Varroa destructor were brought over. The chip also includes SNPs associated with each of three major breeding objectives—honey yield, gentleness, and Varroa resistance. We validated the chip and make recommendations for its use by determining error rates in repeat genotypings, examining the genotyping performance of different tissues, and by testing how well different sample types represent the queen's genotype. The latter is a key test because it is highly beneficial to be able to determine the queen's genotype by nonlethal means. The array is now publicly available and we suggest it will be a useful tool in genomic selection and honey bee breeding, as well as for GWAS of different traits, and for population genomic, adaptation, and conservation questions.High‐throughput high‐density genotyping arrays continue to be a fast, accurate, and cost‐effective method for genotyping thousands of polymorphisms in high numbers of individuals. Here, we have developed a new high‐density SNP genotyping array  for honey bees, one of the most ecologically and economically important pollinators worldwide. SNPs were detected by conducting whole‐genome resequencing of 61 honey bee drones  from throughout Europe. Selection of SNPs for the chip was done in multiple steps using several criteria. The majority of SNPs were selected based on their location within known candidate regions or genes underlying a range of honey bee traits, including hygienic behavior against pathogens, foraging, and subspecies. Additionally, markers from a GWAS of hygienic behavior against the major honey bee parasite  Apis mellifera) is one of the most ecologically and economically important pollinators worldwide, yet to date a high‐throughput, high‐density genotyping array has not been available for the bees. Here, we developed the first high‐density chip for honey bees with probes for over 100,000 SNPs. SNPs were detected by conducting whole genome resequencing of bees from throughout Europe, and the selection of SNPs for the chip was done in multiple steps using several criteria. Photo by Clement Tang.The European honey bee ( It is the most frequent floral visitor in natural habitats worldwide  arrays, or “SNP chips”  populations, and this chip has been used to study the genetic architecture of exploration behavior     was recently developed for performing genomic studies of great tit , the international A. mellifera mellifera breeding program , and the protected A. mellifera carnica conservation region Slovenia (12 samples)  (Eurofins Ebersberg). Genomic libraries were prepared at Eurofins Ebersberg, using the NEBNext Ultra DNA Library Prep Kit E7370 (New England Biolabs). Pools of three individually barcoded samples were sequenced on an Illumina HiSeq 2500 platform at Eurofins. Sequencing was paired‐end, 2 × 100 bp, using HiSeq Flow Cell v3 and TruSeq SBS Kit v3. Details of the coverage for each drone sample are presented in Table To identify SNPs to include on the chip whole‐genome resequencing was performed on 61 unrelated honey bee drones. Samples were sourced from breeders of the international s) Table . Thus, 4Ruttner, . DNA was2.2SD = 26 875 640.2) were mapped to the reference genome Amel_4.5 (INSDC assembly GCA_000002195.1). We identified and marked PCR duplicates using Picard Tools MarkDuplicates . Only reads with a minimum alignment quality of 1  were used for variant detection, meaning all reads with ambiguous alignments were excluded. Variants were called using VarScan  was determined using a custom Python script. The list of candidate regions was compiled through searching the existing literature, using keyword searches of genes in the NCBI database, selecting genes in signaling, and immune pathways , and including all putative genes reported in the Beebase project  and distance criteria (see below) where SNPs within coding regions and with greater estimated regulatory effects were selected at higher density. Different distance criteria (in addition to genomic region criteria) were tested, and distance criteria predicted to enable a final SNP chip density of ~100,000 SNPs were implemented. The predicted effects of all SNPs were determined using SnpEff (version 4.3)  was estimated for all detected SNPs. For each SNP and each pair of bases, a significant association with subspecies was estimated using a Varroa resistance) was also estimated. This was achieved using 37 of the drones that were from known breeding queens of the A. mellifera carnica breeding program and were therefore associated with the breeding values of the 2015' breeding value estimation in www.beebreed.eu. A t test was performed to determine whether the average breeding values differed significantly between bases . No correction for false positives was applied in this test because as this was a filtering step, emphasis was on sensitivity not specificity. The distance allowed between SNPs associated with breeding objectives was 100 bp.The association between all detected SNPs and three breeding objectives . That is, out of any linked SNPs, only one SNP was selected. Loci (SNPs) were designated as belonging to the same cluster if their vectors of bases  for all 61 sequenced drones were identical. It was assumed that the drones were representative of the population, and that different loci in one cluster are redundant (as markers), and therefore the SNPs, except one within a cluster, are obsolete . Single SNPs from each cluster were chosen by selecting the first SNP in the cluster that had a distance of >300 bp to already selected neighboring SNPs. As above, the 300 bp distance was chosen under the objective of developing a final SNP chip with approximately 100,000 SNPs.2.3.4https://dashboard.my.illumina.com) and design scores were calculated. Design scores represent the probability that the probe will be successful in a genotyping assay. Lower probability scores can be caused by the possibility of secondary structures forming, or if a marker is in a duplicated or repetitive region. SNPs with a convert design score >0.72 were retained for inclusion on the chip. This threshold is even more stringent than those used in the design of HD Axiom chips for chicken, catfish, water buffalo, and great tits where thresholds of 0.20, 0.50, 0.60, and 0.69 were used, respectively . Additionally, we tested how well different sample types from a colony (queen cells and different numbers of drone eggs) represent the queen's genotype using empirical data, that is, where the queen's genotype is inferred from her haploid drone offspring (eggs). Discrepancies in genotype representation of the queen can arise due to queen cells potentially being contaminated by worker DNA, as for instance, workers enter the cells to feed the developing queens, and because drones are haploid successfully determining the queen's genotype using drone samples relies on using a sufficient sample size of drones.2.4.1Apis mellifera carnica queens, queen cells, drone pupae, larvae or eggs were collected by selected honey bee breeders in Germany, Austria, the Netherlands, and Switzerland (beebreed.eu) and sent to the authors at the Institute for Bee Research, Hohen Neuendorf. A large resource of samples was collected in this way, and their DNA was extracted and genotyped (as per the methods described below). In addition to forming the basis for setting the call rate thresholds (see below), these data will be utilized in future analyses. For all other analyses conducted here, subsets of the genotyping data were used.2.4.2https://doi.org/10.5061/dryad.gxd2547gp).Honey bee samples were sent to Eurofins Ebersberg, for DNA extraction. DNA was extracted from 3,272 samples, including individual and colony replicates. Samples included replicates of the same individual where different tissues were used for DNA extraction that is, queen and queen cell (see below); and pairs of individuals from the same colony with known relationships, for example, queen–daughter. A summary of the individual unique colonies from which samples were taken is available on Dryad (Table DNA was extracted from queen cells of recently emerged queens using a modified version of the DNeasy Blood and Tissue Kit (Qiagen), for nails, hair or feathers). Frozen queen cells were incubated for 15 min in xylene, washed in ethanol (99%), dried at 37°C, cut into small pieces, and then the manufacturer instructions were followed  to optimize the custom cluster file. The cluster positions were then manually checked using the GenomeStudio software, and 3,637 markers were removed due to bad performance based on manual inspection of the clusters. The 5,000 lowest scoring (GenTrain score) markers were manually adjusted. Cluster positions were then exported, and these positions were used in all subsequent genotyping analyses.Genotyping quality thresholds were set by examining markers (SNP) and samples carried forward under different SNP call rate thresholds . A balance was found between retaining the highest number of SNP markers, while also retaining a high number of successfully genotyped samples. To set the quality control thresholds, all genotyped samples were included in the data examination . First, we set a marker quality threshold of call rate >90%, meaning all markers that were called in <90% of samples were removed. Second, we set a sample quality threshold of call rate >90%, meaning all samples where <90% of all remaining markers were called were removed.2.4.4p‐values of chi‐square tests), and call rates were calculated using a set of quality‐controlled samples . All 103,270 SNPs selected for the chip were considered for these analyses, and included 6,386 SNPs of unplaced contig origin, as well as 580 unmapped SNPs. MAF and HWE for each locus were calculated using the GenomeStudio 2.0 software according to the GenomeStudio Framework User Guide (Illumina) with default parameters.For each SNP that passed the marker quality threshold, the minor allele frequency (MAF), Hardy–Weinberg equilibrium (HWE)   enabled an estimation of the number of inconsistent genotype calls between different genotyping attempts of the same bee. This was achieved by calculating the average of different calls between replicates over all SNPs .2.4.6To determine the SNP chip genotyping success rate of different tissue types, we measured the average genotyping call rate  of all genotyped samples  and these samples  (High‐density honey bee SNP chip: HDHB‐chip), whole‐genome resequencing of 61 unrelated honey bee drones from throughout Europe was completed. SNP selection was subsequently carried out in multiple steps using several criteria  was implemented. A summary of the SNPs selected under the different selection criteria is provided in Table https://www.eurofinsgenomics.eu/en/eurofins‐genomics/service‐corner/).SNPs were selected by searching the existing literature, conducting keyword searches of the NCBI database, and selecting genes in key signaling and immune pathways. Genomic region and distance criteria were also implemented and SNPs associated with 3.23.2.1p < .05). All tissues provide average genotyping call rates above 90%, apart from queen cells where the average call rate (83%) is below 90%  over all SNPs was 0.23%. Additionally, we find that as expected, DNA extracted from queen flight muscle tissue enables the highest average call rate (98%) of all tissue types tested, and is significantly higher than the average call rate obtained from queen cell (83%), pupae (94%), larvae (95%), and egg tissues (96%) ; c) and MAF < 5%. A large proportion of SNPs (41%) were found to have a MAF < 5%. Under a milder threshold of a MAF 1%, and when call rate and HWE estimates are controlled for as above, we find that 27% of SNPs would need to be removed from analyses, and that the resulting average MAF is 14.8%.The newly constructed SNP chip was used to evaluate call rate, MAF and HWE, in 2,734 samples from unique colonies Table . An aver3.2.3F = 6.11, p = .55). The queen cells and all drone egg pools tested  represent the queen's genotype well overall  and test error rates of the chip, the performance of different tissues in genotyping, and how well alternative samples represent the queen's genotype. We show that most SNPs on the chip could be genotyped accurately in repeat genotypings, with an average error rate of <1%. Similar and higher density chips are commonly used in agriculturally important species  (17.6%) is low compared with estimates in other studies utilizing SNP chips to study agriculturally important species such as goats (MAF 25%)  in a GWAS   are also included on the chip, and genomic regions related to subspecies were also mined from the literature and SNPs within these regions were included on the chip. We focused on differentiating these two particular subspecies because A. mellifera carnica is the most widely employed subspecies in Eastern Central Europe, and A. mellifera mellifera is native to this region and still present  used in the beebreed database by breeders across Europe, were also included on the chip. The latter results should be taken as preliminary as the sample size for the analysis for selecting these SNPs was necessarily limited to 37 of the whole‐genome resequenced drones. However, the chip will likely also be particularly useful in bee breeding programs and genomic selection for traits of interest in honey bee breeding in future. We note that because we selected specific SNPs for the chip, the SNPs are not evenly distributed across chromosomes. However, biases in the genomic distribution of SNPs associated with different traits will be interesting to investigate in future studies.The majority of SNPs selected for the honey bee chip fall within an extensive list of candidate genomic regions from previous research, including genes suggested to be important in general honey bee biology and/or associated with a range of honey bee traits, and genes in key signaling and immune pathways. The chip includes 498 SNPs found to be significantly associated with the trait of hygienic behavior against the major honey bee parasite, 4.1As expected, queen flight muscle enabled the highest average call rate, dependent on higher quality, and quantity of DNA yield (DNA was measured using gel electrophoresis before genotyping). Therefore, the best results will be achieved for population genomics and ecology research questions utilizing flight muscle tissue of honey bee samples. However, important for honey bee breeding and genomic selection questions, we find that although drone eggs, pupae, and larvae enable significantly lower average call rates, they are useful alternative tissue types for DNA extraction and genotyping as they avoid sacrificing the queen and the average call rates for these tissues are above the 90% threshold determined.Drone eggs (when pools of 30 eggs and higher are used) are also found to be a good alternative to sacrificing the queen while still accurately representing the queen's genotype. Our comparison of the percentage of different genotype calls shows pools of 30 or 120 drone eggs represent the queen's genotype accurately. Thus, overall, we find that using a minimum pool of 30 drone eggs is a reasonable alternative to sacrificing the queen, while still accurately representing the queen's genotype. Further, using 30 drone eggs as opposed to 120 facilitates simpler and more efficient methods because sampling and extracting DNA from a lower number of drone eggs is easier and less time‐consuming. Of note is that when a call rate above 90% is achieved for genotyping queen cell DNA, this tissue provides the most accurate representation of the queen's genotype . Using DNA from queen cells of newly emerged queens is the most accurate possible alternative to sacrificing the queen, and allows immediate breeding value estimation for selection of queens in breeding studies and programs. We suggest that the continued rapid optimization of extracting good‐quality DNA from small amounts of tissue means queen cells are also a good alternative to sacrificing the queen.High‐density SNP chips continue to offer a robust and relatively straightforward means of investigating different research questions, from genomic selection and breeding topics, to a range of evolutionary genomics and adaptation questions. Eventually whole‐genome sequencing will be used in place of SNP chips, but SNP chips are likely to be used for several more years due to the relatively simple laboratory and bioinformatics protocols and pipelines required, the more affordable costs for investigating population level numbers of individuals, and population genetic questions. We suggest that the methods of development and validation presented here for the honey bee SNP chip will be useful to other researchers developing chips for their study organisms, and that the chip will be a useful tool in a diversity of bee research studies and applications. The honey bee chip we describe is available to other users at Eurofins Genomics.We declare all authors have no conflicts of interest.Julia C. Jones: Conceptualization ; formal analysis ; investigation ; supervision (supporting); writing‐original draft (lead); writing‐review & editing (lead). Zhipei G. Du: Conceptualization ; data curation ; formal analysis ; investigation ; writing‐original draft (supporting); writing‐review & editing (supporting). Richard Bernstein: Data curation ; formal analysis ; investigation ; writing‐original draft (supporting); writing‐review & editing (supporting). Monique Meyer: Data curation ; investigation . Andreas Hoppe: Formal analysis (supporting); writing‐original draft (supporting); writing‐review & editing (supporting). Elmar Schilling: Data curation ; formal analysis . Martin Ableitner: Data curation ; formal analysis . Katrin Juling: Data curation ; formal analysis . Regina Dick: Supervision ; writing‐original draft (supporting); writing‐review & editing (supporting). Anja S. Strauss: Conceptualization ; data curation ; formal analysis ; investigation ; supervision (supporting); writing‐original draft (supporting); writing‐review & editing (supporting). Kaspar Bienefeld: Conceptualization (lead); funding acquisition (lead); supervision (lead); writing‐original draft (supporting); writing‐review & editing (supporting).Appendix S1Click here for additional data file.


Rabbit meat has been widely consumed in China and is considered as an ideal food source due to its high protein, low fat, low cholesterol and low sodium contents. The growth rate, carcass characteristics and meat quality are considered economically important traits in the rabbit industry. Genomic selection (GS) could facilitate genetic selection for important economic traits, however, the lack of molecular markers for these traits limits the application of GS in rabbits. Genome-wide association study (GWAS) has the potential to comprehensively identify trait-associated molecular markers and has been applied in animal and plant research. In this study, GWAS was used to examine growth, carcass and meat quality traits of meat rabbits based on specific-locus amplified fragment sequencing (SLAF-seq) technology to identify significantly associated SNPs and functional genes, to be used as a basis for prompting the application of GS in rabbits.® restriction enzymes. After quality control, a subset of 317,503 annotated single-nucleotide polymorphisms (SNPs) was retained for subsequent analysis. A total of 28, 81 and 10 SNPs for growth, carcass and meat quality traits, respectively, were identified based on genome-wide significance (p < 3.16 × 10−7). Additionally, 16, 71 and 9 candidate genes were identified within 100 kb upstream or downstream of these SNPs. Further analysis is required to determine the biological roles of these candidate genes in determining rabbit growth, carcass traits and meat quality.Growth, carcass characteristics and meat quality are the most important traits used in the rabbit industry. Identification of the candidate markers and genes significantly associated with these traits will be beneficial in rabbit breeding. In this study, we enrolled 465 rabbits, including 16 male Californian rabbits and 17 female Kangda5 line rabbits as the parental generation, along with their offspring , in a genome-wide association study (GWAS) based on SLAF-seq technology. Bodyweight at 35, 42, 49, 56, 63 and 70 d was recorded for growth traits; and slaughter liveweight (84 d) and dressing out percentage were measured as carcass traits; and cooking loss and drip loss were measured as meat quality traits. A total of 5,223,720 SLAF markers were obtained by digesting the rabbit genome using RsaI + EcoRV-HF MC4R) MC4R) [FT1D1 [GHR  genes ar1D1 [GHR ,6. For cGenome-wide association study (GWAS)  represenDespite the great success of GWAS in animal science , includiAll animal experiments were approved by the Institutional Animal Care and Use Committee of Sichuan Agricultural University (Permit Number: No. DKY-B20141401). The experiments were performed in accordance with the institutional regulations (no public availability). 3/h/kg), temperature (18–25 °C) and humidity (20%–70%). A 12 h period of artificial normal environmental lighting was provided. All rabbits of the F1 generation were weaned at the age of 35 days.For the experiment, 53 Californian female rabbits and 22 Kangda5 line rabbits, a commercial strain of meat rabbit from Qingdao Kangda Foodstuffs Co., Ltd., were selected as the parental generation to generate crossbred F1 offspring. Rabbits with the same day of mating (2 May 2017) and the same day of delivery (2 June 2017), including 16 male Californian rabbits and 17 female Kangda5 line rabbits, were enrolled in the study. All rabbits were fed a pellet diet  and housed in cages of 50 cm × 40 cm × 40 cm in size. During day 35–70, two rabbits were placed into one cage block and one rabbit was raised in a separate cage block from 70 days until slaughter. An environmental conditioning control system was used when the conditions were out of the normal range with respect to the ventilation  and drip loss (DL). The drip loss was quantified by the method of Reference . The cooThe rabbit genome (GenBank assembly accession: GCA_000003625.1) was used as the reference genome to predict and characterize the presence of putative restriction endonuclease sites. Genome assembly revealed a genome size of 2.74 Gbp and a GC (guanine-cytosine) content of 43.75%. Restriction endonuclease profiles of the reference genome were determined using self-developed software . The moshttp://soap.genomics.org.cn) [Genomic DNA was extracted from whole blood samples using the phenol–chloroform method and was digested with the selected optimal enzymes. A single “A” nucleotide was added to the 3′ end of the digested fragment (SLAF tag). Dual-index  sequenci.org.cn) . The seq® digestion was evaluated using a positive control sample (Oryza sativa ssp. japonica). SLAF tags were mapped to the reference genome using BWA software (http://bio-bwa.sourceforge.net/) [Efficiency of RsaI EcoRV-HFge.net/)  and SNPsge.net/)  and Samtge.net/) . SNPs idhttp://www.cog-genomics.org/plink/2.0/) (--glm) [The population structure of the rabbits was evaluated using the ADMIXTURE program . The ass (--glm) ,31. The p-value less than the 10%genome-wide Bonferroni-corrected threshold were annotated using ANNOVAR software [Furthermore, Bonferroni correction was applied to determine significance at the genome-wide level. SNPs with an adjusted software . Genome-Bodyweight at six time points, two carcass traits and two meat quality traits were measured. The descriptive statistics for the measured phenotypic traits are listed in A total of 3,127,736,005 paired-end reads was generated in this study. Almost 97.07% of the raw reads were successfully mapped to the rabbit genome. The effectiveness of digestion is the key indicator of a successful SLAF-seq. In this study, 96.05% of the sequences were digested normally, which is an ideal digestion rate. A total of 5,223,720 SLAF tags was identified across the whole genome, with sequencing to a 16.57× average depth. A total of 2,498,314 polymorphic SLAF tags was identified and 8,838,009 SNPs were selected for genome-wide association analysis based on the selection criteria . The density distribution of SNPs was calculated throughout the rabbit genome and is shown in The population structure of the rabbits was analyzed using the ADMIXTURE program. The population was first divided into 1–20 subgroups (K). The cross-validation (CV) error of populations was calculated under different k numbers. The number of the Kvalue with the lowest CV is the most suitable. Our analysis revealed that a K value of 18 was most optimal. The samples were divided into 18 subgroups . p value calculated by the association study fit the expected ones, which suggests that the population stratification was well-corrected and the association analysis using GLM was reliable. The Q-Q plot of each trait is shown following the Manhattan plot of the corresponding trait.As population stratification might affect GWAS, quantile–quantile (Q-Q) plots of all traits were drawn. The observed p value and the annotation of all SNPs linked with each trait.GWAS based on SLAF-seq were successfully used to identify SNPs associated with important economic traits in chickens and pigs ,19. In tFGD4 . This gene encodes a protein involved in the regulation of actin cytoskeleton and cell shape [DNM1L (dynamin 1 like) gene, located within 100 kb of an SNP significantly associated with BW49 and BW56, encodes a member of the dynamin GTPase superfamily, which is involved in regulating mitochondrial metabolism. The gene expression of DNM1L is reported to increase in skeletal muscle following exercise [Based on the GLM and Bonferroni correction, 28 SNPs exhibited genome-wide association with the bodyweight trait at 35, 49, 56, 63 and 70 d  and 16 gll shape , which iexercise . We thusOne SNP at position 61,268,427 of Chr9 was identified to be associated with bodyweight at day 49, 56 and 63. The SNP at position 8,681,239 in Chr8 was identified to be associated with bodyweight at day 49 and 56. SNPs at 83,815,488 and 83,815,516 of Chr13 were found to be associated with bodyweight at day 56 and 63. However, no genes were found within 100 kb upstream or downstream of these SNPs. We speculate these SNPs to have an association at two or more time points of bodyweight and be important for the growth of the rabbit and are potential targets for further molecular breeding research.LIPA  as the nearest gene, which encodes lipase A, the lysosomal acid lipase (cholesterol ester hydrolase). This enzyme catalyzes the hydrolysis of cholesteryl esters and triglycerides in the lysosome [A total of 81 SNPs was found to be associated with two carcass traits and 71 genes were found near those SNPs. Manhattan plots for the growth traits are shown in lysosome . In addiOSBPL11 (oxysterol-binding protein like 11) encodes a member of the oxysterol-binding protein (OSBP) family, a group of intracellular lipid receptors [ADAM7  on chromosome 2 encodes a member of the ADAMs family of zinc proteases and SLC39A6 (solute carrier family 39 member 6) on chromosome 9 encodes a protein with structural characteristics of zinc transporters [For the meat quality phenotype, 15 SNPs were associated with cooking loss and drip loss. Manhattan plots for the growth traits are shown in eceptors . Ten SNPsporters . These fFGD4 and DNM1L genes, linked with BW49 and BW56 in our study, are reported to be involved in cell growth and mitochondrial metabolism. ADAM7 on chromosome 2 and SLC39A6 on chromosome 9 may be associated with drip loss due to its effect on zinc homeostasis that functions in determining meat quality. These findings provide novel insights into the genetic basis of growth, carcass and meat quality traits in rabbits and may contribute to the application of GS in rabbits. However, because only a draft version of the rabbit reference genome is available, there are numerous sequence gaps and missing annotations. These gaps could influence the accurate estimation of LD patterns. Moreover, the missing annotations would inevitably lead to false-negative results when attempting to find causative genes or variants. In addition, relatively high numbers of false positives were observed in the GWAS study. Therefore, a future validation study should be conducted using an independent population.In this study, we performed GWAS of 432 F1 meat rabbits. All samples were genotyped using SLAF-seq technology. The GWAS of 10 economic traits revealed 111 significantly associated SNPs. A total of 98 putative candidate genes were located within 100 kb of significantly associated SNPs. Among them,


SKOR2, SMAD2, VAV3, and NTNG1. Additionally, the functional genes, SLC25A24, PRMT6 and STXBP3, are also located near the fine-mapping region. These results suggest that these candidate genes may have contribute substantially to the birth weight of pigs.Birth weight of pigs is an important economic factor in the livestock industry. The identification of the genes and variants that underlie birth weight is of great importance. In this study, we integrated two genotyping methods, single nucleotide polymorphism (SNP) chip analysis and restriction site associated DNA sequencing (RAD-seq) to genotype genome-wide SNPs. In total, 45,175 and 139,634 SNPs were detected with the SNP chip and RAD-seq, respectively. The genome-wide association study (GWAS) of the combined SNP panels identified two significant loci located at chr1: 97,745,041 and chr4: 112,031,589, that explained 6.36% and 4.25% of the phenotypic variance respectively. To reduce interval containing causal variants, we imputed sequence-level SNPs in the GWAS identified regions and fine-mapped the causative variants into two narrower genomic intervals: a ∼100 kb interval containing 71 SNPs and a broader ∼870 kb interval with 432 SNPs. This fine-mapping highlighted four promising candidate genes,  The birth weight of pigs is an important economic trait in the livestock industry. It is closely associated with early survival, weaning weight, and growth rate after weaning . Pigs haMYOG, MSTN and DBH  techniques such as genotyping-by-sequencing and restriction site-associated DNA sequencing (RAD-seq) . CompareTrait related loci can be identified with GWAS, however, the elucidation of the causative variant rather than the loci is the ultimate goal. The determination of the causative variant requires a high density of SNPs in a particular region of GWAS. If the region is not genotyped at a sequence level, the imputation technique can be used to fill in missing SNPs from the available reference panels. Due to linkage disequilibrium between SNPs, the GWAS signal extends across a large region. Although it is not always possible to directly identify the causative variant, the region containing the causative variant can be narrowed down by sophisticated methods . The keyIn this study, we used the DNA variants from two different genotyping approaches, SNP chip and RAD-seq, to perform GWAS for birth weight. To finely map causative genes, we built a reference panel for a region-of-interest by deep resequencing of 28 boars, by which the merged SNPs of RAD-seq and SNP chip were imputed at the sequence level. Finally, we detected the potential causative genes within or close to the finely mapped region.Pedigree and phenotype records used for this study were provided by our lab. The pedigree contains 26,539 animals from 7 generations, including 14,226 Yorkshire and 12,313 Landrace animals. There were 12,661 and 10,635 records of birth weight for Yorkshire and Landrace piglets, respectively. After excluding disqualified records , 10,267 and 8,919 records Yorkshire and Landrace piglets were included, respectively. A total of 674 purebred sows  born between 2014 and 2016 were selected for RAD-seq. After eliminating abnormal values (deviated from the third quartile), 668 high quality records were analyzed.MspI (Thermo Fisher Scientific) in 30 μL volume containing 20 μL DNA (1 μg). An anneal adapter (10 μM) was ligated to the digestion products by T4 DNA ligase with 23 TaqI-Ms. Then, 24 ligation products were pooled together to form one library with 15 μL per sample. Agencourt® AMPure® XP Reagent was used for library size-selection. The PCR system contained 50 ng size-selection products, 25 μL KAPA HiFi HotStart ReadyMix (kapasystem), and 10 pmol primers. PCR products were purified by Agencourt® AMPure® XP Reagent. The final library quality (concentration and fragment size distribution) was determined by a Qubit 2.0 Fluorometer (Thermo Fisher Scientific) and BiopticQsep100 DNA Fragment Analyzer (Bioptic), respectively. Every four library products (96 different barcodes) were mixed together in equal parts which a total weight at 170 ng. The cycling system contained 48 μL library mix, 1 × T4 DNA ligase buffer, 0.5 μL T4 DNA ligase (600 U/μL), and 100 pmol Splint Oligo, were reactions at 37°C and fragment size distribution were determined by a Qubit 2.0 Fluorometer (Thermo Fisher Scientific) and Bioptic Qsep100 DNA Fragment Analyze sample volume of Agencourt® AMPure® XP Reagent. Finally, the purified cyclizing libraries were sequenced with a BGI-seq500 platform (PE100).Genomic DNA was isolated from the ear tissue of pigs; the double-digest restriction enzyme associated DNA sequencing method (RAD-seq) was performed using the methods of Sscrofa reference genome (version Sscrofa 11.1)1 using the Burrows-Wheeler Aligner (version 0.7.12) software , which contained 50,697 SNPs across autosomes and sex chromosomes. QC of the SNPs was conducted using PLINK (version 1.07) . The SNPp-value < 10–6 for the Hardy-Weinberg equilibrium test. We removed 761,590 additional SNPs with missing genotypes across the 28 boars, leaving 11,668,346 high quality SNPs, which were taken as the reference panel for imputation.We sequenced the whole genome of 28 boars, the ancestors of 453 Yorkshire sows (unpublished), with an average sequence depth ∼19× (ranged from 17.06× to 22.24×). After genome alignment with Burrows-Wheeler Aligner and SNP calling with the Genome Analysis Toolkit, 17,017,067 raw SNPs were detected. These SNPs were filtered using the Genome Analysis Toolkit with parameters “QUAL < 30 || QD < 2.0 || FS > 60.0 || MQ < 40.0 || MQRankSum < −12.5 || ReadPosRankSum < −8.0,” and using PLINK with MAF < 0.05 and https://figshare.com/articles/data2019_tar_gz/9505259). After QC , we obtained 9,012,073 overlapping SNP markers for the two breeds and imputed the RAD_ chip SNPs of the Yorkshire and Landrace pigs to a genome-wide level.The SNPs determined by RAD-seq  and SNP chip  were merged to produce a high density SNP set for sequence level imputation. After removing 427 duplicate SNPs from both SNP sets, 185,701 SNPs remained. We performed sequence level imputation with Beagle by taking the whole genome sequencing data of 28 Yorkshire boars (described above) and 20 Landrace pigs . Heritability of birth weight was estimated as:where Y is the phenotype vector, b is a fixed effects vector, i.e., herd-year-season, sex (only in pedigree-based estimation), breed (2 breeds in SNP-based and 6 strains in pedigree-based estimation) and birth parity, u is a vector of additive genetic effects following the multinormal distribution: u ∼ N , and estimated breeding values are evaluated with the average information restricted maximum likelihood procedure in DMU; b is the estimator of fixed effects including breed, g is the SNP substitution effect and a is the vector of random additive genetic effects following the multinormal distribution a ∼ N . The threshold to declare significance was set at 1.1 × 10–5, which was determined from 0.05 divided by the number of SNPs in the GWAS region. We corrected the phenotypes by subtracting the corresponding breeding values and fixed effects, where the breeding values were estimated via the DMU package.The BayesFM-MCMC package  was used3 and the genomic annotation file (.gff) was downloaded from the web ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/003/025/GCF_000003025.6_Sscrofa11.1/GCF_000003025.6_Sscrofa11.1_genomic.gff.gz.SnpEff (version 4.3t)  was usedWe obtained 139,634 SNPs from RAD-seq and only 45,175 SNPs from SNP chip analysis. First, we compared the allele frequencies (AF) of SNPs garnered from both genotyping platforms . CompareWe estimated heritability prior to the association study to fully understand how much birth weight is inherited. We used pedigree information and genome SNPs to estimate heritability. There were 14,226 and 12,313 individuals in the pedigree, and 10,267 and 8,919 records of birth weight for Yorkshire and Landrace, respectively. Genome-wide SNP information was used to build kinship among individuals and heritability was estimated as 0.094 ± 0.065. Then, once again using the pedigree, we estimated heritability in Yorkshire and Landrace pigs at 0.162 ± 0.026 and 0.131 ± 0.025, respectively see , which ap-values with a Q-Q plot4. The -logP values are well fit by a linear regression against theoretical -logP values . SNP sets from RAD-seq and SNP chip analysis were merged together, with two signals on chromosome 1 and 4 exceeding the threshold . The posP values , suggestr2 among SNPs; then it models multiple causal variants by carrying out a Bayesian model selection across the cluster and generates the posterior probability for each SNP within the cluster, by which a credible set of SNPs with >95% posterior probability is constructed. The advantages of BayesFM-MCMC are that (1) it narrows down potential causative variants by indicating causal variants in the SNP set; and (2) it efficiently identifies more than one variant if multiple variants control the investigated trait.To further refine the regions containing causative genes and variants, we performed fine mapping of the GWAS region 1∼2 Mb around the lead SNP. To increase fine mapping accuracy, we utilized as many SNPs as possible by merging the SNPs from both RAD-seq and SNP chip analysis and removing duplicate SNPs. After applying a stringent filter, we obtained 5,226 and 7,184 SNPs in the fine mapping regions of chromosome 1 and 4, respectively. With this high density of SNPs, we were able to impute SNPs at a sequence level. Sequence-level imputation requires a sequence-level reference SNP set. We therefore re-sequenced 28 Yorkshire boars with an average coverage of ∼19x and downloaded the whole genome sequencing data of 20 Landrace pigs. This resulted in 11,668,346 and 18,954,748 sequence-level SNPs for Yorkshire and Landrace pigs, respectively. With these SNPs as a reference panel, we imputed the merged RAD-seq and SNP chip SNPs at a sequence level using Beagle software separately for each breed. Then, we employed BayesFM-MCMC software to narrow down the clusters containing causative variants. BayesFM-MCMC first clusters the SNPs within a GWAS region using a hierarchy clustering algorithm according to a posterior probability equal to 1 (greater than the threshold 0.5) was identified. To examine which SNPs predominantly explained the posterior probability in this cluster, we plotted the posterior probabilities for each SNP (output from BayesFM-MCMC). Most SNPs have miniscule posterior probabilities and no one SNP gives substantial posterior probability (f.i. greater than 0.5 or 0.2) in the identified cluster . First, we conducted a single variant association for the GWAS chromosome region, 1,96,745,041–98,745,041, which produced a sharp peak in this region . We then cluster . We then cluster , confirma posterior probability equal to 1. As before, we plotted the posterior probabilities for each SNP but most SNPs once again had miniscule posterior probabilities (less than or 0.05) (r2) among SNPs confirmed that they were highly linked, which explains why the individual posterior probabilities of these SNPs are very small.Fine mapping of the region on chromosome 4, 111,031,589–113,031,589 , identifor 0.05) . The 95%or 0.05)  is coverSKOR2 and over 317 kb downstream of SMAD2 , LOC106510207 (covered by 26 SNPs), VAV3 (covered by 160 SNPs), and NTNG1  signaling pathways by binding to Smads , and has not been functionally characterized to this point. As we known, lncRNA transcription plays an important role in both cis- and trans-regulation of nearby gene expression , involved in insulin-regulated GLUT4 trafficking, has been found to be positively associated with body weight in Large White and Tongcheng pigs . Taken together, the region spanning VAV3 and NTNG1 is a very important genetic factor underlying the birth weight of pigs.Several genes near the ∼870 kb tightly linked region were found to be related to growth and development or have been identified in others studies . For exaand mice , and is and mice . Mutation humans . Furthereng pigs . Finallyntration , and theSKOR2, SMAD2, VAV3, and NTNG1, and thereby regulating body development. This research did not confirm such regulatory mechanisms but has highlighted them for further investigation.Most of the finely mapped SNPs obtained herein were located in intergenic regions or within introns. Therefore, we propose that these variants may have a regulatory effect on the expression of nearby genes, such as SKOR2, SMAD2, VAV3, NTNG1, that have been associated with growth related traits in other species including cattle, humans, and dogs. Most SNPs in the fine mapping region were located in the intergenic region or introns, and as such we propose that these variants may have a regulatory effect on the expression of nearby genes, which deserves further investigation. The birth weight of pigs is an important economic factor in the livestock industry, identification of a causal variant would be beneficial to the molecular breeding of pigs.We used the DNA markers from two different genotyping approaches to perform GWAS, and identified significant loci in chromosome 1 and chromosome 4 which explained 6.36% and 4.25% of the phenotypic variance, respectively. To increase the accuracy of fine mapping, we imputed the merged RAD-seq and SNP chip SNPs at a sequence level using the SNPs of high-coverage resequenced pigs as a reference panel. Then, we employed BayesFM-MCMC software to narrow down the genomic region of the clusters that contained causative variants. One cluster was located in an intergenic region, and the other in a gene coding region. Finally, we identified four promising candidate genes, https://figshare.com/articles/GWAS_datasets/9917462.The datasets generated for this study can be found in the This study was carried out in accordance with the guidelines of the Science Ethics Committee of the Huazhong Agricultural University (HZAU). All animal experiments were approved by the Institutional Review Board on Bioethics and Biosafety of Beijing Genomics Institute (BGI-IRB).SZ, MF, and YL conceived and designed the experiments. YL, MY, RW, QW, LY, XL, and XX performed the experiments. YL, BL, MF, TC, HH, ZM, and JS analyzed the data. YL, MF, and BL wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Genetic improvement of fillet quality attributes is a priority of the aquaculture industry. Muscle composition impacts quality attributes such as flavor, appearance, texture, and juiciness. Fat and moisture make up about ~ 80% of the tissue weight. The genetic architecture underlying the fat and moisture content of the muscle is still to be fully explored in fish. A 50 K gene transcribed SNP chip was used for genotyping 789 fish with available phenotypic data for fat and moisture content. Genotyped fish were obtained from two consecutive generations produced in the National Center for Cool and Cold Water Aquaculture (NCCCWA) growth-selective breeding program. Estimates of SNP effects from weighted single-step GBLUP (WssGBLUP) were used to perform genome-wide association (GWA) analysis to identify quantitative trait loci (QTL) associated with the studied traits.Using genomic sliding windows of 50 adjacent SNPs, 137 and 178 SNPs were identified as associated with fat and moisture content, respectively. Chromosomes 19 and 29 harbored the highest number of SNPs explaining at least 2% of the genetic variation in fat and moisture content. A total of 61 common SNPs on chromosomes 19 and 29 affected the aforementioned traits; this association suggests common mechanisms underlying intramuscular fat and moisture content. Additionally, based on single-marker GWA analyses, 8 and 24 SNPs were identified in association with fat and moisture content, respectively.SNP-harboring genes were primarily involved in lipid metabolism, cytoskeleton remodeling, and protein turnover. This work provides putative SNP markers that could be prioritized and used for genomic selection in breeding programs. Fish are excellent source of protein with lower content of total fat, saturated fat, and cholesterol and higher omega-3 fatty acids compared to other animals. These characteristics make fish fillets an ideal source of nutrition according to a consensus dietary studies and recommendation . Thus, fFish fillet is a highly perishable food, at least partially, due to high moisture content (60–70%), which results in off-flavors and faster flesh spoilage because water facilitates enzymatic activity and bacterial growth , 20. Low2 value of 0.50) [Selective breeding can be used to enhance phenotypic traits of interest. A two-way program of selection on muscle fat content was initiated in rainbow trout to produce lean and fat lines where the fat percentage increased by ~ 15 to 31% in the fat line depending on the diet , 29. Theof 0.50) . Muscle of 0.50) , thus maof 0.50) . UnderstGenome-wide association (GWA) studies can identify large-effect variants responsible for phenotypic variations, which can be prioritized in genomic selection. A few GWA studies have been conducted on aquaculture species to identify quantitative genomic loci (QTL) responsible for the genetic variability in body weight , fillet A 50 K transcribed SNP-chip, suitable for GWA analyses, has been recently developed in our laboratory. The array has been used to identify large-effect QTL responsible for genetic variance in fillet yield, firmness, protein content, and body weight gain using the same fish population used in this study , 39, 40.Muscle fat and moisture contents are interrelated attributes that affect the organoleptic quality and nutritional value of muscle foods , 41, 42.In this study, we used genomic windows of 50 SNPs of a 50 K SNP chip to perform GWA analyses, in addition to the single-marker analysis approach, to identify genomic regions associated with the traits. Given that the 50 K, SNP chip contains SNPs of potential association with intramuscular fat content, all fish used to build the SNP-chip were excluded from the GWA analysis in the present study.p-value = 6.3E-262). Further, fat content was regressed on moisture content using phenotypic data from separate year classes to make sure the size and age differences between fish from YC 2010 and YC 2012 are not influencing the correlation between the traits. Interestingly, the negative correlation between fat and moisture content (R = − 0.88) was maintained in fish from the two consecutive generations.The fish population had an average muscle fat content of 9.2 ± 1.91 (%) and moisture content of 69.93 ± 1.75 (%). Variations in fat and moisture content are shown in Fig. n = 124; ~ 91%) were located within 62 protein-coding genes. Genomic loci affecting the additive variance for fat content were clustered in 5 chromosomes   that passed QC were used in the WssGBLUP analysis. A complete list of proportions of additive genetic variance for fat content explained by all genomic windows is provided in Table Sn = 50) and the most significant peaks affecting fat content (up to 5.51%) (Table Sn = 58) as well as their 3’UTR (n = 55). In order to understand the biological significance of the QTL associated with fat content, we annotated the SNP-harboring genes and searched their functions in the literature (described below).Chromosome 19 harbored the highest number  Table S, Fig. 2.n = 165; ~ 93%) were located within 86 genes coding for proteins. Genomic loci affecting the additive variance for moisture content were clustered on 5 chromosomes  , whereas the most significant peaks affecting moisture content (up to 4.46%) were identified on chromosome 19 (Table Sn = 68) as well as their 3’UTR (n = 72).Similarly, a complete list of the proportions of additive genetic variance for moisture content explained by all windows identified in this study is provided in Table SAs shown above, a negative linear relationship has been established between fat and moisture content in this selectively bred rainbow trout population (YC 2010 and YC 2012), suggesting a common mechanism underlying the genetic variation in the two traits. This negative correlation was consistent with other studies in fish and mammals , 26, 32.n = 47) were located on chromosome 19. Thirty-two SNPs, out of 47, involved in lipid metabolism were identified in 16 protein-coding genes on chromosome 19 (Table The current WssGBLUP identified common SNPs affecting the additive genetic variance for fat and moisture content on chromosomes 19 and 29 Tables S. The majn = 5) on chromosome 29. MEF2C is a transcription factor involved in skeletal muscle differentiation; however, it has been reported as a constituent of a mechanism that programs gene expression involved in development of brown adipocytes [Chromosome 29 had 14 SNPs in genomic windows explaining at least 2% of the additive genetic variance for intramuscular fat and moisture content (Tables Sipocytes . MEF2A aipocytes . To our n = 11). Three synonymous SNPs were identified in a gene encoding intersectin-2 (ITSN2). This protein is necessary for the clathrin-mediated endocytosis and actin cytoskeleton remodeling [In addition, twelve SNPs in genes involved in transmembrane transport and cytoskeleton remodeling were identified in common QTL affecting additive variance for fat and moisture content Table . The majmodeling . Six SNPmodeling . Three Smodeling , 72.TabOverall, the analysis revealed that most of the loci in association with both fat and moisture content are involved in lipid metabolic process. Therefore, our results suggest that variation in moisture content is likely to be driven by changes in fat content in an antagonistic fashion. This notion was previously suggested as depletion of macromolecules under catalytic muscle conditions is likely forming voids for water accumulation .n = 14) involved in cytoskeleton remodeling were identified, affecting the additive variance for moisture content in rainbow trout  [A strong positive correlation between moisture and protein content has been established in different species. A simultaneous decline in protein and moisture content was previously reported in mammals . Moistur < 0.01) ; whereas < 0.01) . This wa < 0.01) , 86. The < 0.01) , which a < 0.01)  and has  < 0.01) . Cystati < 0.01) . Pre-mRN < 0.01) , 91. Nuc < 0.01)  and apop < 0.01) .Table 4In addition to the ubiquitin-protein ligases, SNPs in three genes involved in lysosomal/phagosomal pathways were identified. Ras-related protein rab7 (RAB7A) harbored a 3’UTR SNP. RAB7A is a major regulator of endo-lysosomal maturation/ trafficking and protein targeting to lysosome inducing autophagosome formation . Thus, RIn this study, most of the common genomic loci affecting the highest proportion of the additive variance were involved in lipid metabolism, suggesting a common mechanism underlying intramuscular fat and moisture content and, partially, explaining the strong negative correlation between the fat and moisture content in this selectively bred rainbow trout population. Unique loci affecting moisture content were primarily involved in cytoskeleton regulations and protein turnover. Inhibition of protease activity, such as calpains, reduced degradation of proteins responsible for cell membrane-cytoskeleton attachments and postmortem drip channel formation in muscle . The preOverall, the WssGBLUP analysis has enriched the current understanding of the genetic architecture of the fat and moisture content in rainbow trout. Common SNP windows explained a high proportion of the additive genetic variance associated with both fat and moisture content, suggesting common regulatory mechanisms. Knowledge of the heritability of fat and moisture content and their correlations with other traits is needed for establishment of sustained multi-trait selection programs. The aquaculture industry is interested in implementing genomic selection in the breeding programs; however, applying high-density SNP chips is cost-prohibitive for small-sized hatcheries and companies. SNPs with a large-effect on genetic variances of fat and moisture content, identified in this study, could be prioritized to reduce SNP panel density needed to evaluate the predictive abilities for both traits. In another study, we found that prioritizing SNPs based on the proportion of variance explained for muscle yield and firmness allowed to reduce the SNP panel density down to ~ 800 SNPs. Reduced SNP panels outperformed the traditional PBLUP model in predicting the future fish performance, and maintained predictive abilities comparable to the 50 K SNP panel (data will be published elsewhere).n = 29,451) using a generalized score test; this test incorporates multiple covariates in the analysis and accounts for family structure using a kinship matrix [p < 1.69E-06; Figs. To identify single SNP markers associated with variation in fat and moisture content, we analyzed SNPs that passed QC filtration (p matrix . In thisn = 7), and SNP-harboring genes have roles in lipid metabolism (Table SNPs associated with the fat content were mainly located on chromosome 5 (2 = 3.2%) (Table Puromycin-sensitive aminopeptidase (NPEPPS), on chromosome 17, had a nonsynonymous SNP explaining the highest variability in fat content (R%) Table . NPEPPS n = 24) were associated with protein turnover, calcium metabolism, and cytoskeleton integrity  were located on chromosome 17. An SNP in a gene coding for acylphosphatase 2 ranked at the top of the list (R2 = 7.4%) (Table SNPs associated with moisture content (y Table S. Most of%) Table ; however%) Table . These g2+ homeostasis that has a profound effect on the cytoskeleton [A previous report showed that expression of genes involved in the actin cytoskeleton and cytoskeletal organization is positively correlated with drip loss in pig . In thisskeleton , 113. Suskeleton . Translaskeleton .In our previous work, we profiled transcriptome expression of fish families (YC 2010) showing contrasting phenotypes in fat content, which revealed only 17 differentially expressed transcripts associated with fat content . About 9p-value was not the same based on the proportion of variance explained, and this is because the latter depends on allele frequency, i.e., high effect but low frequency decreases the variance explained. This result is consistent with other studied traits, such as fillet firmness, protein content [Compared to our WssGBLUP analysis, the single marker GWA analysis revealed a smaller number of SNP markers associated with variation in intramuscular fat and moisture content. Besides, these two GWA approaches revealed different significant peaks associated with traits of interest. Aguilar et al.  showed t content , and bod content  in rainb content . Common  content . However content .Taken together, controlling muscular fat content can help the aquaculture industry to produce a final product of expected quality, including moisture content, drip loss, firmness, and shelf-life. Dietary lipids are used to increase fillet fat to improve fillet sensory characteristics . HoweverThe current GWA analyses identified novel genomic regions associated with additive genetic variance for fat and moisture content in rainbow trout. SNP-harboring genes encode proteins with a role in lipid metabolism, actin cytoskeleton remodeling, and protein synthesis/degradation. This work reveals significant QTL associated with fat content, which appears to be a polygenic trait. The top common windows affecting additive genetic variance for fat and moisture content are mainly on chromosome 19. These findings provide a genetic basis for description of the molecular mechanisms underlying fat and moisture content in teleost fish. Variation in moisture content is likely to be driven by changes in fat content in an antagonistic fashion. This work provides putative markers that could be prioritized when estimating genomic breeding values for fat and moisture content. GEBV for muscular fat and moisture content can be included in multi-trait selection indices that meet the various producers’ and consumers’ demands .The fish population used in the current GWA analyses was previously described in . BrieflyOver five weeks, a single fish/family was collected and randomly assigned to a harvest group as described in . Fish fr2) values of 0.23 and 0.38 were observed, respectively. Heritability was estimated for fat and moisture content using a genomic relationship matrix (GRM) [Proximate analyses, including crude lipid and moisture content (water content), were previously described . Crude lix (GRM) .A 50 K, transcribed gene SNP-chip was recently developed and used in identifying genomic loci responsible for additive genetic variance in fillet yield . SNPs utIn total, 1728 rainbow trout fish were used for genotyping and quality assessment of the SNP chip. Genotyped samples were filtered by the SNPolisher software using a call rate threshold of 0.97 and Dish QC cutoff of 0.82 . Genotypy is the vector of phenotypes (fat or moisture content), b, a and w are vectors of fixed and random effects, and e is the vector of residual effects. Random and fixed effects were determined according to [X, Z1, and Z2 are incidence matrices for fixed and random effects in vectors b, a, and w, respectively. Whereas the family and residual random effects were considered uncorrelated, the animal effect was correlated. The covariance structure for the animal effect was given by H is a matrix that combines pedigree- and genomic-based relationships [Estimates of SNP effects from WssGBLUP were utilized to conduct the current GWA analyses, as described in . The Wssrding to , 125. X,ionships  and \docThe variance components were estimated using AIREMLF90 . The inbThe filtered SNPs (~ 35 K) were subjected to a two-iteration WssGBLUP analysis. All SNPs were assigned weight = 1.0 in the first iteration. SNP effects , SNP weights were determined as Using OSTGSF90  using geOSTGSF90 :i is the genetic value of the contiguous 50 SNP region, σ2a is the total genetic variance, j is the j-th SNP within the 50 SNP region, Zj is a vector of the gene content of an SNP for all fish, and ûj is the effect of an SNP within the contiguous 50 SNP region.Where i is the i-th region that consists of 50 adjacent SNPs, aThe qqman package  in R was2) of association between the quantitative traits and genotypes. R2 is the proportion of variance in the phenotype explained by the genetic factors in a linear regression model. ONETOOL [p-values were calculated as , where α = 0.05 was used for a genome-wide significance level, and α = 0.3 was used for the suggestive significance level. Manhattan plots showing single markers associated with variation in intramuscular fat and moisture content were generated using qqman package [PLINK  was used ONETOOL  was used package .The genome annotation file was used to annotate the SNP-harboring genes by Bedtools . SNPs weAdditional file 1: Table S1. Genetic variance for fat content explained by all SNP markers in genomic sliding windows. Table S2. SNP markers in genomic sliding windows explaining at least 2% of the genetic variance for fat content. Table S3. Genetic variance for moisture content explained by all SNP markers in genomic sliding windows. Table S4. SNP markers in genomic sliding windows explaining at least 2% of the genetic variance for moisture content. Table S5. SNP markers significantly associated with variability in fat content (highlighted in yellow) using family-based association analysis. Table S6. SNP markers significantly associated with variability in moisture content (highlighted in yellow) using family-based association analysis.


Sclerotinia sclerotiorum (Lib.) de Bary, is an important cause of yield loss in soybean. Although many papers have reported different loci contributing to partial resistance, few of these were proved to reproduce the same phenotypic impact in different populations.Sclerotinia stem rot (SSR), caused by In this study, we identified a major quantitative trait loci (QTL) associated with resistance to SSR progression on the main stem by using a genome-wide association mapping (GWAM). A population of 127 soybean accessions was genotyped with 1.5 M SNPs derived from genotyping-by-sequencing (GBS) and whole-genome sequencing (WGS) ensuring an extensive genome coverage and phenotyped for SSR resistance. SNP-trait association led to discovery of a new QTL on chromosome 1 (Chr01) where resistant lines had shorter lesions on the stem by 29 mm. A single gene (Glyma.01 g048000) resided in the same LD block as the peak SNP, but it is of unknown function. The impact of this QTL was even more significant in the descendants of a cross between two lines carrying contrasted alleles for Chr01. Individuals carrying the resistance allele developed lesions almost 50% shorter than those bearing the sensitivity allele.These results suggest that the new region on chromosome 1 harbors a promising resistance QTL to SSR that can be used in soybean breeding program. Sclerotinia sclerotiorum, a necrotrophic Ascomycota, capable of infecting more than 408 different species [Sclerotinia stem rot (SSR) is a significant disease that causes yield and quality loss in soybean in the northern United States and Canada. This disease is caused by  species . The fun species . SSR was species , 32. How species . Its imp species . The besThe evaluation of SSR resistance is quite challenging in variable environmental conditions. However, a reliable inoculation method was developed by Bastien et al.  wherein To date, complete resistance has yet to be reported in soybean. Partial resistance is controlled by multiple genes or quantitative trait loci (QTL). Numerous mapping studies have been conducted and have identified more than 114 QTL via conventional biparental mapping , 29, 34.Compared to biparental mapping, diversity panels offer a lower level of linkage disequilibrium (LD) between markers and QTLs. Hence, for GWAM, a higher marker density is needed depending on population size and diversity. For higher QTL detection power, the LD between the QTL and any flanking markers should be higher than 0.8. To achieve such a coverage, Bastien et al.  estimateOne alternative to the previously used genotyping approaches is whole-genome sequencing (WGS). However, this approach is still expensive, especially when using large populations. In previous work, Torkamaneh et al.  proposedIn this work, we used such a combined GBS and WGS genotyping approach to genotype an association panel (comprising elite Canadian soybean lines) at millions of SNPs. We then used this exhaustive marker dataset to perform GWAM in the association panel to identify QTLs responsible for partial resistance to SSR in Canadian soybean.Lesion length was measured 7 days after inoculation on young flower buds, and the mean value for each genotype is shown in Supplementary Table SApeKI GBS libraries prepared from DNA of 530 elite Canadian soybean lines) using an improved SNP-calling pipeline (Fast-GBS) and a more recent version of the soybean reference genome. This yielded nearly 150 K SNPs on the panel of 530 lines that included all lines of the association panel. We then used a catalog of 4.1 M SNPs obtained from WGS of 102 lines, also included in the set of 530 lines, as a reference panel to impute genotypes at all the missing loci, thus resulting in a full dataset of 4.1 M markers. Of these, 3.5 M SNPs were polymorphic in our association panel . After removal of SNPs mapping to scaffolds (49.7 K SNPs), 3.4 M SNPs mapped onto one of the 20 soybean chromosomes. Finally, we removed markers with MAF lower than 0.05, thus resulting in a final catalog of 1,493,960 SNPs with which we performed the GWAM analysis.To achieve extensive genome coverage, we re-analyzed previously obtained sequence data , and the remaining 84,708 SNPs were used in fastSTRUCTURE. The results suggested that the panel was composed of between three and six subpopulations. Based on these two results, we chose to perform the ensuing analysis using K = 6 and the corresponding plot is shown in Fig. To characterize population structure, we pruned SNPs in high LD  and explained 15% of the variation. Accessions fixed for the minor allele A (frequency = 0.32) had lesions that were 15 mm shorter than those fixed for the major allele G  and the full set of filtered SNP markers (close to 1.5 M markers). These were analyzed using an MLM (Q + K) and associations with 6:8 lines derived from a cross between OAC Bayfield (S) and Maple Donovan (R). These parents were contrasted for the peak marker on Chr01 as well for SSR resistance; Maple Donovan carries the resistance allele and developed lesions 78.3 mm shorter than those exhibited by OAC Bayfield. The parents were used as checks in the validation trial in addition to 47 recombinant inbred lines (RILs) selected as a validation population. For each line, four plants were genotyped using a CAPS marker developed to assay the QTL on Chr01. Among the 47 RILs, 21 were homozygous for the resistance allele while 26 were fixed for the susceptible allele. These RILs, along with the parents, were then evaluated for SSR resistance in two greenhouse trials. The contrast in lesion length between the parents was still evident (60 mm). Among the RILs, the average lesion length was 63 mm and ranged from 16 to 107 mm. Interestingly, almost all genotypes fixed for the resistance allele developed lesions under the average, ranging between 16 and 78 mm (average of 40 mm), whereas lines homozygous for the susceptible allele averaged 83 mm with lesion length extending from 51 to 107 mm. The phenotypic contrast between the two genotypic classes (43 mm) .As the association on Chr15 had already been validated in previous work , we focu2 ≤ 0.98), all of the markers showing a significant association with lesion length resided in a small block  in elite material was somewhere between 9600 and 29,000 markers. More recently, a genome-wide estimation of LD in telomeric and pericentromeric regions led Bastien et al. [In this study, we used nearly 1.5 M high-quality SNPs. Based on the study of three chromosomal segments, Hyten et al.  had estin et al.  to concln et al.  and 1142n et al. . Later Gn et al.  and 11,8n et al. , while an et al. . The hign et al. . Here, cn et al. . Given tWithin our association mapping panel of 127 Canadian soybean lines, we identified 7 SNPs significantly associated with SSR resistance falling in small segments of only two chromosomes 1 and 15. The first associated region on Chr01, extending over 10 kb, was novel as it did not overlap with any of the previously reported QTLs. In fact, this region didn’t carry any GBS-derived SNPs or those supported by the SoySNP50K BeadChip.The second QTL was discovered on chromosome 15 and is the exact same marker-trait association reported previously by Bastien et al. . These rNumerous previously reported QTLs for SSR resistance were discovered in different genetic backgrounds, environments, and various sampling populations. Before considering the use of such QTLs for marker-assisted selection, a validation step is highly recommended. The QTL on Chr15 was previously validated by Bastien et al.  where reThe two regions discovered in this work, on Chr01 and Chr15, explained 32 and 15%, respectively, of the phenotypic variation for this trait in our association panel. In previous work, QTLs were reported to explain between 3 to 23% of the variance , 31, 34.Despite this, it seems that a large proportion of variation for this trait remains uncaptured in our association panel despite the extensive marker coverage. Thus, there could be other undiscovered, potentially impactful QTLs contributing to SSR in this population. In such panels, however, it will always be difficult to capture rare alleles (MAF < 5%) potentially contributing to resistance as the associated markers will have been filtered out. Expanding this work to larger and more diverse panels could help in discovering additional QTLs. Also, the remaining uncaptured heritability may be explained by numerous small-effect QTLs that will be difficult to discover (given the difficulties associated with phenotypic measurements) and would be of limited use in breeding programs as the benefits of a small increase in SSR resistance might not justify the cost of marker-assisted selection for such minor QTLs.When we investigated the associated region on Chr01, we found a single gene in high LD with the peak marker. Except for the peak SNP (located in an intron), all other significantly associated markers were found in the likely promoter of Glyma.01 g048000. Due to the strong association between the peak SNP and this gene, it represents a strong candidate gene. However, due to the lack of annotation allowing one to hypothesize a role for this gene in resistance to Sclerotinia, functional studies will need to be conducted to provide definitive proof of the role of this gene in contributing to resistance. As many of the associated SNPs are in the 5′ upstream region, it would be particularly interesting to study the regulation of this gene.We conclude that the knowledge that comes out from this study will promote the addressing of the SSR challenges under sustainable agricultural practices. We believe that this work is another step forward in rendering GWAM data more applicable in plant breeding. We expect that genetic region identified in this study will become a key tool in soybean breeding programs for SSR and enable geneticists and molecular biologists to identify causal resistance genes for SSR in near future.The association mapping panel used for this study was composed of 127 lines taken from a private breeding program (Semences Prograin Inc.) and exhibiting a wide variation in their response to SSR . These w6:8 lines segregating for the candidate QTL region on chromosome 1 (Chr01) were selected to serve as a validation panel. These lines were generated from a cross between the partially resistant Maple Donovan and the susceptible OAC Bayfield.A total of 47 FLines were evaluated for SSR partial resistance using the cotton pad method described in Bastien et al. . For theThe potting mix was prepared using a mixture of black earth (50%), perlite (30%) and Promix (20%) . At sowing, seeds were inoculated with RhizoStick® inoculant . Plants were grown under a 16-h photoperiod and the day/night temperature was maintained at 26/22 °C.S. sclerotiorum was cultured in potato dextrose broth (PDA) for 3 days until almost reaching saturation. Inoculation was performed once the plants started to flower. First, the suspension was homogenized for 30 s in a blender. Then, pieces (2.7 × 5.5 cm) of cotton pad were soaked in the suspension. The inoculum was applied on the petiole of the lowest node bearing flowers. After inoculation, plants were transferred to a different greenhouse where day/night temperatures were 22 °C/18 °C and high humidity was maintained at 2.5 g/m3 with a fogging system. For the validation panel, all plants were inoculated on the same day, while for the association panel, several days were needed because of differences in flowering date. Lesion length was measured 7 d after inoculation.The inoculum was prepared from strain NB-5  as described in Bastien et al. . BrieflyApeKI, as per [2) for all marker pairs in a sliding window of 50 Kb using PLINK 1.9 [The association panel was a part of a larger set of 530 Canadian elite lines on which we had previously performed GBS  over ti, as per , 28. To , as per ;) and on, as per . This re, as per . Subsequ, as per . We retaLINK 1.9 .2 ≥ 0.9). The resulting set of 85 K SNPs was used to assess population structure using fastSTRUCTURE [Given the large size of the SNP catalogue , pruning was performed using PLINK 1.9  to removTRUCTURE  with K sTRUCTURE .p-values were below a critical p-value corresponding to a false discovery rate (FDR) of 0.1 [In view of GWAM, only SNPs having a minor allele frequency (MAF) ≥ 5% in the association panel were used, and this resulted in a catalog of close to 1.5 M filtered SNPs. An association mapping analysis for SSR partial resistance was performed using the phenotypic (mean lesion length) and genotypic data described above with the Genomic Association and Prediction Integrated Tool (GAPIT version 2) . To corrNmuCI. The PCR product derived from the allele associated with partial resistance to SSR (present in Maple Donovan) will be cut once while the product obtained after amplification of the allele from the susceptible parent (OAC Bayfield) is not cut. All 47 F6:8 lines of the validation panel (described above), along with the two parental lines were genotyped using this CAPS marker.A codominant cleaved amplified polymorphic sequence (CAPS) marker was designed to genotype one of the candidate SNPs (Chromosome 1: 5594765) residing in the haplotype block containing the peak SNP on Chr01. Two specific primers (5′-GTTGTATGGAAGTGCAACTAAAGTTCT-3′ and 5′- GGTACTTTTTCTTACCTTAC GATGA-3′) were used to amplify an 800-bp region encompassing the targeted SNP. The two alleles can be distinguished by digesting the resulting amplicon with 2 values. Information about the genes found in the LD block containing the peak association were obtained from SoyBase (www.soybase.org). Functional annotation of nucleotide variation in the region was explored using SnpEFF [LD values from PLINK  were extg SnpEFF .Additional file 1: Table S1. Responses of 127 soybean lines to SSR, 7 days after inoculation. (*) Indicates lines used for WGS (Supplementary file “Supplementary table SAdditional file 2: Figure S1. Lesion length distribution across the 127 lines according to alleles at the peak marker on Chr15Additional file 3: Figure S2. LD block plot for the region on Chr01. The arrow shows the position of the significantly associated SNPs. Abbreviations. GBS: Genotyping-by-sequencing. GWAM: Genome-wide association mapping. LD: Linkage disequilibrium. PDA: Potato dextrose agar. PCR: Polymerase chain reaction. QTL: Quantitative trait loci. RILs: Recombinant inbred lines. SLAF-seq: Specific-locus amplified fragment sequencing. SNP: Single nucleotide polymorphism. SSR: Sclerotinia stem rot. USDA: United states department of agriculture. WGS: whole genome sequencing


Berry size is considered as one of the main selection criteria in table grapes breeding programs, due to the consumer preferences. However, berry size is a complex quantitive trait under polygenic control, and its genetic determination of berry weight is not yet fully understood. The aim of this work was to perform marker discovery using a transcriptomic approach, in order to identify and characterize SNP and InDel markers associated with berry size in table grapes. We used an integrative analysis based on RNA-Seq, SNP/InDel search and validation on table grape segregants and varieties with different genetic backgrounds.Thirty SNPs and eight InDels were identified using a transcriptomic approach (RNA-Seq). These markers were selected from SNP/InDel found among segregants from a Ruby x Sultanina population with contrasting phenotypes for berry size. The set of 38 SNP and InDel markers was distributed in eight chromosomes. Genotype-phenotype association analyses were performed using a set of 13 RxS segregants and 41 table grapes varieties with different genetic backgrounds during three seasons. The results showed several degrees of association of these markers with berry size (10.2 to 30.7%) as other berry-related traits such as length and width. The co-localization of SNP and /or InDel markers and previously reported QTLs and candidate genes associated with berry size were analysed.We identified a set of informative and transferable SNP and InDel markers associated with berry size. Our results suggest the suitability of SNPs and InDels as candidate markers for berry weight in seedless table grape breeding. The identification of genomic regions associated with berry weight in chromosomes 8, 15 and 17 was achieved with supporting evidence derived from a transcriptome experiment focused on SNP/InDel search, as well as from a QTL-linkage mapping approach. New regions possibly associated with berry weight in chromosomes 3, 6, 9 and 14 were identified. Vitis vinifera L.) is the main fruit crop of temperate regions, with a harvested area of 6.9 million of hectares in 2017 and 74.3 million tons of fruit produced throughout the world in the same period [Vitis with over 50 species [Grapes , with different purposes in wine and table grapes varieties; in the former, the aim is to augment bunch loosening and aeration, while in the latter it is used mainly to increase berry size [Due to consumer preferences, the main selection criteria in table grape breeding include berry size and seedlessness (or a reduced seed production) , along wrry size , 17, beiBerry size is a polygenic quantitative trait. Numerous processes such as cell multiplication, cell wall modification, water and sugar transport probably define its expression –20, implSeveral QTLs associated to berry size have been reported in linkage groups 1, 12 , 5, 13 , 8, 11, cepages (in particular red ones) require certain degree of astringency, what is provided by the tannins present in seeds [The genes VvCEB1 and VvNAC26 were recently reported as major factors determining berry size. VvCEB1 is a bHLH transcription factor involved in the regulation of cell size in cv. Cabernet Sauvignon . Recentlin seeds . Thus, wBerry size is an economically important trait for grape varieties ; it consThe development of molecular markers for early selection of seedlings is undoubtedly highly valuable , 32. HowSNP/InDel represent a valuable source of genetic variability as drivers of new genes or allelic variants, which might be selected by natural or artificial ways when resultant phenotypes exhibit advantageous traits . Marker N = 139), a population containing segregants with contrasting phenotypes for berry size. Among the hundreds of markers initially identified, 30 SNPs and eight InDels were selected and experimentally validated in (RxS) segregants, in table grape varieties and in representative genotypes of a V. vinifera core collection.The aim of this study was to perform marker discovery using a transcriptomic approach, in order to identify and characterize SNP and InDel markers associated with berry size in table grape. For this we used a ‘Ruby seedless’ x ‘Sultanina’ (RxS) progeny  and insertion/deletion (InDels) related to berry size was performed, using available data from an RNA-Seq experiment based on 14 samples collected at fruit setting and 6–8 mm berry diameter stages, as previously described  (AdditioVitis vinifera reference genome using Tophat [V. vinifera chromosomes (chr); the largest number was found in chr 18  and the lowest in chr 17 , with an average of 37,709 SNPs per chromosome  of 1 [ST analysis selected 382 SNPs and 16 InDels associated to berry size. Putative SNPs were mainly distributed in five chromosomes: 17 (37%), 9 (24%), 15 (12%), 6 (9%) and 8 (7%). The putative InDels were located mainly in chr 6 and 9 (25% each) and chr 11 (12.5%).In order to select putative polymorphisms associated to berry size, we defined a set of criteria that candidate makers should fulfill (See methods section). Missing genotype data  represented the most significant filter, corresponding to 84.5% in the case of SNPs and 77% in the case of InDels  of 1 , which iV. vinifera reference genome (PN40024)  . InDels were analyzed for frameshift, intragenics, intronics, as well as ‘position , 68 SNPsTherefore, there were experimental steps that caused the reduction of the markers to be evaluated, including sequences where primers and PCR products were not possible to be obtained.In summary, primers were obtained for 45 SNPs and 12 InDels. SNP/Indel primers were discarded considering: 1) no amplification products were observed, or 2) ambiguous or not discriminative HRM/melting curves were obtained. Subsequently, sets of 34 and 10 primers were selected, respectively, for SNPs and InDels to be confirmed by Sanger sequencing. From them, 30 SNPs and eight InDelS were confirmed, representing a validation rate of 88% (30/34) and 80% (8/10), respectively.To confirm and validate SNPs and InDels associated with berry size by qPCR-HRM, primers were designed for 30 SNPs and eight InDels, respectively  or large berry segregant (LB) groups, according with ; in the The 38 polymorphisms were distributed in eight chromosomes: 3, 6, 8, 9, 14, 15, 17 and 19, but a large percentage 82%) were located in chr 6, 9, 15 and 17    , to dete5) Table a. A grou5) Table a.Table Significant associations between SNP and InDel markers and berry quality traits including bunch weight (BWe), berry length (BL), berry width (BWi) and berry volume (BV) were determined Table b. Marker   analysis V. vinifera  were identified on chr 3, 6, 8 and 15 (Table p < 0.05)  Table a. In fac15 Table a. Signif5) Table b and c, Association results for BL and BWi traits varied from 11 to 25% and 11 to 32%, corresponding respectively, to the proportion of explained variation. TSRNASNPS120468689 and TSRNASNPS120206839 markers, located in chr 3 and 15, respectively, showed significant associations with the three evaluated traits in at least one season.Physical coordinates were compared between markers and candidate genes to evaluate the co-localization of regions containing candidate SNPs and InDels associated with berry weight and differentially expressed (DE) genes identified in previous transcriptomic analyses . In the Figure One SNP and 25 DE genes were identified in chr 3, four of them significatively correlated to PCA-1 corresponding to genes coding for F-box protein At2g16365 (Vitvi03g00306) (0.96), a putative secretory protein (Vitvi03g01597) (0.98), a 5′-AMP-activated protein kinase gamma subunit (Vitvi03g01483) (0.97) as well as a pathogenesis-related protein 5 (Vitvi03g00124) (− 1); the first three over-expressed in SB, and the last over-expressed in LB segregants. We also identified three SNPs in chr 6, plus four InDels and 26 DE genes, two of them significantly correlated with PCA-1, coding for a GCN5-related N-acetyltransferase (GNAT) family protein (Vitvi06g00679) and a lichenase (Vitvi06g01917), whith correlations of − 0.96 and 1, suggesting high expression in LB and SB segregants, respectively , coding for a putative mitochondrial 2-oxoglutarate/malate carrier (Vitvi08g01801) suggesting over-expression in LB segregants. Eight SNPs plus three InDels and 25 DE genes were found in chr 9, two of them coding for 18.6 kDa class III heat shock protein (HSP) (Vitvi09g00045) and anthranilate N-benzoyltransferase protein 2 (Vitvi09g01229), both negatively correlated with PCA-1 (− 0.95 and − 0.96) and over-expressed in LB segregants. One SNP and 28 DE genes were identified in chr 14, two of them coding for a copper chaperone (Vitvi14g00270) and a probable galacturonosyltransferase 13 (Vitvi14g02028), both significatively correlated with PCA-1 , over-expressed respectively in LB and SB segregants. Five SNPs and one InDel, as well as 15 DE genes were identified in chr 15, one of them coding for one O-acyltransferase WSD1 (Vitvi15g00960) highly correlated with PCA-1 (1), indicating its over-expression in SB segregants. Seven SNPs and 25 DE genes were found in chr 17, seven of them significantly correlated with PCA-1. Two DE genes coding for an expansin-A15 (Vitvi17g01251) and the C2H2-type zinc finger transcription factor (Vitvi17g00658) were negatively correlated (− 0.99 and − 0.97), suggesting their over-expression in LB segregants. Also, five genes coding for a DEAD-box ATP-dependent RNA helicase 30 (Vitvi17g01557) (0.99), a probable metal-nicotinamine transporter YSL7 (Vitvi17g01183) (0.99), a germin-like protein subfamily T member 1 (Vitvi17g01451) (0.96), a protein ruptured pollen grain 1 (Vitvi17g00070) (0.97), and a LON peptidase N-terminal domain and RING finger protein 1 (Vitvi17g00643) (0.96) were positively correlated with PCA-1, and over-expressed in SB segregants. Finally, in chr 19 one SNP and 21 DE genes were found, two of them significatively correlated with PCA-1, coding for 5′-AMP-activated protein kinase gamma subunit (Vitvi19g00252) (0.97) and epidermis-specific secreted glycoprotein EP1 (Vitvi19g00111) (0.95), over-expressed in SB segregants  and insertion/deletions (InDels) are the most abundant polymorphisms in every species studied, including plant genomes , 56; botica rapa , and sweica rapa , among oica rapa , 60–62.Our approach was based on the comparison of the transcriptomes of contrasting individuals (segregants of RxS cross) in early stages of berry development  and InDels (11.6%) in non-coding regions such as intergenic sequences, some of which could be regulatory regions. The most plausible explanation for this finding could derive from the fact that the grapevine annotation, as in other plant genomes, has been basically an automated process, and because of that possibly harboring some errors. The detection of polymorphisms in intergenic regions is concordant with findings previously reported using similar RNA-seq approaches , 69, 70.notation , and RNAnotation , allowednotation .Populus nigra were 15% of SNPs were located at intronic regions, using a similar approach to analyze transcriptomic data for polymorphisms discovery [Interestingly, SNPs and InDels were also found within intronic regions accordingly with our RNA-Seq data. Even though transcriptomic protocols specifically enhance mature mRNA by the selection of polyadenylated RNA or reduce the presence of ribosomal RNAs , the preiscovery . In factV. vinifera, chr 3, 6, 8, 9, 14, 15, 17 and 19  analysis, a post-PCR detection methodology. It was chosen because of its high sensitivity, speed, accuracy and feasibility for the analysis of double-stranded PCR products dissociation, such as SNPs, InDels , 77, 78 Genotype-phenotype association was performed using a subset of 13 segregants of RxS cross, including six segregants with contrasting phenotypes for berry weight which were sequenced, and seven additional segregants with consistently large vs. small berries. Fourteen SNPs distributed in chr 6, 8, 15 and 17 presented significant association with BFW   (5) Table a. In add5) Table b and c. V. vinifera , which included cv. ‘Sultanina’ in one parental’s pedigree, while the second corresponded to the cross of the wine varieties ‘Syrah’ × ‘Grenache’ (SxG). The estimated distance between the QTL interval in LG17 and the region of 6.8 Mbp where the group of six SNPs with highest genotype-phenotype association was 1.9 to 2.1 Mbp, depending on the population analyzed . The ideThus, the multiple evidence presented in this study confirm previously reported QTLs associated with berry size in LG15 , LG8, LGHowever, neither SNP nor InDel markers associated with BFW were identified in this study for the QTLs described in LG1 and LG12 , LG5 andSignificant associations between SNPs and BFW were recently reported in chr 1 related to candidate gene NAC26  and in cThe observed distribution of SNPs and InDels identified in this study allowed us to find four new regions associated with berry weight in chr 3, 6, 9 and 14. The 38 SNP and InDel markers associated with berry size were distributed in 32 genes; none of which was reported as differentially expressed (DE) between segregants with contrasting phenotypes for berry size in the previous work of our research group . The co-For instance, the single SNP reported at chr 3 was associated to a predicted Ca2 + −dependent phospholipid-binding protein Table  possiblyChr 6 presented four InDels and three SNPs, co-localizing a gene coding for GCN5-related N-acetyltransferase (GNAT) considered as a possible transcription activator associated with chromatin assembly and DNA replication  as well Of the eight SNPs and three InDels identified in chr 9, two genes were up-regulated in large berry segregants coding for heat shock protein 18.6 kDa class III and anthranilate N-benzoyltransferase 2 protein, both related to stress and defense response, co-localized with the polymorphism. Genes coding for WAX2 protein and a putative boron transporter 2, respectively associated to drought in plants  and cellV. vinifera, supporting the idea of a positive selection of allele(s) for larger fruit, such as the SNPs described here, during the domestication process. The putative orthologue in V. vinifera of the gene coding for cytochrome P450 78A, a gene that partially controls fruit weight in tomato and has been associated with its domestication [Five SNPs and one InDel found in chr 15 where related to genes coding for a beta-fructofuranosidase (invertase), a phosphatidate cytidylyltransferase, a scarecrow-like protein 6, a transketolase 10 and a serine/threonine-protein kinase AtPK2/AtPK19, possibly involved in signaling, activation and biosynthetic process. Seven out of 21 DE genes found in chr 17 were related to berry size determination  includintication , also cotication . This maOur results are in line with the hypothesis that variations in coding regions as well as in the promotor of the genes are equally important in the determination of complex traits . EquivalFinally, using an integrated analysis based on RNA-Seq, SNP/InDel search and validation on table grape segregants and grape varieties with different genetic backgrounds, we identified a set of informative and transferable SNP and InDel markers associated with berry size. These results are of interest to achieve better understanding of a complex trait like berry size and could facilitate successful transferability of molecular markers suitable to be integrated in marker-assisted grape breeding programs.We identified a set of informative and transferable SNP and InDel markers associated with berry size, using a combined analysis based on RNA-Seq, SNP/InDel search and validation on table grape segregants and grape varieties with different genetic backgrounds. Our results suggest the suitability of SNPs and InDels as candidate markers for berry weight in seedless table grapes. The identification of regions possibly associated with berry weight in chromosomes 8, 15 and 17 was achieved with supporting evidence derived from a transcriptome experiment focused on a SNP/InDel search, as well as a QTL-linkage mapping approach. In addition, new regions possibly associated with berry weight were identified in chromosomes 3, 6, 9 and 14.n = 139) is planted at the La Platina Experimental Station of the Instituto de Investigaciones Agropecuarias (INIA-La Platina), located in Santiago, Chile; vines are conducted through a trellis system (‘Spanish parron’), grafted over cv. ‘Sultanina’, with two to four replicates being managed under standard conditions as previously described by [The population derived of ‘Ruby seedless’ x ‘Sultanina’ cross  with contrasting phenotypes for berry size and weight , i.e., small berry (SB) and large berry (LB), all seedless, were selected and sampled for transcriptome analysis according to [Both parents plus six segregants of the RxS cross . EaV. vinifera were obtained from a group of six segregants and parents from the RxS cross  with SRA Study accession number SRX366617 [Pericarp and mesocarp tissues were homogenized and analyzed together for the RNA-Seq experiments, as previously described by . Total RRX366617 .A global search of SNP and InDel polymorphisms was developed using reads aligments and the Tophat mapper, with previous removal of PCR-duplicates using remove_duplicates tool from Picard Tools , 98 (AddVitis vinifera genome reference (PN40024 12X.1)  –102 (AddV. vinifera (PN40024), searching for possible enrichment in biological processes. Enrichment analyses were based on a hypergeometric distribution followed by FDR correction. Significant GO terms (p < 0.05) were reported.A gene ontology (GO) enrichment analysis was performed ShinyGO v0.61 tool , 50. TheSpecific primers were designed to validate SNPs and InDels using PRIMER 3 software  with theTotal DNA was extracted from young leaves according with . The purThe validation of SNPs was developed according to the protocol of , with moPolymorphisms were confirmed by sequencing amplification products , and sequenced samples were used as controls. The qPCR-HRM amplicons of varieties ‘Sultanina’ and ‘Ruby seedless’ as well as RxS segregants were quantified using the method described by ; samples(ANOVA), using a subset of 13 RxS segregants. Phenotypic data were normalized using logarithmic transformation to fulfill the normality assumption in the statistical analyses [Three methods were used to determine significant associations between SNP/InDel markers and berry quality traits: GLM based on TASSEL, Random Forest and unbalanced variance analysis analyses .p-values for each SNP/InDel marker were recorded. Subsequently, a Random Forest (RF) analysis as well as unbalanced ANOVA were developed in order to determine significant associations between SNP/InDel markers and BFW. RF corresponds to a multivariate analysis mainly used for statistical classification and regression. An RF model is an ensemble of unpruned classification or regression trees created using bootstrap samples of the training data and random feature selection in tree induction [randomForest‘package of R statistical software and a p-value of 0.05.Genotype-phenotype association analysis were performed using SNP/InDel markers for berry fresh weight (BFW), bunch weight (BWe), berry length (BL), berry width (BWi) and berry volume (BV), in seasons 2010, 2011 and 2012, using the Global Linear Model (GLM) implemented in software TASSEL v.5.0 .. The grnduction . The impnduction  based on2) on berry weight according with the proposed model, using the stats package of R (R 3.6.2) [Then a one-way variance analysis (ANOVA) was performed using unbalanced data and the group of 38 candidate markers in order to determine their individual effect (RR 3.6.2) . The coeyij is the observation i of berry fresh weight phenotype according to the genotype of j for marker x; μ is the overall mean; xj represents genotype j of the marker x; and ε is the error term.where p-values for each SNP/InDel marker were recorded.Genotype-phenotype association analyses were performed using phenotype and genotype data derived from a set of 41 varieties and SNP/InDel markers for berry fresh weight (BFW), berry length (BL) and berry width (BWi), in seasons 2016, 2017 and 2018, using the Global Linear Model (GLM) implemented in software TASSEL v.5.0 . For eacAdditional file 1: Table S1. Summary of RNA sequencing data used for global identification of SNPs and InDels. Samples included RxS segregants with contrasting phenotypes for berry size, i.e. small berry (SB) and large berry (LB), and both parents, ‘Ruby seedless’ and ‘Sultanina’. Berries were collected at fruit setting and berry of 6–8 mm stages, according to [rding to .Additional file 2: Figure S1. Bioinformatics workflow for the identification and characterization of SNP and InDel molecular markers derived from RNA-Seq data, associated with berry weight trait in Vitis vinifera.Additional file 3: Figure S2A, S2B. Distribution of SNP (A) and InDel (B) polymorphisms along V. vinifera chromosomes.Additional file 4: Figure S3. SNP distribution along the Vitis vinifera genome according to SnpEffect analysis based on a gene model for V. vinifera (PN40024).Additional file 5: Figure S4. InDel distribution along the Vitis vinifera genome according to SnpEffect analysis based on a gene model for V. vinifera (PN40024).Additional file 6: Figure S5. SNP and InDel density determined in V. vinifera chromosomes. Density was estimated considering the total observed polymorphisms per Kb.Additional file 7: Figure S6. Hierarchical tree summarizing over-represented gene ontology (GO) categories identified in a set of 232 genes containing 382 SNP markers. Dots at branches represent significant FDR values.Additional file 8: Table S2. Primers designed for the validation of 30 SNPs and the subsequent genotyping of seedless segregants from RxS population and table grape varieties, based on High Resolution Melting analysis (qPCR-HRM).Additional file 9: Table S3. Primers designed for the validation of eight InDels and the subsequent genotyping of seedless segregants from RxS crossing and table grape varieties, based on High Resolution Melting analysis (qPCR-HRM).Additional file 10: Fig. S7. Hierarchical tree summarizing over-represented gene ontology (GO) categories, identified in a set of 32 genes containing SNP and InDel markers. Dots at branches represent significant FDR values.Additional file 11: Figure S8. Random Forest regression analysis was applied to identify the most important SNP or/and InDel markers over the phenotype variation. Analysis was performed using observed genotypes of 38 candidate polymorphisms and the 13 segregants with known phenotype for berry weight, including the parents ‘Ruby seedless’ and ‘Sultanina’. Berry weight (BW) was considered as the response variable; 1000 permutations were used and decision trees were obtained, selecting a consensus. Red line indicates significant polymorphisms. At least 10 markers representing nine SNPs and one InDel were significantly associated with BW (p < 0.05).Additional file 12: Table S4. Unbalanced analysis of variance (ANOVA) to analyze possible effects of 30 SNP and eight InDels candidates associated with berry weight.Additional file 13: Table S5. Collection of 21 table grape varieties, representative of table grape diversity cultivated in Chile.Additional file 14: Table S6. International collection of 20 varieties, representative of genetic diversity of Vitis vinifera Prole ‘Orientalis’.Additional file 15: Table S7. Co-localization of 38 SNP/InDel markers associated with berry weight, and differentially expressed genes (DE genes) between large and small berry segregants, previously reported by [orted by . The tot