Solanum lycopersicum) and many other species, seed quality and seedling establishment characteristics are determined by genetic variation, as well as the maternal environment in which the seeds develop and mature. The genetic contribution to variation in seed and seedling quality traits and environmental responsiveness can be estimated at transcriptome level in the dry seed by mapping genomic loci that affect gene expression (expression QTLs) in contrasting maternal environments. In this study, we applied RNA-sequencing to construct a linkage map and measure gene expression of seeds of a tomato recombinant inbred line (RIL) population derived from a cross between S. lycopersicum (cv. Moneymaker) and S. pimpinellifolium (G1.1554). The seeds matured on plants cultivated under different nutritional environments, i.e., on high phosphorus or low nitrogen. The obtained single-nucleotide polymorphisms (SNPs) were subsequently used to construct a genetic map. We show how the genetic landscape of plasticity in gene regulation in dry seeds is affected by the maternal nutrient environment. The combined information on natural genetic variation mediating (variation in) responsiveness to the environment may contribute to knowledge-based breeding programs aiming to develop crop cultivars that are resilient to stressful environments.Seeds are essential for plant reproduction, survival, and dispersal. Germination ability and successful establishment of young seedlings strongly depend on seed quality and on environmental factors such as nutrient availability. In tomato (The online version contains supplementary material available at 10.1007/s00122-023-04322-0. Arabidopsis thaliana is determined during seed development and maturation and depends on temperature  is one of the most important vegetable crops worldwide and is a model organism for research on fruit-bearing crops  in breeding programs and in studies on tomato (quantitative) genetics  model systems, it has been shown that especially trans-eQTLs are dynamic and can be highly specific for a certain environment  and S. pimpinellifolium (G1.1554) parents  and S. pimpinellifolium   of the RILs  in transcribed genes of the parental lines and the RILs. These SNPs were subsequently used to construct genetic and physical maps of the RIL population, to facilitate QTL and eQTL mappings  to depend on the nutritional conditions the mother plant experienced during the seed maturation phase  (Supplementary Table 3). Of these 2871 mRNAs, 922 were more abundant in seeds developed and ripened in HP conditions compared to LN, and 1949 mRNAs were significantly more abundant in LN conditions compared to HP. The mRNAs of genes that were more abundant after LN treatment were among others enriched for Gene Ontology (GO) terms: ‘chloroplast,’ ‘ATP binding,’ ‘proteasome,’ and ‘nitrate transport’ (Supplementary table 4). mRNAs that were more abundant in seeds grown in HP conditions were enriched for the GO terms: ‘cellular response to hypoxia,’ ‘pectin esterase activity,’ and ‘glucosinolate metabolic process’ (Supplementary Table 4).Next, we compared mRNA abundances in all HP-treated lines  with the mRNA abundances in LN-treated lines, to identify genes contributing to differences between the two environments. Principal Component Analysis (PCA) demonstrated the presence of a substantial effect of the maternal nutrient environment on transcript levels in seeds Fig. a. A lineWe also inquired the differences of the mRNA abundances between the MM and PI parental lines, within and between treatments. To this end, we again employed a linear model, but were less stringent in the statistical thresholds (as there were no confounding effects). We found 2976 mRNAs differentially expressed between the two parental lines regardless of treatment and 382 mRNAs that were differentially expressed between the lines due to treatment . We thus conclude that, overall, the number of genes with significant heritability for mRNA abundance was not specifically responsive to the maternal nutrient treatments. The genes with heritable mRNA abundance in HP alone were enriched for the GO terms: ‘translation,’ ‘ribosome,’ ‘mitochondrion,’ and more (Supplementary Table 7b). Those that showed significant heritability only in LN were enriched for the GO terms: ‘ABA metabolic process,’ and others (Supplementary table 7b). The genes that showed significant heritability in both environments were enriched for various GO terms: ‘oxidation–reduction process,’ ‘ribosome/translation,’ ‘nucleolus,’ ‘cell wall,’ ‘heme binding,’ ‘ion binding,’ and ‘vacuole’ (Supplementary Table 7b).To estimate the contribution of genetic variation to differences in mRNA abundance between the genetic backgrounds (plant lines) and treatments (nutrient status), we calculated the Broad-Sense Heritability (BSH). It should be noted that the method used in general gives an upper-bound estimation of the BSH . So, alike heritability, transgression is apparently not linked to a reduction of nitrogen or increase in phosphorus content in the maternal growth environment. Moreover, compared to genes showing significant heritability, many fewer GO terms were enriched in the genes showing transgression, and those GO terms that were enriched generally had a lower level of significance. For genes showing transgression in HP alone, the GO terms ‘cell periphery,’ ‘positive gravitropism,’ ‘cysteine biosynthetic process,’ ‘symporter activity,’ and ‘response to heat’ were enriched, whereas for genes only showing transgression in LN the GO-terms ‘beta-glucosidase activity,’ ‘preprophase band,’ and ‘phragmoplast’ were enriched. The GO term ‘DNA-binding transcription factor activity’ was enriched in genes showing transgression in both environments (Supplementary Table 8b).We next assessed the complexity of the genetic regulation underlying mRNA abundance differences. To this end, the transgression was calculated, i.e., trait values in RILs that extent beyond the parental means. We found significant transgression in mRNA abundance (trait) levels for 1043 genes in the maternal HP treatment and 1145 genes in the maternal LN treatment . This suggests a polygenic genetic architecture for mRNA abundance. Of these, the mRNA abundances of 185 genes showed significant transgression beyond the parental means in both treatments  and a significant influence of genetic variation in the RIL panel (heritability) on the detected mRNA abundance levels. By combining our constructed SNP genetic map Fig. , we weretrans-eQTL landscape, as the distribution of the position of the trans-eQTLs was very different between the two environments. For the HP environment, 4281 eQTLs for 3833 genes were identified, of which 2247 were cis-eQTLs and 2034 were trans-eQTLs. For the LN environment, 7487 eQTLs were detected for 6815 genes, of which 2356 were cis-eQTLs and 5131 were trans-eQTLs  > 3.9; Fig. cis-eQTLs of the two environments was noted . On the contrary, the trans-eQTLs were mainly specific for each tested maternal environment . However, both cis- and trans-eQTLs were not enriched for genes with differentially abundant mRNA levels based on the maternal environment . Together with the significant transgression (Supplementary Table 8a) and considerable heritability of mRNA abundances  were identified, which account for 1206 of the trans-eQTLs in the HP treatment  and 4181 of the trans-eQTLs in the LN treatment  × S. pimpinellifolium )  in the seeds of a tomato RIL population that had matured in different maternal nutrient environments, we show that the maternal environment affects both regulation and the genetic architecture of gene expression in progeny seeds. By design of the experiment which focusses on the role of genotypic variation within an environment, we can only observe the differences between the two (extreme) environments. However, this comparison would benefit from the contrasts between normal nutrient conditions and these more extreme environments. An RNAseq experiment on seeds coming from standard nitrogen/phosphorous conditions would surely benefit this analysis and might help identification of gene expression or even eQTL more specific to high nutrient environments.trans eQTLs proved environment specific, which is comparable to other species  and Solanum pimpinellifolium accession CGN14498 (PI) as well as the derived recombinant inbred lines  for 15 min. Subsequently, seeds were dried at 20 °C for 3 days on a clean filter paper in ambient conditions. The seeds were then stored in paper bags at room temperature.The mother plants  were cultivated as described in Kazmi et al.  and Geshwww.ncbi.nlm.nih.gov/sra) under ID: PRJNA704909.We used 10 mg grinded powder derived from 30 whole, dry, brushed, after-ripened seeds (12 months after harvest) of parental lines, and the RILs grown under different nutrient environments in a generalized genetical genomics design  map.2-transformed byBefore mRNA abundance analysis, between 12 and 31 M reads per sample were mapped to the SL4.0 genome with ITAG4.0 annotation   in RIL j , T is the treatment (HP or LN), and e is the error term. To reduce the chance of detecting differences due to genetic variation, a strict multiple testing correction was applied (Bonferroni) using p. adjust. The threshold for significance was − log10(p) > 5.47 (FDR = 0.05).The principal component analysis comparing the mRNA abundances was done on the ,i,jlog is the abundance level of transcript i  in parental replicate j (n = 3 for both treatments for MM and PI), T is the treatment (HP or LN), L is the line (MM or PI), and e is the error term. Values were corrected for multiple testing using p. adjust following the Benjamini–Hochberg algorithm. The thresholds for FDR = 0.05 were: − log10(p) = 1.71 for line, − log10(p) = 2.08 for treatment, and − log10(p) = 2.89 for the interaction between line and treatment. We took the most stringent p value, − log10(p) = 2.89 as threshold to determine significance.To determine the effect of treatment on the differences in mRNA abundance between the parental lines, we ran a linear model explaining the differences due to treatment and line effects on the MM and PI parental data. The model used wasµ ± 3*σ. This was done for both treatments separately. The lower boundary was established by the parental line with the lowest mean, and the upper boundary was established by the parental line with the highest mean. The standard deviation used to determine transgression (σ) was calculated as the pooled standard deviation of the two parental lines (n = 3 for both).Transgression was calculated by counting the number of lines with expression levels beyond three standard deviations from the mean of the parental lines  = 0.05 threshold.VRIL is the variance within the RIL population and Ve is the pooled variance of both parental lines.The heritability was calculated by estimating the genotypic variance in the RILs and the remaining variance  in the parental lines . This wTo establish whether the heritability was significant and not outlier driven, we applied a permutation approach [as in Vinuela et al. ]. The tr,i,jlog is the expression level of transcript i  in RIL j (n = 49 for the HP treatment and n = 52 for the LN treatment). The expression levels were explained over the genotype on marker location x  of RIL j.For eQTL mapping a single marker model was used and was applied separately for both treatments  was 14,772-RDS, where the number of hypotheses tested (m) was the number of transcripts, 14,772. The q-value was set at 0.05, which led to a threshold of − log10(p) = 3.7 for the LN treatment and − log10(p) = 3.9 for the HP treatment. To keep comparisons straightforward (similar effect sizes), analyses were conducted at the most stringent threshold (− log10(p) > 3.9).To determine the reliability of the detected QTLs and correct for multiple testing, a permutation approach was used. As in the other permutations, the expression levels were randomly distributed over the lines and this randomized set was mapped again according to the procedure described above, which was repeated 10 times. To determine the FDR, we applied a correction for multiple testing under dependency (Benjamini and Yekutieli cis or trans) were called based on distance to the gene encoding the affected transcript. A trans-eQTL had to be located at least 1 Mb from the gene. Furthermore, we calculated the confidence interval of the QTL as a 1.5-drop from the highest − log10(p). For a trans-eQTL to be called, the location of the affect transcript was required to be outside of this confidence interval as well.The eQTL types (trans-bands) was based on assessing whether the number of trans-eQTLs mapped to a locus exceeded the expected number based on an equal genome-wide distribution [as in Rockman et al. trans-eQTL per bin, and for the LN treatment we expected 40.8 trans-eQTL per bin. We used a conservative threshold for calling a bin enriched in trans-eQTL, p < 0.0001. After identifying significant bins, adjacent bins  were merged to a single trans-band.Identification of regulatory hotspots (www.bioinfo.cau.edu.cn/agriGO)  scores are available for download and online exploration in TomQTL at http://www.bioinformatics.nl/TomQTL/, an interactive website based on AraQTL Supplementary file2 (XLSX 11449 kb)Supplementary file3 (PDF 75 kb)Supplementary file4 (PDF 91 kb)Supplementary file5 (PDF 91 kb)Supplementary file6 (PDF 91 kb)Supplementary file7 (PDF 91 kb)Below is the link to the electronic supplementary material.


Five co-located QTL were detected in at least two environments, and two QTLs were co-located with multiple LA-related traits. Forty-seven meta-QTLs were identified based on meta-analysis combing 294 LA-related QTLs extracted from 18 previously published studies, 816 genes were identified within these meta-QTLs, and seven co-located QTLs were jointly identified by both GWAS and meta-analysis. ZmULA1 was located in one of the co-located QTLs, qLA7, and its haplotypes, hap1 and hap2, differed significantly in LA-related traits. Interestingly, the temperate materials with hap2 had smallest LA. Finally, we also performed haplotype analysis using the reported genes that regulate LA, and identified a lot of maize germplasms that aggregated favorable haplotypes. These results will be helpful for elucidating the genetic basis of LA and breeding new maize varieties with ideal plant architecture.Leaf angle (LA) is a key component of maize plant architecture that can simultaneously govern planting density and improve final yield. However, the genetic mechanisms underlying LA have not been fully addressed. To broaden our understanding of its genetic basis, we scored three LA-related traits on upper, middle, and low leaves of 492 maize inbred lines in five environments. Phenotypic data revealed that the three LA-related traits were normally distributed, and significant variation was observed among environments and genotypes. A genome-wide association study (GWAS) was then performed to dissect the genetic factors that control natural variation in maize LA. In total, 85 significant SNPs (involving 32 non-redundant QTLs) were detected ( Leaf angle (LA) is one of the key traits of maize plant architecture. Upright leaves can make maize plants more compact, enable them to adapt to high planting density, and reduce effects of shading between plants, thereby increasing photosynthetic efficiency and final grain yield . Maize y2S3 population was constructed from teosinte and the maize inbred line W22, 12 QTLs underlying LA were identified. UPA1-NILW22 (Near Isogenic Line) has smaller LA  than UPA1-NIL8759. On the contrary, UPA2-NILW22 has larger LA  than UPA2-NIL8759. These results indicated that UPA1 and UPA2 have opposite regulatory patterns, and with the increase of planting density, UPA2-NIL8759 has higher grain yield (kg/ha) than UPA2-NILW22  binds to the promoter of CYP90D1 (Zm00001d039453) and inhibits expression of CYP90D1 through a CYP90D1-mediated cytochrome P450-catalyzed reaction, Brassinosteroids (BRs) can be delivered to the BR receptor BAK1, which binds to the nuclear transcription factors BZR1, BZR2 and BES1, resulting in a smaller LA (Upper LA)  . Li et aitropism , its maielopment . Gao et al cells .fad2) was identified  has been widely used in plant genetics research and has proven to be a powerful tool for mining QTLs or causal genes for complex quantitative traits. The first GWAS report in plants was published in 2008. Single nucleotide polymorphism (SNP) haplotypes at 8,590 loci across 10 maize chromosomes were tested for association with kernel oleic acid content in 553 maize inbred lines, and a putative gene responsible for the target trait  to explore the phenotypic variation of LA 2) to identify natural variation of SNPs/loci and candidate genes significantly associated with LA 3) to select germplasms with the favorable haplotypes of LA to improve maize plant architecture. The results will enrich our understanding of the genetic basis of LA and enhance ideotype-based maize breeding.The association mapping panel (AMP) used in this study consists of 492 maize inbred lines, including 225 tropical/subtropical germplasms and 267 temperate germplasms, which is a subset of 527 inbred lines . In 2020LA data from the AMP were collected using a digital angle ruler . To be consistent, we investigated the flowering date of each line, and LA were investigated at 15 days after pollination for each line at all environments. Three Leaf Angle (LA-related traits) was scored at three positions: the upper leaf , the middle leaf , and the lower leaf . Only ULA was investigated at YYC and YYX, whereas all three LA-related traits were recorded at HB, YC, and YY. The average values of ULA, MLA, and LLA were calculated from five uniformly growing plants in each row. The average of each LA-related traits for the two replications in each environment was calculated. Overall, ULA at all the five environments , MLA, and LLA at the three environments  were collected. These phenotypic data was used for general statistical analysis, Pearson correlation analysis, two-way ANOVA, broad-sense heritability calculation, Best Linear Unbiased Prediction (BLUP) and GWAS.http://www.r-project.org/) was used to calculate the Best Linear Unbiased Prediction (BLUP) value for each trait in the five environments (H2 = [δG2 /δG2 +(δGE2/n)+δe2/(nr)], where δG2 is the genotypic variance, δGE2 is the genotype × environment variance, δe2 is the error variance, r is the number of replications, and n is the number of environments. Considering the unbalanced data for ULA, the harmonic mean (h), h = (492 + 269)/(492/2 + 269/3), was used to instead of the number of environments (n) for calculating the H2  were finished in SPSS Statistics V17.0 after removal of outliers. For the five values measured for one trait of a genotype, if a value is not within the range of the mean plus or minus 1.5 times of standard deviation, it will be regarded as an outlier. A repeated-measures two-way ANOVA, using the following formula: V = G + E + G × E + e, here, V is total variance, G is variance of genotype, E is environmental variance, G × E is variance of genotype-environment interaction, e is error. It was also performed in SPSS Statistics V17.0. The mixed linear model of the lme4 package in R  and have been deposited in the European Variation Archive (EVA) at EMBL-EBI under accession number PRJEB56161 (https://www.ebi.ac.uk/eva/?eva-study=PRJEB56161). This genotype data was inferred from the Illumina MaizeSNP50 array, reduced-representation genome sequencing , the high density Affymetrix Axiom Maize 600K array (600K), and previous deep RNA-sequencing data of whole kernels at 15 days after pollination obtained by Liu et al.  was used as the genome-wide threshold for significant SNP–trait associations, as commonly used in plant genome-wide association studies.Taking into account the linkage disequilibrium (LD) among SNP markers, the effective marker number (En) for the genotypic dataset was 490 548, as previously calculated using GEC software . The sug2, Confidence Interval and so on) were summarized from eighteen studies published in the lasted 15 years. For QTLs whose confidence interval (CI) was unknown, the following formulas were used to calculate CI:N is the number of materials in the mapping population, and R2 is the phenotypic variation. 2 mapping populations, and R2 = 1–10N) . QTLs wihttps://www.maizegdb.org/)  1 (chromosome number) -1 (sequence number)].IBM2 2008 Neighbors   was useddb.org/) . Five didb.org/) . FinallyR2 = 0.1) (http://www.ebi.ac.uk/interpro/scan.html). Phenotypic variation explained (PVE) by each QTL was estimated based on the R2 value of the most significant SNPs within the QTL. Candidate genes within the CIs of meta-QTLs were also obtained. In each non-redundant QTL or co-located QTL, the most likely candidate gene was selected based on its annotation or because it contained the peak SNP (the most significant SNP). If there was no gene in the interval, the neighboring gene of the peak SNP was considered to be the most likely candidate gene.The previously estimated decay distance of LD in this AMP   was usedhttps://www.omicshare.com/tools) (http://www.geneontology.org/), the number of genes in each set was counted, and the list of genes with a specific GO function and the number of genes were obtained. The top 20 GO terms with minimum p values were selected for analysis and plotting (Gene Ontology (GO) enrichment analysis was performed using OmicShare tools (m/tools) . Specifiplotting .Linkage disequilibrium (LD) was estimated by the squared correlation of the paired SNPs, which was calculated with TASSEL 3.0 software. An LD plot was generated using the ‘genetics’ and ‘LDheatmap’ package in R (version 4.1.1).ZmULA1, ZmCLA4, lg1, lg2 and ZmTAC1) were used for haplotype analysis by using 1.25M SNPs genotype data (All SNPs (MAF ≥0.05) in target genes  and environmental (E) effects on LA-related traits, but the effect of their interaction (G × E) was not significant . The resLA was less sensitive to the K model than other two models, and this model could better control type I and type II errors . In addiR2 = 10.01–24.09%). Twenty-six QTLs were identified for ULA, with a mean R2 of 9.34% (4.80%–15.16%), and seven QTLs were detected for MLA, with a mean R2 of 15.14% (9.48%–24.09%). Only one QTL was associated with LLA, and it explained 11.63% of the phenotypic variation.In total, 85 significant SNPs were detected by ULA, MLA and LLA in all environments, and involving 32 non-redundant QTLs. The QTLs were distributed on all chromosomes except chromosome 5 and 9, and there was a QTL hot spot at chromosome 1 and 3 . Twenty-Fourteen non-redundant QTLs were detected in HB and explained 9.08%–21.93% of the phenotypic variation. Ten non-redundant QTLs were detected in YY (10.05%–15.16%), nine in BLUP (4.80%–24.09%), and only four and two in YYC and YYX, respectively. No significant QTLs were detected in YC .qLA2 was detected in YYC, YYX, and BLUP, and qLA3 and qLA27 were detected in YYC and BLUP. For MLA, qLA23 was identified in HB and BLUP, and qLA29 was detected in BLUP and YY. These loci can be stably inherited in different environments and play an important role in the regulation of plant architecture. QTLs detected for different traits may have pleiotropic effects. qLA1 was identified by MLA and ULA and may therefore regulate the size of both ULA and MLA. In addition, qLA21 was also detected in different traits (ULA and MLA), suggesting that qLA21 may have a similar role in the regulation of LA at different positions. Detailed information on the GWAS results, including the physical position, p-value, and R2 for each QTL is provided in Five QTLs were identified in multiple environments . For ULAhttps://www.maizegdb.org/), 82 genes were found within 32 non-redundant QTLs. For example, GRMZM2G049159 within qLA7 was identified for ULA in HB and encodes a GRAS family transcription factor; its homolog OsGRAS23 induces downstream stress-responsive genes and positively regulates drought tolerance in rice  biosynthetic pathway and further regulate plant architectural traits such as tiller number and LA , carbon metabolism (GRMZM2G134256), and the citric acid cycle (GRMZM2G158378) and play important roles in the regulation of plant growth and stress tolerance. GRMZM2G311328 is located in qLA21, which was identified for MLA and ULA in HB. This gene may be pleiotropic; its homolog encodes a vesicle auxin mediated transporter, and regulation of auxin polar transport is important for development and architecture of Arabidopsis  within this QTL, and it encodes a GRAS family transcription factor, involved in the synthesis of organic (GO:1901576)  , plant hbidopsis . We specZmULA1, we extracted all polymorphic sites within one LD decay distance near the lead SNP  to perform LD analysis, there was a strong linkage relationship  between the lead SNP and polymorphic sites  or hap2 (80), and the mean value of hap2 was smaller than that of hap1 for the three LA-related traits (p = 7.20e−10), MLA (p = 3.80e−08), and LLA (p = 1.40e−05) showed extremely significant differences between hap1 and hap2  that regulate LA in previous studies  encodes a protein containing an SBP domain, and its mutation causes the loss of tongues and ears, making plants more compact (lg2 (liguleless2) encodes a basic leucine zipper (bZIP) transcription factor. These two genes are located in the same developmental pathway, in which lg2 plays an earlier role than lg1  that changes ‘AGGA’ to ‘GGGA’ can lead to reduced expression of ZmTAC1 and an LA close to zero, making the rice plant more compact  was found within qLA1, and its rice homolog has been shown to promote the transport and absorption of silicon  and another a vesicle-mediated substance transporter (GRMZM2G311328). Five co-located QTLs , explaining 6.13%–18.89% of phenotypic variation, were identified in at least two environments, indicating that they are genetically stable and less affected by the environment. Most QTLs (∼84%) were detected in only one environment (27/32); a possible explanation is that LA is a complex quantitative trait regulated by a large number of minor genes that are greatly affected by the environment, leading QTL effects to vary in different environments. It is worth mentioning that 21 of the 32 QTLs (∼66%) were major QTLs with R2 values greater than 10%. These results provide new information for understanding the genetic basis of the natural variation in LA.In this study, 85 significant SNPs in 32 QTLs were identified by GWAS and included 82 genes. Seven QTLs could be co-located in different environments or different LA-related traits , they ar silicon . Five geGRMZM2G005066, c1 and GRMZM2G089713, sh1) were involved in kernel colored aleurone and endosperm development (GRMZM2G141399 (du1) encoded starch synthase III, which makes kernels glassy and inhibits glycogen accumulation  were associated with plant height and senescence of maize seedlings (AC195340.3_FG001 (tua1), located in MqLA5-6, is associated with plant architecture at different planting densities and encodes an alpha tubulin family protein; it has been annotated but not functionally characterized  were located in qLA3 and were expressed in leaves, potentially affecting leaf development. GRMZM2G171073 encodes a C2H2-like zinc finger protein, and its homolog IDD1 is involved in gibberellin (GA) signal transduction and transport in Arabidopsis  have been shown to regulate LA, and a lot of maize inbred lines with favorable haplotypes were selected by haplotype analysis (Seven QTLs were jointly identified by GWAS and meta-analysis , and two by GWAS . Three gbidopsis . GA affebidopsis . ZmULA1 d traits . Hap2 wad traits . About 8d traits . Four geanalysis . The resZmULA1, was predicted to play important roles in the regulation of LA. These results will provide reference for improving maize plant architecture.Although GWAS has been recognized as a powerful method for understanding the genetic basis of complex quantitative traits, meta-analysis is complementary to GWAS and can increase precision and accuracy of detected QTLs. Therefore, combining GWAS and meta-analysis, we screened a lot of potential targets/loci regulating LA in maize, and the candidate gene,


Penaeus monodon) is the second most important aquaculture species of shrimp in the world. In addition to growth traits, uncooked and cooked body color of shrimp are traits of significance for profitability and consumer acceptance. This study investigated for the first time, the phenotypic and genetic variances and relationships for body weight and body color traits, obtained from image analyses of 838 shrimp, representing the progeny from 55 sires and 52 dams. The color of uncooked shrimp was subjectively scored on a scale from 1 to 4, with “1” being the lightest/pale color and “4” being the darkest color. For cooked shrimp color, shrimp were graded firstly by subjective scoring using a commercial grading score card, where the score ranged from 1 to 12 representing light to deep coloration which was subsequently found to not be sufficiently reliable with poor repeatability of measurement (r = 0.68–0.78) Therefore, all images of cooked color were regraded on a three-point scale from brightest and lightest colored cooked shrimp, to darkest and most color-intense, with a high repeatability (r = 0.80–0.92). Objective color of both cooked and uncooked color was obtained by measurement of RGB intensities  for each pixel from each shrimp. Using the “convertColor” function in “R”, the RGB values were converted to L*a*b* (CIE Lab) systems of color properties. This system of color space was established in 1976, by the International Commission of Illumination (CIE) where “L*” represents the measure of degree of lightness, values range from 0 to 100, where 0 = pure black and 100 = pure white. The value “a*” represents red to green coloration, where a positive value represents the color progression towards red and a negative value towards green. The value “b*” represents blue to yellow coloration, where a positive value refers to more yellowish and negative towards the blue coloration. In total, eight color-related traits were investigated. An ordinal mixed (threshold) model was adopted for manually (subjectively) scored color phenotypes, whereas all other traits were analyzed by linear mixed models using ASReml software to derive variance components and estimated breeding values (EBVs). Moderate to low heritability estimates (0.05–0.35) were obtained for body color traits. For subjectively scored cooked and uncooked color, EBV-based selection would result in substantial genetic improvement in these traits. The genetic correlations among cooked, uncooked and body weight traits were high and ranged from −0.88 to 0.81. These suggest for the first time that 1) cooked color can be improved indirectly by genetic selection based on color of uncooked/live shrimp, and 2) intensity of coloration is positively correlated with body weight traits and hence selection for body weight will also improve color traits in this population.Black tiger shrimp ( Penaeus monodon) is regarded as a luxury food commodity because of its elegant sensory properties and high nutritional value. For black tiger shrimp, henceforth “shrimp”, visual sensory properties of bright red color after cooking contribute to higher prices and greater consumer acceptance  containing astaxanthin, resulted in higher weight gain, survival, resistance to white spot syndrome virus (WSSV) infection and greater tolerance to stress conditions   . An incr monodon . An exte monodon . Since tloration . The genP. monodon is currently the major aquaculture crustacean species farmed in Australia and economically, is the second most important species in the world with an economic value of USD 5.7 billion and a production base of 750,600 tons in 2018 . Its measurement can be influenced by various external factors, e.g., frequent changing of lighting conditions can affect the visual assessment of the body color of the shrimp. Broadly, there are two means for color phenotyping, namely 1) chemical, and 2) physical measurement approaches. In the chemical measurement approach, the phenotyping is done indirectly by NIR (near-infrared reflectance) or directly through HPLC analysis by quantifying color-producing chemical components in the shrimp. However, chemical analysis is expensive and time consuming and requires destructive sampling of animals, although it provides an accurate measurement of color phenotypes. On the other hand, the physical approach involves either visual determination of color intensity using a standardized color card, or by using an instrumental measurement where a tristimulus colorimeter (chromometer) measures the reflectance of light from the subject  compared to a background calibration plate. Color may then be expressed in the L*a*b* system, with L* measuring lightness, a* redness/greenness and b* yellowness/blueness (a* (intensity of redness) is linearly correlated with the carotenoid pigment content in salmon fish flesh  to estimate the genetic basis of the pigmentation phenotypes, 3) assess genetic correlation among cooked, uncooked color and other economically-important morphological traits and finally, 4) compare genetic gains by direct and indirect selection in color phenotypes.Although the body color phenotype has direct economic benefit to the shrimp aquaculture industry, no study has been conducted so far for genetic improvement of this trait in this species. Establishing the genetic basis for variation in pigmentation in aculture . MoreoveP. monodon raised by Seafarms Group Ltd., as described in −2 at 28°C ± 0.5°C) and broodstock were fed a commercial maturation diet. For each cohort, broodstock were allowed to mate naturally within the tanks, with any unmated females then artificially inseminated following industry practices. Since tracing of broodstock contribution could not be done on farm, all potential broodstock were genotyped and parentage analysis was utilised to determine the contributing parents retrospectively as detailed by 2 grow-out ponds and reared under commercial conditions at a density of 45 m−1 until harvest. Immediately pre-harvest ponds were sampled by random castnet. The current study population comprised of 55 sires and 52 dams. In total 67 full sib and half-sib families were produced across 838 progeny and stocked across seven ponds as shown in All progeny in this experiment were sampled from commercial cohorts of The genotyping method described by r = 0.72)  . Therefo = 0.72) . The rel = 0.72) .L*a*b* (CIE Lab) systems of color properties. The CIE Lab color assessment system aligns more closely with human perception of color  for each pixel from each shrimp sampled. Using the “convertColor” function in “R”, the RGB values were converted to of color . This syYij is the color score of the ith shrimp in the jth pond, and k is the  score threshold, with c being the number of score points /P(Yij > k)] = θk + Pondj + ai , k = 1, … , c–1logL*uncooked, a*uncooked, b*uncooked, L*cooked, a*cooked and b*cooked):yij is the observation of individual i in pond j, µ is the mean and Pondj is the fixed effect of the jth pond, ai is the additive genetic effect, both terms as defined in the ordinal model, and εij is the random error, assumed The following linear mixed model was employed for estimating variance components for each of the growth traits and body color traits  were calculated .y with 1 standard deviation (SD) selection differential in trait x, was calculated from the following equation , the relative efficiency of correlated response (CR) in trait y when selection is applied on trait x as a percentage of gain possible from direct selection for trait y, i.e.,Indirect genetic selection, i.e. correlated response in trait equation :CRy=rg×ai in both ordinal and linear models.Data analysis was performed in R v 4.1.0 , and theThe mean, standard deviation, and coefficient of variation for body color, body weight and body length traits of shrimp at harvest are provided in r = 0.72) of repeated scoring on a sample of 288 images. Instead, using images from current population-specific samples, provided more accurate estimates. This was supported by observing the repeatability analysis of color scoring. The repeatability analysis of scoring using the commercial shrimp cooked color chart and using the reference image developed in this study revealed that the scoring system developed in this study is more reliable  than the commercial image scale .As shown in The observation of the probability distribution of color scores across ponds revealed that the bright cooked color shrimp were more common in ponds ‘C’, ‘F’ and ‘G’. In contrast, medium orange colored shrimp were abundant in ponds ‘A’, ‘D’ and ‘E’ .b* trait for uncooked color was highest at 0.35 ± 0.08, followed by a* trait for uncooked color at 0.29 ± 0.08, whereas they were generally low for cooked color indices. Details of these estimates are presented in Heritability estimates for manually-scored uncooked and cooked body color traits were both 0.12 ± 0.04. Using instrumental measurement of body color phenotypes, heritability of the L*uncooked : L*cooked (r = 0.82), similarly uncooked color score had a strong genetic correlation with cooked color score (r = 0.77). Of note is that L*uncooked was also strongly correlated with both uncooked and cooked color  suggesting this L*uncooked could be a potential indirect selection marker for both cooked and uncooked color. Similarly, both L*cooked and a*cooked had strong genetic correlations with both cooked and uncooked color scores (r = −0.39–0.43) as shown in r = 0.93) and genetic (r = 0.96) correlations . Of these, strong positive genetic correlations were seen among r scores , A mediuelations .L*cooked and a*cooked values with an efficiency of 100% and 114% respectively. Likewise, selection on the L*uncooked trait will lead to a response of 108.5% on the L*cooked color trait and a 135% in the a*cooked values  ranged from 0.82 to 0.92. Instead, there are numerous reports where increased heritability estimates could be achieved by objective phenotyping of color traits using colorimetric instruments. Although, heritability estimates of manually-scored color phenotypes were low, it should be noted that, this manual approach may be very useful when instrumental methods are not available  for both uncooked and cooked color. These findings agreed well with studies with other aquaculture species for color traits . The lowvailable , in addiP. monodon) for just 28 days, either in black or white tanks, had significant effect on body coloration. The black tank reared shrimp were more bright orange in color than the white tank color reared one. Moreover, P. monodon) reared in pond containing mangrove leaf litters had significant darker body coloration. Our study also revealed the variability of body coloration of P. monodon reared in different ponds, in particular cooked color, suggesting further study needs to be carried out to reveal the potential of genotype by environment (G×E) interaction for body color of this species.The effect of rearing environment on shrimp body coloration is well established. For example, Litopenaeus vannamei reared in different environments. Fenneropenaeus merguiensis), respectively. In addition to shrimp, studies with other aquaculture species have shown that body color traits are heritable. Oncorhynchus kisutch). For Atlantic salmon heritability estimates of 0.14 ± 0.03 , 4 ± 0.03  and 0.074 ± 0.03  were repa*uncooked and b*uncooked values, the heritability estimates were 0.29 ± 0.08 and 0.35 ± 0.08, respectively, suggesting strong selection response will be obtained for these color traits. In particular, trait a*uncooked can be a useful candidate trait for selection to improve redness color trait in shrimp, as previous studies have reported a linear relationship between carotenoid concentration and a* value in fish species, where carotenoid contents are the key contributor of bright coloration in crustaceans  for cooked color traits when body color phenotypes were measured instrumentally. This suggests that for genetic evaluation, instrumental measurements are more effective for uncooked body color traits than cooked traits. Given that both manually-scored and chronometrically-assessed body color traits identify significant additive genetic components, this suggests the higher potential of these traits for genetic improvement in A key finding of our study was that the genetic correlation among the key body color traits were moderate to high, ranging from −0.88 to 0.81. Specifically, the genetic correlations between cooked and uncooked color traits were high , suggestP. monodon  and Banana shrimp (Fenneropenaeus merguiensis), respectively. Altogether these positive genetic correlations among color and growth traits suggests that a similar set of genes may be responsible for expression of these traits. Moreover, there might be physical linkage or pleiotropic effect and linkage disequilibrium among the underlying genetic mechanisms responsible for phenotypic expression of these traits (L*cooked (108.48%) color of shrimp , suggesting selection for these traits will lead to significant response in breeding programs. This finding corroborates with the previous findings, where heritability for growth traits were moderate to high, ranging from 0.23 to 0.69 in  monodon , indicate traits . Similare traits , salmonoe traits  and in te traits . The genf shrimp . From a P. monodon. This suggests that this trait can be genetically improved, implying that some individuals of the population studied possess superior ability to convert the carotenoid astaxanthin and also have superior growth compared to others. Of significance is that parameters of uncooked color can improve cooked color based on either subjective scores or objective colorimetric measures . Finally, inclusion of cooked colour in the overall breeding objective for P. monodon will require clear economic benefits associated with the trait to be established through bio-economic modelling procedures as detailed by Studies with banana shrimp , salmon P. monodon have been reported for the first time. The present study indicates that body color traits can respond effectively to selection. The high generic correlation between uncooked and cooked color scores indicates that, selection on dark colored (uncooked) shrimp will lead to enhanced intensity of cooked color. Moreover, positive genetic association among the growth and color traits indicates that, the selection for pigmentation and growth traits can be carried out simultaneously, without any unfavorable outcomes to these economically important traits. In summary, selective breeding can enhance growth and body color traits of shrimp simultaneously, thereby, helping to reduce the amount of food additives containing dietary astaxanthin. This will ultimately increase the overall product value and reduce feed costs.Genetic parameter estimates for body color traits of


Human traits, diseases susceptibility, and clinical outcomes vary hugely among individuals. Despite a fundamental understanding of genetic  contributions, the detailed mechanisms of how genetic variation impacts molecular or cellular behaviours of a gene, and subsequently leads to such variability remain poorly understood.Here, in addition to phenome-wide correlations, we leveraged multiomics to exploit mechanistic links, from genetic polymorphism to protein structural or functional changes and a cross-omics perturbation landscape of a germline variant.cis-acting expression quantitative trait locus in CLEC18A (rs75776403) in which the altered residue (T151→M151) disrupts the lipid-binding ability of the protein domain. The altered allele carriage led to a metabolic and proliferative shift, as well as immune deactivation, therefore determines human anthropometrics (body height), kidney, and hematological traits.We identified a missense CLEC18A rs75776403-regulated pathways.Collectively, we uncovered genetic pleiotropy in human complex traits and diseases via The online version contains supplementary material available at 10.1186/s12929-022-00822-1. Moreover, gene set enrichment analysis permits a multigene  view of the occurrence of human traits. Despite this, a full and integrative links  from genetic changes to subcellular molecular features , and ultimately to phenotypic changes remains a formidable task.Insights acquired from genetic association studies have greatly advanced our understanding of the biology of human traits and the pathophysiology of numerous complex diseases. Combining those studies with pathway analyses has further shed light on the underlying mechanisms that drive traits and/or disease variability among individuals. Due to the statistical limitation of such genetic epidemiological methodologies, it is still a big challenge to parse the molecular and cellular processes that link obscure genetic variation to explicit phenotypic changes. Variability in gene expression imposed by germline variation has been revealed by the identification of CLEC18A (16q22.1), CLEC18C (16q22.1), and CLEC18B (16q22.3). Each of CLEC18 family member encodes a 446 amino acid polypeptide comprising a C-terminal carbohydrate recognition domain (CRD), two epidermal growth factor (EGF) and (EGF)-like domains, and a N-terminal cysteine-rich secretory protein/antigen 5/pathogenesis related-1 (CAP) domain, also known as Sperm-coating protein (SCP) or Tpx antigen 5/pathogenesis related-1/Sc7 (TAPS) domain. CLEC18 protein has been shown to localized in intracellular organelles such as Golgi apparatus, endoplasmic reticulum (ER), and early endosomes, and can also be found in the extracellular milieu ), four metabolic , three hepatic , and four nephrotic parameters  study equation and further cropped into 15 ~ 200) [eGFR], uric acid-to-creatinine ratio [UaCrRatio], blood urea nitrogen-to-creatinine ratio [BunCrRatio] and microalbumin-to-creatinine ratio [mAlbCrRatio]).Notably, for sitting systolic/diastolic pressures and heartbeat speed, values were averaged across three measurement time points. If a quantitative trait presented a censored value due to the detection limit, we directly replaced the record with the censored value.In total, 84 binary traits were included: three alimentary canal diseases , seven arthritis , one bone disease (osteoporosis [Osteo]), 11 cancers , six cardiovascular diseases , seven gynecologic , seven habitual , three headache-induced symptoms , one hepatic symptom , one immune disease (type 1 diabetes mellitus [tp1DM]), seven metabolism disorders , two nephrotic symptoms , six nervous-system diseases , eight ophthalmic symptoms , six psychiatric disorders , two pulmonary syndromes , and six soreness parameters .In total, 19 ordered traits were also included: one habitual (snake consumption frequency [SNKFrq]), seven ophthalmologic , seven soreness , and four psychiatric condition levels .We conducted a phenome-wide association study (PheWAS) of rs75776403 using the TWB cohort. First, a list of genetically unrelated samples by filtering out 1st- or 2nd-order relationships was obtained using the “–unrelated” option implemented in KING v2.2.7 . Next, aFor the 86 quantitative traits, a linear regression model was adopted. The traits were normalized using the inverse normal transformation (Elfving method) before fitting the association model. Hypothesis testing was conducted using the type I  method.For the 84 dichotomized traits, a logistic regression model was applied.Z statistics were calculated using the “coeftest” function implemented in the lmtest package.For the 19 ordinal traits, an ordered logistic regression model was applied using the “polr” function implemented in the MASS package. The To avoid false positivity, a local false discovery rate (FDR) was estimated using the “lfdr” function implemented in the qvalue package.https://yanglab.westlake.edu.cn/software/gcta/#DataResource; accessed Dec 25, 2021) , Fdr = 8.64 × 10–3; and increase waist-to-stature ratio [WSR], Fdr = 3.34 × 10–2), three nephrotic , one hematological  and one hepatic parameter . In addition, traits such as the risk for cardiomyopathy [Crdmyo] (Fdr = 1.80 × 10–2), and retinal detachment eye number [RtDeN] (Fdr = 6.11 × 10–6) were also significantly associated with the rs75776403 c.C-to-T polymorphism  of rs75776403 in the Taiwanese population to examine the correlations between rs75776403 and multifarious human traits. By leveraging a Taiwan Biobank (TWB) cohort with a sample size of 68,080, we parsed the genetic association profile of rs75776403 in 189 traits as follows: (i) Among quantitative traits , pulmonary functionality (forced expiratory volume in 1-s (FEV1), Fdr = 1.35 × 10–8; forced vital capacity (FVC), best measure, Fdr = 1.35 × 10–8; FEV1, best measure, Fdr = 3.16 × 10–8; FVC, Fdr = 3.16 × 10–8; FEV1, predicted percentage, Fdr = 7.09 × 10–6; FEV1, predicted, Fdr = 3.22 × 10–3), smoke intake , hematological profile , hand grip strength , onset age of menarche (Fdr = 2.17 × 10–2), beef intake (Fdr = 3.22 × 10–2), place of birth (Fdr = 3.22 × 10–2), wheezing or whistling in the chest in last year (Fdr = 3.22 × 10–2), and the number of depression episodes , body weight (Fdr = 5.02 × 10–3), and serum creatinine  and Japanese (from Biobank Japan [BBJ]) populations were further investigated. Of 2173 traits from Europeans, 22 were found to relate to rs75776403, which including body height  =  − 0.0127 ± 0.0044, Fdr = 4.52 × 10–4; UKB: β ± s.e. =  − 0.0070 ± 0.0017, Fdr = 7.46 × 10–3; BBJ: β ± s.e. =  − 0.015 ± 0.0022, Fdr = 1.51 × 10–8). Fixed-effect and random-effect models illustrated a combined effect size (β ± s.e.) of − 0.0102 ± 0.0013 (P = 1.918 × 10–15) and − 0.0112 ± 0.0026 (P = 0.0485), respectively  across three populations. Here, only the body height trait was subjected to the meta-analysis because the results passed a phenome-wide significance threshold in all populations (TWB: CLEC18A c.452C-to-T) was elaborated, the next critical question was to know how the genetic variant at this locus impact human traits through perturbing specific molecular or cellular processes. We thus leveraged human cell line-based multiomics  from the Cancer Cell Line Encyclopedia (CCLE) database to conduct the following analyses.Since the phenotypic relevance of rs75776403 . The GSEA analysis further revealed 392 (365 positively and 27 negatively) and 85 (34 positively and 51 negatively) significantly enriched pathways from mRNAs and proteins, respectively  of CLEC18A transcription and/or translation due to the rs75776403 c.C-to-T polymorphism.For each molecular feature from the transcriptome and proteome , a linear regression model was fitted and associations with rs75776403 were tested by exact permutation. Next, gene set enrichment analysis (GSEA) based on the KEGG, GO BP, and Reactome databases was conducted to identify the potential pathways (and/or gene sets). As a result, we found 278 (251 upregulated and 27 downregulated) and 70 (33 upregulated and 37 downregulated) differentially expressed mRNAs and proteins, respectively  and acetylglycine  to the phosphoproteomic profile associated with the rs75776403 c.C-to-T polymorphism  pathways , thyroid hormone receptor beta (THRB), thyroid-stimulating hormone subunit beta (TSHB), thyrotropin-releasing hormone (TRH), thyroid-stimulating hormone receptor (TSHR), thyroid hormone receptor alpha (THRA), and thyrotropin-releasing hormone receptor (TRHR). Indeed, negative associations with TG  and THRB  were found , THRB , TSHR , THRA  and TRHR .Specifically, we noticed a negatively enriched cellular response to thyroid hormone stimulus  and a positively enriched corticosteroid receptor signaling pathway  associated with rs75776403. Coordinating thyroid hormone with the steroid was found to be essential for growth regulation , 24, andARID1A, YWHAH, CRY1, LMO3, and JAK2 and that was negatively associated with THRB  and other six genes  and corticosteroid receptor signaling pathways   tissues . We found 27 (90.0%), 14 (46.7%), and 16 (53.3%) genes that respectively exhibited an association (P < 0.05) with CLEC18A mRNA expression in the brain, pituitary gland, and thyroid  axis maintains thyroid hormone homeostasis in humans, correlations of cis-eQTL (p.T151M) in CLEC18A that was mainly correlated with anthropometrics , nephrotic, and hematological traits (and/or diseases) in Taiwanese, European and Japanese populations. This observation implicates the pleiotropy (associated with more than one trait) of rs75776403 as well as the concept that a genetic polymorphism could exert functional impacts through both regulating gene expressions (as a cis-eQTL) and disrupting a critical protein domain (as a missense variant). Focusing on these pathophysiological aspects may provide novel insights regarding genetic epidemiological profiles of the given trait or disease, as well as facilitate precision medicine.Despite remarkable advances in genetics, little attention has been paid to whether and how genetic variants impact aspects of molecular and/or cellular features in the pathophysiology of human traits beyond simple susceptibility. Herein, we identified a missense CLEC18A has been linked to host immune defence against dengue viral infection through the C-type lectin-like domain (CTLD) . Serum C1/S phase transition [The CAP/SCP/TAPS domain, which is possessed by CLEC18A and other genes, was proposed to bind and transport sterols, acidic glycolipids, and/or acidic phospholipids , 29. Speansition .e.g., dengue virus type 2 [2+-specific serine protease [The underlying mechanisms of immune deactivation through CLEC18A p.T151M can be explained as follows. First, PS-recognized receptors share a common feature that promotes the production of anti-inflammatory mediators , 35. Hens type 2 ) during protease , 38, whiprotease , 40. rs7protease . Since tprotease , 43, we protease .CLEC18A transcripts with the end products of the thyroid hormone biogenesis pathway, thyroglobulin (TG) and thyroid hormone receptor (THRB). We should notice that the upstream receptors of the pathway, thyrotropin-releasing hormone receptor (TRHR) [Additionally, we identified a correlation of rs75776403 p.T151M and r (TRHR)  and thyrr (TRHR) , are alsr (TRHR) , 48. Ther (TRHR) –51, branr (TRHR) , but alsr (TRHR) , 54. ThuMoreover, thyroid hormones modulate the overall synthesis of phosphocreatine (PCr) through the regulations for creatine kinase and mitochondrial oxidative phosphorylation . PCr is cis-eQTL annotations from the GTEx database to confirm the tissue-specific cis-regulatory effects of the rs75776403 c.C-to-T polymorphism on mRNA levels of CLEC18A. Furthermore, a protein domain simulation and lipid strip test were conducted to confirm that missense rs75776403 p.T151M abruptly affects the lipid-binding ability of the CLEC18A. By further incorporating the rs75776403-correlated multiomics to pinpoint metabolic/proliferative shift and implication in thyroid hormonal regulation, these results elaborate a promising way to link the molecular and/or cellular features of rs75776403 to phenotypic landscape of humans.To date, more than 5500 publications and ~ 330 K associations have been elaborated , but most results still need to be functionally validated. One of the possible ways to accomplish this is to integrate data from multiple sources. Herein, we leveraged cis-eQTL in CLEC18A, and linked the polymorphism to various phenotypes. We demonstrated that CLEC18A binds specifically to two phospholipids, PA and PS via the CAP/SCP/TAPS domain. Defects in the lipid-binding ability were identified in the rs75776403 altered allele (p.M151) carriage. By elaborating CLEC18A rs75776403-associated multiomics, CLEC18A has great impact to regulate cellular processes, metabolism, cell cycle, immune activation, thyroid hormone biosynthesis and immune responses. Thus, a single amino acid change T151→M151 ultimately contributes to the variability of human traits and differential outcome of human diseases. This study demonstrates a promising approach to get insights on the potential impact of genetic polymorphism for precision medicine  for CLEC18 family genes . Table S3. The cis-eQTL annotation of rs75776403. Figure S1. Boxplot showing the distribution of CLEC18A transcript (mRNA) levels across rs75776403 genotypes. Table S4. The 86 quantitative traits (11 phenotype categories) included in this study . Table S5. The 24 additional quantitative traits are included in this study. Table S6. Association results of rs75776403 to the quantitative traits. Table S7. The 84 binary traits (16 phenotype categories) are included in this study. Table S8. Association results of rs75776403 and binary traits. Table S9. The 19 ordered traits (4 phenotype categories) are included in this study. Table S10. Association results of rs75776403 and ordinary traits. Table S11. Phenome-wide association study for CLEC18A rs75776403 in UK Biobank (UKB) and BioBank Japan (BBJ). Table S12. Significantly enriched pathways (and/or gene sets) were identified by gene-set enrichment analysis (GSEA). Table S13. Gene-set enrichment analysis (GSEA) results of phosphoproteomics that were associated with rs75776403. Figure S2. Pairwise correlation plot showing a gene–gene correlation between genes that are involved in the cellular response to thyroid hormone stimulus (purple) and corticosteroid receptor signaling pathways (orange). CLEC18A gene is marked using a red dot. Table S14. Association between (A) rs75776403 or (B) CLEC18A mRNA level with thyroid hormone axial genes. Table S15. Gene set of cellular response to thyroid hormone stimulus and corticosteroid receptor signaling pathway in gene ontology (GO) biological process (BP) database. Table S16. Association between rs75776403 and corticosteroid receptor signaling or thyroid-stimulated pathway genes. Table S17. Association between CLEC18A and thyroid stimulated pathways or corticosteroid receptor signaling genes in adrenal gland, liver, ovary, and testis tissues from GTEx portal.


Rht-D1b.FHB resistance shared pleiotropic loci with plant height and anther retention. Genomic prediction allows to select for genomic background reducing FHB susceptibility in the presence of the dwarfing allele Rht) genes is of utmost relevance. In this study, 401 genotypes of European origin without/with dwarfing alleles of Rht-D1 and/or Rht24 were analysed across five environments on FHB severity and the morphological traits such as plant height (PH), anther retention (AR), number of spikelets per ear, ear length and ear density. Data were analysed by combined correlation and path analyses, association mapping and coupling single- and multi-trait genome-wide association studies  and genomic prediction (GP). All FHB data were corrected for flowering date or heading stage. High genotypic correlation (rg = 0.74) and direct path effect (0.57) were detected between FHB severity and anther retention (AR). Moderate correlation (rg = − 0.55) was found between FHB severity and plant height (PH) with a high indirect path via AR (− 0.31). Indirect selection for FHB resistance should concentrate on AR and PH. ST-GWAS identified 25 quantitative trait loci (QTL) for FHB severity, PH and AR, while MT-GWAS detected six QTL across chromosomes 2A, 4D, 5A, 6B and 7B conveying pleiotropic effects on the traits. Rht-D1b was associated with high AR and FHB susceptibility. Our study identified a promising positively acting pleiotropic QTL on chromosome 7B which can be utilized to improve FHB resistance while reducing PH and AR. Rht-D1b genotypes having a high resistance genomic background exhibited lower FHB severity and AR. The use of GP for estimating the genomic background was more effective than selection of GWAS-detected markers. We demonstrated that GP has a great potential and should be exploited by selecting for semi-dwarf winter wheat genotypes with higher FHB resistance due to their genomic background.With the high interest for semi-dwarf cultivars in wheat, finding locally adapted resistance sources against Fusarium head blight (FHB) and FHB-neutral reduced height (The online version contains supplementary material available at 10.1007/s00122-022-04219-4. Triticum aestivum L.) is one of the most cultivated cereal crops worldwide and serves as a staple food for millions of people. However, the production of wheat is hampered by several diseases, including Fusarium head blight (FHB) caused by numerous Fusarium species, with Fusarium graminearum Schwabe and F. culmorum (W.G. Smith) Sacc. being the most predominant in Central Europe. FHB causes severe yield losses and contaminates the grains with several mycotoxins including deoxynivalenol (DON) which is one of the most frequently detected in wheat  caused by climate change , Rht-D1 (syn. Rht2) and Rht24, located on chromosomes 4B, 4D and 6A, respectively  provide only a partial resistance , evaluating the potential of GB in selecting FHB-resistant genotypes with short Rht alleles based on the single-trait GWAS and genetic estimated breeding values (GEBV) from the GP. GB refers to all QTL affecting the traits, except those associated with plant height on chromosomes 4D and 6A corresponding to Rht-D1 and Rht24 genes, respectively.In this study, we aimed to (i) describe the nature and magnitude of the complex interactions between FHB resistance and morphological traits, including plant height, anther retention, number of spikelets per ear, ear length and ear density using combined correlation and path analyses; (ii) dissect the genetic architecture of FHB severity, plant height and anther retention, thus uncovering the genetic basis of the complex interactions among these traits through a joint implementation of single- and multi-trait genome-wide association study (GWAS), and genomic prediction (GP); and (iii) separate for each trait, the effects of The materials consisted of 401 winter wheat cultivars from European origin (Table S1). These cultivars were evaluated in Germany at three locations in 2020 and two in 2021, resulting in five environments (location × year combinations) in total. In 2020, the cultivars were evaluated in Hohenheim (HOH) near Stuttgart , Oberer Lindenhof (OLI) near Reutlingen , and Wohlde (WOH) near Bergen . In 2021, field experiments were conducted in HOH and OLI only. At each location, the cultivars were randomized using an incomplete lattice design with two replicates. Experimental units were planted in double rows of 0.9 m length in WOH and single rows of 1.2 m length in HOH and OLI. Experiments were sown with a density of 40 kernels/row in WOH and 60 kernels/row in HOH and OLI.F. culmorum . Inoculations were repeated four to five times at intervals of two to three days to inoculate each genotype at least once during mid-anthesis. The first inoculations were done when early cultivars started flowering.Inoculum of the highly aggressive single-spore isolate FC46 (IPO 39-01) of −1), ear density , days to flowering  and heading stage (HS). Days to flowering, the number of days when 75% of heads showed extruded anthers after May 1, was assessed in all environments except in WOH. Heading stage (1–9) was recorded on one a single day (different dates in different environments) and is equal to BBCH stage 51–59 (Meier i) was counted and ear (spike) length  was measured. Ear density EDi was calculated as the number of spikelets per centimetre:Eight traits were recorded: Fusarium head blight severity , plant height , anther retention , ear length , number of spikelets per ear  from seed samples of the 401 genotypes. Genotyping was done using an Illumina 25 K Infinium single-nucleotide polymorphism (SNP) array which produced a total of 24,145 SNP markers. About 58 and 14% of the markers overlapped, respectively, with 90 K iSelect and 820 K Axiom® arrays, which are publicly available at CerealsDB   , days to flowering was added as a cofactor (fixed effect) to the mixed linear model for calculating FHB severity. Because days to flowering was not assessed in WOH, the model was further extended by adding heading stage as a second cofactor in order to correct FHB severity. This correction reduced the correlation coefficient between FHB severity and days to flowering from − 0.32 to − 0.16. Similarly, the correlation between FHB severity and heading stage was reduced from 0.29 to 0.11. The genotype was treated as fixed effect, while block, replicate, environment and genotype-environment interaction were included as random effects. Variance components were firstly estimated for all genotypes as one group. Secondly, genotypes were grouped based on Rht genes and variances components were estimated to describe the extend of genetic variation within each group for PH, FHB severity and AR. Prior to genotypes grouping, a single-trait genome-wide association study was conducted on each trait to identify significant markers associated with PH on chromosomes 4D and 6A, which correspond to Rht-D1 and Rht24 genes. Based on the presence/absence of Rht-D1b and Rht24b, genotypes were categorized into four Rht groups, namely NoRht, Rht24b, Rht-D1b and Rht24b + Rht-D1b. NoRht was composed of genotypes with tall alleles of both genes. Rht24b included genotypes carrying Rht24b only, while Rht-D1b was constituted of genotypes with Rht-D1b only. Similarly, Rht24b + Rht-D1b included genotypes possessing semi-dwarfing alleles of both genes. Dummy variables  as described by Piepho et al.  based on factor-wise comparisons of a model with all factors and a model without the respective factor  was estimated for each trait following the procedure described by Piepho and Möhring (G) was calculated for each trait as follows:ε) was also calculated for each trait by replacing the genotypic variance by the residual variance (Broad-sense heritability ( Möhring :3\docume) in Eq. .yijkl is the phenotypic value of the first trait; y’ijkl is the phenotypic value of the second trait; gi is the effect of the ith genotype; ej is the effect of the jth environment; geij is the effect of the interaction between the ith genotype and the jth environment; bk is the effect of the kth block; rl is the effect of the lth replicate; and εijkl is the residual error. Bivariate models were fitted with unstructured variance–covariance using the corh-option for the genotype, environment and genotype by environment interaction and diagonal variance–covariance for replicate and blocks. In addition to genotypic correlations, Pearson’s product-moment correlations analysis was performed based BLUEs and the results plotted using the GGally package v.2.1.2. Correlation coefficients were classified and interpreted as described by Zou et al.  and indirect path  effects of morphological traits on FHB severity  of the path model was estimated as:r15, r25, r35 and r45 are the genotypic correlations between FHB severity and PH, AR, EL and NS, respectively. The direct effect (pres) of the residual factors was calculated as the part of the variation of FHB severity which was not explained by PH, AR, EL and NS  and one filtering process, which are performed iteratively  (based on indicated threshold) that are not in linkage disequilibrium (Pearson’s correlation < 0.7) as covariates. Based on this, the two FEMs can be written as follows:yi is the BLUE across environments of the ith genotype; Si1, Si2, …, Sik are the genotype scores of k pseudo-QTNs, initiated as an empty set; b1, b2, …, bk are the respective effects of the pseudo-QTNs; Sij is the genotype score of the ith genotype and jth marker; dj is the corresponding effect of the jth marker; and ei is the residual with ei ~ N. In FEM1, Sijdj represents a testing markers term where all remaining markers are tested one at a time. In FEM2, all t pseudo-QTNs selected after the filtering are re-examined together using the Bayesian information content (BIC) as an optimization criterion. Afterwards, k out of the t pseudo-QTNs with the best BIC values are selected and included in FEM1 as covariates to test the remaining markers.To dissect the genetic architecture of FHB severity, PH and AR, single genome-wide association study (ST-GWAS) analysis was performed based on all 19,969 SNP markers with MAF ≥ 0.05 to identify significant marker–traits associations (MTAs), using the Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) method available in the Genomic Association and Prediction Integrated Tool (GAPIT) R package v.3.1.0 (Wang and Zhang 2), we implemented two ST-GWAS analyses (ST-GWAS1 and ST-GWAS2) on each of the trait. In ST-GWAS1, all markers were included in the analysis, while in ST-GWAS2 significant MTAs identified on chromosomes 4D (Rht-D1) and 6A (Rht24) from ST-GWAS1 were used as covariate variable to increase the detection of small-effect markers. In wheat, the use of Rht markers as covariate variables in BLINK was reported to increase the number of significant markers  = 6 and a Bonferroni-corrected threshold of − log10  = 6.30. The corrected Bonferroni threshold  was determined as follows:α = 0.01 is the type 1 error and Me is the number of markers included in the analysis. The total proportion of genetic variation (pG) explained by significant markers for each trait was determined as follows  from the analysis of variance of the linear model . To identify causal loci with common effects (COM) as well as opposite/interaction effects (IE) on each pair of traits, we performed three generalized least squares (GLS) F tests  was estimated by fitting the significant markers as fixed effects in the bivariate model described in Eq.  using the multi-trait mixed-model (MTMM) method proposed by Korte et al. . MT-GWASk et al. . The gend in Eq. . Markersfrom Eq. .R2 values between pairs of significant markers to identify chromosomal positions of unmapped SNP markers. Markers were considered to be in LD when R2 ≥ 0.70.For both ST-GWAS and MT-GWAS, the genomic relationship matrix was estimated using the kinship algorithm, VanRaden  in GAPIT1) included all markers as random effects and was fitted as follows:Y is the vector of BLUEs; β is a vector of fixed effect such as the grand mean; Z is the design matrix; u ~ N is the vector of random markers effects; A is a relationship matrix and the residuals are normal with constant variance; and ε is a vector of residual errors. GP1 model was validated using the fivefold cross-validation approach where the training and validation sets were constituted by splitting the phenotypic and genotypic data into five sets, consisting of 80–81 genotypes each  and accuracy (rMG/H) for each of the traits. Genomic prediction ability was the Pearson correlation coefficient between predicted and observed phenotypes for each of the five validation sets  using only significant markers from ST-GWAS which explained at least 5% of the genetic variation. MAS prediction was done based on the markers effects deducted from the GP1 model, and the prediction accuracy was calculated.Genomic prediction analysis was performed using the mixed linear ridge regression model in the rrBLUP package v.4.6.1 Endelman . The modpG values) explained by GB and Rht markers. pG values were estimated separately for GB and Rht markers by fitting a linear regression model and using the adjusted R2 as described in Eq. (2) was built and the prediction ability (rMG) and accuracy (rMG/H) were evaluated following the fivefold approach. Training and validation sets were the same as described for GP1. In GP2, significant MTAs for PH on chromosomes 4D and 6A were used as covariates, and the prediction accuracy was estimated for each trait based on small-effect markers only. GP2 was fitted as follows:Y is the vector of BLUEs; β is the vector of fixed effects of the covariates; X and Z are the design matrices; u ~ N is the vector of random markers effects; A is a relationship matrix and the residuals are normal with constant variance; and ε is a vector of residual errors. Furthermore, we selected the ten best genotypes with low FHB severity and Rht-D1b allele based on the calculation of genetic estimated breeding values (GEBV) using the effects of random markers from the GP2 model as:Y0 and Z0 are the vectors of GEBV and design matrix of the genotypes, respectively. The correlation between the GEBV-based approach and stacking of additive effects (SAE) of GB markers from ST-GWAS was also determined in order to check the relative effectiveness of GWAS for exploiting genomic background. SAE was calculated only for GB markers which explained at least 5% of genetic variation.Based on the ST-GWAS, we evaluated the contribution of GB to FHB severity, PH and AR using the additive effects and estimates of total genetic variation (d in Eq. . In addiG) were low for DTF, HS, EL, NS and ED and moderate to high for PH, FHB severity and AR (Table G observed for AR was due to the considerable existing genetic variation among the genotypes. The coefficient of variation due to error (CVε) was low for all traits (Table Genetic coefficients of variation (CVAR Table . The higts Table . Geneticrp = 0.70) and negatively and moderately correlated with PH (rp = − 0.62). The outlying point in the scatter plot FHB severity/AR belongs to the cleistogamic cultivar “Anapolis” that has a very high AR (99.6%) and a low FHB severity (20.9%). PH was negatively and moderately correlated with AR (rp = − 0.53), while NS showed positive and low correlation with EL (rp = 0.39) and ED (rp = 0.37). Highly negative phenotypic correlations were detected between EL and ED (rp = − 0.71) , PH and AR (rg = − 0.55) and negatively high between ED and EL (rg = − 0.70). Genotypic correlations were positively high between FHB severity and AR (rg = 0.74) and low between NS with EL (rp = 0.38) and ED (rp = 0.37) . One MTA was common to PH and FHB severity, one to PH and AR and two to FHB severity and AR. MTAs explained 69.7, 60.7 and 44.2% of total genetic variation for PH, FHB severity and AR, respectively. Markers rs20873 on chromosome 4D linked to Rht-D1 and rs10110 on chromosome 6A linked to Rht24 were major MTAs for PH  for identified MTAs.The first two principal coordinates (PCs) indicated that the 401 genotypes included in the study were not structured . Therefore, we corrected in the GWAS for relatedness among genotypes using the kinship matrix and did not include PCs. The ST-GWAS was performed to depict the genetic architecture of FHB severity, PH and AR (Table S2). Considering all traits, a total of 25 significant MTAs were identified, of which seven were specific to ST-GWASels Fig. a, b. Thers9660 on chromosome 4D was associated with FHB severity only, but it exerted an opposite effect on the two traits. Similarly, all common effects MTAs were associated with FHB severity only. Common effect MTAs explained 6.3–26.6% of the genotypic correlation between FHB severity and PH, while 3.1–6.3% of the correlation was attributable to interaction effects MTAs  between markers rs19377 and rs13973 located on chromosome 5B for PH . Very low R2 values were observed among all other MTAs.In both ST-GWAS and MT-GWAS, the inspection of Q–Q plots of the two GWAS revealed a good control of false positives and negatives, demonstrating the existence of true MTAs controlling the genetic architecture of each trait as well as their interactions . The linkage disequilibrium analysis revealed strong linkage . This guaranteed the accurate estimation of both rMG and rMG/H, resulting in low standard errors of the estimates across validation sets (Table S3). In contrary, the prediction accuracy of marker-assisted selection based on SNP markers with pG ≥ 5% was moderate for all traits, with the lowest value (0.4) observed in AR . FHB severity and AR showed similar and lower prediction accuracy than PH. Genotypes with Rht markers  implemented using Rht markers as covariates showed moderate prediction accuracy for the three traits (Table S5). This showed that the estimated contribution of GB was higher in the three traits with GP2 compared with GWAS. The highest accuracy was observed for AR, while the lowest value was obtained in PH, confirming the trend revealed by the GWAS. Standard errors were also low for GP2, indicating a high consistency of the prediction accuracy among the five validation sets.The contribution of GB to the genetic variation was different among the three traits Fig. . The toters Fig. a. Ten, eers Fig. b. Non-RhRht groups, GEBV from GP was positively correlated (r = 0.64–0.77) with stacking of additive effects (SAE) of GB markers with pG ≥ 5%  in Rht-D1b and Rht24b + Rht-D1b groups are summarized in Table Rht-D1b allele only (e.g. Faktor and Anapolis), or the combination Rht24b + Rht-D1b alleles (e.g. Kranich and Kamerad)  at position 521 Mbp (Table S6). This falls within the QTL region Qfhb.nmbu.5A.1 (480–552 Mbp) which was reported through a meta-QTL analysis by Venske et al.  is an indication that Rht24b did not affect FHB resistance  on plant height was about twofold lower than the effect of Rht-D1b  between the GEBV-based and stacking of additive effects (SAE) approaches, many genotypes with similar additive effects from GWAS exhibited different GEBV and FHB severity. This shows the limitations of SAE to accurately discriminate among genotypes based on their genomic background contrary to the GEBV-based approach and hence confirms the superiority and efficiency of the genomic prediction over fixed effects selection methods as reported by several previous studies  of genotypes based on the effects of all loci, except WAS Fig. , and thaFurthermore, the GEBV approach would select Anapolis as one of the best genotypes which exhibited the highest anther retention (99.6%). This is a typical cleistogamous genotype which possesses a structural floral barrier for pathogens, resulting in low FHB severity  taller genotypes within the Rht-D1b group as long as they have a high (negative) FHB-related resistance GB  explained a high proportion of the genetic variance of FHB severity and anther retention and has the potential to increase FHB resistance in genotypes with Supplementary file1 (XLSX 52 KB)Supplementary file2 (PDF 914 KB)Below is the link to the electronic supplementary material.