F statistic, and their relationship with the Hardy–Weinberg equilibrium (WHE) and the polymorphic information content (PIC) were determined, followed by principal component analysis (PCA). As much as 32 SNPs out of the 48 initially studied were successfully genotyped. A total of 9 SNPs, 4 SNPs in TLR5, 1 SNP in TLR8, and 4 SNPs in TLR2 genes, were polymorphic. The variable genotype and allele frequency of the TLRs gene indicated genetic variability among the studied sheep breeds, with the Hungarian Merino exhibiting the most polymorphisms, while Dorper was the population with the most SNPs departing from the HWE. According to the PIC value, the rs430457884-TLR2, rs55631273-TLR2, and rs416833129-TLR5 were found to be informative in detecting polymorphisms among individuals within the populations, whereas the rs429546187-TLR5 and rs424975389-TLR5 were found to have a significant influence in clustering the population studied. This study reported a moderate level of genetic variability and that a low to moderate within-breed diversity was maintained in the studied populations.This pilot study used an alternative and economically efficient technique, the Kompetitive Allele-Specific Polymerase Chain Reaction (KASP-PCR) to examine 48 SNPs from 11 parasite-resistance genes found on 8 chromosomes in 110 animals from five sheep breeds reared in Hungary; Hungarian Tsigai, White Dorper, Dorper, Ile de France, and Hungarian Merino. Allele and genotype frequencies, fixation index, observed heterozygosity, expected heterozygosity,  Small ruminants account for a substantial percentage of the worldwide livestock sector, with estimates of 1173 million heads for sheep and 1003 million heads for goats, respectively FAO . Sheep iOne of the main causes of production losses in small ruminants worldwide is represented by parasitic infections in grazing animals. For instance, Charlier et al.  estimateFasciola hepatica  over the last two centuries.In this study, the Kompetitive Allele Specific Polymerase Chain Reaction (KASP-PCR) was used. Fluorescence resonance energy transfer (FRET) generates signals in KASP-PCR. Two luminous cassettes detect bi-allelic SNP allele-specific amplification  or Ensembl (http://www.ensembl.org). In the end, only 32 SNPs out of the 48 initially studied were successfully genotyped.A panel of 48 SNPs from 11 parasite-resistance genes located on 8 chromosome was chosen based on previous genome-wide association studies (GWAS) and marker-assisted selection studies  was used to perform bi-allelic discrimination of the selected 48 SNPs. The data were visualized using SNP Viewer software version 1.99 . All genotype data were exported for statistical analysis. Only SNPs that were found in at least 90% of the breeds were included. Quality control of genotyped data comprised of eliminating a number of animals with more than 10% missing SNP calls and a number of SNPs with call rates less than 90%, resulting in disparities for the number of individuals among SNPs.F statistic (Fst), and their accordance with or deviation from the Hardy–Weinberg equilibrium(HWE). Polymorphic information content (PIC) was determined online at https://gene-calc.pl/pic.The raw allele calls provided from LGC Genomics were examined using LGC Genomics’ KlusterCaller program. POPGENE software version 1.31  was performed using FactoMineR , White Dorper (A = 0.650 and C = 0.350), and Ile de France (A = 0.533 and C = 0.467), even though the A allele was dominant in Dorper and Merino breeds, with a frequency of 1.00 and 0.79, respectively. For the rs55631273, both alleles C and T were found moderately in each of the sampled populations, except for the White Dorper and Merino. In rs430457884, the genotype GG was highly observed in all populations, although the heterozygote GA was dominantly observed in the White Dorper population, while the G allele dominant for rs160821602 with the allele frequency varied between 0.85 and 1.00.For the TLR2 (0.269–0.375), rs430457884-TLR2 (0–0.375), and rs416833129-TLR5 (0–0.375). In general, the obtained results showed that the PIC values were smaller than 0.5, with the minimum and maximum values of 0.044 and 0.361, suggesting that all investigated markers are considered moderately informative.The Ho, He, PIC, and Fst values for the various breeds studied were determined and are listed in Table TLR8, rs412232316-TLR5, and rs416833129-TLR5 in the Dorper population, with a value of 0.700, 0.600, and 0.600, respectively. With a similar pattern for rs424975389-TLR5 and rs55631273-TLR2 in the Ile de France population, with the value of 0.600 for both SNPs. The Fst value ranged from 0.104 to 0.462, indicating a moderate relationship among the observed breeds.The highest observed heterozygosity, which indicated a high within-population diversity, was obtained for rs403288183-x2) is also shown in Table TLR5 in Hungarian Tsigai, rs430457884-TLR2 in White Dorper, rs55631273 and rs430457884 of TLR2 in Dorper, and rs55631273-TLR2 in the Hungarian Merino population. The South African Dorper population was found to be a population with most SNPs deviating from the HWE. It was found that the Hungarian Merino population had the highest proportion of polymorphic markers (100%), and Ile de France had the lowest one (33.33%), with a fixed allele in 6 SNPs, except for rs403288183-TLR8, rs401390846-TLR2, and rs55631273-TLR2.The HWE test (TLR5 (SNP1) and rs424975389-TLR5 (SNP4) had maximum values in PC1, as they showed maximum variance.The result of the PCA is shown in Figs. This study aimed to examine the genetic polymorphism of 48 SNPs in five European and African sheep breeds reared in Hungary, with an emphasis on gastrointestinal parasite resistance, using the KASP-PCR technique. As many as 16 (33.33%) of 48 SNPs were failed for genotyping, resulting in the success rate of the KASP technique in this study (66.67%) being lower than in previous studies on similar genes in goats , TLR8 , and TLR2 .In this study, 9 polymorphic markers located in 3 genes associated with the parasite resistance were observed, namely the TLR genes in the species are highly comparable to caprine TLR, also showing over 95% similarity to bovine orthologs , being found on the membranes of intracellular vesicles such as endosomes  are frequently investigated, considering that they are proteins pattern-recognition receptors (PRRs) that initiate the inflammatory processes .Numerous studies have implicated is Yaman , MycoplaTLRs genes in this study demonstrated genetic diversity amongst Hungarian sheep breeds. These results are encouraging for future development of selection and genetic improvement programs for disease and environmental adaptation, as well as utilization for conservation efforts of Hungarian local sheep breeds. The Dorper and Hungarian Merino showed the most distinctive genotype and allele frequency for the studied SNPs compared to the other three breeds. An indigenous breed such as the Hungarian Tsigai is assumed to maintain high levels of genetic diversity, compared to commercial breeds, as indigenous breeds are usually under less selection pressure; however, in this study, a high proportion of monomorphic loci were found in the Hungarian Tsigai (n = 10) as well as in the Ile de France population (n = 30). In some instances, monomorphic loci associated with disease resistance are observed, such as Tendinopathies in the Greek native horse breed (Giantsis et al. Mycobacterium bovis infection in Hostein Friesien cattle (Richardson et al. The different genotype and allele frequency of igai n = 0 as wellThe Hungarian Merino observed in this study showed the highest polymorphism levels among all. The breed is a commercial breed and has been developed through crossbreeding with a variety of Spanish Merino and Merino-derived breeds over years of history, which has resulted in extensive genetic admixture, results in line with those reported previously by Ciani et al. , which sThe South African Dorper is also a widely spread commercial breed, being a hardy composite breed established by crossing the Black-headed Persian and the Dorset Horn. Due to its outstanding characteristics, such as the thermotolerance ability (Joy et al. Our current findings serve as a starting point for the characterization of European and African sheep breeds based on gastrointestinal parasitism resistance genes. They can be utilized to establish precise conservation measures aimed at improving disease resistance and monitoring the genetic variability of sheep breeds, especially those reared in Central Europe. However, populations with a larger sample size would be beneficial to obtain a better figure of the genetic diversity of the European sheep breeds.TLR5, TLR8, and TLR2 genes associated with parasite resistance in Hungarian Tsigai, White Dorper, Dorper, and Hungarian Merino sheep using the KASP assay, giving room for genetics selection for parasite resistance traits in Hungarian sheep population. In general, the study reported a moderate level of genetic variability and that a low to moderate within-breed diversity was maintained in the studied populations. Although these SNPs require additional research into marker associations and their marker-quantitative trait locus phase relationships in each population to precisely define each SNP effect, and the number of samples is limited in this pilot study, the results obtained may prove valuable and contribute to the future molecular marker studies on disease resistance in sheep.This study describes polymorphisms in parasite resistance genes in European and African sheep breeds reared in Hungary. Our study revealed nine polymorphic markers in


HvSs1. Some genes involved in cold stress and regulation of flowering time were detected near eight signatures. Taken together, these results highlight the existence of putative adaptive loci in naked barley on QTP and thus improve our current understanding of the genetic basis of local adaptation.Understanding the local adaptation of crops has long been a concern of evolutionary biologists and molecular ecologists. Identifying the adaptive genetic variability in the genome is crucial not only to provide insights into the genetic mechanism of local adaptation but also to explore the adaptation potential of crops. This study aimed to identify the climatic drivers of naked barley landraces and putative adaptive loci driving local adaptation on the Qinghai-Tibetan Plateau (QTP). To this end, a total of 157 diverse naked barley accessions were genotyped using the genotyping-by-sequencing approach, which yielded 3123 high-quality SNPs for population structure analysis and partial redundancy analysis, and 37,636 SNPs for outlier analysis. The population structure analysis indicated that naked barley landraces could be divided into four groups. We found that the genomic diversity of naked barley landraces could be partly traced back to the geographical and environmental diversity of the landscape. In total, 136 signatures associated with temperature, precipitation, and ultraviolet radiation were identified, of which 13 had pleiotropic effects. We mapped 447 genes, including a known gene  Natural and artificial selection have varying degrees of influence on plant genomes. The effects on genetic polymorphisms are reflected in adaptive differences among different crop cultivars  is one of the founding crops of Old-World agriculture   is famous for being the highest and largest plateau in the world, with an average altitude exceeding 4000 m. The high altitude results in strong solar radiation, low temperature, a large temperature difference between day and night, dry climate, low annual precipitation, and an obvious regional microclimate. The harsh habitats force plants living on QTP to adapt to extreme conditions. Therefore, QTP provides an ideal ecological environment for dissecting the genetic basis of plant ecological adaptation. Barley , Tibet (38), Gansu (46), Sichuan (15), and Yunnan (7) Fig. . In geneApeK I (G|CWCG), barcoded libraries were prepared to track each accession, and the DNA sequence corresponding to the region flanking the ApeK I site was obtained using the Illumina HiSeq 2000 platform. The raw sequence data were filtered by SOAPnuke (http://soap.genomics.org. cn/). Then, clean sequences were mapped to the Hordeum vulgare Hv IBSC PGSB v2 reference genome  ≥ 0.2, and missing data rate ≥ 0.2 were removed from 118,183 SNPs. After filtering, 37,636 high-quality SNPs were retained, and they were annotated for functional effects based on the reference genome using SnpEff  for 1970–2000 at 30-s resolution, and 30 variables  during the growth period were downloaded from WorldClim (s Tables  and S3 ws Tables .https://megasoftware.net/), and the output was visualized using iTol (https://itol.embl.de/)  as response variables, while geography, climate, and genetic structure were treated as explanatory variables. First, the missing values of the SNP dataset were obtained by simple imputation, and then, the 3123 SNPs were converted to 0, 1, and 2 forms, which represented homozygosity for the most frequent allele, and heterozygosity and homozygosity for the least frequent allele  . FarmCPU is a method aimed at overcoming the limitation of the general linear model (GLM) and mixed linear model (MLM) in dealing with population structure, so as to generate a higher statistical capacity  implemented by a memory-efficient, visualization-enhanced, and parallel accelerated tool  to find the function of the protein domain. Enrichment of these genes could be performed using the “GO Enrichment” option implemented in TBtools  was evaluated as the pairwise We used a sliding window approach  to describe the pattern of variation and over-divergence across the genome  in the BIG Data Center at the Beijing Institute of Genomics (BIG), Chinese Academy of Sciences, under accession number CRA007788. For analysis of population genetic structure and pRDA, we selected 3123 SNPs with MAF ≥ 0.1, Het. Rate ≤ 0.2, missing values ≤ 0.2, and overall missingness of 6.42%; this dataset was LD-pruned with PLINK using an r2 threshold of 0.5. In this dataset, 707 (22.64%) SNPs were located in the exon region  individuals had a higher ancestry coefficient of more than 0.8. There were 17 (35.4%), 9 (56.3%), 28 (49.1%), and 10 (41.7%) individuals with membership coefficients of Q ≥ 0.8 in Group 1, Group 2, Group 3, and Group 4, respectively. The first two PCs represented the groups corresponding to the previously determined results. On the first PC axis, Group 3 was separated from the other three groups. On the second PC axis, Group 1, Group 4, and Group 2 could be distinguished from each other. The confidence interval was 0.95 and the two PC axes explained 10.54% and 7.76% of the variation, respectively , srad5 (solar radiation in May), srad6 (solar radiation in June), and srad7 (solar radiation in July) related to solar radiation, while prec4 (precipitation in April) and tmax6  described the precipitation and temperature patterns, respectively  to explore the patterns of the seven chromosomes’ diversity. The average SNP density was 17 SNPs per million base pairs, the average nucleotide diversity (pi) was 0.30 per million base pairs, the average Watterson’s theta estimator (θ) was 0.18 per million base pairs, and the average Tajima’s D was 1.87 per million base pairs Fig. A. Then, Changing environmental conditions force organisms to become phenotypically plastic, migrate, or adapt to avoid extinction  in shaping the population structure of the 157 naked barley landraces. By applying outlier methods to 37,636 SNP markers, we identified potential signatures contributing to local adaptation. Within the naked barley landraces, we identified 136 significant loci associated with temperature, precipitation, and ultraviolet radiation, of which 68 (50%) were located in intergenic regions and 13 had pleiotropic effects. Furthermore, we mapped 447 genes, including a known cold stress-responsive gene Supplementary Tables


Introduction: Age-related macular degeneration (AMD) is a progressive neurodegenerative disease and the leading cause of blindness in developed countries. Current genome-wide association studies (GWAS) for late-stage age-related macular degeneration are mainly single-marker-based approaches, which investigate one Single-Nucleotide Polymorphism (SNP) at a time and postpone the integration of inter-marker Linkage-disequilibrium (LD) information in the downstream fine mappings. Recent studies showed that directly incorporating inter-marker connection/correlation into variants detection can help discover novel marginally weak single-nucleotide polymorphisms, which are often missed in conventional genome-wide association studies, and can also help improve disease prediction accuracy.Methods: Single-marker analysis is performed first to detect marginally strong single-nucleotide polymorphisms. Then the whole-genome linkage-disequilibrium spectrum is explored and used to search for high-linkage-disequilibrium connected single-nucleotide polymorphism clusters for each strong single-nucleotide polymorphism detected. Marginally weak single-nucleotide polymorphisms are selected via a joint linear discriminant model with the detected single-nucleotide polymorphism clusters. Prediction is made based on the selected strong and weak single-nucleotide polymorphisms.Results: Several previously identified late-stage age-related macular degeneration susceptibility genes, for example, BTBD16, C3, CFH, CFHR3, HTARA1, are confirmed. Novel genes DENND1B, PLK5, ARHGAP45, and BAG6 are discovered as marginally weak signals. Overall prediction accuracy of 76.8% and 73.2% was achieved with and without the inclusion of the identified marginally weak signals, respectively.Conclusion: Marginally weak single-nucleotide polymorphisms, detected from integrating inter-marker linkage-disequilibrium information, may have strong predictive effects on age-related macular degeneration. Detecting and integrating such marginally weak signals can help with a better understanding of the underlying disease-development mechanisms for age-related macular degeneration and more accurate prognostics. AMD is a progressive neurodegenerative disease characterized by reduced retinal pigment epithelium (RPE) function and loss of photoreceptors in the macula . By 2040A recent landmark genome-wide association study (GWAS) has identified 52 independently associated variants across 34 susceptibility loci for AMD by comparing the late-stage AMD patients and unaffected controls of European ancestry . Even thr2 statistic, which spans from 0 to 1, is one of the most widely used LD metrics. A higher r2 value indicates a stronger correlation between two SNPs. In this study, we use r2 to explore the whole-genome LD spectrum. For each genomewide significant SNP identified from a GWA scan, we detect its high-LD connected SNP cluster using connected component searching algorithms (We use linkage disequilibrium (LD) as the metric to measure the inter-SNP correlations. In genetics, “LD” refers to the non-random correlations between alleles at different loci . It has gorithms , such thgorithms .International Age-Related Macular Degeneration Genomics Consortium (IAMDGC) has collected genetic data from AMD patients and control subjects across 26 different studies. IAMDGC dataset is by far the largest-scaled AMD GWAS dataset . In thisOur analysis included a total of 5,002,684 common SNPs from chromosomes 1 through 22 with minor allele frequency (MAF) larger than 0.1. Each SNP is coded by its number of minor alleles . It is known from previous studies that this sample population does not contain an obvious heterogeneous structure. We also carried out a principal component analysis (PCA). The PCA results showed no obvious population structures. We calculated the kinship matrix using 100,000 randomly sampled SNPs across the whole genome. The absolute values of the KING-robust estimators on 100,000 randomly chosen sub-principal matrices were very small . All the above evidence suggests that the samples used in our study are independent. Following previous studies using the same data cohort , we did Xn×p, where n is the number of subjects and p is the total number of features. We use Xj to denote the length-n sample vector for feature j, j = 1, … , p. Let y denotes the length-n vector of outcome class labels, and yi denotes the class indicator for subject i, i = 1, … , n. We use nk to denote the size of class k, k = 0, 1, where k = 0 for normal controls and k = 1 for late-stage AMD patients.Denote the observed data matrix by j, j = 1, … , p, we calculatedp-values less than 5 × 10−8 were selected as the marginally strong SNPs.Marginal Student t-tests were used to select marginally strong signals. Specifically, for each SNP r2 values for all SNPs on each chromosome. We then thresholded r2 values at 0.7 so that only SNPs in high LD were considered connected and included in the downstream analyses. For each marginally strong SNP, we employed the connected component searching algorithms in B such clusters detected, we denote those clusters by Cl, where l = 1, … , B.We used PLINK 1.9 , a tool Cl, l = 1, … , B, we calculated the multivariate t-statistic by Cl. Cl. For each SNP j in Cl, its LD-adjusted differentiating statistic is the corresponding entry in j has in differentiating the diseases and controls after integrating the LD information with its connected SNPs. Marginally weak SNPs were selected based on Cls, which were of much smaller sizes, thus making the calculation feasible. Not every SNP within an LD-connected cluster would necessarily be informative for classification. Only the top 1,000 SNPs with the greatest LD-adjusted effect sizes were then calculated for SNPs in the detected connected components. Specifically, for each etection . For an m = 1, … , M, was then generated by repeating the process M = 120,000 times. The percentage of the occurrences that the permuted statistics p-value for each selected SNP j. Last, to further control for the family-wise error rate, a Bonferroni correction was made for all selected SNPs.To evaluate the statistical significance of the selected SNPs, a post-selection non-parametric permutation test was performed. We first constructed a permutation dataset by re-sampling (with replacement) the class labels but keeping the genotype matrix unchanged. We kept the detected strong SNP set and the connected components unchanged to preserve the connection patterns. We then recalculated the statistics p-values were provided for each canonical pathway. A pathway with a p-value less than 0.01 was considered a significantly enriched pathway.All SNPs in the analysis were first mapped onto genes in the Human Genome GRCh37/hg19 assembly using the R package “biomaRt” . A SNP nFor each significantly enriched pathway, we reported its leading-edge genes. Leading edge genes can be viewed as the pivot genes in the pathway because of which the pathway got enriched . We evalWe fitted a logistic regression with a Lasso penalty and the selected marginally strong and weak SNPs for outcome prediction. We included age and gender as adjusting covariate variables.The whole analysis pipeline is summarized in Algorithm 1Marginally strong SNPs: From marginal t-tests.1: LD connected SNP clusters: For each strong SNP, detecting its high-LD connected SNP clustered by searching for its connected component in the thresholded LD matrix.2: Marginally weak SNPs: Detected by ranking the LD-adjusted statistics 3: Functional enrichment analysis.4: Late-stage AMD prediction: Prediction using Lasso-logistic regression with selected SNPs and adjusting covariates.5: Procedure for detecting strong and weak signals and late-stage AMD classification.r2 > 0.7 with at least one of the marginally strong SNPs. Those 3,446 SNPs were clustered within 410 connected components. Variants selection was conducted on each of the 22 autosomes separately. There were 4,679 genome-wide significant SNPs detected as the marginally strong SNPs, of which 26 had been reported by eak SNPs .p-value = 1.124 × 10−4. As a result, it is likely to be missed from downstream fine mappings in a conventional GWAS. However, Once the LD information is integrated, SNP rs7907130 has the most significant permutation test p-value among all the 32 SNPs. Therefore, its predictive effect is manifested through integrating its LD connections with the strong SNPs. Moreover, SNP rs7907130 was mapped to a previously identified gene BTBD16 . Based on the permutation test results, 55 out of the 4,679 selected marginally strong SNPs  is mapped by one marginally strong and one marginally weak SNP. Chronic inflammatory illnesses like asthma (DENND1B encoded protein and its interaction with tumor necrosis factor α (TNF-α)  or HMHA1 (minor histocompatibility antigen 1) is mapped by one marginally strong SNP. It controls endothelial integrity, whereas endothelial barrier dysfunction can lead to uncontrolled leaks or edema  is associated with two marginally strong SNPs. It can induce genotoxic stress and DNA damage  . Althoug and CNV . ARHGAP4or edema . In CNV,A damage , which i the eye  and is a the eye .BAG6 (BAG Cochaperone 6) and C6orf47 (chromosome 6 open reading frame 47) are mapped by one significant marginally weak SNP. BAG6 gene is found to be a component of a cluster of immune-relevant genes located within the human major histocompatibility complex class (MHC) III region  is a neuroprotective response to amyloid-β (Aβ), a major component of Alzheimer’s disease plaques that may cause increased neuronal death  and its ligand PD-L1 play critical roles in the immune response and autoimmunity beyond cancer  can encourage the retro-differentiation of Müller cells in the retina into retinal progenitor cells by regulating the expression of OCT4 in these cells  are confirmed as leading edge genes within the AMD-related gene sets, including “GOBP: activation of the immune response,” “GOCC: photoreceptor inner segment,” “HP: choroidal neovasularization,” “HP: macular degeneration” among others. These serve to further illustrate the role that marginally weak genes have significant co-regulatory effects, even no significant differentiating effects, in the onset and course of the disease. Identification of these weak signals could provide new insights into a better understanding of the underlying disease mechanisms.We also conducted a gene set enrichment analysis (GSEA) using the curated gene sets in Molecular Signature Database (MSigDB v7.5.1 c2). Multiple gene sets linked to AMD-related phenotype changes or biological processes were enriched. They include extracellular matrix organization, complement system, lipid metabolism, immune, and inflammation responses . MarginaFor prediction, we applied a five-fold cross-validation, where the whole dataset was partitioned into five folds of sub-datasets with similar sample sizes. In each round of cross-validation, we aggregated four folds of data into a training set for model training. We used the remaining fold as the test set for AMD status prediction. Through the five rounds of cross-validations, each sample in the original dataset would have been predicted once. Prediction performance was evaluated by comparing the predicted labels to the observed ones.Our method, which takes into account both weak and strong signals, has a prediction accuracy of 0.769 with an area under the receiver operating characteristic curve (AUC) of 0.768. With only the 4,679 marginally strong SNPs, it achieves a prediction accuracy of 0.732 and an AUC of 0.730. This demonstrated that marginally weak signals could help to improve the prediction performance.BTBD16, C3, CFH, CFHR3, HTARA1) were confirmed in our study. Novel AMD-related genes DENND1B, PLK5, ARHGAP45, and BAG6 were detected as weak signals. More AMD-related biological pathways were enriched when marginally weak signals were considered.Our study employed a novel machine-learning method to detect marginally weak variants in a GWAS. By exploring and integrating the genome-wide LD spectrum into GWAS, we were able to detect novel susceptibility genes for AMD and explain more disease variability. The previously identified genes like For the effect size estimates of the selected SNPs, compared to the marginal approaches, the estimates from our approach would be closer to their oracle estimates, which are the estimates from fitting a joint model assuming one knew prior to the analysis the subset of true causal SNPs . It is pIn our approach, each weak signal detected must have a high-LD path linking it to some marginally strong SNPs. It is cognizant that other than linking to strong signals, some weak signals work together by themselves as a group to exude their overall predictive effect on the outcomes, such as a group of rare variants . Our appOur weak variants detection method can be directly applied to GWA studies with other types of trait variables: continuous, ordinal, counting, and survival outcomes. We envision its wide range of applications.


Apis cerana cerana larvae raised in vitro and challenged with SBV. After filtering, a total of 337.47 gigabytes of clean data and 31,000,613 high-quality SNP loci were detected in three populations. We used ten databases to annotate 9359 predicted genes. By combining population differentiation index (FST) and nucleotide polymorphisms (π), we examined genome variants between resistant (R) and susceptible (S) larvae, focusing on site integrity (INT < 0.5) and minor allele frequency (MAF < 0.05). A selective sweep analysis with the top 1% and top 5% was used to identify significant regions. Two SNPs on the 15th chromosome with GenBank KZ288474.1_322717 (Guanine > Cytosine) and KZ288479.1_95621 (Cytosine > Thiamine) were found to be significantly associated with SBV resistance based on their associated allele frequencies after SNP validation. Each SNP was authenticated in 926 and 1022 samples, respectively. The enrichment and functional annotation pathways from significantly predicted genes to SBV resistance revealed immune response processes, signal transduction mechanisms, endocytosis, peroxisomes, phagosomes, and regulation of autophagy, which may be significant in SBV resistance. This study presents novel and useful SNP molecular markers that can be utilized as assisted molecular markers to select honeybees resistant to SBV for breeding and that can be used as a biocontrol technique to protect honeybees from SBV.Sacbrood virus (SBV) is a significant problem that impedes brood development in both eastern and western honeybees. Whole-genome sequencing has become an important tool in researching population genetic variations. Numerous studies have been conducted using multiple techniques to suppress SBV infection in honeybees, but the genetic markers and molecular mechanisms underlying SBV resistance have not been identified. To explore single nucleotide polymorphisms (SNPs), insertions, deletions (Indels), and genes at the DNA level related to SBV resistance, we conducted whole-genome resequencing on 90  Apis cerana cerana (A. c. cerana) and Apis mellifera (A. mellifera) are widely used model species for apiculture and crop pollination, offering significant economic benefits [A. mellifera [A. cerana comprises 144 genes and 12 pathways [A. cerana than in A. mellifera [Both benefits ,2. Howevbenefits . Sacbrooellifera ,5. SBV cellifera ,7,8. Dueellifera ,10. The ellifera ,12. The ellifera . Latent ellifera . Honeybeellifera . Honeybepathways . SBV is ellifera .A. cerana using exogenous double-stranded RNA (dsRNA) produced by the pathogenic insect bacterium Bacillus thuringiensis (Bt), demonstrating the potential for using Bt as a platform for effective dsRNA production to control viral infections in host insects. Additionally, Radix isatidis extract has been shown to have antiviral properties against SBV [To develop effective disease control strategies for preventing SBV development, it is essential to gain a deeper understanding of bee resistance and the molecular mechanisms involved in resistant bee larvae. Various methods have been explored for managing SBV infection in honeybees, such as RNA interference (RNAi). However, large-scale RNAi isolation and purification are necessary to prevent SBV infection in bees . The useinst SBV .A. cerana have reported the usefulness of SNPs and simple sequence repeats (SSRs) as markers [A. c. cerana genome is smaller than the A. mellifera genome due to length inconsistency and has been sequenced to investigate adaptation and diversity in different climates [A. mellifera are more widespread than those in A. cerana [A. cerana was generated in 2015 by Park et al., with an estimated size of 228.32 Mb, 2430 scaffolds, and a 152.0 N50 genome coverage [A. c. cerana, with a genome size of 228.79 Mb [A. c. cerana is suitable for studying population differentiation index (FST), gene variations, and genetic structure. Illumina sequencing is a precise platform that can generate millions of short-read sequences [ST and population differentiation index (π) has been used to assess the high royal jelly production of the honeybee A. mellifera [ST comparison pools and individual sequencing have been reported for studying the populations, structure, and diversity of European honeybees [A. c. cerana are required to identify which SNPs alter cellular function and contribute to disease to cover any protection gaps for this bee species. SNP molecular markers are commonly used in genetic research in various organisms [Molecular markers and genomic DNA sequencing technologies are commonly used in studying honeybees from various perspectives. Genome-wide association studies (GWAS) in  markers . The A. climates . Studies. cerana ,27. The . cerana . The fircoverage . In 201828.79 Mb . A. c. cellifera . SNP molrganisms .A. c. cerana remains unknown. To address this, we conducted experiments to explore the molecular mechanism of larval bee resistance to SBV. We raised larvae in vitro and challenged them with SBV, and the results are shown in All studies have shown that identifying SNPs and molecular mechanisms of larval resistance to SBV is crucial for disease control in honeybees. However, the exact mechanism by which SNPs and alleles affect honeybee susceptibility or resistance to A. c. cerana) larvae. The results of this study may be useful for accelerating molecular breeding, assisted selective breeding to develop SBV resistance, and as a biocontrol method to manage SBV infection in honeybees.The primary aim of this study was to identify SNPs linked to SBV resistance in honeybees and to explore genomic DNA variations between R and S larvae using whole-genome resequencing. Additionally, this study aimed to identify SNP loci, genotypes, and associated genes to gain a better understanding of the molecular mechanisms underlying SBV resistance in honeybee . The comparison of survival function between the treatment and control groups of resistant (CR) and susceptible (CS) colonies showed a difference. However, the mortality rates of the control groups between CR and CS were not significantly different , SBV detection, transmission electron microscopy (TEM) characterization, sequencing, and phylogenetic analysis. The results revealed that SBV was the only infection detected in the pure viral samples, and none of the six common bee viruses , Kashmir bee virus (KBV), black queen cell virus (BQCV), Israeli acute paralysis virus (IAPV), and chronic bee paralysis virus (CBPV)) were detected. Colony screening for common bee viruses showed single virus infection, multiple virus infections, and no infection A. The q-ifferent D. A. c. cerana S and R larvae was completed, and the results are presented in A. c. cerana, all variants of SNP loci, and Indels are summarized.The resequencing of 90 individual samples from The pipeline was utilized to identify all variations through data processing, bioinformatics analysis, and functional annotation, as shown in Based on the location of the mutation site in the reference genome and the location of the gene in the reference genome, we can identify the region of the genome, including intergenic regions, coding sequence regions (CDS), and the impact of the mutation, such as synonymous and nonsynonymous mutations. Using the VCF format file as both input and output, we can assess various fields in the info column of the VCF file, including EFF = Effect , which are added as part of the output. All 1,048,575 SNPs and 424,131 indels identified were annotated, and gene information was obtained from the reference genome to detect and annotate the location of small indels in CDS regions. The results show that most of the SNPs and indels were annotated in intergenic regions. ST and compared with the reference genome, and INT < 0.5 and MAF < 0.05 with their significant region genes are shown  nonsynonymous mutated genes between the reference genome and the sample. The differences in genes between the R and S individuals of 90 samples compared with the reference genome are shown in re shown .ST and π from COG, KOG, GO, and KEGG and the results of the classification are shown in The Basic Local Alignment Search Tool (BLAST) was used to compare mutant genes with functional databases  such as To investigate the evolutionary relationships between R and S from the same species, a phylogenetic tree was constructed based on a neighbor-joining (NJ) tree composed of 45 R and 45 S samples from three populations of all informative SNPs used in the phylogenetic analysis. The results showed that the group samples from three colonies were well differentiated in clades; however, the R and S from the sample colony were still slightly mixed together and differentiated from other colonies. Therefore, the three populations were the same species and in the same location, and there was no distance between the sampled geographic areas. However, the phylogenetic tree of SNP validation in the field from different geographic locations and a comparison of RC and SC showed a distinct evolution between R and S larvae, which were well differentiated into separate clades and branches .A population structure analysis can quantify the number of ancestors of the studied populations and infer each sample’s blood origin. It is currently a more widely used group cluster analysis method that helps us understand material evolutionary processes. The number of subgroups  was set from 1 to 10 for the clustering of the study population. The optimal number of groups was determined based on the valley value of the cross-validation error rate in the admixture, and the individual ancestries from SNP genotypes were investigated. The clustering results of K-values from 1 to 10, the cross-validation error rate corresponding to each K value, and the model K = 6 obtained from 90 samples in the two groups is shown in C,D.Through a principal component analysis (PCA), it is possible to determine which samples are relatively close and which samples are relatively distant, which can assist in understanding the evolution between traits. The results showed that total of 20.76% variance between R and S. PCA 1, PCA 2, and PCA 3 showed some differences. Most of the SNPs in R were relatively separately clustered; however, simultaneously, the clusters were close to S in distance. A similar result was observed in the phylogenetic tree PCA 3D plot E. To estST and θπ based on the calculation results, and a particular region was selected , the frequency of the neutral linkage relationship of neutral mutation increased. In contrast, the frequency of no linkage neutral site mutations decreased, resulting in a decrease in polymorphism in this region. The method for detecting selective sweep regions in the genome-wide range is to calculate the population genetic indicators of all SNP sites within a sliding window . A specific step size  was chosen, such as Fselected . The samST and θπ, independent and combined FST, and θπ genome-wide analysis of each candidate site showed that R and S are considerably different from the reference genome in the 457 scaffolds region of the chromosome-level selective sweep analysis. Comparison results of each chromosomal region were extracted at 19,551 positions. A selective sweep analysis showed that the window and step size statistics intersection in 6,464 scaffolds between R and S were significantly different  and their corresponding alleles  were significantly different in the three colonies with the same colony samples used for DNA sequencing. Further SNP validation was performed to ensure the reliability of these two SNPs with comparisons of R and S larvae from different geographic locations and populations. The results showed a highly significant difference between the R and S larvae. To verify the candidate SNPs, we selected 10 SNPs based on the combined FAPCC_06899 gene at 1804 bp distance. The second SNP was an intergenic SNP relatively far from the APCC_01833 gene with a 53,591 bp distance. According to SNP annotation, the first SNP (KZ288474.1_322717) was an upstream effect transcript ID = RNA 9701 with a gene close to the The comparison results of multiple SNP validations from the same individual sample and different samples from different geographic locations were consistent between two SNPs in the R, S, and genotyped queens conformed to each other with a slight change.In addition, in the R samples, the frequencies of G allele at the SNP KZ288474.1_322717 position and C allele at the SNP KZ288479.1_95621 position were high percentage allele frequencies. On the other hand, in the S samples, high percentage allele frequencies of C and low percentage allele frequencies of T were observed; however, in the R samples, C and T at two SNPs position were very low and even were not observed in some colonies. A comparison of two SNPs with same sample was conducted, which indicated the reliability of each SNP and the accuracy of the Sanger sequencing method and supported the reliability of the first SNP detection and validation results.A. c. cerana) with SBV from 90 individual samples from three populations in Fuzhou, China, then resequenced and analyzed them to screen SNP genetic markers associated with SBV resistance [ST and π, independently and in combination using Popgenome [ST and π ratios. All regions were significantly different for segregation sites and the INT was 0.05 and the MAF was < 0.05 for R and S. The significant regions under artificial selection with FST and π 0.99 and 0.95 (top 1% and top 5%) were then selected by integrating the FST and π statistics. We cataloged 84 unique SNPs with seven predicted genes and 735 unique SNPs, with ten anticipated genes based on combined FST and π. FST and π R divided S and FST and π S divided R were significantly detected in 90 samples.In this study, we challenged larvae of honeybees  and KZ288479.1_95621 (C > T), located on the 15th chromosome with GenBank IDs, which were associated with SBV resistance based on their allele frequencies.To identify and validate the SNPs of interest, we selected the top ten SNPs with the highest proportion of genotypes based on the Fp > 0.05), indicating that the three colonies were resistant to SBV. For the second SNP (KZ288479.1_95621), three symptomatic colonies were selected in Minhou after larval rearing, sequencing, and analysis. The allele frequency was significantly different. During the validation process, it was discovered that the honeybee colony might have evolved naturally to become resistant to SBV and not carry the susceptible allele C/C at the SNP KZ288474.1_322717 position. Each colony or population exhibited different levels of resistance and susceptibility [The validation of the SNPs began with 16 R and 16 S samples from three colonies, followed by validation with 32 R and 32 S samples from the same colonies using DNA sequencing. Out of the 20 candidate SNPs, three were not well amplified, and 15 were not significantly different, shown in Therefore, to further validate the SNPs and assess their association with SBV resistance across different geographic locations, we selected additional colonies from various regions. The SNP validation data showed significant variations in allele frequency between 32 R and 32 S samples in each symptomatic colony. Asymptomatic colonies had high frequencies of G/C and C/C alleles but very low frequencies of G/G alleles, while symptomatic colonies had low allele frequencies. In addition, asymptomatic colonies had a high C/C allele frequency at both SNPs, whereas symptomatic colonies had a high T/T allele frequency at the second SNP (C/T). To investigate whether these genotypic and phenotypic characteristics were consistent across different geographic regions, we obtained symptomatic samples from three colonies in Yunnan Province, located 2306.5 km away from Fuzhou City. Although these colonies were infected with DWV and did not show visible SBV symptoms, we selected R samples (pupae) for analysis. Screening and analysis of both SNPs revealed that most of the pupae samples had G/G and C/C alleles, and susceptible alleles (C/C and T/T) were not detected. These results provide further evidence of the association between these SNPs and SBV resistance, regardless of geographic location. Additional evidence was gathered from 14 resistant queens from Nanping and three apiaries, as well as one apiary in Fuzhou. Genotyping revealed that only one queen carried the G/C and C/T genotypes for the two SNPs, while the remaining queens had G/G and C/C genotypes. These results, combined with those of the genotyped queen, suggest that honeybee colonies with homozygous resistant G/G alleles at the SNP position KZ288474.1_322717 and C/C alleles at the SNP position KZ288479.1_95621 are likely to be strongly resistant to SBV. Conversely, colonies with homozygous susceptible alleles (C/C and T/T) are expected to be extremely susceptible to SBV, while those with heterozygous susceptible alleles (G/C and C/T) may only be moderately susceptible. These findings underscore the importance of choosing colonies and queens for breeding programs.A. melliferra [The fact that the alleles generating SBV resistance in both SNPs were found to be correlated with one another in the 90 samples of genotype sequences and SNP validation highlights the accuracy of these SNPs. To further ensure their accuracy, we compared the results of 210 samples for the two SNPs using the same sample, as well as 48 samples from two different sequencing companies. We aimed to verify whether the G/G allele at SNP position KZ288474.1_322717 and C/C allele at SNP position KZ288479.1_95621 corresponded with one another, and vice versa. We found that the results from both groups were nearly identical after peak assessment, indicating that sequencing errors were minimized. We further validated the successful SNPs in a large number of samples from various geographic locations to confirm their reliability. The use of these SNPs as valuable genetic markers in selective breeding programs for improving multiple honeybee traits, including SBV resistance, has been suggested in previous studies that found the same corresponding alleles related to SBV resistance, high royal jelly production, and chalk-brood resistance in lliferra ,27,31.Late SBV infections were observed in the prepupae during the larval-rearing experiment. The larvae and prepupae were not identified as SBV-resistant until the larvae had fully transformed into pupae at the 12–14 day instar and were then individually sampled as R . The morThe clean reads were mapped to the reference genome to obtain the mapped reads. A high sequencing read ratio (~70%) indicates appropriate reference genome selection and the absence of contamination during the experiment. The assembly of the reference genome is important for the quality of sequencing reads and the locatability of reads in the reference genome, which is directly proportional to the degree of similarity between the species. On average, the mapping rate of the sequence data was 87.92% . CompareWhole-genome sequencing data were utilized to investigate the evolutionary relationships among three populations using various analyses, including phylogenetic tree, population structure and admixture, PCA, and kinship analyses. These analyses were conducted to describe the taxonomic and evolutionary relationships between the traits that were assumed to have a common ancestor. The three populations showed clear differentiation in clades with some mixing within groups. Additionally, some distance was observed in the ancestries between the three populations, and the best K = 6 was obtained in admixture analysis . ST and π were selected for SNP validation, considering the narrow range of significant regions and the high allele frequency. The SNP genotypes were present in the R group, and they differed from the selected verified reference genome alleles in the S group. However, only a few genes were annotated, which could not explain the more positive effects; therefore, we considered the top 5% annotation results from the same site where we detected two SNPs [The mRNA level genes were evaluated using Illumina sequencing data. Selective sweep, INT < 0.5, and MAF < 0.05 were applied to obtain highly consistent genes. All genes within 100 kb of significant SNP regions were extracted, and their annotations were considered. All predicted gene directions were upregulated. The top 1% of genes based on combined Ftwo SNPs .ST and π top 5%. The functional annotation of 90 individual samples was conducted across 10 databases of 9,359 different genes, including integrated functional annotation statistics, GO gene list 1399, function, 1812 in the biological process (BP), 820 in cellular component (CC), and 10,583 unique genes in molecular function (MF). In the 116 KEGG pathways, 2908 and 3842 unique genes with positive effects were annotated, most of which were involved in hypothetical protein signal transduction mechanisms [A. c. cerana, 9359 different predicted genes against ten databases were annotated, which is close to the previously identified and annotated genes in the A. cerana genome (9627) [SNP and indel annotation, as well as a prediction of different genes, were performed on individual samples from R and S, comprising 90 annotated samples, and on the specific region detected by the combined Fchanisms ,46. Somechanisms ,48,49,50e (9627)  ST and π values, R and S were divided into the top 5% gene annotations, resulting in 119 genes, of which 116 were annotated in various databases, such as COG, GO, Nr, Nt, Pfam, SwissProt, and TrEMBL (p < 0.05). Notably, APICC_03984 was found to be involved in the activation of MAPKK activity, pathogenesis, cellular hyperosmotic response, and signal transduction involved in filamentous growth in the biological process category. The curated GO BP annotation dataset shows that 63 genes participate in the activation of MAPKK biological processes, such as HRAS, JAK2, and BRAF. In the cellular component category, 79 out of 520 genes were related to the cytoplasm (GO:0005737), with the most enriched genes involved in metabolic processes (10), catalytic activity (10), cellular process (7), binding (10), biological regulation (5), response to stimulus (3), and signaling (2). The other genes were involved in different positive effects, and no genes were involved in the death pathway. APICC_00122 was found to be involved in exopeptidase activity. While the insect midgut has been primarily associated with digestion and detoxification, endopeptidases such as serine proteases (trypsin and chymotrypsin-like) and exopeptidases with varying terminal amino acid specificity (aminopeptidases and carboxypeptidases) are believed to play a key role in protein hydrolysis [Based on the Fd TrEMBL . Out of APICC_00398 and APICC_04707) having the highest enrichment factor (14.77) and a q-value of 0.052. Other enriched pathways included beta-alanine metabolism (APICC_01290 and APICC_10032), lysine degradation, glycerophospholipid metabolism, phagosome (APICC_03257 and APICC_08196), fatty acid metabolism (APICC_10032), protein processing in the endoplasmic reticulum, and endocytosis (APICC_01294 and APICC_10111) [In the GO molecular function category, two genes were significantly enriched in ATPase activity (GO:0016887). Among the 90 genes in KOG, 46 were annotated in KEGG, with ABC transporters (C_10111) ,54.A. cerana has been previously reported to have a de novo assembly with 10,182 predicted genes [A. cerana with a data size of 228 Mbp and 879 scaffolds [A. c. cerana genome still needs further sequencing and improvement. Although the genomic size of A. cerana is slightly smaller than that of A. mellifera, more evidence is required to explain the significant differences between the two species [The genome of ed genes . Anothercaffolds . The A.  species . Bee viruses are usually asymptomatic but can significantly affect honeybee health and their lifespan under certain conditions. While SBV symptoms in larvae are evident, they are not clear in adult bees ,56. WorkExperimental colonies were established at the experimental apiary at the College of Animal Sciences (College of Bee Science), Fujian Agriculture and Forestry University, to conduct experiments. Some samples were directly collected from apiaries. The samples and colonies were selected from Fujian Province and Yunnan Province. The details of sample collection and the number of colonies used in this study are summarized in To purify SBV from diseased larvae and to select healthy colonies, this study was conducted in different geographic locations in Fujian Province. We selected three apiaries in Yongtai and one apiary in Fuzhou. A symptomatic colony was sampled from each apiary. Three frames with obvious SBV symptoms were selected from each colony and extracted from the hives. The frames were marked and immediately placed on dry ice. The samples were quickly transferred to the laboratory. From each colony, 250 old, infected larvae were collected in pooled samples. All the samples were directly stored at −80 °C for isolation and purification of SBV.Three out of eight healthy colonies were selected for larval rearing after screening against seven common bee viruses, see the details in the virus isolation and purification section. After larval rearing from three colonies challenged with SBV 90 samples, each colony was composed of 30 samples (15 S larvae between 7–10th instars and 15 R larvae (pupae) at 12–14th instars) randomly selected for genomic DNA extraction and genome sequencing. The first SNP validation was conducted using the same colony samples that were previously used for genomic DNA sequencing, which were composed of 16 R and 16 S. The second SNP validation of each colony was composed of 32 R and 32 S from the same three colonies for SNP 95621 and SNP 322717; two colonies from the same colonies and one extra colony from Fuzhou were used.Three asymptomatic bee colonies from the Fuzhou area were selected for in vitro culture to verify the effectiveness of these two SNPs in the field. Each colony consisted of 64 samples (32 R and 32 R). By SNP verification, three asymptomatic colonies in Fuzhou and three symptomatic colonies in Minhuo were compared. The SNP 95621 was validated in two symptomatic colonies in Putian, one asymptomatic colony in Fuzhou, and three symptomatic colonies in Minhou County. Each population consisted of 64 larval and pupal samples (32 R and 32 S) of SNP 322717.To verify whether these two SNPs function reliably in different geographical locations, we conducted further SNP verification in three symptomatic colonies  and selected SNP 322717. Another SNP (95621) was identified in two symptomatic colonies in Putian, one colony in Nanping, and two colonies in Fuzhou. Each colony consisted of 32 R and 32 S. Samples in frame form, one symptomatic and two asymptomatic from Yunnan Province, were obtained by express, and 48 R samples (pupae at the white eye stage) were randomly sampled from combs and analyzed.Further SNP validation in resistant queens was conducted on 14 queens from four apiaries (three apiaries in Nanping: twelve queens and two apiaries in Fuzhou: two queens), which were directly collected from the hives, kept in queen cages, and transferred to the laboratory. In addition to comparing the genotypes, we investigated whether the allele frequencies of the two SNPs corresponded to each other and whether the Sanger sequencing was accurate. Of 210 samples from R and S, the queen samples included in SNP validation were compared separately. Overall, a total of 926 samples for SNP 322717 and 1022 samples for SNP 95621 were used for SNP verification in this study.g for 20 min. The supernatants were collected in new clean tubes and the pellets were discarded. The high-speed centrifuge  was turned on for a while to cool to 4 °C. The supernatant was transferred into ultracentrifuge tubes and balanced before centrifugation at 27,000× g for 4 h. The supernatants were discarded, the pellets were dissolved using 0.4% sodium deoxycholate acid (Na-DCA), and 4% Brij 58  was added, vortexed, and centrifuged again at 800× g for 15 min for clarification. The supernatants were carefully transferred into the same tubes we used before, then 5.5 g cesium chloride (CsCl) was added to each tube, well-mixed, then balanced, and transferred into the ultracentrifuge again and centrifuged at 27,000× g for 24 h at 18 °C. Two typical cloudy bands were formed in the supernatant and in the middle of the tubes. The bands were carefully collected separately in fresh tubes. To remove CsCl from the solution, three dialysis cycles were performed in double-distilled water (ddH2O) at 4 °C, and the water was replaced with fresh water after eight hours. Then, the solution was prepared in different vials  and kept at −80 °C for viral load quantification and inoculation.To isolate SBV, all necessary materials were autoclaved before virus purification. Prior to purification, 15 infected larvae were randomly selected. Additionally, 15 uncapped larvae were sampled from eight candidate colonies. Total RNA was extracted, cDNA was synthesized, and seven common bee viruses were screened using the RT–PCR method, as described by Hassanyar et al. . After sThe first observation of SBV was conducted using TEM, following the protocol developed by Zhou et al. . Briefly®Top Green qPCR SuperMix + Dye II . The cDNA was diluted 5 and 10 times, and templates of plasmid (Chinese sacbrood virus) CSBV DNA were used to create a standard curve and serially diluted from 10−2 to 10−8. cDNA and plasmid CSBV were used as templates for qPCR. The qPCR solutions were prepared according to the following components, and the total reaction system was 20 µL: cDNA purified virus 2 µL, Chinese Sacbrood virus (CSBV) forward primer 0.4 µL, reverse primer 0.4 µL, 2 × TransStart® Top Green qPCR SuperMix 10 µL, and ddH2O 7.2 µL. qPCR amplification consisted of 40 cycles of 95 °C for 30 s, 95 °C for 5 s, 58 °C for 15 s, 72 °C for 10 s, and 95 °C for 0.5 s. Data were analyzed based on the molecular weight and the CSBV DNA plasmid sequence length [qPCR was performed to measure the exact copy number of the virion particles . Purifiee length .2O). All the materials were weighed one by one, poured into a 100 mL beaker containing 37 mL of ddh2O, and mixed well. Royal jelly was stored at −20 °C and placed in a water bath at +34.5 °C for 10 min, then 50 g of royal jelly was poured off and placed into a 100 mL beaker and some of the nonessential particles were removed. Then, the larval diet was pureed and placed into a 20 mL test tube, and the date was marked. We fed this diet to larvae for three days, and the larval diet was prepared using the same method for subsequent experiments.Larval rearing was conducted between Mar and Jun 2018 and continued until Nov 2018. This was based on the protocol developed by Crailsheim et al. . Larvae 2SO4) 360 g/L water to prevent dehydration. which was prepared in a plastic box. The larvae were fed with a pipette and modified tips, and the remaining food was removed daily. Inoculation tests were conducted based on the method described by Hu et al. [6 copies/1 µL of the purified virus) were mixed well with 10 µL of food and placed into cell culture plates using a micropipette with a modified tip. The larvae were then allowed to thoroughly consume the food. After 24 h, in treatment groups 1, 2, and 3 and control groups 4, 5, and 6, no virus was added to the diet in all subsequent portions of the larval food. At 7 days, the larvae began defecating, determined by glycol uric acid crystals and fibrous substances on the well. The larvae were gently transferred by plastic forceps to the new cell culture plates, which were lined with two layers of sterilized filter paper (autoclaved and dried). Culture plate larvae were cleaned with sterilized tissue paper before being transferring to new cells. The plates were placed in incubators at ±34 °C and 75–80% RH  until pupation. The larvae were reared for an additional six days.Before grafting, the larvae were placed in cell culture plates, and the food and plates were prewarmed at ±34 °C for 15 min. Then, 20 µL of food was added to the culture plates, and second instar larvae (within 48 h of hatching) were gently grafted by Chinese grafting tools . The grau et al. . The 3rdMortality was recorded daily, and larvae were observed under a microscope for immobility, swelling, tissue breakdown, and infection with SBV. The larvae that showed symptoms of SBV were individually collected in a 1.5 mL Eppendorf tube and marked. When the larvae showed discontinued respiration and reduced flexibility, developed edema, and exhibited a color change to brownish, they were removed from the cell culture plates every day and recorded. Therefore, they could be contaminated and decomposed by bacteria and fungi. Larvae infected with SBV were distinguished, collected during observation, recorded, and stored at −80 °C for subsequent experiments .A. c. cerana colonies with three biological replicates. Each colony was composed of 15 R and 15 S for genomic DNA extraction. Genomic DNA was extracted from whole larvae and pupae using the Cetyl Trimethyl Ammonium Bromide (CTAB) method of Paterson et al. [260/288- OD 260/230 nm using a Nanodrop . A spectrophotometer was used to measure 1 µL of the DNA sample, and it was recorded directly. The DNA quality was visually monitored by electrophoresis on 1.0% agarose gel. Qubit 4.0  was used to quantify DNA using a fluorometer and diluted to 50 ng/µL for sequencing. The amount of DNA used for whole-genome sequencing was 2 µg per sample. For SNP validation, larvae were reared using the same method described previously. The samples were removed at −80 °C to a refrigerator  and dissected with sterilized scissors. The thorax from the pupae and half of the larvae were used for DNA extraction.After four months of larval rearing, we collected a sufficient number of samples. We randomly selected 90 samples (45 S at the 7–10th instar larva stage and 45 R at the 12–14th pupa stage) from three n et al. . The samData sequencing and bioinformatics analyses were conducted based on the pipeline. The experiments were conducted according to the standard procedure developed by Illumina . Qualified DNA samples were resequenced by whole-genome sequencing using a commercial Illumina NOVA HisSeq X-Ten Biomarker Technologies Corporation . Sample quality detection, genomic DNA library construction, library quality detection, and sequencing were also included. After the genomic DNA test, qualified DNA samples were fragmented by mechanical interruption (ultrasound), fragmented purification, end pair, 3′ ends plus A, and connection sequencing joints; electrophoresis on 1.0% agarose gels was used to select fragment sizes. Polymerase chain reaction (PCR) amplification was performed to construct a sequencing library. DNA libraries were constructed using the DNA Library Prep Reference Guide with an insert size of 150 bp and were constructed based on Illumina, following the manufacturer’s instructions , and paired-end reads were generated using Illumina. The DNA library was first built to conduct a quality inspection, and a qualified library was used for sequencing. The quality of the first reads (double-end sequence) was obtained by sequencing, evaluation, and filtering. Clean reads were used for subsequent bioinformatics analyses. Clean reads were compared with reference genome sequences, and variations in SNPs, indels, and other variants were detected and annotated based on the comparison results. Subsequent genetic evolution analyses were performed based on the detection of variation.A. c. cerana. Low-quality reads containing adaptor sequences, duplicate reads, and reads containing adaptor sequences were filtered based on the following three rules: the main steps of data filtering were the removal of adapter reads, removal of >10% Ns reads, and removal of >50% reads with low bases . The raw data or raw reads and the results were in the FASTQ format. They were stored in fq file format, containing sequence information for the sequencing reads and corresponding sequencing quality information. Clean sequencing data were used for SNP calling.Quality control (QC) processed low-quality data by filtering the sequenced reads or raw reads . To ensuA. c. cerana  using the ‘mem’ algorithm of Burrows–Wheeler Aligner (BWA version 0.7.5a-r405) [http://broadinstitute.github.io/picard/ (accessed on 26 December 2018) and local realignment around Indels. The Indels-Realigner in GATK Haplotype Caller algorithm in GATK software version 3.8 was used for SNP SNPs and small Indels mutations calling across the 90 samples with default parameters [The clean reads were calculated using the SAMTOOLS flag stat command . Filtere5a-r405) , with th5a-r405) . Clean dhttp://sourceforge.net/projects/picard/ (accessed on 26 December 2018) (Picard) were used to filter redundant reads (mark duplicates) to ensure the accuracy of the test results. The variation detection of SNPs and indels was carried out using the GATK haplotype caller  algorithm, and each sample was first generated in genome variant call format (gVCF). Then, the group joint-genotyped all VCF SNPs  software . Based oVCF SNPs . FinallyAnnotation was conducted to classify SNPs and Indels as genetic variants using the SnpEff toolbox , which wp ≤ 0.5) was used from 90 samples using the neighbor-joining (N-J) method under the Kimura 2-parameter model implemented by MEGA X Software, with bootstrap replicates of 1000 [http://itol.embl.de/ (accessed on 13 March 2019).To study the relationships and evolution between SNPs and traits, a phylogenetic analysis of all detected SNP genotypes after filtering was used to construct the phylogenetic tree SNPs with low MAF (<0.05). A low genotyping rate  was determined to calculate the significance of enrichment with a ST and π S divided R top 1% and FST and π R divided S top 1% with a high percentage genotype. For significant SNPs, 40 pairwise primers were designed to amplify the desired SNP for SNP validation (Apis-β-actin primer pair (181 bp) was used as an internal positive control [2O, was used for all PCR amplification assays. Amplified PCR products were analyzed using 5 μL of 2% agarose gel electrophoresis and visualized under UV light. The lengths were compared to that of a standard Trans 2 K molecular weight DNA ladder . After visualization, the PCR product of 20 μL of amplified target DNA was purified, positive strands (forward primer) were used in di-deoxy Sanger Sequencing, and the ABI file data were analyzed.To verify the candidate SNPs, we first conducted an (MAF < 0.05) analysis to select high-consistency SNPs based on the combination of Flidation . In the  control . A negatp value of < 0.01 was considered significantly different. Additionally, the survival functions of CR and CS were compared. For SNP flanking validation, specific primer pairs were designed using the default parameters of the BatchPrimer3 v1.0 online software. Chromas, BioEdit sequence Alignment Editor, and DNASTAR Lasergen SeqMan Pro were used to manually evaluate each sample peak and compare the variance in the genotype differences. The distributions of allele frequencies between R and S were analyzed using the chi-square test in Hardy–Weinberg equilibrium, where a p value of < 0.05 was considered significantly different. The ggplot2 package in R was used to plot the significant region graphs.GraphPad Prism version 9.00 (121) for Windows  was used to compare the control group with the treatment group to evaluate the survival function analysis. The Kaplan–Meier chi-square log-rank (Mantel–Cox) test was performed, where a Apis, including bumblebees, and effective methods to control this viral disease are urgently needed. Genome sequences are crucial for basic biological research on bees, and Asian honeybees provide an excellent model for understanding the genetic basis of SBV resistance at the genomic level. To catalog the genetic variations and nucleotide polymorphisms associated with SBV resistance in the A. c. cerana genome, bee larvae were challenged with SBV and raised in vitro, and 90 individual samples from three populations were resequenced. This study identified 31,000,613 high-quality SNPs with 696,352 individual genotypes compared to the reference genome. Two SNP molecular markers related to SBV resistance were identified and successfully validated by multiple verifications. The results showed that the two SNPs were significantly associated with SBV resistance, and these significant SNP loci were found on the 15th chromosome, composed of five putative candidate genes. The favorable G/G and C/C allele frequencies in the R samples at the two SNP loci revealed the molecular mechanism of SBV resistance by validation and analysis of the allele frequencies. Functional and enrichment analyses of predicted genes showed that genes involved in cellular processes might play a significant role in SBV resistance. The findings of this study may support and improve the honeybee A. c. cerana genome and may be employed in breeding to enhance SBV resistance and decrease SBV infection. Hence, SNP molecular markers can quickly and accurately identify SBV-resistant colonies, and further investigation is needed to determine if the two discovered SNPs are also responsive to other closely related species, such as A. mellifera.SBV is prevalent in the genus This study received two patents from the China Innovation Center which have already been published. Patent. Issue No. 2021030301132160. Publication No. CN 112430675 A, SNP labeling at KZ288474.1_32271. Another Invention Issue No. 2021100418633. Publication No. CN 112430675 A, SNP labeling at KZ288479.1_95621. Invention method for identifying SNP molecular markers related to sacbrood virus resistance.


EARLY FLOWERING 3 (ELF3) is an important regulator of various physiological and developmental processes and hence may serve to improve plant adaptation which will be essential for future plant breeding. To expand the limited knowledge on barley ELF3 in determining agronomic traits, we conducted field studies with heterogeneous inbred families (HIFs) derived from selected lines of the wild barley nested association mapping population HEB-25. During two growing seasons, phenotypes of nearly isogenic HIF sister lines, segregating for exotic and cultivated alleles at the ELF3 locus, were compared for 10 developmental and yield-related traits. We determine novel exotic ELF3 alleles and show that HIF lines, carrying the exotic ELF3 allele, accelerated plant development compared with the cultivated ELF3 allele, depending on the genetic background. Remarkably, the most extreme effects on phenology could be attributed to one exotic ELF3 allele differing from the cultivated Barke ELF3 allele in only one single nucleotide polymorphism (SNP). This SNP causes an amino acid substitution (W669G), which as predicted has an impact on the protein structure of ELF3. Consequently, it may affect phase separation behaviour and nano-compartment formation of ELF3 and, potentially, also its local cellular interactions causing significant trait differences between HIF sister lines. EARLY FLOWERING 3(ELF3) are determined and associated with agronomic traits, suggesting a central role for ELF3in barley adaptation.Novel naturally occurring exotic alleles of  The performance of crops such as barley depends on their ability to adapt to different environments, which ultimately determines the yield potential. In the context of an ever growing world population and climate change, maximizing crop yields for further food supply will be pivotal  and coulPPD-H1, a pseudo-response regulator, which promotes VRN-H3, a homologue of Arabidopsis thaliana (Arabidopsis) FLOWERING LOCUS T (FT), through CONSTANS (CO), but also independently of CO, leading to the induction of flowering  or earliness per se (eps) loci  loci .EARLY FLOWERING 3 (ELF3), which is the focus of this study. ELF3 is an integral part of the circadian clock in both Arabidopsis and barley (Flowering time is a complex trait which is controlled by a large regulatory network (d barley . It reguELF3 functions as a core component of the clock (ELF3 (AtELF3) is an oscillating gene with an expression peak in the early evening. AtELF3 encodes a multifunctional protein that in turn regulates various physiological and developmental processes , several clock orthologues from Arabidopsis have been identified with a high degree of conservation (Praematurum-a (Mat-a)/EARLY MATURITY 8 (EAM8) was identified as a barley homologue of AtELF3  has been observed, and increased expression of HvFT1 (VRN-H3) was observed independently of PPD-H1  contributed significantly to barley domestication and adaptation to higher latitudes by conferring a day-neutral flowering phenotype.In barley  population ‘Halle Exotic Barley’ (HEB-25). The population originates from crosses of 25 highly divergent wild barley accessions  with the elite cultivar Barke . A previous study on plant development traits in barley identified a quantitative trait locus (QTL) region containing the ELF3 locus  accelerated plant development and decreased plant height compared with the cultivated ELF3 allele (ELF3Hv). Furthermore, ELF3Hsp effects for the mentioned traits varied for the 25 HEB families. Results for QTL1H10 (128–133.1 cM), the respective QTL for ELF3, were extracted from ELF3Hsp has varying effects among HEB families, but it is, in most cases, accelerating flowering (up to 2 d) compared with the cultivated barley ELF3Hv allele. Except for family 24, ELF3Hsp effects were stronger in Dundee (UK). Here, the maritime climate in 2014 and 2015 was characterized with colder summers, more and equally distributed rain, and greater day lengths compared with Halle (Germany) with moderate-to-continental growing conditions . Of these, the novel eam8.l mutation was assumed to be responsible for an early flowering phenotype because of a mutation at position 3257 which probably contributed to intron retention and a truncated protein. However, other haplotypes also exhibited early flowering phenotypes, indicating the existence of other reasons for early flowering . HIFs can be derived from advanced generations of lines with initial heterozygosity at a genomic region of interest. In this manner, allele effects can be estimated in a nearly isogenic background  in one exotic ELF3 allele promotes expression of VRN-H1, potentially through a predicted altered protein structure of ELF3, which might reshape the phase separation behaviour and nano-compartment formation of ELF3.Besides time to flowering (HEA), additional phenological traits were investigated such as time to shooting (SHO), duration of shoot elongation (SEL), duration of ripening phase , and time to maturity (MAT). Furthermore, plant height (HEI), ears per square metre (EAR), grain number per ear (GNE), thousand grain weight (TGW), and grain yield (YLD) were investigated. Here, we describe significant effects of exotic 1S3 lines that were developed by an initial cross of the spring barley cultivar Barke (H. vulgare ssp. vulgare) with 25 highly divergent wild barley accessions (H. vulgare ssp. spontaneum and agriocrithon). For detailed information about the population design, see 1S3:11, which were heterozygous at ELF3 in generation BC1S3. In addition, HIF pair 25_002_BC2 originates from a backcross of HEB line 25_002 (BC1S3:7), carrying the ELF3Hsp allele, with Barke. Here, the HIF pair was selected from the segregating progeny of the resulting BC2 plant. With the chosen plants (11 HIF pairs from nine HEB families), two field trials were conducted in 2019 and 2020.For this study, HIFs were selected from the multiparental barley NAM population HEB-25 . A gene-specific marker (JHI_Hv50k_2016_57670), which is located inside ELF3, and flanking markers were used to determine whether heterozygosity was present at ELF3  markers covering the ELF3 region  to gather phenotypic data for 11 selected HIF pairs. Both field trials were sown in March (4 March 2019 and 19 March 2020), with fertilization and pest management carried out according to local practice. In 2019, the field trial was conducted in a randomized complete block design consisting of four blocks, each containing a randomized replication of the 11 selected HIF pairs. The plots consisted of three rows (50 seeds each) with a length of 1.5 m and a distance of 0.15 m between rows. Plots were evenly spaced by 0.3 m. The field trial in 2020 was conducted in six randomized blocks. The plots consisted of eight rows with a length of 3.2 m, a distance of 0.15 m between rows, 0.3 m between plots, and a seeding density of 300 seeds mThe growth period had the same length in both years, except that in 2020 sowing was carried out 2 weeks later than in 2019 (4 March 2019 and 19 March 2020) as earlier sowing was not possible due to too much rain and wet soil in 2020. Therefore, maturity of the latest line was 2 weeks earlier in 2019. During the respective growth periods of the field trials, the mean temperature was 0.5 °C higher in 2020 (13.4 °C); in the third month of the vegetation period, in particular, when heading started, the temperature was on average 3 °C higher in 2020. However, during the last month of the respective growth period, the temperature was almost 2 °C higher in 2019 (21.3 °C) with high daily average temperatures of up to 29 °C. In 2020, the highest daily average temperature was 23.8 °C . The sum2)  .ELF3 effects in a controlled environment, a phenotyping experiment was conducted using the LemnaTec system at the Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) in Gatersleben. One HIF pair (10_190), the cultivar Bowman, and two elf3 mutants in a Bowman background  and three AtELF3 protein domains were defined according to https://mafft.cbrc.jp/alignment/server/, and subsequently the respective HvELF3 protein domains were retrieved.The structure of Barke ELF3 coding sequences were extracted from https://www.ncbi.nlm.nih.gov/) and https://galaxy-web.ipk-gatersleben.de/). Determination of ELF3 haplotypes based on exon sequences was done using the ‘haplotypes’ package in R.Furthermore, for comparison purposes of the coding sequence variation in the HEB-25 wild donors with already described variation in the literature, –2 s–1. On day 17, starting from the onset of light (ZT00), leaf samples were harvested every 4 h, using three biological replicates. Total RNA was isolated using the NucleoSpin RNA Plant Kit (Macherey-Nagel), cDNA was synthesized using the PrimeScript RT Reagent Kit , and quantitative real-time PCRs were performed on an AriaMx Real-Time PCR System (Agilent) using Absolute Blue Low Rox Mix (Thermo Fisher Scientific). The reference genes HvGAPDH and PTF were used for normalization. Primer sequences are described in t-test using GraphPad QuickCalcs .To test for differences in gene expression between HIF sister lines, HIF pairs 10_003 and 10_190 as well as plants of Bowman and BW290 were grown for 16 d under LDs with 16 h light and 8 h darkness, day/night temperatures of 20 °C/18 °C, and a light intensity of 300 μmol mFor global structure prediction, a local installation of Alphafold v2.1.0 ’ and the ELF3 subsequences ‘SSRGSELQWSSAASSPFDRQ’ and ‘SSRGSELQGSSAASSPFDRQ’ were used. The derived hits were analysed by an in-house PyMOL script (available upon request) regarding their structural completeness (minimum of five resolved residues in the pdb file) and their annotated secondary structure. The weblogo was generated using the Berkley weblogo webserver  effects for genotype and year as well as for genotype×year interaction, except for SEL, MAT, and TGW between the two years and RIP, EAR, TGW, and YLD for genotype×year interaction (–1 (–1) whereas in 2020 it was far lower (38.8 dt ha–1). Due to the small plot size in 2019, yield was probably overestimated. Repeatabilities (Rep) for YLD confirm this fact, as Rep for YLD in 2019 is much lower than for YLD in 2020  in previous trials , the effects observed in the current study are more diverse and more extreme. This is most likely to be due to the relatively rough effect estimation in the GWAS model of In general, HIF sister lines carrying an th years . These fELF3Hsp allele, for HEA up to 3.75 d earlier, and for MAT up to 40.39 GDD earlier, which corresponds to 1.75 d in 2019  and gene expression analysis .Several significant effect differences were found, especially for SHO and HEA, most of which could be confirmed in both years. The strongest and greatest number of effects were found in HIF 10_190, where SHO was up to 5.00 d earlier for the  in 2019 . SignifiELF3Hsp allele-carrying lines were found for HIFs 16_105, 17_041, and 25_002 BC2, with yield differences of up to 15.96 dt ha–1 in HIF 16_105. In HIF 17_041, the ELF3Hsp-carrying line shows the strongest increase in yield (13.21 dt ha–1 in 2020). This is of great importance considering the absolute yield and the average yield for spring barley in Germany .However, to confirm that the effects found came from the respective ELF3Hsp effect is larger under specific environmental conditions, as shown before (ELF3Hsp effects on heading were stronger in Dundee (2014 and 2015) with colder summers (up to 16 °C on average), more and equally distributed rain (>800 mm), and greater day lengths (maximum of 17.45 h) compared with Halle. In Halle, the average temperature in July was up to 21 °C, 50% of the annual precipitation (514 mm) fell during July and August, and maximum day length was 16.63 h . AtELF3 also controls growth in response to ambient temperature and photoperiod  and compared with cultivar Bowman and the two enotypes . HIF paienotypes .The method of phenotyping the traits heading, tiller number, and plant height was slightly different compared with the field trials. Here, heading was scored when the first awns of a plant appeared, which is well comparable with HEA in the field trials, where it was scored when the awns were visible for 50% of all plants of a plot. In the greenhouse, the number of tillers was counted manually on day 64 and all tillers were included, while in the field trials, the trait EAR was counted by using a representative 50 cm frame in the centre of a plot and only tillers actually carrying ears were counted. Plant height in the greenhouse was measured continuously and was obtained by analysing images and, in the field trial, it was solely measured at the end of maturity with tillers pulled upright.ELF3Hsp allele flowered ~18 d earlier than the line carrying the ELF3Hv allele, even outperforming the results of the field experiments and the previous QTL studies, probably due to the optimal conditions in the greenhouse .As expected, the mutants showed earlier flowering of ~24 d compared with Bowman . For theELF3 had an impact in controlling vegetative growth, the three growth parameters plant height, area, and volume were measured or estimated (for volume) , while growth of cultivar Bowman and 10_190_ELF3Hv continued to increase. The same trend as for plant height was visible for plant area and plant volume  . Plant hshooting . Just aft volume  where th0_ELF3Hv . This ca0_ELF3Hv . In the 0_ELF3Hv . This maELF3Hv- and wild ELF3Hsp allele-carrying HIF sister lines with each other, we sequenced the full-length genomic DNA of ELF3  could be determined as novel alleles  contains two deletions, one inversion, and two small insertions (eam8.w) has a C-to-T point mutation, resulting in a premature stop codon  and CONSTITUTIVE PHOTOMORPHOGENIC 1 (COP1) . Moreover, the exotic ELF3 in family 10 (HID_102) only differs in one amino acid from the cultivated ELF3 of Barke. This amino acid is located at position 669 in the C-terminal region of the ELF3 protein (PIF4 gene in Arabidopsis controls thermomorphogenesis (PIF4 expression and elongation growth can be explained by genetic variation in AtELF3 (ELF3Hv in the greenhouse experiment (ELF3Hsp (ELF3Hsp without showing strong phenotypic effect differences) strengthens the assumption that there are further factors determining the ELF3Hsp allele effect differences, such as structural variation on the protein level or in the remaining genome as already suggested previously. Therefore, we compared the genetic background of all HIFs and conducted gene expression analyses for ELF3 and downstream genes in HIF 10_003 and 10_190, and performed a sequence-/structure-based analysis of the ELF3Hv and ELF3Hsp proteins from family 10 (see below).A summary of all ELF3 protein polymorphisms present in the field trials and their respective phenotypic effects in both years as in  can be f protein . In Arabn AtELF3  and has periment  although alleles , closest SNP: SCRI_RS_146425, ELF3 difference. Apart from these two lines [16_105_959 (ELF3Hsp) and 17_041_1000 (ELF3Hv)], all other HIF lines carry cultivated alleles at btr1/btr2   . These ddentical .HvCO1 in Bowman was not detected in BW290 , and HvBM8, initiating inflorescence development , with an isogenic background, in the future. Here, the concept is to detect a HEB line which is heterozygous at both loci of interest and select segregating offspring genotypes in all four possible combinations of wild and elite alleles at these two loci . Future experiments should then include phenotyping and diurnal gene expression analysis to examine whether the different combinations of wild and cultivated alleles of the respective genes show distinct effects on flowering time or other traits as well as changes in diurnal expression of themselves and downstream genes, making further statements about their interactions in a more isogenic background possible.Overall, these results confirm periment . FurtherN-H3/FT1 , and twoand PIF4 , severalHvELF3 transcript abundance itself (Hv of Barke (ELF3Barke) and ELF3Hsp of HID_102 (ELF3HID102), the exotic donor of HEB family 10. Since HIF sister lines of 10_190 are nearly isogenic  the Trp-containing sequence can adopt all types of secondary structure, whereas the Gly substitution induces a reduced β-sheet content due to the sheet-breaking properties of Gly. This might directly affect secondary structure transitions, needed in disordered regions to adapt for self-interacting .Table S3. IBD genotype data from the Infinium iSelect 50k SNP chip of all HIFs used in the field trial (with a homozygous Table S4. Day lengths and weather conditions during the respective growth periods in 2019 and 2020.2).Table S5. Repeatabilies (Rep) and heritabilites .Table S21. Data from the local sequence analysis, including the identified pdb file, chain, residue range, and occurrence of secondary structure elements.Table S22. Accession codes for the barley homologues and the disorder content prediction.Fig. S1. Weather data.Fig. S2. Boxplots for all traits and both years separately.Fig. S3. Correlations of traits for 2019 and 2020 separately.ELF3Hsp compared with ELF3Hv) across years.Figure S4. Trait differences between the two sister lines of each HIF pair (Fig. S5. Segregating regions between HIF sister lines.Protocol S1. Repeatabilities and heritabilities. Correlations. Impacts of the environment on phenotypes.erad127_suppl_Supplementary_TablesClick here for additional data file.erad127_suppl_Supplementary_MaterialClick here for additional data file.