59 and A. yangbiense67, were extracted using TBtools68 and viewed in JCVI74 (v.0.8.4) with a parameter setting of ‘–cscore = .99’.Protein sequences from five species, DNAs from leaf tissue of 72 lychee accessions were used for the construction of 450-bp paired-end libraries at BGI , and were then sequenced using the Illumina HiSeq2000 platform. In total, 405.4 Gb (average 13×) of data were obtained.75 and adapter trimming using Trimmomatic30 , the reads were mapped to the reference genome using Bowtie2 (ref. 76) (v.2.3.5) with parameters ‘–no-mixed–no-unal’. Reads were then counted using Samtools39 at the whole-genome level and compared between the HY and HH haplotypes.After initial quality control using FastQC37 mem (v.0.7.17). The mapped reads were then sorted according to genomic coordinates using Samtools39. Sequence data generated from different Illumina lanes were combined using ‘samtools merge’. After merging, duplicates were removed using Picard77 (v.2.5.0), and then HaplotypeCaller from the GATK40 (v.3.8) was used to call individual-specific gvcf files. Finally, the GenotypeGVCFs was used for joint calling of SNPs. After quality control of the SNPs using bcftools78, the SNPs were hard filtered using GATK VariantFiltration (DP < 300 || DP > 3000 || QD < 2.0 || FS > 60.0 || MQ < 40.0 || MQRankSum < −12.5 || ReadPosRankSum < −8.0), and only biallelic SNPs were selected for further analysis.Resequencing data for the 72 accessions were mapped to the reference genome using BWA9 software (v.1.3.0). Before analysis, SNPs in LD were dropped out in Plink79 using a sliding window of 50 SNPs with 10 SNP step size, SNPs with R2 >0.1 were filtered out, these settings were the same as recommended in the ADMIXTURE user manual (–indep-pairwise 50 10 0.1). The optimal number of populations (K) was selected using tenfold cross-validation.We carried out analysis of admixture using the ADMIXTURE80 (v.20180901). The SNPs were filtered by >10% minor allele frequency (-m 0.1) and missing rate <40% (-M 0.4) and R2 <0.4 (-l 0.4). The resulting fasta file was used as input to RAxML81 (v.8.2.12), where the tree was run using the GTR-GAMMA model and 1,000 bootstrap replicates, and thereafter rooted by the outgroup species.The data filtered for ADMIXTURE analysis were also used for estimating the population SNP tree with SNPhylo79 . PCA was calculated using SNPs where genotypes were called for over 80% of the individuals. Identity-by-state analysis was carried out in Plink using the ‘–genome full’ option, and inbreeding coefficient with ‘–het’.Population statistics were calculated using Plink82 (v.0.933) with -doFasta 2 -doCounts 1 options. The ancestral states were set as reference in the .vcf file using bcftools fixref, and the ancestral state was also incorporated in the .vcf file information field with bcftools annotate.The ancestral states were obtained by aligning the outgroup species longli, longan and rambutan against the haploid lychee reference assembly using BWA mem (v.0.7.17). The ancestral allele was called using a majority vote in ANGSD28) (v.1.15.3) format using vcf2smc; the repeat regions annotated with RepeatModeler and the SNPs with no ancestral state prediction were filtered out. An unfolded composite likelihood using HNW13 and HNW03 individuals as distinguished samples was estimated for the Hainan population; for the Yunnan population, YNW01 and YNW05 individuals were used. These four individuals had the highest coverage among the populations under inspection. The model parameters for SMC++ (ref. 28) (v.1.15.3) were obtained with threefold cross-validation after fixing the estimated time interval to 10 to 200,000 generations and by varying the mutation rate estimates, as described by Salojarvi et al.83. The population split time was estimated with smc++ split. The predicted demography was produced using smc++ plot and then visualized in R84 (v.3.6.0) using the ggplot2 (ref. 72) package.The .vcf file was converted into SMC++ (ref. 82 (v.0.933) and used as input for Stairway plot85 (v.2.1) with 200 bootstrap estimates.For input data, the .vcf file containing SNPs with ancestral allele calls was filtered for Yunnan and Hainan subpopulations and repeat regions were removed using vcftools. The derived site frequency spectrum was obtained with ANGSD86 was estimated from read mappings where the repeat regions were masked out. Standard parameter settings (N25 -t15 -r5 -p 4 + 25*2 + 4 + 6) were used in estimating the population history.The pairwise sequentially Markovian coalescent model87 (v.2.6.0.3). The two-dimensional derived allele site frequency spectrum was obtained with easySFS (https://github.com/isaacovercast/easySFS), using a .vcf file filtered for repeat regions and sites with no ancestral allele call. A projection to 22 samples was found optimal. In fastsimcoal87 (v.2.6.0.3), three models were compared: (1) a model with constant population sizes and a single split event fitted to data; (2) a model with constant population sizes, with bottlenecks in each of the populations and split time estimated from data; and (3) a model with population growth rates, bottlenecks and split time estimated from data. For each of the models, 100 parameter files were simulated. For each parameter file, 1,000,000 simulations were run; monomorphic sites were not used. Maximum composite likelihood estimation of parameters was carried out with 40 expectation-conditional maximization iterations. The best model (3) was selected using Akaike information criterion.Population split time and the presence of subsequent bottlenecks was assessed with FastsimcoalTo correct the differences in population trajectories due to higher level of inbreeding Yunnan population, the generation time was adjusted to three times the generation time in the Hainan population  was used to estimate kinship among individuals. Relatedness was estimated using the ‘–related’ option and default settings. The method calculates an estimator of the kinship coefficient that is independent of sample composition or population structure based on the difference between shared heterozygosity and shared homozygosity29. The order of relationship was obtained using the ranges recommended in KING user manual, and compared with identity-by-state values calculated with plink. The detected relationships were then illustrated using Apple Keynote.The KINGR2 values >0.01 were reported. In R, a linear model was applied to the data using the ‘lm’ function with log10 transformed distance versus R2 value data, and the point where R2 dropped to 50% of the initial value was reported as the measure of decay. LD levels in wild and cultivated population groups were calculated by PopLDdecay88 (3.40) software using the correlation coefficient (r2) of alleles.For LD analysis, the .vcf file was filtered by dropping out the outgroup species and by selecting SNPs present in all individuals and having minor allele count of greater than four. LD was calculated for a 500 kb window, and all 10 package (v.4.1). The Plink formatted file was first converted into eigenstrat format using the ‘convertf’ function, and then qp3Pop was used to estimate the F3 introgression statistics. The wild individuals were grouped into populations according to sampling location, whereas the cultivars were analyzed separately. Visualization and false discovery rate adjustment of the Z-score statistics were done using R, as in Salojarvi et al.83.F3 statistics were calculated using the Admixtoolsst estimation was carried out using vcftools89 (v.0.1.15) by grouping the wild populations according to sampling site, and cultivars according to maturation time. Fst was estimated in windows of 100 kb, and the average of the Weir and Cockerham weighted Fst values90 was calculated. Nucleotide diversity was estimated for each of the populations, and for all individuals together, using vcftools in 100-kb windows with a 10-kb step size.Fn and πs statistics from whole-genome sequencing data. First, for each population to be assessed, the .vcf file containing all individuals was filtered to contain only the members of the population, and ANGSD82 (v.0.933) was used to calculate sitewise nucleotide diversity. Then, the impacts of SNPs were predicted with SnpEff91 (v.4.3t).We developed a custom pipeline for assessing the πn estimation, all predicted gene models were scanned and only high-quality predictions containing a methionine as the first amino acid and where the total sum length of the exons was divisible by three were selected. The positions with nonsynonymous changes inside high-quality gene models were then selected based on ‘high’ or ‘moderate’ SNP effect annotation using SnpEff91 (v.4.3t). The nucleotide diversities in the identified positions were then summed together using a custom script in R with a parallel implementation for higher speed. In the presence of missing data, the mean nucleotide diversity estimate is heavily affected by the number of called SNP positions. To obtain this number, GATK40 (v.3.8) GenotypeGVCFs was run by also calling monomorphic positions, and the resulting .vcf files were filtered using the Phred quality thresholds used for filtering the .vcf file containing only variant positions in vcftools. For the πn estimate the number of called positions inside high-quality gene model regions was then calculated. We operationally assumed that every third position in the exons caused a synonymous mutation and therefore multiplied the number of nucleotide calls inside high-quality gene models by two-thirds.For πs estimation, all regions with predicted gene models were filtered out from the ANGSD82 (v.0.933) file containing sitewise nucleotide diversities. To obtain the number of called positions, GATK40 (v.3.8) GenotypeGVCFs was run by also calling monomorphic alleles and the resulting .vcf file was filtered using the Phred quality thresholds used for filtering the .vcf file containing only variant positions. Subsequently all regions with predicted gene models were filtered out. The sums of diversities outside of gene models were then calculated using a script in R with parallel implementation for higher speed. and the sums were then divided by the number of called positions.For π92. All values are comparable with other plant species as well as the πn/πs ratio. The draft pipeline containing the R codes is given in Zenodo93.To verify that the values obtained from the pipeline are correct, they were compared with results from several species reported in Chen et al.91 (v.4.3t), and processed with custom made bash and R scripts. The significance of the difference between the counts for different populations was assessed in R using ANOVA and TukeyHSD post hoc analysis; ANOVA was used since the normality assumption of the residuals could not be rejected .In addition to nucleotide diversity calculation, also the actual numbers of SNPs with different functional impact were quantified. The impacts of SNPs were first predicted using SnpEff94 (v.5.2.52).For GWAS analysis, 62 accessions had fruit maturation period recorded, which were encoded ‘1’ for extremely early maturing and early maturing accessions and ‘0’ for late-maturing accessions. Comparing with a general linear model (glm), we accepted a mixed linear model (mlm) with structure result , and kinship together with PCA results to conduct association analysis using TASSEL95 was downloaded from the National Center for Biotechnology Information (NCBI). Population resequencing reads from different lychee accessions were mapped against the reference chloroplast genome using Bowtie2 (ref. 76) (v.2.3.5), and only those with both pair-end reads mapping were retained. We randomly selected approximately ×150 reads in depth to the reference chloroplast genome to construct new chloroplast genomes using MIRA96 (v.4.0.2) and MITObim97 (v.1.9.1) software with default parameters. The chloroplast genomes of different lychee accessions were retrieved from the last iterations and aligned using MAFFT98 (v.7.429). All gaps were removed using trimAl99 (v.1.4.rev22), and a maximum likelihood phylogenetic tree was constructed with complete chloroplast genome sequences using IQ-TREE100 (v.1.6.10).A reference lychee chloroplast genome from previous publicationRemaining method descriptions are included to Supplementary Note Further information on research design is available in the Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41588-021-00971-3.Supplementary InformationSupplementary Notes I–III, Note Figs. 1–3 and Figs. 1–13.Reporting SummarySupplementary Table 1Supplementary Tables 1–36 are included in this file.Peer Review Information


Thirteen ROH islands were detected in our study, and 86 genes were found within those regions. Some of these genes were correlated with economically important traits, such as meat quality , immunity capacity , production , and reproduction (ODF1 and CDK2). A total of six significant Gene Ontology terms and nine significant Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were identified, most of which were correlated with disease resistance and biosynthesis processes, and one KEGG pathway was related to lipid metabolism. In addition, we aligned all of the ROH islands to the pig quantitative trait loci (QTL) database and finally found eight QTL related to the intramuscular fat trait. These results may help us understand the characteristics of Laiwu pigs and provide insight for future breeding strategies.Laiwu pigs, distinguished by their high intramuscular fat of 7–9%, is an indigenous pig breed of China, and recent studies also found that Laiwu pigs showed high resistance to Porcine circovirus type 2. However, with the introduction of commercial varieties, the population of Laiwu pigs has declined, and some lineages have disappeared, which could result in inbreeding. Runs of homozygosity (ROH) can be used as a good measure of individual inbreeding status and is also normally used to detect selection signatures so as to map the candidate genes associated with economically important traits. In this study, we used data from Genotyping by Genome Reducing and Sequencing to investigate the number, length, coverage, and distribution patterns of ROH in 93 Chinese Laiwu pigs and identified genomic regions with a high ROH frequency. The average inbreeding coefficient calculated by pedigree was 0.021, whereas that estimated by all detected ROH segments was 0.133. Covering 13.4% of the whole genome, a total of 7,508 ROH segments longer than 1 Mb were detected, whose average length was 3.76 Mb, and short segments (1–5 Mb) dominated. For individuals, the coverage was in the range between 0.56 and 36.86%. For chromosomes, SSC6 had the largest number ( Laiwu pigs, a precious Chinese indigenous pig breed, are mainly distributed in Shandong Province, China. They are distinguished by a good meat quality, especially a high intramuscular fat (IMF) content of 9–12%, compared with the major commercial lean pig breeds . Recent In addition, during the last 40 years, with the introduction of western pig breeds such as Duroc and Large White pigs, the Laiwu pig population has shrunk, and some lineages have disappeared. This caused a decrease in both the nucleotide diversity and additive genetic variance in the Laiwu pig populations and led to inbreeding, which can undermine fitness traits due to the expression of highly homozygous detrimental recessive alleles . Thus, mFROH)  , which hFROH) . Runs ofFROH) . InbreedFROH) , indicatFROH) , and theFROH) .In addition to inbreeding, other population events can also result in the formation of ROH, including genetic drift, population bottleneck, and selection . Thus, tThe purposes of this study were to detect and characterize the ROH patterns in Laiwu pig populations using the data obtained by the Genotyping by Genome Reducing and Sequencing (GGRS)  method, According to the genealogical records, 93 unrelated or distantly related Laiwu pigs  were selected from the National Conservation Farm for Laiwu Pigs in Shandong Province, China. DNA samples were extracted from their ear tissues with commercial kits  and then genotyped using the GGRS method .The GGRS method includes the following steps: First, the DNA sequencing libraries were prepared. Next, they were sequenced using paired-end (2 × 150 bp) sequencing methodology with an Illumina HiSeq2000 sequencer. Quality control for this procedure was conducted by filtering raw reads, as reads with base average quality scores less than 30  were excluded using NGS QC Toolkit v2.3 . The othHo) and expected heterozygosity (He) by PLINK v1.9  50 FPED) for all individuals (FROH) (FSNP). FROH was calculated according to the following formula:We used several different methods to calculate inbreeding coefficients according to the pedigree and genomic information, and these inbreeding coefficients were further compared by Pearson’s correlation analysis. The R package “pedigree” was used to estimate the inbreeding coefficients based on pedigree   and fromLROH is defined as the total length of the genome covered by all ROH segments for each individual, and Lauto is the length of the sequenced genome, which equaled 2.26 Gb in this research. Following the study of FROH into four types of estimates based on the length of the observed ROH: FROH_all (>1 Mb), FROH1–5 Mb (1–5 Mb), FROH5–10 Mb (5–10 Mb), and FROH>10 Mb (>10 Mb). It has been reported that a lower limit of ROH length of 1 Mb has a genetic distance of 1 cM approximately  to trace the ancestral inbreeding events that occurred 50 generations ago. Similarly, the corresponding spans for the last three FROH estimates are 10 to 50 generations, 5 to 10 generations, and 5 generations.where ximately , which aFSNP1, FSNP2, and FSNP3) with the option “-ibc”  and n is the amount of SNPs.In which Y1. Further analysis of gene functions was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses by DAVID 6.82, and terms with a p-value greater than 0.05 were filtered.To detect the ROH islands, we calculated the frequency of occurrences within the ROH regions of each SNP across the individuals and made a Manhattan figure by plotting these values in conformity with the position of each SNP on chromosomes. The SNPs in the top 1% of the frequency of occurrence were selected as a hint of a potential ROH island . A chainHo and He of this Laiwu pig population were 0.27 and 0.38, respectively. The value of Ho is lower than the result reported before (0.284), whereas He is higher (0.281)  . In addiFROH>10 Mb < FROH5–10 Mb < FROH1–5 Mb. Compared with them, FPED, the average inbreeding coefficient calculated from the pedigree, was the lowest (0.021).The inbreeding coefficient of each individual estimation based on pedigree, ROH of different lengths, and the SNPs are shown in FROH_ was strongly correlated with the other ROH-based estimates, and the correlation coefficients between FROH_ all and FROH1–5 Mb were the highest (0.910), whereas the weakest correlation among the ROH-based estimates was between F5–10 Mb and F>10 Mb, which was 0.593. The correlation among all the values calculated by the SNPs was strong and statistically significant. However, the correlations between the estimates based on the SNPs and ROH data were relatively strong, except for FSNP2, which was significantly correlated with all of the ROH-based inbreeding coefficients.The correlation coefficients among all of the results estimated by different methods are shown in In our study, 7,508 ROH segments were found in 93 individuals, with an average length of 3.76 Mb. The segment with the longest segment (45.15 Mb) was SSC15, which contains 978 SNPs. In contrast, the shortest segment (1 Mb) only consisted of 265 SNPs . In tota5–10 Mb had a lower quantity of 1,197 , and it made up 28.81% of the total ROH length. The number of long ROH segments was the lowest, only 5.57%, but it still had a coverage ratio of 22.76%.The statistics of ROH number and length classified by ROH length are shown in For individuals, high variability was observed in both the number of ROH segments and the genome coverage; the former varied from 6 to 166, and the latter varied from 0.56 to 36.86%. For chromosomes, the number and coverage of ROH on each chromosome are shown in To identify the genomic regions with high ROH frequency, we first calculated the frequency of SNP occurrence in the ROH and selected the top 1% among those with the highest frequency (present in at least 33.3% of the samples) . There wECI1, LRP12, NDUFA4L2, GIL1, and LYZ), immunity capacity , production , and reproduction (ODF1 and CDK2). All genes found were further subjected to GO and KEGG enrichment analyses. Six significant GO terms and nine significant KEGG pathways are shown in Thirteen ROH islands were detected in our study, and there were 86 genes within those regions according to the University of California, Santa Cruz database . The posIn our study, the number of small ROH segments was the highest, whereas the number of medium and large segments was much lower, especially large segments (>10 Mb). Small ROH can reflect ancestry inbreeding history, whereas large segments are usually formed by recent inbreeding. These results indicated that both ancient and recent inbreeding events might affect this Laiwu population, but recent inbreeding was not frequent. However, it should be noted that the length of ROH may not always reflect the generations from inbreeding events that took place, as randomness exists during the formation of ROH, which can be affected by the dynamic rates of recombination, as well as the stochastic nature during gamete formation . Kirin eThe variability of the number of ROH among individuals was high, which coincides with the results of studies on cattle and sheep . ROH numThe SNPs used to detect ROH regions were obtained from GGRS, a high-throughput reduced representation sequencing method in our study. It is a cost-effective approach and is designed for outbreed population, making it suitable for the population of Laiwu pigs in our research. However, the challenge is that the distribution of the data generated by it is unbalanced among individuals, which often causes a large portion of missing genotypes, which would impact subsequent analysis. To address this issue, imputation is inevitably used, and it has been included in the GGRS protocol. After imputation, we have also taken quality control measures, including filtering out the SNPs with a MAF < 0.05. This filter is necessary to ensure the precision of imputation .There might be concerns about the necessity and appropriateness of imputation and the subsequent filtration with MAF, but several researchers have conducted studies that may help address such concerns. Imputed data have been used to detect ROH as found in several published works . It has To date, few studies have been done comparing the performance of software used for ROH detection and the ways of determining appropriate parameters . ActuallFPED only indicates the expectation of homozygosis, whereas FROH and FSNP can reflect the realized homozygous loci  can be found in the article/YP and QWe designed the experiment. YF, XH, ZX, and HS performed the experiment. QZ, PM, and ZZ developed some analysis software used in this study. YF wrote the manuscript with RC’s kind help. All authors have read and approved the final manuscript.YS, ZQ, and QWe were employed by Breeding Company of Pigs, Laiwu, China. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.