LEPR, ACACA, FTO, LIPE or SCD on fatness, growth and fatty acid composition) and also disclosed interesting effects of new SNPs in less known genes such as LRIG3, DENND1B, SOWAHB, EPHX1 or NFE2L2 affecting body weight, average daily gain and adiposity at different ages, or KRT10, NLE1, KCNH2 or AHNAK affecting fatness and FA composition. The results provide a valuable basis for future implementation of marker-assisted selection strategies in swine and contribute to a better understanding of the genetic architecture of relevant traits.Iberian pigs and its crosses are produced to obtain high-quality meat products. The objective of this work was to evaluate a wide panel of DNA markers, selected by biological and functional criteria, for association with traits related to muscle growth, fatness, meat quality and metabolism. We used 18 crossbred Iberian pigs with divergent postnatal growth patterns for whole genome sequencing and SNP discovery, with over 13 million variants being detected. We selected 1023 missense SNPs located on annotated genes and showing different allele frequencies between pigs with makerdly different growth patterns. We complemented this panel with 192 candidate SNPs obtained from literature mining and from muscle RNAseq data. The selected markers were genotyped in 480 Iberian × Duroc pigs from a commercial population, in which phenotypes were obtained, and an association study was performed for the 1005 successfully genotyped SNPs showing segregation. The results confirmed the effects of several known SNPs in candidate genes (such as  Iberian × Duroc crossbred pigs are the main genotype employed to produce Iberian meat products in intensive production systems, although their sensorial meat quality is considered lower than that of pure Iberian pigs5. Furthermore, crossbred Iberian pigs are characterised by very heterogeneous developmental, productive and quality traits6, with great potential to identify the underlying genes. Knowledge of the molecular genetic basis of relevant traits would help in the design of marker-assisted selection programs.Iberian pig is an autochtonous fatty breed characterized by a high adipogenic potential, high appetite and outstanding meat quality. These features are consequence of their genetics and the traditional extensive production system, both contributing to the deposition of subcutaneous and intramuscular fat with a high content of oleic acid and antioxidantsLEPR8, ELOVL69 or ACSL411. Recently, Fernandez-Barroso et al.12 found significant associations of mutations in PRKAG3, CAPN1 and several other genes on meat quality traits in pure Iberian animals. Also, functional genetic studies have been performed in Iberian pigs which have provided information on functional candidate genes and pathways potentially involved in phenotypic variation15. On the other hand, the use of commercially available high-density SNP arrays for genome-wide association studies (GWAS) provides a systematic and powerful approach for deepening into the genetic basis of complex traits. This approach has led to the identification of many interesting genomic regions and candidate genes in different populations and breeds, including the Iberian22. Nevertheless, the applicability of commercial SNP arrays is limited because they only identify a fraction of genetic variation. This is due to the fact that they have been designed from SNPs detected in a few cosmopolitan breeds resulting in limited informativity and power, specially for the analysis of local breeds23. In contrast, whole-genome sequencing (WGS) can potentially detect all genetic variants, including causal ones24 and may allow the design of customized, highly-informative SNP panels, useful to uncover the genetic basis of relevant traits in local breeds. Such advances make genetic improvement achievable for relevant productive and quality traits in non-cosmopolitan pig breeds.Different candidate gene studies have been performed in Iberian pig populations, mostly in purebred animals or experimental crosses and with low-scale structural approaches, in order to deepen in the genetic architecture of productive traits. Relevant genes and mutations have been identified so far with this approach, mainly those detected in the IBMAP experimental populations such as The objective of this work was to discover and evaluate a wide panel of DNA markers for association with traits related to muscle growth, fat deposition and composition, metabolism and meat quality in Iberian crossbred pigs. For this purpose, a medium density custom genotyping protocol was used, designed with SNPs obtained and selected from a combination of whole genome sequencing data, transcriptome sequencing data and a candidate gene approach.https://arriveguidelines.org/arrive-guidelines).The study was performed according to the Spanish Policy for Animal Protection RD53/2013, which meets the European Union Directive 2010/63/UE about the protection of animals used in research. The experiment was specifically assessed and approved (report CEEA 2012/036) by the INIA Committee of Ethics in Animal Research, which is the named Institutional Animal Care and Use Committee (IACUC) for the INIA. The study was carried out in compliance with the ARRIVE guidelines  and local anesthesia  applied with a 25G needle inserted directly into the spermatic cord through the skin. Piglets were weaned at an average age of 24 days-old, and housed in groups of 12 piglets/pen distributed by sex and body weight (BW) during the transition phase. In the growing-fattening phase (from 70 days-old until slaughter), animals were housed by sex and BW in groups of 40 pigs/pen. Females received immunocastration using VACSINCEL  with two vaccinations at 120 and 148 days-old, respectively. From day 240 until day 340 of age, pigs were marketed whenever they reached the minimum market weight, which for Iberian crossbred pigs is set at 115 kg of carcass weight. At day 340 of age, all remaining pigs were sent to market, independently of their weight. The phenotype evaluation followed the methods already described in a previous paper using part of the animals involved in this study6.We used an Iberian × Duroc crossbred population. The animals were housed at a commercial farm, Ibéricos de Arauzo 2004 S.L. . A total of 477 crossbred piglets  born from 47 third and fourth-parity Iberian sows were involved in this study. The Iberian sows were inseminated with cooled semen from Duroc PIC boars . The sows were individually identified with an electronic ear tag and housed in groups until day 101 of pregnancy, when they were moved to individual pens until the end of the suckling phase. Sows and offspring were fed standard diets for the Iberian breed, adjusted to fulfil individual daily requirements, based on recommendations from De Blas et alDifferent measurements were carried out at the following time points: birth, weaning (24 days-old), days 70, 110, 150, 180, 215 and 240 of averaged age and at slaughter. Body weight was determined individually at all these time points. Morphological measures  were recorded at birth and at weaning for all piglets, with a measure tape. Backfat thickness and loin diameter were determined at weaning and at 110 and 215 days-old. Weight and length of carcasses and backfat thickness were recorded at the slaughterhouse.The average daily weight gain (ADWG) was calculated during the suckling phase (1–24), the transition phase (25–70), and during the five selected time periods of the growing-fattening phase until the day 240 of age . The feed conversion ratio (FCR) was calculated by pen during the five selected periods of the growing-fattening phase. Average daily weight gain and FCR were also calculated for the whole periods from birth to slaughter and from weaning to slaughter, respectively.longissimus dorsi (LD) muscle, right liver lobe and subcutaneous fat were taken at slaughter and biobanked at − 20 °C until fatty acid (FA) composition was analysed. Muscle drip-loss analysis was also carried out26.Samples of blood were obtained in vivo (110 and 215 days-old) and at slaughter, and employed for DNA extraction and metabolic status evaluation. Samples of Metabolic status was assessed at the time points of 110 and 215 days-old and at slaughter. Blood samples were assayed for determination of parameters related to metabolism of glucose (glucose and fructosamine) and lipid profiles , by means of a clinical chemistry analyzer .27 and expressed as a percentage of dry matter. These lipids were fractionated into the two main fractions of the fat tissue: neutral lipids (NL) and polar lipids (PL)27. Subcutaneous fat was extracted and separated in outer and inner layers to be analysed individually. From individual FA values, proportions of saturated, monounsaturated, and polyunsaturated FA , the unsaturation index (UI), and the sum of total n-3 FA (n-3) and n-6 FA (n-6) and its ratio (n-6/n-3) were calculated28. Moreover, the activity of stearoyl-CoA desaturase enzyme 1 (SCD1) was estimated using two desaturation indexes, the ratios of C18:1/C18:0 and MUFA/SFA29.Liver fat (LF) and intramuscular fat (IMF) were extracted as described by Segura and Lopez-BoteOut of the crossbred Iberian pig population, 18 male pigs with divergent postnatal growth patterns were selected for whole genome sequencing. Birth weight and BW at 240d were considered in order to select animals corresponding to three different growth categories: low birth weight and low final weight , low birth weight but normal final weight  and normal values for both birth and final weights  . A NanoDrop 2000 spectrophotometer  was used to measure the concentration and quality of the DNA. Whole-genome resequencing of the selected 18 animals was performed on an Illumina HiSeq 2000 platform by CNAG-CRG . About 1 μg genomic DNA was randomly fragmented. The genomic library was prepared according to the manufacturer’s protocol  using the TruSeq DNA sample preparation kit. The paired-end library was sequenced on an Illumina HiSeq 2000 using the v4 chemistry and 2 × 125 reads.Genomic DNA was obtained from the blood samples of all animals using the NucleoSpinhttp://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Trimmomatic package version 0.3830 was used for paired-end read trimming. Reads were trimmed to remove adapters, eliminate low quality or N-bases at the start or end of the read and scan the reads from the 3′-end with a 4 bases sliding window, cutting when the average Phred quality drops below 20. After trimming, reads were removed if they were shorter than 40 bp. Filtered paired reads were aligned to the porcine reference genome build 1131 by the Burrows-Wheeler Aligner (BWA version 0.7.10)32, employing the “bwa-mem” algorithm. SAMtools version 1.633 was used to filter unmapped or unproperly mapped reads, sorting, indexing and adding read groups. Picard tools version 2.18.9 (http://broadinstitute.github.io/picard/) was employed to mark duplicates. From this step on till the Variant Call Format (VCF) file, the reads were processed using the Genome Analysis Toolkit (GATK version 4.0.6.0)35. Base quality was recalibrated using BaseRecalibrator and variant calling was carried out for each sample using HaplotypeCaller, resulting in Genomic Variant Call Format files (GVCFs), which were combined to a VCF file using CombineGVCFs using default parameter values. Finally, variants were called using GenotypeGVCFs using default parameters. A total of 14,871,405 and 3,613,138 raw SNPs and INDELS were detected, respectively. The SNPs were quality filtered using the GATK VariantFiltration tool, excluding those with parameters “QUAL < 40 || QD < 5.0 || FS > 60.0 || MQ < 40.0 || DP < 30” and resulting in 13,324,328 filtered SNPs. VCFtools36 was employed to filter those SNPs with Minor allele frequency (MAF) lower than 0.05, leading to a final set of 11,756,179 filtered SNPs.FastQC software was employed to perform the quality control of the raw data (37 was used to annotate the variants from our WGS dataset with information in the VEP database (the merged cache file Sscrofa11.1 ver. 99). The default parameter of --distance 5 kb of VEP was applied to define the upstream and downstream variants. We also applied --sift b option to the prediction of the SIFT score38 of missense variants.Ensembl variant effect predictor (VEP ver. 99)39 removing a total of 20,049 SNPs with r2 > 0.6 in 1000 kb windows. Finally, a total of 8419 SNPs mapped on autosomes were used for downstream analyses. Allele frequencies within growth groups were calculated with PLINK 1.9. We kept those SNPs having an allelic frequency difference between groups higher than 0.25 in at least one comparision . As a result, a total of 2259 SNPs remained.We focused on missense SNPs located on annotated genes and showing differences in allele frequencies between groups differing in growth patterns. A total of 32,688 markers were classified as missense out of the 11.7 million, according to VEP. LD-pruning was implemented with PLINK 1.9Genotyping by Sequencing (GBS) in the Iberian × Duroc population, from which phenotypes were also available. The list of markers genotyped by GBS is included as Supplementary Table Among these preselected SNPs, 1023 were technically adequate for Agriseq genotyping. These were included in the design and genotyped by 40 (Suplementary Table ® OpenArray™ (OA) Genotyping platform . Genotyping was carried out in a QuantStudio™ 12 K Flex Real-Time PCR System  at the Veterinary Molecular Genetics Service . DNA samples were loaded and amplified on the arrays following manufacturer instructions. Detection of allele-specific signal intensities was performed using OpenArray NT Imager, and the genotypes were called using OpenArray SNP Genotyping analysis software. In addition, SNPs images were visually inspected to detect any clustering issues.Besides, we complemented this panel with 192 candidate SNPs obtained from the literature mining and from pure and Duroc-crossbred Iberian muscle RNAseq dataleave-one-chromosome-out option from the software GCTA41. This option implements a mixed linear model-based association analysis using the following general model:y is the phenotypic value corresponding to each trait, a is the mean term, b includes the additive effects (fixed term) of the analysed SNP, X includes the genotype indicator variable that is coded as 0, 1 or 2, f includes the remaining fixed effects and covariates that were different depending on the group of traits analysed  except those mapped on the same chromosome than the SNP analysed and e is the residual. The genetic variance var(g-) is estimated based in the null model y = a + g- + e and then fixed while the association between each SNP and the trait is tested. This variance is re-estimated each time that a chromosome is excluded when the GRM is calculated. False discovery rate (FDR) was controlled for using the library q-value42 in Rstudio43. SNPs with a p-value and q-value lower than 0.05 and 0.10, respectively, were considered as significantly associated with the trait tested.The total SNP dataset (1215 markers) was filtered for association analyses using PLINK 1.9 by MAF value (0.1) and excluding markers with genotyping errors, with 1005 SNPs remaining after the filtering. These analyses were carried out on 477 individuals with genotyping and phenotyping data. SNP effects were analysed with the An average of 83 million paired-reads were generated per sample by Illumina sequencing, ranging from 67 to 100 million, with a mean coverage of 8.4 ×. After quality control, 98% of the reads were mapped, on average, to the reference genome (Sscrofa11.1), and over 14 million variants were detected. After the application of several quality filters and functional and technical selection criteria, 1023 missense polymorphisms were finally genotyped by GBS.GBS quality metrics confirmed the reliability of the genotyping procedure. Both average sample and marker call were 93.9%, and 90.2% of the markers had a call rate above 90%. Average coverage was 183 ×, well above the Agriseq recommended coverage of 100 × for genotyping purposes. Uniformity (percentage of target bases that have at least 0.2 times the mean depth) averaged 92.9%.Out of the 1023 SNPs included in the GBS marker panel, 120 polymorphisms were discarded due to lack of segregation (MAF < 0.10) or genotyping errors, resulting in 903 GBS markers with useful data for the association study.In parallel, 192 markers in candidate genes were genotyped by openarray. Out of them, 60 were monomorphic and a total of 30 discarded due to low variability (0 > MAF < 0.10) or genotyping errors, thus 102 OA markers remained informative.Joint results of GBS and OA genotyping resulted in 1005 markers being included in the association study . Minimum allele frequencies (MAF) distribution for the genotyped markers in the Iberian crossbred population is shown in Fig. p-values, for those genes showing at least one significant association (q < 0.10).The association study was performed for the 1005 successfully genotyped SNPs showing segregation within our population and all growth, fatness, FA composition and metabolic traits available  for which previous biological or functional hypothesis were available.ACACA, EPHX1 and FREM2 genes  for slaughter weight, 10 kg for carcass weight (1.25 SD) and 0.125 kg/d (1.9 SD) for global ADG  included LEPR, NFE2L2, FTO and LIPE candidate genes .Backfat was affected by ts Table  and withLongissimus dorsi muscle and liver tissues . A total of 100 significant associations were found for these traits  only in the backfat outer layer; while other genes such as AHNAK, LIPE, EPHX1 or TNF had effects on different locations affecting similar traits (Table Fatty acid composition was determined in backfat (inner and outer layers) and in 0 Tables  and 5, ws Tables  and 6, bts Table .Table 4SSCD gene were associated with palmitoleic acid and the desaturation ratio oleic/stearic only in the neutral lipid fraction of liver   and palmitoleic acid (C16:1n-7), respectively, in both the backfat outer layer and the neutral lipids from muscle (Table Three different SNPs located in ) Tables , 4. Howe) Tables . Also, tIRX3, ADIPOQ, MSTN or MYOD1, had significant effects on just one trait  after the multiple test correction.In this study, a medium-throughput genotyping approach was performed after an initial SNP discovery step, in order to uncover DNA polymorphisms related to relevant productive traits. SNPs analysed were mostly obtained from WGS data, and complemented with a panel of biological and functional candidate genes/SNPs, obtained from literature and RNAseq data.Genotyping results showed a good level of informativity of the markers derived from WGS, with a high proportion of markers showing intermediate frequencies (56% showed 0.3 < MAF < 0.7) and scarce proportion being discarded due to low segregation (10.6%). These results support the validity of the SNP discovery approach perfomed from WGS data. On the contrary, markers obtained from bibliography and RNAseq data were less informative (33% showed 0.3 < MAF < 0.7) and a high proportion was detected as monomorphic or scarcely informative (50%). This finding was not unexpected as WGS markers were obtained from sequencing data from animals of the same Iberian crossbred population employed for the association study, while candidate genes/SNPs were previously identified in different pig breeds and populations.In spite of a lower number of previously known candidate SNPs, and their lower informativity, in comparison to WGS-derived markers (102 vs 903) a much higher number of significant associations were observed for the SNPs with previous evidences of association or differential expression. This is not surprising as those fulfill more functional criteria than the ones discovered in the present work from WGS data. In fact, out of 174 significant associations, 152 involved previously identified SNPs in candidate genes and only 22 involved WGS-derived markers. Besides, out of the 152 associations related to previously characterized SNPs, 62 corresponded to well-known markers with previous evidences of association, and 90 corresponded to markers discovered from RNAseq data, within genes showing differential expression between Iberian pig genotypes or with a potential regulatory role. This finding supports the usefulness of deep sequencing transcriptome data mining to uncover structural variants with interesting effects on phenotype.ACACA and SCD, were found to affect, respectively, birth measures and livelong performance traits. These genes are involved in fat synthesis and metabolism and, indeed, significant effects were also found for both genes on FA composition. However, their potential role on growth traits is not clear so far. Acetyl-CoA carboxylase (ACACA) is a multifunctional enzyme system that catalyzes the carboxylation of acetyl-CoA to malonyl-CoA, the rate-limiting step in FA synthesis. In agreement, our results show a significant effect of this gene on palmitoleic acid abundance in backfat and muscle and suggestive effects on many main FA and FA ratios. Besides this main role, the ACACA gene influences mitochondrial membrane potential, thus it might condition cell proliferation and tissue growth44. In fact, a deficiency in ACACA activity was associated to defects in early growth and muscle development in humans45. Stearoyl-CoA desaturase (SCD) catalyzes the ∆9-cis desaturation of palmitoyl- and stearoyl-CoA, which are converted into palmitoleoyl- and oleoyl-CoA, respectively. In addition to being components of tissue lipids, MUFAs also serve as mediators of signal transduction, cellular differentiation, food intake, apoptosis, and mutagenesis, and therefore, variation in SCD function in mammals would be expected to have an effect on a variety of key physiological events, including growth46. Thus, it has been proposed that BW gain may be associated with SCD increased activity. In fact, a different polymorphism in SCD gene was associated with ADG and FCR in lean pigs47. Nevertheless, the three SNPs analysed in this study , located in the promoter region and in full linkage disequilibrium, had not been previously associated with performance traits50. This novel finding is also striking because of the size of the effect, especially on BW traits, with an estimated additive effect close to 30 kg. This finding should be cautiously interpreted, as the frequency of the homozygous genotype was low and the effect could be overestimated. Interestingly, Estany et al.48 found that C-T-A haplotype was additively associated with enhanced C18∶1/C18∶0 desaturation index both in muscle and subcutaneous fat, but not in liver, in a purebred Duroc line. According to our results, the main effect of SCD gene regarding FA composition is detected in liver neutral lipid fraction, with C-T-A haplotype enhancing the desaturation index as well as C16:1n-9 content. Also, a potential effect on IMF was reported for this mutation49 which has not been validated in the present work.Regarding growth and development traits, two main genes, FREM2, EPHX1, SOWAHB or TFRC15. Thus, those genes fulfill a functional criteria to be considered involved in the phenotypic differences observed between Iberian and Duroc genotypes. The FREM2 gene (FRAS1 related extracellular matrix protein 2) encodes an extracellular matrix protein with a role in morphogenetic processes51. In agreement with our findings, the FRAS/FREM complex is particularly important during early development. In humans, mutations in this gene cause the Fraser syndrome, a multisystem disorder involved in the structural adhesion of the skin ephitelium to its underlying mesenchyma52 and in pigs this gene has been proposed as candidate for developmental morphological traits such as presence of wattles53, The genes SOWAHB and EPHX1 showed somewhat similar association results, both affecting livelong growth traits and some fat deposition traits. The gene SOWAHB (sosondowah ankyrin repeat domain family member B) is part of the Ankyrin family, involved in membrane skeleton organisation, ionic transport, protein recognition as well as cell–cell adhesion regulation54. Moreover, SOWAHB is activated in response to insulin and possibly involved in insulin resistance55. Besides its effect on growth traits, significant nominal p-values were observed in the test of association with fatness traits at different ages  gene showed significant association results for growth as well as FA composition traits, specifically for C20:1(n-9) and for C20:3(n-6) contents. The enzyme encoded by this gene is able to either detoxify or bioactivate a wide range of substrates56 including epoxides derived from endogenous polyunsaturated FA, thus mediating several biological processes such as inflammation or angiogenesis, and regulating crucial signaling pathways for cellular homeostasis, adipocyte differentiation and insulin response59. This gene was shown to be upregulated in biceps femoris, longissimus dorsi and backfat of pure Iberians in comparison to Duroc genotypes15. The upregulation of SOWAHB and EPHX1 genes in pure Iberians in different tissues may be related to their potential role in insulin response and adiposity, which is in agreement with the suggestive association of their structural variants with fatness traits and significant association with weight at growing stages, when Iberian pigs are known to develop a compensatory growth and a more intense development than Duroc genotypes5. The TFRC gene (Transferrin Receptor) encodes a cell surface receptor necessary for cellular iron uptake and has been studied as a positional candidate gene for disease susceptibility60. In addition, it acts as a lipid sensor that regulates mitochondrial function by regulating activation of the JNK pathway61. Interestingly, TFRC was upregulated in loin muscle of Duroc × Iberian crossbred pigs at birth14, but it was upregulated in biceps femoris and backfat of Iberian purebred growing pigs15. According to our results, the SNP in this gene affects BW and ADG. Thus, the opposite regulation observed at birth and growing stages would be related to the different paucity of muscle developmental processes observed in Iberian vs Duroc genotypes, with the latter showing higher prenatal development and birth weight and the Iberian purebreds showing increased muscle development at growing stages.Other interesting genes and SNPs that have not yet been established as candidates for growth traits were also identified. Some of them were obtained from RNAseq data and were differentially expressed between Iberian and Iberian × Duroc crossbred or Duroc purebred pigs, such as LRIG3, DENND1B or TMCC1. Although none of these genes has been studied for association with any productive trait, all have roles or evidences which suggest involvement in early developmental processes. LRIG3 (leucine-rich repeats and immunoglobulin-like domains 3) plays a role in embryo development, including cranio-facial morphogenesis and neural crest formation62. Also a direct relationship for LRIG3 and early growth was observed since KO mice were smaller than wild-type ones63. In agreement, our results show significant effects on birth weight and thoracic circumference and suggestive effects on all remaining traits recorded in neonates, including those related to head development. DENN Domain Containing 1B (DENND1B) gene has an important role on cytokine production and regulation of T cell receptor signaling and mutations or loss of this factor were associated with immune diseases in neonates64. Interestingly, methylation of DENND1B gene in venous umbilical cord blood at delivery has been associated to birth weight in humans65. According to our results, this gene influences birth weight, abdominal circumference and biparietal diameter and is suggestively associated to most early traits recorded at birth and at weaning. TMCC1 (Transmembrane And Coiled-Coil Domain Family 1) is an integral endoplasmic reticulum (ER) membrane protein that has been associated with central adiposity and waist circumference66 and is supposed to be involved in regulation of myogenesis67. Our results indicate effects on biparietal and thoracic diameter as well as thoracic circumference, and also at a suggestive level, on several other birth traits and later fatness traits such as backfat.Newborn developmental traits were also affected by several SNPs detected through WGS, located in relatively unknown genes such as LEPR, LIPE or FTO, with the clearest results detected for LEPR. Leptin receptor is known to have a determinant role on energy homeostasis and has been widely studied as functional and positional candidate gene for fat deposition traits. Iberian pigs are known to carry a fixed mutation in this gene which is considered a causal mutation contributing to their characteristic trend for adiposity and high appetite as well as their leptin resistant phenotype8. This mutation has been evaluated in different genetic backgrounds, mainly in experimental pig populations, but also in other breeds and commercial populations70. According to the present results, the effects of this SNP on fatness are further validated in our Iberian × Duroc commercial population, with significant effects on different backfat thickness measures and global ADG, and suggestive associations with BW, FA composition and cholesterol, HDL and LDL levels. As expected, the Iberian T allele increases fattening, with an additive effect close to 0.5 cm for backfat, which means an estimated difference of almost 1 cm between homozygous animals for alternative alleles .Fattening traits were mainly affected by SNPs located in known candidate genes such as NFE2L2 gene (nuclear factor erythroid 2-related factor 2) was associated with fatness. This gene codes a transcription factor whose main role involves the regulation of the expression of antioxidant proteins that protect against oxidative damage. Moreover, this transcription factor is determinant in the maintenance of homeostasis, participating in the regulation of metabolism, inflammation, autophagy, proteostasis, mitochondrial physiology, and immunity71. This gene was selected from RNAseq data for SNP discovery, despite not being differentially expressed, due to its potential role as predicted regulator for the differential expression observed between Iberian pig genotypes14. Our results show a significant effect on backfat thickness and suggestive effects on intramuscular fat content and FA composition, especially in loin, in agreement with its known role in muscle mitochondrial biogenesis and the predicted role as regulator of muscle metabolism.Besides the known candidate genes, the KRT10 and NLE1, both affecting the proportions of SFA and MUFA in backfat and loin. KRT10 (keratin 10) was studied because it was overexpressed in muscle from pure Iberian in comparison to crossbred pigs13, although it does not have a known role in relation to fat metabolism. However, it has been recently shown that KRT10 expression is suppressed by serum lipids72 and, interestingly, different keratin genes had been associated to FA composition traits in a chromosome 12 survey in Iberian × Landrace backcrossed pigs73. In fact, the KRT10 gene maps on SSC12, where several QTL for FA composition traits have been discovered. Moreover, KRT10 was found as the most significant differentially expressed gene according to ACACA genotype regarding polymorphism ALGA0066302A, not analysed here, which in turn is associated with FA composition. On the other hand, the NLE1 gene (Notchless homolog 1) plays a role in regulating the Wnt pathway, which is known to play a central role in adipocyte differentiation and lipid metabolism74. In fact, Wnt pathway plays a dual function in adipocytes, including a well-known repressive effect on adipogenesis and the stimulation of leptin production in mature adipocytes75. In agreement with the antiadipogenic effect, a higher expression of this gene was observed in loin of crossbred Iberian pigs, which are leaner than purebreds14. Our association results do not relate this gene with adiposity traits, but clearly demonstrates its involvement in relevant traits related to FA metabolism and profile, which influence meat quality, in the two main productive tissues, fat and muscle, and especially on this last one. The allele that increases oleic acid content and monounsaturated to saturated fat proportions, with effects ranging from 0.5 to 1 SD in magnitude, also shows suggestive positive effects on BW and ADG at late growing stages, meaning it could be an interesting marker to improve meat quality without negative pleiotropic effects on other relevant traits.The main genes affecting FA composition were KCNH2 gene (Potassium Voltage-Gated Channel Subfamily H Member 2) was associated with the main PUFA contents in backfat, although, suggestive effects were observed on the muscle FA profile and on some growth and carcass traits. This gene was previously found overexpressed in loin and biceps of pure Iberians in comparison to crossbreds and Duroc animals14. Our results agree with previous findings in humans showing that mutations in KCNH2 gene are associated with alteration of insulin homeostasis and glucose and lipid metabolism76 and its methylation is related to obesity77. AHNAK gene (Neuroblast differentiation-associated protein) codes a nucleoprotein involved in adipogenesis and lipid metabolism. AHNAK KO mice have reduced fat accumulation and decreased serum triglyceride levels as well as increased expression of genes involved in lipolysis and FA oxidation78. In agreement, its expression was higher in pure Iberian animals14, which are characterised by a higher adipogenic potential than crossbreds. In addition, our results show effects of this SNP on different FA in backfat and liver and a suggestive effect on backfat thickness at weaning.Several other SNPs showed significant effects limited to a few FA. FASN, LIPE and TNF had similar effects, mainly on C14:0 in backfat and muscle. As mentioned previously, LIPE gene (hormone sensitive lipase) was one of the genes significantly affecting backfat depth at slaughter. The LIPE allele with a positive effect on backfat also positively affected C14:0 and C16:0 in backfat and loin and had suggestive negative effects on several MUFA parameters and glucose metabolism indicators. LIPE is a key enzyme in the mobilization of FA from acylglycerols79, which has been related to different fat deposition and FA profile traits in different species. On the other hand, FASN gene (fatty acid synthase) codes a multifunctional enzyme that catalyzes the synthesis of FA and has been repeatedly associated with FA composition traits, especially affecting short chain saturated FA80, as in our results. This gene, together with ACACA, regulates de novo synthesis of FA. Thus, FASN, ACACA and LIPE are involved in the regulation of fat deposition by balancing lipogenesis and lipolysis, and significant association results are obtained for all three of them. On the other hand, tumor necrosis factor (TNF) is an adipokine that promotes insulin resistance and is associated with obesity-induced type 2 diabetes81. In agreement, our results indicate a significant effect on C14:0 in backfat and loin, but also many other suggestive associations are found for other saturated and monounsaturated FA, as well as carcass and meat quality traits  and BW records along growth.On the other hand, the genes TBC1D16 gene, which has a role in membrane trafficking and molecule transport and is known to be dysregulated in obesity82. Besides the significant effect on HDL, this SNP showed suggestive effects on many different FA traits related to MUFA contents and MUFA/SFA ratios in loin and liver, which are known to be correlated with physical markers of obesity such as body mass and adiposity indexes83, in agreement with the role of this gene, although no effect was observed on fatness.Scarce significant associations were found for metabolic traits. Only HDL concentration was significantly associated with two different SNPs, both derived from WGS data, at two different ages. The association found at 110 days old is interesting as it involves LEPR, SCD, ACACA, FASN or LIPE). Moreover, our approach allowed for the discovery of interesting new effects of SNPs in less known genes  affecting BW and ADG at different ages, as well as fatness and FA composition . Most significant associations came from SNPs identified through structural mining of transcriptome deep-sequencing data. In spite of a relatively small number of significant associations detected for WGS-derived markers, the approach employed here is proposed as a useful genome-wide strategy for the discovery of untapped genetic basis of productive traits. Our results contribute to a better understanding of the molecular genetic basis of relevant traits in pigs, including both growth and yield-related traits as well as traits involved in sensory and technological quality of meat which are essential for Iberian pig cured products. Besides, our results provide interesting SNPs and new candidate genes for association studies in different pig breeds and populations and for future implementation of marker-assisted selection strategies. Different markers can be highlighted as most promising, such as KRT10, NLE1, TFRC, NFE2L2, SCD or LEPR, and some of them may allow the improvement of meat quality without negatively affecting growth efficiency, or viceversa.A combined approach of candidate SNP recovery from literature and SNP discovery from WGS and RNAseq data has been successful in validating the effects of different SNPs in candidate genes previously associated with fatness, growth and FA composition traits (such as Supplementary Legends.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.Supplementary Table S4.Supplementary Table S5.Supplementary Table S6.


However, the power of GWAS is limited by sample size in a single population. Alternatively, meta-analysis of GWAS (metaGWAS) is an attractive method to increase the statistical power by integrating data from multiple breeds and populations. The aim of this study is to identify shared genetic characterization of BFT across breeds in pigs via metaGWAS. Average backfat thickness (BFT) is a critical complex trait in pig and an important indicator for fat deposition and lean rate. Usually, genome-wide association study P < 0.05) and defined five breed-shared QTLs in across-breed metaGWAS. Markers within the five QTL regions explained 7 ~ 9% additive genetic variance and showed strong heritability enrichment. Furthermore, by integrating information from multiple bioinformatics databases, we annotated 46 candidate genes located in the five QTLs. Among them, three important  and seven suggestive candidate genes  were identified.In this study, we performed metaGWAS on BFT using 15,353 pigs  from 19 populations. We detected 40 genome-wide significant SNPs (Bonferroni corrected QTLs and candidate genes underlying BFT across breeds were identified via metaGWAS from multiple populations. Our findings contribute to the understanding of the genetic architecture of BFT and the regulating mechanism underlying fat deposition in pigs.The online version contains supplementary material available at 10.1186/s12864-022-09036-z. Human obesity has become an increasingly common social phenomenon in the past decades related to both quality of life and life expectancy . Pig wasMC4R [PPARD [LEPR [Previous studies showed that BFT is a complex trait –7 with mMC4R , 16, PPAR [PPARD , and LEPRD [LEPR , 19) havThe objectives of this study were to identify QTLs and candidate genes via metaGWAS and to explore the genetic architecture of BFT across pig breeds. In this study, we performed GWAS for BFT in 19 different populations genotyped with Porcine SNP BeadChip. Further, metaGWAS was performed for within-breed and across-breed strategies. Our findings contribute to the understanding of the genetic architecture of BFT and the regulating mechanism underlying fat deposition in pigs.The detail information  of 19 populations used in this study were presented in Table Principal component analysis (PCA) based on genotypes of all pigs Fig.  and eachP < 1 × 10− 5) ranged from 0 to 43. 105 SNPs were significant (P < 1 × 10− 5) only in one of 19 single population GWAS. 85 SNPs were significant (P < 1 × 10− 5) only in one of four metaGWAS (within-breed and across-breed), and 49 SNPs were significant in at least one metaGWAS. 28 SNPs were significant both in single population GWAS and metaGWAS  1, 7, and 18 respectively  which was located in the gene body of NUDT3. The QTL on SSC1 included the second most significant lead SNP rs81284646  which was located in the gene body of ENSSSCG00000004911. And the QTL on SSC18 was detected via the lead SNP  which was located in the gene body of ZC3HAV1.In Duroc within-breed metaGWAS, a total of 24 significant SNPs were detected  which was located in the downstream of ENSSSCG00000045579 with distance equal to 69,671 bp. The QTL on SSC2 included the second most significant lead SNP rs81358998  which was located in the gene body of SBF2.In Yorkshire within-breed metaGWAS, we identified seven significant SNPs  was located in the gene body of RMND5B.In Landrace within-breed metaGWAS, we only identified one significant SNP  were not observed in within-breed metaGWAS, while the remaining three QTLs were overlapped with those in within-breed metaGWAS.rs80877507  which was located in the gene body of ENSSSCG00000048538. The QTL on SSC7 was identified by the second most significant lead SNP rs80962638  which was located in the gene body of SNRPC. The QTL on SSC18 included the third most significant lead SNP rs81236473  which was located in the gene body of ZC3HAV1. The QTL on SSC2 included the lead SNP rs81359652  which was located in the downstream of ENSSSCG00000013869 with distance equal to 14,538 bp. The remaining QTL was detected on SSC1 by the lead SNP  which was located in the downstream of CEP162 with distance equal to 224,490 bp.The QTL on SSC1 was defined by the most significant lead SNP We also examined the linkage disequilibrium (LD) blocks across breed for each of the five QTLs .To evaluate whether the five QTLs are effective across-breed, we conducted genomic prediction with the lead SNPs within these QTLs, and calculated the Pearson’s correlation coefficient between predicted genetic value and average phenotype value in all populations. Finally, we observed an significant predicting ability across breed for these QTLs , PPARD , RELCH , and ITPR3 , of which MC4R was widely reported in both pig (11 evidence in backfat) and human (24 evidence in body mass index). SLC27A1  was the gene annotated in five fat metabolism processes in GO and KEGG including lipid, fatty acid, and triglyceride metabolic pathways, insulin signaling pathway and homeostasis regulation pathway. Two genes, ILRUN (SSC7 at ~ 30.6 Mb) and KCNQ5 (SSC1 at ~ 52.4 Mb) were not previously reported in pig but reported in human research.To investigate the gene that potentially regulates BFT, we conducted candidate gene mapping from total 138 positional candidate genes via four external biological annotations  was associated with fatness, growth, as well as meat quality traits reported in recent decades [MC4R had been validated by several studies in pigs [MC4R was located in the QTL on SSC1 (~ 160 Mb) and was annotated with several homeostasis regulation pathways in GO biological processes. Second, PPARD is recognized as an important gene associated with fat deposition traits in gene and gene expression layers [SLC27A1 was another detected important genes that showed multiple evidence associated with fat deposition [PHLPP1, NUDT3, ILRUN, RELCH, KCNQ5, ITPR3, and U3. RELCH, the candidate gene near MC4R, belonged to sterol and lipid metabolism pathway in GO database and was also identified in previous researches [ITPR3, the candidate gene near PPARD, belonged to insulin secretion pathway in KEGG database and both annotated in pig [PHLPP1  and NUDT3 (SSC7), two candidate genes near major genes MC4R and PPARD respectively, were found to be associated with backfat in pig [ILRUN, KCNQ5, and U3 were reported in human associations studies in BMI and body weight for 15, 7, and 9 times respectively but not reported as the candidate genes in pig.Among the discovered candidate genes, three genes associated with BFT were well studied. First,  decades , 34–36.  in pigs –39 and i in pigs –42. In tn layers , 43, 44,position –47. In asearches , 48. ITPd in pig  and humat in pig , 50–53. The sample size is a crucial factor to the power of GWAS. Generally, there are two strategies to enlarge the sample size. One is to combine individual level data (genotypes and phenotypes) from multiple populations, then to conduct GWAS analysis. The other is metaGWAS, in which the GWAS summary statistics from multiple populations are analyzed jointly, rather than put individual data together. In this study, we aimed at testing the metaGWAS strategy, in which individual data from different populations are not allowed to be combined together. The other strategy would be an interesting solution deserve further attempt in our further investigation. Integrating data from multiple populations could further increase the power of association analysis. However, the weakness of metaGWAS still could not be neglected. For example, the available SNPs were different for different populations, hence the number of common SNPs across all populations were usually too small to perform GWAS effectively. Therefore, a large pig genotype imputation reference panel was necessary for unifying the SNP maps for different populations.MC4R, PPARD, and SLC27A1, had been well studied. Additionally, seven candidate genes, PHLPP1, NUDT3, ILRUN, RELCH, KCNQ5, ITPR3, and U3, were identified to be promising candidate genes associated with BFT. Our results provided useful reference for understanding the genetic architecture of BFT and the regulating mechanism underlying fat deposition in pigs.In this study, we conducted metaGWAS for BFT on 15,353 pigs from diverse genetic backgrounds. We identified 40 genome-wide significant SNPs located in five QTL regions and annotated 46 candidate genes based on across-breed metaGWAS. Among the candidate genes, A total of 15,353 pigs  from 19 populations in 12 Chinese pig farms were used in this analysis. The average age of pigs was 156, 172, and 164 days for Duroc, Yorkshire, and Landrace respectively. The backfat thickness was measured by living B-ultrasonic within each population at the end of test. The measurements were adjusted to body weight at 100 kg using within farm genetic evaluation system. Details about the populations, phenotypes, and genotypes were shown in Table Sus scrofa 11.1 reference genome version. And the missing genotypes were phased within each population using Beagle 5.1 [P > 1 × 10− 6 for Hardy-Weinberg equilibrium test. SNPs with no position information or located on sex SSC were removed. After quality control, the number of SNPs within each population was shown in Table All pigs were genotyped with one of the following SNP chips, GeneSeek GGP-Porcine Beadchip 50 and 80 K , Illumina PorcineSNP60 BeadChip 60 K , “ZhongxinI” Porcine Breeding Chip 50 K . The annotation of all SNPs has been unified to Ensemble agle 5.1 . Qualityagle 5.1  with theTo correct the population structure in GWAS model and investigate the population structure, we performed PCA using GCTA-PCA (1.94.0beta) , 56 in sWe performed GWAS in each single population separately using GCTA-LOCO , 56 withwhere  formula : \docume− 5) for both method in the comparative analysis.To perform meta-analysis of GWAS, we utilized the GWAS summary statistics from single population GWAS as input for METAL (released on 2011-03-25) , in whiccument}λ  were perIn this study, we defined QTL as the genomic region containing a set of significant SNPs, where physical distance of each neighbor pair was less than 5 Mb. If the length of a QTL region was less than 2 Mb, the region was defined as the 1 Mb on the either side of the lead SNP. A lead SNP was defined as the most significant SNP within a QTL.2) in the largest population within each breed  using PLINK v1.9 [To assess the LD event in each QTLs across breeds, we computed the coefficient of LD  genotype combinations via the formula :\documenwhere To identify the candidate genes from QTLs, we firstly retrieved them based on physical location (within QTLs detected in across-breed metaGWAS) and multi external biological annotations. Second, we matched genes within QTLs with those belong to fat regulation biology process  in GO , 64 and Additional file 1: Figure S1. PCA plot of each of three breeds.Additional file 2: Table S1. Summary information of 19 single population GWAS.Additional file 3: Figure S2. Venn diagram of the number of significant SNPs (P<10-5) between 19 single population GWAS and four metaGWAS.Additional file 4: Table S2. Summary information of significant SNPs in four metaGWAS.Additional file 5: Figure S3. Q-Q plot of  four metaGWAS.Additional file 6: Figure S4. LD blocks of each of the five QTLs in three breeds.Additional file 7: Table S3 (Sheet1). Annotation of candidate genes belongs to fat regulation biology process in GO and KEGG. Table S3 (Sheet2). Annotation of candidate genes in pigQTLdb. Table S3 (Sheet3). Annotation of candidate genes in human NHGRI-EBI Catalog.


Vitis vinifera L. cultivars planted in 5 blocks in the vineyard. This panel was phenotyped over several years for 127 traits including yield components, organic acids, aroma precursors, polyphenols, and a water stress indicator. The panel was genotyped for 63k single nucleotide polymorphisms by combining an 18K microarray and genotyping-by-sequencing. The experimental design allowed to reliably assess the genotypic values for most traits. Marker densification via genotyping-by-sequencing markedly increased the proportion of genetic variance explained by single nucleotide polymorphisms, and 2 multi-single nucleotide polymorphism models identified quantitative trait loci not found by a single nucleotide polymorphism-by-single nucleotide polymorphism model. Overall, 489 reliable quantitative trait loci were detected for 41% more response variables than by a single nucleotide polymorphism-by-single nucleotide polymorphism model with microarray-only single nucleotide polymorphisms, many new ones compared with the results from bi-parental crosses. A prediction accuracy higher than 0.42 was obtained for 50% of the response variables. Our overall approach as well as quantitative trait locus and prediction results provide insights into the genetic architecture of target traits. New candidate genes and the application into breeding are discussed.To cope with the challenges facing agriculture, speeding-up breeding programs is a worthy endeavor, especially for perennial species such as grapevine, but requires understanding the genetic architecture of target traits. To go beyond the mapping of quantitative trait loci in bi-parental crosses, we exploited a diversity panel of 279  With the 2 major challenges facing perennial fruit crops in general and grapevine in particular, i.e. decreasing phytosanitary products such as fungicide treatments and adapting to climate change, harnessing existing genetic diversity . As a reTo overcome these limits, a few genome-wide association studies (GWASs) have been performed in cultivated grapevine diversity panels but, due to various reasons, failed to identify many new QTLs. Several articles  and is weakly structured in 3 genetic groups  for some traits, data were acquired in different years and conditions, and hence analyzed separately; (2) we also combined several traits to define new variables. In the end, we thus analyzed 152 response variables from 127 traits.13C (D13C) as previously detailed  and harvested 3 clusters at 20°Brix, which provided the sampling date . We recorded mean cluster weight , mean cluster length (in cm), mean cluster width (in cm), and cluster compactness . One hundred berries randomly sampled from the central third of clusters were weighed, providing the mean berry weight . In the winters of 2011–2012 and 2012–2013, the number of woody shoots (NBWS) and pruning weight  were recorded for each plant. In 2011, the veraison date  was also recorded. Two variables were computed from these traits: the veraison-maturity interval , and plant vigor . In 2011 and 2012, juices were made from the sampled berries and analyzed to measure δ13C measurements and analysis are described elsewhere (−1) (−1) .https://bioweb.supagro.inra.fr/collections_vigne/Home.php?l=EN).The sanitary status of cultivars regarding the presence of 5 viruses  was assessed by ELISA from plants at INRAE Vassal . Flower sex (OIV 151) and berry skin color (OIV 225) of each panel cultivar were retrieved from , and while subsequent filtering for MAF below 0.05 resulted in 63,105 SNPs , thereafter referred to as the “microarray-GBS” SNPs. We also imputed the Syrah × Grenache SNP genotypes similarly using Beagle.The 13,925 microarray SNPs and 184,145 GBS SNPs for 277 common cultivars were combined into a set of 197,885 common SNPs  using coordinates on the 12Xv2 reference sequence  as advisSwarts We performed a 2-stage analysis of each response variable using univariate regression models. In the first stage, estimates of total genotypic values were obtained (detailed in this section). In the second stage (see next section), these were regressed on SNP genotypes to identify QTLs, estimate their allelic effects and assess prediction accuracy.To decrease the influence of potential outliers, all polyphenols  had their raw data automatically transformed with the natural log. For the other traits, when their raw phenotypic data were too skewed as visually assessed, they were also log-transformed .In 2011–2012, phenotypic data for the control were spatially analyzed  in a wayIn 2014–2015, the control was not phenotyped, an irrigation treatment was applied, and samples from different blocks with the same irrigation level were pooled  to obtain unbiased estimates of variance components and empirical BLUPs of genotypic values. The acceptability of underlying assumptions  was visually assessed by plotting residuals and BLUPs. Broad-sense heritability on a genotype–mean basis (H2) was computed using 2 estimators. The first assumes a balanced design (2g/[σ2g + (σ2g:y/ny) + (σ2e/(ny × nr)] where σ2g is the variance of the genotypic values, σ2g:y is the variance of the genotype–year interactions, ny is the arithmetic mean number of trials (years), σ2e is the variance of the errors , and nr the arithmetic mean number of replicates per trial. The second estimator, For each response variable, a global linear mixed model was defined with multiple fixed effects , and all 5 viruses, as well as the volume and weight of juice samples for BDAM and PDMS) together with 2 random effects (genotype and genotype–year interaction). The global model was fitted by ML with R/lme4 version 1.1.19 (d design : HC2 = σdata see  in the Rg) = 1 μ + Ma,p βp + u + e where eBLUP(g) is a vector of responses of length N, Ma,p is a vector of length N with the genotypes at the pth SNP (additive coding), βp is its effect modeled as fixed, u ∼ NN is a vector of length N corresponding to a polygenic effect modeled as random where the covariance matrix A contains additive genetic relationships  with N the Normal distribution of dimension N, 0 a vector of zeros, and Id the identity matrix of dimension N×N. eBLUPs of g were used instead of BLUEs as they are known to be more accurate for prediction and selection purposes, notably thanks to the shrinkage property  = 1 μ + Maβ + e, where Ma is a NxP matrix of SNP genotypes (additive coding), with the so-called spike-and-slab prior for each SNP p, βp ∼ π0 δ0 + (1 - π0) N1, δ0 being a point mass at zero. We fitted it with the variational algorithm, faster than MCMC, implemented in R/varbvs version 2.5.7 (p = Pr(βp ≠ 0), was higher than 0.80.The second type of models jointly analyzes all SNPs with the goal of selecting a subset of those with large effects while handling LD. This SNP selection can be achieved in a frequentist setting via stepwise regression  and slightly edited for automatic processing (see Supplementary Text 3); and (2) a second list of significant hits from a few GWAS publications after converting their coordinates on the genome reference we used.QTLs were defined as intervals around significant SNPs based on LD decay (Results) across all response variables, which resulted in 134 distinct genomic intervals (Supplementary Table 9). These intervals had a median length of 100,001 kb . We then searched for overlaps between them and the Vcost version 3 annotations totalizing 42,413 gene models from https://urgi.versailles.inra.fr/Species/Vitis/Annotations).In terms of annotation, as a given locus can be a QTL for multiple response variables, we first merged our 489 reliable QTLs , we used the well-known multi-SNP model termed ridge regression  which assumes a dense architecture: eBLUP(g) = 1 μ + Maβ + e where β ∼ NP. It is known to be equivalent to the “GBLUP” model (g) = 1 μ + ag + e where ag ∼ NN with A, the N×N matrix of additive genetic relationships, proportional to the matrix product Maa is centered using allele frequencies. It is similar for the dominance genotypic values dg ∼ NN where D is the N×N matrix of dominance genetic relationships. Because the estimators of additive and dominance relationships from SNPs assume linkage equilibrium, a 0.5 LD threshold was applied when computing A and D. We fitted the models with R/lme4 and computed confidence intervals for variance components by bootstrap as above.To estimate the proportion of variance of empirical BLUPs of genotypic values explained by the cumulative contribution of SNPs (R2), and the P-value of the test for no bias.Out-of-sample prediction was assessed within the panel by 5-fold cross-validation repeated 10 times with R/caret version 6 , using ROut-of-sample prediction was also assessed by training rrBLUP and varbvs methods on the whole panel and predicting empirical BLUPs of genotypic values for the 23 genotypes of the Syrah × Grenache cross.V.vinifera L. panel of 279 cultivars suitable for GWAS and representing the INRAE Vassal germplasm repository to set up a randomized-complete-block field trial . It was phenotyped for 127 traits from which 25 extra variables were computed. All 152 response variables displayed substantial variation . For some polyphenol variables, part of the variation was obviously associated with skin color, 137 cultivars out of 279 having colored skin berries. When phenotyped, the control cultivar allowed us to establish that (1) part of this variation was due to genetic differences between panel cultivars , and that (2) spatial heterogeneity was negligible . The amount of missing data among response variables ranged from 15.78% to 43.93% (Supplementary Table 4). To account for such unbalance, we fitted linear mixed models and obtained BLUPs of genotypic values. After model selection, the final set of fixed and random effects differed between response variables (Supplementary Table 4), with year and genotype–year interaction effects being selected in most cases.We took advantage of the We then assessed the accuracy with which genotypic values were estimated using broad-sense heritability . As shown in We then aimed to explain the variance of these genotypic BLUPs with SNP genotypes. For that purpose, we used 2 sets of SNPs, the “microarray-only SNPs”  and “microarray-GBS SNPs” .V.vinifera L. (Because LD is known to be short in SNPs). Assuming an additive-only, polygenic architecture, PVESNPs was higher with microarray-GBS SNPs than with microarray-only SNPs for 97.8% of responses variables . We then estimated the additive genetic relationships between cultivars , confirming the weak structure in 3 subgroups corresponding to wine west, wine east, and table east. The matrix of genetic relationships was used to estimate the proportion of variance in genotypic BLUPs explained by SNPs .The GWAS methods used in the following were first checked on 2 previously phenotyped traits, flower sex and berry skin color, for which the already known genetic architecture consists in a major QTL. Results were coherent with the literature . For each response variable, QTLs were defined as LD-based intervals around each significant SNP (Supplementary Table 7), and then merged when overlapping (Supplementary Table 8). As summarized in To benefit from a potential gain in power, we fitted 2 multi-SNP models that both provided more response variables with at least 1 QTL compared with the SNP-by-SNP model, whatever the SNP set . Within By summing over the 150 response variables with at least 1 QTL, a total of 3,490 QTLs were found (Supplementary Table 8), which corresponded to an increase of 196% in the number of QTLs and of 70% in the number of response variables with at least 1 QTL, compared with applying the SNP-by-SNP method on the microarray-only SNPs. Among these QTLs, 136 were found by all 3 methods, while 3,001 were found by a single method only and 1,598 by multi-SNP methods only . Furthermore, over these 150 response variables, 26 had no QTL according to the SNP-by-SNP method but at least one found by both multi-SNP methods .All chromosomes harbored at least 1 QTL , and most QTLs found only by the multi-SNP mlmm.gwas method fell far from QTLs found by other methods . Moreover, 90% of the QTLs found only by the SNP-by-SNP method GEMMA clustered on chromosome 2 for 64 response variables, all of them being polyphenols, in relation with the anthocyanin-related MYB genes on this chromosome . They corresponded to 59% less QTLs but 41% more response variables with at least 1 QTL, compared with applying the SNP-by-SNP method on the microarray-only SNPs. All chromosomes harbored at least 1 such reliable QTL, except chromosome 19 .The 489 reliable QTLs were compared with the largest list of QTLs detected in grapevine bi-parental crosses compiled so far  were phenotyped in at least 1 other study with at least 1 significant GWAS hit found , but didThe comparison of our reliable QTLs with the reference gene annotations detected 1,926 distinct gene models (Supplementary Table 10). Out of these, only 980 had a proposed putative function (Supplementary Table 11).We assessed the accuracy of genomic prediction by cross-validation within the panel of 279 cultivars (Supplementary Table 12). We compared 2 methods assuming contrasted genetic architectures: additive infinitesimal for rrBLUP and additive sparse for varbvs. Both the median Pearson and Spearman correlation coefficients between observed and predicted genotypic values fell between 0.37 and 0.44, similarly for both SNP sets and methods . These cP value of the test for no bias, varbvs showed similar values across both SNP sets, higher than rrBLUP in general and above 0.05, suggesting an absence of bias. On the contrary, rrBLUP showed lower P values with the microarray-GBS SNPs, suggesting that its assumption of all SNP effects being nonzero may be too strong for these traits, especially when SNP density is high.Moreover, rrBLUP results did not seem to depend on the SNP set whereas they were slightly better with the microarray-GBS SNPs for varbvs. This suggests that, among the extra SNPs provided by GBS, varbvs managed to identify those that improved its prediction accuracy. Concerning the P value of 1.6x10−4 (3.5x10−3) when testing for no bias. The correlation is particularly promising for rrBLUP compared with varbvs, in agreement with the results obtained by cross-validation within the panel (Pearson correlation of 0.71 with rrBLUP and 0.61 with varbvs).We also used the 279 cultivars panel as a training set to predict MBW in a subset of a Syrah × Grenache progeny. With rrBLUP , this gave a Pearson correlation of 0.56 (0.35), an adjusted coefficient of regression of 0.28 (0.08), and a Finally, combining results from both genome-wide association and genomic prediction studies provides insight into the genetic architecture of the studied traits. In Overall, the median of this metric is positive only for response variables corresponding to biochemical traits (mostly polyphenols), suggesting they display a sparse genetic architecture. All other trait classes have a negative median metric, suggesting a dense genetic architecture. When taking into account the distribution of the metric, the classification in sparse or dense architecture is deemed more trustworthy when the quantile interval does not include 0, which is the case for phenological and morphological traits.13C, all response variables had a high median broad-sense heritability (around 0.7 and above), indicating a higher measurement quality, hence also contributing to increased trustworthiness in the suggested genetic architecture. Moreover, in the case of the biochemical response variables, the median number of reliable QTLs is higher than for the other trait classes, although there is a large variation. This is consistent with their genetic architecture deemed sparse, for which one expects to have QTLs with an effect large-enough to be found significant.Apart from the abiotic stress variable δAcquiring phenotypic data from which genotypic values can be deduced with sufficient accuracy is a major challenge, especially because a large panel is a prerequisite to provide enough statistical power to detect QTLs . AutomatAt the first stage of the analysis, we chose to include PRUW, the number of wooding shoots and vigor as explanatory factors in the global model, but neither flower sex nor berry color. Our rationale was that the former 3 are more influenced by the way the field trial is conducted than the latter 2, which are under a stronger genetic determinism (SNPs). The improvement obtained with the microarray-GBS set increased our trust in the genotyping and imputation procedures. Yet, PVESNPs did not equal 1 for all response variables. Several factors can underlie this discrepancy. First, empirical BLUPs of genotypic values are not fully accurate versions of the “true” genotypic values, as reflected by broad-sense heritability. Second, the microarray-GBS SNPs may not be in strong-enough LD with all “true” QTLs. The number of SNPs required might reach half a million in grapevine  the composition of the association panels, (2) the genotyping densities, (3) the phenotyping protocols, and (4) the statistical models. Reanalyzing these data sets was out of the scope of this work but could be done in the future depending on data availability.13C.For various traits, our association study identified many QTLs (Supplementary Tables 8 and 9) containing numerous genes (Supplementary Tables 10 and 11). As such, this large database is of interest per se for further investigations. We chose to focus here our discussion on a subset of traits, i.e. phenolic compounds, organic acids and δVvLAR2 . LAR was initially characterized as being able to catalyze the formation of catechin terminal units  orthologuous of AtWRKY55 and AtWRKY54/70  that could be involved in this reaction.Arabidopsis participates in oil synthesis in seed endoplasmic reticulum, where its overexpression triggers the activation of genes involved in glycolysis , and the long chain acyl coA synthase 2 (Vitvi03g00388). Oxylipins formed by allene oxide synthases are precursors of jasmonates  involvedChlorella  transcription factor.Relevant candidate genes were also found under novel QTLs for δThe accuracy of genomic prediction, assessed for the first time for such a large number of traits by cross-validation within a grapevine diversity panel, reached promising levels according to the median Pearson correlation (around 0.4), even though the coefficient of determination remains substantially lower  performed better than the sparse regression model (varbvs), which may be due to the essentially infinitesimal architecture of the trait despite a few larger QTL segregating for this trait in the progeny , hence making QTL detection easier. On the opposite, the other trait classes are more integrated, in the sense of resulting from multiple developmental and ecophysiological processes . MoreoveThis work demonstrated the feasibility of performing a GWAS in a perennial fruit crop such as grapevine for numerous, mostly complex traits related to various aspects of plant biology and breeding. A key ingredient for field trials remains the experimental design necessary to achieve high broad-sense heritability. We also provided dense genotyping data for further studies on the panel, although, given the low LD, an even higher number of SNPs would be advantageous. In terms of GWAS, we confirmed that a gain in power is possible when using multiple-SNP models. Overall, we identified new QTLs as well as promising genes under them, leading to mechanistic hypotheses yet to be tested. In terms of genomic prediction, we provided a distribution of prediction accuracy across many traits likely to have various genetic architectures. We confirmed the usage of the RRBLUP/GBLUP model assuming a dense architecture as a relevant default. Yet, we showed that a model assuming a sparse architecture can reach higher prediction accuracy for some traits, notably in the case of traits closer to the genetic variation. As such, our work provided important results for the contribution of genomic prediction to perennial crop species breeding.The authors declare that the experiments comply with the current laws of the country in which they were carried out.https://doi.org/10.15454/8DHKGL. The code that supports the findings of this study is openly available at the Data INRAE repository at https://doi.org/10.15454/8DHKGL. Many of the custom functions we used are available in a R package for reproducibility purposes, rutilstimflutre (The data that support the findings of this study are openly available, for sequences at the NCBI as BioProject PRJNA489354 and, for all the rest, at the Data INRAE repository at imflutre .https://doi.org/10.15454/8DHKGL.Supplemental material is available at


Salmonella reference laboratory in England, in terms of its impact on public health and whether these methods remain a fit for purpose test under UKAS ISO 15189. The evaluation of the genomic methods were mapped against the value of detecting microbiological clusters to support the investigation of food-borne outbreaks of Salmonella in England between 2012–2020. The analysis of WGS with both SNP- and allelic-based methods provided an unprecedented level of strain discrimination and detection of additional clusters when comparing to all of the previous typing methods. The robustness of the routine genomic sequencing at the reference laboratory ensured confidence in the microbiological identifications, even in large outbreaks with complex international food distribution networks. There was evidence that the phylogeny derived from the WGS data can be used to inform the provenance of strains and support discrimination between domestic and non-domestic transmission events. Further insight on the evolutionary context of the emerging pathogenic strains was enabled with a deep dive of the phylogenetic data, including the detection of nested clusters. The public availability of the WGS data linked to the clinical, epidemiological and environmental context of the sequenced strains has improved the trace-back investigations during outbreaks. The global expansion in the use of WGS-based typing in reference laboratories has shown that the WGS methods are a fit for purpose test in public health as it has ensured the rapid implementation of interventions to protect public health, informed risk assessment and has facilitated the management of national and international food-borne outbreaks of Salmonella.We aim to provide an evidence-based evaluation of whole genome sequence (WGS) methods, employed at the  Salmonella is a gram negative rod belonging to the family Enterobacteriales, and human infection is primarily caused by the Salmonella enterica subspecies enterica. Clinically speaking, S. enterica is further subdivided into the invasive typhoidal group, which causes enteric fever , often requiring treatment and can lead to further complications if not treated [Salmonella (NTS); although they generally cause a self-limiting gastroenteritis, they can cause invasive disease, such as bacteremia [Salmonella isolated from human clinical diagnostic samples in public health laboratories is mandatory under national legislation, and the United Kingdom Health Security Agency (UKHSA), Gastrointestinal Bacteria Reference Unit (GBRU) receives approximately 10,000 presumptive Salmonella isolates each year from diagnostic microbiology laboratories, private laboratories, industry and food, water and environmental (FW&E) laboratories for confirmation of the identity and typing [Salmonella are prospectively typed to detect any emerging threats and to put public control measures into place. treated ,2,3. Ent treated . The othcteremia , lesionscteremia  or osteocteremia . The repd typing ,8. NTS pSalmonella enterica was classified into serovars on the basis of the extensive diversity of the lipopolysaccharide (O) antigens and the flagellar protein (H) antigens [Salmonellae) and/or those that are most frequently detected  has beeeritidis . Therefoeritidis ,22,23 aneritidis ,25,26,27Whole genome sequencing (WGS) involves the sequencing of the whole genome, in which the data can then be used in downstream analysis, such as strain identification and subtyping, to assess the strain relatedness. In addition, WGS can be upscaled and used to streamline the laboratory process, reduce processing and turnaround times, increase the fine typing discriminatory power for emerging outbreaks, improve food safety and bridge one-health surveillance .Salmonella in the reference laboratory and the utilisation of these typing methods for the detection and investigation of outbreaks.In 2015, the Gastrointestinal Bacteria Reference Unit (GBRU) at the United Kingdom Health Security Agency  fully implemented WGS methods based on clonal inference via E-burst groups (eBG)  and SingSalmonella’, ‘sequencing’, ‘outbreak’ and ‘England’  guidelines . The relEngland’ . The litEngland’ .Salmonella risk assessments between the European Centre for Disease Prevention and Control (ECDC), the European Food Safety Authority (ECDC) and the UKHSA was performed. Relevant risk assessments available via ECDC (Risk assessments (europa.eu) were retrieved until the end of February 2020. The eligibility of the published risk assessments in this review was based primarily on WGS in which additional typing techniques may or may not have been used  and 24 risk assessments were included where cases were found in England , and sequenced using the Illumina HiSeq 2500 platform  in rapid run mode (2 × 100 bp reads). The quality of the raw FASTQ files is evaluated using an in-house program, qa_and_trim, which determines the metric yield of the sample  and trims the files with Trimmomatic [https://github.com/phe-bioinformatics/kmerid (accessed on 7 December 2022)) is used to compare the sequenced reads with the published genomes in order to identify the bacterial species and Salmonella subspecies [https://github.com/phe-bioinformatics/MOST (accessed on 7 December 2022)) [Salmonella serovar determination is predicted based on the Salmonella eBURST group (eBG) or Sequence Type (ST) [Since 2014, all mmomatic  (using tbspecies  and deter 2022))  associatr 2022)) . Each saype (ST)  and checype (ST) .Salmonella sequence data to NCBI BioProject PRJNA248792 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA248792 (accessed on 7 December 2022)) and Enterobase to enable other institutions to utilise the data [All of the FASTQ sequences are made publically available by routinely uploading the the data . The basSalmonella strain depending on the eBG  relative to a reference genome is recorded and can be used to quantify the genetic relatedness between isolates. This is undertaken by hierarchical single linkage clustering, which is performed on the pairwise SNP difference between all of the isolates at descending distance thresholds . The result of the clustering is a SNP profile, or SNP address, that is used to describe the population structure based on the clonal group membership, as indicated by the number at each level of the seven-number SNP address . The resSalmonella. EnteroBase performs daily scans of the SRA, via its Entrez APIs [Salmonella comprising of 3002 loci for the improved discrimination of the genotype as compared to 7-locus MLST. Hierarchical Clustering (HierCC) supports the analyses of population structures based on cgMLST at multiple levels of resolution [Salmonella, 13 HierCC levels are reported, ranging between HC0 (indistinguishable except for missing data) and HC2850 [EnteroBase is an integrated software environment that supports the identification of global population structures within several bacterial genera, including rez APIs  for noverez APIs . The corsolution . To idend HC2850  which enSalmonella to replace phenotypic methods. The definition of a Salmonella ‘lineage’ changes depending on the context of the study, and has been described in many ways; for example, in terms of serovar [Whole genome sequencing has laid the framework to define the clonality of  serovar ,55, sequ serovar ,56 or di serovar ,58,59,60Salmonella species and subspecies lineages were traditionally defined by biochemical properties, which was subjective to variable interpretation and added potential disparity in the subspecies definitions, routine typing, and the ability to detect novel subspecies. A large-scale analysis of the WGS data from the routine sequencing of clinical isolates was employed to define and characterise the Salmonella subspecies’ population structure. This evaluation study clearly demonstrated that the Salmonella species and subspecies were genetically distinct and the proposed species and subspecies structure was sufficiently biologically robust for routine application [Salmonella enterica and Salmonella bongori remain as separate species, (ii) S. enterica subspecies IIIa should be reclassified as the separate species, S. arizonae, (iii) S. enterica subspecies enterica (I), S. enterica subspecies salamae (II), S. enterica subspecies diaraizonae (IIIb), S. enterica subspecies houtenae (IV) and S. enterica subspecies indica (VI) remain as S. enterica subspecies, (iv) five novel subspecies (S. enterica subsp. londinensis (VII), S. enterica subsp. brasiliensis (VIII), S. enterica subsp. hibernicus (IX), S. enterica subsp. essexiensis (X), S. enterica subsp. reptilium (XI) are identified as additional S. enterica subspecies .lication ,61, whiclication . The defbspecies . The impandensis . FurtherSalmonella serovars. For example, when multiple serovars belonged to the same sequence type, or how to define novel Salmonella sequence types and serovars as they evolved. In order to address this issue, a large-scale validation study was undertaken to genomically define the serovars of Salmonella using Major Antigenic Clustering or ‘MAC typing’ [Salmonella enterica subspecies enterica to define the clusters in two stages: Multi Locus Sequence Typing followed by antigen prediction to provide a MAC type and reported as ST-Serovar. This genomic framework of defining serovar lineages was able to assign 99.96% of the isolates to a MAC type and is the current method used in the reference laboratory, enabling the complete withdrawal of phenotype serological testing [The lineages are then further defined in terms of E-Burst groups (EBG) and sequence types (ST) with the parallel reporting of the predicted antigenic formula or serovar nomenclature to retain the historical naming conventions and maintain the backwards compatibility with the information linked to these serovars. Although the use of Multi Locus Sequence Typing (MLST) as a replacement for serotyping had been validated  and impl typing’ . In this testing .Finally, the strain lineages are defined by the differences in the core genome, SNP typing or cgMLST , as descS. Newport, associated with the consumption of watermelon, in 2012. The sequences of the outbreak isolates from humans and food differed from each other, by 0–1 SNPs apart, compared to several thousand SNPs from the nearest non-outbreak strain [S. Typhimurium DT8, where an increase in this phage type associated with the consumption of mayonnaise made from raw eggs was reported in the summer of 2012, was performed [The first study in England to compare the PFGE with the WGS data was a prospective comparison of 24 isolates from a multi-country outbreak of k strain . In 2014erformed . Whole gerformed . The seqerformed .Salmonella at the UKHSA was fully implemented and was used prospectively to investigate a prolonged restaurant outbreak of S. Typhimurium, MLVA profile (3–14–9-0–0211) [By 2015, the routine sequencing of -0–0211) . Despite-0–0211) .These initial investigations showcased the highly discriminatory nature of WGS and provided preliminary evidence that isolates that fall within a five SNP single linked cluster (SLC) are more likely to be part of the same temporally linked outbreak and demonstrated that we were able to detect the same clusters, as defined by PFGE, PT and MLVA ,33,36.S. Enteritidis, which had been extensively characterised using SNP-based approaches [Salmonella outbreaks to be analysed and compared across laboratories and jurisdictions.Preliminary evaluation studies have highlighted that the use of SNP typing is a robust method for detecting outbreaks, but a key international method that is used for detecting clusters is core genome multi locus sequencing typing (cgMLST) ,64. Two proaches ,35. Thisproaches . They coS. Enteritidis outbreak associated with Polish eggs, but with various European institutes using different analysis workflows to identify the isolates potentially related to the outbreak [S. Enteritidis isolates, thus providing public-health researchers with comparable tools for the detection of infectious-disease outbreaks [A further study in 2019 reanalysed the data from another large European multi-country outbreak . The objutbreaks . Hence, Salmonella belonging to the most commonly identified serotypes in England and Wales between April and August 2014. identified 32 clusters (566 of the 1445 isolates into 32 clusters of five or more) with a statistically significant epidemiological link for 17 clusters, whereas. during the same time frame, only one cluster was detected using the traditional methods [A study by Waldram et al., analysing WGS of isolates of S. Enteritidis and S. Typhimurium infection during the first year of implementation (2014–2015) and determined the distribution of these clusters by the UKHSA operational levels [S. Typhimurium and S. Enteritidis, in which the majority had common phage types. Even when PFGE and MLVA could be used to provide further resolution, they were mainly used reactively to suspected Salmonella outbreaks, and not on a routine scale [A follow up study, by Mook et al., was undertaken in order to summarise how the WGS data was used to inform the implementation and development of a national gastrointestinal infection surveillance system . This stl levels . A constl levels . Previoune scale , whereasS. Enteritidis is the most common serovar in England and Wales for both sporadic and outbreak-related cases [S. Enteritidis clusters that were further investigated [It is well known that ed cases . As desced cases ,37,43, aed cases  or due ted cases  and therstigated ,37,43 , now superseded by EpiPulse  and/or TS. Newport in a ready-to-eat watermelon slice from a supermarket retailer in England and communicated via RASFF [S. enterica subspecies enterica serotype  associated with sesame seeds [Food distribution is a global franchise; therefore, a large proportion of non-domestic outbreaks in England are due to imported food . Prospecia RASFF . Subsequia RASFF . In someme seeds . In respme seeds , and witS. Enteritidis phage type 14b associated with the consumption of eggs from a German producer [S. Enteritidis, mentioned above, in relation to Spanish and English cases being exposed to a common source of Salmonella-contaminated chicken eggs [As described above, preliminary evaluation studies for the detection of clusters with respect to the epidemiological data have shown that 0–5 SNP level thresholds work well in detecting point source outbreaks and are more likely to have an obvious epidemiological link ,41, as sproducer , which fproducer . In thisproducer . Howeverken eggs , a ten SBayesian approaches such as BEAST have proven useful for producing dated phylogeny to estimate the introduction of specific strains within a population and the measure of population size in case of intervention methods. However, these methods are greedy in terms of computing capacity, they are dependent on the size of the population to assess and request fine prior information (time sampling) and good sequencing quality, which makes them difficult to apply in real time. Due to the complexity of implementation, non-Bayesians methods, and specifically maximum likelihood phylogeny based on SNP differences, have been the implemented choice for GRBU to complemented clusters detected by the SNP address.S. Adjame outbreak that had been detected in a local area in 2017 [S. Adjame outbreak strains produced a heterogeneous phylogeny showing multiple microbiological sub-clusters that were, in general, temporally and geographically linked, with some strains being genetically distinct (up to 1273 SNPs different). It was concluded that the contamination from an endemic source with a mixed population of strains could explain the heterogeneous genealogy, consistent with the hypothesized source of the contamination being imported herbs or spices from South Asia. This study again highlights the importance of integrated epidemiology when interpretating the WGS data and that WGS whole genome sequencing for outbreak detection and the use of SNP- or allele-based clustering methods as part of the case definition may not always be appropriate in heterogeneous outbreaks or rare/unusual serovars involving small numbers of cases [The use of employing single linkage SNP or allelic threshold for cluster detection for outbreak investigations helps to simplify the process, but it is important that we are dynamic with defining the threshold. Otherwise, this may over-simplify the definition and may lead to outbreaks being missed  or clusters being defined as outbreaks when they are not , hence the importance of an epidemiological investigation. In some outbreaks, the use of SNP thresholds may not be appropriate at all, as was the case with a rare  in 2017 . The seqof cases .Salmonella lineages; (ii) robust comparative SNP and cgMLST methodologies, enabling international collaboration; (iii) an unprecedented level of strain discrimination, including the detection of nested clusters; (iv) improved case ascertainment; (v) an insight into the evolutionary context for emerging pathogenic strains; and (vi) inferring the geographical origins from the phylogenetic signal. Collecting epidemiological data is essential when interpretating phylogenetic clusters, and defining clusters by the number of SNP or allelic differences between isolates provides information regarding the strain relatedness. Isolates originating from the same source population with less time to diverge form a common ancestor and are more likely to have fewer nucleotide or allelic differences between a pair of isolates. However, when analysing a source population, the amount of diversity is dependent on the duration of the infection, the effective size of the population and the time colonised in a particular niche. Hence, the definition of the absolute thresholds of nucleotide or allelic differences for exclusion and inclusion within an outbreak can vary, and the epidemiological information should always be used, where possible, to inform the outbreak definition. With the expansion of WGS-based typing analysis on a global scale, we will continue to see an improvement of trace-back investigations during food-borne outbreaks. This will ensure the rapid implementation of interventions to protect public health. The utility of the publicly available WGS databases linked to the clinical, epidemiological and environmental context of each strain cannot be underestimated for the purpose of the risk assessment and management of food-borne outbreaks. This review provides an evidence base that the genomic methods at the GBRU continue to be a fit for purpose test, not just for the initial report of typing data, but for further downstream analysis in a public health setting.This perspective provides an overview of the use of reference microbiology WGS data during food-borne outbreak investigations in England, demonstrating the key advantages, including: (i) establishing genomic approaches in the routine identification and detection of novel


DiKTa. The emission of the charged derivatives is red-shifted compared to the parent compound. For instance, DiKTa-OBuIm emits in the green  while DiKTa-DPA-OBuIm emits in the red . In 1 wt % mCP films, both emitters showed good photoluminescence quantum yields of 71% and 61%, and delayed lifetimes of 316.6 μs and 241.7 μs, respectively, for DiKTa-OBuIm and DiKTa-DPA-OBuIm, leading to reverse intersystem crossing rates of 2.85 × 103 s−1 and 3.04 × 103 s−1. Light-emitting electrochemical cells were prepared using both DiKTa-OBuIm and DiKTa-DPA-OBuIm as active emitters showing green (λmax = 534 nm) and red (λmax = 656 nm) emission, respectively.We designed and synthesized two new ionic thermally activated delayed fluorescent (TADF) emitters that are charged analogues of a known multiresonant TADF (MR-TADF) compound,  Light-emitting electrochemical cells (LEECs) are thin film light-emitting devices typically consisting of an emissive layer containing ionic species that facilitate charge transport and an emissive semiconductor material. The emissive layer is sandwiched between two air-stable electrodes . Upon apTwo families of widely investigated emitters for LEECs are ionic transition metal complexes (iTMCs) –10 and cI (2 in  rep repDiKTaompounds . The Stoat 75 nm 61 cm−1. in MeCN) –36. The  in MeCN . The S1 DiKTa-OBuIm and DiKTa-DPA-OBuIm, respectively. The emission spectrum of DiKTa-OBuIm is slightly blue-shifted and narrower than that in MeCN, which is expected due to the higher polarity of the solvent than mCP. Surprisingly, for DiKTa-DPA-OBuIm the emission is red-shifted by 14 nm, and with negligible change in the FWHM. This suggests that the conformation of the emitter in the solid state is slightly more conjugated than that in solution or that there are specific host–guest interactions with the DPA unit that perturbs the energy of the excited state. The emission is broader than that of a structurally similar emitter, QAD-mTDPA, a derivative of DiKTa containing two DPA substituents, reported by Zhang et al. [QAD-mTDPA  is shown in DiKTa  in the same host [PL values in the mCP film at 71% and 61% under nitrogen, and these reduced to 57% and 53% in air for DiKTa-OBuIm and DiKTa-DPA-OBuIm, respectively. As neat thin films, the emission for both compounds are red-shifted and significantly quenched  [1 and T1 levels were measured from the onsets of fluorescence and phosphorescence spectra in the 1 wt % doped mCP film at 77 K  [EST values are smaller than those computationally predicted . However, the trend of decreasing ΔEST is in line to the findings from DFT. The temperature dependent time-resolved PL decays in the 1 wt % doped mCP films are presented in DiKTa  [Cz-DiKTa , DMAC-DiKTa , and QAD-mTDPA  in DiKTa-OBuIm and DiKTa-DPA-OBuIm are long at 317 μs and 242 μs, respectively. RISC rate constants, kRISC, were calculated for both emitters, which are 2.85 × 103 s−1 and 3.04 × 103 s−1, respectively for DiKTa-OBuIm and DiKTa-DPA-OBuIm, compared to that of DiKTa (4.6 × 104 s−1) in toluene [The thin film PL behavior of both emitters was then assessed in 1 wt % doped film in 1,3-di-9-carbazolylbenzene (mCP) . At thisg et al.  The struame host . Both emnder N2) . The S1  toluene .DiKTa-OBuIm and DiKTa-DPA-OBuIm as emitters. The device stack was the following: ITO/PEDOT:PSS/emitter/Al :poly(styrenesulfonate)). The PEDOT:PSS and the emitter layers were prepared from solution and the device was finished with an evaporated Al top contact. Details of the LEEC fabrication can be found in the General Methods section of PL LEEC devices using DiKTa-OBuIm and DiKTa-DPA-OBuIm as 1 wt % doped films in mCP as the emitter layer were prepared. The devices showed no turn-on, both in lifetime measurements and in current density and luminance versus voltage sweeps (JVL) up to 8 V. Most likely the low content of ionic species in the neutral matrix hindered the required ionic transport for LEEC operation. To solve this, we fabricated devices adding an ionic liquid (lithium hexafluorophosphate (LiPF6) or 1-butyl-3-methylimidazolium hexafluorophosphate (BMIM:PF6) in a 4 to 1 molar ratio) and, in some cases, an electrolyte matrix (PEO (polyethylene oxide)), to improve the ionic mobility on the active film [DiKTa-OBuIm and DiKTa-DPA-OBuIm were directly used as active layers. Non-doped small molecule films have shown recently promising results in LEEC devices [DiKTa-DPA-OBuIm in DiKTa-OBuIm was also used, the latter acting as a host matrix for the former. The electroluminescence (EL) of the three device stacks is shown in DiKTa-OBuIm and DiKTa-DPA-OBuIm occurs at λEL of 534 and 656 nm, respectively. Both neat-film EL spectra are red-shifted from the solution state and the 1 wt % in mCP film PL spectra. The origin of this red shift could be ascribed to the presence of emissive aggregates in the emissive layer [EL at 586 nm, between the emission of the neat films. JVL characterization (from −2 to 8 V) was carried out on the three stacks  values; however, they are also highly affected by the luminance levels, giving as a result very low efficiencies (<0.01%).LEECs were fabricated using ive film ,41. Howe devices . As bothve layer . Interese stacks –d. As itDiKTa. Our MR-TADF green emitter, DiKTa-OBuIm exhibited efficient green luminescence and TADF in 1 wt % mCP film . This emitter represents a rare example of an ionic MR-TADF emitter for LEEC applications. The red emitter, DiKTa-DPA-OBuIm, was obtained by coupling a methoxy-modified diphenylamine unit onto the DiKTa fragment. Addition of a donor unit red-shifted the emission to red region with TADF . Different strategies were explored to prepare LEECs based on DiKTa-OBuIm and DiKTa-DPA-OBuIm as emitters. The devices showed green and red emission, respectively.Two new ionic TADF emitters were designed and synthesized for LEECs application using a known MR-TADF emitter The research data supporting this publication can be accessed at https://doi.org/10.17630/6ef45b8f-579d-4075-891e-595516c56e47.File 11H NMR and 13C NMR spectra, GC–MS, and HRMS; supplementary computational data and coordinates; additional photophysical.