SBF2, a gene associated with neuroticism in previous GWAS, with total psychiatric problems. The genetic effects underlying the total score were shared with common psychiatric disorders only  (rG > 0.49), but not with autism or the less common adult disorders  (rG < 0.01). Importantly, the total psychiatric problem score also showed at least a moderate genetic correlation with intelligence, educational attainment, wellbeing, smoking, and body fat (rG > 0.29). The results suggest that many common genetic variants are associated with childhood psychiatric symptoms and related phenotypes in general instead of with specific symptoms. Further research is needed to establish causality and pleiotropic mechanisms between related traits.Substantial genetic correlations have been reported across psychiatric disorders and numerous cross-disorder genetic variants have been detected. To identify the genetic variants underlying general psychopathology in childhood, we performed a genome-wide association study using a total psychiatric problem score. We analyzed 6,844,199 common SNPs in 38,418 school-aged children from 20 population-based cohorts participating in the EAGLE consortium. The SNP heritability of total psychiatric problems was 5.4% (SE = 0.01) and two loci reached genome-wide significance: rs10767094 and rs202005905. We also observed an association of  Psychiatric disorders are moderately heritable, on average about 30–50% of the variability in symptoms can be explained by genetic differences between individuals . The joiRecent family and molecular genetic studies demonstrated that much of the genetic effects underlying psychiatric disorders are not unique to particular diagnoses, but rather shared across several psychiatric diagnoses and symptoms , 6–10. TGWAS derived polygenic risk scores (PRS) for single disorders are good predictors of general psychopathology. For instance, a PRS for ADHD was more strongly associated with a general psychopathology factor than with specific hyperactivity or attention problems adjusted for general psychopathology . In anotPrevious GWAS of childhood disorders, such as autism spectrum disorders, ADHD, aggression and internalizing disorders, , 17–19 hhttps://www.eagle-consortium.org/). Finally, we estimated genetic correlations of the total psychiatric problem score with various single child and adult psychiatric, psychological, neurological and lifestyle or educational characteristics.Our aim was to identify genetic loci associated with a total psychiatric problem score representing a variety of psychiatric problems including internalizing, externalizing, attention, neurodevelopmental and other psychiatric problems. To identify these genetic variants, we performed a GWAS meta-analysis within the EArly Genetics and Lifecourse Epidemiology (EAGLE) consortium  was performed using established protocols. In all cohorts, SNPs were imputed to the 1000 Genomes Phase 1 or Phase 3 reference panel . Each cohttps://github.com/kaustubhad/gwas-power) [The z-scores of the total psychiatric problems scores were related to the SNP dosages in a linear model. Covariates included gender, age at assessment and principal components of ancestry. The number of dimensions (1–10) were specified by each cohort. CATSS and TCHAD additionally used a random effect to account for familial relatedness. FinnTwin12 and NTR applied a mixed model with two random effects to control for population stratification and relatedness. We pooled the results from the individual cohorts using an inverse-variance weighted fixed-effects meta-analysis. R 3.4.3 was used for QC, data preparation and analysis of results . Meta-sos-power) .We also used the FUMA web tool  to exploWe performed gene-based tests using MAGMA  in FUMA.Second, we tested, whether the results from the gene-based tests were related to gene expression in several tissues. Specifically, we used MAGMA to test whether the strength of association between genes and the total psychiatric problem score was related to the mean gene expression level in a specific tissue, while considering average expression levels. Given that we expected gene variants to act via brain pathways, we tested expression in 13 brain regions . HoweverThird, we further examined whether the predicted gene expression of selected genes was related to total psychiatric problems. We selected genes, that were  annotated to genome-wide hits, or that were genome-wide significant according to gene-based tests. To correlate gene expression with total psychiatric problems, we used a transciptome-wide association study (TWAS) approach . In shorWe estimated the SNP heritability of total psychiatric problem scores with LD score regression . We usedThe GWAS meta-analysis included cohorts using different instruments to measure total psychiatric problems with mean age ranges from 5 to 13 years. While our inclusion criteria maximized sample sizes and also increased the potential for generalizability, this approach may have increased the study heterogeneity. We therefore performed two sensitivity analyses to investigate effect consistency. In the first, we performed three separate meta-analyses for each of the major instrument groups. These meta-analyses included only cohorts using the CBCL, SDQ or Rutter questionnaires, respectively. We then tested consistency of genetic effects by computing the genetic correlation between each instrument.In the second sensitivity analyses, we tested, whether the genetic correlation between total psychiatric problems and thought disorders changed depending on the age at which total psychiatric problems were assessed. Specifically, we hypothesized, that total psychiatric problems scores assessed during early adolescence would correlate more strongly with schizophrenia and bipolar disorders, than total psychiatric problems occurring before the onset of puberty. We therefore performed two meta-analyses consisting of the cohorts assessing youths below and equal or above age 12 years. We then examined the genetic correlation of total psychiatric problems in both age groups with schizophrenia and bipolar disorder.0 = 1.01, SE = 0.01), suggesting that the inflation was due to a true signal rather than spurious associations. The SNP heritability was estimated at 5.4% (SE = 0.013).We tested 6,844,199 SNPs after quality control. The QQ-plot  showed s2). Information on this locus was only available in 27% of participants (6 cohorts). The SNP showed a moderate amount of effect heterogeneity (I2 = 47.6%). Also on chromosome 11 an insertion/deletion variant (InDel) was genome-wide significant. A deletion of the A allele at rs202005905 was associated with an increase of 0.08SD in total psychiatric problems .Two loci on chromosome 11 were genome-wide significant, see The SNP rs10767094 lies in the intron of the long non-coding RNA Loc105379880 and rs202005905 lies in an intergenic region with no nearby genes. A FUMA eQTL and chromatin interaction analysis did not reveal any interactions with genes. The mQTL database did not list any associations with DNA methylation.SBF2. The T allele was associated with 0.05SD lower total psychiatric problems  (2 = 0.0%) among the cohorts. The T allele occurred on average in 14% across cohorts, with a very good imputation quality of 96%. FUMA eQTL and chromatin interaction analysis, as well as a lookup in mQTL DB did not reveal any further information on functional association. Results for all SNPs with genome-wide suggestive p-values (p<5E-06) can be found in https://doi.org/10.6084/m9.figshare.17170994.v1.The third top locus did not reach genome-wide significance, but is of interest for its location in a gene previously implicated in neuroticism , 48 as w 38,330) . This asSBF2. The aggregate of 1,508 SNPs in SBF2 showed a nominal significance of p = 0.0004 .Next we tested the association of 18,290 protein coding genes with the child total psychiatric problem score. None of the genes reached genome-wide significance  Figs. WeWe performed a MAGMA tissue expression analysis in 13 specific brain tissues . Genes mSBF2. We found that higher predicted levels of SBF2 in the basal ganglia were related to higher scores of total psychiatric symptoms  based on the best linear unbiased predictions (BLUP) of a random variable representing 489 SNPs. A lookup in the TWAS Hub database revealed, that predicted levels of SBF2 gene products associate most with following phenotypes: neuroticism, body fat measures, red blood cell count, nervous feelings and worrying (http://twas-hub.org/genes/SBF2/).The top two genome-wide significant loci were not linked to a characterized gene, thus we decided to perform a TWAS analysis only for G = 0.86, SE = 0.39, p = 0.03, q = 0.06). Subjective wellbeing, childhood IQ, college completion, years of schooling, intelligence and age of smoking initiation showed significant negative correlations with the total psychiatric problem score, ranging from -0.66 to -0.42. Of the psychiatric phenotypes tested, the less common psychiatric disorders like schizophrenia, bipolar disorder, autism spectrum disorder, and anorexia were not genetically correlated with the total psychiatric problem score (rG < 0.01).Next we quantified the extent to which the genetic associations of child psychiatric problems scores were shared with other phenotypes. After adjustment for false discovery rate, insomnia, depressive symptoms, neuroticism, cigarettes smoked per day, body fat, body mass index, number of children, and age of smoking initiation all showed positive genetic correlations between 0.29 and 0.60 with the total psychiatric problem score  based onG = 0.84 . The Rutter questionnaire correlated more modestly with both CBCL  and SDQ . The genetic correlations of total psychiatric problems assessed both before or after age 12 with schizophrenia and bipolar disorder were small and consistent with a null effect irrespective of age group , but not with less common and on average more severe ones .The current study reports the first GWAS examining global psychopathology in children. Two genetic loci were genome-wide significant in the total sample. Additionally, we found support for the involvement of gene The two genome-wide significant variants are one SNP (rs10767094) and one InDel (rs202005905). To the best of our knowledge these variants have not been associated with psychiatric traits before. It is unclear, how exactly these variants or tagged causal variants may affect general psychopathology, as functional annotation for these loci is sparse. The modest imputation quality possibly affected study results as both variants failed quality control in most cohorts. Measurement error of the genotypes could explain the relatively high estimates heterogeneity. An important next step would therefore be to replicate these SNPs using direct genotyping or denser arrays.SBF2 with the lead SNP rs72854494 in total psychiatric problems is more convincing. This locus has been implicated in neuroticism based on two GWAS. In a GWAS of neuroticism [SBF2, showed genome-wide significance. In a second larger independent GWAS of 449,484 participants, SBF2 showed a genome-wide significant effect for both neuroticism and worry in gene-based tests [SBF2 correlate with neuroticism based on several GWAS. Neuroticism describes a disposition to experience negative emotions and a higher stress reactivity. It robustly and substantially associates with general psychopathology in children, [G = 0.41). These results suggest that SBF2 pleiotropically affects neuroticism and psychopathology, but the mechanisms would need to be explored further. Neuroticism has been hypothesized to contribute strongly to general psychopathology, [SBF2 alterations [SBF2 affects psychiatric problems via white matter development.While just not genome-wide significant, the evidence for an involvement of roticism  rs155734hildren,  and adulhildren,  . Considering that these correlations represent agreement across independent cohorts with numerous methodological and population differences, a genetic correlation above 0.8 provides very strong support for jointly analyzing total psychiatric problems scores assessed by either CBCL or SDQ. The total psychiatric problems assessed by the Rutter questionnaire had a lower genetic correlation with the other assessment methods. However, the Rutter sub-group meta-analysis was only based on three cohorts, representing only 20% of the total study population, which makes it difficult to distinguish instrument effects from other cohort-specific characteristics. Overall, the genetic correlation analyses suggest limited heterogeneity between the majority of cohorts. We thus argue that the lack of power to identify more loci probably stemmed from insufficient sample size, measurement error and the relatively low number of children with severe psychiatric problems in the general population.Another limitation is that the study sample was not sufficient to consider potential sex interactions. The prevalence of child psychiatric problems differs by gender, with school-age boys typically showing more externalizing problems and girls more internalizing problems. Total scores tend to be less different on average, with boys showing only slightly higher scores . Gene-seSBF2 with total psychiatric problems. However, the association of predicted SBF2 gene products with neuroticism and psychiatric problems, as well as the influence on myelination in an experimental mouse model, suggest a causal role.Finally, as in any other GWAS study, the extent to which the found associations can be interpreted causally is difficult. Due to linkage disequilibrium it is unclear whether the two top variants have causal influence on psychopathology or are a marker for other causal variants. The same is true for the association of SBF2, which was previously known to be associated with neuroticism, is also implicated in general psychopathology in children. Our results merit further investigation for confirmation and exploration of potential causal mechanisms.In conclusion, this GWAS of total psychiatric problem scores suggests that common genetic variants exist that are associated simultaneously with internalizing, externalizing, attention and other psychiatric problems in childhood. The pleiotropy was not restricted to psychiatric phenotypes, but also included intelligence, educational attainment, wellbeing, smoking, body fat and number of children in adulthood. Interestingly, we did not find shared genetic effects with autism, schizophrenia and bipolar disorder. Two novel loci were genome-wide significant, though, the low sample size and modest imputation quality necessitate replication before firm conclusions can be drawn whether they influence total psychiatric problems. Furthermore, we found evidence that the gene S1 File(PDF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 Table(PDF)Click here for additional data file.S1 Fig(PNG)Click here for additional data file.S2 FigNote: SNP only available in subset of cohorts, due to insufficient imputation quality in most studies.(PNG)Click here for additional data file.S3 FigNote: SNP only available in subset of cohorts, due to insufficient imputation quality in most studies.(PNG)Click here for additional data file.S4 Fig(PNG)Click here for additional data file.S5 FigQuantile-quantile plot of observed -log 10 p values vs expected -log 10 p values assuming chance findings in gene based analysis. Diagonal line indicates a p value distribution compatible with chance finding. Upward deviations indicate p values more significant than expected.(PNG)Click here for additional data file.S6 FigManhattan plot of -log 10 p values vs SNP position for gene based analysis. Genes above the red horizontal line indicate genome-wide significant findings.(PNG)Click here for additional data file.


Introduction: Porcine reproductive and respiratory syndrome virus (PRRSV) causes respiratory disease in piglets and reproductive disease in sows. Piglet and fetal serum thyroid hormone  levels decrease rapidly in response to Porcine reproductive and respiratory syndrome virus infection. However, the genetic control of T3 and T4 levels during infection is not completely understood. Our objective was to estimate genetic parameters and identify quantitative trait loci (QTL) for absolute T3 and/or T4 levels of piglets and fetuses challenged with Porcine reproductive and respiratory syndrome virus.Methods: Sera from 5-week-old pigs (N = 1792) at 11 days post inoculation (DPI) with Porcine reproductive and respiratory syndrome virus were assayed for T3 levels (piglet_T3). Sera from fetuses  at 12 or 21 days post maternal inoculation (DPMI) with Porcine reproductive and respiratory syndrome virus of sows (N = 145) in late gestation were assayed for T3  and T4  levels. Animals were genotyped using 60 K Illumina or 650 K Affymetrix single nucleotide polymorphism (SNP) panels. Heritabilities, phenotypic correlations, and genetic correlations were estimated using ASREML; genome wide association studies were performed for each trait separately using Julia for Whole-genome Analysis Software (JWAS).Results: All three traits were low to moderately heritable (10%–16%). Phenotypic and genetic correlations of piglet_T3 levels with weight gain (0–42 DPI) were 0.26 ± 0.03 and 0.67 ± 0.14, respectively. Nine significant quantitative trait loci were identified for piglet_T3, on Sus scrofa chromosomes (SSC) 3, 4, 5, 6, 7, 14, 15, and 17, and collectively explaining 30% of the genetic variation (GV), with the largest quantitative trait loci identified on SSC5, explaining 15% of the genetic variation. Three significant quantitative trait loci were identified for fetal_T3 on SSC1 and SSC4, which collectively explained 10% of the genetic variation. Five significant quantitative trait loci were identified for fetal_T4 on SSC1, 6, 10, 13, and 15, which collectively explained 14% of the genetic variation. Several putative immune-related candidate genes were identified, including CD247, IRF8, and MAPK8.Discussion: Thyroid hormone levels following Porcine reproductive and respiratory syndrome virus infection were heritable and had positive genetic correlations with growth rate. Multiple quantitative trait loci with moderate effects were identified for T3 and T4 levels during challenge with Porcine reproductive and respiratory syndrome virus and candidate genes were identified, including several immune-related genes. These results advance our understanding of growth effects of both piglet and fetal response to Porcine reproductive and respiratory syndrome virus infection, revealing factors associated with genomic control of host resilience. Porcine reproductive and respiratory syndrome virus (PRRSV) infection causes respiratory disease in swine of all ages and reproductive disease in pregnant females . PRRSV iThyroid hormones affect a wide range of biological processes, such as growth , metabolThis study used samples from two of the largest studies conducted to date on host responses to PRRSV infection. This includes samples on respiratory PRRS using the nursery piglet challenge model from the PRRS Host Genetics Consortium (PHGC) . Groups For the piglet trials, all experimental protocols were approved by the Kansas State University Institutional Animal Care and Use Committee. For the fetal samples, all experimental protocols were approved by the University of Saskatchewan’s Animal Research Ethics Board (Protocol # 20160023) and adhered to the Canadian Council on Animal Care Guide to the Care and Use of Experimental Animals. Animals were donated from commercial companies for the PHGC experiments. Pregnant gilts were purchased from Fast Genetics Inc., for the PGM experiments. This study is reported in accordance with ARRIVE guidelines.Mycoplasma hyponeumoniae, and swine influenza. Animals were transported to Kansas State University at approximately 21 days of age, randomly placed into 10–15 pens, and acclimatized for 7 days. Piglets were inoculated intramuscularly and intranasally with either NVSL-97-7985 or KS-2006-72109 PRRSV2 isolates; all animals used in the current study were challenged and animal was considered the experimental unit. Authors were aware of group allocation at the different stages of the experiment. A subset of phenotypic and genotypic data collected as part of the PHGC trials and stored in the secure PHGC database was used in the present study. Specifically, to study WG, body weight (BW) was measured at 0, 21, and 42 DPI. Piglet WG from 0 to 21 DPI (WG21) and WG from 0 to 42 DPI (WG42) were included in the genetic parameter analyses. PRRSV2 RNA concentration tested from sera at 11 DPI and then log10 transformed for future analyses. Pigs were euthanized at 42 DPI except in Trials 7 and 8, where they were euthanized at 35 DPI due to facility availability limitations. Weights from animals in trial 9 were excluded from the dataset as the animals were from the Iowa State University Residual Feed Intake selection lines  genotype, associated with PRRS tolerance , for eacFor fetal genotyping, only SNPs that were present on both the Illumina Porcine SNP60 Beadchip version 2 and the Affymetrix Axiom Porcine Genotyping Array 650 K Beadchip were retained for downstream analyses. SNPs were matched between platforms using both RS identifiers and chromosomal map location, and MAF were verified between platforms. The WUR SNP was not included as a fixed effect in the fetal models as the impact of WUR on the fetal response to PRRS using the PGM model has been previously shown by our group to not be associated with response to infection . This rep < 0.05. The Iowa State University High Performance Computing Nova cluster was used for these analyses. The following univariate mixed linear animal model was used for piglet_T3;The piglet and fetal genotype data were used separately to compute a genomic relationship matrix using the Van Raden Method . GeneticThe following univariate mixed linear animal model was used for both fetal_T3 and fetal_T4;For all traits, the phenotypic variance was calculated as the sum of variance from dam, animal, and residuals. Heritability was calculated as the ratio of animal variance to phenotypic variance. Bivariate animal models were also run in ASReml to estimate phenotypic and genetic correlations between T3 and/or T4 traits and other important traits , using the same fixed and random effects as in the univariate models, and this applied to WG and VL traits as well as previously described .π = 0.999, and a Monte Carlo Markov Chain (MCMC) of length 50,000, with 5,000 for burn-in. The following mixed model was used for both the piglet and fetal datasets, with the same fixed and random effects as the univariate mixed linear animal model was used for piglet_T3, fetal_T3, and fetal_T4, respectively;Y is a vector of phenotypes, X is an incidence matrix for fixed effects in the model, b is a vector of fixed effects, W is an incidence matrix to account for other random effects in the model, u is a vector of random effects, zj is a vector of genotypes for SNP j based on the number of B alleles , αj is the allele substitution effect for SNP j, δj is a parameter that indicates whether SNP j was included in that sample of the MCMC, and e is the vector or residuals. The analyzed SNPs were split into 2,265 non-overlapping 1-Mb windows across the genome. Based on the infinitesimal model, each window is expected to explain 0.04% (100%/2265) of the genetic variation (GV). We considered windows that explained more than 1% of the GV as significant.GWAS for T3 and T4 levels during PRRSV infection were performed for each trait separately using the Julia for Whole-genome Analysis Software (JWAS) . The ASRp < 0.05 after multiple testing correction was considered significant. The linkage disequilibrium (LD) between SNPs that were within 1 Mb from the SNP with the highest PPI in the top 3 significant 1-Mb windows were estimated based on r2, using the Plink software  within the top 3 significant 1-Mb windows based on percentage of GV explained were investigated for impact on phenotypic traits. We fitted these SNP genotypes separately as fixed effects using univariate mixed linear animal model that was used for piglet_T3, fetal_T3, and fetal_T4, respectively in ASReml. The LS means and corresponding SE were estimated and pairwise T-tests between SNP genotypes were conducted with Tukey’s multiple testing correction; a software .For each trait, the SNP with the highest PPI was identified for each significant 1-Mb windows . Annotated genes that were located within 400 Kb (200 Kb upstream and 200 Kb downstream) of these SNPs were identified using ENSEMBL biomart  release Our investigations focused on serum thyroid hormone levels at 11 DPI in piglets, because T3 levels were most extremely suppressed at this timepoint post PRRSV challenge . Fetal Tg) with serum VL (rg = −0.34). Moreover, there was a positive high genetic correlation between piglet_T3 and both WG21 and WG42 with rg = 0.81 and rg = 0.67, respectively. Our results were consistent (within the margins of the SE) as those previously calculated as high genetic correlations between serum VL and WG21 or WG42 . The least-square (LS) means for each SNP genotype were calculated and the statistical difference between individual genotypes were determined (p < 0.05) for thyroid hormone levels between animals with different genotypes at the individual SNP. Three of these SNPs had patterns of additive inheritance , whereas five showed the homozygous recessive was needed for a favorable phenotype , and one of these SNPs had a pattern of dominance inheritance (ALGA00009329).We quantified the impact of the top three SNPs for each trait based on PPI in the 1-Mb window that explained the largest amount of GV for each trait. We fit univariate mixed models and included the individual SNP genotype in the model as a fixed effect. We found that all 9 SNPs  were highly significantly associated with thyroid hormone levels (termined . Detailsr2) were calculated for the top SNP (±1 Mb) within all significant windows  of the top SNP in each significant window. The top SNP was determined based on the SNP with the highest PPI within each of the 1-Mb windows that explained >1% of the GV. Results for all significant SNP revealed a total of 93 annotated genes, enumerated in Our study estimated genetic parameters, identified QTL, and characterized QTL regions for thyroid hormone  levels in piglets and fetuses challenged with PRRSV2. Our main findings were: 1) thyroid hormone levels following PRRSV infection were lowly to moderately heritable in piglets and fetuses; 2) thyroid hormone levels had negative moderate to high genetic correlations with VL and positive moderate to high genetic correlations with weight-related traits, both economically important disease resilience traits; 3) QTL were identified and the significant 1-Mb windows collectively explained a large amount of the GV. These windows were identified based on individual SNP that had the highest PPI within significant 1-Mb windows for thyroid hormone levels; and 4) biologically relevant candidate genes were identified near SNP markers that had the highest PPI within significant 1-Mb windows. These results advance our understanding of both piglet and fetal response to PRRSV infection, bringing us one step further to characterizing the genomic control of phenotypes associated with host resilience and susceptibility.Estimates of heritability of piglet_T3, fetal_T3, and fetal_T4 during PRRSV2 challenge were 16%, 10%, and 10%, respectively. We chose to use the animal model for both piglet and fetal traits with dam included as an independent random effect to account for common environmental effects. The direct-maternal genetic model was not employed for fetal traits due to the absence of pedigree records or genotypes of dams. We hypothesize that heritability of this response phenotype is greater in the piglet samples from the PHGC trials due to the fixed inoculation time compared with the more complex dynamics associated with maternal challenge and transplacental infection in the reproductive PGM trials. Furthermore, while the porcine placenta is known to act as an enzymatic barrier for thyroid hormone , maternain utero.Serum thyroid hormone levels in humans during periods of homeostasis have been estimated to be moderately to highly heritable, at 23%–64% and 28%–65% for T3 and T4, respectively . There hin utero, compared to non-IUGR fetuses , along with the critical outer ring deiodinase (DIO2), required to convert T4 to the more bioactive T3  which suggests that large gains in selection would be difficult. Genetic correlations of the fetal brain:liver ratio with other traits were estimated as very high (−0.79 to −0.99) but were largely uninformative due to the large SE and low heritability of this trait. However, this may be data dependent. Here, we used samples from the largest reproductive PRRS challenge models with a total of 1,184 fetuses analyzed for brain:liver ratios. Potential exists to add more fetal phenotypes to reduce SE and potentially capture more GV, given the uniqueness of this trait. In contrast, we identified a moderate positive genetic correlation between fetal_T4 with BW (0.67), indicating animals with higher levels of T4 could be selected to increase BW of fetuses. We expected, and confirmed, a positive phenotypic correlation between fetal VL and BW and a negative correlation between fetal VL and brain:liver ratio. Previous research reports IUGR fetuses  tend to have lower VL compared average growth fetuses . Our stuHere we report for the first time QTL identified for thyroid hormone levels in swine. Our study found a total of 18 QTL across 11 chromosomes for T3 and/or T4 levels during challenge with PRRSV, collectively explaining 10%–31% of the GV for a trait. The GWAS analyses were completed in JWAS software partly because of the option to fit additional random effects, that could be biologically important , in the model compared to other software . The Pig QTL Database containing 34,793 QTL was used to identify relevant (previously identified) nearby (±2 Mb) QTL with the top three significant regions identified in the current study .versus to use for growth. The overlap between QTL for thyroid hormone levels and adaptive immunity traits identified here may indicate animals were able to successfully initiate adaptive immunity while maintaining sufficient levels of thyroid hormone during challenge.A QTL for piglet_T3 was found on SSC3, explained 3% of the GV, and co-localized with a previously identified QTL for PRRS VL in the host using an expanded dataset from the PHGC . This reA QTL for fetal_T3 that explained 3% of the GV was identified on SSC1 that had two consecutively significant 1-Mb windows. The SNPs with highest PPI in these windows were located at 252973691bp and 253112371bp. This QTL overlapped with 145 previously reported QTL, and co-localized with a QTL for mean corpuscular hemoglobin concentration identified in a study investigating immune-related traits in two pig lines . ReproduKCNA2, KCNA3, KCNA10, LAMTOR5, PROK1, RBM15, and SLC16A4. Potassium voltage-gated channel gene expression have previously been shown to change in expression in the heart in response to hypothyroidism in rats , including several for average daily gain , birth w in rats . Previou in rats . The LAM in rats . AlthougOne of the most important findings of the PHGC was identification of the WUR genetic marker on SSC4, which explained a large proportion of GV for both VL and WG in PRRSV infected piglets  but not Actinobacillus pleuropneumoniae  cytokine , leukocylmonella . Four prlmonella , with imlmonella . Thus, tAll annotated positional candidate genes were identified for each trait within ±200 Kb for all significant SNP , as wellIRF8 (interferon regulatory factor 8) as a putative candidate gene nearby QTL for fetal_T4 . The IRF8 protein is a transcription factor that regulates differentiation of myeloid progenitor cells into monocyte precursor cells . The CD247 protein is T-cell receptor zeta that has an important role in antigen recognition and signal transduction and found to be differentially expressed during PRRSV infection  were identified in the current study as candidate genes near fetal_T4 QTLs . In addition, we found transcriptional regulator genes near QTL  including CREG1, and POU2F1. Gene expression is regulated by thyroid hormones via interactions with thyroid hormone receptors, which are DNA-binding transcription factors (HSDL2 (hydroxysteroid dehydrogenase like 2) gene near a QTL for fetal_T3 levels . In humans HSDL2 modulates cervical cancer cell proliferation and metastasis (PTH1R (parathyroid hormone 1 receptor) as well as three growth factor and cellular proliferation genes; BMP5, CSPG5, and PROK1. It has been shown that PRRSV infection in the fetus results in changes in cellular proliferation and angiogenesis at the maternal fetal interface (PTH1R gene has been shown to be expressed in the thyroid gland and in one study human carriers of a mutation in this gene exhibited hypothyroidism (Thyroid hormones have been shown to depend on solute carrier proteins . We iden SLCO1C1 , and man SLCO1C1 . Several SLCO1C1 . Althoug SLCO1C1 . Recent  SLCO1C1 ; two cad factors . In the tastasis . Recent tastasis . In addinterface . The PTHyroidism . The ideOverall, a variety of putative candidate genes were identified nearby QTL with interesting biological connections especially the immune-related genes. The genes identified here are good candidates for future investigations into the complex interaction of thyroid hormones and animal health.For the first time, we report genomic control of thyroid hormone levels in both piglets and fetuses during PRRSV infection. Our main findings were that thyroid hormone levels following PRRSV infection were heritable and had positive genetic correlations with growth rate. Multiple QTL with moderate effects were identified for T3 and T4 levels during challenge with PRRSV; candidate genes were identified, including several immune-related genes. These results advance our understanding of both piglet and fetal response to PRRSV infection by characterizing the genomic control of a novel phenotype associated with host resilience and susceptibility. Future work should continue to probe the complex interaction of thyroid hormones and immunity.


Biobank scale genetic associations results over thousands of traits can be difficult to visualize and navigate.P-value, effect size, case counts, trait class and fine-mapping posterior probability at a single-nucleotide polymorphism (SNP) across a range of traits from a large set of genome-wide association study. We find that user interaction with association results in LAVAA can enrich and enhance the biological interpretation of individual loci.We have created LAVAA, a visualization web-application to generate genetic volcano plots for simultaneously considering the https://geneviz.aalto.fi/LAVAA/) and will be available in future releases of the finngen.fi website starting with release 10 in late 2023.LAVAA is available as a stand-alone web service ( P-value, and the magnitude of the association, reflected in the effect size. The Manhattan plot and the regional association plot are two popular methods to explore the distribution of the P-values across the genome for one trait at a time  has been a very successful technique for elucidating the role of inherited variation in human biology and pathophysiology. The importance of a statistical association between a genetic variant and a trait of interest is conveyed by the strength of the association, reflected in the P-value. Indeed, in the limit we might expect that eventually all genes in relevant tissues will show statistical significance with all traits  (For continuous traits (e.g. biomarker levels) at a single-nucleotide polymorphism (SNP) the trait with the strongest  traits)  (Sham an traits) . However traits) .P-value for a trait at a particular SNP can reach genome-wide significance (P < 5 × 10−8) but may be entirely explained by a much stronger signal 10 s or 100 s of kilobases away. Statistical fine-mapping techniques    the strength of the association  on the y-axis; (iii) the case count displayed as the diameter of the halo around each dot; and (iv) the category of each trait conveyed by the color of the halo around each dot. In this way, every association for a particular variant can be viewed and explored  > 7.3) or can sweep and select an arbitrary region of the display. The latter action generates a small table of the association results for all selected variants.LAVAA provides multiple mechanisms for the user to further explore the data. If fine-mapping results are available, traits which have been fine-mapped to the current variant can be highlighted with a dark circle around the central dot. Mousing over any dot displays the association statistics and provides an option to label the dot with those details. A user can zoom in to just the genome-wide significant results . This TSV can be directly uploaded to the LAVAA tool here: https://geneviz.aalto.fi/LAVAA/. The LAVAA visualization tool is integrated into the finngen.fi website as of release 10, due to be available to the general public by the end of 2023.Users can generate their own LAVAA plots by downloading the TSV summary of association results from any variant page in the finngen.fi website . However, as the LAVAA plot reveals, more specific definitions of this disease category can have much stronger associations (P-value of 8 × 10−11. Similarly, the LAVAA plot illustrates the bifurcation of lipid and diabetic traits with the diabetes risk allele being associated with lower lipid levels and reduced use of statins (We demonstrate the utility of LAVAA plot with an example from the FinnGen. FinnGen employs a rich variety of case-control definitions, refining or expanding the number of subjects included. For example, the broadest definition of ‘Endocrine, nutritional and metabolic diseases’ encompasses over 118 000 cases out of 309 154 subjects. This broad category shows no significant association at the well-known ciations . The lar statins  and 3.LAVAA is a novel web-based visualization tool for assorted traits at a particular locus. By simultaneously representing multiple features per association, LAVAA permits researchers to more deeply explore biobank-scale genetic results such as from FinnGen.


CHARGE Gene-Lifestyle Interactions Working Group conducted series of genome-wide interaction studies (GWIS) involving up to 610,475 individuals across four ancestries for three lipids and four blood pressure traits, while accounting for interaction effects with drinking and smoking exposures. Here we used GWIS summary statistics from these studies to decipher potential differences in genetic associations and G×E interactions across phenotype-exposure-ancestry combinations, and to derive insights on the potential mechanistic underlying G×E through in-silico functional analyses. Our analyses show first that interaction effects likely contribute to the commonly reported ancestry-specific genetic effect in complex traits, and second, that some phenotype-exposures pairs are more likely to benefit from a greater detection power when accounting for interactions. It also highlighted modest correlation between marginal and interaction effects, providing material for future methodological development and biological discussions. We also estimated contributions to phenotypic variance, including in particular the genetic heritability conditional on the exposure, and heritability partitioned across a range of functional annotations and cell types. In these analyses, we found multiple instances of potential heterogeneity of functional partitions between exposed and unexposed individuals, providing new evidence for likely exposure-specific genetic pathways. Finally, along this work, we identified potential biases in methods used to jointly meta-analyze genetic and interaction effects. We performed simulations to characterize these limitations and to provide the community with guidelines for future G×E studies.The role and biological significance of gene-environment interactions in human traits and diseases remain poorly understood. To address these questions, the  The precise role of gene-environment interactions (G×E) in complex human traits and disease traits remains unclear. Although genome-wide G×E studies have been conducted for many phenotypes, the number of identified G×E is very small relative to the large number of genetic variants identified in traditional genome-wide association studies (GWAS). A number of issues related to the identification of G×E have been well described in the literature –3, inclui.e. the estimate of genetic effect from the interaction model) and G×E interaction effects [The Gene-Lifestyle Interactions Working Group  within t effects  has been effects  and SNP- effects , 9 on bl effects  and SNP- effects .Here we first synthesize the GWIS results for all phenotype-exposure combinations. We highlight the importance of our large-scale initiative, providing evidence that interacting variants might differ by genetic ancestry, and show that accounting for G×E can help to discover new loci, especially for certain phenotype-exposure pairs. We then performed a series of analyses comparing interaction effects against marginal genetic effects derived from both our studies and from previous GWAS. Contrary to a commonly assumed hypothesis , we founcurrent smoking and ever smoking, were considered and measured similarly across all smoking GWIS. The current smoking variable was coded as 1 if the subject smoked regularly in past year and as 0 otherwise. Ever smoking status was coded as 1 if the subject smoked at least 100 cigarettes during his/her lifetime and 0 otherwise. For alcohol consumption, two binary variables were considered, referred further as current drinking and drinking habit. All studies conducted a two-stage approach. In stage 1 (referred to as Discovery), a standard GWIS was performed using up to 18 million genetic variants. In stage 2 (referred to as Replication), only a subset of variants with a p value for either 1df or the 2df test below a certain threshold (P < 10−6 or P < 10−5) at stage 1 were further considered. For each outcome exposure, we had access to complete meta-analysis summary statistics of both the discovery and the replication stages for four different ancestries  after quality control filtering. To ensure a fair comparison, we re-processed all results for each outcome-exposure-ancestry combination using the same pipeline. More details are provided on the data and pre-processing are available in the supplementary notes and in the corresponding publications [We considered four blood pressure phenotypes , and three lipids levels . Two binary smoking exposures, ications –11.P < 5 × 10−8): independent signal (lead SNPs after clumping) and associated locus . Independent signals represent independent SNPs associated at genome-wide significance level. Independent signals were defined using the clumping framework from the PLINK software [i.e., the most associated variant) of ±500 kb. The LD was derived using 1000 Genomes Project [Associated loci are genetic regions of 1 Mb or more harboring at least one genome-wide associated SNPs. To define associated loci, we first derived region 500 kb upstream and downstream of each and every independent signal (as defined above). All overlapping regions were then merged to form the loci. Further details are provided in the Supplementary NoteWe defined two levels of association when reporting genome-wide significant variants in the combined meta-analyses . Note that the aforementioned clumping of SNPs avoids any biased enrichment due to pairwise SNP correlation. For the last bin, including only SNPs previously identified at genome-wide significance level (5 × 10−8) in marginal effect GWAS, we also performed three complementary association tests [We assessed potential enrichment for interactions effects for SNPs displaying marginal genetic association. To increase independence between our interaction effect GWIS and the marginal GWAS, we used summary statistics from previous studies on blood pressure traits –17 and lon tests  to assesVarExp [LDSC approach [LDscore relative to EA samples provided with the software. When unavailable from the original studies, stratified results were derived from the interaction model using J2S [baseline and GenoSkyline+. The significance of the annotation enrichment was assessed using a Bonferroni corrected significance threshold of P < 0.000277. Tissue-specific heritability was also derived following Finucane et al. [For each ancestry and each phenotype-exposure combination, we derived from the combined (stage 1 and stage 2) results the fraction of phenotypic variance explained by top SNPs was decomposed into main effects, interaction effects and those effects jointly using the R package VarExp . The sigapproach . We usedsing J2S . For eacsing J2S  using twe et al. . Except We investigated results from 28 GWIS on three lipid and four blood pressure phenotypes, each examining G×E interaction with two smoking and two alcohol exposures Table . All outLINC01517, interaction with current smoking on MAP, P1df = 3.19 × 10−8). For the ancestry-specific meta-analysis, the 1df interaction test identified 8 loci reaching genome-wide significance, all observed in the African ancestry population (Table P2df between 1.9 × 10−6 and 6.3 × 10−8).Despite the reasonably large sample size available in our studies, there was only one significant interaction signal with the 1df interaction test across the 28 trans-ancestry GWIS when combining discovery and replication (rs1626071 on chr10 near gene on Table . They inN = 926) harbored a single independent association signal, while others display multiple independent signals . However, sample size for all other phenotype-exposure pairs were fairly very similar (<14%) and unlikely to explain difference in number of replicated signal . We also derived the association signal from the combined stage 1 and 2 SNPs that would have been obtained using a standard marginal genetic effect in the CHARGE data, while adjusting for the effect of the exposure, but not modeling the interaction. The marginal model replicated only at a similar proportion of signal, 48% (N = 289) as compared to 49% (N = 294) of the 599 previously reported associations. Among the 119 new associations detected by the 2df test, 29% (N = 35) did not passed the genome-wide significance level with the marginal model, highlighting the importance of accounting for G×E to detect new associated variants.We compared the trans-ancestry 2df results against 599 significant marginal genetic effect association on the two primary blood pressure traits (DBP and SBP) –17 and tP = 8.7 × 10−5 and 1.58, P = 1.2 × 10−4) as compared to the smoking exposures , suggesting a potential contribution of SNP-by-drinking interaction effect  were of European, 27%  of Asian, 6%  of African and 4%  of HAs. For the 2df joint test, the total number of significant associations per ancestry was, as expected, significantly positively correlated to the available sample size  (Table N = 6), AA (N = 36), EA (N = 41), and HA (N = 1). To account for sample size differences and assess whether top variants were consistent across ancestries, we extracted the top variants for each ancestry-specific association and checked for nominal significance (P < 0.05) in other ancestries screenings from stage 1. Figure Over the two stages, 63% of the individuals (5) Table . When me5) Table . ConversN = 12,302, see Table ρ = 0.99) among the significant 2df variants. Overall, the direction of marginal and interaction effects estimated using the 1df test tended to be randomly distributed among those SNPs, although we observed a slight enrichment, with 13 out of 91 trios showing disequilibrium for either concordant or discordant effects : LDL showed larger genetic effects in both current (P = 4.3 × 10−4) and ever smokers (P = 3.7 × 10−4), DBP showed larger genetic effects in current drinkers (P = 1.6 × 10−4), and SBP showed smaller genetic effects among ever smokers (P = 9.9 × 10−5). Among sets of variants displaying interaction effects discordant with marginal genetic effects, we also searched for those inducing an opposite effect between exposed and unexposed individuals. Although the 2df joint test is supposed to outperform substantially the marginal test in this scenario [To understand further the contribution of G×E to significant 2df results, we derived for each phenotype-exposure-ancestry combination , a missense variant in GCKR previously found associated with alcohol consumption [p value larger than a given α1 significance threshold. To assess the potential of this strategy in our data, we quantified the enrichment of nominally significant variants (i.e. P < 0.05) for G×E interaction effect while varying α1 between 0.1 and 10−6 applied to the aforementioned previous marginal GWAS summary statistics. Some phenotype-exposure pairs show a slight increase in the proportion of significant G×E interactions, including in particular TG and drinking habits (11% of the SNPs against the 5% expected for α1 = 10−5). However, as illutrated in Fig. −4 confidence interval, no enrichment remains significant after correction for multiple testing in our data. We also considered using marginal genetic effect derived from the stage 1 in CHARGE .We next assessed potential enrichment for interaction effects across variants previously identified in marginal effect GWAS , 20, 27 sumption , 29. Howsumption  did not sumption , 32 , and this difference did not remain statistically significant after correction for multiple testing.Second, we estimated potential changes in the heritability of the three lipids and two blood pressures (DBP and SBP) traits across all individuals and in strata defined by exposure, using the approach  applied LDSC package and the GenoSkyline [N = 12,578 in the smallest strata, see Table P = 9.5 × 10−24), 0.60 (P = 5.8 × 10−13) and 0.40 (P = 7.7 × 10−6) for HDL, LDL and TG, respectively), suggesting that potential G×E interactions for those phenotypes do not involve new pathways. Conversely, LDL shows substantial variability in enrichment for the three other exposures , 0.22 (P = 0.01), and 0.17 (P = 0.07), for current drinking, current smoking, and ever smoking, respectively), suggesting those exposures might activate new genetic pathways while reducing the effect of genetic variants involved in unexposed individuals. We also noted substantial variability for the phenotypes-exposure pairs showing the largest differences in heritability  and lipWe fully appreciate that the results from the several experiments we conducted are challenging to aggregate into a single uniform framework. Our analysis rather suggests first that even though related traits share some features; they can also display substantial heterogeneity at other levels. For example, all lipids harbor more signals when accounting for drinking exposures, but at the same time display very different patterns when investigating functional enrichment. It also suggests that the links between heritability, genetic mechanisms involved, and the resulting distribution of G×E effect across SNPs are not straightforward. Finally, our careful assessment of each step of the analyses highlights that complexity also shows up at the methodological level, with a potential for introducing bias at several stages, and so the extra care needed for interpretation. Despite those limitations, we argue that systematic and careful evaluation of G×E across multiple phenotype-exposure-ancestry combinations, as done in this study, still provides critical insight of the interplay between genetic and environmental factors, offering long-term opportunities for numerous additional follow-up analyses down to the biological mechanisms underlying the phenotypes and their interaction with the environment.Supplementary TablesSupplementary Notes and Figures


Oryza sativa) is one of the most important cereal crops in the world, and yield-related agronomic traits, including plant height (PH), panicle length (PL), and protein content (PC), are prerequisites for attaining the desired yield and quality in breeding programs. Meanwhile, the main effects and epistatic effects of quantitative trait nucleotides (QTNs) are all important genetic components for yield-related quantitative traits. In this study, we conducted genome-wide association studies (GWAS) for 413 rice germplasm resources, with 36,901 single nucleotide polymorphisms (SNPs), to identify QTNs, QTN-by-QTN interaction (QQI), and their candidate genes, using a multi-locus compressed variance component mixed model, 3VmrMLM. As a result, two significant QTNs and 56 paired QQIs were detected, amongst 5219 genes of these QTNs, and 26 genes were identified as the yield-related confirmed genes, such as LCRN1, OsSPL3, and OsVOZ1 for PH, and LOG and QsBZR1 for PL. To reveal the substantial contributions related to the variation of yield-related agronomic traits in rice, we further implemented an enrichment analysis and expression analysis. As the results showed, 114 genes, nearly all significant QQIs, were involved in 37 GO terms; for example, the macromolecule metabolic process (GO:0043170), intracellular part (GO:0044424), and binding (GO:0005488). It was revealed that most of the QQIs and the candidate genes were significantly involved in the biological process, molecular function, and cellular component of the target traits. The demonstrated genetic interactions play a critical role in yield-related agronomic traits of rice, and such epistatic interactions contributed to large portions of the missing heritability in GWAS. These results help us to understand the genetic basis underlying the inheritance of the three yield-related agronomic traits and provide implications for rice improvement.Rice ( Oryza sativa) is one of the most important cereal crops in the world. It currently contributes to the daily energy needs of half of the world’s population [Rice  main effects and epistatic effects, namely QTN-by-QTN interactions (QQIs). PLINK ,13 offerFrom the perspective of controlling population structure and polygenic background, and improving computational accuracy, the mixed linear model (MLM) method for GWAS has been established ,23,24, aAll the above single- and multi-locus methods under the framework of MLM are based on the main effect model, the epistatic interaction of alleles between different loci is not considered, which is thought to play a crucial role in quantitative trait genetic analysis and is suggested as a key reason for ascribing missing heritability as well. However, there are two main challenges to discovering epistasis: computational complexity, and statistical power ,35. For In order to efficiently identify more epistatic interactions with both statistical and biological significance, a multi-locus linear mixed model with compressed variance components was proposed, named 3VmrMLM , which cIn the last two decades, there have been several reported epistatic interactions of yield-related traits in rice. Xing et al.  detectedHowever, yield-related agronomic traits are usually controlled by various polygenes and epistatic interactions; thus, they are undetectable for analysis by the single-locus methods. Therefore, we should apply a multi-locus linear mixed model to identify loci and epistatic interactions related to yield-related quantitative traits. In this study, 413 rice germplasm resources with 44 K SNPs and three quantitative traits  were analyzed using the 3VmrMLM method. We identified the yield-related agronomic QTNs and QQIs in rice, and mined candidate genes in the neighborhood of the significant QTNs and QQIs. In addition, we performed a pathway enrichment analysis and tissue expression analysis, which helped us to understand the genetic bases underlying the inheritance of the three traits, and provided implications for rice improvement.https://www.ricedata.cn/gene/ (accessed on 19 May 2022)) were used in this study for GWAS. The dataset consists of 413 inbred accessions with high-quality 44,100 SNPs from 82 countries. After filtering, 36,901 SNPs with minor allele frequency (MAF) >0.01 were analyzed in this study. The phenotypic datasets are available from the Rice Diversity website (https://www.ricedata.cn/ (accessed on 19 May 2022)). To gain insight into the three yield-related agronomic traits, we implemented descriptive statistics for phenotypic data using R software (https://www.r-project.org/ (accessed on 18 June 2022)), the results of descriptive statistics included the mean, standard deviation (SD), coefficient of variation (CV), minimum, maximum, and range for the three traits.To identify the significant genomic loci and interactions associated with yield-related agronomic traits in rice, 413 rice germplasm resources from the Rice Diversity Database  analysis, and linear model (LM) regression analysis. LD is a non-random association between alleles at different loci, which does exist for many pairs of SNPs. It is estimated as the square of the correlation coefficient of the paired alleles. More studies are subject to an inflated level of false-positive results when the two SNPs are highly correlated and/or both have significant marginal effects ,40, it ws is 0.8 . If the s is 0.8 . The p-vta and td for tth SNP, sa and sd for sth SNP, and additive-by-additive (aa), additive-by-dominance (ad), dominance-by-additive (da), and dominance-by-dominance (dd) interaction effects using a two-way ANOVA model. All the effects are estimated by EM empirical Bayes [p-value < 5.00 × 10−8 or LOD ≥ 3.00 in genome-wide detection is applied [SNP filtering after the dimensionality reduction was used to construct the multi-locus model for the 3VmrMLM method. There are fifteen variance components in the QQI (epistatic interactions) mixed model , and thial Bayes . The thr applied ,43.IIIVmrMLM” package was downloaded from https://github.com/YuanmingZhang65/IIIVmrMLM (accessed on 26 June 2022). We conducted main-effect QTN detection and QTN-by-QTN detection, both using the “IIIVmrMLM” function, specifying the parameter of “method = c(“Single_env”)” for the main-effect QTN detection model and “method = c (“Epistasis”)” for the QTN-by-QTN detection model.The “https://ricedata.cn/ (accessed on 3 July 2022)) was used to annotate the significant loci (main effect SNPs and epistatic interactions) identified by 3VmrMLM in this study. The regions within 200 kb of all significant loci were used to search for the candidate genes.The China Rice Data Center (CRDC) database (http://systemsbiology.cau.edu.cn/agriGOv2/# (accessed on 25 July 2022)) [p-value < 0.05) was utilized to select enrichment GO terms. The R package “pheatmap” was used to plot the heatmap according to the results of the GO analysis for the candidate genes.To further understand the genetic basis, we performed gene ontology (GO) enrichment analysis, based on the nearest candidate genes at significant loci, which provides more information on biological functions, pathways, or cellular localizations . The only 2022))  was usedhttp://rice.uga.edu/ (accessed on 3 August 2022)) was adopted to show the expression levels of the candidate genes in various tissues or organs, and we used the R package “pheatmap” to draw the heat map, which illustrates the fragments per kilobase of exon model per million mapped fragments (FPKM) expression values of the candidate genes through the tissues or organs.The Rice Genome Annotation Project (RGAP) database  were analyzed, to examine whether there were significant phenotypic variations among the 413 rice varieties. p value < 5.00 × 10−8). The total QTNs and QQIs explained 59.85%, 31.97%, and 67.36% of the phenotypic variations , which were calculated as the proportion of the variance of all the QTNs to the variance of each phenotype using the “IIIVmrMLM” R package. For PH 1 QTN and 14 paired QQIs were found to be tightly associated with the target trait, which were widely located on all 12 chromosomes, except chromosome 10. The PVE of QTN was 12.83%, with a LOD score of 6.79, while the PVE of QQIs ranged from 1.14% to 7.15% with the LOD score from 3.23 to 8.16  by red QQIs ; that isred QQIs  were adjp-value < 0.05) enriched for 37 GO terms associated with various BPs  . The mai3VmrMLM is a powerful multi-locus tool to identify the main effects QTNs and epistatic interaction effects QQIs, simultaneously estimate and test their genetic effects, and so on. As an advanced approach, its merits can be explained as the following points: (1) 3VmrMLM, based on a multi-locus approach, adds the polygenic effect and population structure, to decrease the bias in effect estimations by controlling the genetic background, and it might be relatively close to the true genetic models of plants and animals, thus 3VmrMLM produces high-quality results, with higher statistical power and lower false positive rate (FPR). (2) To reduce the computational complexity, 3VmrMLM employs the two stages: first, all putative QTNs are chosen by a single-locus model, then the QTNs after filtering are being included in a multi-locus model for true QTN detection. This is the key point for 3VmrMLM to efficiently solve the “big P, small N” problem . (3) In the QQI detection model, the number of variance components is decreased from fifteen to three, and this is one of the most obvious advantages for solving the problem of the huge computational resources needed by too many markers. (4) For the single-locus method, the multiple test correction of the significance test (Bonferroni correction) was too stringent to capture all true QTNs. Differently from this, 3VmrMLM first uses single marker genome-wide scans to select potentially relevant markers and then uses empirical Bayes and likelihood ratio tests to detect the above loci in a multi-locus model. The appropriate threshold and polygenic model are the desirable features of 3VmrMLM , thus itThis study conducted a GWAS for yield-related agronomic traits in rice, including PH, PL, and PC. As a result, two significant QTNs and 56 paired QQIs were detected by 3VmrMLM, while REMMA failed to identify significant QQIs. For the QTNs and QQIs, 26 key genes were identified as confirmed yield-related genes, such as LCRN1, OsSPL3, and OsVOZ1 for PH; and LOG and QsBZR1 for PL. Subsequently, an enrichment analysis and expression analysis indicated that most of the 114 candidate genes were significantly involved in all three GO terms of the target traits. These results help us to understand the genetic bases underlying the inheritance of yield-related agronomic traits and provide implications for rice improvement.


Very low-coverage (0.1 to 1×) whole genome sequencing (WGS) has become a promising and affordable approach to discover genomic variants of human populations for genome-wide association study (GWAS). To support genetic screening using preimplantation genetic testing (PGT) in a large population, the sequencing coverage goes below 0.1× to an ultra-low level. However, the feasibility and effectiveness of ultra-low-coverage WGS (ulcWGS) for GWAS remains undetermined.We built a pipeline to carry out analysis of ulcWGS data for GWAS. To examine its effectiveness, we benchmarked the accuracy of genotype imputation at the combination of different coverages below 0.1× and sample sizes from 2000 to 16,000, using 17,844 embryo PGT samples with approximately 0.04× average coverage and the standard Chinese sample HG005 with known genotypes. We then applied the imputed genotypes of 1744 transferred embryos who have gestational ages and complete follow-up records to GWAS.CRHBP, ICAM1, and OXTR were more frequently reported as preterm birth related. By joint analysis of gene expression data from previous studies, we constructed interrelationships of mainly CRHBP, ICAM1, PLAGL1, DNMT1, CNTLN, DKK1, and EGR2 with preterm birth, infant disease, and breast cancer.The accuracy of genotype imputation under ultra-low coverage can be improved by increasing the sample size and applying a set of filters. From 1744 born embryos, we identified 11 genomic risk loci associated with gestational ages and 166 genes mapped to these loci according to positional, expression quantitative trait locus, and chromatin interaction strategies. Among these mapped genes, This study not only demonstrates that ulcWGS could achieve relatively high accuracy of adequate genotype imputation and is capable of GWAS, but also provides insights into the associations between gestational age and genetic variations of the fetal embryos from Chinese population.The online version contains supplementary material available at 10.1186/s13073-023-01158-7. Detection and characterization of genetic variants associated with traits and diseases are fundamental to the study of human genetics. Genome-wide association study (GWAS) is an approach widely used in genetic research that aims to decode the associations of specific genetic variations with particular diseases or traits in sample populations. In the past decade, GWAS has facilitated discovery of over one hundred thousand variants associated with complex traits in human . Whole g2 of imputation accuracy observed by using 10 low-coverage WGS (~0.5×) as compared to known genotypes [Genotype imputation can be used to infer missing genotypes and to increase the accuracy of detecting genetic variants, such as SNVs. In general, performance of genotype imputation is largely affected by sample size, sequencing coverage, analysis methods, and other parameters . A main enotypes . Pasaniue WGS ~0.× as compenotypes . Even thGestational age is an important complex trait associated with biological processes and human disease. Biologically, gestational duration plays a vital role in both mental and physical health of children at an age of 5 years old . GestatiIn this study, we devised a pipeline for analyzing and applying the ulcWGS of 17,844 embryo samples for GWAS. Our result shows that a large sample size is effective to increase the accuracy of genotype imputation even at an ultra-low coverage. Furthermore, using the imputed genotypes of 1744 embryos that were successfully transferred and born with a widespread of gestational ages, we demonstrate the power of using ulcWGS in GWAS and provides insights into understanding genetic association of gestational age in embryos acquired from Chinese population. Refreshing the lowest coverage used in GWAS, our finding also provides a foundation for exploring the utilization of an even lower coverage for dissecting genotype-phenotype associations.The whole PGT dataset of 17,844 embryos  was from the Clinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of China International Trust Investment Corporation - Xiangya. The samples used in this study were not used in any previous studies. The protocol of embryo culture and biopsy was published in a related study . Three WWe developed a three-step pipeline to carry out genotype imputation using ulcWGS data and to perform GWAS Fig. . FirstlyIn the first step of Fig. In the population SNV calling stage, we modified the method of Liu et al. . The firSNV calls of the raw population were filtered following the rules, (1) calls that overlapped with the 35-kmer problematic alignment regions in hs37d5 were removed , 23; (2)k were set as 20, the assumed number of generations nGen was set to 2000, and the reads were binned into windows with gridWindowSize 10000. The diploid mode in STITCH was used. Although using a reference panel is optional in STITCH, we used the IMPUTE2 1000 genome haplotypes phase 1 reference panel [r2 of the estimated AFs between imputation and population SNV calling in a randomly chosen 5 Mbp genomic region (chr3:180–185Mbp). When applying STITCH to the whole genome, we divided the genome into 5-Mbp windows with a 500-kbp overlap between two windows.We used STITCH  version ce panel  as it imFor benchmarking, seqtk was used to subsample the HG005 Illumina WGS raw reads to 0.01×–0.1×  [p-value >1e−6” were applied to select the imputed genotypes.All bi-allelic SNPs with MAF ≥ 0.01 found in chromosome 1 with the population SNV calling from the 17,844 samples are included for genotype imputation. We used the 167,814 SNPs both in our SNP callset and HG005 known genotypes for benchmarking. The imputed genotypes were compared to the truth released by GIAB for estimating the imputation accuracy. Then, genotype imputation was applied to all 17,844 embryo datasets at the 31,622,332 bi-allelic sites with MAF ≥ 0.01 found in population SNV calling. The entire imputation process spent 19 days and used 15 machines with 16 cores (two 8-core Intel Xeon Silver 4108 CPU). Two filters “INFO score ≥ 0.4 and HWE p-value > 1e−6. To remove biases, we specified 16 covariates for ANGSD, 8 most significant principal components calculated from the inputs of PCA  instead of 10 to achieve better accuracy with a large sample size. ANGSD did not create an output beta-coefficient, so we followed the ideas of Skotte et al. [To conduct GWAS, we used score statistics  that is e et al.  by incorp-value ≤ 4.515e−8) and is independent of other significant SNPs (with LD r2 < 0.6). FUMA also generated a set of lead SNPs with low LD and with other (r2 < 0.1) from the independent significant SNPs. The genomic risk loci were identified by starting from these lead SNPs and through iteratively merging related genomic regions to them according to FUMA’s rules.The significant SNPs from our GWAS were mapped to genomic risk loci using FUMA pipeline  and the r2 ≥ 0.6). For condition 2, the SNPs can be from our imputed genotypes if p-value is below 0.05 or from the reference panel of 1000G EAS. ANNOVAR was used to annotate the candidate SNPs [Also, the FUMA pipeline sorted out a set of candidate SNPs from our inputs that meets one of two conditions, (1) the independent significant SNPs and (2) SNPs that are linked to the independent significant SNPs  methods and four different sequencing platforms were used in the PGT dataset  MAF ≥ 0.01, 2) known in dbSNPv150, and (3) passed the filter “INFO score ≥ 0.4 and HWE  known inWe used FUMA  pipelineCNTLN was reported as a PTB-related gene [PIN1 involves inhibition of breast cancer [CRHBP was linked through chromatin interaction mapping to two loci, chr5:75101342-75164623 and chr5:78251511-78271282. Enrichment analysis of DEGs in 30 tissue types in GTEx v8 [By integrating strategies positional, eQTL, and chromatin interaction mappings, we identified a total of 166 genes mapped to the 11 risk loci, including 48 and 19 genes from two and three strategies, respectively , inflammatory response , coagulation (MBL2), apoptosis (PLAGL1), and cell adhesion (ICAM1 and THBS4), emphasizing their associations with PTB.GWAS of gestational age related PTB has been implicated in biological functions that include immune response, inflammatory response, and coagulation factors , 54–56. CRHBP and OXTR were identified in two datasets, while overexpressed  and underexpressed (THBS4) genes were present in one dataset  is the most common respiratory disorder among children born preterm , 60. Theher Fig. d, supporDKK1, ICAM1, DKK1, EGR2, PLAGL1, GCNT4, and THBS4 in all three subtypes. Increased ICAM1 and decreased PLAGL1 were consistently identified in these datasets −, HER2+, or triple-negative breast cancer (TNBC). We collected gene expression data mainly presenting three subtypes of breast cancer, TNBC, HER2+, and ER or progesterone receptor (PR) , which is related to gestational age [Gestational age is a multi-factor phenotype involved in maternal and fetal biological activities . To inven of CRH , 70. ICAregnancy –73. Oxytonal age . We valiEBF1, EEFSEC, AGTR2, WNT4, ADCY5, and RAP2C) associated with gestational duration [EBF1, EEFSEC, and AGTR2) strongly associated with PTB in a European ancestry cohort of 43,568 women. However, we did not detect such 6 genes from the reported PTB markers and only identified them as DEGs from few published datasets of PTB. This could be due to the heterogeneous sources of samples used in different PTB studies or be explained in part by the genetic complexity of incomplete gestation-induced PTB complications. Here, we collected almost 2000 PTB-related genes from previous PTB studies. The differences in study design, source and subtype of samples, and statistical methods would be important factors that could account for the diversity of PTB variants and genes among the various studies. Considering the possible association of PTB with other traits, we compared differential gene programs on multiple phenotypes between mother and infant reported in previous studies, and demonstrated a high risk of CRHBP, ICAM1, DNMT1, CNTLN, PLAGL1, DKK1, and EGR2 with PTB among our sample cohorts of Chinese ancestry. To support this finding, we reconstructed co-expressive networks linking the PTB-related genes in GWAS with the reported PTB markers. Indeed, the correlated PTB genes are mainly involved in immune and inflammation-related processes and signaling pathways, as well as coagulation factors. Thus, these findings have biological implications for dissecting genetic associations of gestational factors with disease or traits in different human ancestries.PTB is neonatal birth occurring before 37 weeks of gestation age and is a leading cause of infant morbidity and mortality. Understanding of genetic and molecular mechanisms of PTB and its association with gestation duration is currently insufficient. Recently, Zhang et al. reported replicable loci in six genes  derived from analyzing the genome-wide gene expression datasets listed.Additional file 2: Figure S1. Sequencing coverage statistics of the 17,844 embryos. Figure S2. Visualizing the linear regression model for genotype accuracy prediction. Figure S3. Quality control of SNV calling. Figure S4. Principal component analysis of all 17,844 embryo samples. Figure S5. Distribution of gestational age in the 1,744 born embryo samples. Figure S6. Quantile-Quantile plot of the 1,107,198 studied SNPs. Figure S7. Pie charts of the candidate SNPs. Figure S8. Zoom in locus plots of the 11 genomic risk loci. Figure S9. Circos plots of chromatin interactions and eQTL mapping. Figure S10. Gene set analysis of the 166 mapped genes. Figure 11. Co-expression network of PTB-related genes in maternal and infant subtypes.Additional file 3: Supplemental Methods.


The agricultural scenario of the upcoming decades will face major challenges for the increased and sustainable agricultural production and the optimization of the efficiency of water and fertilizer inputs. Considering the current and foreseen water scarcity in several marginal and arid areas and the need for a more sustainable farming production, the selection and development of cultivars suitable to grow under low-input conditions is an urgent need. In this study, we assayed 42 tomato genotypes for thirty-two morpho-physiological and agronomic traits related to plant, fruit, and root characteristics under standard (control) and no-nitrogen fertilization or water deficit  treatments in two sites (environments), which corresponded to organic farms located in Italy and Spain. A broad range of variation was found for all traits, with significant differences between the applied treatments and the cultivation sites. Dissection of genotypic (G), environmental (E), and treatment (T) factors revealed that the three main factors were highly significant for many traits, although G was the main source of variation in most cases. G × E interactions were also important, while G × T and E × T were less relevant. Only fruit weight and blossom end rot were highly significant for the triple interaction (G × E × T). Reduction of water supply significantly increased the soluble solid content in both locations, whereas both nitrogen and water stress led to a general decrease in fruit weight and total yield. Despite so, several accessions exhibited better performances than the control when cultivated under stress. Among the accessions evaluated, hybrids were promising in terms of yield performance, while overall landraces and heirlooms exhibited a better quality. This suggests the possibility of exploiting both the variation within ancient varieties and the heterosis for yield of hybrids to select and breed new varieties with better adaptation to organic farming conditions, both under optimal and suboptimal conditions. The results shed light on the strategies to develop novel varieties for organic farming, giving hints into the management of inputs to adopt for a more sustainable tomato cultivation. Water and nitrogen (N) are major inputs in agriculture, representing key elements to ensure the high productivity and quality of crops. It is estimated that plants globally consume over 70% of the freshwater from surface or groundwater resources (blue footprint)  and up tIn plant nutrition, N is an important component of many organic structural compounds and secondary metabolites , playingTherefore, the future risks for the entire ecosystem linked to the uncontrolled use of resources posed the basis for new plans aiming at a more sustainable and environmentally friendly agriculture . HoweverSolanum lycopersicum) is a highly demanding vegetable in terms of N and water supplies  . GloballIt is estimated that yields under organic systems are typically lower than in conventional ones. Meta-analyses highlighted a reduction of 20–25% in organic farms , althougS. lycopersicum) genotypes belonging to diverse cultivar groups including elite cultivars  and heirlooms  with different origins, landraces for the fresh market  from Spain and Italy, and long shelf-life cultivars “de penjar/da serbo”  originating from different regions in the Spanish eastern coast and Southern Italy. The panel set was assembled from the germplasm collections of the Research Centre for Vegetable and Ornamental Crops  and the Universitat Politècnica de València  and selected from a core collection previously assayed  (3-0-0) and “Prodigy4” (7-6-1)  was applied. The rest of the N and potassium units were supplied in fertigation with the water soluble based on hydrolyzed animal epithelium “Gold Dust” (15-0-0)  and the water soluble based on crystalline potassium sulfate plus amino acids “Prokton” (6-0-17) . In SP, the fertilization was supplied with the irrigation system using “Fenorganic Nitrógeno” (16-0-0)  for N, “Fosfoser” ECO GR  for P, and “Summum Líquido Quality” (0-0-15)  for K to provide the desired fertilization levels. All these fertilizers are authorized in organic farming. The N-stress plot (NS) consisted in the absence of N supply (0 kg N ha–1).The plants were grown during the spring-summer of 2020 in two fields certified for organic farming. The first field was the experimental station of the Research Centre for Vegetable and Ornamental Crops  , and the second was a private farm of the municipality of Alcàsser  . The temperature and humidity during the growth season as well as the soil chemical properties determined before the start of the experiments at each site are reported in 3/ha plus a rainfall of 938 m3/ha considering a daily global solar radiation of 257 W/m2, a mean reference crop evapotranspiration (mean Eto) of 45.8 m3/ha, and the total reference crop evapotranspiration  of 4,719 m3/ha. For SP, the irrigation volume was 3,212 m3/ha plus a rainfall of 1,126 m3/ha, with a daily global solar radiation of 279 W/m2, mean reference crop evapotranspiration (mean Eto) of 46.9 m3/ha, and total reference crop evapotranspiration  of 4,451 m3/ha.The total amount of water supplied was based on the calculation of crop evapotranspiration (ETc). In this way, for IT, the irrigation volume was 3,801 m3/ha in IT and 937 m3/ha in SP. Considering rainfall, these were equal to about 44 and 47% of total water with respect to the controls in IT and SP, respectively. The plants were irrigated throughout the entire cultivation period using a drip irrigation system. At both locations, a randomized block design was followed with three blocks for each treatment  for a total of nine blocks. In each block, four plants/accessions were considered for a total of 3,024 plants across the two locations. A biodegradable plastic mulch film was used to avoid competition with weeds. When necessary, weeding was done manually. Plants were grown at a density of 2.6 plant/m2 using a system of double rows in which canes from adjacent rows were tied together forming a triangle-shaped structure.The water stress treatment (WS) target consisted in 30% of water supply with respect to the control, finally resulting in 1,146 mAll plants in each block were individually phenotyped for 9 pseudo-qualitative traits related to plant architecture and fruit characteristics and 16 agronomic traits, including both pseudo-qualitative and quantitative measurements. All traits were scored at both locations except for plant vigor, which was scored only in Spain. The details of traits and scale or method of measurement are listed in A bulk of representative fruits for each accession/block/treatment were sampled and subsequently washed and dried. Soluble solid content was measured using 0.5 ml of liquid extract with digital refractometers . The titratable acidity and pH were measured with a pH-Matic 23 analyzer titroprocessor equipped with a pH electrode including a temperature sensor (model 5011T) , using 10% (w/v) aqueous tomato extract and NaOH 0.1 M as titrating reagent. Titratable acidity was expressed as g citric acid/L juice.The roots were assessed at the end of the cycle firstly removing soil and taking care not to damage the root system, then removing the substrate residues by carefully shaking the root. Two quantitative traits were measured including the radicular crown angle and diameter of the main root . The den2G) and phenotypic variances (σ2P = σ2G + σ2E + σ2T + σ2G × E + σ2G × T + σ2G × E × T) of each trait were obtained from the mean squares (MS) of the genotype, location, treatments, interactions, and residuals of the ANOVA performed, in order to estimate the broad-sense heritability (H2) using the formula G and σP) and the mean value of the trait (μ). The genetic advance as percent of population mean was also derived as GAM = [(σp × k × H2)/μ] × 100, where k is the selection intensity at 5% (2.06) (The genotypic (σ=σG2/σP2 . The coe% (2.06) .p = 0.05 threshold was considered to indicate a statistically significant difference. The traits with significant differences between treatments at each location were considered displaying a stable significant difference. All analyses were performed using the R statistical software v4.0.2.A three-way ANOVA for determining genotype (G), location (E), and treatment (T) effects and the double and triple interactions was performed for every trait studied. In the ANOVA, treatments considered were the control , WS , and NS . Mean square values (MS) were used to estimate the magnitude of the observed effect, while the total sum of squares in percentage (TSS%) was calculated dividing the TSS of the effect by the total TSS. The data from replicates per accession were used for removing the block effect. The mean values, standard deviation, coefficient of variation, and range for all traits were calculated from accession means and for the three treatments in both locations. Average differences between treatments and control were compared using the Dunnett tests; multiple group comparisons were performed using the Tukey range tests. A psych and corrplot R packages. The Spearman linear coefficients of correlation (r) were calculated between pairs of traits and the significance of correlations was evaluated at p < 0.01. A principal component analysis (PCA) was carried out among accession means considering the three treatments and two locations in order to determine which are the most effective traits in discriminating among accessions. PCA loading and score plots were drawn using the computer package XLSTAT 2012.1. The prediction ellipses with a 95% level of confidence were added to the PCA score plot.The correlations among traits scored in the two locations and for each treatment were calculated from accession means using the p < 0.001) were found in the studied set for all traits except for disease in foliage and fruit that showed significance at p < 0.01 or no significance, respectively (H2) was found for locules number (0.93), with fruit weight, fruit set truss, and fruit set plant displaying H2 values above 0.90. The lowest heritability values were instead found for the presence of pests and diseases, with the lowest H2 for disease in fruits (0.37). Among trait categories, root traits showed, in average, the lowest heritability. For all traits, the coefficient of PCV was from moderate to high, with values ranging from 11.37% (pH) to 161.71% (fruit set plants). The same trend was observed for the GCV. In general, the greatest GCV and PCV were found for agronomic traits, being in average 103.36 and 91.80, respectively. As expected, PCV values were higher than GCV in all instances, with a ratio PCV/GCV ranging from 1.02 to 1.36 for locules number and disease in fruits, respectively. Fruit features showed the highest genetic advance as a percentage of the population mean (GAM) with values above 100% for puffiness appearance, fruit fasciation, blossom end rot, locules number, fruit weight, fruit set truss, and fruit set per plant. On average, agronomic traits exhibited the highest GAM, while lower average values were instead observed for chemical traits. The lowest level (<12.04%) was observed for pH.Considering locations and applied treatments, highly significant differences  in two experimental locations (E) are given in p < 0.05). The effect of E was statistically significant for all categories of traits, except for fruit firmness, ribbing at calyx end, stem diameter, disease in foliage, ripening uniformity, fruit set truss and plant, and soluble solids.A highly significant variation due to T was found for 14 traits including most agronomic quantitative traits, chemicals, and root traits, except for the density of fine roots . On the p < 0.001) have been detected for 19 out of the 30 traits scored at both locations. Instead, for G × T and T × E, only 2 and 7 out of the total 30 traits, respectively, exhibited significant values. A variable degree of significant interactions was found for the environmental × treatment effects in several traits. Overall, only the fruit weight (p < 0.001) and the blossom end rot (p < 0.05) showed high level of significance for the interaction among the three factors (G × E × T).Most interactions among factors were found between G and E for which highly significant differences  showed an increase of yield in 3 out of the 4 trials. In most cases, we found an opposite trend in yield for the same type of stress in the two grown sites, with more consistent increase and decrease effects in IT. A remarkable increase of yield has been found for BT00890 (CL) for NS (+64%) and WS (+84%) in IT , BT04140BT04090 reached values up to +46% under WS in SP. For long shelf-life accessions, BT02020 was found to be very promising with an increase of soluble solids of +31% and + 27% in IT and SP under WS, respectively. Finally, among heirlooms, BT06400 and BT06440 showed a statistically significant increase of soluble solids over the control under WS in SP, although the trend was not confirmed in the other stress treatments.Contrary to the yield-related characteristics, the reduction of cultivation inputs has determined an increase of soluble solids in different accessions, with very evident effects under water stress . Indeed,1) and the second (PC2) components accounting for 24.1 and 11.60% of the total variation, respectively  and fruit weight (r > −0.87 in SP and r = −0.82 in IT). Positive correlations were detected between fruit weight and locule number (r > 0.8 in both locations), ripening earliness (r > 0.6 in both locations), and total yield (r = 0.74 in SP and r = 0.58 in IT). No correlations were detected for root traits, whereas, among chemicals, only soluble solids showed negative correlations with different agronomic traits although with a coefficient of less than 0.50 in all instances. Regarding the NS treatment, we found fewer significant correlations than for the control, whereas those confirmed showed higher coefficients , suggests additive gene action and an enhanced expression in offspring . On the Better yield-related performances were evidenced at the SP location, suggesting pedo-climatic conditions more suitable for the cultivation of the tomato set studied. The most evident differences between locations were the clay-loam composition and the greater organic matter content of the SP soil. These properties would confer an intermediate compaction degree able to increase the available water capacity and aeration, enhancing furthermore the efficiency of roots for nutrient uptake, in particular the N stored in organic form . In addiWe found the variation of pH and acidity was highly influenced by environmental effects rather than water and N stress. Contrariwise, the soluble solid content had a stronger genotypic control, being more influenced by the variability due to treatment, and less by that due to cultivation site. This highlights the complexity of breeding for taste-related traits, thus suggesting a different difficulty level of achievement in the improvement objectives for sourness and sweetness.The deficit of water has strengthened the correlations among traits, highlighting the complexity of the physiological mechanisms of adaptation in response to drought , suggestThe tomato set examined in this study is part of a larger collection developed in the frame of the H2020 EU project BRESOV  which aims at improving the competitiveness of crops in organic and sustainable environment. We investigated 40 accessions selected based on the information retrieved from high-density genotyping  and largAmong traditional varieties such as landraces and heirlooms, genotypes with high-soluble solid content have been detected, highlighting their potential for further exploitation of quality traits. Overall, individual accessions improving trait performances under either N- or water-stress conditions were also identified in each cultivar group; among elite cultivars, BT00900 and BT08270 showed good performances in terms of yield, soluble solid content, and fruit set per plant under stress conditions, whereas several landraces for the fresh market , heirlooms , and long shelf-life genotypes  were highly promising for several agronomic traits in one or both locations. This highlights how there are different genotypes that can be used for organic farming, allowing the reduction of cultivation inputs, but also how new breeding programs can be established for combining traits of interest and exploiting heterosis.In this study, we evaluated 42 tomato accessions in two organic farms located in typical Mediterranean environments in Italy and Spain by applying diverse cultivation management conditions based on the reduction of water and N supply. To broaden the selection of promising cultivars for low-input growing conditions, we considered a diversified panel including different groups of accession for specific consumer/market segments. We found how the epigeal part of the plant was minor influenced by the stress applied with respect to the hypogeal one, confirming how the root architecture plays a major role in plant adaptation to nutrient and drought stress. Water stress was instead more responsible for physiological malformations such as blossom end rot and fruit fasciation, while the general sanitary status of the fields linked to the occurrence of pests and disease was almost unchanged between the control and the stress trials. Beyond the wide diversity found for the traits assayed, we observed that a reduction in water and N fertilizer can be applied without affecting dramatically the overall performance. The results highlighted several accessions with either better production or soluble solid content in the stress trials, thus suggesting the possibility to improve the organoleptic quality by maintaining, furthermore, adequate yields. The partitioning of the G, E, and T effects and their interactions suggested the possibility to select broadly adapted genotypes able to maintain main morpho-agronomic characteristics in diverse cultivation environments and/or following different agricultural practices, shedding light on the possibility to promote different cultivars for organic farming, as well as establishing new cross-breeding programs. In addition, the comparison of the performances of individual accessions highlights the possibility to exploit the hybrid vigor as a strategy to improve yield-related traits and root architecture in organic cultivation; indeed, among accessions essayed, the hybrids had an optimal performance for most of the traits. The results of this work represent a step forward toward the selection of promising cultivars for low-input growing conditions, being also the first effort toward the assessment of both drought and N stress under organic cultivation. The established panel of tomato accessions provides a framework for further investigations on nutritional and sensorial properties, providing novel information to enhance the consumers’ preference for organically developed products.The original contributions presented in this study are included in the article/PT, JP, TC, and MD conceived the project and coordinated the experiments. GC, FL, SS, and MF designed the experimental layout, managed the field trials, and performed the phenotypic characterization. FL and SS collected and curated the data. PT analyzed the data and drafted the manuscript. All authors critically revised the manuscript and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.