However, dozens of high-yield rice varieties in this region still exist with low quality or susceptibility to blast disease, severely limiting their application in rice production.Balancing the yield, quality and resistance to disease is a daunting challenge in crop breeding due to the negative relationship among these traits. Large-scale genomic landscape analysis of germplasm resources is considered to be an efficient approach to dissect the genetic basis of the complex traits. Central China is one of the main regions where the japonica rice varieties grown in central China over the past 30 years and analyze the genetic structure of these cultivars using 2.4 million polymorphic SNP markers. Genome-wide association mapping and selection scans indicate that strong selection for high-yield and taste quality associated with low-amylose content may have led to the loss of resistance to the rice blast fungus Magnaporthe oryzae. By extensive bioinformatic analyses of yield components, resistance to rice blast, and taste quality, we identify several superior alleles for these traits in the population. Based on this information, we successfully introduce excellent taste quality and blast-resistant alleles into the background of two high-yield cultivars and develop two elite lines, XY99 and JXY1, with excellent taste, high yield, and broad-spectrum of blast resistance.Here, we re-sequence 200 japonica rice varieties grown in central China and we demonstrate a balancing of multiple agronomic traits by genomic-based strategy.This is the first large-scale genomic landscape analysis of The online version contains supplementary material available at 10.1186/s13059-021-02488-8. Oryza sativa L.), one of the most important staple crops, feeds more than half of the population in the world. Although rice yield has undergone enormous leaps through semi-dwarfism and genetic heterosis in the last 60 years, rice production now faces several new challenges, including continuing population growth [Rice  and selection scan results, we observed a key genomic linkage drag that caused excellent taste quality and high yield negatively correlated with rice blast resistance in the present breeding population. To overcome this dilemma, excellent taste quality or blast-resistant alleles have been introduced into the high-yield cultivars. Finally, we successfully bred two elite lines with a yield, quality, and blast resistance balance. This study develops a novel molecular design breeding strategy and closes the gaps between genomic studies and crop breeding.The area of ica rice . Howeverjaponica rice varieties in central China, we re-sequenced 200 japonica rice varieties bred from 1989 to 2017 in this region  was used as a sequencing reference. A total of 2,410,743 single-nucleotide polymorphism (SNP) markers covering the whole genome (approximately 15× coverage) were obtained  value. Based on these predicted values, we noticed that the yield, GW and GN increased over the past 30 years, while PN did not significantly change , the number of grains per panicle , and the panicle number  to further detect the gene loci related to GW, GN, and PN. The results revealed that the MLM model was appropriate to analyze these traits  value > 8.3) were identified as being associated with these yield-related traits  , 1000-grain weight (GW), grain number per plant (GN), and panicle per plant (PN), were identified in four environments    with 14–18% AC mainly carried the Wxb genotype, and the other 3 MV varieties with 18–20% AC mainly carried the Wxin genotype  is one of the essential traits that influence rice taste quality, and low-amylose varieties usually have better-taste quality . Nangeng it Fig. : Fig.S3dBG3, OsPL3, and OsSNB; GN-related genes NOG1 and OsSPX1; and the amylose content-related gene Wx  to scan the selective sweep regions at the genome-wide level. The results revealed that 655 regions were enhanced through artificial selection , Zhejiang, and Shandong provinces, which are the main japonica rice-growing areas in the central region  18 varieties with 14–18% AC showing resistance to more than six isolates were classified to the blast-resistant, medium-amylose varieties (BRMVs); (2) 22 varieties with 8–12% AC showing susceptibility to more than three isolates classified to the blast-sensitive, low-amylose varieties (BSLVs); (3) 157 varieties with 14–18% AC showing susceptibility to more than three isolates classified to the blast-sensitive, medium-amylose varieties (BSMVs)  were associated with rice blast resistance on chromosomes 2, 6, and 11, respectively  were distributed near the nucleotide-binding site–leucine-rich repeat (NBS-LRR) resistance gene (Os06g0286700) in the Nipponbare genome  was observed Fig. c, in mosjaponica elite lines with high yield, excellent taste, and blast resistance , and finally we acquired twenty-four F2 plants with superior genes related to grain weight , grain number , blast resistance (Piz-t), and excellent taste (Wxmp). Then, in each backcross and selfing generation, we strengthened the selection of the above superior gene. We then selected twenty BC2F5 generation lines for predicting the yield potential by constructing the rrBLUP prediction model. Finally, we selected one elite line (XY99) with high consistency between the predicted yield value and the observed yield value  were strongly selected in each backcross and selfing generation. Using the above strategies, the beneficial genotypes in the breeding population can be enriched to the maximum in the shortest generation time. The selected JXY1 and XY99 lines had a higher yield potential than the lines selected by traditional breeding methods . The 200 varieties were planted and phenotyped across four environments: Hangzhou city in Zhejiang Province in 2017 and 2018, and Yangzhou city in Jiangsu Province in 2017 and 2018 in China. A randomized complete block design with two replicates was used in all four trials . Each plot contained eight rows with 10 plants per row. The row and column spacings were set to 30 and 12 cm, respectively. At least eight plants in the middle of the plot were selected for phenotyping, including panicle number per plant (PN), grain number per plant (GN), 1,000-grain weight (GW), yield per plant (YP), and amylose content (AC), according to the standard evaluation system for rice.In total, 200 elite https://cran.r-project.org/web/packages/agricolae/).To obtain a best linear unbiased predictor (BLUP) value, the above trait values of four yield traits across all trials were fitted using a linear mixed model in R using the lme4 package  to obtaiM. oryzae isolates were collected from Jiangsu, Zhejiang, and Shandong provinces from 2016, the polymorphisms of cloned avirulence (Avr) genes among these isolates were analyzed, and the primers used for amplification are presented in Additional file Avr genes in each M. oryzae isolate, we selected seven isolates  to screen the seedling blast resistance of the 200 japonica varieties. Single spore isolation, strain cultivation, and inoculum preparation were conducted manually to identify resistance rice varieties. Ten plants of each japonica variety were grown in a plastic tray filled with sieved garden soil in a greenhouse maintained at 27–30 °C until three leaves emerged. Three-week-old rice seedlings were inoculated with 40 mL of an M. oryzae conidial suspension (5 × 104 conidia/mL) with 0.02% Tween 20 using a hand atomizer (100 kPa) connected to an air compressor [M. oryzae were classified as resistant varieties, and those that showed resistance to 1–4 isolates of M. oryzae were classified as susceptible varieties.A total of 35 mpressor . Inoculampressor ; rice vajaponica varieties were collected, and their genomic DNA was extracted using the cetyltrimethylammonium bromide (CTAB) method. The genome sequencing libraries were sequenced using an Illumina X-ten sequencer (depth range 15.0× to 30.9×). The genomic DNA of the 35 blast isolates was extracted using the Fungi Genomic DNA Extraction Kit . The genome sequencing libraries were sequenced using an Illumina X-ten sequencer (average depth range 30×).Young seedlings at the three-leaf stage of the 200 japonica varieties, and M. oryzae isolates against the Nipponbare reference genome (IRGSP-1.0) and blast isolate (70-15) reference genomes, respectively, with default parameters. The mapping results were processed using SAMtools (v.1.3.1) [−5). Whole-genome SNPs on chromosomes and evolutionary trees were visualized using the R package CMplot (v1.0.1) and online iTOL software (https://itol.embl.de).The quality of the short sequencing reads was firstly evaluated using FastQC (v.0.10.1), and the reads were trimmed using Trimmomatic (v.0.36). BWA-MEM (v.0.7.13) was used to map the remaining clean reads of the v.1.3.1) . GATK  in the study was calculated using SNPs with MAF > 0.05 and missing rate < 0.5, using Tassel (v.5.2.64) [r2 between two SNPs within 200-kb windows was calculated, and the distance of LD decay was represented as the physical distance over which r2 dropped to 0.2.We conducted a model-based method implemented in the ADMIXTURE tool . We dete.5.2.64) . The genr2 < 0.5, they were considered independent). The confidence intervals of the GWAS loci were determined by local LD block analysis, where the pairwise r2 of the SNPs with P < 1 × 10−4 should be > 0.3. Genes located directly in or within 50 kb (genome-wide average distance of LD decay to r2 = 0.2) around the confidence intervals were selected as candidate genes for the GWAS loci. The GWAS threshold P value was determined using the Bonferroni correction method: threshold P value = 0.01/n, where n is the number of SNPs used in the GWAS analysis.We selected 2,410,743 SNPs  to perform GWAS for all traits. GWAS was conducted using the mixed linear model (MLM) method, which was implemented in Tassel (v.5.2.64) . We perfhttp://vcftools.sourceforge.net), with a window size of 50 kb and a step of 5 kb for all chromosomes and 1 kb non-overlapping windows for candidate genes.To detect the potential selection signals during rice breeding, we used the cross-population composite likelihood ratio (XP-CLR) , to scan1F1 and BC2F1, two BC2F1 plants carrying all superior alleles were identified and self-crossed to obtain 242 BC2F2 individuals. Finally, 36 BC2F3 individuals were obtained, which carried the above favorable alleles. To use NG9108 as the recurrent parent, we used the same strategy, and the superior alleles detected during each backcross and selfing generation. In a later generation (BC2F5), ten recombination lines were selected to sequence the genome with a 5× resequencing depth, and the identified SNPs were used for genome-wide selection based on the constructed prediction model. The lines with the best prediction values were selected to further calculate the yield, blast resistance, and taste quality. The KASP marker information identified the genotype of grain weight , grain number (NOG1 and DEP1), panicle number (OsIAGLU), taste quality (Wxmp), and blast resistance (Pigm) are shown in Additional file The 2,410,743 SNPs and the phenotype data of the 200 sequenced varieties were used to construct a genomic prediction model using the R package rrBLUP , 56. ForThe dried grains were broken in the middle section of the kernel with tweezers. Fractured grains were fixed on the specimen mount with the fractured surface facing upward and sputter-coated with gold before being viewed using a Hitachi S4800 scanning electron microscope at 10 kV.g for 5 min), the yellow impurities on the precipitate’s surface were removed. The precipitate was then washed three times with water and twice with ethanol. The starch was oven-dried at 40 °C for 2 days and passed through a 100-mesh sieve. Dry starch (10 mg) was placed in a test tube, and the sample was immediately dissolved in 5 mL of dimethyl sulfoxide (DMSO) containing 10% 6.0 M urea. The mixing was performed at 95 °C for 1 h with intermittent vortexing to ensure full dissolution of starch. A 1.0 mL aliquot of the starch-DMSO solution and 1.0 mL of iodine solution  were mixed and made up to 50 mL with water and kept in the dark for 20 min. The control was prepared using the same method but without a starch sample. The amylose content was calculated from the absorbance measured at 620 nm, and the amylose content standard curve was produced using waxy corn amylopectin and potato amylose. The experiments were performed in triplicate.The seeds were ground thoroughly in a mortar and mixed with water to form a homogenate. The homogenate was filtered using 100- and 400-mesh sieves. After centrifugation . First, 3 g of milled rice flour was dispersed in 25 mL of water. The starch suspension was first kept at 50 °C for 1 min and then heated to 95 °C at a rate of 12 °C /min. The temperature was maintained at 95 °C for 2.5 min, and then the starch suspension was cooled down to 50 °C at a rate of 12 °C /min and maintained at 50 °C for 2 min.Additional file 1: Fig S1 Geographical location and genetic structure of the 200 re-sequenced japonica rice varieties. Fig S2 Quantile-quantile plots related to genome-wide association studies of yield-related traits. Fig S3 Identification of candidate genes for yield-related traits. Fig S4 Gene combinations contribute to yield level. Fig S5 Artificial selection trend of GWAS mapped genes. Fig S6 Genetic diversity analysis and selection of representative isolates of blast bacterium. Fig S7 The haplotypes of avirulence genes in 36 sequenced isolates. Fig S8 Yield decrease caused by Hap2 and artificial selection signals. Fig S9 BSLVs show similarity of genomic structure on chromosome 6. Fig S10 JXY1 and XY99 showed high-yield and broad-spectrum blast resistance.Additional file 2: Table S1: The agronomic traits of 200 sequenced japonica rice varieties in four environments. Table S2: The descriptive statistics of yield-related traits in four environments. Table S3: Results of ANOVA analyses for yield-related traits. Table S4: GWAS mapped results relate to grain weight, grain number and panicle number. Table S5: Selection regions between japonica rice varieties before and after 2010. Table S6: Summary of cloned genes related to the yield, disease resistance, taste quality and heading day traits detected in this study. Table S7: Pasting properties of milled rice flour. Table S8: PCR amplication primer sequences for Avr genes. Table S9: KASP primer sequences for identification of the GWAS mapped genes relate to grain weight, grain number, panicle number and eating quality.Additional file 3. Review history.


Lens culinaris Medik. subsp. culinaris) was first cultivated in Southwest Asia (SWA) 8000–10,000 years ago but archeological evidence is unclear as to how many times it may have been independently domesticated, in which SWA region(s) this may have happened, and whether wild species within the Lens genus have contributed to the cultivated gene pool. In this study, we combined genotyping-by-sequencing (GBS) of 190 accessions from wild (67) and domesticated (123) lentils from the Old World with archeological information to explore the evolutionary history, domestication, and diffusion of lentils to different environments. GBS led to the discovery of 87,647 single-nucleotide polymorphisms (SNPs), which allowed us to infer the phylogeny of genus Lens. We confirmed previous studies proposing four groups within it. The only gene flow detected was between cultivated varieties and their progenitor  albeit at very low levels. Nevertheless, a few putative hybrids or naturalized cultivars were identified. Within cultivated lentil, we found three geographic groups. Phylogenetics, population structure, and archeological data coincide in a scenario of protracted domestication of lentils, with two domesticated gene pools emerging in SWA. Admixed varieties are found throughout their range, suggesting a relaxed selection process. A small number of alleles involved in domestication and adaptation to climatic variables were identified. Both novel mutation and selection on standing variation are presumed to have played a role in adaptation of lentils to different environments. The results presented have implications for understanding the process of plant domestication (past), the distribution of genetic diversity in germplasm collections (present), and targeting genes in breeding programs (future).Protein-rich legumes accompanied carbohydrate-rich cereals since the beginning of agriculture and yet their domestication history is not as well understood. Lentil ( Lens culinaris subsp. culinaris) is a diploid (2n = 14), self-pollinating, and annual legume. With 6.3 million tons produced globally, in 2018, it constitutes an important source of dietary protein1. Lentil cultivation increases soil fertility via nitrogen fixation. Two broad varietal types of lentils are recognized based on morphological traits: the large-seeded macrosperma and the small-seeded microsperma; with a wide diversity of seed color and nutrient content , Lens lamottei, Lens ervoides, and Lens nigricans  is the wild progenitor of cultivated L. c. subsp. culinaris (henceforth referred to as culinaris). Orientalis presently occurs in Southwest Asia (SWA) and, occasionally, in Central Asia and Cyprus  L. nigricans is found in Southern Europe from Spain to Turkey, the Crimean Peninsula, Georgia, and, occasionally, in Morocco and Algeria; (iii) L. lamottei is found predominantly in Morocco  and Grotta dell’Uzzo , in the sites of Jerf el Ahmar  and Netiv HaGdud  where this could have happened is still uncertain. Wild lentils were gathered, by humans in the region, as early as the Upper Paleolithic, as attested by the Ohalo II , Abu Hureyra , and Mureybit  sites. Outside SWA, d barley . Seed sid barley . During d barley .A study matching archaeobotanical findings with molecular data to determine how many times lentil may have been domesticated and pinpointing the place(s) where this domestication may have taken place is lacking, unlike for other major crops domesticated in the same area . LikewisWhen agriculture spread outside the Fertile Crescent, lentils were part of the first set of crops introduced in Europe and Egypt . In the Lens genus  allows for the detection of thousands of genome-wide genetic markers. This has been used to study crop domestication and evolution in rice , common ns genus  and the ns genus . The latns genus . We defirovement . AccessiLens relatives to investigate the domestication and spread of this crop. We aimed to determine how many times L. culinaris may have been domesticated, in which region(s) this domestication may have happened, whether introgression from other wild Lens species may have occurred, and which genes may have been affected by domestication.Here, we present GBS results for cultivated lentil varieties, accessions of its known progenitor, and other wild Lens genus was analyzed (L. ervoides (11), L. nigricans (6), L. odemensis (4), L. culinaris subsp. tomentosus (2), and L. culinaris subsp. orientalis (44). These taxa are found in the Near East and thus could have been involved in lentil domestication. The cultivated culinaris included breeds (13), landraces (106) from Eurasia and Africa, and accessions with unknown improvement status (4). Seeds were ordered from the ICARDA (Lebanon), USDA-GRIN (United States), IPK (Germany), and Nordgen (Sweden) germplasm banks. For cultivated accessions, seeds were germinated in petri dishes and DNA was extracted from hypocotyls. For wild accessions, germination was not always successful and the number of seeds provided was small, so DNA was extracted directly from single seeds pulverized inside a tin foil wrap to prevent dust contamination. All extractions were made with the DNeasy Plant Mini Kit (Qiagen) and DNA was quantified with a Qubit dsDNA HS assay on a Qubit 2.0 Fluorometer. DNA integrity for GBS was checked by running all extractions on a 1% agarose gel. Two 96-well plates were prepared, with each well containing 300 ng of DNA.A set of 190 wild and cultivated accessions within the analyzed . The wilPstI and ApeKI restriction enzymes. Libraries were pooled and sequenced in an Illumina NextSeq 550 System (150-bp single reads). The raw sequencing reads were deposited in the ArrayExpress database at EMBL-EBI2 under accession number E-MTAB-9222.Genotyping-by-sequencing was outsourced to LGC Genomics GmbH . Libraries, including indexing, were prepared using a combination of Demultiplexing barcoded samples and verification of restriction sites were done using the Illumina bcl2fastq2 v.2.17.1.14 software . Sequencing adapter remnants were clipped from all reads and reads with final length <20 bp or 5′ends not matching the restriction enzyme site were discarded. Further trimming, which was done with CUTADAPT v.3.2 , include3 using BWA-MEM v.0.7.12 , and .vcf files were generated (henceforth referred to as Dataset 1). Different filters were subsequently applied to the VCF files using TASSEL 5 v. 5.2.52  and VCFtv.0.7.12 .4. First, VCF files were converted to phylip and nexus formats using the python script vcf2phylip.py , using RAxML v.8.0.0 , and Bayhylip.py .L. culinaris subset of 157 accessions, phylogenetic analyses were carried out also accounting for the proportion of invariable sites (GTR + I + Γ model).We determined the best-fit evolutionary model for the dataset using jModelTest v.2.1.4 . For allThe best ML tree was obtained by coupling 100 rapid bootstrap iterations and searching for the best-scoring ML tree in a single RAxML run. Two independent runs, with four chains and 1 million generations each, were computed using MrBayes. Markov chains were sampled every 500 generations with swaps of states between chains being tried on each generation of the run. The burnin was set to 25%.L. nigricans was used as the outgroup; for the L. culinaris set, orientalis accessions were used as the outgroup .For the complete set, vcfR objects using the vcfR package  and discriminant analysis of principal components (DAPC) to explore similarities between the accessions in the dataset. Firstly, the raw VCF files were read as  package  and then package  to a gen package , with sc package , plotly  package , gridExt package , and RCo package . Additio package , after cK-values between 1 and 20, 40,000 burnin, 80,000 MCMC iterations, and 10 independent runs for each value of K. The most likely values of K were chosen based on the ΔK method . The former aimed to investigate relationships across genus Lens, and the latter pertained to the domestication process. Q-matrixes were plotted in MS Excel and displayed on geographic maps using ArcMap v.10  data (L. ervoides was here used as the outgroup.The four-taxa ABBA-BABA test was used to detect introgression between wild  methods . Dsuite NP) data . L. ervosee above).To test isolation-by-distance in the dispersal of domesticated lentils outside the Fertile Crescent in SWA, the correlation between pairwise genetic distances and pairwise geographic distances between individual accessions was performed with a Mantel test, and a density plot was obtained with the MASS package v. 7.3-53.1 . This wadomestication as a trait with two states: wild vs. domesticated). Potential hybrid accessions and outliers in population structure analysis were excluded , and missing data were also removed (9617 SNPs). A mixed linear model (MLM), using the first five components of a PCA as input for population structure and a kinship matrix (Q + K model), was also computed in TASSEL 5. A Bonferroni correction with α = 0.005 was used to highlight significant marker–trait associations.We used GWAS to identify markers associated with the domesticated status . Four environmental variables were tested as proxies for temperature and water stress: (1) “maximum temperature of the warmest month,” (2) “minimum temperature of the coldest month,” (3) “precipitation of the wettest quarter,” and (4) “precipitation of the driest quarter.” The values of temperature and precipitation for each location were extracted from the bioclimatic variables available at WorldClim5 with a 5-min spatial resolution and using the raster and rgdal packages in R  to 0.3 million in PI 572329 (L. ervoides) (Dataset 1), mapping of reads against the chickpea reference genome was met with a mapping rate of 44.10% . We therefore used only the cluster reference method.The use of cessions , but thervoides) . While morientalis together with culinaris, and the other comprised L. c. subsp. odemensis, L. ervoides, and L. nigricans; each of those groups comprise discrete sub-groups of their own. However, the general dispositions of the sub-groups and their proximity to culinaris/orientalis changed significantly depending on the different SNP filtering options. For example, in filtering conditions A and C in L. nigricans was the more distinct (distant) species from the culinaris/orientalis complex, whereas in conditions I and K, that position was taken by L. ervoides. This indicates that filtering conditions applied on GBS-developed SNPs can indeed affect data interpretation (Dataset 1) and the second had more conservative filtering criteria .We explored population structure by looking at clusters revealed by the PCA, using datasets with different filtering conditions for minimum number of reads for each SNP (between 5× and 8×), proportion of missing data, number of accessions where a SNP is detected, and MAF values . Regardlretation . We therIG72553 was classified as L. nigricans, but in the PCA, it is clearly grouped with the bulk of L. ervoides accessions , as the most likely substitution model; we rooted the topology with L. nigricans as the outgroup  and culinaris (in light blue) were placed in a separate clade from wild L. nigricans (in dark green), L. ervoides (in light green), and, notably, L. c. subsp. odemensis (in pink). Subspecies odemensis (in pink) was inferred to be sister to ervoides (in light green) in the Bayesian topology (L. culinaris clade (dark and light blue). Within the L. culinaris clade, all culinaris accessions are clustered together within orientalis .Exploratory analysis using Maximum Likelihood (ML) methods (RAxML) for outgroup . The Bayoutgroup . Both ortopology , while itopology , it was L. culinaris  accessions was computed, domesticated culinaris accessions were in a clade nested within wild orientalis ones , L. nigricans, L. ervoides, and L. c. subsp. odemensis  and nucleotide polymorphism (θ = 0.172). The smallest nucleotide diversity was observed within L. ervoides (π = 0.063), although nucleotide polymorphism levels were similar to those of L. c. subsp. odemensis . The same result was obtained when the original Dataset 1 was filtered to exclude missing data (4421 SNPs), meaning missing data do not seem to be misleading the comparisons of genetic diversity between taxa , L. c. subsp. odemensis and L. ervoides had positive values and all the remaining taxa were in the negative , so did orientalis (dark blue), whereas L. nigricans and L. c. subsp. odemensis got their alleles, on one hand, from the ervoides group and, on the other, from the orientalis group  and C (purple) had the lowest proportion of orientalis alleles  accessions were analyzed, a complete separation between the wild and cultivated forms was observed. In the PCA, four orientalis accessions  were considered distinct from the rest along both axis: PI572389, PI572390, PI572393, and IG72699 , with characteristic genetic identity, i.e., only six accessions showed more than 10% of their alleles coming from culinaris clusters: IG72715 (Syria), IG72848 (Jordan), IG72833 (Turkey), IG135443 (Syria), IG72893 (Syria), and IG72715 (Syria) (IG136658 (Israel) appeared in the model K = 4 as a hybrid between orientalis and culinaris (PI572374 (Iran)  . Also, nulinaris , and the4 (Iran) . Supplemculinaris, accessions were grouped in accordance with the STRUCTURE genetic analysis for K = 4. We named these group A (light blue), group B (red), and group C (purple). Groups A and B were further divided in the K = 6 model  can be found throughout the range of lentil cultivation. However, regional patterns were revealed by the interpolation of proportional membership with spatial occurrence  was present in southern Turkey, Syria, and Iraq, whereas group A2 (gray) extended westwards into Greece and Italy, and it was more frequent in northern Turkey and Iran  was restricted to the southern Levant, Arabian Peninsula, and Horn of Africa , lower in fact than between any group of culinaris accessions , and another one mainly found in Syria, Jordan, the Caucasus, and western Turkey  or PI 345635 (Armenia)  . This suculinaris with respect to the wild orientalis. We assumed P1 and P2 to be two of the three domesticated lentil groups singled out by STRUCTURE, P3 was the wild orientalis, and P4 was L. ervoides (here as the outgroup). In a scenario of solely incomplete lineage sorting (ILS), and in the absence of gene flow between either domesticated group (P1 or P2) with wild orientalis (P3), the value of the D-statistic is expected to be zero. If there were to be more gene flow between P2 and P3 than between P1 and P3, D would be expected to be negative, and if gene flow were more intense between P1 and P3, D would be positive. In all the scenarios we tested, the D-statistic was positive, albeit very low (<0.06), indicating a small amount of gene flow between the orientalis and the culinaris groups A and B , and more between groups A and C, than between groups B and C , more introgression is detected between groups B and C than between groups A and C (We used the four-population (P1–P4) ABBA-BABA test, under different scenarios, to investigate the possibility of gene flow between each of the three groups of domesticated  A and B . The higr groups . A resid B and C . When L. A and C .L. culinaris accessions or for the groups identified by population structure methods (R2 remained low.No correlation for pairwise genetic and geographic distances was observed for either the complete panel of  methods . This in methods  were remAn MLM Genome-Wide Association Study (GWAS) computed to identify markers associated with the domesticated traits yielded only three SNPs above the Bonferroni correction threshold . A BLASTorientalis accessions. This was meant as a means to infer if adaptation was mostly based on standing variation, existing in the wild progenitor, or whether novel mutations had had to evolve in the domesticated crop. In the case of two of these three markers, only the reference allele was present in the orientalis accessions, and in the other two, both alleles could be found in the wild. For the “maximum temperature of the warmest month” trait, two SNPs in the same contig, with the alternate allele present in the same accession, were detected. These accessions were from Egypt and Saudi Arabia, and this mutation was not present in any of the orientalis accessions. Thus, this contig could be a good candidate for an adaptative mutation emerging after domestication. This sequence codes for a zinc finger BED domain-containing protein DAYSLEEPER in both chickpea and soya.A GWAS using a GLM was computed in order to detect SNPs associated with adaptation to four climatic variables. No markers were found associated with “precipitation of the driest quarter,” nor for “minimum temperature of the coldest month.” Three markers were identified for “precipitation of the wettest quarter” and in two of them, the alternate allele was observed only in lentil accessions of group B, which is distributed in very hot or desert areas . HomologOur work confirms the usefulness of GBS to investigate crop domestication histories as well as taxonomic relationships at the genus level and below. GBS was able to detect SNPs for the particular set of individuals used, thus avoiding the ascertainment bias that occurs when panels of SNPs developed in a small number of commercial varieties are used to screen landraces or wild plants . Other tWe attempted to map reads against a well-annotated reference genome. During the time this work was developed, there was no assembled lentil genome, and we therefore chose the chickpea genome, because it has a similar domestication history to that of lentil . HoweverLens genus distinctively recovered time and again. For questions pertaining to population structure and phylogenetic relationships, filtering conditions did not affect the results. Population structure analyses and phylogenies inferred were similar to previous studies: four or five groups, when different species within Lens are considered   ; and thr or not) .L. culinaris accessions  were revealed by STRUCTURE  can be seen as an argument against the hypothesis of a single domestication event. The geographic distribution of our group A and group B accessions broadly correspond to the clusters K6 and K2 identified by orientalis accessions shown in the STRUCTURE model for K = 4 (with L. culinaris accessions only) have a high proportional membership to culinaris group B  could have led to the emergence of groups A and B in cultivated lentil. This is likely to have been a protracted process, as evidenced by the slow change in the form and size of lentils in the archaeobotanical record . An exchientalis . Semi-doOnce established, cultivated lentil spread out of SWA. The geographic distribution of groups A and B suggests that both groups were part of the westward introduction of lentils into Europe . It is nThe GWAS based on GBS-generated SNP data has previously been used to identify loci or genes underlying domestication and adaptation, e.g., in common bean  and barlIn the case of domestication, we found three associated SNPs but could not identify a putative gene associated with any of them other than an uncharacterized protein expressed in chickpea . Regardiorientalis. If so, we could hypothesize that adaptation emerged from standing variation of the wild progenitor and, if not, that novel mutations emerged in the domesticated gene pool and were positively selected for. For four SNPs, only the reference allele was identified in orientalis, and for one (SC00006020_87), both alleles were detected in the wild. It is noteworthy that, for adaptation-related SNPs, the alternative allele was always found only in group B accessions, which are distributed in the driest and warmest regions.We investigated if both alleles for each of the five adaptation-related SNPs were present in Lens genus, already revealed in previous studies. Orientalis was shown to be the sole wild progenitor of cultivated lentil (culinaris), with insignificant contribution from other wild species to the domesticated gene pool. Three groups were identified within cultivated lentils, and these correspond broadly to geographic regions. Lentil was likely domesticated from wild stands from somewhere between southern Turkey, to the north, and Jordan, to the south, in a protracted and incremental fashion. Two regional groups of cultivated lentils emerged in SW Asia, which further spread into different regions. A third group probably resulted from lentil cultivation expanding into Central Europe. Introgression between cultivated lentil and its wild progenitor seems to have occurred at low levels. New mutations and selection from standing variation have probably resulted in local varieties becoming adapted to harsher environments in some areas, and these make a target for lentil breeding programs.Genotyping-by-sequencing is an effective approach to study the domestication and spread of lentil. We confirmed the existence of four gene pools within the https://www.ebi.ac.uk/arrayexpress/, E-MTAB-9222.The datasets generated for this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: HRO: conceptualization, plant germination and DNA extraction, and project supervision. HRO, ML, ID, and ATM: methodology. ML, HRO, and ID: data analysis. ML and HRO: writing—original draft. ML, ID, and ATM: writing—review and editing. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


MSTN and RSAD2, and several potential novel candidate genes such as ACTN2 and THOC1. The results of the LASSO regression analysis demonstrated that CNVs can be used to detect associations with carcass traits which were not detected using the set of SNPs available in the present study. Therefore, the CNVs and SNPs available in the present study were not redundant forms of genomic data.The relative contributions of both copy number variants (CNVs) and single nucleotide polymorphisms (SNPs) to the additive genetic variance of carcass traits in cattle is not well understood. A detailed understanding of the relative importance of CNVs in cattle may have implications for study design of both genomic predictions and genome-wide association studies. The first objective of the present study was to quantify the relative contributions of CNV data and SNP genotype data to the additive genetic variance of carcass weight, fat, and conformation for 945 Charolais, 923 Holstein-Friesian, and 974 Limousin sires. The second objective was to jointly consider SNP and CNV data in a least absolute selection and shrinkage operator (LASSO) regression model to identify genomic regions associated with carcass weight, fat, and conformation within each of the three breeds separately. A genomic relationship matrix (GRM) based on just CNV data did not capture any variance in the three carcass traits when jointly evaluated with a SNP-derived GRM. In the LASSO regression analysis, a total of 987 SNPs and 18 CNVs were associated with at least one of the three carcass traits in at least one of the three breeds. The quantitative trait loci (QTLs) corresponding to the associated SNPs and CNVs overlapped with several candidate genes including previously reported candidate genes such as  Carcass value is an important component of both beef-on-beef  and beefThe objective of the present study was to quantify the relative contribution of CNV data and SNP genotype data to the additive genetic variance of carcass traits in cattle. A secondary objective was to jointly evaluate SNP genotypes and called CNVs in a least absolute selection and shrinkage operator (LASSO) regression model to identify genomic regions associated with carcass traits in cattle.All animals in the dataset were genotyped on the Illumina BovineHD  SNP genotype array . The chromosome and positions of the SNPs were taken from the UMD3.1.  assemblyEstimated breeding values (EBVs) for carcass weight, carcass fat, and carcass conformation were obtained for each genotyped animal in the dataset from the January 2019 national genetic evaluation of the Irish Cattle Breeding Federation (ICBF) database . Each of the animals in the present study were sires. The EBVs of these sires were estimated from their descendants; the EBVs of the sires were the equivalent to >150,000 effective phenotypic records. Carcass weight is the weight of the animal, in kg, after organs, visceral fat, limbs, and head have been removed . CarcassCopy number variants were called from the edited high-density SNP array  data using PennCNV  and QuanM is the matrix of genetic marker genotypes recoded as -1, 0, 1 representing the homozygote, heterozygote, and the other homozygote, respectively, P is a matrix of allele frequencies for each genetic marker in the dataset, ith column vector of P is given by 2 were calculated separately for the CNV data and the SNP genotype data within each of the three breeds using method 1 described in  FImpute .For each breed, the CNV-derived GRM was calculated twice using two separate procedures. In the first procedure, double-deletions and double-duplications were recoded as -1, single-deletions and single-duplications were recoded as 0, and the normal state (i.e. the absence of a CNV) was recoded as 1. In the case of mixed-CNVs (CNVs which are present as both deletions and duplications in the population), in order to avoid treating deletions and duplications as identical variants, each mixed-CNV was treated as two distinct loci, one to represent the deletions and the other to represent the duplications. For the deletion locus, duplications were recoded as normal state (i.e. no CNV); similarly for the duplication locus, any deletions were recoded as normal state. In the second procedure to calculate the CNV-derived GRM, mixed-CNVs were not treated as two distinct loci but instead all deletions were recoded as -1, the normal state was recoded as 0, and all duplications were recoding as 1.A GRM of the entire population  was constructed using the imputed SNP genotype data  for all available animals. Principal components analysis of this GRM was used to determine population substructure within the entire population of available animals . SeparatdEBV was a vector of deregressed EBVs of each animal in the population. The intercept of the model was denoted by μ and was treated as a fixed effect, the vector a was a random effect with distribution N was used as the dependent variable in a second linear mixed model. The fitted model was:.The variance component of the random effect of the second model, A LASSO regression model was conducted to jointly evaluate the association between individual SNPs and CNVs and the deregressed carcass EBVs. The principal components of the SNP-derived GRM and the CNV-derived GRM were also included in the model as covariates to account for population stratification. The LASSO models were solved separately for each breed and trait using the R package “glmnet” . Only thy, was the weighted deregressed EBVs for each animal. The matrix X consisted of the full set of SNPs and CNVs, the principal components the SNP and CNV GRMs, and an intercept term. The vector β was the effect of each independent variable in matrix X, λ was the penalty factor in the model, and the solutions, The dependent variable, Since none of the CNVs detected had at least 5 animals of a given breed carrying either a double-deletion or a single-deletion, the identified double-deletions were assumed as a single-deletion and were collapsed into a single deletion class. Similarly, all duplications were collapsed into a single duplication class and considered as single-duplications because there was no CNV with at least 5 animals within breed carrying either a double-duplication or a single-duplication. The number of animals and CNVs available for each breed and trait is in The LASSO model is a biased estimator of the regression coefficients for the associated genetic markers due to the inclusion of the penalty term in the LASSO model . To obtaTo compare the results of the LASSO analysis with a more traditional genome-based association analysis, each of the associated variants identified by the LASSO analysis were individually re-analysed, within breed, using a linear mixed model approach. Along with an intercept term and the SNP genotype, the direct additive genetic effect of the animal was accounted for by fitting the SNP-derived GRM. The association of the genetic variant with the trait of interest was tested using a t-test under the null hypothesis that there was no association between the genetic variant and the trait of interest.http://ensembl.org), which overlapped in genomic position with each of the QTLs.For each associated SNP and CNV, a quantitative trait locus (QTL) region was designated as 50 kb upstream and 50 kb downstream of the SNP or CNV. When an associated SNP or CNV was located within 50 kb of another associated SNP or CNV, respectively, then they were merged into the same QTL; the boundary of these QTLs were 50 kb upstream and 50 kb downstream of the outermost variants in the QTL. The genomic position for each associated SNP and CNV were updated to the genomic positions given in the ARS-UCD1.2 assembly of the cattle genome  in orderp-value for the observed enrichment score under the null hypothesis that there is no gene set enrichment.Gene set enrichment analysis was performed on the set of genes that overlapped with the QTL regions of each of the associated CNVs and SNPs identified by the LASSO regression analysis using the Database for Annotation, Visualisation, and Integrated Discovery (DAVID). This enrichment analysis was carried out separately per breed for the QTLs associated with each of the three traits. The DAVID algorithm identifies clusters of genes, assigns an enrichment score for the gene cluster, and gives a The population structure of the entire population was examined using principal components analysis; the first principal component accounted for 6.2% of the total variance in the SNP-derived GRM and the second principal component accounted for 3.3% of the total variance in the SNP-derived GRM . The topAfter edits 13,969, 3,954, and 2,805 CNVs were available to calculate the CNV-derived GRMs for the Holstein-Friesian, Charolais, and Limousin populations, respectively, whereas for each breed, 712,555 SNPs were used to calculate the SNP-derived GRMs. For each breed, the CNV-derived GRM was calculated twice using the two separate procedure described in the methods. The variance components analysis was conducted separately for these two sets of CNV-derived GRMs; there was no difference in the results of the variance components analysis using either of these two sets of CNV-derived GRMs. The proportion of the variance accounted for by the SNP-derived GRM when considered in a linear mixed model was between 0.032 and 0.291 for the three carcass traits analysed in the three breeds . In compA total of 987 SNPs and 18 CNVs were associated with at least one of the three carcass traits in at least one of the three breeds analysed. The number of CNVs and SNPs associated with each of the three carcass traits per breed is given bta-mir-584-7. Single nucleotide polymorphisms close to this QTL region were also associated with carcass conformation and weight in the Limousins.The QTL with an associated CNV and an associated SNP was associated with carcass fat in the Charolais and was located on chromosome 29 between 36.70 and 36.77 Mb. The CNV in this QTL was a duplication present in 8 animals and the associated SNP in this QTL, rs109364153, was located within the genomic position of the associated CNV; this QTL overlapped in genomic position with a single gene which was the RNA gene MSTN is the most likely candidate gene for these two QTLs identified in the Limousins and the Charolais. None of the 26 SNPs in the genomic region of these two QTLs were associated with any of the three carcass traits in the Holstein-Friesians, even though each of the 26 SNPs were segregating in the Holstein-Friesians. A list of the top five SNP QTLs associated with each of the three carcass traits within breed is given in Twenty-eight SNP QTLs were associated with more than one trait within breed  and anotp-values for each of the tested variants was between 0.027 and 1.4 × 10−15  to two SNP QTLs. The first SNP QTL was located on chromosome 3 between 90.89 and 90.90 Mb and was associated with carcass conformation in the Charolais; the second SNP QTL was located on chromosome 3 at 90.76 Mb and was associated carcass conformation in the Limousins. This gene has previously been associated with milk yield and mastitis resistance in cattle (ACTN2 was located 230 kb from a SNP positioned on chromosome 28 at 9.09 Mb which was associated with carcass fat in the Charolais. ACTN2 also overlapped with a SNP on chromosome 28 at 9.35 Mb which was associated with carcass fat in the Limousins. In mammals, ACTN2 is highly expressed in skeletal and cardiac muscle (ACTN2 cause cardiac myopathy (THOC1; this gene overlapped with a SNP QTL on chromosome 24 between 35.22 and 35.25 Mb which was associated with carcass fat in the Charolais and Limousins. THOC1 may have a role in muscle cell proliferation in pigs (In addition to the candidate genes which have previously been identified in genome-wide association studies of carcass traits in cattle, several novel candidate genes were also identified in the present study. The protein coding gene n cattle  and grown cattle , but hasc muscle  and mutamyopathy  and muscmyopathy  in human in pigs .MSTN overlapped with a SNP QTL that was associated with all three traits in the Charolais as well as carcass fat and conformation in the Limousins. MSTN is known to have a large effect on carcass traits in cattle (The 28 SNP QTLs which were associated with more than one trait within breed may be contributing to the genetic correlation between carcass traits in cattle . Two SNPn cattle .MSTN, NCAPG, and LCORL were associated with at least one of the carcass traits in the Charolais and the Limousin populations. The dataset used in the present study had previously been used as part of a larger dataset which consisted of 28,470 sires across six different breeds with each of the sires imputed to 41,389,526 SNPs. This dataset was used by PTPRU and novel candidate genes which overlapped with SNP QTLs included ACTN2, THOC1, and USP24.The CNV-derived GRM did not account for any additive genetic variance in the three carcass traits when jointly evaluated with a SNP-derived GRM in a linear mixed model for each of the three breeds. A LASSO regression analysis which jointly evaluated SNPs and CNVs did, however, identify 18 CNVs associated with at least one of the three carcass traits in the three breeds analysed although the proportion of genetic variance explained was small. Some of these associated CNVs overlapped with genes that have previously been associated with carcass traits, which suggests that these CNVs represent true associations. In addition to reaffirming candidate genes that have previously been associated with carcass traits in cattle, several novel candidate genes where also identified. Novel candidate genes which overlapped with CNV QTLs included


The aquaculture industry has been dealing with salmon lice problems forming serious threats to salmonid farming. Several treatment approaches have been used to control the parasite. Treatment effectiveness must be optimized, and the systematic genetic differences between subpopulations must be studied to monitor louse species and enhance targeted control measures. We have used IIb‐RAD sequencing in tandem with a random forest classification algorithm to detect the regional genetic structure of the Norwegian salmon lice and identify important markers for sex differentiation of this species. We identified 19,428 single nucleotide polymorphisms (SNPs) from 95 individuals of salmon lice. These SNPs, however, were not able to distinguish the differential structure of lice populations. Using the random forest algorithm, we selected 91 SNPs important for geographical classification and 14 SNPs important for sex classification. The geographically important SNP data substantially improved the genetic understanding of the population structure and classified regional demographic clusters along the Norwegian coast. We also uncovered SNP markers that could help determine the sex of the salmon louse. A large portion of the SNPs identified to be under directional selection was also ranked highly important by random forest. According to our findings, there is a regional population structure of salmon lice associated with the geographical location along the Norwegian coastline. The combination of IIb‐RAD sequencing and random forest enables the discovery of SNP markers for the population structure and sex determination. Our data suggest that salmon lice have a regional population structure associated with their geographic position along the Norwegian coast. Additionally, the sex of lice can be deduced by using marker SNPs. Figure There is great interest in the population genetic assignment of salmon louse  samples were collected from 12 different geographical locations along the Norwegian coast from commercial Atlantic salmon pens in 2019   were false discovery rate (FDR) adjusted for multiple comparisons using the R package p‐adjust, which implements the Benjamini‐Hochberg step‐up procedure , discriminant analysis of principal components (DAPC), and population assignment probabilities were calculated using Paradis, . IsolatiParadis,  using R 2.5randomForest R package  is a novel nonlinear dimension reduction method built on the theoretical foundations of Riemannian geometry and algebraic topology  ranged from 0.152 to 0.209, while expected heterozygosity within population (Hs) ranged from 0.176 to 0.196  . A negative value of Tajima's D was calculated, indicating an excess of low‐frequency polymorphisms relative to expectation . No population structure for geographical areas was observed using PCA, and no clusters were formed for sex differentiation. The visualization using UMAP did not show any clustering for the geographical locations and sex differentiation  Table  suggesti Figures .3.5The supervised machine learning technique Random Forest was used to detect important SNPs for population structuring. We reduced the dataset dimension based on the regional geography (Geo) and gender‐disparity (Sex) features. For geography, a subset of 91 SNPs (RfGeo) was identified, and a model built on these SNPs was able to classify each sample's originating region with an accuracy of 43.75%. Similarly, a sex‐specific dataset of 14 SNPs (RfSex) was derived and was used to correctly classify the sex with 87.5% accuracy on the unseen data. These reduced datasets with highly discriminatory loci were used to conduct downstream analyses that were similar to those conducted on the full‐SNP dataset.PCA performed on the RfGeo dataset revealed a greater spatial distinction between the regional populations Figure . When cop < .01) values ranging from 0.04 to 0.067 suggesting a considerable degree of differentiation within geographical regions. Additionally, the pairwise difference for population in each sampling site (P01–P12) is shown as a heatmap , suggesting a population structure of salmon lice. Results from UMAP analysis showed that it was possible to distinguish between geographical regions for RfGeo . Likewise, the gender‐specific clustering was strongly observable using the RfSex dataset with both PCA and UMAP methods  are shown as a heatmap Figure . Pairwisp Figure . Similaro Figure , and loco Figure . The eucs Figure .3.6Boruta algorithm . Four of the SNPs were found to be intron variants. The rest of the SNPs were between intergenic regions of different genes. Table We investigated the regions upstream and downstream of the outlier SNPs in the annotated genome to classify genes possibly involved in local adaptation and under positive selection in salmon lice. We also used the We have also outlined and annotated the important SNPs in sex determination Table . The SNP4L. salmonis populations in coastal geographical locations along Norway and to identify potential markers of selection. Despite the significant effect of salmon lice on Norwegian aquaculture industry, few studies have been conducted to determine the importance of population genetics in salmon lice management. There is also gap in the knowledge of the sexual dimorphism in salmon lice. Sexual dimorphism, the state in which there is a difference between individuals of different sex other than that of morphology of sexual organs, delineates the critical components of higher eukaryotic biology , which is similar to the previous studies, and genetic drift had little impact on population structure  was recently linked to sex reversal from female to male ; Data curation (lead); Formal analysis (lead); Investigation (lead); Methodology (lead); Resources ; Validation (lead); Visualization (lead); Writing – original draft (lead); Writing – review & editing . Anna Solvang Båtnes: Conceptualization (supporting); Funding acquisition ; Project administration (lead); Supervision ; Validation (supporting); Writing – review & editing . John Zobolas: Data curation (supporting); Software ; Visualization ; Writing – review & editing . Yngvar Olsen: Funding acquisition (lead); Project administration (supporting); Resources ; Supervision ; Validation ; Writing – review & editing . Atle M. Bones: Conceptualization ; Funding acquisition ; Resources (lead); Supervision ; Validation ; Writing – review & editing . Per Winge: Conceptualization (lead); Data curation ; Formal analysis ; Funding acquisition ; Investigation ; Methodology ; Project administration ; Resources ; Software ; Supervision (lead); Validation ; Visualization (supporting); Writing – review & editing .https://www.ncbi.nlm.nih.gov/bioproject/PRJNA762270; https://doi.org/10.5061/dryad.p8cz8w9r9; https://github.com/g3prashan/IIb‐RADseq_popgen.This article has been awarded Open Materials, Open Data, Preregistered Research Designs Badges. All materials and data are publicly accessible via the Open Science Framework at Supplementary MaterialClick here for additional data file.


Carcass length is very important for body size and meat production for swine, thus understanding the genetic mechanisms that underly this trait is of great significance in genetic improvement programs for pigs. Although many quantitative trait loci (QTL) have been detected in pigs, very few have been fine-mapped to the level of the causal mutations. The aim of this study was to identify potential causal single nucleotide polymorphisms (SNPs) for carcass length by integrating a genome-wide association study (GWAS) and functional assays.P = 4.49E−07) on pig chromosome 17 for carcass length, which was further validated in two other DLY populations. Within the detected 1 Mb region, the BMP2 gene stood out as the most likely causal candidate because of its functions in bone growth and development. Whole-genome gene expression studies showed that the BMP2 gene was differentially expressed in the cartilage tissues of pigs with extreme carcass length. Then, we genotyped an additional 267 SNPs in 500 selected DLY pigs, followed by further whole-genome SNP imputation, combined with deep genome resequencing data on multiple pig breeds. Reassociation analyses using genotyped and imputed SNP data revealed that the rs320706814 SNP, located approximately 123 kb upstream of the BMP2 gene, was the strongest candidate causal mutation, with a large association with carcass length, with a ~ 4.2 cm difference in length across all three DLY populations . This SNP segregated in all parental lines of the DLY  and was also associated with a significant effect on body length in 299 pure Yorkshire pigs (P = 9.2E−4), which indicates that it has a major value for commercial breeding. Functional assays showed that this SNP is likely located within an enhancer and may affect the binding affinity of transcription factors, thereby regulating BMP2 gene expression.Here, we present a GWAS in a commercial Duroc × (Landrace × Yorkshire) (DLY) population that reveals a prominent association signal (Taken together, these results suggest that the rs320706814 SNP on pig chromosome 17 is a putative causal mutation for carcass length in the widely used DLY pigs and has great value in breeding for body size in pigs.The online version contains supplementary material available at 10.1186/s12711-021-00689-0. Body or carcass length (CL) of pigs varies considerably between pig breeds [Body size, usually measured as body height or length, is not only an important indicator of human growth and health  but is ag breeds . Due to g breeds –6.To uncover the genetic architecture of CL in pigs, quantitative trait loci (QTL) for this trait have been detected by linkage mapping or genome-wide association studies (GWAS), usually using an F2 cross between outbred lines, such as a European commercial breed with wild boar or with a local native breed or another commercial breed –13. GWASMany GWAS in pigs have used the Illumina porcine SNP60 BeadChip with 62,163 single nucleotide polymorphisms (SNPs) to genotype animals. In general, trait-associated SNPs identified by GWAS are in strong linkage disequilibrium (LD) with the causal mutation in the studied population, but this may not be the case in other populations with the same QTL because different populations can have different LD patterns. Therefore, for practical applications in breeding, it is important to identify the causal gene and causal mutation for a QTL that has a strong effect on an economic trait.Many QTL have been detected in pigs but very few have been fine-mapped to the level of the causal mutation . The resFine-mapping is a statistical analysis approach that is designed to assign probabilities of causality to candidate variants located in regions identified by GWAS. Recently, several Bayesian methods, including CAVIARBF, CAVIAR, and PAINTOR, have been developed to incorporate GWAS summary statistics into fine-mapping analyses and to calculate posterior probabilities of causality for SNPs across all loci of interest –25.In this study, we identified a unique significant QTL for CL in DLY pigs by 60 K GWAS and then fine-mapped the causal variant through both regional association analysis in multiple populations and Bayesian analysis with information on genotyped and imputed SNPs in the QTL region. We also conducted functional validation and molecular biology studies to determine the causal SNP for this QTL.This study involved three crossbred DLY and a purebred Yorkshire pig population. As far as we know, the four populations are not related to each other. The three DLY populations  comprised 686, 608, and 207 pigs, respectively, were raised by different farm enterprises, and slaughtered at approximately 180 days of age in the same commercial abattoir in Nanchang city in 2011, 2012, and 2015, respectively. Carcass length (CL) was measured from the first cervical vertebra to the pubis on all pigs, while in the DLY-P3, the length of the tenth thoracic vertebra was also measured. For the Yorkshire population, body length was measured on 299 pigs at approximately 5 months of age on a farm owned by Fujian Yichun Agricultural Development Co., Ltd.Genomic DNA was isolated from ear tissue using a standard phenol/chloroform extraction method and was adjusted to a concentration of 50 to 80 ng/µL using a Nanodrop-1000 spectrophotometer . All pigs from the DLY-P1 population were genotyped with the Illumina PorcineSNP60 BeadChip for the purpose of GWAS analysis. Quality control was conducted using the PLINK v1.07 software . SNPs wia, respectively. The suggestive (P = 2.16E−05) and genome-wide (P = 1.07E−06) significance thresholds were determined by the Bonferroni method [reduce−Vfull)/Vreduce, where Vfull and Vreduce are estimates of the residual variance of ordinary linear models with and without SNP terms, respectively.Genome-wide association analyses of body size were conducted using the GenABEL package in the R software . Associai method . The phePopulation stratification was assessed by examining the distribution of test statistics and assessing their deviation from the null distribution  in a quantile–quantile (Q-Q) plot . The Q-QSus scrofa reference genome sequence (Sscrofa11.1) and the SNP dataset from the whole-genome resequencing data of western commercial pigs, we selected 267 SNPs that were nearly evenly distributed across a QTL region on Sus scrofa (SSC) chromosome 17 that was identified by GWAS and segregated in at least one of the three commercial lines . To save on costs and improve the power of QTL detection, we also selected 200 (top 100 and bottom 100) and 300 individuals (top 150 and bottom 150) with extreme CL phenotypes from DLY-P1 and DLY-P2, respectively, to form the DLY-Pf population for QTL fine-mapping analysis. Then, the 267 SNPs were genotyped in the DLY-Pf using the SNP scan technology based on double ligation and multiplex fluorescence PCR . Potential population stratification was assessed by principal component analysis (PCA) using the PLINK software. Then, imputation from the genotyped SNPs to whole-sequence for the QTL region on SSC17 was performed on the mixed population using the Beagle 4.1 software with default parameter settings. Ultimately, we obtained genotype data for 11,029 SNPs in this region, of which, 9149 passed the quality control criteria mentioned previously and were included in the subsequent regional association analysis.Based on the SNP annotation information from the The Bayesian method provides a powerful way to refine association signals or identify potential causal SNPs in GWAS-detected regions . We usedbone morphogenetic protein 2 (BMP2) gene is a strong candidate gene for the QTL on SSC17. Using the online Primer3 software, we designed 19 and three pairs of primers to amplify the full-length DNA and cDNA sequences of porcine BMP2, respectively. The primer sequences and the lengths of the corresponding amplified fragments are listed in Table S1 [see Additional file BMP2 gene, we used the DNA of 10 DLY pigs with different genotypes  at the 60 K GWAS tag SNP rs80965549. To sequence BMP2 cDNA, total RNA was extracted from the articular cartilage tissue of 10 DLY pigs with 5 AA and 5 AG genotypes at the SNP rs80965549. Then, cDNA was synthesized from 1 µg of total RNA by using the PrimeScript™ RT Reagent kit (Takara). Polymerase chain reaction (PCR) was performed in a 25 µL reaction mixture containing 50 ng template DNA, 0.5 µL of each dNTP, 0.5 µL of each primer, 2.5 µL buffer, 1.5 µM MgCl2, and 0.5 µL of Tag DNA polymerase (Takara) and using a PTC-200 thermal cycler (Bio–Rad). The PCR products were sequenced on both strands by using a BigDye™ Terminator v3.1 kit (Applied Biosystems) on a 3130 DNA Analyzer (Applied Biosystems). The genomic sequences were assembled and analyzed for SNP detection using the SeqMan program in the DNASTAR software.The AA and GA) at the GWAS tag SNP rs80965549. The purified mRNA was fragmented for sequencing on the MGISEQ-2000 platform at BGI Co. Ltd. The raw sequencing data of these samples averaged 10.92 G. Using the STAR software, clean reads were mapped to the Sus scrofa reference genome build 11.1. Gene expression levels were estimated using the StringTie software [Total RNA was isolated from cartilage tissue from eight DLY-P3 pigs. They represented two groups  with extremely different CL phenotypes and different genotypes  was added to terminate the digestion and for subculture. Live cells were then cryopreserved in liquid nitrogen at − 196 °C.A total of 102O to a final volume of 15 μL; (2) for the experimental group: 1.5 μL 10 × binding buffer, 5 μL nuclear extract, 1 μL (20 fmol) wild-type or mutant biotin-labeled probe, and 7.5 μL ddH2O to a final volume of 15 μL; and (3) for the competition assay group: 1.5 μL 10 × binding buffer, 5 μL nuclear extract, 1 µL (20 fmol) biotin-labeled probe, 1 μL (1–4 pmol) nonlabelled competitor probe, and 6.5 μL ddH2O to a final volume of 15 μL. The binding reaction was incubated at 20–25 °C for 30 min. After adding 1.5 μL of loading buffer, the DNA–protein complexes were separated by electrophoresis on a 5% polyacrylamide gel in 0.5 × TBE buffer at 10 V/cm for 1 to 1.5 h at 4 °C. The reaction samples were transferred to a nylon membrane by wet electroblotting at 390 mA for 60 min. Transferred DNA was crosslinked for 60 s under UV light (254 nm) at 120 mJ/cm2, and the protein-DNA complexes were detected by a chemical imaging system .Nuclear protein was extracted from the cultured cells (MC3T3-E1 and MLO-Y4) using the Nuclear Extraction Kit (Abcam) and protein concentration was measured with the BCA Protein Assay Kit (Abcam). The following two 31-bp biotin-labeled oligonucleotides were used as probes for the wild-type (Wt) and mutant (Mt) sequences, respectively: 5’-CAAGGTAAATCTCAACAATATTTTGCAGTTT-3’ (Wt) and 5’-CAAGGTAAATCTCAATAATATTTTGCAGTTT-3’ (Mt). The composition of the reaction system varied according to the experimental design: (1) for the negative control group: 1.5 μL 10 × binding buffer, 0.5 μL biotin-labeled probe, and 13 μL ddHBMP2 gene on transcriptional activity, both allelic forms (TT and CC) of a 201 bp fragment centered around the QTL were PCR-amplified from genomic DNA and subcloned into the Kpnl and BgIII sites of the pGL3-promoter vector (Promega). MC3T3-E1 and MLO-Y4 cells were grown to approximately 80% confluence. Cells were transiently co-transfected with the firefly luciferase reporter construct (100 ng) and a Renilla luciferase control vector  using the Hieff TransTM kit (YEASEN) according to the manufacturer’s recommendations. Cells were incubated for 48 h before lysis. The luciferase activities were measured using the Dual-Luciferase Reporter Assay System (Promega). At least three independent experiments were performed in triplicate for each cell line. The activities of the mutant promoter were compared to those of the wild-type promoter based on normalized luciferase expression. Statistical analysis was performed with analysis of variance (ANOVA). A P value lower than 0.01 was considered as statistically significant.To measure the effect of the putative causal SNP (rs320706814) upstream of the P = 4.49E−07) on SSC17  had a 2 cm greater CL than noncarriers  and also obtained a significant result (P = 2.52E−05).To identify QTL for body size in pigs, we performed a GWAS for CL using 60 K SNP genotype data from a discovery population of 686 DLY pigs (denoted as DLY-P1). The genomic inflation factor (λ) was equal to 1.0018  dropped below 0.2 at distances greater than 200 kb in the DLY-P1 population [see Additional file FERM1, ENSSSCG00000043546, and BMP2. The FERM1 gene encodes the fermitin family member 1 protein, which is involved in integrin signaling and cell adhesion. Comparative genomic analysis of the ENSSSCG00000043546 gene showed that it has no homologs in humans, dogs, cows, or mice, and its function is unknown. The BMP2 gene encodes bone morphogenetic protein 2, a secreted ligand of the TGF-beta superfamily of proteins, which is known to play an important role in bone and cartilage development [BMP2 to be the most likely candidate gene for the QTL on SSC17.By LD decay analysis, we found that the LD r dropped elopment . TherefoBMP2 gene that could underlie the observed QTL effect. To this end, we sequenced the full-length DNA sequences of the BMP2 gene of 10 DLY pigs with different rs80965549 genotypes  and CL phenotypes (high versus low) and detected 46 variants in the BMP2 gene [see Additional file BMP2 in 10 other DLY pigs (including 5 AA and 5 GA genotypes at SNP rs80965549), which revealed no splicing site mutation that might cause deletions or insertions in the BMP2 transcript sequence [see Additional file BMP2 causes a serine to leucine change at amino acid residue 257 . However, this missense mutation was predicted to be “neutral” by both the SIFT (score: − 2.29) and Polyphen-2 (score: 0.117) software. Moreover, the S257L mutation was not significantly associated with CL in DLY-P1 (P = 0.0778), which suggested that it was unlikely to be a causal mutation for the SSC17 QTL. Besides the S257L SNP, no variants in BMP2 showed a genotypic segregation pattern similar to that of the GWAS tag SNP rs80965549 [see Additional file BMP2 were likely related to the SSC17 QTL.Next, we determined whether there was any variant within the BMP2 gene, we hypothesized that a regulatory variant that affects BMP2 transcription might be responsible for the SSC17 QTL. Thus, to examine whether the expression level of BMP2 differed between QTL genotypes, we compared the transcriptomic profiles in cartilage samples between AA and GA genotype groups (n = 4 per group) at the GWAS tag SNP rs80965549. The results showed that there were 486 differentially-expressed genes (DEG) between the two groups across the genome [see Additional file BMP2 gene was identified as one of the DEG [see Additional file FERM1 and ENSSSCG00000043546) for the SSC17 QTL were not. The expression level of BMP2 was nearly twofold lower in the heterozygote group than in the wild-type group. Furthermore, we used transcriptomic data from four heterozygotes and one wild-type homozygote for the SSC17 QTL, all of which were heterozygous for the SNP rs45434988 in an exon of BMP2, to detect allelic imbalances in BMP2 transcripts. Based on the allelic expression ratio (AER) of the transcribed SNP rs45434988, we found that allelic expression imbalance  occurreP = 5.22E−23)  with its neighboring SNPs, and no haplotype block contained rs345818757 [see Additional file To fine-map the QTL on SSC17, we genotyped 267 additional SNPs within the candidate region from15 to 16 Mb and in the fine-mapping population DLY-Pf of 500 pigs that were selected from DLY-P1 and DLY-P2 based on high or low CL phenotype. No obvious problem of population stratification was observed in the selected individuals by PCA [see Additional file P = 3.07E−29; at 15,626,425 bp), with a significance level that was five times higher than that of nearby SNPs  after correcting the genotypes of these 15 pigs. In addition, the conditional association analysis of SNP rs320706814 indicated that SNP rs318639793 had an independent association with CL  .To maximize the effectiveness of the association analysis, we imputed the genotypes of all SNPs within the QTL interval for the 500 pigs in the DLY-Pf population using a reference panel containing 561 re-sequenced individuals. In total, 9149 SNPs in the QTL interval met the quality control criteria and were included in the subsequent association analysis. The most significant SNP associated with CL was rs320706814 (P = 2.01E−23), while the P values of the other 21 SNPs ranged from 1.82E−22 to 1.75E−10 [see Additional file To determine the most likely causal variant underlying the QTL for CL, we genotyped the top 22 most significant SNPs that were detected in the DLY-Pf population by imputation-based association analysis for a total of 1501 pigs from the three DLY populations and examined their associations with CL phenotypes. Again, SNP rs320706814 showed the strongest association with CL (C/T) at rs320706814  with other significant SNPs in the QTL interval but also confirmed that rs320706814 was likely a major contributor to the QTL.We also performed haplotype analysis in the DLY-Pf population, which was used for QTL fine-mapping. Haplotypes were constructed based on the top seven significant SNPs associated with CL. Four major haplotypes with frequencies higher than 3% were identified. By comparing the associations of these four haplotypes with CL, they could be divided into favorable and unfavorable haplotypes for CL, which were consistent with the segregation of alleles (TT genotype at SNP rs320706814 was estimated to increase CL by 4.39 cm compared with the CC genotype (Table P = 9.2E−4), with a difference in CL between the TT and CC genotypes of 2.31 cm , LTV10th (P = 3.13E−6), and LTV (P = 1.35E−2) but not with NTV (P = 0.83) , the length of all thoracic vertebrae (LTV), and the length of the tenth thoracic vertebra LTV10th) were also recorded in DLY-P3, which allowed us to estimate the effect of the SSC17 QTL on these traits. We found that SNP rs320706814 was significantly associated with CL  and Yorkshire (13.5%) pigs  showed that the rs320706814 SNP results in loss of the SOX5 transcription factor binding site and creation of a CDXA binding site. Then, we performed EMSA to evaluate differences in the binding of nuclear proteins between alleles of the rs320706814 SNP (C > T) in nuclear extracts from MC3T3-E1 cells and MLO-Y4 cells. Results showed that this SNP increased the DNA–protein binding affinity in mouse embryonic osteoblast precursor (MC3T3-E1) cells  in cells transfected with vectors containing the oligonucleotides, which suggest that the region surrounding SNP rs320706814 acts as an enhancer to stimulate gene expression. Moreover, in both MC3T3-E1 cells and MLO-Y4 cells, the mutant allele T at SNP rs320706814 caused a decrease in luciferase activity compared to the wild-type allele C  three-way cross has been the most widely used hybrid in commercial pig production. Understanding the genetic basis of phenotypic variation in DLY pigs will help to improve the use of heterosis and the effectiveness of purebred selection. Our GWAS unveiled the genetic architecture underlying carcass length in DLY pigs for the first time and revealed a unique significant associated SNP (rs80965549) at approximately 15.4 Mb on SSC17. This SNP was estimated to increase CL by 2.34 cm and explain 8% of the phenotypic variation in the DLY-P1 population; thus, it represents a major QTL. This result is consistent with empirical evidence that there are some common mutations with moderate to large effects on body size in livestock, e.g., dogs and cattle . In contLike for most detected QTL, to date no in-depth analysis of the QTL on SSC17 for CL has been reported. Here, we applied a series of statistical methods to refine the associated region and identify putative causative variants. First, we genotyped dense markers along the QTL region in a group of 500 individuals that were selected for this purpose (DLY-Pf). Second, non-genotyped SNPs in the target region were imputed by combining the high-density genotyped SNP data and a sufficient amount of WGS data. Third, we used a Bayesian method to improve the precision of the QTL location and extract a limited number of candidate causal variants. Finally, among the candidate variants, the most likely causal variant was pinpointed through a meta-analysis of genetic associations based on 1501 individuals. The results show that rs320706814 is likely the causal SNP for the trait. However, rs320706814 did not contribute to the haplotype block identified for the QTL region in the DLY pigs, which can be explained either by the much smaller extent of LD in the DLY crossbreeds compared with their parent lines or by the fact that the rs320706814 SNP is likely an ancient mutation, since it is also present in the Chinese wild boar. It is worth noting that we have not completely ruled out the possibility that SNP rs320706814 is just a marker that is closely linked to the causal mutation. Other non-SNP mutations  were not included in this analysis, and within the 383-kb fine-mapped QTL region, the current genomic assembly may contain gaps that overlap with the actual causal mutation. Another possibility is that more than one causal mutation underlies the QTL and that the SNP rs320706814 is one of these. Indeed, the conditional analysis on rs320706814 revealed a secondary association signal in the QTL region. The mechanistic basis underlying this association needs further investigation.BMP2 locus with body size have been identified by GWAS in humans [BMP2 can induce chondrogenic differentiation, osteogenic differentiation, and endochondral ossification [BMP2 gene has been prioritized as a human growth-associated gene by a number of supporting studies and by DEPICT, which is a data-driven, integrative method that uses gene sets reconstituted on the basis of large-scale expression data to prioritize genes and gene sets [BMP gene. These results support the previous inference that BMP2 underlies the QTL for body size.Associations of SNPs that are adjacent to the n humans  and pigsfication , 44. Theene sets . By tranBMP2, BMP4, BMP5, and GDF6, are known to be under the control of distant cis-regulatory elements [BMP2 gene. Luciferase assays in both MC3T3-E1 and MLO-Y4 cells consistently showed that this mutation resides in an enhancer but downregulates gene expression. However, EMSA showed that the rs320706814 SNP had opposite effects on DNA–protein binding affinity in the two cell types, which suggests the existence of different transcription factors (TF) that interact with the enhancer motif in different types of cells.It is known that many QTL are caused by mutations in regulatory elements. In addition, members of the BMP gene family, including elements . Here, wNR6A1 gene [VRTN gene [BMP2 on bone growth.To the best of our knowledge, only two strong putative causal mutations have been identified for QTL for vertebrae number in pigs: the Pro192Leu missense mutation in the 6A1 gene  and the RTN gene . These tRTN gene . For theC/T) segregated in all three parental lines of the DLY cross and in some Chinese native breeds. The frequency of the favorable T allele for CL of the rs320706814 SNP was higher in Chinese wild boars than in any of the seven tested Chinese domestic pig breeds  decay as a function of inter-SNP distance in the DLY-P1 population. LD decay is a fast and effective tool for linkage disequilibrium decay analysis based on variants. LD (r2) dropped below 0.2 at distances greater than 200 kb in the DLY-P1 population.Additional file 3. Table S2. Screening for mutations in the BMP2 gene in ten DLY pigs with different genotypes at the GWAS tag SNP rs80965549.Additional file 4. Table S3. Mutations identified by sequencing the BMP2 cDNA from ten DLY pigs with AA or GA genotypes at the GWAS tag SNP rs80965549.Additional file 5. Table S4. List of differentially expressed genes identified in cartilage between different QTL genotypes (Qq vs. qq).Additional file 6. Figure S2. Transcriptome analysis of cartilage tissue. a Volcano plot of differentially expressed genes (DEG). b Allelic expression ratio (AER) analysis with a transcribed SNP rs45434988 (G>A) in BMP2 from four heterozygotes (Qq) and one wild-type homozygote (qq) for the SSC17 QTL. The five individuals were all heterozygous for rs45434988. The Y-axis represents the ratio of RNA-seq reads carrying different alleles of rs45434988 (G/A) in a sample. Allelic expression imbalance was determined by an AER greater than 1.2 or less than 0.8, indicated by dotted lines.Additional file 7. Figure S3. Assessment of the population stratification of all DLY animals by PCA. The red dots represent DLY-P1, the green dots represent DLY-P2, and the blue dots represent DLY-P3.Additional file 8. Figure S4. Haplotype block view of the region from 15.395 to 15.487 Mb. Haplotype structures viewed using Haploview software. The red mark was the top SNP rs345818757.Additional file 9. Table S5. The associations between some candidate casual mutations and carcass length in all three DLY populations (N=1501).


Animal breeding in recent years has benefited greatly from the availability of large-scale genetic information. The most widely applied genomic tools in selective breeding are specialized arrays that use DNA hybridization. However, the high financial investments accompanying this practice impair the profitability of emerging aquaculture species, including Arctic charr. The aim of the current study was to assess and compare the potential of two cost-efficient genotyping strategies applicable in a variety of breeding-related tasks, such as pedigree verification, genetic diversity screening and detection of genomic regions that are associated with phenotypes of economic importance. Both strategies are based on reduced representation sequencing but differ in sequencing coverage (low and high). The low coverage strategy offers a higher density of DNA markers, but also presents a greater proportion of missing data in the marker set and is characterized by more uncertainty in determining heterozygosity compared to high coverage. Our results show that while high coverage genotyping performs better in genetic diversity and kinship analyses, a low coverage strategy is more successful in identifying genomic regions associated with phenotypic traits, leading to the conclusion that both strategies could be of value into selection schemes.Salvelinus alpinus), where production volume is low. The requirement to genotype a large number of animals for breeding practices necessitates cost effective genotyping approaches. In the current study, we used double digest restriction site-associated DNA (ddRAD) sequencing of either high or low coverage to genotype Arctic charr from the Swedish national breeding program and performed analytical procedures to assess their utility in a range of tasks. SNPs were identified and used for deciphering the genetic structure of the studied population, estimating genomic relationships and implementing an association study for growth-related traits. Missing information and underestimation of heterozygosity in the low coverage set were limiting factors in genetic diversity and genomic relationship analyses, where high coverage performed notably better. On the other hand, the high coverage dataset proved to be valuable when it comes to identifying loci that are associated with phenotypic traits of interest. In general, both genotyping strategies offer sustainable alternatives to hybridization-based genotyping platforms and show potential for applications in aquaculture selective breeding.Incorporation of genomic technologies into fish breeding programs is a modern reality, promising substantial advances regarding the accuracy of selection, monitoring the genetic diversity and pedigree record verification. Single nucleotide polymorphism (SNP) arrays are the most commonly used genomic tool, but the investments required make them unsustainable for emerging species, such as Arctic charr ( Over the last decade, genomic technologies have transformed the field of aquaculture, opening up exciting new avenues for in-depth studies of practically any trait of interest . ImplemeThe RAD-derived platforms supported by a plethora of available restriction enzymes have proven to be particularly flexible in their applications . Most coA number of common enzymatic cutters used for genotyping by sequencing (GBS)  have beeSalvelinus alpinus) is an attractive candidate for diversifying the Nordic aquaculture industry, with an ongoing breeding program having been operational in Sweden for approximately 40 years [Arctic charr  and 188  samples, respectively, of Arctic charr originating from the national Swedish breeding program. During the first genotyping scenario, we applied less frequent cutting restriction enzymes (SbfI-SphI) while aiming to identify SNPs supported by high read coverage. In the second scenario, we constructed shallow coverage ddRAD libraries through the usage of more frequent enzymatic cutters (PstI-NlaIII). SNPs were identified under both scenarios and were used for estimating genetic diversity metrics, genomic relationships, and identifying growth-related quantitative trait loci (QTLs).5 × weight/length3. Finally, fin-clips were collected for DNA extraction and ddRAD library preparation.Arctic charr from the national Swedish breeding program was used for the needs of our study. The animals were located at the facilities of Aquaculture Center North (ACN) in Kälarne, Sweden. Growth measurements  were recorded from 277 animals (10 months old) of the 2017 class, representing 10 full-sibling families (with two families having the same sire). In addition, the condition factor (K) was calculated for all the genotyped animals using the formula K = 102 were collected and preserved in 100% ethanol. Genomic DNA from individual samples was extracted using a salt precipitation method [Fin clips of Arctic charr of about 3 mmn method  and elutn = 277; n = 188; ddRAD libraries were simulated in silico using RADinitio v1.1.1  and wereSalvelinus sp. reference genome assembly GCA_002910315.2 using bowtie2 [gstacks module. An alpha threshold of 0.01 and 0.05 for calling SNPs and genotypes respectively was applied using the marukilow model of gstacks. From each putative ddRAD locus, only a single SNP was used for downstream analysis. SNPs with minor allele frequency (MAF) < 0.05 and maximum heterozygosity > 0.7 across the tested samples were discarded using the populations module of Stacks. Only SNPs found in at least 50% and 70% of the samples in the low and high coverage datasets respectively were retained for downstream analysis.Reads of low quality (Q < 20) and missing the expected restriction sites were discarded. The retained reads were aligned to the  bowtie2 . Stacks  bowtie2  was usedn = 175) was used. Expected (He) and observed (Ho) heterozygosity metrics were calculated separately for the two datasets using the R package adegenet 2.1.3 [predict.dapc) performed on the training data sets was used to predict the family of origin for the members of the testing sets and assignment accuracies were calculated. Finally, in order to determine the effect of MAF on the reassignment performance of the low coverage set, we applied a series of MAF filters  and subjected the yielded sets to the same cross-validation method.For the direct comparison of the two genotyping strategies, the intersecting population  and compare the two datasets in regard to kinship estimations among full siblings. For this purpose, we employed R/rrblup 4.6.1 ,38 and c2 transformed K factor. A linear mixed model was applied:α is the vector of fixed effects other than marker effects , β is the fixed effect of each SNP marker, ω is the vector of animal random effect ~N  and ε is the vector of residuals ~N . X, Z and S are incidence matrices relating Y with α and β respectively. G is a genomic relationship matrix computed with the GRM function, I is an identity matrix, e2σ is the residual variance and α2σ represents the additive genetic variance. Multi-test adjustment of the genome-wide significance thresholds was achieved using Bonferroni corrections (0.05α level/n), where n is the number of tested SNPs. Furthermore, Benjamini–Hochberg (BH) adjustment of p-values was performed to control for false discovery rate.Association studies for the recorded growth traits were performed separately for the two genotyping strategies and their respective populations. Analyses were performed using the R package gaston 1.5.5 . The tesDue to high rate of missing data, 24 animals from the high coverage dataset (HC) and nine animals from the low coverage dataset (LC) were removed. Therefore, the HC dataset was comprised of 253 animals, and 179 were finally represented in the LC dataset. The number of individuals successfully genotyped with both SbfI-SphI and PstI-NlaII was 175 (intersecting population) and was used for analyses where direct comparisons were necessary. The expected number or RAD loci as estimated with the in-silico library stimulations was 1524 for SbfI-SphI (HC) and 305,077 for PstI-NlaIII (LC). In contrast, the filtered variant sets in our study consisted of 1034 SNP genotypes for SbfI-SphI, and 38,224 SNPs for PstI-NlaIII. The mean coverage for the former was 428X, while for the latter the mean coverage was 3X. The distributions of MAF and SNP call rates after filtering are shown in 2 transformation was performed to obtain a normalized distribution  and observed (Ho) heterozygosity were 0.34 and 0.31 respectively; and the same metrics for the shallow coverage scenario were 0.23 and 0.22. Differences in subpopulations detection were visualized when dimensionality reduction was used through PCA . The firDAPC provided further insights of the formed genetic clusters . The optA cross-validation scheme was employed to assess the accuracy of family reassignment (DAPC) using the two datasets. The relative frequency (observed minimum) of assignment to the original (according to pedigree records) family was 95.83% for SbfI-SphI and 68.75% for PstI-NlaIII. Frequencies of family assignments as calculated during cross-validation are visualized in Mean genomic relatedness  among full siblings was found to be 0.23 (sd = 0.09) for SbfI-SphI and 0.19 (sd = 0.16) for PstI-NlaIII, and the same metric for non-sibling pairs was −0.05 (sd = 0.06) for SbfI-SphI and −0.05 (sd = 0.11) for PstI-NlaIII. The density plots in 2 transformed condition factor (K) when using genotypic information from the high coverage dataset  from the chromosomal position of each variant  and founIn the current study, both high and low coverage ddRAD genotyping scenarios provided genotypic information that is useful for different applications in a wide range of breeding related tasks. Reduced representation sequencing can be a valuable tool for selective breeding practices in emerging aquaculture species, such as Arctic charr . GenomicAs expected by RADinitio library simulations, the PstI-NlaIII library resulted in significantly higher number of SNP biallelic genotypes than SbfI-SphI. The actual counts of filtered variants were lower than estimated in silico with the deviation being larger and more profound for the low coverage scenario. This could be attributed to a number of factors that among others include the low depth in the case of PstI-NlaIII (~ 3X) and the fact that the reference genome was derived from a distant population and might not be adequately representative of the nucleus of the national Swedish breeding program.The deep coverage strategy outperformed shallow coverage genotyping. A comparison of heterozygosity metrics indicates that the latter underestimates the proportion of heterozygotes in the population, possibly due to missing information in the marker set . ObserveFurthermore, genotyping in the SbfI-SphI scenario resulted in better and more robust detection of subpopulations and clustering at the family level in the PCA and DAPC analytical procedures. This finding was further supported by a cross-validation scheme where the estimated accuracy of family reassignment was higher for deep coverage, suggesting that high quality genotypic information is a priority over marker density for genetic diversity and population structure analysis. Despite providing a much more defined view on genetic clusters, DAPC for the SbfI-SphI scenario resulted in two individuals not grouping with their original families, but this could be explained by putative errors in the pedigree records.It should be highlighted that in our study we tried to decipher the population structures and genetic clustering of full-sibling families in a closed breeding nucleus, a task that is more challenging and demanding than studying genetic diversity and differentiation among distant populations . Even thKinship estimations among full-sibling pairs were higher for the high coverage dataset and provided better discrimination from the GR values of non-sibling pairs, despite incorporating remarkably less genotypic information than the low coverage dataset. As with genetic diversity analyses, this might have been due to the higher SNP calling uncertainty entailed by shallow coverage genotyping-by-sequencing approaches. Overall, both genotypic datasets underestimated the average relationship between full-sibling pairs, thereby performing worse compared to similar studies in livestock that used SNP arrays . The abo2 transformed condition factor (K) confirmed that the density of the marker set is crucial when conducting genome-wide regression studies in aquatic animal populations [2K. Further analysis showed that some of those loci are located in genomic regions containing genes that are affiliated with development and metabolism. Among them, a SNP was located 570 bp upstream of the apolipoprotein M (apom) gene, which has been previously associated with obesity in mice [Our association study for length and logulations ,50,51. A in mice ,53 and h in mice ,55. More in mice ,57,58, bBoth deep and shallow coverage genotyping by sequencing strategies provides valuable genomic information that can be incorporated in fish breeding programs. While deep coverage results in high quality but less dense genotypes, shallow coverage provides higher genotypic density with a greater proportion of missing data and higher uncertainty. The first strategy performed better in genetic diversity and population structure analyses, while low coverage genotyping proved to be more informative regarding genome-wide regression for association, with phenotypic traits demonstrating the potential of both approaches in aquaculture breeding schemes.