A complex set of perturbations occur in cytokines and hormones in the etiopathogenesis of obesity and related cardiometabolic conditions such as type 2 diabetes (T2D). Evidence for the genetic regulation of these cytokines and hormones is limited, particularly in African-ancestry populations. In order to improve our understanding of the biology of cardiometabolic traits, we investigated the genetic architecture of a large panel of obesity- related cytokines and hormones among Africans with replication analyses in African Americans.n = 7990). The effects of sex, body mass index (BMI), and T2D on results were investigated through stratified analyses.We performed genome-wide association studies (GWAS) in 4432 continental Africans, enrolled from Ghana, Kenya, and Nigeria as part of the Africa America Diabetes Mellitus (AADM) study, for 13 obesity-related cytokines and hormones, including adipsin, glucose-dependent insulinotropic peptide (GIP), glucagon-like peptide-1 (GLP-1), interleukin-1 receptor antagonist (IL1-RA), interleukin-6 (IL-6), interleukin-10 (IL-10), leptin, plasminogen activator inhibitor-1 (PAI-1), resistin, visfatin, insulin, glucagon, and ghrelin. Exact and local replication analyses were conducted in African Americans  loci across all 13 traits. Notably, 14 loci were African-ancestry specific. In this first GWAS for adipsin and ghrelin, we detected 13 and 4 genome-wide significant loci respectively. Stratified analyses by sex, BMI, and T2D showed a strong effect of these variables on detected loci. Eight novel loci were successfully replicated: adipsin (3), GIP (1), GLP-1 (1), and insulin (3). Annotation of these loci revealed promising links between these adipocytokines and cardiometabolic outcomes as illustrated by rs201751833 for adipsin and blood pressure and locus rs759790 for insulin level and T2D in lean individuals.GWAS identified 39 significant (Our study identified genetic variants underlying variation in multiple adipocytokines, including the first loci for adipsin and ghrelin. We identified population differences in variants associated with adipocytokines and highlight the importance of stratification for discovery of loci. The high number of African-specific loci detected emphasizes the need for GWAS in African-ancestry populations, as these loci could not have been detected in other populations. Overall, our work contributes to the understanding of the biology linking adipocytokines to cardiometabolic traits.The online version contains supplementary material available at 10.1186/s13073-021-00971-2. A complex system of cytokines and hormones is implicated in the pathogenesis of obesity and related cardiometabolic conditions, such as insulin resistance and type 2 diabetes (T2D) . CytokinThe role of these cytokines and hormones in the etiology of obesity and T2D is highly complex. Dysfunction of cytokines and hormones such as leptin, ghrelin, insulin, GLP-1, resistin, and visfatin has been implicated causally in the pathogenesis of obesity but also as a mediator between excess fat mass and insulin resistance, T2D, and cardiovascular disease –6. ThereTwin studies report that the circulating levels of obesity-related cytokines and hormones are highly heritable , 19. ThiWe used data on 4432 sub-Saharan Africans from the Africa America Diabetes Mellitus (AADM) study for the discovery cohort. The AADM study is a cross-sectional study comprising data on individuals residing in sub-Saharan Africa and has been described in detail elsewhere –31. DataReplication analyses were performed using data on 7990 AA from five different cohorts: the Howard University Family Study (HUFS) , the MulP value ≤ 1 × 10−6). Imputation for all cohorts was performed using the African Genome Resources Haplotype Reference Panel via the Sanger imputation Service [Genotyping for the AADM study was performed using either the Affymetrix Axiom PANAFR SNP array or the Illumina’s Multi-Ethnic Global Array (MEGA) . Quality Service . Quality Service  and iden Service .2 (kg/m2). Type 2 diabetes (T2D) status was determined in all cohorts using the American Diabetes Association (ADA) criteria. This entailed a fasting plasma glucose cut-off of ≥ 7.0 mmol/L (126 mg/dL), or a 2-h post load value of ≥ 11.1 (mmol/L) on an oral glucose tolerance test (OGTT) on more than one occasion, or the reported use of glucose-lowering medication as prescribed by a physician confirmed by review of clinical records.In all cohorts, demographic data such as age and sex were obtained through questionnaires. Height and weight were measured in light clothing and without shoes to the nearest 0.1 cm and kg. Body mass index (BMI) was calculated as weight/heightn = 594). PAI-1 was measured by a two-site sandwich ELISA in all three cohorts [n = 7645). Insulin was measured via a radioimmunoassay method using the Linco Human Insulin Specific RIA kit (Linco Research) in the MESA and in the ARIC study, and using the Vitros 950 or 250, Ortho-Clinical Diagnostics analyzer  in the JHS. IL-6 was available in MESA, CFS, and HUFS (n = 2517) and measured using ELISA in all three cohorts .In the AADM study, the obesity and diabetes-related cytokines and hormones adipsin, GIP, GLP-1, leptin, PAI-1, resistin, visfatin, glucagon, and ghrelin were measured on fasting serum samples using multiplex bead-based flow cytometric immunoassays—containing dyed microspheres linked with monoclonal antibodies specific for each protein plex—according to the manufacturer’s instructions . These commercial kits measure the levels of cytokines and hormones involved in human obesity and diabetes. Data were collected using Bio-Plex 200®System  equipped with Bio-Plex Manager™ Software . IL-1RA, IL-6, and IL-10 were analyzed using Enzyme-Linked Immuno Sorbent Assay (ELISA) . Insulin was measured by electrochemiluminescence immunoassay (ECLIA) on Roche Modular-E or Elecsys 2010 analyzers . The obesity and diabetes-related cytokines and hormones available in the AA replication cohorts differed by cohort. Ten cytokines and hormones were only available in the HUFS dataset   and based on this GRM we filtered for relatedness > 0.2 as the GCTA-GREML method assumes unrelated individuals, which removed 1382 individuals. Age, sex, T2D, and the first three PCs were included as covariates in heritability analyses. To perform genome-wide quantitative linear regression analyses on each of the traits, we used the EPACTS software package (version 3.2.6) [EPACTS. Inclusion of the genotyping array as a covariate in our analysis models was evaluated but array was not included in final models as this adjustment did not alter findings. Bayesian colocalization analyses were performed on GWAS summary statistics with expression Quantitative Trait Loci (eQTL) data using the coloc package in R version 4.0.5 [2) and overweight (including obese) individuals (BMI ≥ 25 kg/m2), and T2D controls and T2D cases. A P value of < 5 × 10−8 was considered genome-wide significant in all discovery analyses. Loci passing a more stringent threshold taking into account the multiple traits and the stratification have been annotated in the tables . The sex, BMI, and T2D strata were each meta-analyzed in METAL [P values for all variants identified in stratified analyses. Variants identified, in either the base (non-stratified) model or the stratified models, that are observed in African populations but are not observed in populations without African ancestry in the Allele Frequency Aggregator (ALFA) database [n = 2082) were excluded from all fasting insulin analyses. Lastly, the list of genes from the base model was submitted to Ingenuity Pathway Analysis (IPA)  for canonical pathway analyses through the use of the core analysis feature of IPA [P values of overlap, using the right-tailed Fisher’s exact test, where we considered a P value of < 0.05 as statistically significant.Heritability of all 13 obesity and diabetes-related cytokines and hormones was calculated using the Genome-wide Complex Trait Analysis – Genomic-Relatedness-based restricted Maximum-Likelihood (GCTA-GREML) approach  on the iLINK 1.9 . The GCTn 3.2.6) . Estimaton 4.0.5 , 47. Expon 4.0.5 . Colocalin METAL  to obtaidatabase  were condatabase  on the ie of IPA . IPA calEPACTS), models, and transformations were used to perform genome-wide quantitative linear regression analyses in each of the replication cohorts. For HUFS, the first PC was included as a covariate in all models and the first two PCs were included for the other replication cohorts, based on assessment of residual population stratification, which was low after adjusting for the respective PCs. For PAI-1, insulin, and IL-6, subsequent fixed-effects meta-analyses were conducted with the METAL software [P values were also calculated using METAL. To harmonize, the first two PCs were included in all models and cohorts that were merged in meta-analyses. Either HUFS linear regression results  or combined meta-analyses results  were subsequently used for follow-up analyses. First, we performed exact replication, where we evaluated per trait and per model the genome-wide significant loci in AADM for significance  in the replication cohorts. Secondly, local replication was performed based on a linkage disequilibrium (LD) block of 500 kb around the AADM significant variants, retaining only those variants with a r2 of > 0.3. To determine statistical significance in the local replication, P value thresholds were adjusted for the effective number of variants  in the LD block as described by Ramos et al. [Neff was estimated by spectrally decomposing the covariance matrix and then using the following formula: Neff = (∑k = 1Kλk)2/(∑k = 1Kλ2k)2, in which λk is the kth eigenvalue of the K × K covariance matrix for the K SNPs. The nominal significance threshold α = 0.05 was subsequently divided by Neff to obtain the adjusted P value thresholds. In both exact and local replication, only variants below the P value thresholds that had the same direction of effect as in the discovery cohort were considered replications. In addition, in silico replication was performed by lookup in the GWAS catalog (https://www.ebi.ac.uk/gwas/) [The same association analysis software  . A 500 k2) compared with AA (ranging from 29.7 kg/m2 in ARIC to 34.5 kg/m2 in CFS). Half of AF participants had T2D due to the T2D case-control study design of AADM, which was also reflected in the mean BMI being higher than expected for the general population of the countries sampled. The T2D prevalence in the AA cohorts ranged from 11.2% (HUFS) to 28.3% (CFS). Mean circulating levels of GLP-1, PAI-1, visfatin, and IL-1RA were higher in AF than in AA. For leptin, GIP, resistin, insulin, and ghrelin, mean circulating levels were higher in AA compared with AF. Mean circulating levels were similar between AF and AA for adipsin, IL-6, IL-10, and glucagon.The discovery cohort included 4432 continental Africans (AF) enrolled from Nigeria, Ghana, and Kenya as part of the Africa America Diabetes Mellitus (AADM) study who had at least one obesity and diabetes-related cytokine or hormone measure available Table . Out of We calculated SNP-heritability for each of the adipocytokines and hormones in AF. Heritability was highest for glucagon (70.6%) and lowest for PAI-1 (13.3%) Table . No reliThe SNP heritability estimates for the adipocytokines and hormones in AF differ for several traits from what has been reported among other populations Table . HeritabP value < 5 × 10−8 and four loci that passed a more stringent P value threshold adjusted for the number of traits and the stratification  on chromosome 2, replicated in AA at a P value of 0.002 . Across adipocytokines and hormones, associations were detected with 14 African-specific variants, i.e., variants that are observed in African populations but are not observed in populations without African ancestry in the Allele Frequency Aggregator (ALFA) database  and the leptin loci for the lean and overweight models  when meta-analyzed together  for insulin, which has been reported previously in European-ancestry populations [P value of 0.024) and KIR3DL1 on chromosome 19   and the AG genotype . The variant annotated to KIR3DL1 did show a significant differential effect on adipsin in men compared with women for both the AA genotype  and for the AG genotype  compared with the GG genotype. A locus associated with GLP-1 in men (GLTSCR1) was replicated in AA  . The association of this locus with GLP-1 also had a larger effect size in men than in women for both the CC genotype  and the AG genotype  compared with the AA genotype. For insulin, two men-specific novel loci were replicated in AA: locus NALCN-AS1 on chromosome 19   . One women-specific intergenic locus replicated in AA , but in LD-based local replication the variant rs62460948 was replicated at P value 0.021 . This locus showed a larger effect size in women for both the CC genotype  and the CT genotype  compared with the TT genotype.Sex was most relevant for GIP and PAI-1, for which we respectively detected nine and five genome-wide significant loci in women, but none among men  and overweight (BMI ≥ 25 kg/m2) individuals. We replicated one novel lean-specific locus associated with insulin   that showed that the AC genotype compared with the CC genotype had a significantly larger effect size for the association with insulin in lean compared with overweight individuals. The effect of the AA genotype compared with CC did not significantly differ between lean and overweight individuals . The replicated overweight-specific locus for adipsin did not show differential associations between lean and overweight individuals  due to reduced sample size in the lean stratum (n = 1547) compared with the overweight (n = 2166)  or weakly (r2 = 0.20–0.39) correlated with each other , whereas none of the adipocytokines and hormones showed high correlation (r2 ≥ 0.60).Both phenotypic and genetic correlation between the studied cytokines and hormones was low. Most adipocytokines and hormones were very weak . The interleukins  were excluded from factor analyses because of low sample size. Factor analysis was performed on the 1538 samples that had complete data on all 10 remaining adipocytokines and hormones and revealed four factors with an eigenvalue of < 1 that combined explained 65% of variance. Traits GLP-1, glucagon, and GIP were related to the first factor, PAI-1 and visfatin to the second factor, ghrelin and adipsin to the third factor, and insulin and leptin to the fourth factor. Resistin did not relate well to any of the factors. Figure The Bartlett test of sphericity indicated that sufficient correlation existed to perform factor analysis (R2 = 0.0004). The GLP-1 variant is annotated as in an intron of WDR89 and the leptin variant is intergenic. In the stratified models, we found three additional loci that were physically close of which one had an R2 > 0.3. Variant rs75275574, associated with visfatin in T2D cases, was 12 kb upstream of rs552964654, associated with IL-1RA in T2D controls (R2 = 0.34).In addition, there was little evidence of pleiotropic loci influencing multiple adipocytokines and hormones. In the base model, only one locus overlapped (within 500 kb region) in the results across traits. GLP-1 variant rs1445210817 is 51.4 kb away from leptin variant rs113453972. These variants were not in LD  to perform pathway analysis of genome-wide significant loci in the base model. We found three significant enriched canonical pathways when we evaluated genes annotated to genome-wide significant loci   included more than one gene; the Adipogenesis Pathway included the LEP gene from the leptin analysis and the LPIN1 gene from the Adipsin analysis  was replicated in AA. Fourteen of the total 39 identified loci were African-ancestry specific. In addition, we found that genetic loci associated with the studied adipocytokines and hormones vary greatly between strata of phenotypes: men, women, lean, overweight, T2D controls, and T2D cases. An additional 237 loci were discovered in stratified models and 8 loci from stratified analyses were replicated in AA. Lastly, we observed low phenotypic as well as low genetic correlation between the traits.In this work, we report the genetic architecture of thirteen obesity- and diabetes-related cytokines and hormones in continental Africans. The heritability of these adipocytokines and hormones ranged from 13.3% for PAI-1 to 70.6% for glucagon. We report the first GWAS for these adipocytokines and hormones in Africans and for adipsin, ghrelin, and visfatin this is the first GWAS for these three adipocytokines reported in any population. We found 39 associations across traits, of which 36 represent novel loci. For ten of these 36 novel loci, replication could not be attempted because of low MAF in AA and out of the remaining 26 one adipsin locus . Both loci were associated with adipsin. Variant rs199890456 is African-ancestry specific and evaluation of the regional plot for this intergenic locus . We, therefore, urge caution in interpreting this finding. Locus AC092684.1 is one of four replicated loci that have previously been reported in relation to cardiometabolic traits in other populations and their annotation suggests possible mechanisms by which these variants may influence cardiometabolic traits. This locus, which we not only replicated in the base model but also in the overweight stratum for adipsin, has previously been associated with blood pressure among those with European ancestry, Asian ancestry and AA [AC092684.1 on both adipsin levels and blood pressure could explain variation in adipsin levels in relation to cardiometabolic traits. Using Haploreg v4.1 [Two novel loci in the base model passed the stringent threshold for genome-wide significance  within the KIR3DL1 locus reached genome-wide significance in AF men for adipsin and one of these, rs527885640, replicated in AA. rs78852323 and rs527885640, annotated to an intron of KIR3DL1, are African-ancestry specific and the alternative alleles were associated with lower adipsin levels. rs527885640 has previously been associated in Finnish-ancestry individuals with IL-7, an interleukin that plays an important role in B and T cell development as part of the immune respons e[JDP2 and NFE2L 1[NFE2L1 was found to disrupt glucose metabolism and impair insulin secretion [Five variants in LD with each other  [Intergenic insulin locus rs73216105, which was detected and replicated in men, has been reported in relation to peak insulin response and acute insulin response in Hispanic, Europeans, and Pima Indians, but it did not reach genome-wide significance in these analyses (ombined) . Multiplombined) , 81–84.P value threshold adjusted for the number of traits and the stratification. For 17 of these 33 loci, data were available through the GTEx Portal V8  [TBCD (rs139302892), associated with GIP in women, is an eQTL for TBCD in adipose tissue, whole blood, and muscle tissue, and for FN3KRP in multiple tissues including colon, adipose, heart, whole blood, and pancreas. The FN3KRP gene has been tentatively associated with glucose metabolism and T2D [FAHD2CP in muscle and heart tissue, which is a pseudogene. The insulin locus RP11-31F15.2 in lean individuals is an eQTL for SLC16A1 in adipose tissue. Overexpression of SLC16A1 has in mouse pancreatic β cells been found to induce hyperinsulinism during exercise [SLC24A4 in skin. An intergenic locus for adipsin in T2D cases (rs2853934) has been reported as an eQTL for 15 genes in multiple tissues including 4 HLA genes .Thirty-three novel loci across stratified models passed a more stringent 3, 2020)  of which and T2D . Intergeexercise . Locus rP value threshold and that was replicated in lean individuals in our analyses is an eQTL for 18 different genes across multiple tissues. Most notable is the eQTL GPAT2 in muscle, heart, and adipose tissue. GPAT2 codes for the enzyme glycerol-3-phosphate acyltransferase (GPAT), which a rate-limiting enzyme in glycerolipid biosynthesis. GPATs have been reported to play a critical role in the development of obesity and insulin resistance [An intergenic insulin locus (rs759790) that passed the stringent sistance . This sasistance . An incrsistance , suggestDifferences in environmental and genetic background between AF and AA may have limited our replication efforts. The AF participants of our discovery cohort were recruited in Ghana, Nigeria, and Kenya, which greatly differ in physical environment and related health behaviors such as physical activity, dietary intake, and smoking from the AA participants in the replication cohorts who reside in the USA. These environmental and genetic background differences between AF and AA highlight the need for more studies in AF in order to be able to replicate findings within this population.I-square value. The higher number of detected loci in the stratified models may, therefore, be an indication of the importance of sex, BMI, and T2D on these phenotypes: ignoring these factors by considering them in unstratified models may hamper the ability to detect and replicate association signals. Furthermore, the striking differences in detected loci between sub-groups of phenotypes  suggest that genetic variants for obesity and diabetes-related cytokines and hormones are specific to sub-groups. Previous studies have reported sex-specific SNP-trait associations for coronary artery disease and Crohn’s disease [Detection of loci linking adipocytokines and hormones to cardiometabolic traits was increased in the sex-stratified and BMI-stratified models. In addition, replication ability was higher in the sex-stratified model (13.3% replicated) compared with the non-stratified base model (3.8%) and the BMI-stratified models (3.6%). By stratifying we reduced the variability in the environmental context, which may have limited replication in the non-stratified model. These strata also show statistically significant heterogeneity according to an  disease , longevi disease , anthrop disease , and lip disease . It has  disease . In this disease , 94. In Despite efforts to increase diversity in genomics studies, African-ancestry populations are still underrepresented in GWAS , 96. OurA recent study reporting a GWAS of a cytokines network found shared causal variants between cytokines . In contIn conclusion, our findings contribute to the growing body of evidence on the genetic basis of obesity- and diabetes-related cytokines and hormones. The loci we identified and replicated for several cytokines and hormones provide insight into how these cytokines and hormones may influence cardiometabolic traits. The high number of African-specific loci detected emphasizes the need for GWAS in African-ancestry populations, as these loci could not have been detected in other populations. The strong effect of sex, BMI, and T2D status on GWAS findings highlights that stratified analyses facilitate the discovery of novel loci that have previously been undetected in combined analyses.Additional file 1: Table S1: Number of samples per cohort and per obesity-related cytokine and hormone. Table S2: Total number of genome-wide significant  loci detected in the discovery cohort, number of novel loci, and number of loci replicated in African Americans per trait and per model. Table S3: Variants at genome-wide significance of P-value < 5×10-8 with low MAF (<5%) before and after removal of high leverage points based on Cook's distance. Table S4: Variants at genome-wide significance of P-value < 5×10-8 per adipocytokine/hormone for the meta-analysis of discovery and replication cohorts combined. Table S5: Genome-wide significant loci from base model analyses with moderate or strong evidence for colocalization with eQTL data from GTEx. Table S6: Variants at genome-wide significance of P-value < 5×10-8 per adipocytokine/hormone for men only. Table S7: Variants at genome-wide significance of P-value < 5×10-8 per adipocytokine/hormone for women only. Table S8: Variants at genome-wide significance of P-value < 5×10-8 per adipocytokine/hormone for lean (BMI < 25.0 kg/m2) individuals only. Table S9: Variants at genome-wide significance of P-value < 5×10-8 per adipocytokine/hormone for overweight (BMI ≥ 25.0 kg/m2) individuals only. Table S10: Variants at genome-wide significance of P-value < 5×10-8 per adipocytokine/hormone for T2D controls only. Table S11: Variants at genome-wide significance of P-value < 5×10-8 per adipocytokine/hormone for T2D cases only. Table S12: Canonical pathways for genes annotated to genome-wide significant and suggestive loci in the base modelAdditional file 2. Fig S1: Principal Components 1 and 2 in continental Africans from the AADM study (A) and for the AADM study combined with 1000 Genomes Project populations (B), colored by ethnic group. Fig S2: Regional plots of all loci detected in the base model  and all replicated loci from stratified models. Fig S3: Manhattan plots for meta-analysis of discovery and replication cohorts combined for all 13 obesity- and diabetes-related cytokines and hormones. Fig S4: Miami plots for sex-stratified analyses for all 13 obesity- and diabetes-related cytokines and hormones. Fig S5: Miami plots for analyses stratified on BMI status for all 13 obesity- and diabetes-related cytokines and hormones. Fig S6: Miami plots for T2D status-stratified analyses for all 13 obesity- and diabetes-related cytokines and hormones.


We used the single-trait model and the two-trait model to conduct single-step genome wide association study (ssGWAS) on seven body size traits; a total of 198 significant SNPS were finally identified according to the P-value and the contribution to the genetic variance of individual SNP. 11 candidate genes  were found to be associated with body size traits in pigs; KDM6B and CHD3 jointly affect AC and CC, and MUC13 jointly affect RW and CW. These genes are involved in the regulation of bone growth and development as well as the absorption of nutrients and are associated with obesity. HMGA1 is proposed as a strong candidate gene for body size traits because of its important function and high consistency with other studies regarding the regulation of body size traits. Our results could provide valuable information for pig breeding based on molecular breeding.The body shape of a pig is the most direct production index, which can fully reflect the pig’s growth status and is closely related to important economic traits. In this study, a genome-wide association study on seven body size traits, the body length (BL), height (BH), chest circumference (CC), abdominal circumference (AC), cannon bone circumference (CBC), rump width (RW), and chest width (CW), were conducted in Yorkshire pigs. Illumina Porcine 80K SNP chips were used to genotype 589 of 5,572 Yorkshire pigs with body size records, and then the chip data was imputed to sequencing data. After quality control of imputed sequencing data, 784,267 SNPs were obtained, and the averaged linkage disequilibrium ( Pork is widely used as an important animal protein resource and has become one of the main sources of human protein. Commercial pigs  have the characteristics of fast growth, high feed utilization rate, high lean meat rate, and obvious economic benefits. Therefore, it is not only used for a large number of breeding production, but also has been the focus of research. The body size trait is an important phenotypic trait that can reflect the overall appearance of animals. Compared with the description of physical appearance, body size traits can objectively reflect the response of pigs to their environment and other aspects . In pig 1. Although a range of research has been done in QTL mapping, wide confidence intervals (covering more than 20 CM) for the positions of QTL remain that have rarely been replicated  at the China Agricultural University. The IACUC of the China Agricultural University approved this study (permit number DK996).Yorkshire pigs born from 2013-2016 from one pig breeding farm in Beijing were collected in this study. A performance test on seven body size traits were carried out at the body weight of about 100 kg for pigs. In total, 5,572 Yorkshire pigs with phenotypic records and pedigree information were selected. The seven body size traits included body length (BL), body height (BH), chest circumference (CC), abdominal circumference (AC), cannon bone circumference (CBC), chest width (CW), and rump width (RW). P < 10–6) were excluded and only variants located on autosomes were used for further analysis. SNP with call rates less than 0.95 were removed. Individuals with call rates less than 0.90 were excluded. In addition, in order to decrease the influence of the dependence of adjacent markers on the high false positive of GWAS analysis, the SNP were further pruned, and the SNP with linkage disequilibrium (r2) in slide window of 50 SNPs less than 0.9 were selected. Finally, all the genotyped animals and 784267 SNPs were retained.In this study, 589 out of 5572 Yorkshire pigs with body size records were genotyped using the PorcineSNP80 Bead Chip , which includes 68,528 SNPs across the whole pig genome. In order to improve the marker density, the genotyped animals with another 6103 pigs genotyped with PorcineSNP80  were impAccording to the information of 5,572 pigs in this study, the restricted maximum likelihood method (AI-REML) in DMU v6.0 software  was usedThe animal model was used to estimate the genetic parameters:withy is the vector of phenotypic values of each body size trait; μ is the population mean; b is the fixed effect of herd-year-season; a is the vector of additive genetic effects; t is the covariate vector of body weight effects; and e is a vector of residual error effects. X, 1,Z and 2Z are incidence matrices associating b, a, and t with y, respectively. A is the genetic relationship matrix, five generations of pedigree were traced back to construct A, and a2σ is the additive genetic variance. I is the identity matrix of appropriate dimension, t2σ is the variance of body weight effect, and e2σ is the residual variance.where, As the following two-trait model was constructed, the same designs and similar methods were used to combine seven body size traits to construct a matrix equation to form a seven-trait model, which was mainly used to calculate the genetic correlationwhere all letter representations are the same as the single trait model above and the subscript numbers 1 and 2 represent the two body size traits.Subsequently, genetic correlations were calculated based on the variance components as follows:A is the genetic correlation between trait 1 and trait 2, a1 and a2 represent the additive genetic values of trait 1 and trait 2 for the same individuals, cov  and σa1, σa2 refer to the genetic covariance of two traits and the genetic standard deviation of trait 1 and trait 2, respectively.where, rIn this study, single-step GWAS (ssGWAS), which can simultaneously use all the SNP information and utilize the ungenotyped animals with phenotypic records , was impThe single-trait ssGWAS model was used for three body size traits: BL, BH, and CBC.y is the vector of phenotypic values, b is the vector of fixed effects including herd-year-season-sex, W is the covariate of body weight, γ is the regression coefficient associating W, g is the vector of additive genetic effects, following a normal distribution of H is the matrix of additive genetic relationships incorporating both pedigree and genomic information, e is the vector of random residuals with distribution of N were carried out using two-trait ssGWAS model.b1 and 2b are the vector of fixed effects of herd-year-season-sex of trait I and II, 1X and 2X are the incidence matrix associating 1b and 2b with 1y and 2y, 1 and γ2 are the regression coefficient associating 1W and 2W, N, where M = 1Z and 2Z are the incidence matrix associating 1g and 2g with 1y and 2y, and N, where I is the identity matrix and R = where P-values were estimated via postGSf90. The significance test of SNP effects was performed using two-sided t-test and the P-value of each marker was calculated as follows   method fPMax is the genome-wide significance level empirical P-value of FDR adjusted. Based on the P-values of SNPs obtained by ssGWAS, the empirical P-value of FDR adjusted at the genome-wide significance level of 0.05 was calculated on each trait in this study.where m is the number of times to be tested and n is the number of significant SNPs at assigned FDR level, e.g., 0.05. 2 Sus scrofa 11.1 genome version). The R package bioconductor3 was used to identify the related pathways and perform functional annotation. QTLdb4 was used to annotate significant SNPs located in previously mapped QTLs in pigs. R package ‘Cluster Profiler’  and five  genes were found to be related to the corresponding body size traits by the two methods. The biological processes and pathways involved in these genes include calcium channel proteins, lipid metabolism, and cell proliferation.All the significant SNPs identified by the two methods were annotated within the 50 Kb downstream and upstream region with reference to the Sus scrofa 11.1 genome assembly. According to the two methods of SNP significance and explained genetic variance, 88 and 110 SNPs were identified without overlapping, and 64 and 40 genes were found near these SNPs, with only two of them in common , 3. Six Genotype marker density is one important factor affecting the efficiency of GWAS . With thr2 was 0.191 and 0.195, respectively. This not only retains the original SNPs but also increases a large number of SNPS, and does not cause an increase of LD.Increasing marker density could lead to high linkage disequilibrium (LD) to improve the resolution of gene mapping, although it may also be a burden . Too higThe single SNP regression model is widely used in GWAS to identify the association of SNP with traits of interest, although it usually yields a high false-positive rate due to ignoring the linkage disequilibrium between adjacent SNPs. P-value) and 60% of large effects SNPs obtained by the four-trait model on average overlapped with those from the two-trait model for each trait. Moreover, the computation of the multi-trait model is demanding and difficult to converge.In this study, different analysis models were used for body size traits. Two-trait GWAS model was used for traits with high genetic correlation, which can simultaneously use SNPs affecting two traits, resulting in reducing the false positives and improving the statistical power to detect genes . HoweverP-values were supposed to explain relatively high proportions of genetic variance. Likewise, the SNPs with large effects should be significantly associated with the trait of interest. However, our results indicated that the SNPs with the smallest P-values did not have large effects, and there was no overlap between the top 20 SNPs with the smallest P-values and with the largest SNPs effects for each trait. Spearman correlation of P-values and effects of all SNPs showed that the rank correlations of seven body size traits were low (ranging 0.38–0.4), although they were significant. As pointed out by P-value explains whether markers have apparent effects that are seemingly different from 0 with statistical assessment, while SNP effect mainly explains part of the genetic variance, with no statistical assessment. The consistence of SNPs fitting well these two criteria could not be high due to the linkage disequilibrium of SNP, the impact of environment, and the interaction of SNPs. However, it provides an efficient way to determine the SNPs related to traits of interest. Therefore, in order to locate QTLs related to traits more accurately and comprehensively, this study identified significant SNPs from both P-value and SNP effect. The proportion of genetic variance explained by most of the significant SNPs was small (0.00004–0.00653%) for all traits, and the maximum genetic variance of all SNPs was also not large (0.0557–0.1205%), perhaps because too many SNPs were used in the sequencing data in this study, leading to a small effect of each related SNP for each trait. It also indicates that SNPs controlling body size traits are widely distributed on the genome, fitting the infinitesimal model well. It was reported that for complex traits such as height, action sites are widely distributed across the entire genome, indicating that almost all genes are involved in the regulation of height  were found in each pair of genetic related traits, e.g., the top 20 SNPs with largest effects for AC and CC were completely overlapped, and these SNPs were adjacent to each other and located near SSC12:53132997. Therefore, it is speculated that these SNPs constitute an important QTL and jointly affect AC and CC. Similarly, there may be QTLs associated with RW and CW around SSC6:39553559 and SSC13:135373704. In addition, we took 20 SNPs as a sliding window, and found that the top 20 windows with the largest genetic effects, respectively, for AC and CC were overlapped, as well as for RW and CW. The above results further reflect ‘one factor produces multiple effects’, suggesting that highly genetically related traits are probably regulated by the same QTL.Pleiotropic effects can lead to genetic correlation between traits. From the aspect of CDH13 near SSC6:5671575 could be used as a candidate gene affecting body length. CDH13 is a unique cadherin , a protein subunit of transporter particle II (TRAPPII), can lead to abnormal embryonic development and abnormal dietary behavior, and is associated with body mass index (The body length (BL) is an important index to investigate the breeding performance of animals. According to bioinformatics analysis, cadherin  that regcadherin , and placadherin . The ingcadherin , therefoody size . CBC ref in mice . TMPRSS1ss index .CTNND2 was closely related to AC according to the P-value. Studies have shown that CTNND2 participates in the regulation of cell proliferation and affects the body node number of zebrafish (KDM6B and CHD3 jointly affect AC and CC. KDM subfamily 6 enzymes B (KAM6B) plays an important role in the repression of developmental genes protein species and plays an important role in cell growth and differentiation  of the China Agricultural University.XD and CW conceived and supervised the study. HL, HS, and YL helped complete the imputation of the chip data and provide technical guidance. HL, YaJ, HS, and YiJ collected the samples and recorded the phenotypes. YaJ and YL extracted the DNA for genotyping. HL, FZ, YL, and YS contributed to the visualization of data. HL and XD wrote and revised the manuscript. All authors read and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The COVID-19 pandemic has led to an unprecedented daily use of RT-PCR tests. These tests are interpreted qualitatively for diagnosis, and the relevance of the test result intensity, i.e. the number of quantification cycles (Cq), is debated because of strong potential biases.We explored the possibility to use Cq values from SARS-CoV-2 screening tests to better understand the spread of an epidemic and to better understand the biology of the infection.We used linear regression models to analyse a large database of 793,479 Cq values from tests performed on more than 2 million samples between 21 January and 30 November 2020, i.e. the first two pandemic waves. We performed time series analysis using autoregressive integrated moving average (ARIMA) models to estimate whether Cq data information improves short-term predictions of epidemiological dynamics.Although we found that the Cq values varied depending on the testing laboratory or the assay used, we detected strong significant trends associated with patient age, number of days after symptoms onset or the state of the epidemic  at the time of the test. Furthermore, knowing the quartiles of the Cq distribution greatly reduced the error in predicting the temporal reproduction number of the COVID-19 epidemic.Our results suggest that Cq values of screening tests performed in the general population generate testable hypotheses and help improve short-term predictions for epidemic surveillance. Molecular testing is a key component of screening policies to control the spread of infectious diseases, and the coronavirus disease COVID-19) pandemic has led to an unprecedented testing rate using reverse transcription PCR (RT-PCR) assays 9 pandemi or studyFew studies analyse Cq values at a population level. One explanation is that these are known to suffer from several, potentially strong, biases. Firstly, sample type and sampling quality directly affect the amount of genetic material available. Secondly, the choice of RT-PCR assay matters. Even the quality of the reagents may have a considerable effect on the number of amplification cycles required to achieve the same level of fluorescence for the same amount of target genetic material. Combining data from different laboratories helps control for these sources of variation in statistical analyses. Furthermore, the larger the dataset, the more we can detect small statistical trends even after having controlled for non-informative variables.Supplementary Figure S2 shows the daily number of tests and the number of tests per French department). Our aim was to determine firstly, if this analysis at the population level can identify cofactors of interests  and, therefore, new virological or immunological hypotheses and, secondly, if it can improve our ability to anticipate epidemic trends.Here, we present a cross-sectional analysis of SARS-CoV-2 RT-PCR tests performed on samples from 2,220,212 individuals in France during the COVID-19 pandemic between 21 January and 30 November 2020 . The median and upper bound of the 95% CI were unaffected by the removal of these values and the lower bound increased marginally from 16.87 to 16.89. Overall, we were left with 793,479 tests from the same number of individuals, i.e. 35.7% of the whole database. The whole database contains tests with both negative and positive clinical results. However, we only kept tests with a Cq value and the former were less represented in the final database since samples that test negative usually do not have any reported Cq value .We studied SARS-CoV-2 RT-PCR tests performed in 2020 in France on samples from individuals aged between 1 and 89 years. The national French database for SARS-CoV-2 RT-PCR tests (SI-DEP) collects qualitative results but Cq values are not reported. In order to focus on Cq values, this study relied on the French Society of Microbiology (SFM) network of hospital-based and private laboratories. Nationwide, databases from 21 laboratories, listed in the idelines , it is rRt) of the epidemic on the sampling date, and a control variable. The latter corresponds to the last digit of the patient anonymity number and is expected to be independent of the Cq value. Therefore, the lowest p value associated with the control variable, which we expect to be pure noise, can be used to set the significance threshold for the other variables. We also included in the model an interaction term between sampling date and Rt. For this analysis, we excluded Cq values from internal controls. Univariate analyses are extremely sensitive to heterogeneity in the dataset. For instance, the age distribution from patients sampled in aged care homes is different from that in city screening facilities, and analysing the ‘sampling facility’ factor alone could yield misleading results. This is why the analysis used here was multivariate and considered all the factors listed above simultaneously. In particular, it allowed us to control for variations in the way the data were collected, e.g. the intensity or the context of the sampling.We used a linear regression model to explore how Cq values can be explained by the following variables: patient age and sex, the number of days since the onset of symptoms (if known), the clinical sampling site (if known), the sampling facility (if known), the RT-PCR assay used, the target gene, the test’s qualitative result, the sampling date, the temporal reproduction number , we also performed the analyses only on the tests that were reported as 'positive' or 'weakly positive' . These are shown in Rt, which can be interpreted as the number of secondary infections caused by a person infected at a given date t, was estimated using national hospital admission data and the EpiEstim method [Supplementary Methods.The m method ,7. FurthThis study was approved by the Institutional Review Board of the Centre Hospitalier Universitaire de Montpellier. It is registered at ClinicalTrials.gov under the identifier NCT04738331.Supplementary Figure S3A). Care should be taken in the interpretation given that the data were unbalanced, which is why we performed an analysis of variance (ANOVA) with type II sums of squares. All factors except the Rt were significantly associated with Cq values using a classical 5% p value criterion. Even for the control variable, the p value was 0.013, and patients with final digits 1 and 3 in their identification number had Cq values slightly lower (−0.19 and −0.17 cycles) than patients with a 0 as the final digit. Therefore, we set our significance thresholds to 5% of that of the control variables, i.e. 6.5 × 10−4, to analyse the main effects .The intercept of the linear model indicates the average Cq value for a positive test performed with the reference assay, with all factors being set to their reference value. Its magnitude (19.1 cycles) is in line with clinical practice. The importance of the noise in the dataset is illustrated by the strong effect of the testing laboratory, as well as the RT-PCR assay used  communicated by the testing laboratory , with CqSupplementary Table S3 for these sensitivity analyses).The effect associated with the number of days since symptom onset was particularly strong. The number of days between symptoms onset and testing dates was known for 8.5% of the participants; their Cq values increased gradually over the reported range of days with a maximum difference of 5.73 cycles . The effThe effect of sex had the same order of magnitude as that of the control variable and could therefore be treated as non-significant. Conversely, the factor age had a strong effect, with a decrease of 0.541 cycles per 20.1 years of age increased .The target gene of the RT-PCR assay used also yielded a significant effect. The Cq values obtained when using a probe targeting the ORF1 and S regions of the virus genome were significantly higher than when using the N gene, which was the genomic region of reference in the model . Supplementary Figure S2A shows that the number of tests performed varied strongly during the year), but also to variations in the epidemic trend. Indeed, although the Rt  on the date of sampling was not significantly associated with the Cq value according to our threshold , the interaction between the sampling date and Rt was nearly significant with a p value of 10−3 , but this effect was nonlinear . This co of 10−3 , suggestRt and the Cq values of the tests performed in a population is consistent with population dynamics theory, which predicts that in an expanding population of infected individuals, the ‘age’ of the infections, i.e. the number of days post infection, is skewed towards lower values [Rt [The existence of a correlation between the r values . Since Cr values , which wlues [Rt .Supplementary Methods). We analysed the temporal correlation between the time series with a 7-day rolling average of this median, skew and Rt . For the median Cq value, we found a significant correlation with Rt that was maximised for a delay of 6–7 days . This is consistent with Rt being calculated using data from hospital admissions for COVID-19, which occur at a median of 10 to 14 days after infection [To investigate this question, we focused on screening data collected in the general population only from individuals aged 5 to 80 years because younger or older individuals may be associated with specific epidemiological clusters, e.g. in daycare facilities or nursing homes. We estimated the median and skewness values of the daily distribution of the Cq values. To correct for potential confounding factors, these were adjusted using a linear model  was low in periods where Rt variations were limited. Furthermore, we found that adding exogenous data improved the prediction, especially when strong shifts in Rt were occurring . The Cq values tended to provide a better reduction in the error of the prediction than the ratio of positive tests.To further assess the usefulness of Cq data, we used ARIMA models to predict ve tests ). As expThis analysis of a large database of RT-PCR tests performed in all of the French regions during the first two COVID-19 pandemic waves in 2020 confirms that population-level Cq values are noisy since even a linear model that featured 91 degrees of freedom did not explain the majority of the variance. However, owing to the law of large numbers, we detected several effects that are in line with biological observations and with virological properties. For instance, our finding that Cq values decreased as a function of the number of days after symptoms onset is consistent with longitudinal follow-up . AnotherCoronaviridae transcripts can produce subgenomic mRNAs that lack part of the genome [Finally, we found Cq differences associated with the gene targeted by the RT-PCR assay that are consistent with the life cycle of the virus. As stressed by Michalakis et al. , since ce genome . As a coe genome , genes ae genome .Supplementary Figure S2). Although the testing behaviour can be assumed to be homogeneous, epidemic spread was different between regions, which could blur the relationship between Cq values and Rt at the national level. Performing similar analyses in other European countries and regions can also be particularly informative. In our study, we chose to analyse all the tests performed that had a Cq value. This is debatable since high Cq values can be due to noise and this is the point of implementing cut-offs. However, Cq values are known to increase during the course of an infection [A limitation of our study is that although our dataset stands out by its size and its level of details, it is restricted to a single country where testing effort varied, both on a temporal and on a spatial scale  and biological issues (coronaviruses produce subgenomic RNAs of different lengths) . Howevernfection ,11. The d States . Note thOur results show that analysing a large dataset of Cq values from screening tests can filter out the important amount of noise in these values. Inclusion of Cq values in routine surveillance calls for an adaptation to the current state of the epidemic, especially the evolution of variants and the increase in vaccination coverage, but also the integration with other types of data such as mobility data -28. In many European Union countries, the qualitative outcome of SARS-CoV-2 screening tests are already aggregated in national databases to monitor epidemic spread. Adding Cq values as well as basic metadata  could be done while there should be minimal economical and ethical challenges. Our results call for a better integration of Cq values in national and European surveillance programmes to monitor epidemics caused by SARS-CoV-2 or other human viruses, especially since these data raise fewer ethical concerns than other sources such as mobility data.


To further annotate the main QTL intervals and identify candidate genes, we used Oxford Nanopore long-read sequencing in combination with Illumina short-read resequencing, to assemble the parental genomes de-novo. In addition to 2.5 million genome-wide SNPs and short InDels detected between the parents, we also highlight here the structural variation between these lines and the reference melon genome. Through systematic multi-layered prioritization process, we identified 18 potential polymorphisms in candidate genes within multi-trait QTLs. The associations of selected SNPs with earliness and ripening traits were further validated across a panel of 177 diverse melon accessions and across a diallel population of 190 F1 hybrids derived from a core subset of 20 diverse parents. The combination of advanced genomic tools with diverse germplasm and targeted mapping populations is demonstrated as a way to leverage forward genetics strategies to dissect complex horticulturally important traits.Earliness and ripening behavior are important attributes of fruits on and off the vine, and affect quality and preference of both growers and consumers. Fruit ripening is a complex physiological process that involves metabolic shifts affecting fruit color, firmness, and aroma production. Melon is a promising model crop for the study of fruit ripening, as the full spectrum of climacteric behavior is represented across the natural variation. Using Recombinant Inbred Lines (RILs) population derived from the parental lines “Dulce” ( Earliness of maturity is an important trait of crop plants with a direct impact on production efficiency and stress tolerance. Horticultural earliness, also referred to as days to harvest (DtH), was previously dissected in tomato to its components – time from sowing to first female flower (flowering time), and number of days for fruit development and ripening . Floweriinodorus group and climacteric  lines have enabled QTL mapping of abscission formation [CmNAC-NOR, an orthologue to the tomato ripening mutant NOR gene [CmACS, CmACO), cell wall integrity, carotenoid accumulation and sugar metabolism [In fleshy fruits, fruit development and ripening are considered as the main components determining earliness. Fruit development consists of carpel cells expansion and differentiation, and ripening is a complex process that typically includes modifications in fruit color, texture, composition and profile of sugars, acids, and volatiles , 4. Ripeormation , ethylenormation –19, follNOR gene . AnotherNOR gene . Comparatabolism . Varioustabolism , 23.Flowering initiation is an integrated response to environmental and endogenous cues through a network of pathways responding to factors such as photoperiod, vernalization, aging, autonomous flowering, and gibberellic acid (GA) . RecentlThe genomic resources for melon are constantly improving. Since the first melon reference genome, published in 2012 , updatedDe novo assemblies are becoming more common for model and non-model organisms, and pan-genomes are becoming the new references [de novo assembly of the semi-climacteric “Harukei-3” provides insight to the effect of transposable elements on ripening related gene expression [Recent advances in long-read sequencing have presented an important addition to the available tools that simplify assemblies and can further elucidate genomic context of QTLs. ferences –44. The ferences –47, and ferences . In meloferences . A recenferences . The genferences . A recenpression .de novo assemblies of their genomes and highlight the structural variations between them, some of which are in context of the detected QTLs. Associations of selected candidate genes and polymorphisms within them were validated across a diverse collection and a large diallele population.In the current study, we used a RILs population derived from melon inbred lines differing in their earliness and ripening behavior, to map QTLs related to these traits. Resequencing of parental genomes facilitated detailed genomic analysis of QTL intervals, an expansion of the genomic comparison between our parental lines and an improved QTLs annotation. We also present ,Table 1,r = 0.55–0.6, h2 = 0.56–0.72, h2 = 0.34.The TAD×DUL RILs population was analyzed over three years for earliness and ripening related traits. We characterize agronomic earliness as days from transplanting to harvest (DtH) and further break it down to its components – days to flower (DtF) and fruit development time . A total of 3963 fruits were sampled for DtH across the different experiments, averaging 10 fruits per line per year. Variation in DtH, analyzed on line-mean basis, is substantial and distributes in a transgressive manner across nearly 20 days  of the RILs, parental lines and their F1 hybrid was limited to two fruits from separate plants per replicate. A total of 1258 fruits were sampled in the open field experiments and 536 fruits from the net house, averaging 11 fruits per line. This trait was also found to be highly heritable (eriments . The F1 periment .r = 0.57) with RF values in the open field between 3–26 kg cm −2 and in the net house 3–17 kg cm −2. FF values range between 0.6 and 2.7 kg cm −2, with “Dulce” about twice as firm compared to “Tam Dew” in both tissues. RF values display a much wider range than FF . Both traits are characterized by transgressive segregation across the population, with approximately a third of the RILs softer or harder than the parents. In the net house, both RF and FF display dominant inheritance with the F1 fruits not significantly different from “Tam Dew”  was evaluated in one open field experiment (2017) and in the net house experiment (2018), while flesh firmness (FF) was only evaluated in the net house experiment. There is moderate positive correlation between the open field and the net house  was measured on 3510 mature fruits across all experiments with a mean of 8 fruits per line in the open field experiments and 4 fruits per line in the net house. Interestingly, while both parents have high TSS, with “Tam Dew” constantly a couple of degrees brix sweeter than “Dulce” (~15 vs 13°brix), substantial transgressive segregation is observed across the RILs (9.4–16.4°brix). The environmental effects and G × E interactions in this trait are apparent, as distributions are moderately correlated between the open field experiments but not so between the open field and the net house, where TSS values are lower 1b. TSS det house .p = 0.0004). A stronger negative correlation with EtE is observed for both FtH and DtH in the open field, ranging between r = −0.60 and − 0.65. This negative relation is even more pronounced in the net house . Interestingly, this analysis also shows that ripe fruit TSS is not correlated with ripening behavior or with earliness traits . Significant epistatic interaction between FtH3.3 and FtH8.2 was detected only in the net house , to support the validity of QTLs for this trait, we analyzed the correlation between the five replications in the net house experiment. All correlations were significant and above r = 0.55 (FF5.1 and FF8.3 can significantly distinguish between three levels of flesh firmness (1.16–1.74 KgF cm−2) and account for a third of the total genetic variation in this trait , out of 41 genes annotated across the confidence interval, MELO3C011432, a WRKY family transcription factor, received a high score, with a codon deletion in “Dulce” . In the HDA20 population, similar allelic effect on DtH is shown with a clear additive mode of inheritance, where heterozygotes are intermediate to the homozygote genotypes . The effect of this locus on EtE was validated in the HDA10 population where the deletion alleles are associated with increased ethylene production by nearly 60 μL kg−1 h−1, with additive mode of inheritance . Significant association of this gene with fruit firmness was shown also across the HDA20 population, where the deletion alleles were softer in 3 Kg cm−2 than wild-type  and heterozygotes are intermediate to both homozygotes .MELO3C011365. Another candidate gene in EtE3.3 QTL is MELO3C011365, a transducin/WD40 repeat-like superfamily protein, described as a large family of proteins involved in signal transduction and coordinating protein–protein interactions. Forty-eight genes are annotated within EtE3.3 and MELO3C011365 is located 20 Kb from the QTL peak. We detected several high impact polymorphisms in this gene, including a nonsense mutation, leading to a premature stop codon, two missense mutations, and a splice site region SNP . To test the combined effects of EtE3.3 and EtE8.2 across our diallel population, we analyzed MELO3C011365 with MELO3C24520–a recently suggested EtE candidate located within EtE8.2 [HDA10 population, with a difference of 120 μL kg−1 h−1 between the combination of contrasting alleles at both loci  and in ripe fruits  substitution in exon 5  and shares a minor QTL with DtF8.2 , to test the association of this polymorphism. Significant association was found between SNP S08_4442666 at MELO3C007661 and DtH across the multi-allelic HDA20 population  with the FtH3.3 candidate, MELO3C011432, was tested and significantly accounted for 36% of the genetic variation in DtH  reads of “Tam Dew” and 23.3 Gb of “Dulce”, representing ~43× and ~ 64× coverage of the estimated 400 Mb melon genome, respectively. N50 for ONT read lengths was 16.3Kb and 20.2Kb for “Tam Dew” and “Dulce”, respectively. The initial assembly of “Tam Dew” was comprised of 386 contigs with an N50 of 3.4 Mb and “Dulce” assembly was comprised of 190 contigs with an N50 of 7 Mb , we manually scanned for inversions and translocations. To increase the confidence in the reported events, we only considered segments with at least two anchor sequences present. Between “Tam Dew” and the reference genome we identified 42 events in total, 12 inversions of which 3 were larger than 1 Mb, and 18 translocations between chromosomes, 4 larger than 1 Mb – the largest being 3.6 Mb from chromosome 1 in the reference to chromosome 2 in “Tam Dew”. In Dulce we identified 32 events in total, 17 inversions of which one was larger than 1 Mb, and 6 translocations between chromosomes, none larger than 1 Mb. Interestingly, the most substantial SV that we detected was on chromosome 6 where we report five large translocation events encompassing nearly half the chromosome , yielded 7973 structural variants encompassing 27 Mb. Here too, the majority (66%) were within repetitive elements, 7% larger than 50 Kb, and 28% were InDels, the largest between 10 Kb and 50 Kb, altogether encompassing approximately 2 Mb Fig. 6d, and 11. ,We further analyzed how the structural variation between “Tam Dew” and “Dulce” affected genome-wide gene distribution, and we report that 1119 genes common to both parents (96% single-copy), were re-localized to different chromosomes. 305 genes from chromosome 8 of “Tam Dew” are located on different chromosomes of “Dulce”, mainly on chromosomes 3 and 4. 292 genes from Dulce’s chromosome 1 were mainly translocated to chromosomes 2 and 7 of Tam Dew . These rFig. 6dinodorus type) melon, and climacteric types . In the current study, we used a RILs population originating from a cross between the inodorus line, “Tam Dew” (a Honey Dew variety) and the climacteric line, “Dulce” (reticulatus type). A comparison between EtE from our RILs to a recent study using RILs derived from “Piel de Sapo” (inodorus) and a cantalupensis variety, “Vedrantais” [−1 h−1, compared to 0–286 μL kg−1 h−1) a difference that may be attributed to the fact that “Vedrantais” is much more climacteric than “Dulce” (225 vs 90 μL kg−1 h−1). Nevertheless, several common genetic loci related to ripening were mapped in both populations. Earliness and ripening related traits displayed transgressive segregation across our population, as RILs surpassed the parental range  and Makuwa (agrestis) parents [RF2.1, “Dulce” allele is associated with firmer fruit, while in RF8.2, “Tam Dew” allele is associated with increased firmness. These two QTLs are acting additively (no interaction) and therefore the trans-allelic combination DULRF8.2TADRF2.1 is significantly firmer than all other combinations between these QTLs  and 8 (QTL8.2) provide a genetic explanation for the correlations between the different earliness and ripening-related traits. These two multi-trait QTLs are responsible for more than 30% of the genetic variation  we identified possible candidate genes and polymorphisms. We combined detailed genotypic profile of parental genomes with functional annotations of sequence variation. Gene expression information was also included in the process. Another important layer was the validation of significant associations in two additional multi-allelic populations derived from our diverse melon collection  protein involved in degradative sorting mechanism of plasma membrane proteins [2 in our population  [C. melo ssp. melo), we report here a similar PAV between these lines in Vat proteins, as six open reading frames on chromosome 5 are present in “Tam Dew” but missing from the “Dulce” genome . Out of the 1412 genes missing from either “Tam Dew” or “Dulce”, none were found within a QTL interval. Nonetheless, recent publications report on PAVs related to melon domestication in a region on chromosome 5 containing resistance genes, such as the protein coding mission) , 50. Tho” genome .MELO3C007661 gene was missing from “Tam Dew” annotation, and was manually added after the PCR validation. It is possible that ab-initio gene annotation supported by expression data originating from each of the parents would greatly increase the confidence of both SV and gene annotation from their present draft status.We believe that further examination of the genomic data generated in this study will expose additional cases of SVs within genes, some of which with potential impact on phenotypic variation. However, the lift-over process used in this work is limited to the reference transcriptome, and at times found inaccurate—e.g. the above mentioned de novo assemblies of parental lines of a segregating population is an effective way to identify and prioritize candidate genes within QTL intervals, towards the complementary use of reverse genetic approaches (e.g CRISPR-Cas9 mediated genome editing) for breeding improved varieties.Earliness and ripening behavior in melon are shown here and in other studies to be under complex genetic control , 16–18. 7 recombinant inbred lines originating from a cross between the late non-climacteric “Tam Dew”  and the early climacteric “Dulce”  growing conditions and experimental design previously described in Oren et al., 2020 [1 and their parents were represented by five plants per plot in two replicates and grown in a randomized block design (RCBD) in the open field in the summers of 2016 and 2017. In the summer of 2018, each line was represented by five replicates of a single plant and were grown in a 50-mesh net-house in RCBD. The second population, Melo180 GWAS panel, is composed of 177 diverse accessions representing the two melon subspecies (ssp. agrestis and ssp. melo) and eleven horticultural groups. Here, each line was represented by three plots of five plants each in an RCBD in the open field in the summer of 2015 [HDA20 –multi-allelic population of 190 F1 hybrids derived from intercrossing in a half-diallele mating scheme of 20 diverse core accessions, selected to represent the genetic variation in our Melo180 GWAS panel [HDA10 is a core subset of 10 parents and 45 half-diallele F1 hybrids that are included in the HDA20 populations.The germplasm in this study included three sets which were grown at Newe-Ya’ar Research Center, northern Israel (32°43′05.4″N 35°10′47.7″E). The first population, TAD×DUL RILs, is composed of 164 Fl., 2020 . Briefly of 2015 . The thiAS panel . The 190http://www.restek.com/). Ethylene emission rate (EtE)—μL Kg−1 fresh weight per hour—was calculated from the sample peak area based on the standard peak area (1 ml of 1 ppm ethylene in N2). Fruits were then cut along the longitudinal section, and firmness — KgF cm−2 —was measured on each fruit at two opposite points in both flesh and rind, using a digital force gauge . Fruit rind and flesh firmness  scores were an average of the two sampling points. Flesh sugar content, evaluated as total soluble solids (TSS) was measured by refractometer  in juice squeezed from five fruits per plot. Genotype least square means for EtE, RF, FF and TSS were calculated on a minimum of four fruits per genotype.At maturity a single fruit from each plant was harvested at maturity based on abscission in climacteric fruits, or rind color and days after fruit set (45–50 days) and rind color in non-climacteric fruits, giving a total of five mature fruits per plot (10 per genotype). In the open field, female flowers were routinely tagged at anthesis, over the course of three weeks, and the flowering date of tagged fruits was collected during harvest. Earliness (DtH) is defined as the number of days from sowing to harvest. Time to flower (DtF) is the number of days from sowing to anthesis and fruit development time (FtH) was the number of days from anthesis to harvest. In the net house, flowers were manually pollinated and due to variation in setting, DtF data from this experiment was not reliable enough, therefore only FtH data was used . Ethylenh2) was estimated for each trait in each year separately using ANOVA based variance components [JMP ver. 14.1 statistical package  was used for statistical analyses as described in Oren et al., 2020 . Brieflymponents . Trait cExtraction of DNA was done using the GenElute™ Plant Genomic Miniprep Kit , and the quantity and quality was determined using Nanodrop spectrophotometer ND-1000 , electrophoresis on agarose gel (1.0%) and Qubit® dsDNA BR Assay Kit .et al. [. Map construction was based on 89 343 SNPs across 146 lines. SNP filtration were done with TASSEL v.5.2.43 [et al. [HDA20 population was extracted and shipped to the Genomic Diversity Facility at Cornell University  for WGS to an estimated 30× coverage, yielding 4 million informative SNPs as previously described [Genotyping of the TAD×DUL RILs was based on GBS, and map construction were previously described by Oren et al. . Map conv.5.2.43  and linkv.5.2.43 . Genotyp [et al.  and the escribed .et al. [For expression analysis, fruit rind tissue was sampled into two biological replicates from “Tam Dew” and “Dulce” at flowering day, 5, 10, 15 and 20 days after anthesis (DAA) and at mature stage. Each biological replication consisted of bulked tissue from three fruits sampled from different plants from each line. Fruit tissue was frozen in liquid nitrogen and stored in −80°C. Total RNA was extracted from 24 tissue samples  as previously described  and 50 μet al.  . In esset al. ) and foret al. [A modified CTAB protocol based on Fulton et al.  was usedHigh-quality HMW DNA libraries for Oxford Nanopore MinION were constructed and DNA size selection was performed using BluePippin system . Library preparation was performed with 1–1.7 μg DNA using the Ligation Sequencing Kit SQK-LSK109  following manufacturer’s guidelines. Libraries were loaded on MinION FLO-MIN106D flow cell. Base calling was done using the GPU version of Guppy v2.1. “Dulce” samples produced 1.7 million sequences with a sum length of 23.3 Gb between 70 bp and– 148 592 bp with an average length of 13 729 bp. “Tam Dew” produced 1.7 million sequences with a sum length of 15.7 Gb between 76 bp and 117 396 bp with an average length of 117 396 bp. Mean read qualities for both samples were equal or above Q10.De-novo assemblies and their annotations were created for both parental lines of the TAD×DUL RILs. The assembly workflow is described in http://broadinstitute.github.io/picard/; Broad Institute). Both long and short-reads were aligned using Minimap2 (v2.17) [ (v2.17)  with par (v2.17) . Polishe (v2.17)  using Ra (v2.17) . Assembl (v2.17)  and QUAS (v2.17) . Finally (v2.17) .de-novo repetitive elements in both “Dulce” and “Tam Dew” genomes. Gene models were annotated using a lift-over approach based on the Melon v4.0 data previously published [RepeatModeler2  was usedublished , 33. We ublished  based onublished  using deTo observe SV variation between “Dulce”, “Tam Dew” and Melon genome V4.0, we first aligned the assemblies to each other and to the reference using Nucmer , with 1000 permutations and p-value of 0.05 as detection threshold using 1.5 LOD scores confidence intervals. Composite interval mapping (CIM) was done based on a 10 cM marker window size.QTLs were analyzed as previously described . In brieTo classify and rank polymorphisms within predicted genes, we used SnpEff  that preWe wish to thank Uzi Saar, Fabian Baumkoler and the Newe-Yaar farm team for technical assistance in setting the field trials and for plant maintenance. We thank Dr. Vitaly Portnoy for technical assistance in ethylene emission measurements, and Dr. Nurit Katzir for sharing raw data on the PI414xDUL RILs population. Funding for this research was provided by the United States-Israel Binational Agricultural Research and Development Fund (BARD) grant no. IS-4911-16 and by the Israeli Ministry of Agriculture Chief Scientist grants no. 20-01-0141.de novo assembly pipeline. Bioinformatic, genomic and statistical support were provided by YE, SF, YT and AAS. EO analyzed the results. EO and AG wrote the manuscript. All authors discussed the results and approved the final version of the manuscript.EO and AG conceived the research plan and designed the experiments. YB, AD and AG were responsible for development of plant genetic materials. EO, AD, GT, TI and AG performed the experiments and collected the data. ERR, BS and ESB contributed to the genomic sequencing and sec id="sec35"><title>Funding</title><p>Amit Gur, United States - Israel Binational Agricultural Research and Development Fund (BARD), Funder id 100006031, IS-4911-16.</p><p>Amit Gur, Ministry of Agriculture and Rural Development , Funder id 501100004576, 20-01-0141.</p></secThe data supporting the findings of this study are available within the paper and within its supplementary materials published online. Raw sequences and FASTA files of genome assemblies can be found at NCBI BioProject: PRJNA775626.The authors declare that they have no conflict of interest.at Horticulture Research Journal online.Web_Material_uhab081Click here for additional data file.


Root hair, a special type of tubular-shaped cell, outgrows from root epidermal cell and plays important roles in the acquisition of nutrients and water, as well as interactions with biotic and abiotic stress. Although many genes involved in root hair development have been identified, genetic basis of natural variation in root hair growth has never been explored.Here, we utilized a maize association panel including 281 inbred lines with tropical, subtropical, and temperate origins to decipher the phenotypic diversity and genetic basis of root hair length. We demonstrated significant associations of root hair length with many metabolic pathways and other agronomic traits. Combining root hair phenotypes with 1.25 million single nucleotide polymorphisms (SNPs) via genome-wide association study (GWAS) revealed several candidate genes implicated in cellular signaling, polar growth, disease resistance and various metabolic pathways.These results illustrate the genetic basis of root hair length in maize, offering a list of candidate genes predictably contributing to root hair growth, which are invaluable resource for the future functional investigation.The online version contains supplementary material available at 10.1186/s12864-021-07961-z. On tstresses . The devstresses .Arabidopsis [OsEXPA8 [OsEXPA17 [OsCSLD1 [OsFH1 [OsSNDP1 [OsPHR [Roothairless 1 (rth1), rth3, rth5 and rth6 mutants are deficient in root hair formation in all root types, and exhibit defect in different stages of root hair development [ZmLRL5 gene, encoding a basic helix–loop–helix (bHLH) transcription factor, was demonstrated to play a positive role in orchestrating the translational process by directly regulating the expression of translational processes/ribosomal genes during maize root hair growth. The loss-of-function of ZmLRL5 resulted in a dramatic reduction in the elongation of root hair [ZmTIP1, encoding a functional S-acyltransferase, was identified to participate in drought tolerance by regulating root hair growth [As a unique single cell type in plant biology, the development, physiology, and cell biology of root hair have been intensively studied in bidopsis , 6, 9–11bidopsis . In ricebidopsis , 14. In [OsEXPA8 , OsEXPA1OsEXPA17 , OsCSLD11 [OsFH1 , OsSNDP1[OsSNDP1  and OsPH1 [OsPHR . So far,elopment –24. The oot hair . Recentlr growth . AlthougIn recent years, genome-wide association studies (GWAS), which is based on linkage disequilibrium (LD) in a panel, has offered high mapping resolution and could effectively benefit the exploration of the genetic basis associated with complex quantitative traits –30. In mThe association population in this study consists of a global collection of 281 diverse maize inbred lines , 43. TheAll 281 lines used in this study include three subpopulations and one mixed group, which are referred to TST, SS, NSS, and MIXED . TST subAs root hairs play a crucial role in the plant acquisition of nutrients and water, we postulated that root hair morphology is likely coordinated with other agronomic traits and amino acid metabolism. To verify our hypothesis, the Pearson-correlations were calculated after comparing root hair length with 17 agronomic traits and 18 amino acid contents in maize kernel, which were previously measured in the same association panel , 45. TheP ≤ 0.05), which were EH, ELW, TBN, LNAE, and all three flowering-related traits , which were Gly, Lys and Arg , and the leading SNP_ 75630277 was selected as the representative of this locus  ≥ 0.05, we performed GWAS to explore the genetic loci underlying the root hair length. Under the mixed linear model (MLM) , 50, whiNPs Fig. . As therP-value = 1.70E-07, R2 = 9.0%) locates in the fourth exon of GRMZM2G403003  locates in the third intron of GRMZM2G044851 , which is the one with other 7 SNPs within the same LD region, locates at the promoter region of GRMZM5G825276  about 8000–9000 years ago. Nowadays, modern maize displays a large geographic distribution from tropical to temperate climate. Therefore, population structure is associated with significant differences in maize morphology [Maize was domesticated from its wild ancestor, teosinte , which is well known as a key hub of cell signaling networks mediating various hormone, stress, and immune responses [GRMZM2G064644 encodes a protein containing a CRIB motif required for its specific interaction with GTP-bound Rop1 (plant-specific Rho GTPase), promoting root hair development [GRMZM2G403003 encoding a TON1 recruiting motif (TRM) proteins is able to target TON1 to cortical microtubules [We identified a total of 11 candidate genes associated with root hair elongation, and the homologs of three genes have been reported to influence root hair development in esponses –65. Rootesponses –68. GRMZelopment –71. GRMZotubules . It has otubules , 74.BNT1 encodes an atypical TIR-NBS-LRR protein and works as a regulator of the hormonal response to stress. The disruption of BNT1 could result in dramatic alteration in root hair distribution [Nucleotide binding site leucine-rich repeat (NBS–LRR) comprises a large class of disease resistance proteins that play a widespread role in plant protection against pathogens , 76. Surribution .GRMZM2G000471 and GRMZM2G044851 encode an aquaporin and a nitrate transporter, respectively. The loss-of-function of HvEXPB7 and OsWOX11 severely suppressed root hairs under drought conditions [The function and morphology of root hairs in relation to the acquisition of water and nutrients have been well studied previously . Root handitions . AtNPF7.nditions .GRMZM2G147446 encodes a UDP-rhamnose/UDP-galactose transporter situated in the Golgi lumen where UDP-galactose is used for synthesis of noncellulosic polysaccharides and glycoproteins [GRMZM5G825276 encodes a GDSL-like lipase involved in lipid biosynthesis [Root hair requires robust activity of various metabolism pathways to support its polar elongation , 88–90. proteins –94. GRMZynthesis , 96. OvePrior to this study, genetic architecture and genes controlling natural variation in maize root hair development remains unclear. We elucidated a broadly natural variation in the root hair length in a maize association population. Several genetic loci putatively regulating the natural variation in root hair were revealed by performing GWAS, shedding light on novel knowledge about the genetic basis of root hair development in maize. Given that the importance of root hair in plant nutrient acquisition and water uptake, candidate genes identified in this study provide a list of research targets for future functional characterization to understand how root hair growth is naturally regulate, and benefit the breeding program to improve maize varieties with proper root hair morphology assisted by molecular breeding and engineering.http://maizego.org/. The maize lines in this association panel were clustered into three subpopulations, including 22 SS lines, 30 NSS lines and 112 TST lines, and the remaining 117 lines fall into a mixed subpopulation. Detailed information about the origins of these lines was described in previous studies [Association tests were conducted in an association panel consisting of 281 inbred lines, which are publicly available at  studies , 43. The studies , 98.Maize seeds were surface-sterilized in a 10% NaClO solution for 5 min, rinsed several times with distilled water, and germinated in moist germination paper rolls (Anchor Paper) at 28 °C under constant darkness as previously described . The rooP value thresholds [r2 ≥ 0.2) [P value was computed by 1/n and 0.05/n , and we obtained the suggestive threshold 6.05 × 10− 6 for MLM and 3.03 × 10− 7 for GLM, respectively.A GWAS on root hair length was performed using Tassel 5.2 under both GLM and MLM. Considering the non-independence of SNPs caused by strong LD, it is usually too strict for significant association detection when the threshold is derived from the total number of markers. Thus, the effective number of independent markers for the multiple adjustment were used to obtain the resholds , 100. Th2 ≥ 0.2) . Then, w2 ≥ 0.2) , 44, 102Y = αX + βP + ε (1) and Y = βP + ε (2). In this model, Y is the phenotype, X is the SNP genotype, P is the matrix of three subpopulations , α is the SNP effect, β is the subpopulation effects, and ε is random effects. Thus, the R2 of each significant SNP after adjusting for the population structure effects were reported as previously described [To estimate the phenotypic variance explained by each significant SNP, we used ANOVA to construct linear models of escribed .https://maizegdb.org/past. The resulting SNP-trait association data and effects data generated by TASSEL were implemented in the pathway analysis. During the process of loading data, the LD data is filtered to drop rows where the loci are not the same, and then unneeded columns from the TASSEL output are dropped [The pathway analysis was performed in  dropped . Only paR2 < 0.2) to represent the locus, and candidate genes were nominated by the leading SNP positioned. The physical locations of the SNPs were recorded according to the B73 RefGen_v2 (www.maizesequence.org). The corresponding genes were annotated based on the literatures describing the function of their homologs in other species or the information retrieved from conserved domain database (CDD).To search the candidate genes underlying associated SNPs, we selected the most significant/leading SNPs within the same LD block . (a) Manhattan plots of GLM for root hair length. The horizontal dashed line represents the significance threshold -log10(P) = 6.5. (b) QQ plot of GLM showing the ratio of the observed and the expected P-values for root hair length. The solid diagonal lines represent agreement between observed and expected probability distributions assuming null SNP–trait association.Additional files 2: 0Figure S2. The allele effects of significant SNPs located around representative genes for root hair length.  Regional plots showing association mapping results for SNPs located around GRMZM2G100288 (a), GRMZM2G147446 (b), GRMZM2G078013 (c), GRMZM2G000471 (d), GRMZM2G064644 (i), AC193598.3_FG002 (j), GRMZM2G180244 (k), GRMZM2G091579 (l).  Allele effects of the most significant SNPs for root hair traits. (e) GRMZM2G100288, (f) GRMZM2G147446, (g) GRMZM2G078013, (h) GRMZM2G000471, (m) GRMZM2G064644, (n) AC193598.3_FG002, (o) GRMZM2G180244, (p) GRMZM2G091579. Each dot represents an SNP. The horizontal dashed black line represents the significant threshold –log10(P) = 5.2. *Significant at P ≤ 0.05; **significant at P ≤ 0.01.Additional files 3: Table S1. Root hair length ratio (Inbreds/B73) of the 281 inbred lines from different subpopulations.Additional files 4: Supplemental Table 2. Pathways associated with increased root hair length at statistical significance P < 0.05.Additional files 5: Supplemental Table 3. Pathways associated with decreased hair length at statistical significance P < 0.05.