Significance testing for genome‐wide association study (GWAS) with increasing SNP density up to whole‐genome sequence data (WGS) is not straightforward, because of strong LD between SNP and population stratification. Therefore, the objective of this study was to investigate genomic control and different significance testing procedures using data from a commercial pig breeding scheme. A GWAS was performed in GCTA with data of 4,964 Large White pigs using medium density, high density or imputed whole‐genome sequence data, fitting a genomic relationship matrix based on a leave‐one–chromosome‐out approach to account for population structure. Subsequently, genomic inflation factors were assessed on whole‐genome level and the chromosome level. To establish a significance threshold, permutation testing, Bonferroni corrections using either the total number of SNPs or the number of independent chromosome fragments, and false discovery rates (FDR) using either the Benjamini–Hochberg procedure or the Benjamini and Yekutieli procedure were evaluated. We found that genomic inflation factors did not differ between different density genotypes but do differ between chromosomes. Also, the leave‐one‐chromosome‐out approach for GWAS or using the pedigree relationships did not account appropriately for population stratification and gave strong genomic inflation. Regarding different procedures for significance testing, when the aim is to find QTL regions that are associated with a trait of interest, we recommend applying the FDR following the Benjamini and Yekutieli approach to establish a significance threshold that is adjusted for multiple testing. When the aim is to pinpoint a specific mutation, the more conservative Bonferroni correction based on the total number of SNPs is more appropriate, till an appropriate method is established to adjust for the number of independent tests. These associated SNPs could however also be a false positive, leading to wrong conclusions about the genetic architecture underlying a trait or to a suboptimal subset of SNPs for genomic prediction. With the increasing number of SNPs used for GWAS, especially up to imputed whole‐genome sequences (iWGS), the number of false‐positive associations is expected to increase and choosing an appropriate significance threshold becomes an issue. Although the whole concept of significance thresholds should not be misused to assume causality and reproducibility of SNP effects  aim to associate single nucleotide polymorphism (SNP) with a trait of interest in order to get a better understanding of the genetic architecture and to improve the accuracy and persistency of genomic prediction  between SNPs, strong association between SNPs and phenotypes, and systematic bias  >7.2 . The family‐wise type 1 error rate aims to minimize the probability of finding at least one false positive. The family‐wise type 1 error rate is usually controlled by applying a Bonferroni correction that adjusts the significance threshold by the number of independent tests. In livestock, the number of independent tests is usually defined as the total number of SNPs or as the number of independent chromosome fragments, defined as regions of the genome that explain unique genetic variation  ranged from 3.3 to 6, using either no multiple testing correction, a Bonferroni correction, the false discovery rate, or genomic control  > 0.6 were considered for iWGS and the average Beagle R2 was 0.93.The data set for a Large White (LW) line was provided by Topigs Norsvin. The data set included precorrected phenotypes for number of teats of 4,964 Large White (LW line) pigs , applying a mixed linear association model with a leave‐one‐chromosome‐out (LOCO) approach as implemented in GCTA version 1.25.2  with σe2 being the residual variance. The random polygenetic effect followed a normal distribution u ~ N, where G is the genomic relationship matrix . The established significance thresholds were compared between medium‐density, high‐density and iWGS genotypes.Three approaches to establish significance thresholds were evaluated: (a) permutation testing, (b) the Bonferroni correction and (c) the FDR. The definition of the significance threshold was expressed as the –log10  of each permutation was recorded. The –log10  significance threshold was defined as the 95th percentile of the ordered recorded values. Using either medium‐density genotypes, high‐density genotypes or iWGS, the permutation test was performed for only 3 chromosomes to limit the computational costs. Chromosomes 4, 7 and 10 were chosen to represent different levels of association.The permutation test followed the procedure for the estimation of the experimental critical values that was proposed by Churchill and Doerge . The pheH0) is true (α) by the number of independent tests. In this study, α was set to 0.05. The number of independent tests was either the total number of SNPs or the number of independent chromosome fragments. The number of independent chromosome fragments (Me) was calculated as follows  across the whole genome.where VanRaden  with SNPVanRaden  showed tp‐values were ordered in ascending order. Second, k was determined as the rank of maxp(i)<iαm, with α = 0.05, and m being the total number of SNPs. Third, the p‐value at rank k was defined as the threshold and all SNPs with a rank smaller than k are declared significant. The second procedure evaluated was proposed by Benjamini and Yekutieli (BY) <iαm∗∑i=im1i. Both false discovery rates were estimated using the R package “mutoss”   of 28.5 using iWGS. Furthermore, highly significant peaks were found with all densities at chromosomes 2, 6, 10 and 12.In comparison with lower densities, more peaks are observed with iWGS Figure . These p3.2p‐value) > 5 for iWGS, 67,784 SNPs were considered as significant without genomic control, 2,154 with genome‐wide control and 1,362 with chromosome‐specific genomic control. To investigate potential causes for these high genomic inflation factors, first the average genomic inflation factors over the 1,000 permutations were calculated for the chromosomes 4, 7 and 10. Values were around 1 and remained constant across chromosomes and densities  and were similar for the medium density, high densities, and iWGS. At the chromosome level, the genomic inflation factors also remained constant across the different densities . The significance thresholds found with permutation testing for the medium‐density SNP chip were 4.1 and increased to 5.5 when the marker density increased to iWGS. In contrast to the genomic inflation factors, significance thresholds were constant across chromosomes , suggesting that we need to adjust for population stratification in the data. Accounting appropriately for population stratification is important to avoid strong genomic inflation and consequently avoid false positives  or the FDR without applying genomic control. Fitting the full GRM based on all chromosomes might alleviate the need for genomic control.The authors declare that there is no conflict of interest regarding the publication of this article.6


Quercus robur) reference genome, for the sequencing of 3 Mb of genic and intergenic regions. Using a mixed stand of 293 Q. robur and Q. petraea genotypes we successfully captured over 97% of the target sequences, corresponding to 0.39% of the oak genome, with sufficient depth (97×) for the detection of about 190,000 SNPs evenly spread over the targeted regions. We validated the technique by evaluating its reproducibility, and comparing the genomic relatedness of trees with their known pedigree relationship. We explored the use of the technique on other related species and highlighted the advantages and limitations of this approach. We found that 92.07% of target sequences in Q. suber and 70.36% of sequences in Fagus sylvatica were captured. We used this SNP resource to estimate genetic relatedness in the mixed oak stand. Mean pairwise genetic relatedness was low within each species with a few values exceeding 0.25  or 0.5 (full-sibs). Finally, we applied the technique to a long-standing issue in population genetics of trees regarding the relationship between inbreeding and components of fitness. We found very weak signals for inbreeding depression for reproductive success and no signal for growth within both species.Anticipating the evolutionary responses of long-lived organisms, such as trees, to environmental changes, requires the assessment of genetic variation of adaptive traits in natural populations. To this end, high-density markers are needed to calculate genomic relatedness between individuals allowing to estimate the genetic variance of traits in wild populations. We designed a targeted capture-based, next-generation sequencing assay based on the highly heterozygous pedunculate oak ( From an evolutionary perspective, the principal challenge is predicting the evolutionary changes required to track ongoing environmental changes and to identify key traits likely to respond to ongoing natural selection. These concerns are particularly important in the case of forest trees, which have long-generation times. Their evolutionary response must therefore occur within a very small number of generations. The prediction of evolutionary responses requires the estimation of essential genetic parameters, such as selection gradients, heritability, and evolvability, s acting . Trait hs acting .in situ , as reported by Targeted sequence capture coupled with NGS constitutes an efficient alternative approach to the exploration of genetic diversity in a very large number of genomic regions and specimens. The use of sequence capture techniques provides evolutionary biologists with easy access to nucleotide diversity, for addressing various research questions, as already demonstrated in arable crops , fruit , and forFurthermore, these techniques provide high sequence coverage for a small set of target sequences, making it possible to multiplex several samples, thereby reducing the cost of large-scale applications, for population genetics studies, for example. Sequence capture techniques require access to a reference genome, but provide highly reproducible SNPs and markers with greater transferability across species than for other pangenomic marker systems  . Intra- Quercus petraea and Quercus robur. We thus developed a targeted sequence enrichment strategy, explored its transferability to related species, and applied the detected markers to a long-standing question in tree population genetics: the relationship between inbreeding and fitness components.Our main objective here was to develop a large number of SNPs for estimating the genetic relatedness and inbreeding coefficient in a mixed oak stand containing two sister species: Q. petraea and 7 Q. robur) in a mixed oak stand (Q. petraea–Q. robur) located in the Petite Charnie State Forest in Western France . This population corresponds to cohort #1b described by Figure 1).Leaves were collected from 278 adult oak trees and 15 siblings (8 Fagus sylvatica) from St. Symphorien, on a tributary of the Ciron river, in south-west France  and two adult cork oak trees (Quercus suber) growing at the INRA Research Station at Pierroton in south-west France . We considered a total of 300 samples in all, as three adult trees from the Petite Charnie forest were sampled twice.We also collected leaves from two adult beech trees  and a fluorometer  with a Broad Range Quant-iT dsDNA kit . For each replicated individual, DNA was extracted independently from each of the two samples, independent libraries were constructed, and replicates were sequenced in separate proton sequencing runs.We used in-solution hybridization-based sequence capture technology, based on the results of Q. robur genome (haplome V2.3), available from: http://www.oakgenome.fr/ and described by Table 1). As our resources were limited to a total sequence length of 2.9 Mb for capture, we could not consider entire genes as targets for probe design. We therefore selected target sequences within each gene, depending on its length. For genes of less than 1.5 kb in length, we identified a single 150 bp target sequence located in an exon. Longer genes were artificially subdivided into three regions, and we selected two 150 bp target sequences located in two extreme regions of the gene: one within an exon, and the other within an intron–exon transition (Supplementary File Table 1). If the target sequence colocalized with a TE, it was shifted to 150 bp further along in the genome.The haploid version of the Once target sequences had been identified, we retained only those with a GC content between 30 and 60%, as suggested by Following this strategy, we identified 15,623 candidate target sequences, which were sent to Agilent Technologies  for the design of 120 bp probes.® Fast DNA Library Prep Set for Ion TorrentTM from New England Biolabs  according to the manufacturer’s instructions: 400 ng of genomic DNA was sheared, with an M220 focused ultrasonicator , to yield 200 bp fragments. Each sheared DNA sample was subjected to end repair and ligated to barcoded adapters. We then selected DNA fragments of 300 bp in size by two consecutive Agencourt® AMPure® XP steps : 0.7× then 0.15×. The libraries were subjected to 11 cycles of amplification. Each library was quantified with a Qubit Fluorometer, with the QubitTM dsDNA HS Assay Kit . Then equimolar pools of three libraries were prepared (250 ng for each library) for target enrichment.Our targeted enrichment procedure was based on Agilent’s SureSelect target enrichment system for Ion Torrent Proton sequencing . We randomly assigned the 300 DNA samples to 20 groups, each corresponding to a proton sequencing run. The 15 samples in each run were labeled (indexed 1–15). We assessed reproducibility, by duplicating three samples corresponding to three individuals. For the three duplicated samples, DNA was extracted separately from the two samples, independent libraries were constructed and sequencing was performed in separate runs. A pre-capture library was prepared for each sample, using the NEBNext® 480 System (Roche Molecular Diagnostics), with the Ion Library TaqManTM Quantification kit. In total, 20 pools of 15 libraries were used in equimolar amounts, with a final concentration of the pooled samples of 5 pM for sequencing on an Ion Proton System .The size of the DNA library was limited by the use of in-solution capture, which requires an excess of probe over template. Hybridization to the probes was carried out for 24 h at 65°C, according to the Agilent protocol, in a thermocycler, with 750 ng of library. Following the hybridization and washing steps, the recovered targeted DNA fragments were amplified in KAPA HiFi HotStart ReadyMix  for 40 s at 98°C, followed by eight cycles of 30 s at 98°C, 30 s at 62°C, and 30 s at 72°C, with a final extension for 5 min at 72°C. The captured library pools were quantified by qPCR on a LightCyclerFor each sample, high-quality Ion Torrent proton reads were demultiplexed and subjected to quality control with Torrent suite V5.0.5 (Thermo Fisher Scientific). Reads were independently aligned with the oak genome, using the Torrent Mapping Alignment Program  and the default parameters for the Torrent suite. We estimated target enrichment by quantifying the proportion of sequencing reads correctly aligned with the target sequences. For each sample, this “on-target” set of reads was considered for further analysis. We investigated the coverage of target sequences and calculated the percentage of the length of the target covered by at least one read. These analyses were performed with custom scripts developed in Python V2.7.2.mpileup function of samtools V1.3.1, and then with the bcftools function V1.1-60-g3d5d3d9 , SNPs were independently called, first with the g3d5d3d9 . We consg3d5d3d9 .Q. robur or Q. petraea) excluding admixed individuals on the basis of the posterior probability of each individual belonging to one of the clusters.For the assignment of each individual to a species, we retained markers in Hardy–Weinberg equilibrium located at least 1,000 bp apart, to avoid a redundancy of marker information due to linkage disequilibrium. We assigned each individual to a species  with the fastSTRUCTURE V1.0 algorithm . We allor2 > 0.4) with their neighbors, using the indep-pairphase function of PLINK v1.90b3.34  with the function Fst from the R package pegas  between individuals was estimated as:For each species and each set of SNPs, the genomic relatedness matrix , ip is the maf at locus i, n is the number of individuals, and m is the number of markers, as described by where Q. petraea trees, whereas seven were the offspring of seven adult Q. robur trees. Four different pedigree relationships were identified among these 54 pairs of trees: parent–offspring selfed, parent–offspring, full-sib–full-sib, and half-sib–half-sib. These relationships corresponded to three different expected coefficients of relationship: 1, 0.5, and 0.25 . For the 54 pairs of trees, we compared genomic relatedness (G) with the expected pedigree relatedness. Finally, we also calculated the genomic relatedness based on the 82 SNPs obtained in a previous study (G), diagonal elements (Gii) correspond to the relatedness of each individual i to itself relative to population allelic frequencies. In a theoretical population, at equilibrium, with no inbreeding, each individual should have a Gii of 1. Inbreeding is thus assessed as Gii–1  the reproductive success of each adult tree, as assessed by the parentage analysis of 2,500 offspring and the adult trees, and (ii) the growth of each tree, as assessed by measuring stem circumference at breast height when the trees were cut. The method used to assess reproductive success has been described elsewhere . For eacFi is the reproductive success of individual i, α is the intercept, β1 is the regression coefficient associated with the first axis of principal component analysis (PCA) on the five environ is the first PC value extracted from this PCA, iI is the inbreeding coefficient of individual i associated with the regression coefficient γ, 𝜀i is the residual error, and g is a log-link function associated with the Poisson distribution data. Independent variables were centered such that the intercept of the model corresponded to the phenotypic mean for the population. This transformation had no effect on the regression coefficient values, their standard error or the associated P-values. We applied a similar approach to the circumference, except that we used a linear model, as circumference is a normally distributed quantitative variable, and we added the age at which each tree was cut as an independent variable (range: 78–102 years).where Q. robur reference genome was used for probe design (The 3P e design .Table 2). In total, 11,120 probes (46.91%) targeted exons, whereas 6,731 (28.40%) targeted intronic regions and 5,853 (24.69%) targeted exon–intron regions.The mean size of the target sequences was 150 bp and the probes were 120 bp long. One or two non-overlapping probes were therefore designed per target sequence, resulting in a total of 33,931 120 bp probes designed with SureSelect eArray software . These probes covered a total of 2,897,647 bp . In total, 23,704 probes targeted 11,446 (44.35%) of the 25,808 predicted protein-coding genes and 10,227 probes targeted intergenic regions .Table 1. Agilent Technologies successfully designed 33,931 probes for 15,477 target sequences (99.07%). Among the target sequences, 4,031 (26.05%) corresponded to intergenic regions and 11,446 (73.95%) corresponded to genes (Table 2). In total, 4,960 (43.33%) sequences corresponded to exons, whereas 2,991 (26.13%) sequences were located in intronic regions, and 3,495 (30.54%) were located in exon–intron regions. The 4,031 intergenic target sequences were distributed as follows: an initial set of 1,796 intergenic target sequences (Table 1), with 2,235 sequences of 150 bp in length used as putative selectively neutral control regions for population genetic analyses. These control regions were evenly distributed over the genome.We selected a total of 15,623 genomic regions for capture , as described in Q. robur DNA. In total, 20 pools of 15 individuals each were independently sequenced with the Ion Torrent Proton sequencing system , with three samples sequenced twice. Each sequencing run produced between 65,426,948 and 134,977,869 reads . The size of the sequenced reads ranged from 140 to 190 bp (mean: 174 bp). The length of the sequencing reads was significantly positively correlated with the percentage of on-target sequences  . Most of the target sequences displaying polymorphism  contained between 1 and 20 SNPs. The SNPs were, thus, evenly spread over most of the target sequences. We classified these SNPs into genic and intergenic sites on the basis of the oak gene model . On average, 7.28 and 10.49 SNPs were detected every 100 bp in genic and intergenic regions, respectively. Intergenic regions were much less covered than genic regions, with a median sequencing depth of 97 and 63 in genic and intergenic regions, respectively. Finally, we detected a mean of 13,219 SNPs per tree within the Petite Charnie population.We identified 191,281 polymorphic sites in one of the 297 trees, distributed between 13,572 target sequences (87.69%). The number of SNPs in target sequences ranged from 1 to 603 , the number of SNPs detected , the number of captured target sequences , and the mean length of the captured sequences .Mean sequencing depth differed considerably between proton sequencing runs (Supplementary File Table 3). Given the different sequencing depths of the different runs and the stringent filters applied for SNP detection , for each individual, we did not capture the entire set of targeted SNPs (Table 3) . Nevertheless, when captured in both replicates, the same alleles were almost systematically correctly retrieved (Table 3) (99.9% similarity). When considering all sites (polymorphic sites and monomorphic sites covered by at least by 20×), the percentage of genotype similarity among replicates was 99.86, 99.65, and 99.27% for tree #049, tree #288, and tree #402, respectively. As expected, decreasing the intra-individual MAF for SNP detection from 30 to 10% increased the number of SNPs detected. This also made it possible to increase the proportion of targeted SNPs detected for all samples . Again, when variants were detected in both replicates, allele similarity was maintained (99.9%). For all samples, sequencing depth exceeded 10× for most of the SNPs detected in only one of the two replicates (Table 4). We conclude that the individuals were monomorphic at these loci. However, increasing the sequencing depth threshold from 10 to 20× should significantly increase the number of SNPs detected in both replicates.The genomic capture assay was repeated twice for three oak genotypes of the Petite Charnie population. For each genotype, the number of captured targets and the length of the capture sequence were similar  and 25 of these SNPs were also used in this study. The SNPs identified by the two methods were similar for the two methods except for two trees, for which differences were observed at multiple SNPs. We suspect that these differences result from labeling errors, given that the two analyses were conducted 3 years apart, with different DNA extracts. These two trees were therefore removed from subsequent analyses. A total of 25 SNPs and 250 individuals was scored with both methods (sequence capture and sequenom) giving two sets of 6,250 genotypes. We thus compared the two sets, and over the 6,250 repeated genotypes, 97.67% was concordant  between the two methods.Finally, we were also able to test for SNP reproducibility, as 25 SNPs identified by SNP calling were included in an earlier SNP scoring method used in a previous study of the same trees . Indeed,Q. suber) and two beech (F. sylvatica) samples in our study. An alignment of cork oak reads against the 3P oak genome showed a significant level of target enrichment: on average, for both samples, 15.86% of the reads captured 92.07%  of the target sequences (Table 5). When captured, target sequences were covered over 87.18% of their length on average, and the mean depth of coverage over the two samples was 56.03×. Lower values were obtained for the two beech specimens. On average, 8.93% of the reads captured 70.63%  of the targeted sequences. Length coverage was only 51.60%, and sequencing coverage was significantly lower, at 26.30×.We studied the transferability of the targeted sequence capture technology to other species, by including two cork oak , we identified 13,219 SNPs per sample, on average (Table 5). Smaller numbers of SNPs were detected in the other two species: 9,093 and 3,000 SNPs in cork oak and beech, respectively.When considering Q. suber and 2 F. sylvatica genotypes), we identified a total of 191,281 polymorphic sites heterozygous in at least one of these trees . In total, 177,232 polymorphic sites were identified in Q. robur and Q. petraea, and 13,354 and 4,295 sites were identified in Q. suber and F. sylvatica, respectively. A set of 36 SNPs was found to be common to all three species, as 10,181 SNPs were specific to Q. suber  and 3,836 SNPs were specific to F. sylvatica . As expected, more SNPs were shared between the Quercus sp. than between Quercus and Fagus.When considering all 297 trees studied here (including 2 q) obtained with fastSTRUCTURE software. Trees were assigned to three groups on the basis of threshold values of q: Q. petraea purebreds (q ≥ 0.9), admixed trees (q 0.1–0.9), and Q. robur purebreds (q ≤ 0.1), as described in Q. robur by Truffaut et al. but considered admixed in our study. These two trees had admixture values very close to the q threshold values in study of Q. petraea and the 51,886 SNPs detected in Q. robur, 21,331 were common to these two species. Fst values for all the 21,331 markers common to Q. petraea and Q. robur showed an L-curve distribution, with a large number of SNPs displaying very low levels of interspecific differentiation . The mean and median Fst values between the two species were 0.069 and 0.019, respectively, suggesting that these two species display no clear differentiation over a large part of their genome.According to fastSTRUCTURE, the most probable number of clusters was 2, consistent with the findings of a previous analysis performed on oak trees in the same forest, with 82 SNPs . IndividQ. robur and 1,454–32,047 for Q. petraea, respectively . Considering only genomic relatedness estimated by genomic capture, very minor differences in mean values were observed for numbers of markers between 32,500 markers (maf = 0.01) and 1,500 markers (maf = 0.4). However, this difference in the number of markers had a slight impact on precision, as the variance of the estimate was lower for larger numbers of markers , a finding supported by the overall distribution of relatedness between individuals . Thus, the use of numerous rare alleles has no major effect on the prediction of genomic relatedness. Realized genomic relatedness was slightly lower than expected, in both species . Conversely, when estimated with 82 SNPs only, genomic relatedness was scattered around the expected value . The fact of including non-neutral markers (located in exons) in the SNPs sets had no impact on the genomic relatedness estimation (data not shown here).We first compared the expected relationship coefficient derived from pedigree relationships and realized genomic relatedness in the two parent–offspring groups of known pedigree relationships, for 54 individual pairs , as expected, given the method used to calculate relatedness. However, we can consider overall mean genetic relatedness to be low within natural populations of Q. petraea and Q. robur. Among parents  with a population maf = 0.05, only 20 (Q. robur) and 40 (Q. petraea) pairs of trees had a genomic relatedness of more than 0.25  and only three (Q. robur) and two (Q. petraea) pairs had a genomic relatedness of more than 0.5 (expected for full-sibs), among 8,151 (Q. robur) and 10,150 (Q. petraea) pairwise estimates.At population level, relatedness coefficients were distributed around a mean value of 0 , and was discarded from the analysis. Overall, the individuals of Q. robur were more inbred  than the individuals of Q. petraea . GLM analysis showed the number of offspring to be significantly negatively correlated with inbreeding level in Q. petraea , whereas this relationship was not significant in Q. robur  . There was no significant correlation between genomic inbreeding and circumference at breast height . These results were slightly modified when the G matrix was computed with the markers selected with a maf threshold of 5%: the relationship between number of offspring and inbreeding in Q. petraea became positive while remaining non-significant. Thus, whatever the significance and sign of the relationship, inbreeding depression signals were found to be very weak for both traits, within both species . Finally, when G matrix is computed over all the individuals without subdividing by species, inbreeding had a significant negative effect on both growth  and reproductive success .Genomic inbreeding coefficients were estimated separately for each species from the Q. robur individual to design the capture probes. We evaluated the performance of target enrichment according to several parameters . Two of these parameters varied considerably between experiments, providing a cause of concern, at first sight, for SNP detection. First, as observed in other studies (Q. robur genome), making it difficult to design highly specific capture probes. The duplicated nature of many of the genes in most plants, particularly in trees . However, 99.9% of the SNPs identified in each replicated individual were identical. For the retrieval of all polymorphic sites, we would need to increase sequencing effort  at the targeted loci. Finally, we also obtained reproducible results (in 97.67% of the cases) with another genotyping assay (mass spectrometry), for a set of 250 trees genotyped for 25 SNPs with both technologies .Q. robur genome  and another species (F. sylvatica) from a different genus belonging to the same botanical family (Fagaceae). No whole-genome sequence is yet available for Q. suber or F. sylvatica. However, transcriptomic assemblies are available for both species (Q. robur/Q. petraea trees (293). It would, therefore, be reasonable to expect the detection of a much larger number of markers if a larger number of genotypes were considered. As expected, we clearly showed that, with increasing divergence time, the number of captured target sequences and the fraction of their length captured decrease. The number of SNPs detected in Q. suber and F. sylvatica also decreased with decreasing sequencing depth. Given that the probes were designed based on the Q. robur reference genome , beyond a few thousand markers, suggests that several thousand markers are sufficient for the estimation of relatedness between individuals and, thus, of the level of inbreeding of each individual (relative to the population). By contrast, we found that the use of a much more limited number of markers (a few tens), as in most traditional population genetic investigations, results in a broad scattering of genomic relatedness values around the expected value, with a tendency toward a large sampling variance. However, our results also show that the genomic relatedness estimated with thousands of markers is systematically slightly lower than that predicted on the basis of pedigree, at least for oaks. A similar trend has been reported for other forest trees e.g.,  and thisInbreeding depression can be defined as a decrease in fitness trait  values in the most inbred individuals . StudiesQ. petraea, pure Q. robur, and admixed genotypes) to belong to the same population, individuals assigned to the species Q. robur tend to be more inbred overall than individuals assigned to the species Q. petraea, and, as expected, individuals classified as admixed are less inbred than “pure” individuals (data not shown).The method we used to estimate the genomic relatedness matrix provides values for inbreeding (and relatedness) relative to population allelic frequencies. Thus, inbreeding values cannot be used to compare homozygosity levels between individuals from different species. However, when estimating inbreeding by considering all individuals . Thus, as individuals compete principally with the surrounding trees, competition on this plot can be considered to occur principally within species. Given that both these species are outcrossers and form large populations  of the Q. robur genome (haplome V2.3) has been deposited on the EMBL – ENA database under accession OLKR01000000. The set of 191,281 polymorphic sites between Q. petraea/Q. robur, Q. suber, and F. sylvatica associated with each trait detailed in Table 1 is available through the EVOLTREE eLab service: available from: http://www.evoltree.eu/index.php/snp-db. The list of candidate genes included in the set of target sequences, the 33,931 probe sequences, the description of the probes along with their transferability, and the analysis scripts used in our study can be found on the TreePeace website under the Publications tab: available from: http://www.treepeace.fr/?page_id=1401.Sequencing data for the 300 samples considered in this study are available in the NCBI – SRA database under the BioProject IL contributed to the conception of the work, performed SNP detection, evaluated the success of the sequence capture experiment, and contributed to the writing of the draft manuscript. HA analyzed the genomic relatedness between individuals and contributed to the writing of the draft manuscript. CB was responsible for library construction and sequencing. EC was involved in sampling. CP supervised the sequence capture experiment and revised the manuscript. AK contributed to the conception of the work and revised the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


In plant breeding the use of molecular markers has resulted in tremendous improvement of the speed with which new crop varieties are introduced into the market. Single Nucleotide Polymorphism (SNP) genotyping is routinely used for association studies, Linkage Disequilibrium (LD) and Quantitative Trait Locus (QTL) mapping studies, marker-assisted backcrosses and validation of large numbers of novel SNPs. Here we present the KeyGene SNPSelect technology, a scalable and flexible multiplexed, targeted sequence-based, genotyping solution. The multiplex composition of SNPSelect assays can be easily changed between experiments by adding or removing loci, demonstrating their content flexibility. To demonstrate this versatility, we first designed a 1,056-plex maize assay and genotyped a total of 374 samples originating from an F2 and a Recombinant Inbred Line (RIL) population and a maize germplasm collection. Next, subsets of the most informative SNP loci were assembled in 384-plex and 768-plex assays for further genotyping. Indeed, selection of the most informative SNPs allows cost-efficient yet highly informative genotyping in a custom-made fashion, with average call rates between 88.1%  and 99.4% (384-plex assay), and average reproducibility rates between duplicate samples ranging from 98.2% (1056-plex assay) to 99.9% (384-plex assay). The SNPSelect workflow can be completed from a DNA sample to a genotype dataset in less than three days. We propose SNPSelect as an attractive and competitive genotyping solution to meet the targeted genotyping needs in fields such as plant breeding. State-of-the-art genotyping solutions have greatly accelerated the speed and efficiency of plant breeding in fields such as cultivar identification, hybrid seed purity testing, (gene) association studies or marker-assisted selection (MAS) –4. CustoMaize is widely cultivated throughout the world, with its production increasing yearly . Maize iApproximately 80 percent of the maize genome is derived from highly repetitive sequences, interspersed with single-copy, gene-rich regions . The higThe development of Next Generation Sequencing (NGS) has fueled the discovery of large numbers of Single Nucleotide Polymorphism (SNP) markers and enabled high-resolution, sequence-based genotyping. To date, numerous NGS-based polymorphism discovery and genotyping workflows have been described, which are either based on whole genome (re-)sequencing or involve a form of genome complexity reduction or target enrichment. For example, the Complexity Reduction of Polymorphic Sequences (CRoPS) , RestricFor routine detection of selected SNP marker sets, a wide range of genotyping methods are available. When working with small numbers of markers, monoplex assays such as KASP  and rhPCDNA chips  and micrAltogether, the ideal multiplexed SNP genotyping workflow is highly robust, NGS-based, easily scalable from hundreds to thousands of markers and provides the flexibility to change assay content at will without loss of performance, comparable to the NGS-based target enrichment methods mentioned above.Here we present KeyGene SNPSelect, a targeted genotyping solution, which combines the multiplexing capabilities of oligonucleotide-ligation assays with the scalability, flexibility and accuracy of NGS for detection of selected SNP loci. SNPSelect is based on multiplexed ligation oligonucleotide probes containing allele- and locus-specific identification barcodes, followed by multiplexed PCR amplification with generic primers containing sample- and plate-specific barcodes . The ampTotal genomic DNA was isolated from leaf material using a modified CTAB procedure .F2 population from a crossing of maize lines PH207 and CO125: leaf material of an F2 population including the parental lines was kindly provided by KWS SAAT AG . Genomic DNA from 184 samples of the F2 population and both parental lines was used in the genotyping experiments.RIL population: seeds of the Main Set of IBM RILs and their corresponding parents (B73 and Mo17) were obtained from the Maize Genetics Cooperation Stock Center , grown in the greenhouse, and subsequently leaf material of young plants was harvested. Genomic DNA from 78 RILs and the parental line B73 was used in the genotyping experiments.Germplasm: seeds of germplasm samples were obtained from the U.S. National Plant Germplasm System , grown in the greenhouse, and subsequently leaf material of young plants was harvested. Genomic DNA from 53 germplasm lines was used in the genotyping experiments.An overview of all samples including sample names is provided in SNPSelect is based on the oligonucleotide ligation assay . For eac20): 5’-TGGAGTTCAGACGTGTGCTCTTCCGATCTxxxx N20-3’. Each locus-specific probe contains a locus-specific sequence ranging in size between 20 and 28 bases (for simplicity denoted as N20), an 8-nucleotide identifier sequence . The mixture was incubated for 60 minutes at 37°C, followed by 10 minutes at 70°C.The allele-specific probe pool was generated by adding 5 μl of each allele-specific probe. Sixty microliters of the allele-specific probe pool was combined with 100 μl of the phosphorylated locus-specific probe pool, and further diluted to 780 μl (384-plex), 3,900 μl (768-plex) or 2,840 μl  using Milli-Q (MQ). The diluted final probe mixtures were stored at -20°C until further use.Taq DNA Ligase  in a total volume of 10 μl of 1x Taq DNA Ligation buffer, denatured for 90 seconds at 94°C, after which hybridization and ligation was performed during a cool down to 60°C and an overnight incubation at 60°C. Ligation reactions were diluted 4-fold, from which 10 μl was amplified in a 40 μl reaction using Phusion Hot Start Flex 2x Master Mix (New England Biolabs) and 2 pmol of primers containing the Illumina P5 or P7 flow cell oligo nucleotide sequences as well as identifiers and (parts of) the Illumina TruSeq paired-end sequencing primers. Sequences of the amplification primers were as follows: P5-primer 5'-AATGATACGGCGACCACCGAGATCTACACxxxxxxACACTCTTTCCCTACAC GAC-3' and P7-primer 5' CAAGCAGAAGACGGCATACGAGATyyyyyGTGACTGGAGTTCAGACGTGT-3' respectively. In these sequences xxxxxx and yyyyy denote 6-base and 5-base combinatorial sequence barcodes  was performed using the HiSeq2500 . Clusters for each library were generated on a HiSeq flow cell v4 using a TruSeq Single-End Cluster Kit v4, according to the manufacturer’s instructions. Upon completion of the sequencing run using HiSeq2500 SBS v4 chemistry, image analysis, error estimation and base calling were performed using the Illumina Pipeline (HCS 2.2.68 / RTA v1.18.66.3). Bcl2fastq v2.17.1.14 was used to sort and pool sequences into single (zipped) .fastq files per lane / per sample. All sequence files were deposited in the NCBI Sequence Read Archive (SRA) with accession number SRP157886.De-multiplexed sequencing files were processed using customized scripts implemented in a Galaxy environment . The scrIn order to determine the accuracy of the genotypes determined using the SNPSelect technology, reproducibility was assessed by comparing the genotypes obtained for DNA samples that were included multiple times. Further validation was performed by comparison of the genotypes to published data obtained using Illumina MaizeSNP50 arrays  to deterGenetic maps for the F2 and RIL populations were calculated using the CarteBlanche software package . First, Genetic maps for the populations were calculated individually and subsequently compared.A 1,056-plex maize SNPSelect assay was designed, developed and applied to a collection of 374 samples from F2 and RIL populations and a germplasm collection. SNPSelect amplicons sequenced on a HiSeq2500 produced a total of 205,270,919 filtered and de-multiplexed reads, with an average of 548,853 reads per sample.Sequencing reads for each sample were processed, and the resulting allele frequencies for each locus and sample were exported to a read count file in tab-delimited format  Tables. The read count file containing the sample, locus and allele frequencies was subsequently imported in proprietary scoring software and the genotypes were determined. Allele frequencies are presented in a graph as “raw” frequencies and as ratio scores  Figs.Resulting genotypes were exported in the .loc file format. A summary of the genotype calls is shown in Genotype concordance  was determined by comparing previously published genotypes from 29 germplasm samples  with theReproducibility  was calculated for 4 F2, 12 RIL and 40 germplasm samples and resulted in 99.9%, 98.8% and 97.9% reproducibility rates, respectively, i.e. 98.2% on average based on a total of 56 samples.To get an impression about the quality of the genotypes, linkage mapping within the F2 and RIL populations was performed. First, genotyping data was filtered for informative high-quality markers. Filtering removed markers with an overrepresentation of a single genotype (A vs H vs B). To achieve this, per marker the fraction of the most occurring genotype score was determined and only markers that contained less than 90% of the same genotype remained. A total of 432 informative high-quality markers were obtained, of which 145 were scored in the RIL population, 287 in the F2 population and 43 were informative in both . Hence, In order to increase the information content of the assay and thereby decreasing the genotyping costs further, the most informative loci were selected from the 1,056-plex SNPSelect assay. To make this selection, first loci containing <25% unknown genotype (U) scores in at least one of the three genotype datasets were selected. Subsequently, from these loci a total of 876 loci were selected.A 384-plex and a 768-plex SNPSelect assay was assembled using a subset of the 876 selected loci. The selection of loci for both assays was done as follows. First, for the 384-plex, 342 loci were selected from the total of 552 which segregated in at least two datasets. The remaining 42 loci were segregating in a single dataset. For the 768-plex, these 384-plex loci were supplemented with 193 loci segregating in at least two datasets, 190 loci segregating in a single dataset and one locus which did not segregate in the samples tested, such that a total of 535 loci of the 768-plex segregated in at least two datasets.The 384-plex and 768-plex assays were subsequently applied to a collection of 283 samples (including 29 duplicates) consisting of F2, RIL and germplasm samples, of which the majority was also used for genotyping with the 1,056-plex assay. Amplification products were generated and subsequently sequenced on a HiSeq2500. The total number of filtered and de-multiplexed reads generated was 116,971,732 and 118,530,099 for the 384-plex and 768-plex, respectively. Average reads per sample was 413,328 (384-plex) and 418,834 (768-plex). An example of the read distribution across the samples genotyped using the 384-plex assay is shown in Concordance rates for the 384 SNP loci present in the 384-plex, 768-plex and 1,056-plex SNPSelect assays were determined. Concordance rates were 97.9% (384-plex vs. 768-plex), 98.0%  and 98.4%  .Genotype reproducibility in the 384- and 768-plex SNPSelect assays was calculated by determining the percentage of identical genotypes between the genotypes obtained from 29 duplicated samples, and were 99.9% and 99.3% for the 384-plex and 768-plex, respectively.We have developed SNPSelect and presented results of a 1,056-plex assay applied in 374 maize samples of F2 and RIL populations and a germplasm collection, with call rates between 81.2% and 91.6% and reproducibility rates of samples tested in duplicate of at least 97.9%. SNPSelect genotypes were benchmarked with published Illumina MaizeSNP50 array data for a subset of 1,037 loci scored in 29 germplasm samples with a concordance rate of 97.6%. The majority of the small proportion of discordant results were attributable to a subset of six samples, and this was not investigated further. Furthermore, we demonstrated the versatility of the SNPSelect technology by creating 384-plex and 768-plex assays based on the most informative subsets of markers from the 1,056-plex assay, and presented concordance rates between genotypes obtained using these assays of 97.9% and higher. These results showcase the potential of the SNPSelect technology for routine genotyping of selected SNPs in a highly customizable and easily adjustable multiplexed format without additional protocol optimization in a crop with a complex repetitive genome. The SNPSelect workflow can be completed from genomic DNA to genotype dataset in less than three days when starting with pre-validated assays. SNPSelect combines highly reliable multiplexed ligation-based allele discrimination with the throughput, scalability and cost advantages of short-read NGS platforms. Contrary to sequence-based genotyping methods based on genome complexity reduction methods, all NGS reads produced from SNPSelect assays are expected to contribute to a genotype data point, which lays the basis for obtaining extremely high numbers of genotypes per run at minimal costs. Indeed, with an average redundancy of more than 375 reads per SNP locus per sample, at least 400 samples can be scored for 1,000 SNPs  using a single lane of a HiSeq 2500 flow cell. This equals production of 400,000 SNP genotypes per lane or 6.4 million genotypes per run with two flow cells. It is expected that this throughput can be increased further by accepting a lower average redundancy of reads per genotype. In practice, the combination of an easily adjustable SNPSelect assay incorporating our proprietary combinatorial sequence barcoding methods for sample pooling and the availability of a variety of NGS platforms covering a wide range of output levels  allow users to tailor SNPSelect to their specific project requirements to optimize sample throughput, turnaround times and/or cost per data point. To date, in addition to maize we have performed SNPSelect in a variety of crops of different genome sizes and complexities including oil palm, carrot, melon, eggplant, radish and lettuce, with multiplexing levels ranging from 192 to 10,000 SNPs per assay using either MiSeq or HiSeq2500 platforms.A unique feature of SNPSelect assays is the use of locus- and allele-specific identifier sequences in the ligation probes. This enables counting reads representing alleles based on the combination of locus- and allele- specific barcode sequences instead of detecting the actual SNP position in the sequence read. Our data analysis process incorporates this approach and further takes into account the nucleotide distance between the locus- and allele-specific barcodes. In this way, sequence reads obtained from incorrect ligation products are excluded from further processing. These filtered data are subsequently used for (automated) genotype scoring. Our scoring software delivers A, H, B and intermediate C (B or H) or D (A or H) single character genotype calls which can directly be used for downstream applications such as genetic map construction using CarteBlanche. Besides scoring in a fully automated way, the software allows user intervention such as manually setting the boundaries between genotypic classes, which may be advantageous when scoring sample collections with skewed allele frequencies.Continuous improvements of short-read NGS platforms in terms of throughput, run times and cost per base have sparked development of numerous sequence-based genotyping methods screening either random loci based on genome complexity reduction approaches or targeting specific loci using probes or primers. Random and targeted sequence-based genotyping methods are complementary as both come with specific advantages and limitations such that a “one-fits-all” solution in terms of optimal information density, cost and speed does not apply. For example, specific advantages of genome complexity reduction methods are that no genome sequence information is required and that generic reagents (such as barcoded adaptors) can be deployed across multiple species, which reduces upfront investments costs. Furthermore, genome complexity reduction methods enable both marker discovery and genotyping in a single process. However, downsides of genome complexity methods are that the number of informative markers is difficult to predict and requires fine-tuning per species and that the cost per data point may be higher than for targeted methods as not all sequence reads are informative. By contrast, targeted sequence-based genotyping methods do require prior genome sequence information to enable locus-specific primer- or probe design and upfront investment in these assay reagents. However, advantages are that they can also be used for combined marker discovery and genotyping in a single process and deliberately targeted to known polymorphisms linked to important traits. In addition, the genotyping datasets are consistent for all samples and the cost per marker is often lower than for random methods, provided that (cumulatively over time) a sufficient number of samples are processed to amortize the probe- and primer reagents costs to minimal levels. In this landscape SNPSelect represents a unique technology enabling routine genotyping of hundreds to thousands of known SNPs or small indels per sample in a single reaction vessel based on the proven OLA for allele discrimination. Improvements of SNPSelect are directed at reducing the upfront investment costs of ligation probes and establishing the upper boundaries for multiplexing such that in the future it will be possible to genotype thousands of markers per sample for high-density genetic mapping or genome-wide selection processes in crops at significantly lower costs and with higher fractions of informative markers than fixed content DNA chips.In conclusion, KeyGene SNPSelect offers a fully customizable, highly flexible, multiplexed SNP genotyping solution leveraging the power and accuracy of NGS-based detection. It provides scalable flexibility in terms of numbers of SNP loci and samples per project, and assay content that can be adjusted between projects to reduce costs. SNPSelect genotyping can find widespread application for (highly) multiplexed genotyping in any organism.S1 FigEach sample is represented as a single dot. The x- and y-axis represent the obtained read counts for the A and B allele, respectively.(TIF)Click here for additional data file.S2 FigEach sample is represented as a single dot.(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(TXT)Click here for additional data file.S4 Table(TXT)Click here for additional data file.S5 Table(TXT)Click here for additional data file.S6 Table(TXT)Click here for additional data file.S7 Table(TXT)Click here for additional data file.S8 Table(TXT)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.S10 Table(XLSX)Click here for additional data file.S1 Scripts(ZIP)Click here for additional data file.


To satisfy an increasing demand for dietary protein, the poultry industry has employed genetic selection to increase the growth rate of broilers by over 400% in the past 50 years. Although modern broilers reach a marketable weight of ~ 2 kg in a short span of 35 days, a speed twice as fast as a broiler 50 years ago, the expedited growth has been associated with several negative detrimental consequences. Aside from heart and musculoskeletal problems, which are direct consequences of additional weight, the immune response is also thought to be altered in modern broilers.Given that identifying the underlying genetic basis responsible for a less sensitive innate immune response would be economically beneficial for poultry breeding, we decided to compare the genomes of two unselected meat control strains that are representative of broilers from 1957 and 1978, and a current commercial broiler line. Through analysis of genetic variants, we developed a custom prioritization strategy to identify genes and pathways that have accumulated genetic changes and are biologically relevant to immune response and growth performance. Our results highlight two genes, TLR3 and PLIN3, with genetic variants that are predicted to enhance growth performance at the expense of immune function.Placing these new genomes in the context of other chicken lines, reveal genetic changes that have specifically arisen in selective breeding programs that were implemented in the last 50 years. Clostridium perfringens. Global losses due to NE are estimated to be $2B/year and they are expected to rise 1, while the Silkie line, was originally bred for ornamental purposes in the 19th century2. Of note, the RJFswe line did not group with the RJF line, suggesting that pooling of samples from two sources may have impacted variant calls in this dataset.To further examine the potential impact of sequencing platform on variant calls, we performed a phylogenetic analysis on the variants common to lines sequenced by SOLiD and Illumina platforms Fig. . ReassurFocusing specifically on the three broiler lines , we found that while each line possessed ~ 1.7–1.8 million SNPs not present in the other two lines, they share a common core of ~ 3.9 million SNPs. Amongst this core were 14,686 non-synonymous SNPs, together with 1796 frameshifts and 80 stop gains – variants that were likely introduced in the common ancestor of the three lines. Further, we found that the 1978 and Ross308 genomes share more SNPs  than the 1957 and the Ross308 genomes . In addition to analyzing single-nucleotide polymorphisms (SNPs), we also compared copy number variants . CNVs that encompass coding regions have the potential to alter gene expression , with a Notable genes associated with CNVs include two copies of CD8alpha in Ross308, two copies of tumor necrosis factor superfamily member 13b (TNFS13B) in both 1978 and Ross308, and the loss of one copy of Apolipoprotein-AII (APO-AII) in both 1978 and Ross308. CD8alpha is a co-receptor on CD8+ T cells, a specialized group of T-cells that recognizes antigens presented by MHC class I molecules, then respond to damaged cells by triggering production of cytokines and the release of cytotoxic molecules . RelatedSNP density as the number of mutations divided by the coding sequence length. We then constructed a set of SNP dense genes as the 10% of genes displaying the greatest SNP density (and hence most likely impacted by genetic variation) and performed a gene set enrichment analysis for enriched functional categories as defined through GO and pathways defined by the Kyoto Encyclopedia of Genes and Genomes  defines transcripts that have accumulated the greatest number of SNPs over the past 60 years. Here we compared increases in non-synonymous SNP density between the 1957 and the 1978 lines, and the 1978 and Ross308 lines. Under this strategy, we select transcripts that are in the 95th percentile in terms of increase in SNP density either for the 1957/1978 comparison or the 1978/Ross308 comparison. We additionally specify that both changes in SNP density must be positive (i.e. there are more SNPs in more recently bred lines). In Strategy 2 we define transcripts based on unique SNPs in faster-growing lines. Transcripts are selected based on possessing the highest (90th percentile) density of SNPs that are unique to the 1978 and/or Ross308 lines relative to all other lines. Strategy 3 focuses on transcripts that possess high impact variants  restricted to either the 1978 or Ross308 lines, but not the 1957 line. Similarly, Strategy 4 identifies transcripts based on decreasing scores from application of the Sorting Intolerant From Tolerant  algorithm [Strategy 5) we identify transcripts that have accumulated deleterious SNPs. Here we select for transcripts that show an increase in the number of deleterious variants (SNPs with SIFT scores less than 0.05) when comparing the 1957, 1978, and Ross308 broilers and, additionally, possess the highest (99th percentile) density of deleterious variants.The first strategy (lgorithm . Under t). This final filtering step ensures the selection of transcripts with SNPs that result in functional consequences (use of only one or two strategies might result in the selection of transcripts that have simply accumulated many low-impact variants) and identified a total of 78 transcripts. From this list, manual reviews of GO and domain annotations allowed us to identify 12 transcripts with mammalian homologs involved in immune response and/or promoting growth, that have likely been compromised in the selection of the 1978 and Ross308 lines . After manual review of the potential functional impact of their associated SNPs, we identified TLR3 and a putative homolog of mammalian PLIN3 (ENSGALT00000003248), for more focused analysis.Applying each strategy resulted in the generation of five complementary sets of transcripts Fig. . Strateg3TLR3 is part of the Toll-like receptor family that plays a crucial role in innate immunity by recognizing double stranded viral RNA and promoting anti-viral defenses in other cells . In the ENSGALT00000003248, a PLIN3 homolog, is the only gene that was identified by all five strategies; of the ten SNPs found in the Ross308 line, five were unique, including the deleterious variant G199R and a stop codon W298X predicted to cause premature termination  for a long period of time in response to the same stimulus. Thus, we may expect production traits are interlinked with immune traits. To address this question, we developed a selection strategy to identify genes that have acquired variants over the course of selective breeding programs, impacting their function. Our initial expectation is that this strategy would result in the identification of genes that contribute to both immune and growth-related processes.Our combinatorial strategy incorporates three considerations neglected by other studies. First, different genes could experience different levels of selective constraints which has been observed in domestication of cattle  and maizSalmonella enterica serovar Typhimurium and Escherichia coli, is sufficient to induce anti-inflammatory signals via TLR3. Another study that used a high-density SNP array to investigate polymorphisms in a commercial broiler line also found that TLR3 falls within a region of positive selection [It should be appreciated that rather than sampling across each population, only a single individual from each line was sequenced. Consequently, given their heterogeneous genetic backgrounds, we expect that the variants identified here may only represent a small fraction of those that distinguish the three lines. Nevertheless, we are encouraged by the enrichment of variants in genes associated with immune functions, with many of the genes that passed our final filter (prioritized by more than 3 strategies) contributing to both immune response and growth Figs.  and 3. Telection . This raPLIN3 belongs to the perilipin family of proteins that associate with lipid droplets to regulate lipase access to lipid storage. With known roles in exercise-induced lipid degradation  and lipoSalmonella revealed the fast-growing chickens relied on T-cell activation and were slower at clearing S. enterica serovar Enteritidis infection than slow growing chickens, which induced macrophage activation post infection [In addition to identifying genes of interest, we observed that all three broilers that we sequenced are over-represented in SNP dense genes from several functional categories such as phagosome, cytokine-cytokine receptor interaction, and intestinal immune network for IgA production. That suggests that commercial broiler immune response, irrespective of lineage, is distinct from the Red Jungle Fowl immune function. The Ross308 line, however, is the only one of the three broilers that we sequenced with SNP dense genes enriched in the GO term: “Defense response to other organisms” (GO:0098542). This result is supported by previous studies; one study found that commercial broilers had a lower febrile response (ability to generate fever) and lower cytokine expression in response to gram-negative bacteria than commercial layers . Comparinfection . TherefoTogether this study illustrates how comparisons of the genome sequences derived from temporally related lines can help reveal the genes and biological processes that help drive livestock breeding programs.The genomes of two female chickens from each of the University of Alberta Meat Control strains unselected since 1957 and 1978, respectively, and a commercial Ross 308 broiler  were generated as follows:Genomic DNA was isolated from 30 ul of chicken blood samples using the Promega Wizard Genomic DNA Purification Kit  according to the manufacturer’s instructions. Genomic DNA was quantified with a Qubit 2.0 fluorometer using the Qubit dsDNA BR assay kit  and diluted in buffer EB to the 20 ng/ul input for library preparation. DNA samples were also assessed for quality by gel electrophoresis. Library preparation was performed following the Illumina Truseq DNA PCR-Free LT Library Prep Kit  protocol using 1μg of isolated genomic DNA. The input DNA was sheared to 350 bp using Covaris microTUBES and the S2 Ultrasonicator . End repair, A-tailing, adaptor ligation and library nomalization were performed according to Illumina’s instructions. Libraries were quantified for normalization by qPCR using the KAPA Library Quantification Kit for Illumina sequencing platforms . The final libraries were also assessed using the DNA1000 kit for the Bioanalyzer 2100 . The final libraries were denatured and diluted to a final loading volume of 1.3 ml at a concentration of 1.8pM. Sequencing was performed on the NextSeq500 sequencer , according to the manufacturer’s instructions, using the 300 cycle High Output V2 sequencing kit and a spike-in of 8% PhiX control library, generating 150 bp paired end reads. Sequence data is available through the sequence repository archive, hosted by the NCBI, under BioProject PRJNA561435.Additional genomes were downloaded from the sequence read archive (SRA) at the National Center for Biotechnology Information (NCBI)  and inclGallus gallus reference genome  removed duplicates in the alignment. Genome analysis toolkit (GATK) [For lines sequenced using the Illumina platform , adapters and low quality reads were identified and filtered using FastQC  and Trimry, 2016 ) with BWry, 2016  using det (GATK)  was usedPutative copy number variants (CNVs) of the 1957, 1978, and Ross 308 genomes were detected using CNVnator    convertevcf-isec applied to compare broiler variants to the merged file to identify unique variants. SNP density for each gene was defined as the number of SNPs normalized by gene length, global SIFT score was defined as the average SIFT score of all variants in a gene normalized by gene length, and number of variants predicted to be deleterious for each gene. Ortholog [SNPs were annotated with both ANNOVAR (v2016-02-01)  and VariOrtholog , Gene OnOrtholog , and funOrtholog  and UnipOrtholog .p-value < 0.05 after Benjamini-Hochberg false discovery rate [Genes with SNP densities in the 90th percentile in each breed were subject to pathway enrichment analysis based on Gene Ontology (GO)  and Kyotery rate  correctiBased on SNP analyses described above, we defined five prioritization strategies to define transcripts of interest Fig. :Strategy 1. Transcripts that have accumulated most SNPs in the last 50 years. Nonsynonymous SNP density increase between the 1957 and the 1978 broiler was compared to the nonsynonymous SNP density increase between the 1978 and Ross 308 broiler. For a transcript to be prioritized by this strategy, the difference in SNP density between the 1957 and 1978 broiler OR between the 1978 broiler and Ross 308 must be in the 95th percentile. Additionally, all SNP density changes between 1957 and 1978 and 1978 and Ross 308 must be positive (i.e. more SNPs in more recently bred lines).Strategy 2. Transcripts with the most SNPs unique to fast-growing broilers. This strategy selected for transcripts with the highest number of variants  that belonged exclusively to the 1978 and/or Ross 308 subset OR the subset of SNPs exclusive only to Ross 308. The former subset (transcripts with high number of unique variants in 1978 and Ross 308 combined) includes transcripts that have not gained unique variants between 1978 and Ross 308 but have accumulated unique variants since the 1950s.Strategy 3. Transcripts containing high impact variants  only in Ross 308 or the 1978 broiler.Strategy 4. Transcripts with decreasing SIFT score. For every transcript, the mean SIFT score is calculated, only transcripts that show a decrease in SIFT score when comparing the 1957, 1978, and Ross 308 broilers and are also in the bottom 50th percentile for either the 1978 or Ross 308 broiler are selected.Strategy 5. Transcripts that have accumulated deleterious SNPs. Transcripts that show an increase in number of deleterious variants (SNPs with SIFT scores less than 0.05) when comparing the 1957, 1978, and Ross 308 broilers and possess the highest number of deleterious variants (99th percentile) after normalizing by transcript length.Additional file 1: Table S1. Breeds and Naming Convention for the 15 Samples Analyzed in this Study. The main text uses the abbreviation to refer to each breed. Genomes sequenced from pooled blood samples are specified in brackets in the “Name” column.


These settings can have a large impact on the outcome and default values are not always appropriate for medium density SNP array data. Guidelines for a robust and uniform ROH analysis in PLINK using medium density data are lacking, albeit these guidelines are vital for comparing different ROH studies. In this study, 8 populations of different livestock and pet species are used to demonstrate the importance of PLINK input settings. Moreover, the effects of pruning SNPs for low minor allele frequencies and linkage disequilibrium on ROH detection are shown.PLINK is probably the most used program for analyzing SNP genotypes and runs of homozygosity (ROH), both in human and in animal populations. The last decade, ROH analyses have become the state-of-the-art method for inbreeding assessment. In PLINK, the ROH and to check the validity of ROH analyses. The effect of pruning for linkage disequilibrium and low minor allele frequencies on ROH analyses is highly population dependent and such pruning may result in missed ROH. PLINK’s minimal density requirement is crucial for medium density genotypes and if set too low, genome coverage of the ROH analysis is limited. Finally, we provide recommendations for the maximal gap, scanning window length and threshold settings.We introduce the genome coverage parameter to appropriately estimate FIn this study, we present guidelines for an adequate and robust ROH analysis in PLINK on medium density SNP data. Furthermore, we advise to report parameter settings in publications, and to validate them prior to analysis. Moreover, we encourage authors to report genome coverage to reflect the ROH analysis’ validity. Implementing these guidelines will substantially improve the overall quality and uniformity of ROH analyses. ROH) [Runs of homozygosity (ROH) are the state-of-the-art method for inbreeding analyses in livestock populations . ROHs arROH) .--homozyg function. The PLINK algorithm for ROH detection relies on a scanning window approach which roughly consists of three steps.PLINK , 8 is th--homozyg-window-snp) with a maximal number of heterozygous SNPs (--homozyg-window-het) and a maximal number of missing SNPs (--homozyg-window-missing). The defined window stepwise scans an individual’s genome and scores for each SNP the proportion it appears in a homozygous window.First, the scanning window is defined by a predefined number of SNPs (--homozyg-window-threshold). For a window size of 100 SNPs and a threshold of 0.05, a SNP has to appear in at least five homozygous windows before it is identified as part of a segment. Note that such homozygous windows may contain missing or heterozygous SNPs, depending on scanning window settings.Second, segments of homozygous SNPs are identified genome wide by using a threshold for these scores per SNP: the scanning window hit rate (--homozyg-gap) as well as the maximal amount of heterozygous SNPs allowed in the final ROH segment (--homozyg-het). Next, ROH segments that do not meet these two requirements are split and re-evaluated. This may lead to detecting ROH segments smaller than the scanning window size. Thereafter, the minimal SNP density (in kb/SNP) per segment is evaluated (--homozyg-density) as well as the minimal length and number of SNPs (--homozyg-kb and --homozyg-snp). ROH segments which do not fulfill any of these three conditions are removed.Third, extra constraints are set to these homozygous segments to identify the final ROH segments. The maximal interval between two SNPs in a segment is checked (R2 >  0.5 (using bins of 50 SNPs), resulting in a reduced set of 7997 and 14,366 SNPs , respectively [In literature, there is no consensus whether SNP data should be pruned for linkage disequilibrium (LD) and/or minor allele frequency (MAF) before ROH analysis. In Table ectively , 15. HenThe effect of minimal ROH length, either by the minimal number of SNPs or minimal kb length, has been thoroughly studied by Purfield et al. and Ferenčaković et al. , 34. Pur--homozyg-window-snp, −-homozyg-window-het, --homozyg-window-missing, --homozyg-window-threshold, --homozyg-snp) [Howrigan et al. simulated genotypes to test PLINK’s ROH detection ability and varied several PLINK detection settings (zyg-snp) . They cozyg-snp) .There is a large variation in parameter settings considering the maximal gap, minimal density and the scanning window size Table . Moreove--homozyg-density), the maximal interval between two SNPs in a ROH (--homozyg-gap), scanning window size (--homozyg-window-snp) and scanning window hit rate (--homozyg-window-threshold). Third, we introduce the genome coverage parameter to evaluate the validity of the ROH analysis and to estimate inbreeding based on ROH more accurately. These guidelines facilitate an adequate and robust ROH analysis, resulting in a higher overall quality and uniformity across studies.In this paper, we provide guidelines for choosing PLINK parameter settings that ensure a robust and reliable ROH analysis. We used medium density genotypes in eight different livestock and pet species . First, we evaluated the effect of MAF and LD pruning on ROH analysis. Second, we investigated effects of the minimal density , not all ROH were detected and FROH estimates were lower than without pruning for LD. In PIT, maximal genome coverage was reached more slowly in comparison to other populations (e.g. BB).The results of pruning for varying LD levels prior to ROH analysis for PIT, BB, MER and BUR are shown in Fig. ROH estimates by varying MAF pruning levels.In PIT and BUR, we observed that even mild MAF pruning (0.01) had an impact on ROH detection in several genomic regions. Figure ROH, aut and FROH, cov by varying density for PIT, BB, MER and BUR   for all populations (no LD nor MAF pruning performed).In general, the model based approach (RZooRoH) yielded higher FNK) Fig. . This caResults for varying LD levels prior to ROH analysis using a model based approach (RZooRoH) for PIT, BB, MER and BUR are shown in Fig. To unravel the effects of PLINK parameter settings on ROH estimation using medium density SNP data we analyzed these settings on eight different livestock and pet species. We examined the effects of pruning for LD and/or MAF on ROH detection and genome coverage. Next, we investigated the effect of the previously unstudied PLINK parameters.R2 >  0.5, genome coverage is only 16.3%). For PIT and BUR, FROH shows a strong decrease for more stringent LD conditions, whereas in BB, MER and SAA this strong decrease could not be observed.The effect of LD pruning on ROH analysis is highly population dependent , deviant results were observed possibly due to the high inbreeding level, presence of microsomes and the small chicken genome.Howrigan et al. suggested to perform LD pruning before ROH analysis, based on their study using simulated genotypes . HoweverTherefore, we argue that caution has to be taken in performing LD pruning prior to ROH analysis, due to differences in population structure. We suggest to correct for false positive ROH, caused by LD, by increasing the stringency criteria for ROH detection  instead of pruning SNPs for LD, as previously reported , 34.In literature, there is no consensus whether or not genotypes should be pruned for low MAF before ROH analysis. Our inventory of recent studies in livestock Table  showed tROH estimates and genome coverage in other populations (e.g. BB), indicating that MAF pruning does not improve ROH detection. Therefore, we recommend not to perform MAF pruning before ROH analysis.Pruning for low MAF as a quality control measure was introduced in genome wide association studies (GWAS) for two main reasons. First, genotype accuracy declines with decreasing MAF . Second,ROH sharply increase between 40 and 60 kb/SNP. At the default PLINK setting (50 kb/SNP), genome coverage in PIT was only 47% and for MER barely 0.6%. Calculating the genome coverage parameter proves to be a good method to check whether the density parameter is set appropriately. For the studied populations maximal genome coverage was reached between 60 and 70 kb/SNP.We showed that the minimal density setting (in kb/SNP) can strongly affect the outcome of the ROH analysis. This is most relevant for medium density SNP arrays since in PLINK the default density setting is roughly equal to the average density for most livestock and pet species (50 kb/SNP). This is clearly visible in Fig. density as --homozyg-window-density instead of --homozyg-density). This forces the algorithm to check every window for the required minimal density instead of only performing the check on (large) homozygous regions.Moreover, SNP densities can differ along the genome (e.g. > 150 kb/SNP on SSC3 in PIT) and therefore certain regions could be overlooked. A possible solution is to implement a check for density on the scanning window level in PLINK’s algorithm (implement Note that the average chicken genome length (±1000 Mb) is about half the size of the average mammalian genome, and therefore the minimal density at which maximal genome coverage is reached lies around 25 kb/SNP  as follows:out the desired number of final outer SNPs on either side of the homozygous segment that should not be included in the final ROH and L the scanning window size. In this formula, ‘+ 1’ is included as this denotes the first SNP that will be tolerated of the final ROH and ‘, 3’ points at flooring with three decimals. For example, with L = 100 and Nout = 4, the threshold will be set at 0.05. By doing this, we will scan windows of 100 SNPs and in the obtained homozygous segment we discard the four outer SNPs on each side of the homozygous segment.We propose to calculate the scanning window threshold .As an example, we evaluated the genome coverage of the ROH analysis as performed by Yang et al. on 146 pig populations . We founThis study has shown that MAF and LD pruning as well as PLINK input settings can severely impact ROH analyses on medium density genotypes.Pruning for low MAF and LD was historically introduced in genomic analyses but seems to provide little benefits for ROH studies. Our findings show that MAF and LD pruning can be problematic for ROH detection, regardless of the method used (rule based or model based). Therefore we recommend to skip MAF and LD pruning prior to all ROH analyses using medium density genotypes.It is clear from our results that a low minimal density setting (in kb/SNP) can lead to an incomplete genome coverage of the analysis and should be evaluated thoroughly. Moreover, the default PLINK setting of 50 kb/SNP is often not suitable. Furthermore, we advise to minimize the maximal gap setting while still assuring maximal genome coverage. The scanning window size should be kept equal to the minimal desired ROH length. Finally, a proposal to calculate the scanning window threshold is given.ROH can be more accurately estimated. We strongly believe that using these recommendations will improve quality and comparability of ROH analyses.Overall, we advocate to always report PLINK input settings in publications, and to validate them prior to analysis. Moreover, we encourage to use and report genome coverage to reflect the ROH analysis’ validity. Based on this genome coverage, FThe effect of LD and MAF pruning on ROH detection, as well as the effects of the density, gap and window size settings were evaluated on different livestock and pet species . We demonstrated our findings in one example population per species Table .Table 2All populations were genotyped on medium density arrays. We performed our analyses on the autosomal genome, discarding all SNPs with unassigned chromosomal information and all SNPs with a low call rate (< 95%). Detailed information on the quality control for all populations is given in Additional file ROH) as [LROH is the total length of all ROHs in the individual’s genome, and Laut is the length of the autosomal genome. The population’s mean FROH is calculated as the average FROH of all individuals. However, the length of the autosomal genome depends on the genome assembly used for SNP mapping and can therefore differ between genotyping arrays. Moreover, regional differences in SNP density can result in genomic regions where it is impossible to detect ROH. The detection of ROH in these regions is not only dependent on the SNP density of the array, but also on the specific criteria assigned to PLINK to detect ROH.Many studies estimate the inbreeding coefficient based on ROH (FROH) as :\documeROH. First FROH, aut is the estimated degree of inbreeding based on the length of the autosomal genome, with Laut calculated as the length between the first SNP and the last SNP per chromosome for all autosomal chromosomes. Second, FROH, cov is the estimated degree of inbreeding based on the length of the covered genome, where Laut is equal to the length of the autosomal genome where ROH detection is possible. This was calculated by simulating an individual with a completely homozygous genotype (based on the population’s .map file) and performing the ROH analysis with all specified parameters on this homozygous individual. The total ROH length found for this homozygous individual is the maximal detectable ROH length for any individual in this population, given the parameter settings. Similarly, we calculated the genome coverage of the ROH analysis as the proportion of the maximal detectable ROH length over the length of the  genome. This genome coverage was as an indication of the validity of the ROH analysis.Therefore, we propose two different methods for estimating F--maf function for MAF equal to 0.01, 0.05, 0.10 and 0.20 and compared this to the ROH analysis without MAF pruning. To analyze the effect of LD pruning on ROH analyses, we used PLINK’s --indep-pairwise function with a scanning window of 50 (step size of 5) and pruned SNPs with R2 values between 0.01 and 0.99. These results were compared to the ROH analysis without LD pruning (R2 = 1). To test the minimal density setting, we varied --homozyg-density from 10 to 125 kb/SNP. To examine the effect of the maximal gap setting, we varied --homozyg-gap from 1 to 1000 kb. The scanning window size setting (--homozyg-window-snp) was investigated by varying this setting from 1 to 150 SNPs. The scanning window threshold (--homozyg-window-threshold) was varied between 0.05 and 0.95. The PLINK settings were evaluated on non-pruned genotypes. When unvaried, ROH detection settings were set to a small scanning window (20 SNPs), a large gap (2 Mb), a high density level (200 kb/SNP) and a scanning window threshold level of 0.05. All ROH detection was performed with a minimal ROH length of 1 Mb, maximum one missing SNP and no heterozygous SNPs in the scanning window. The minimal number of SNPs in a ROH was determined by the formula proposed by Lencz et al. and adapted by Purfield et al. [ns the number of genotyped SNPs per individual, ni the number of genotyped individuals, α the percentage of false positive ROH (0.05) and het the mean heterozygosity across all SNPs.To study the effect of MAF pruning, we used PLINK’s d et al. , 33:\doROH results from both PLINK and RZooRoH, an individual with a completely homozygous genotype was simulated, using the same method as previously described. This individual was analyzed in RZooRoH to calculate the maximal detectable ROH length for any individual in the population, given the model settings.We validated our results using PLINK with a non-rule based ROH detection method by analyzing all populations using the model based software RZooRoH, developed by Druet and Gauthier . The RZoAdditional file 1: Table S1. Quality control metrics for all evaluated populations. Abbreviations as in Table Additional file 2: Figure S1. The effect of linkage disequilibrium (LD) pruning on genome coverage and FROH estimates for ICE, SAA, LAB and BAR in PLINK.Additional file 3: Figure S2. The effect of the density setting (in kb/SNP) on genome coverage and FROH estimates for ICE, SAA, LAB and BAR.Additional file 4: Figure S3. The effect of maximal gap setting (in kb) on genome coverage and FROH estimates for ICE, SAA, LAB and BAR.Additional file 5: Figure S4. The effect the scanning window size on genome coverage and FROH estimates for all evaluated populations.Additional file 6: Figure S5. The effect of the scanning window threshold on genome coverage and FROH estimates for all evaluated populations.Additional file 7: Figure S6. The effect of linkage disequilibrium (LD) pruning on genome coverage and FROH estimates for ICE, SAA, LAB and BAR in RZooRoH. For BAR, an increase in FROH was detected at very low R2 values (> 0.35), probably linked to a high degree of inbreeding and a strong decrease in number of markers in a small genome to reliably estimate HBD.


Haliotos rubra) and greenlip (H. laevigata) parental populations. To assist applied breeding and investigate genetic divergence, this study applied genome sequencing and variant detection to develop and validate a SNP genotyping tool. Skim short read Illumina sequencing was performed using 24 individuals from each of the two parental species and a hybrid population. Raw reads were assembled into three population specific pools (each 12–15 fold coverage), before mapping was performed against a draft greenlip abalone reference genome. Variant detection identified 22.4 M raw variants across the three populations (SNP and indels), suggesting they are highly heterozygous. First stage filtering defined a high quality SNP collection of 2.2 M variants independently called in each of the three populations. Second stage filtering identified a much smaller set of variants for assay design and genotyping using a validation set of 191 abalone of known population and pedigree. Comparison of allele frequency data revealed a high proportion of SNP (43%) had divergent allele frequency (< 0.2) between the two parental populations, suggesting they should have utility for parentage assignment. A maximum likelihood approach was used to successfully assign 105 of 105 progeny to their known true parent amongst a set of 86 candidate parents, confirming the genotyping tool has utility for applied breeding. Analysis of pairwise allele sharing successfully discriminated animals into populations, and PCA of genetic distance grouped the hybrid animals with intermediate values between the two parental populations. The findings present a library of DNA polymorphism of utility to breeding and ecological application, and begins to characterize the divergence separating two economically important aquaculture species.Abalone breeding in southern Australia often involves the production of interspecies hybrids through crossing blacklip ( Haliotis) has lagged behind other species. This is changing with development of first draft reference genome assemblies for both pacific . Further, generation of de novo transcriptomes  and greenlip (H. laevigate) abalone which are often hybridized to generate an interspecies offspring favored for its improved growth and production. At Jade Tiger Abalone a family based selective breeding program has been in operation since 2003, aiming to maintain diverse populations of both parental species (blacklip and greenlip) and produce hybrids selected for growth, meat yield and product quality  using BWA-MEM  for assay design for genotyping using the kompetitive allele-specific PCR (KASP) technology of LGC genomics .A total of 24 animals were obtained from the 2012 year class managed by Jade Tiger Abalone at Indented Head in Victoria, Australia . Pedigre BWA-MEM  with def BWA-MEM . SNP and BWA-MEM . VariantThe population used for validation consisted of 191 animals collected from the Jade Tiger Abalone breeding program. This included 65 blacklip abalone comprising 33 broodstock (year class 2012–2014) and 32 offspring. The BL offspring were generated using two known dams and unknown sire due to the design of the breeding program . The valPN), expected heterozygosity (HE), and allele frequency were calculated using PLINK v1.9. The relative genetic distance between animals was assessed using two methods. First, principal component analysis (PCA) of pairwise genetic distance was estimated as implemented in PLINK v1.9 using the –pca flag. Second, pairwise allele sharing (AS) was estimated as an identity by state matrix using the – distance ibs flag. The AS matrix was used to generate a heatmap and unsupervised dendogram using R software package RColorBrewer. The ability of the SNP genotyping tool to accurately estimate parentage was evaluated using Cervus v 3.0.7 , greenlip (GL) and BL × GL hybrid abalone (HY) were used for Illumina short read skim sequencing. This returned a total of 58.88 Gb of raw data, however, the average depth of coverage per individual was approximately 2.5 fold prompting us to pool sequence reads within population prior to variant discovery. This generated three pools , each with 12 – 15 fold genome coverage which is considered more than adequate for variant detection . Reads dTo drive selective breeding, a key objective was to develop a low density SNP genotyping tool suitable for parentage assignment. In response to the commercial preference for BL × GL hybrids, the genotyping tool was enriched with SNP displaying polymorphism within each of the two parental species and hybrid populations. The pooled NGS approach used for SNP discovery generated an estimate of population minor allele frequency for each of the 2.9 M high quality variants. This was used to identify a subset of 156,000 SNP with allele frequency between 0.25 and 0.75 in all three pools. To prioritize loci further, additional filters were applied that considered the proximity of surrounding variants and their location on the reference genome assembly . A total of 384 prioritized loci were subjected to KASP assay design before the tool was assessed by genotyping a validation set of 213 abalone. Preliminary assessment revealed nearly a quarter of assays (93/384) returned no genotypic data due to failed synthesis and a further quarter (88/384) reported monomorphic results across all animals (Supplementary Table AS). This may seem counter intuitive, however, it likely reflects the presence of diverse broodstock in the BL and GL populations not present in the hybrids. Further, fewer hybrids were genotyped and all were generated in matings involving only two greenlip sires . While a slightly lower proportion of SNP were polymorphic in the hybrids, they displayed an enrichment of SNP with high allele frequency  or unknown . This raises the possibility that complex repetitive structures such as CNV or segmental duplications have occurred that were not accounted for in our approach to identify prioritized SNP for assay design. A third possible explanation concerns undetected polymorphism immediately flanking assay SNP, however, we rate this as unlikely given i) genetic diversity was present in the panel of animals used for sequencing and SNP discovery and ii) the presence of flanking polymorphism was considered during SNP filtering.Application of the SNP tool to genotype a validation population of 191 animals successfully demonstrated its ability to estimate population diversity and to accurately identify known pedigreed relationships. Analysis of pairwise genetic distance Figure  suggestsThe DNA samples used are from the Jade Tiger Abalone breeding program, as part of the commercial operations of Jade Tiger Abalone Pty Ltd. Their use was in accordance with authorized management practices of the company.JK conceived the experiments, performed the analysis, and wrote the manuscript. MH, SM, HK, and NB performed the analysis and drafted the manuscript. LM and AK contributed animal material and experimental design.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.