Betta splendens and whole-genome sequenced 98 individuals across five closely related species. We find evidence of bidirectional hybridization between domesticated ornamental betta and other wild Betta species. We discover dmrt1 as the main sex determination gene in ornamental betta and that it has lower penetrance in wild B. splendens. Furthermore, we find genes with signatures of recent, strong selection that have large effects on color in specific parts of the body or on the shape of individual fins and that most are unlinked. Our results demonstrate how simple genetic architectures paired with anatomical modularity can lead to vast phenotypic diversity generated during animal domestication and launch betta as a powerful new system for evolutionary genetics.Siamese fighting (betta) fish are among the most popular and morphologically diverse pet fish, but the genetic bases of their domestication and phenotypic diversification are largely unknown. We assembled de novo the genome of a wild  A Lego-like genetic logic for ornamental traits underlies the diversification of betta fish during domestication. Domesticated animals have provided important insights into the genetic bases of a wide range of morphological, physiological, and behavioral traits. Because of their intimate relationship with people, domesticates have also furthered our understanding of human history and culture and of our interactions with other species  may have contributed to modern varieties  .We generated a high-quality reference genome assembly of wild B. splendens reference to three ornamental betta references (Anabas testudineus (climbing perch) as an outgroup .To discover structural chromosomal rearrangements that may have arisen during domestication, we performed whole-genome alignments of our wild B. splendens complex, we sequenced to ~15× coverage the whole genomes of (i) 37 ornamental betta from different sources, representing a diversity of ornamental traits (B. splendens complex (except for Betta stiktos), and four populations of B. splendens from different parts of its natural range (Betta compuncta). We aligned the sequencing reads to our B. splendens reference genome then called and filtered variants to generate a final set of 27.8 million phased biallelic single-nucleotide polymorphisms (SNPs).To determine the genetic origin of ornamental betta and understand their relationships with species of the al range ; and (iiB. splendens complex by constructing neighbor-joining (NJ) and maximum likelihood–based phylogenies .We then tested for evidence of evolutionary processes that violate tree-like species relationships, such as hybridization, by computing ABBA-BABA statistics . This rate is similar to the rate previously inferred for cichlids . Since, in the absence of very large sample sizes, Markovian coalescence–based approaches such as Relate do not accurately estimate recent demographic changes, we used fastsimcoal2  in ornamental betta relative to wild simcoal2  with an H-scan, which can detect soft and hard selective sweeps and performs well in dogs, another domesticated species . Equivalent selection scans using whole-genome sequencing of 24 wild B. splendens did not reveal clear signals, which is unlikely due to the smaller sample size, as H-scan and G12 peaks remained in a downsampled set of 24 ornamentals . Notably, selection peaks were not located in genomic regions with low recombination rates, suggesting that selection signals are unlikely to be driven by long haplotypes resulting from reduced recombination (fig. S13C). These results are consistent with footprints of selection in ornamental betta being related to the domestication process.Genetic variants that increase fitness in captivity or that are associated with phenotypic traits actively selected by breeders are expected to increase in frequency during domestication. To discover these loci with signatures of selective sweeps in ornamental betta, we searched for extended homozygosity tracts across 37 ornamental betta using omosomes , and then = 17), solid red (n = 17), veiltail (n = 18), and crowntail (n = 16), represented by males (n = 20) and females (n = 17). Veiltails are characterized by large, flowing caudal fins, and crowntails have fins that are webbed between the rays (H-scan and haplotype frequencies across subsets of fish representing these traits (The majority (34 of 37) of the ornamental betta that we sequenced represent four of the most popular varieties along two phenotypic dimensions: color and fin morphology. The fish were royal blue , which is required for the formation of fin rays in zebrafish , became apparent when comparing males to females , which encodes alpha- and beta-melanocyte–stimulating hormones; T-box transcription factor 19 (tbx19), which encodes a transcription factor expressed specifically in pituitary cells that will express pomc (xanthine dehydrogenase (xdh), which encodes an enzyme whose homologs synthesize yellow-red pteridine pigments , which encodes a cell signaling molecule important for the development of iridophores (beta-carotene oxygenase like-1 (bco1l), which encodes an enzyme whose homologs metabolize orange-red carotenoid pigments  using gonadal sex as the trait. We focused on ornamental betta since we had a large enough sample size  to detect variants with large effect on sex. A ~30-kb region overlapping dmrt1 was strongly associated with sex, with 16 of 17 females being homozygous at the most strongly associated SNPs, while 16 of 20 males were heterozygous , indicating that there are no major sex-specific regions that are absent from the reference (fig. S14A). To test whether smaller-scale sequence differences were associated with sex, we performed a GWAS independent of the reference genome using k-mers from the sequencing reads. We found k-mers significantly associated with sex, and when we assembled those k-mers into contigs, they corresponded to dmrt1, consistent with the results from SNP-based GWAS (fig. S14B). We also assessed copy number variations across the genome, and none were significantly associated with sex (fig. S14C). Although sex chromosomes often carry chromosomal rearrangements, we found no evidence of an inversion in X or Y  was lower than on the Y . In contrast, in wild B. splendens, Y had slightly lower diversity (4.07 × 10−4/bp) than X , indicating that the reduced diversity on X in ornamental betta is unlikely caused by a higher mutation rate in males compared to females, which would contribute to excess diversity on Y. As they evolve, proto-sex chromosomes often stop recombining in the heterogametic sex, leading to reduced diversity in the chromosome specific to that sex (dmrt1 (fig. S13C) nor evidence of rearrangements (which can prevent recombination) involving this region  of XX individuals were female, and 91%  of XY individuals were male  of XX individuals were female, and 93%  of XY individuals were male  and suggest that this effect contributed to the selective sweep around dmrt1.We further explored the association between ere male  and G. Were male . These rdmrt1 XX-XY status was not perfectly related to gonadal sex, we searched for additional sex-linked loci that may have been missed by GWAS. To do so, we performed two quantitative trait locus (QTL) mapping experiments, one in a cross between an XX female and an XY male, and another between an XX female and an XX male. In the XX × XY cross, 52% of the offspring were female, and we detected a single sex-linked locus encompassing dmrt1 (Oryzias latipes (medaka fish) that also bear a dmrt1 XX-XY sex determination system  and that this allelic bias progressively decreases at 8 and 12 dpf, until it reverses in adult testis, where only 45% of the dmrt1 transcripts originate from the Y allele , a teleost-specific gene essential for testis development (antimullerian hormone (amh), a gene that promotes vertebrate male development  and blue (n = 17) fish that were used for the selection scans (2) hybrid population of red-blue fish in which individual coloration components could segregate . We also measured the hue, brightness, and saturation of the red and blue areas on each body part and used these phenotypes for QTL mapping.Ornamental betta breeders have generated a vast array of fish varieties  that differ along multiple axes of coloration: hue, brightness, saturation, and the anatomical distribution of these features. To determine whether any of the genes we found to be under strong selection, as well as any others, contribute to coloration in ornamental betta, we performed a GWAS of the red  and bco1l on chromosome 2  in the skin of the body of blue fish compared to red fish but not significantly different in the fins (fig. S18A). alkal2l encodes a ligand of leukocyte tyrosine kinase . We did not detect differential biochemical activity of the two alleles in vitro, but it is possible that their activity or stability differs in vivo, as there are no significant differences in skin bco1l mRNA levels between red and blue fish (fig. S19H). Therefore, variation in the locus containing alkal2l and bco1l likely affects both blue and red coloration through these two genes located only ~50 kb apart. The tight linkage might explain why breeders struggle to make the “perfect” red fish without any iridescence.Notably, the the body  and G. T animals . One of adenylosuccinate lyase (adsl), and the strongest QTL at this locus was for the brightness of blue areas on the body  , a gene necessary for the development of larval yellow xanthophores in medaka , whose orthologs affect melanin pigmentation in other fish and in mammals , two enzymes , and a membrane solute transporter (slc2a15b). Candidate genes that we identified might correspond to those inferred, but not molecularly identified, by betta geneticists beginning in the 1930s .Larvae were grown from 5 to 14 dpf in 2.8-liter static tanks at a density of 30 larvae per tank and fed https://github.com/wtsi-hpag/Scaff10X) with 183× Illumina HiSeqX, 10X Chromium reads from a different individual (SplPhe0). Following scaffolding, the assembly was gap-filled with PBJelly (https://github.com/PacificBiosciences/pbbioconda) using the long-reads data and then polished further using Freebayes ; these chromosomal units, including any discrepancies, were confirmed using the Bionano optical map data, presence of retained haplotypic contig overlaps, and general sequence contig integrity . Chromosomes were named by synteny to medaka (O. latipes) assembly GCA_002234675.1 . Curation resulted in 67 manual breaks, 92 manual joins, and the removal of 113 regions representing false duplications. The final assembly consists of 69 scaffolds totaling 441 Mb, with a scaffold N50 of 20.1 Mb. A percentage of 98.62% of the assembly has been assigned to the 21 chromosomes.The resulting assembly was further improved with manual curation by building a gEVAL database and correcting mis-scaffolding by leveraging aligned Bionano consensus optical maps generated from SplPhe0  were generated using the NEBNext Ultra Directional RNA Library Prep Kit for Illumina  and sequenced to an average coverage of 72 × 106 reads (range: 33 × 106 to 145 × 106) with 2 × 150 bp reads in a NextSeq 500. Reads were used by NCBI to annotate the B. splendens genome in Annotation Release 101.For RNA sequencing for genome annotation, we used fin, gonad, brain, liver, and gill from two adult wild B. splendens panel to ~15 ×.Sequencing libraries were generated from genomic DNA extracted from ethanol-preserved fin clips of wild samples and were sequenced on an Illumina platform, following Illumina’s polymerase chain reaction (PCR)–based library protocols. Whole-genome sequencing libraries for the ornamental samples were generated using the QIASeq FX DNA Library Kit . Libraries were PCR-free with the exception of Orn30, Orn27, and Orn37, which were PCR-amplified in 6 cycles. Through receiver operating characteristic (ROC) curves of total variants called and the number of Mendelian errors in the trio and quartet sequenced to >30× coverage, we determined that 15× coverage was enough for our sample set and sequenced our ornamental Samples and the associated metadata included in the study are listed in table S1. Field sampling in Indonesia was conducted according to research permits 1/TKPIPA/FRP/SM/I/2011 and 3/TKPIPA/FRP/SM/III/2012 for L.R. Fieldwork in Peninsular Malaysia and Sarawak was conducted under permits issued by the Economic Planning Unit, Prime Minister’s Department, Malaysia (UPE 40/200/19/2417 and UPE 40/200/19/2534) and the Forest Department Sarawak (NCCD.970.4.4[V]-43).http://broadinstitute.github.io/picard/). We identified filter criteria for variants based on Mendelian errors from a trio and quartet using two variant callers: bcftools mpileup v1.11 , filtering out genotypes with GQ < 50 and for which deletions carried a DP > 0.7× of the flanking region . We additionally filtered out duplications that were <1.3× relative to other similar GC-content bins across the genome  and inversions/translocations with DP < 4. We manually inspected these variants through Integrated Genomics Viewer (For short (<100-bp) indel analysis, we used GATK4 for calling indels as it uses local realignment around indels and filtered with more stringent criteria including requiring indels to have GQ > 50, fraction missing < 0.2, and are present in more than one individual. These variants were required to be concordant with Mendelian expectations if present in either the trio or quartet, leaving 228,066 indels. For structural variant analysis , we used smoove  with –l 200 and –r 0.5. Most regions (98.0%) that were masked through SNPable were also removed on the basis of our read-coverage filter (table S4).To further filter out regions with poor mappability, we used SNPable . Block bootstrap support was determined by randomly subsampling window-pairwise differences with replacement and testing how often a node of the whole genome tree was supported in 1000 random replicates. Maximum likelihood trees were generated using IQ-TREE v2.0.3  for f4 > 0 and -f4 otherwise.We computed ABBA-BABA tests for all triplets of samples with Dsuite v0.4 r38 . We thus filtered for segments with fdM > 0.2 and merged adjacent remaining segments that were within 5 kb of each other to determine the start and end points of the introgressed regions. Local gene trees were constructed for each of these high-fdM genomic regions using IQ-TREE across the haplotypes of the ornamental bettas and other B. splendens species complex samples that did not show recent introgression through the ABBA-BABA tests. For each region, we determined the ancestry of each haplotype for the ornamental sample (p2) with high fdM by calculating the average branch length distance of the sample’s haplotype to the haplotypes of other samples for each species. If the average distance of the p2 sample’s haplotype to the haplotypes of a non-splendens species was shorter than the average distance to other B. splendens haplotypes and less than 2 SD away from the average distance between B. splendens haplotypes, we determined that the haplotype likely shared ancestry with that non-splendens species. We further validated ancestry assignments by visual inspection of the trees, all of which showed the topology inferred by these analyses .Windowed fB. splendens populations and ornamental betta. We generated an ancestral sequence reconstruction with IQ-TREE v2.0.3 using the phased variant call set from B. compuncta, B. smaragdina, B. mahachaiensis (only Mah0 sample because it had no evidence of introgression from B. splendens), and wild B. splendens. We did not include ornamental betta as many samples have introgressed regions from other species. We constructed trees for each chromosome using the HKY+F+R2 model with 1000 bootstraps (-bnni). Tree topologies supported the species tree  were assigned the International Union of Pure and Applied Chemistry ambiguity code “N” as the ancestral state.We used Relate v1.1.7 . The genome-wide average rate was 6.6 centimorgan (cM)/Mb. We ran Relate using our point estimate for the de novo mutation rate and the upper and lower CI boundaries. We assumed a diploid starting effective population size of 91,300 based on the nucleotide diversity of 0.00137 of wild B. splendens from Kanchanaburi and the point estimate of the mutation rate. Inferred demographies were similar when using a starting population size of 37,000 or of 378,000, based on the CI from the mutation rate (fig. S10D). We excluded the following samples with signs of recent introgression or that are the offspring of the quartet or trio: Orn29, Orn21, Orn5, Orn38, Orn39, SplKan8, SplPhe2, SplBan0, and SplBan1. We further excluded all genomic regions with evidence of non–B. splendens introgression in the included samples (amounting to 11% of the genome) and found negligible differences in the demographies inferred by Relate (fig. S10B). We ran the Relate EstimatePopulationSize algorithm for one iteration to avoid introducing biases generated from the assumption that our samples belong to one panmictic population. We performed jackknife resampling of chromosomes and observed negligible variation when removing individual chromosomes. We also performed 100 runs of 5-kb block bootstrap resampling across chromosome 1 to assess the variability of inferred demographics (fig. S10C).We used a constant recombination rate based on the genetic length of each chromosome  (B. splendens chromosome length). First, we assumed constant effective population sizes of 10,000 or 100,000 individuals. Second, we ran simulations following the inferred Relate demographic history for times older than 1000 generations ago. Since, given our sample size, Relate results for more recent times are less reliable (fig. S10B), we assumed linear growth toward the inferred long-term effective population size of 72,000. On top of these demographic models, we added bottlenecks of different timings and intensities. We added a single instantaneous bottleneck in the past 100, 250, 500, 1000, and 2000 generations ago of varying bottleneck intensities  and a demography without a bottleneck. A constant recombination rate of 6.6 cM/Mb and mutation rate of 3.75 × 10−9/bp per generation based on our point estimate of the de novo mutation rate were applied. For each parameter combination, we ran five independent replicates of the fsc2 instbot model and one “null model” in which the bottleneck intensity was fixed to zero (fig. S10E).To check that such a simple model can accurately time a relatively recent bottleneck in a variety of “background” demographic histories, we used msprime v1.0.1 , and the sum is divided by the total accessible (invariant and variant) sites remaining after filtering. CIs were calculated on the basis of jackknife resampling of chromosomes. We calculated windowed nucleotide diversity in 10-kb windows with 100-bp slide across the genome (fig. S12). We removed windows where 3 kb or less of the window was accessible.We calculated genome-wide nucleotide diversity (pi) in ornamental and wild r2 value of SNPs with a minor allele frequency (MAF) of 0.2  and --ld-window-kb 999. To assess LD decay, we generated bins that incrementally increased by 3 bp from 0 to 999 kb and calculated the mean r2 value of SNPs where the distance between the SNPs fell within each bin. Calculations of interchromosomal r2 value after thinning SNPs that were within 100 kb of each other within a chromosome (--bp-space 100000) served as the baseline using --r2 interchrom and --ld-window-r2 0.We assessed LD using plink v1.90, calculating the H-scan  were greater than expected by chance, we performed permutation testing. As neighboring loci may share high H-scan values due to linkage, representing the same putative sweep  across the distribution of differences of all permutations across chromosomes, which served as our genome-wide threshold.We performed three types of genome-wide scans to identify signatures of selection: (i) r2 > 0.8. We performed genome-wide association using biallelic variants for sex (confirmed by inspection of gonads after euthanasia) across 37 ornamentals . For color, we performed genome-wide association across 34 ornamentals . For fin morphology, we performed genome-wide association across 34 ornamentals .Genome-wide association analysis for sex, color, and fin morphology across ornamental betta was performed using GEMMA version 0.98.1, with a linear mixed model that incorporated the kinship/relatedness matrix to account for population structure  = 6.03.We considered haplotype blocks to represent the number of independent statistical tests. We calculated the number of haplotype blocks using plink --blocks with a maximum kilobase window of 999 and MAF of 0.05. We identified 53,844 haploblocks in ornamental betta. Thus, for a Bonferroni-corrected We also performed a k-mer association analysis using HAWK v1.3  and a female (dmrt1_XX) ornamental splendens. We crossed the F1 (dmrt1_XY) males and F1 (dmrt1_XX) females to generate 211 F2 progeny. We sexed each F2 based on the presence of an ovipositor and confirmed after euthanasia by the presence of ovaries or testis. We generated Tn5-tagmented whole-genome sequencing libraries  with base quality > 20 and MQ > 35.We performed an F2 intercross design. We removed SNPs where the genotype likelihood was less than 90% across more than 50% of the samples. We additionally removed SNPs where the difference of the posterior probability of the SNP relative to the preceding SNP was less than 10%. Using the remaining 15,572 SNPs, we performed QTL analysis for sex using Haley-Knott regression in R/qtl  and female (dmrt1_XX) ornamental betta, resulting in 100 F2s. None of the founders of these crosses were close relatives of each other. Following the same filtering criteria, we performed QTL analysis for sex  on 8561 SNPs. To minimize environmental biases, we raised the dmrt1_XY male × dmrt1_XX female and dmrt1_XX male × dmrt1_XX female crosses at the same time using the same housing, water, and temperature conditions.To investigate whether a secondary sex determination system exists for males carrying the dmrt1 locus, we used the filtered, phased, and biallelic SNP variants within the LD block of chromosome 9 spanning 28,850,248 to 28,884,157 bp. We included both invariant and variant sites within the region. We excluded the following samples due to signs of recent gene flow, due to being the offspring of the quartet or trio, or due to being sexually immature: Orn29, Orn5, Orn38, Orn39, SplPhe1, SplPhe2, SplBan0, SplBan1, SplChi1, SplChi2, and SplKan6. We used IQ-TREE v2.0.3 . We performed a local chi-square analysis of the regions carrying the highest associated GWAS SNPs for sex in the ornamentals to select the SNPs most associated with sex and present in the Y branch. We PCR-amplified and Illumina-indexed products spanning these SNPs (tables S3 and S4) and sequenced the amplicons using NextSeq 500/550. We removed aligned reads that had an MQ of less than 10 and then performed bcftools mpileup with -C 50. We removed genotypes with DP < 50, GQ < 30, and where the SNP VAF was between 0.2 and 0.3 or 0.7 and 0.8 from further analysis.dmrt1_XX versus dmrt1_XY. We identified an SNP with r2 > 0.8 with the top 0.5% of the highest associated GWAS SNPs for sex that overlapped the restriction enzyme site for MluCI. We PCR-amplified the region and overnight digested the amplicons with MluCI  (tables S3 and S4). The PCR fragments were then visualized on a 2.5% agarose gel (fig. S21). To confirm the accuracy of the RFLP assay, we Sanger-sequenced the undigested PCR amplicons of five dmrt1_XY samples and five dmrt1_XX samples and found perfect concordance.We developed a diagnostic restriction fragment length polymorphism (RFLP)–based assay for genotyping dmrt1_XY) male and a blue (dmrt1_XX) female ornamental betta, crossed two pairs of F1 hybrids, and generated 211 F2 progeny (one of the crosses used in QTL mapping of sex). We genotyped F2s as described in the “QTL analysis of sex” section and then performed Haley-Knott regression in R/qtl.We generated an intercross between a red  provided on the 24ColorCard from CameraTrax.com. Each image included the color checker card.We photographed both sides and top of sexually mature Fhttps://dahtah.github.io/imager/). Color analysis was based on the hue-saturation-value color space, using the hue range ,  for red and  for blue. These ranges maximized the difference between the red (n = 17) and blue (n = 17) ornamental betta through iterative subtractive binning. We similarly determined the range [0 to 0.3] to assess blackness based on the value axis along the color space.We used a custom python script to automatically identify the color matrix and calibrated each picture based on the reference color values on the color checker card with a pipeline built on python packages, rawpy, colour, and PlantCV. We segmented images using a recursive-convolutional neural net model across 122 images that were annotated and trained in MATLAB 2019b. Images that were poorly segmented were manually segmented with ImageJ for downstream analysis. Downstream analysis was performed using a custom R script based on imager (2 intercross consisting of 139 F2 progeny from a veiltail (dmrt1_XX) male with a crowntail (dmrt1_XX) female ornamental betta . We pinned sexually mature euthanized fish to a sylgard-coated petri dish with the fins expanded and photographed them as described in the “QTL analysis of color” section. Using Fiji, we annotated the length of the ray and webbings between the primary rays measured from the base of the tail and. We genotyped F2s as described in the “QTL analysis of sex” section and performed Haley-Knott regression using R/qtl.We generated an F


Inbreeding depression can adversely affect traits related to fitness, reproduction and productive performance. Although current research suggests that inbreeding levels are generally low in most goat breeds, the impact of inbreeding depression on phenotypes of economic interest has only been investigated in a few studies based on genealogical data.SYK, IL27, CCL19 and CCL21. Moreover, one region on chromosome 2 was significantly associated with inbreeding depression for milk yield.We genotyped 1040 goats with the Goat SNP50 BeadChip. This information was used to estimate different molecular inbreeding coefficients and characterise runs of homozygosity and homozygosity patterns. We detected 38 genomic regions with increased homozygosity as well as 8 ROH hotspots mapping to chromosomes 1, 2, 4, 6, 14, 16 and 17. Eight hundred seventeen goats with available records for dairy traits were analysed to evaluate the potential consequences of inbreeding depression on milk phenotypes. Four regions on chromosomes 8 and 25 were significantly associated with inbreeding depression for the natural logarithm of the somatic cell count. Notably, these regions contain several genes related with immunity, such as Although genomic inbreeding levels are low in Murciano-Granadina goats, significant evidence of inbreeding depression for the logarithm of the somatic cell count, a phenotype closely associated with udder health and milk yield, have been detected in this population. Minimising inbreeding would be expected to augment economic gain by increasing milk yield and reducing the incidence of mastitis, which is one of the main causes of dairy goat culling.The online version contains supplementary material available at 10.1186/s40104-022-00684-5. FHOM) just reflect identity-by-state (IBS) allele-sharing proportions [FROH) based on measuring the fraction of the genome covered by runs of homozygosity (ROH) estimate IBD allele sharing [Inbreeding is defined as the mating of individuals that are related to each other more closely than the average relationship within the concerned population . In liveportions , coeffic sharing –8, makin sharing –9.The increase of inbreeding might have adverse consequences on the fitness of livestock populations due to the loss of genetic variability, which can entail a long-term reduction of genetic variance  and, consequently, a slowing down of the rate of response to selection in breeding schemes –12. MoreFROH < 0.10), with the only exception of certain local breeds with small population sizes (e.g. Dutch Landrace goats) as well as of breeds with insular origins  which happened to be highly inbred [Several studies have used genomic methods to determine the levels of inbreeding in goat populations with a broad geographic distribution –21. A rey inbred , 20. Desy inbred , 23. In y inbred  and DeroThe goals of the current work were: (i) to measure the levels of inbreeding in a Murciano-Granadina resource population by using different genomic coefficients, and (ii) to use this information to infer the impact of inbreeding depression on dairy phenotypes recorded in this population.The animal material comprised 1040 Murciano-Granadina female goats from 15 farms located in the autonomous region of Andalusia (Spain). Murciano-Ganadina is a local Spanish breed officially created in 1975 by the crossbreeding of Murciano and Granadina goats . Current3 anticoagulant and stored at − 20 °C until processing. Phenotypic records for milk yield and composition traits were recorded in the framework of the selection program of the Murciano-Granadina goat breed [lnSCC × 103 cells/mL), fat percentage (FP), protein percentage (PP) and lactose percentage (LP). Milk composition traits were standardised to a lactation of 210 days. Summary statistics of phenotypic records are displayed in Table Blood samples were extracted from goats using vacuum tubes coated with EDTA Kat breed . Only phg. The supernatant was discarded and the pellet was resuspended in 3 mL of lysis buffer  plus 100 μL proteinase K (20 mg/mL) and incubated for 3 h at 55 °C. The lysate was chilled, and 1 mL of ammonium acetate 10 mol/L was added to it. After centrifugation at 2000 × g for 10 min, the supernatant (~ 4 mL) was transferred to a new tube with 3 mL of isopropanol 96%, and this mixture was centrifuged at 2000 × g for 3 min. The resulting DNA pellet was washed with 3 mL of ethanol 70% and an additional centrifugation step at 2000 × g for 1 min was performed. The DNA pellet was dried at room temperature, and it was subsequently resuspended in 1 mL of TE buffer . Murciano-Granadina goats were genotyped with the Goat SNP50 BeadChip  by following the instructions of the manufacturer. The goat ARS1 genome [P < 0.00001) from the Hardy-Weinberg equilibrium, and (iv) with a genotype call rate over 98%. Data were filtered using PLINK 1.9 [The isolation of genomic DNA was carried out following a salting-out protocol . Three m1 genome  was used1 genome . Only inLINK 1.9 . In addiLINK 1.9  in orderThe definition of ROH followed six criteria: (i) the minimum length of ROH is 1 Mb, (ii) a ROH must contain at least 15 SNPs, (iii) the density of SNPs per ROH was set to at least 1 SNP every 100 kb, (iv) the maximum distance between consecutive SNPs is 250 Kb, (v) one heterozygous position per ROH is allowed, and (vi) one missing position per ROH is allowed. Said criteria were established based on the density of the genotyping panel, with a mean distance between consecutive SNPs of 51.73 Kb and a mean number of 19.35 SNPs/Mb  or larger (FROHLong) than 5 Mb. Such calculations were made to assess the relative importance of distant (FROHShort) versus recent (FROHLong) inbreeding [g corresponds to the number of generations to the closest common ancestor [FROHShort indicates the inbreeding of an individual from 10 to 50 generations in the past, while FROHLong estimates the inbreeding from 1 to 9 generations in the past. by SNPs . The sammentclass2pt{minimancestor , and alsancestor , FROHShoFIS coefficient of Wright [f Wright  was calci. This coefficient was estimated from the output of the --hardy command of PLINK 1.9 [Where LINK 1.9 .FYANG coefficient was estimated from the diagonal of the matrix of genomic relationships of Yang based on the correlation between uniting gametes [xk is the genotype of the individual i for the SNP k, and pk is the frequency of the reference allele in the studied population. The command --ibc of PLINK 1.9 [ gametes , 38. It LINK 1.9  was usedFHOM and FROH were also estimated at the chromosomal level.The coefficients y is the vector of observations for each phenotype, β is a vector of fixed effects, including farm (15 levels), year of birth (10 levels), and the linear regression on F as a covariate; a is the vector of additive genetic effects, e is the vector of random residual effects, and X and Z are incidence matrices relating fixed and random effects to observations.The effects of inbreeding depression on dairy traits were investigated using data from 817 goats with available phenotypes . Analyses were performed with the REMLF90 software  to impleThe significance of the inbreeding effect on the analysed traits was determined by applying a two-tailed hypothesis test. A Z-statistic was estimated with the following general formula:μ0 is the coefficient of inbreeding corresponding to the null hypothesis , and s.e. is the standard error. The transformation of Z-scores into P-values was accomplished with the function pnorm implemented on R [Here, ted on R .FHOM, FROH, FROHLong, and FROHShort). In order to detect genomic regions associated with inbreeding depression, analyses at the chromosomal level were performed for traits that in the whole-genome analysis were identified as significantly affected by inbreeding depression (P < 0.05). Following Saura et al. [The above analysis was performed by regressing each phenotype onto four genomic inbreeding coefficients .A total of 46,689 SNPs and 1040 animals were selected to investigate the population structure and patterns of homozygosity of Murciano-Granadina goats. The PCA . Particularly significant were GO terms related with ferric and copper import into the cell  that were scattered on 20 goat chromosomes (CHI), i.e. CHI 1–8, 11, 13–18, 20, 21, 24, 26 and 29 ; 0.053 ± 0.046 (FROH); 0.040 ± 0.041 (FROHLong); 0.014 ± 0.008 (FROHShort); − 0.016 ± 0.035 (FIS) and 0.023 ± 0.047 (FYANG). The magnitude and dispersion of these coefficients are shown in Fig. FHOM, FROH, FROHLong, FIS and FYANG coefficients were highly correlated, being especially high the correlations between FHOM and FROH, FROH and FROHLong  and between FHOM and FIS . In contrast, FROHShort showed the weakest correlations with the remaining inbreeding coefficients (|r| = 0.33–0.64), although their statistical significance (P < 2.2 × 10−16) was very high  and 0.127 (P = 0.038) units, respectively  displayed inbreeding depression for this trait exclusively when it was regressed onto FHOM  and genes with protein kinase activity chemotaxis and cell signalling activities . Several of these genes display functions related with innate immunity and inflammatory response e.g. lymphocyte chemotaxis, cellular response to interferon-γ, immunological synapse formation, positive regulation of chemotaxis, cellular response to interleukin-1, monocyte chemotaxis etc.  as well as milk yield at three different time points  showed significant evidence of inbreeding depression when analysing  P = 0.03 units, rFHOM or FROH involved a decrease of 48.50 kg (P = 0.011) and 20.49 kg (P = 0.014) of milk for MY210; 51.02 kg (P = 0.017) and 21.30 kg (P = 0.023) for MY240; and 55.72 kg (P = 0.032) and 23.20 kg (P = 0.040) for MY305, respectively .For the majority of Murciano-Granadina goats, ROH number and total length were below 50 and 350 Mb, respectively. Moreover, short ROH (< 5 Mb) were more abundant than the medium or long ones Fig. A. These FROH, FROHShort, and FROHLong of Murciano-Granadina goats were mainly in the range of 0 to 0.05. In their study, Bertolini et al. [FROH coefficients (< 0.10), while the remaining ~ 30% and ~ 10% of breeds showed moderate (0.10 < FROH < 0.20) or high (> 0.20) FROH values. The patterns of low homozygosity that we have observed in Murciano-Granadina goats contrast strongly with what has been reported in certain local breeds, such as Mallorquina, Pyrenean, and Valdostana, which have undergone sharp population bottlenecks [https://www.mapa.gob.es/es/ganaderia/temas/zootecnia/razas-ganaderas/razas) and other countries. Noteworthy, the Murciano-Granadina breed was founded by crossing, during the 1970s, two Murciana and Granadina populations with different historical origins [The inbreeding coefficients i et al.  reportedtlenecks , 20. Low origins . Althoug origins , this ad origins , so theiFHOM, FROH, FROHLong, FIS and FYANG were high, FROHShort displayed the lowest correlations with the remaining inbreeding coefficients, in line with previous studies focused on other livestock species [While correlations between  species , 13, 41. species , 42 and  species . Besides species , thus liDrosophila melanogaster by genotyping more than 100 million SNPs made it possible to detect 106,964 recombination events displaying a remarkable degree of intra-specific variation [We have identified several genomic regions in which ROH are particularly frequent (ROH hotspots). Similar patterns were found when the proportion of homozygous individuals was analysed at a genome-wide level i.e. 35.46% of the most common homozygous regions overlapped with ROH hotspots and these overlapping regions represented 35.42% of the total ROH hotspots. No positional coincidence was detected between ROH hotspots identified by us and those reported by Bertolini et al.  in a worariation . FactorsP-value < 0.05)  and insulin-like growth factor binding protein 1 (IGFBP1) that participate in the growth and postnatal development of cattle [Regarding the gene content of genomic segments co-localising with both high homozygosity regions and ROH hotspots , the functional enrichment analysis highlighted several gene ontology terms with nominally significant enrichment  and Saanen (5.32 ± 1.19 units) breeds . CompareFROHShort strongly differed from estimates based on the other inbreeding coefficients (Table r| = 0.33–0.64) correlations between FROHShort and other molecular inbreeding coefficients . Milk production showed a positive quadratic correlation with inbreeding levels, but such effect was not significant. Deroide et al. [FROH involved a 0.86 ± 0.28 unit increase in somatic cell score (days 150 through to 400) recorded in Dutch Holstein-Friesian dairy cattle. In Iranian cattle, individuals with high inbreeding coefficients tended to have higher somatic cell scores than animals with low inbreeding coefficients [In Murciano-Granadina goats, a previous study performed by Deroide et al.  reportede et al.  also repe et al.  reportedficients , and simficients . Doekes ficients  indicateficients . Inbreedficients –62, so tficients .TLR4 polymorphisms and haplotypes have been associated with milk somatic cell count and susceptibility to mastitis [ITGAM also known as CD11b) which maps to CHI25: 27,221,164–27,264,350 bp and encodes a receptor for lipopolysaccharide [When we investigated which enriched clusters are present in the set of genes mapping to chromosomes 8 (37–56 Mb and 75–93 Mb) and 25 (0.082–7 Mb and 21–28 Mb) regions associated with inbreeding depression for lnSCC  , 21 (CCL21), 24 (CCL24), 26 (CCL26) and 27 (CCL27). Chemokines are essential for the development of the innate immune response since they orchestrate and control the migration of the immune cells  to sites of infection [P-value = 0.03), we detected the interleukin 27 (IL27) gene which encodes a molecule with both pro and anti-inflammatory effects, thus enhancing the immune response and, at the same time, preventing tissue damage caused by inflammation [As previously indicated, genes related to chemotaxis were significantly enriched at the nominal level  with genes associated with multiple unrelated biological processes  influences milk production and composition in cattle. Bai and collaborators [The genomic region displaying inbreeding depression for milk yield  was significantly enriched at the nominal level , a finding consistent with the large census size and demographic history of this breed. Four genomic regions on CHI8:37,557,623–56,336,435, CHI8:75,115,244–93,894,055 bp, CHI25:82,419–7,143,084 and CHI25:21,429,255–28,572,340 bp were associated with inbreeding depression for lnSCC. Moreover, one region on CHI2:22,751,824–68,255,473 was consistently associated with inbreeding depression for three milk yield traits . Genes encoding integrins, chemokines and pathogen recognition receptors, which play relevant roles in the elicitation of innate immune responses against microbes, mapped to regions associated with inbreeding depression for lnSCC. These results suggest that keeping inbreeding to a minimum, through an adequate reproductive management, might be a useful approach to decrease the incidence of mastitis in Murciano-Granadina goats.Murciano-Granadina goats display low levels of inbreeding .Additional file 3: Figure S2. Number of ROH per chromosome  and the percentage of each chromosome covered by ROH  in 1040 Murciano-Granadina goats.Additional file 4: Table S2. Genomic regions associated with the top 1% of homozygosity for each SNP marker in a population of 1040 Murciano-Granadina goats genotyped with the Goat SNP50 BeadChip.Additional file 5: Table S3. ROH hotspots identified in the genomes of 1040 Murciano-Granadina goats.Additional file 6: Table S4. Genes mapping to regions consistently identified as ROH hotspots and regions with high homozygosity.Additional file 7: Table S5. Functional enrichment analysis of genes mapping to regions consistently identified as ROH hotspots and regions with high homozygosity.Additional file 8: Table S6. Inbreeding depression estimates (s.e: standard error) per goat chromosome (CHI) for the natural logarithm of the somatic cell count divided by 1000 (lnSCC) and their 95% confidence intervals (C.I).Additional file 9: Table S7. Inbreeding depression estimates (s.e: standard error) for the natural logarithm of the somatic cell count divided by 1000 (lnSCC) in specific regions of goat chromosomes (CHI) 8 and 25 and their 95% confidence intervals (C.I).Additional file 10: Table S8. Genes mapping to goat chromosome (CHI) 8 (37–56 Mb and 75–93 Mb) and 25 (0.082–7 Mb and 21–28 Mb) regions associated with inbreeding depression for the natural logarithm of the somatic cell count divided by 1000 (lnSCC).Additional file 11: Table S9. Functional enrichment analysis of genes from the goat chromosome 8 (37–56 Mb and 75–93 Mb) and 25 (0.082–7 Mb and 21–28 Mb) regions associated with inbreeding depression for the natural logarithm of the somatic cell count divided by 1000 (lnSCC).Additional file 12: Table S10. Inbreeding depression estimates (s.e: standard error) per goat chromosome (CHI) for milk yield at 210, 240 and 305 days  and their 95% confidence intervals (C.I).Additional file 13: Table S11. Inbreeding depression estimates (s.e: standard error) for milk yield at 210, 240 and 305 days  in specific regions of goat chromosomes (CHI) 2 and their 95% confidence intervals (C.I).Additional file 14: Table S12. Genes mapping to goat chromosome (CHI) 2: 22–68 Mb region associated with inbreeding depression for milk yield at 210, 240 and 305 days .Additional file 15: Table S13. Functional enrichment analysis of genes from the goat chromosome (CHI) 2: 22–68 Mb region associated with inbreeding depression for milk yield at 210, 240 and 305 days .


Takifugu rubripes). High values for predictive ability (0.563–0.606) were obtained with 4000 SNPs for all traits under a genomic best linear unbiased predictor (GBLUP) model. These values were still within an acceptable range with 1200 SNPs (0.554–0.588). However, predictive abilities and prediction accuracies deteriorated using less than 1200 SNPs largely due to the reduced power in accurately estimating the genetic relationship among individuals; family structure could still be resolved with as few as 400 SNPs. This suggests that the SNPs informative for estimation of genetic relatedness among individuals differ from those for inference of family structure, and that non-random SNP selection based on the effects on family structure  is unlikely to increase the prediction accuracy for these traits.Aquaculture production is expected to increase with the help of genomic selection (GS). The possibility of performing GS using only a small number of SNPs has been examined in order to reduce genotyping costs; however, the practicality of this approach is still unclear. Here, we tested whether the effects of reducing the number of SNPs impaired the prediction accuracy of GS for standard length, body weight, and testes weight in the tiger pufferfish ( However, most cultured species are genetically still very similar to their wild conspecifics because of their relatively short history under culture. Therefore, significant genetic variation exists in cultured populations that can be harnessed to boost aquaculture production by means of selective breeding2. For example, an average of 13% of genetic gain in growth rate per generation has been achieved in salmonids3. Such genetic improvements can be further accelerated by incorporating recent developments in molecular tools, such as genomic selection (GS)4.Aquaculture as an industry has taken root worldwide and has become the fastest growing in the food production sector5 and is now implemented in many aquaculture species4. In this approach, a prediction model is trained by regressing the phenotype on the genome-wide genotype of a reference population, and genomic estimated breeding values (GEBVs) of selection candidates are predicted by substituting their genotypes in the model . Genomic selection is particularly advantageous for aquaculture species because many of economically important traits are polygenic , and cultured populations often consist of many full-sib/half-sib families resulting in highly accurate GP6. Among GP models, the genomic best linear unbiased predictor (GBLUP) model is widely used for polygenic traits8. GBLUP is statistically very similar to the traditional pedigree-based BLUP (PBLUP); the main difference is that the pedigree-based relationships used in PBLUP are replaced by genomic relationships inferred from genome-wide SNP information. Overall GBLUP often outperforms PBLUP in prediction accuracy because genomic relationships can estimate the fraction of a genome shared between individuals more accurately than pedigree-based relationships; the latter are based on expected values while the former evaluate deviations around the expected values 9. The major drawback of GS is that frequent genotyping for thousands of SNPs in hundreds of individuals is required for accurate prediction. Therefore, extensive efforts are underway to reduce genotyping costs, such as studying the effect of sib-test frequency on prediction accuracy10, using genotype-by-sequencing methods11, and using low-density markers with imputation13. It is expected that a relatively small number of SNPs might be sufficient to perform GP in aquaculture because breeding populations often include many full- and half-sib families, and the genomic relationships among population members can be accurately captured with a few thousand SNPs. Indeed, recent studies have suggested that near-maximal prediction accuracy can be gained with a low-density SNP panel (1000 SNPs) regardless of the differences in population and family structure, phenotype, and trait definition14. However, the practical utility of GS with a small number of SNPs is still unclear and needs to be tested with more species and traits.The theory of GS was first proposed by Meuwissen et al.Takifugu rubripes) and sought to determine the minimum number of SNPs required to achieve good prediction accuracy for TW, standard length (SL), and body weight (BW). The tiger pufferfish  is one of the most valuable aquaculture species in Japan17. Artificially raised seedlings have been used in aquaculture of this species since the 1990s when technologies for broodstock management and artificial seed production were established21. Aquaculture production using these methods now dominates (approximately 90%) overall production of this species (Japan Fisheries Research and Education Agency (FRA), Japan: http://abchan.fra.go.jp/digests2019/index.html, accessed 12 Nov 2020). Although a selective breeding program for this species is still in its infancy17, the possibility of using GS for SL, BW, and for resistance against the monogenean parasite, Heterobothrium okamotoi, has already been tested22. Precociousness is another important economic trait in this species23. While the ovary is highly poisonous, the testis is edible and is the most expensive of the edible parts24. In most individuals, the testes reach marketable size (> 100 g) in late January or February (when the fish is about 22 months old); however, some individuals have testes larger than 100 g in early December (at about 20 months old), a time when prices are also at their highest. As precociousness is a polygenic trait25 then TW at harvest can potentially be improved by GS. To test this possibility, we raised an experimental population and applied GP for TW, SL, and BW using a genome-wide medium density SNP panel. Pedigree information was reconstructed using genomic information to enable application of the BLUP method. We also examined the effect of reducing the number of SNPs (from 4075 to 200) on the prediction accuracy and estimation of genetic relatedness among individuals to test the feasibility of using a low-density SNP panel for GS.In this study, we examined the feasibility of GP for testes weight (TW) in the tiger pufferfish (1), consisting of 129 full-sib families, was produced from nine females and nineteen males of the founder generation (F0) in April 2018. The parental individuals were obtained from 996 fish purchased from aquaculture farms in Nagasaki prefecture (Japan) in December 2016. Of these, 652 were used in the previous study26 and the remaining 344 were utilized in this study. The F1 generation included 712 individuals; these were at 20 months old and fin-clips were collected. Body size data (SL and BW) were collected from 695 individuals while TW was obtained from the 246 males. The phenotypic data  of the F1 specimens is shown in Fig. The test generation F, consistAs the genetic origin of each parent and the parental pair of each individual were unknown, pedigree information was reconstructed using 4075 genome-wide SNPs obtained using the Ampliseq custom panel (Thermo Fisher Scientific Inc). The results of the kin relationship analysis among parents and the parent–offspring assignments were combined to reconstruct pedigree information of the population. Based on this inferred pedigree, a numerator relationship matrix (A matrix) was constructed for further genetic analyses.27; with the exception of one female parent, the parental individuals comprised six full-sib families and two pairs of half-sibs. The exceptional female parent was estimated to be a third-degree relative of one pair of half-sibs  to examine the effect of the number of SNPs for the assignments. We selected the optimal K value for each SNP set based on a five-fold cross validation: K = 34 for the full SNP data set, and K = 33, 38, and 34 for the 800, 400, and 200 SNPs subsets, respectively , predictive ability, and prediction accuracy were estimated along with those for SL and BW under GBLUP using the full SNP data (4075 SNPs)  Fig. . These vh2 = 0.686; SL: 0.538; BW: 0.557), these values were smaller than those estimated under the inf-PBLUP model . The predictive ability and prediction accuracy were also high for each trait under the GBLUP model. The predictive ability (measured as the correlation coefficient between GEBV and observed phenotypes) were similar among the three traits (0.551–0.591), while the prediction accuracy (the prediction ability divided by square-root of the heritability) varied somewhat (0.665–0.792). Both values were highest for BW and lowest for TW. These values were higher than those estimated under the inf-PBLUP model .The estimated heritability under the GBLUP model was moderate for every trait, with that for TW being the highest  from the full SNP set . For each subset, SNP extraction was performed independently ten times . Unsurprisingly, the ten replicates of the subset with 4000 SNPs were nearly identical to each other as these were selected from 4075 SNPs. Genetic parameters were estimated under the GBLUP model using these subsets and compared with the results obtained using the full SNP data sets  between the GEBV estimated from the full SNP set and those estimated from each of the 11 subsets. As for heritability and predictive ability estimation, the value of the correlation coefficient for each trait dropped when fewer than 1200 SNPs were used  was observed for each trait even between the full SNP set and 400 SNPs, with which family structure could be captured  constructed with the full SNP set and with each replicate of the 11 SNP subsets by means of the Mantel test Fig. . The ManSalmo salar; length: 0.61, weight: 0.60)29, common carp 30, Nile tilapia 31, channel catfish 32, large yellow croaker 34, yellowtail kingfish 35, and the tiger pufferfish (length: 0.41)22. Prediction accuracy was relatively high for all of the traits (prediction accuracy: 0.665‒0.792), but also within the range of previous studies: Atlantic salmon 29, common carp (length: 0.71)30, Nile tilapia (weight: 0.60)31, channel catfish (weight: 0.37)32, large yellow croaker 33, yellowtail kingfish 35, and the tiger pufferfish (length: 0.73)22. The high heritability and prediction accuracy indicate that GS can be applied for selective breeding on TW in addition to SL and BW of the tiger pufferfish.In this study, we tested the feasibility of GP for TW SL, and BW in cultured tiger pufferfish at harvest using the GBLUP model and examined the effect of varying the number of SNPs on the estimations. Moderate heritability values were obtained for the three traits 0.538‒0.686)  between the GEBVs estimated with the full SNP dataset and with each of the SNP subsets  , regardless of the species, population and family structure, traits, and genotyping platforms. This is particularly the case when the training population used for the prediction model construction and the selection candidates are closely related, because long genomic segments which can be captured with a small number of SNPs are shared among individuals. Our results also suggest that a medium number of SNPs (2–4 K) is sufficient for GS programs in the tiger pufferfish population. The prediction performance of the GBLUP model is superior to that of the BLUP model mainly due to the improvement in estimation of genetic relationship between each pair of individuals. Our results revealed that a larger number of SNPs are required for accurate estimation of genetic relatedness compared to estimation of family structure. This suggests that the SNPs informative for the estimation of genetic relatedness among individuals and for the inference of family structure are different. Therefore, SNP selection based on, for example, site FST, principal components analysis (PCA), and random forest, which account for the effects of each SNP on the family structure, would not help increase the prediction performance, especially for polygenic traits. To further decrease the number of SNPs for cost effective prediction, genotyping imputation from using a low to medium density panel37 would be a better choice compared to non-random SNP selection schemes.Kriaridou et al.22. A medium size dataset of SNPs (4075 SNPs) is sufficient for accurate prediction, but the number of SNPs can be reduced to 1200 without much loss of accuracy. Below this threshold, however, genetic relationship between each pair of individuals is likely to be obscured and the prediction accuracy will deteriorate.In conclusion, our data revealed that GS is available for improvement of TW as is the case for SL and BW in selective breeding of the tiger pufferfish0 generation) from aquaculture farms in Nagasaki prefecture (Japan) in December 2016. Some of these fish (n = 652) were used in a previous study26, and the remaining (n = 344) were analyzed specifically for this study. Approximately half of the entire group of fish (n = 495) was transferred to Nagasaki Prefectural Institute of Fisheries  as the broodstock candidates without phenotype recording, while the rest of fish were sacrificed for phenotyping at site. A fin-clip was obtained from each individual for genotyping, stored in 99.5% ethanol and kept at − 30 °C until DNA extraction. In April 2018, nine females and nineteen males were crossed to produce the F1 generation . The genetic relationships among the parents were assessed as explained below prior to the mating to minimize inbreeding between full sibs. The F1 generation was raised at NPIF in the conventional manner25. At 20 months old, 712 individuals were sampled; 500  were euthanized with an overdoze of 2-phenoxy ethanol (> 600 mL/ton) and body size data (SL and BW) was collected. Testes weights was also measured for each male. A fin-clip was collected from each fish for genotyping. The remaining 212 fish were retained as broodstock. The sample information is presented in Supplementary Tables 38. All the other figures were also created using R/ggplot2 (v3.3.5) with the exception of Supplementary Fig. We purchased 996 individuals . Paired-end sequencing was carried out using an Illumina MiSeq with a MiSeq Reagent Kit , or a MiSeq Reagent Micro Kit (for the library consisting of 39 samples). Quality-trimming was performed using Trimmomatic (v0.38)39 with the following filtering parameters: ILLUMINACLIP TruSeq2-PE-2.fa:2:30:10, SLIDINGWINDOW:30:20, AVGQUAL:20. The read pairs surviving at both ends were mapped onto the reference sequence covering 50 bp upstream and downstream of the targeted loci extracted from FUGU5/fr316 using BWA-mem40. The file format was converted from SAM to BAM using SAMtools41 and used for SNP genotyping. GATK HaplotypeCaller (v4.1.4)42 was used to detect polymorphisms with following parameters: –output-mode EMIT_ALL_CONFIDENT_SITE, –stand-call-conf 30 and –ERC BP_resolution. Genotyping of each sample was performed using GATK GenotypeGVCFs with –all-site TRUE option. This per-base genotyping allows us to distinguish between the reference homozygote and the missing genotype. Low quality SNPs were filtered out using VCFtools (v0.1.17)43 with options of –minDP 5 and –minQ 30. Genotype files were merged per generation using BCFtools merge (v1.9)44, and meanwhile, only SNPs with two alleles, minor allele frequency (MAF) larger than 0.01 and MAF less than 0.4 were selected for each generation using BCFtools view. Subsequently, SNPs with genotyping rate less than 60% were removed from the F0 generation using VCFtools. The remaining SNPs were then extracted from the F1 generation and SNPs with genotyping rate less than 60% were excluded. As a result, 4075 high quality SNPs surviving in both generations were retained.Genotyping of genome-wide SNPs was performed using the Ampliseq custom panel (Thermo Fisher Scientific Inc) as described previously45 with default setting. The imputation strategy adopted in the software depends on LD-kNNi , which is free from pedigree information. Subsequently, subsets of SNPs  were randomly extracted from the imputed SNP panel using the bash shuf command. Ten replicates were produced for each subset independently. Genotype vcf files were converted into ped, bed or 012 (recodeA) format using VCFtools and PLINK v1.0746 as needed.The effect of the number of SNPs was examined after genotype imputation. The imputation step was performed for each generation using LinkImpute (v1.1.4)27 to infer kin relationships among them based on the coefficient range  following the manual. Meanwhile, parentage assignment was carried out using ADMIXTURE v1.3.028. Optimal K-value (the number of subpopulations) was selected as 26–42 based on five-fold cross validation implemented in the software. Parentage assignment was also carried out using one of the ten replications of the imputed SNP subsets . Finally, the pedigree was reconstructed from the results of kin relationship among parents and parentage.As no pedigree information was available for the original aquaculture population, we have reconstructed the pedigree from the pre-imputed data set (4075 SNPs). Pair-wise kinship coefficients were estimated among the 28 parental individuals using KING software1 generation with phenotypic data from 695 individuals for SL and BW, and 246 males for TW, using rrBLUP package47 for the 11 subsets of imputed SNPs (including ten replicates). The following genomic BLUP (GBLUP) model was fitted:a2), where G is the genomic relationship matrix, calculated using A.mat function, and σa2 is the additive genetic variance. Narrow sense heritability was calculated as h2 = σa2/(σa2 + σe2).Narrow-sense heritability was estimated for SL, BW, and TW in the Fa2). The A matrix was calculated using Amatrix function of AGHmatrix48, and the model was solved using rrBLUP.Narrow-sense heritability of each trait was also estimated based on the inferred pedigree information (inf-PBLUP). The same model as GBLUP was fitted, but the G matrix was replaced by the numerator relationship matrix (A); the additive genetic effects follows the normal distribution ~ N of which phenotype recodes were masked. Predictive ability was determined as the correlation between the observed phenotype and predicted GEBV of the validation set. Prediction accuracy was obtained by dividing predictive ability by the square root of the heritability estimated above. This set was repeated ten times while rotating the validation set among the ten groups, and the whole procedure was repeated ten times independently to obtain the mean and the standard error of the measure of prediction accuracy.The GEBV and inf-EBV of each trait of each fish were predicted under the linear model described above. The estimation of GEBV was also performed using the ten replicates of the 11 SNP subsets. The predictive ability and the prediction accuracy were estimated for each dataset using ten replicates of tenfold cross-validation as described in Hosoya et al.r) were calculated among GEBVs and among the family means of GEBVs calculated with the full SNP set and each replicate SNPs subset. The correlation among the G matrices averaged over the replicates and A matrix was also calculated as the Mantel statistics using the mantel function implemented in vegan package50.Pearson’s correlation coefficients  (#NPIF-0001) and carried out in accordance with the Guidelines for Fish Experimentation in NPIF. This study was carried out in compliance with the ARRIVE guidelines for fishes.Supplementary Figures.Supplementary Tables.


Strigops habroptilus), wherein a highly inbred island population and one individual from the mainland of New Zealand founded the entire extant population. Genotyping-by-sequencing (GBS), and a genotype calling approach using a chromosome-level genome assembly, identified a filtered set of 12,241 single-nucleotide polymorphisms (SNPs) among 161 kākāpō, which together encompass the total genetic potential of the extant population. Multiple molecular-based estimates of inbreeding were compared, including genome-wide estimates of heterozygosity (FH), the diagonal elements of a genomic-relatedness matrix (FGRM), and runs of homozygosity . In addition, we compared levels of inbreeding in chicks from a recent breeding season to examine if inbreeding is associated with offspring survival. The density of SNPs generated with GBS was sufficient to identify chromosomes that were largely homozygous with RoH distributed in similar patterns to other inbred species. Measures of inbreeding were largely correlated and differed significantly between descendants of the two founding populations. However, neither inbreeding nor ancestry was found to be associated with reduced survivorship in chicks, owing to unexpected mortality in chicks exhibiting low levels of inbreeding. Our study highlights important considerations for estimating inbreeding in critically endangered species, such as the impacts of small population sizes and admixture between diverse lineages.Events of inbreeding are inevitable in critically endangered species. Reduced population sizes and unique life-history traits can increase the severity of inbreeding, leading to declines in fitness and increased risk of extinction. Here, we investigate levels of inbreeding in a critically endangered flightless parrot, the kākāpō ( Extensive inbreeding between close relatives and subsequent fitness effects are a major threat to the resilience of critically endangered populations  and empii.e., overdominance) maintained at moderate frequencies by balancing selection  can elevate extinction risk in endangered populations through increased susceptibility to disease  is endemic to New Zealand, and unique among parrots in that it is flightless, nocturnal, and possesses a polygynous lek mating system  and the diagonal elements of a genomic-relatedness matrix . Several studies using whole-genome sequencing have demonstrated that runs of homozygosity provide the most robust estimator of genome-wide patterns of inbreeding  (RoH) in the GBS dataset and evaluated its accuracy relative to FH and FGRM. In addition, levels of inbreeding were compared between the descendants of the mainland and Stewart Island founding populations to further investigate the impacts of previous bottlenecks  . Therefon = 10). In 1977, a small insular population on Stewart Island was also rediscovered and eventually translocated (n = 61) to predator-free islands (n = 153). Some relationships between individuals of the founding population are currently unresolved , and a selection of blood, chorioallantoic membrane, and liver were sampled from surviving and deceased chicks from the 2016 breeding season (n = 38). Genomic DNA was isolated from blood or tissue using standard phenol-chloroform extractions following Kākāpō samples were obtained as blood and stored in lysis buffer   was used to convert and sort SAM files to BAM files, and to print statistics for checking alignment mapping rate . Stacks populations with default parameters and filtering for one SNP per RAD locus (–write_single_snp) was used to output genotypes in VCF and PLINK formats for further downstream analyses , since these markers can overestimate the proportion of heterozygous sites , leaving 12,207 SNPs for 151 individuals.A principal component analysis (PCA) based on filtered SNPs was conducted in .9 and R  to rule .9 and R , were apinbreedR v0.3.2 package in R, with bootstrapping    and diagonal elements of the genomic relatedness matrix (FGRM) are relative measures of inbreeding within the total population, indicating the probability that an individual carries alleles that are identical by descent (IBD)  quantifies allelic similarity between gametes and gives more weight to homozygous rare alleles (RoH) is an absolute measure of individual autozygosity: the realized proportion of the genome that is IBD (The inbreeding coefficient (Fnt (IBD) . FGRM (aH), the probability that an individual carries two IBD copies of an allele at a given neutral locus, was calculated using VCFtools (–het) and confirmed with PLINK (–het) using method of moments  uses allele frequencies to provide unbiased estimates of individual inbreeding while accounting for read depth in the genotype calls, implemented in the R package “kinship using GBS with depth adjustment”  described by i.e., if there are at least 2 reads). Individuals more inbred than average have positive values, whereas less inbred individuals are expected to have negative values. The Python script vcf2ra.py (available from https://github.com/AgResearch/KGD) was used to convert VCF to the “Tassel” format for input into KGD. Applying KGD filtering to the total dataset, 876 SNPs with a depth <0.01 or minor allele frequency (MAF) of zero were removed, leaving a total of 11,365 SNPs to construct the GRM; FGRM = diagonal of GRM − 1. To identify regions containing repetitive elements, KGD provides a framework for further diagnostics by outputting plots illustrating SNP call rates and depth, MAF, and Hardy-Weinberg disequilibrium plotted against MAF for validation. This is referred to as a fin plot, which illustrates SNP average depth using a color gradient. For kākāpō, the fin plot showed that SNPs were concentrated in appropriate regions , with an intermediate depth across MAFs, and no large concentration of SNPs at the upper and lower boundaries; the latter suggesting no excess levels of heterozygosity. To investigate the influence of Hardy-Weinberg disequilibrium filtering on self-relatedness estimates, SNPs with deviations less than −0.05 were removed, which had a negligible effect on self-relatedness estimates (results not shown). GRMF is defined as,j indexes SNPs with depth at least two in the individual, The genomic relatedness matrix (GRM) and its diagonal elements of self-relatedness  . To account for occasional mutations or sequencing error, a single heterozygous position was allowed in inferred RoH. The following parameters were used to define RoH while ensuring that the edges of RoH are delimited: a minimum of 25 contiguous homozygous SNPs (–homozyg-snp 25), minimum SNP density of one SNP every 130 Kb (–homozyg-density 130), a maximum distance between neighboring SNPs of 1 Mb (–homozyg-gap 1000), and a maximum of one heterozygous site (–homozyg-het 1). Allowing up to three heterozygous sites (–homozyg-het 3), as suggested by The accurate detection of runs of homozygosity (RoH) using reduced-representation sequencing approaches is highly dependent on read depth, SNP density, and the distribution of SNPs across the genome. However, dense markers from GBS and the availability of a high-quality chromosome-level genome assembly provide the necessary framework to map and compare RoH coordinates across the genome (RoH), a minimum length threshold of >1 Mb was required to qualify a RoH as homozygous in order to exclude RoH resulting from background relatedness or with strong linkage disequilibrium, which typically can extend into shorter regions of up to 100 Kb  . The FRoH and FRoH10 estimates were compared between mainland and Stewart Island founders and descendants, and between deceased (n = 9) and surviving chicks (n = 25) from the 2016 breeding season.To calculate individual autozygosity or inbreeding ; e.g., crushed eggs, chicks deceased after conflict or drowning after a storm. The developmental stage was not considered, as age at death could not be resolved for all samples; thus, all samples are referred to as chicks hereafter. Differences in inbreeding between deceased (n = 9) and surviving (n = 25) chicks from the same breeding season were compared with FRoH using the lm linear regression function in R  and inbreeding (FRoH), and chick survivorship  as the response variable , to evaluate if a relationship between ancestry, FRoH and chick survival exists.The inbreeding estimate Fion in R . A generR Studio v1.3.959, using the following packages: ggplot2 v3.3.2, ggpubr v0.4.0, ggfortify v0.4.10, and inbreedR  . The PCA revealed no unexpected population structure; the mainland founder and its descendants separated from the Stewart Island founders consistent with their ancestry .We generated high-density genome-wide SNPs for 123 adult kākāpō and 38 chicks from the 2016 breeding season using GBS with reference-based genotype calling. Processed raw reads were aligned to a high-quality reference genome, resulting in a mean mapping rate of 98.51%. Calling SNPs with the referenced-based approach yielded 56,218 SNPs. After filtering for minimum and maximum depth, removing sex-linked markers and individuals with high SNP call missingness, and excluding variants with more than 20% missing data, 12,241 SNPs with a mean depth of 9.82 and density of 84.03 kb/SNP remained. After applying KGD filtering, a total of 11,782 SNPs with a mean depth of 9.55 were used to construct the GRM for all individuals . Autosomal SNPs were distributed across all chromosomes and concentrated toward the ends of the chromosomes . Mean obg2 confirmed nonzero variance , with a strong positive heterozygosity-heterozygosity correlation coefficient , indicating that the SNP markers meet the requirements to detect inbreeding depression  but no correlations were found between FH and FGRM , or FRoH and FGRM . For all kākāpō, the mean FH was 0.09, mean FGRM was 0.04, and mean FRoH was 0.18 . When the Stewart Island and mainland descendants were considered separately , the Stewart Island mean FH was 0.15, mean FGRM was −0.03, and mean FRoH was 0.19 . For the mainland descendants, mean FH was −0.77, mean FGRM was 1.07, and mean FRoH was 0.08 .We compared multiple estimates of genome-wide inbreeding among all kākāpō. First, ID using the metric e.g., chromosome 11, 15, and 19 for male kākāpō Hillary; RoH for Stewart Island descendants was 0.19 and 0.08 for mainland descendants . In addition, a linear regression showed a significant difference between the FRoH of mainland and Stewart Island descendants (P < 2 × 10−16)  .RoH to compare inbreeding between deceased (n = 9) and surviving (n = 25) chicks from the 2016 breeding season. Deceased chicks had a mean number of 55.33 RoH with a mean length of 175.75 Mb (15.08% of genome in RoH), and surviving chicks had a mean number of 55.92 RoH with a mean length of 180.26 Mb (15.46% of genome in RoH). The number of RoH did not significantly differ between deceased and surviving chicks (P = 0.87) or between length of RoH (P = 0.75). There was no significant relationship between chick survival and their FRoH (P = 0.76) or FRoH10 . Similar to the mainland founder (Richard-Henry) and his three offspring, all but one of his descending grand-chicks (F2) had no RoH >10 Mb. Notably, despite the absence of a difference in FRoH between surviving and deceased chicks, the single mainland descendant chick that had two RoH >10 Mb was an early-death embryo. Furthermore, one chick suspected of dwarfism also had the highest value of FRoH out of all deceased chicks (P = 6.25e-07) and length of RoH (P = 3.32e-08) per individual significantly differed between Stewart Island and mainland descendant chicks. FRoH  were both significantly different between Stewart Island and mainland descending chicks.We used Fd chicks . Comparie.g., GBS) is a cost-effective approach to evaluate inbreeding in populations under conservation management (n = 123) and chicks from the 2016 breeding season (n = 38), representing virtually the total managed kākāpō population up until 2018.Reduced-representation sequencing across the genome  may be more powerful for detecting inbreeding depression . A limited number of long RoH were found in kākāpō using this approach  (RoH (>2 Mb) in a subset of the extant Stewart Island population compared to the extinct mainland population (including Richard-Henry).SNPs were distributed across the kākāpō genome and were concentrated toward the ends of chromosomes , consistelection , suggestelection , where sapproach . The comH and FRoH remained correlated with each other , revealing that the majority of variation in inbreeding levels in kākāpō is driven by differences between the two divergent founding populations. The weakest correlations between inbreeding estimates invariably involved GRM methods (FGRM), which use estimated population allele frequencies and are highly influenced by what initial population is provided , as GRM-based approaches give too much weight to rare alleles causing biases when there are subdivided populations and admixture between individuals with diverse allele frequencies ; comparable recommendations are given by H values result from excess heterozygosity relative to Hardy-Weinberg proportions and indicate that parents are, on average, less closely related than expected under random mating  and its descendants than for Stewart Island descendants , indicating that individuals with mainland ancestry are relatively more outbred within the extant population. Both FH and FRoH similarly reflected elevated levels of inbreeding in Stewart Island descendants , and overall, FRoH estimates were elevated (maximum FRoH 0.248) compared to those estimated from RADseq data of the vulnerable New Zealand hihi (maximum FRoH 0.158)  nor ancestry predicting survivorship (Supplementary Table S1). This pattern was driven by mortality in chicks descended from mainland ancestry, despite mainland descendants exhibiting the lowest levels of inbreeding in the population, as well as mortality in Stewart Island chicks exhibiting both high and moderate levels of inbreeding. Genetic rescue aims to increase fitness in endangered populations through the introduction of unrelated individuals, with demonstrated success in numerous species . Specifiet al.Current management strategies to mitigate inbreeding in kākāpō include the prevention of consanguineous matings, removal of infertile or overly successful males from breeding islands, and favoring matings with mainland descendants et al., 2019.i.e., genetic load) . Outcomes of strategies such as translocations and artificial insemination may be improved if a greater emphasis is placed on the selection of individuals that carry desirable alleles for breeding (to minimize deleterious variation) rather than individual relatedness alone  . A studyss alone .RoH value of all deceased chicks  should also incorporate homozygosity mapping and targeted-gene approaches to identify susceptible individuals and minimize their exposure to sources of transmission.Markers of homozygosity can be used to detect causal mutations associated with malformations and disease (d chicks . MappingH) and absolute measures of autozygosity (FRoH). Future studies should compare estimates of inbreeding with additional quantitative phenotypic traits  to further evaluate evidence for inbreeding depression in kākāpō  and corresponding results are available on figshare at


The key-ancestor approach has been frequently applied to prioritize individuals for whole-genome sequencing based on their marginal genetic contribution to current populations. Using this approach, we selected 70 key ancestors from two lines of the Swiss Large White breed that have been selected divergently for fertility and fattening traits and sequenced their genomes with short paired-end reads.ST) analyses indicated considerable genetic differentiation between the lines. Genomic inbreeding quantified using runs of homozygosity was higher in the sire than dam line (0.28 vs 0.26). Using two complementary approaches, we detected 51 signatures of selection. However, only six signatures of selection overlapped between both lines. We used the sequenced haplotypes of the 70 key ancestors as a reference panel to call 22,618,811 genotypes in 175 pigs that had been sequenced at very low coverage (1.11-fold) using the GLIMPSE software. The genotype concordance, non-reference sensitivity and non-reference discrepancy between thus inferred and Illumina PorcineSNP60 BeadChip-called genotypes was 97.60, 98.73 and 3.24%, respectively. The low-pass sequencing-derived genomic relationship coefficients were highly correlated (r > 0.99) with those obtained from microarray genotyping.Using pedigree records, we estimated the effective population size of the dam and sire line to 72 and 44, respectively. In order to assess sequence variation in both lines, we sequenced the genomes of 70 boars at an average coverage of 16.69-fold. The boars explained 87.95 and 95.35% of the genetic diversity of the breeding populations of the dam and sire line, respectively. Reference-guided variant discovery using the GATK revealed 26,862,369 polymorphic sites. Principal component, admixture and fixation index (FWe assessed genetic diversity within and between two lines of the Swiss Large White pig breed. Our analyses revealed considerable differentiation, even though the split into two populations occurred only few generations ago. The sequenced haplotypes of the key ancestor animals enabled us to implement genotyping by low-pass sequencing which offers an intriguing cost-effective approach to increase the variant density over current array-based genotyping by more than 350-fold.The online version contains supplementary material available at 10.1186/s12864-021-07610-5. Swine production follows the classical breeding pyramid. Genetic gain is generated in nucleus herds and transmitted via the multiplier to the production unit. Swiss pig production relies on maternal and paternal Swiss Large White (SLW) lines at the top level of the breeding pyramid. For decades, the SLW breed has been maintained as a universal breed, selected for production and fertility traits. In 2002, the population was divided into sire and dam lines that have been divergently selected for fattening and reproduction since then. Approximately 32.5 and 30% of the genes of 2.5 million fattening pigs slaughtered in 2020 in Switzerland originate from the dam and sire line, respectively . Both liThe SLW breeding boars are selected based on genome-based breeding values that are predicted using genotypes obtained with a customized version of the Illumina PorcineSNP60 BeadChip. Apart from a small number of putatively causal variants that are included in the custom part, the content of the currently used microarray was designed in a way that it is useful for mainstream breeds . HoweverThe availability of sequence variant genotypes from key ancestor animals enables imputing sequence-level genotypes for animals that had been genotyped at lower density –10. In lHere we obtain whole-genome sequencing data from key ancestor animals to characterize genetic diversity, population structure, and signatures of selection in two divergently selected commercial pig breeds. Using the haplotypes of the key ancestor animals as a reference panel, we accurately genotype more than 22 million variants in animals that have been sequenced at low coverage.Using pedigree records, the average inbreeding coefficients of the active breeding animals of the sire and dam line were 0.06 ± 0.02 and 0.05 ± 0.01, respectively. Based on these values and the inbreeding coefficients of the parents, we estimated the effective population size of the sire and dam line of the Swiss Large White (SLW) breed to 44 and 72, respectively. In order to assess sequence variation within the two lines, we prioritized 70 boars for whole-genome sequencing based on their marginal genetic contributions to the active breeding populations with a key ancestor approach. Of the 70 boars, 38 and 32 represent the sire and dam line, respectively, explaining 95.35 and 87.95% of the genetic diversity of the active breeding populations.Following quality control , between 81.15 and 377.01 million read pairs (2 × 150 bp) per sample (mean: 165.55 ± 60.32 million read pairs) were aligned to the SSC11.1 assembly of the porcine genome. Using reads with high mapping quality , the average sequencing coverage of the 70 boars was 16.69 ± 5.93-fold across all autosomes. Raw sequence read data of 70 pigs have been deposited at the European Nucleotide Archive (ENA) of the EMBL at BioProject PRJEB38156 and PRJEB39374.A reference-guided multi-sample variant discovery and genotyping approach yielded genotypes at 28,407,060 sites  that had average sequencing coverage between 8.72 and 36.85-fold (average: 16.79-fold) also had Illumina PorcineSNP60 BeadChip-called genotypes. Using the array-called genotypes at 54,600 autosomal SNP for which we were able to determine reference and alternate alleles as a truth set, we calculated genotype concordance, non-reference sensitivity and non-reference discrepancy between array-called and sequence-called genotypes as proposed by DePristo et al. . Of the Of the 48,224 SNP that were either polymorphic or fixed for the alternate allele in the array-called genotypes, 46,009 (95.41%) and 45,951 (95.29%) were also present in the raw and filtered sequence variants, respectively. 1232 SNP of the Illumina PorcineSNP60 BeadChip complement were missing in the sequenced set because they were either genotyped as INDEL or multiallelic sites using GATK and thus excluded from the comparison due to incompatible alleles. 983 and 1041 SNP were not among the raw and filtered sequence variants, respectively, although the frequency of the minor allele was > 5% in the array-called genotypes for most (> 80%) of them. It is likely that these variants could not be matched with the sequence set due to either incompatible or ambiguous map coordinates.Non-reference sensitivity was greater than 99% and non-reference discrepancy around 1% for the raw genotypes called by the GATK, suggesting that the high sequencing coverage facilitated accurate variant discovery Table . The conST) analyses. The principal components were extracted from a genomic relationship matrix constructed from 23,691,198 autosomal sequence variants that had minor allele frequency greater than 0.01.To investigate the population structure, ancestry and genetic diversity among the 70 sequenced pigs, we performed principal component, admixture and fixation index , was 0.26 ± 0.03 and 0.28 ± 0.03 for the dam and sire line, respectively. We classified the ROH into short (50–100 kb), medium (100 kb - 2 Mb) and long ROH (above 2 Mb)  for 19,146,365 biallelic SNP to investigate genomic inbreeding in both lines. In total 111,201 ROH with an average length of 391.28 kb (ranging from 50 kb to 11.1 Mb) were detected . The ROH contained an average number of 3176 SNP . The boars from the dam and sire line had 1604 ± 133 and 1575 ± 91 ROH with an average size of 377,928 and 402,731 bp, respectively. The genomic inbreeding (FSus scrofa in the Ensembl database (release 101), 5,745,790  and 5,693,068  variants were novel, of which the majority were INDEL and 14.66 and 14.64% were biallelic SNP.In 32 boars from the dam line, we annotated 23,774,053  variants, including 2,567,754 variants that were not detected in the sire line. In 38 boars of the sire line, we annotated 23,531,919  variants, including 2,325,620 that were not detected in the dam line. When compared to 63,832,658 germline variants listed for We used the Ensembl Variant Effect Predictor software  to preSus scrofa curated in the OMIA database  containKIT gene and a splice site variant in intron 17 of the KIT gene are associated with the dominant white phenotype [KIT. An increase in coverage between 41.22 and 41.78 Mb confirmed the presence of a previously reported 560 kb duplication  which manifests in a dominant white phenotype [A duplication of the henotype , 24. Bechenotype , 24. TheMC1R gene in the sequenced pigs. All boars were homozygous carriers of a 2-bp insertion , that causes a frameshift and premature translation termination, which is associated with recessive white color [MC1R gene , for which the reference alleles had been associated with red color in the Duroc breed [We detected three OMIA-listed pigmentation-associated variants in the te color . All anioc breed .FUT1 gene enables adhesion of enterotoxigenic Escherichia coli F18 fimbriae (ETEC F18) to receptors at the brush border membranes of the intestinal mucosa [A missense variant  in the l mucosa . The alldam = 19,015,058, Nsire = 18,808,294) that were grouped into non-overlapping 100 kb windows. For the CLR tests, we considered an empirical 0.5% significance threshold to identify putative signatures of selection  test. Signatures of ongoing selection were identified by the integrated haplotype score (iHS) test. For both analyses, we used biallelic autosomal SNP . Only 6 regions, detected by either CLR or iHS, overlapped between both lines. A strong signature of selection was detected in both lines with both methods on SSC13 between 140 and 152.4 Mb. The candidate region encompassed 125 genes  at low coverage using Gencove’s low-pass sequencing solution. The pigs also had Illumina PorcineSNP60 BeadChip-called genotypes. A principal component  read pairs (2 × 150 bp) to the porcine reference genome, achieving an average depth of coverage of 1.11-fold (from 0.38 to 1.51). On average, 54% of the reference nucleotides were covered with at least one read. Following the reference-guided low-pass sequence variant genotyping approach (GLIMPSE) proposed by Rubinacci et al. [We assessed the accuracy of genotyping by low-pass sequencing based on Illumina PorcineSNP60 BeadChip-called genotypes at 54,600 SNP, for which we were able to determine reference and alternate alleles. Of the 54,600 SNP, 6176 and 965 were fixed for the reference and alternate allele, respectively, in the 175 pigs according to the array-called genotypes. Of 48,424 SNP that were either polymorphic or fixed for the alternate allele, 46,001 (94.99%) were also among the GLIMPSE-imputed genotypes. 2423 SNP had microarray-derived genotypes but were missing in the GLIMPSE-imputed genotypes because these SNP were missing in the haplotype reference panel constructed from the key ancestor animals.The genotype concordance, non-reference sensitivity and non-reference discrepancy between GLIMPSE-imputed and array-called genotypes at 46,001 autosomal SNP was 97.60, 98.73 and 3.24% in 175 low-pass sequenced pigs  than corresponding elements of the GRM constructed from low-pass sequencing data . While the correlation of the off-diagonal (r = 0.99) and diagonal (r = 0.96) elements was high between both GRMs, the values of the diagonal elements were higher for all samples using the GLIMPSE-imputed than microarray-derived genotypes  from the microarray-derived and GLIMPSE-imputed genotypes of the 175 sequenced pigs based on a subset of 44,268 SNP that were detected at minor allele frequency greater than 0.01 in both datasets. Both the off-diagonal and the diagonal elements of the GRM constructed from array-derived genotypes had greater variance , which agrees well with estimates obtained in other European pig populations [− 3 – 1.90 × 10− 3) and human (0.98 × 10− 3 – 1.41 × 10− 3) populations that have considerably larger current effective population sizes [We applied a key ancestor animal approach to prioritize 38 and 32 boars that accounted for 95.35 and 87.95% of the genetic diversity of the SLW sire and dam line, respectively. The contributions of the SLW key ancestor animals to the current populations are considerably higher than reported for other populations. For instance, 43 key ancestor animals explained 69% of the genetic diversity of the Fleckvieh cattle population  selectedulations . The nucsire = 23,531,919; Ndam = 23,774,053). While the average number of heterozygous variants detected per animal was higher in the dam line , the number of variants homozygous for the alternate allele was higher in the sire line . Because the average depth of sequencing was similar in the sire and dam line, these differences are unlikely to be due to uneven coverage between the lines. These differences are likely attributable to a smaller effective population size and higher genomic inbreeding in the sire line. The presence of many long ROH (> 2 Mb) suggests that recent inbreeding is higher in the sire than the dam line. Small effective population size and increasing inbreeding make both lines susceptible to the phenotypic manifestation of recessive alleles. For instance, a recessive sperm defect has recently been discovered in the sire line [Although our sequencing cohort contained more animals from the sire line, we detected somewhat more autosomal variants in the dam line  were selected based on their marginal genetic contribution to the current breeding populations using a key ancestor approach . The marWe used the fastp software  to removWe used the BaseRecalibrator module of the Genome Analysis Toolkit  to adjThe concordance between sequence- and array called genotypes was calculated for 68 pigs that also had Illumina PorcineSNP60 BeadChip microarray-derived genotypes. We considered only autosomal SNP. We converted the TOP/BOT alleles of the microarray-derived genotypes to REF/ALT allele coding to make them compatible with the sequence-derived genotypes. This was possible for 54,600 SNP. Sequence variant genotyping accuracy was quantified using genotypic concordance, non-reference sensitivity and non-reference discrepancy , 44.Invariant sites and variants within regions with an excessive depth of coverage (> mean coverage + 2 * SD) were removed using VCFtools . The rstats .We downloaded genomic coordinates of 47 likely causal variants from the Online Mendelian Inheritance in Animals (OMIA) database . Genes hr2 > 0.6) of pairwise LD using PLINK . To avsion 1.9 ;) with tST and signature of selection analyses (1 from the dam line and 3 from the sire line).A genomic relationship matrix was built using 23,691,198 autosomal sequence variants that had a minor allele frequency higher than 0.01 using PLINK. The principal components of the genomic relationship matrix were calculated using the GCTA  softwaST, [ST values were calculated in 10 kb sliding windows with an overlap of 5 kb using the “--weir-fst-pop” flag of VCFtools  based ov.1.2.11 ;). The mv.1.2.11 .Nucleotide diversity (π) was calculated over all biallelic autosomal variants in 10 kb sliding windows with an overlap of 5 kb using VCFtools.ROH) was calculated assuming an autosomal genome length of 2,265,774,640 bases. Following a recent study by Bhati et al. [Runs of homozygosity (ROH) were estimated with BCFtools/ROH  using thi et al. , we classcan_hh to estimate the integrated extended haplotype homozygosity (EHH) on variants with MAF > 0.05 for each chromosome separately. Subsequently, we applied the function ihh2ihs to obtain standardized iHS values in 100 kb non-overlapping windows.Putative signatures of selection were detected using integrated Haplotype Scores (iHS) and composite likelihood ratios (CLR). The iHS  reveals calc_candidate_regions from the rehh 2.0 package [The function  package  was applA median number of 16,131,419 paired-end (2x150bp) reads were generated for 96 pigs from the dam line and 96 pigs from the sire line. Adapter sequences and bases and reads with low sequencing quality were removed with fastp . Subsequmpileup and call commands of BCFtools [To compile the reference haplotypes, we retained 22,618,811 biallelic autosomal SNP that were polymorphic  among the 70 key ancestor pigs. Following the approach proposed by Rubinacci et al. [BCFtools  to calcuBCFtools .Additional file 1 Fig. S1. Admixture analysis. Ancestry of 70 pigs with K = 2 ancestral populations estimated using 1,207,189 biallelic SNPs after LD pruning. Ancestry proportions were estimated using the ADMIXTURE software. Each bar represents an individual and the colors indicate the proportion of genes originating from K ancestral populations. The animals are ordered by population.Additional file 2 Fig. S2. Manhattan plot of FST values. Weir and Cockerham FST estimates were calculated in 10 kb sliding windows between 31 dam and 35 sire boars. Black dotted line indicates a value of 0.25.Additional file 3 Table S1. List of variants listed in the OMIA database and their corresponding frequency in the two pig lines.Additional file 4 Fig. S3. Depth of coverage at a region on SSC8 encompassing the KIT gene. Representative plots of different depth of coverage detected in the sequenced pigs at a large duplication  encompassing the KIT gene. Grey vertical bars represent the absolute coverage observed in four animals. The green dotted lines represent the median and 2*median coverage along SSC8. In order to determine the number of extra copies, we divided for each sequenced animal the average coverage observed at SSC8 by the average coverage observed at DUP1 . The number of additional copies ranged from 1 to 4. 16, 27, 4, 20, 2 and 1 animal had 1.5–1.9x, 2x, 2.1–2.4x, 2.5–2.9x and 3x the average coverage of SSC8 at DUP1, respectively. The average copy number was 2.07 and 2.19 in the dam and sire line, respectively. The 560 kb duplication (DUP1) encompasses two smaller duplications DUP2 and DUP3/4. DUP2 is 4.3 kb long and upstream, while DUP3 and DUP4 are 23 kb and 4.3 kb duplications downstream of the KIT gene.Additional file 5 Table S2. Candidate signatures of selection based on CLR and iHS analyses. Genes annotated to the region are given for each signature of selection.Additional file 6 Fig. S4. Principal components analysis of key ancestor and low-pass sequenced animals. Plot of the first two principal components showing the relationship of 96 dam and 96 sire animals sequenced at low (< 1.5-fold) coverage and 32 dam and 38 sire animals sequenced at high (~ 16.5-fold) coverage.