To reduce the cost of genomic selection, a low-density (LD) single nucleotide polymorphism (SNP) chip can be used in combination with imputation for genotyping selection candidates instead of using a high-density (HD) SNP chip. Next-generation sequencing (NGS) techniques have been increasingly used in livestock species but remain expensive for routine use for genomic selection. An alternative and cost-efficient solution is to use restriction site-associated DNA sequencing (RADseq) techniques to sequence only a fraction of the genome using restriction enzymes. From this perspective, use of RADseq techniques followed by an imputation step on HD chip as alternatives to LD chips for genomic selection was studied in a pure layer line.Genome reduction and sequencing fragments were identified on reference genome using four restriction enzymes  and a double-digest RADseq (ddRADseq) method (TaqI-PstI). The SNPs contained in these fragments were detected from the 20X sequence data of the individuals in our population. Imputation accuracy on HD chip with these genotypes was assessed as the mean correlation between true and imputed genotypes. Several production traits were evaluated using single-step GBLUP methodology. The impact of imputation errors on the ranking of the selection candidates was assessed by comparing a genomic evaluation based on ancestry using true HD or imputed HD genotyping. The relative accuracy of genomic estimated breeding values (GEBVs) was investigated by considering the GEBVs estimated on offspring as a reference. With AvaII or PstI and ddRADseq with TaqI and PstI, more than 10 K SNPs were detected in common with the HD SNP chip, resulting in an imputation accuracy greater than 0.97. The impact of imputation errors on genomic evaluation of the breeders was reduced, with a Spearman correlation greater than 0.99. Finally, the relative accuracy of GEBVs was equivalent.RADseq approaches can be interesting alternatives to low-density SNP chips for genomic selection. With more than 10 K SNPs in common with the SNPs of the HD SNP chip, good imputation and genomic evaluation results can be obtained. However, with real data, heterogeneity between individuals with missing data must be considered. Genomic selection, as described in 2001 by Meuwissen et al. [The cost of such HD SNP chips is still a problem for all livestock species. However, it is possible to reduce the cost of genomic selection through the use of a low-density SNP chip by selecting a subset of markers from the HD SNP chip and to impute the genotypes at missing markers. This is a very common method used in many livestock species such as cattle –7, pigs In parallel to these SNP chip methods, next-generation sequencing (NGS) techniques to simultaneously detect and genotype SNPs have been increasingly used in livestock species. Nevertheless, they remain expensive to routinely use for genomic selection. An alternative and cost-efficient solution is to sequence only a fraction of the genome using restriction enzymes. This solution was first named restriction site-associated DNA sequencing (RADseq) or genotyping-by-sequencing (GBS) but now refers to a large range of techniques relying on the use of restriction enzymes to detect and genotype SNPs . ConsequIn addition, these techniques can also differ by the presence or absence of size selection of the DNA fragments during library preparation.These techniques were first tested on plants , 30, 32 As stated by Andrews et al. , these tTo date, initial RADseq , GGRS 3, 37, andhttps://arriveguidelines.org) for the reporting of animal experiments.All methods are described in accordance with ARRIVE guidelines . SNPs with unknown locations, located on linkage group LGE64 or on sexual chromosome W, were not included to assure consistency with sequence data used in this study. SNPs located on chromosomes 16 and 33 were also removed since some enzymes used in this study did not detect more than 2 SNPs in common with the SNPs of the HD SNP chip, thus preventing the possibility of imputing these chromosomes. Pedigree incompatibility problems were also checked. Finally, 300,028 SNPs and 2,362 individuals were used in this study.Genotypes were filtered in six successive steps Table  with PliAmong the individuals genotyped with the HD SNP chip, 90 individuals of the first generation (G0) were also sequenced with Illumina HiSeq2000 technology with a target coverage of 20X at the Genomics and Transcriptomics platform GeT-PlaGe . The sequenced individuals were chosen to best represent the haplotype diversity of the chickens of the first generation (G0). Haplotype diversity was assessed by clustering on a genomic relationship matrix. Among these 90 individuals, 53 individuals were breeders of the second generation (G1), and 37 were collaterals of the breeders.Data were aligned to the fifth annotation release of the chicken reference genome using the Burrows-Wheeler Aligner V0.7.15  with defPreviously, imputation tests were carried out on 90 individuals, which were sequenced at 20X and genotyped on the HD chip. These individuals were selected as representative of the genetic diversity of the line. A reference population of 50 individuals was established. Then, cross-validation tests were produced by successively drawing 8 groups of 5 individuals from the 40 remaining individuals. For each draw, a Pearson correlation was calculated between 20X sequences and imputed sequences. We obtained an average correlation of 0.98, indicating a very good imputation accuracy for HD genotyping up to the sequence level .Four distinct restriction enzymes were used to simulate in silico digestion of DNA. Consistent with Liao et al.  and PértIn addition, double digestion of DNA was simulated by simultaneously using TaqI and PstI. In silico digestion of DNA with these different enzymes was realized using R and the Bioconductor packages : BiostriFinally, among the list of SNPs extracted from the 1,027 imputed sequenced individuals, the SNPs in common with the HD genotyping after quality control were identified. The HD genotyping of the 1,027 individuals was then reduced to the SNPs previously identified, thus allowing simulation of GGRS and ddRADseq approaches.In this study, the selection candidates were 580 individuals of the second generation (G1) with simulated low-density genotyping obtained through GGRS and ddRADseq simulations. Based on shared SNPs between RADseq approaches and HD SNP chips, these candidates were imputed from the HD genotyping of the 447 individuals of the first generation (G0). The selection candidates were directly related to the 447 individuals of the first generation, the fathers or the fathers’ half-brothers of the selection candidates.For each simulated RADseq approach, imputation accuracy was estimated as the mean correlation between true and imputed HD genotypes . CorrelaThe four traits studied in this paper were named according to Animal Trait Ontology for Livestock . MeasureDuring this period, all eggs were collected and transferred at Zootests  to study egg quality traits. Analyses started by measuring egg weight . A Minolta Chroma Meter was used to estimate three eggshell traits: redness (a*), yellowness (b*) and lightness (L*). Egg shell colour (ESC) was then calculated as One of the major points of interest for any breeder is to obtain good genomic evaluations from low-density genotyping simulated based on GGRS and ddRADseq approaches. The previous work of Herry el al.  showed aEW, ESC, ESS and AH were evaluated using single-step GBLUP methodology  using BLThe first objective was to investigate the impact of imputation errors on genomic evaluation based on ancestry. A genomic evaluation based on ancestry was performed with phenotypes of the individuals of the first generation (G0) and with HD genotyping of the 1,027 sires of the two first generations (G0 + G1). A second genomic evaluation was performed by replacing the HD genotyping of the selection candidates G1 with their imputed HD genotyping obtained using the two RADseq approaches. For each simulated GGRS and ddRADseq method and for each trait, the reordering of the selection candidates was estimated using Spearman correlations calculated between the true HD genomic estimated breeding values (GEBVs) and imputed HD GEBVs. Spearman correlations were calculated for the 67 breeders from G1 with at least 10 offspring in G2. They were also calculated for the top 150 individuals from G1 according to each trait. The percentage of individuals kept in top 150 was also estimated for each trait and each enzyme. The 67 breeders from G1 were not all included in the top 150 individuals from G1 because the 67 breeders were selected according to a multitrait index, whereas the top 150 individuals were different according to the trait studied.The second objective was to study the attainable relative accuracy with imputation. The “Full_HD” GEBVs represented the maximum relative accuracy attainable with this genomic evaluation considering all information. Thus, the results of the “Full_HD” genomic evaluation of the selection candidates G1 were compared with those from the genomic evaluation based on ancestry with imputed HD genotyping of the selection candidates G1. For each simulated RADseq method and for each trait, Pearson correlations were calculated for the 67 breeders of G1 between “Full_HD” GEBVs and GEBVs based on ancestry with imputed HD genotyping of the selection candidates G1.The enzymatic digestion of the genome was simulated with the four different restriction enzymes as well as with double digestion with TaqI and PstI. Changes in the number of DNA fragments obtained using different enzymes according to the size of the fragments is summarized in Fig. In silico digestion of DNA showed, for each enzyme, a similar pattern for the changes in the number of fragments according to their length. EcoRI was the enzyme generating the fewest number of fragments , whereas PstI and AvaII generated the greatest number of fragments . The results were similar for the number of fragments ranging between 200 and 500 bp, with a lower number of fragments for EcoRI  and a higher number for PstI and AvaII . With double digestion using TaqI and PstI, a fragment obtained through the action of only one of the two enzymes cannot be considered in the ddRAD-Seq approach. The number of fragments obtained with double-digest TaqI and PstI  and after size selection of fragments ranging between 200 and 500 bp  did not correspond to the sum of the fragments obtained using TaqI and PstI separately.Finally, PstI and AvaII and the double digestion of DNA using TaqI and PstI generated greater proportions of DNA fragments between 200 and 500 bp: 19.99%, 20.58% and 24.30%, respectively. In contrast, EcoRI produced only 7.85% fragments of interesting size.For each enzyme, the number of fragments of interest (200–500 bp) per Mb Fig.  was alsoBased on their physical positions, fragments ranging between 200 and 500 bp were used to produce the bed file used by Plink to extract all SNPs located in the windows covered by the fragments from the 1,027 imputed sequenced individuals. This allowed us to extract 46,568 SNPs with EcoRI, 122,248 SNPs with Taq1 and 427,141 and 470,425 SNPs with PstI and AvaII, respectively. The double digestion of DNA with TaqI and PstI also enabled 318,408 SNPs to be obtained.The overlap afforded by the number of SNPs detected with the GGRS and ddRADseq approaches and the SNPs that can be genotyped with the HD SNP chip was then compared. The number of SNPs in common was quite reduced for EcoRI, with only 1,797 SNPs. For TaqI, AvaII and PstI, the number of SNPs increased from 4,126 to 12,453 and to 14,390 SNPs, respectively. The number of SNPs in common between ddRADseq and the HD SNP chip was 11,193 SNPs. Finally, among all the SNPs detected with the GGRS and ddRADseq approaches, the proportion of SNPs in common with the HD SNP chip was 2.65% and 3.86% for AvaII and EcoRI, respectively.− 1 to 2.3 ± 0.7 SNP. Mb− 1. However, for the other enzymes and the double digestion of DNA with TaqI and PstI, the results showed an increase in the ratio going from macrochromosomes to microchromosomes. Indeed, for TaqI and PstI, the ratio increases from 3.0 ± 0.3 SNP. Mb− 1 and 8.9 ± 1.4 SNP. Mb− 1 to 9.4 ± 3.5 SNP.Mb− 1 and 42.6 ± 16.3 SNP. Mb− 1. The ratios for double digestion with TaqI and PstI were intermediate to the ratios for TaqI and PstI. The sexual chromosome Z in particular showed a lower ratio than those observed for the other type of chromosome for each enzyme tested. The ratio was only 0.8 SNP. Mb− 1 for EcoRI and 4.4 SNP. Mb− 1 for PstI.Before studying imputation accuracy based on the SNPs in common between the HD SNP chip and those detected using RADseq methods, the distribution of these SNPs according to the type of chromosome was studied Fig. . From maWe examined the impact of mutations that create new restriction sites or destroy existing ones on the Taq1/Pst1 enzyme pair to assess the impact of these creations or destructions. We determined the number of sites destroyed or created for each enzyme using genotyping data based on 20X sequencing: 57,596 sites destroyed, and 55,937 sites created for Pst1 and 32,692 destroyed and 29,482 created for Taq1. This information about restriction site creation or destruction was then combined with all previously identified site positions. We obtained a list of 129,648 restriction fragments and compared it to the previous list of 130,187 fragments without including mutations by reusing the conditions for obtaining a restriction fragment (i.e. presence of both sites in this example and size greater than 150 bp and less than 500 bp): 105,529 fragments are shared by both lists, and 24,938 and 24,389 fragments are unique to the lists without and with mutations, respectively. Finally, we extracted the SNPs potentially present in these new fragments to obtain a total of 321,090 SNPs with 10,978 SNPs shared by the HD DNA chip, compared to the previously identified 318,408 SNPs and 11,193 SNPs shared by the HD chip without mutation inclusion. When the SNP lists with and without mutations are compared, there are 227,851 common SNPs in total and 8,363 common SNPs with the HD chip. With or without considering mutations and counting common SNPs with the HD chip, we observe that 3/4 of the SNPs  are identical (the difference being explained by the difference in predicted enzyme fragments). The presence of mutations in the restriction sites does not affect the total number of predicted SNPs common with the HD chip .The imputation accuracy of the 580 selection candidates of the second generation (G1) from low-density genotyping obtained with the RADseq approaches to HD genotyping was studied for each enzyme used. These individuals were imputed from the HD genotypes of the 447 individuals of the first generation (G0).The mean correlation between true and imputed HD genotyping was 0.7906 from 1,797 SNPs with EcoRI, 0.9121 from 4,126 SNPs with TaqI, 0.9691 from 11,193 SNPs with TaqI and PstI, 0.9699 from 12,453 SNPs with AvaII and 0.9735 from 14,390 SNPs with PstI  were calculated regardless of the trait studied with ddRADseq and with AvaII or PstI. The results for these three last cases were not significantly different from each other for the different traits studied.In addition, when focusing on the number of individuals in the Top150 that remain in the Top150 after imputation, results are consistent with Spearman correlations. The lowest numbers are obtained for EcoR1 with 132, 121, 130 and 129 individuals staying in the Top150 for EW, ESC, ESS and AH, respectively. The highest numbers are obtained for ddRAD-Seq with 146, 147, 146 and 147 individuals remaining in the Top150 for EW, ESC, ESS and AH, respectively.Concerning the 67 breeders of G1, the results for each trait were also lower for EcoRI compared to those obtained using ddRADseq and for simple digestions with TaqI, AvaII or PstI. Spearman correlations were 0.9450, 0.8854, 0.9255 and 0.9096 for EW, ESC, ESS and AH, respectively, with the enzyme EcoRI. However, except for ESC and given the standard deviations, the results were not significantly different from the results obtained using TaqI. The results for ddRADseq or the use of AvaII or PstI were not significantly different from each other, and all resulted in correlations above 0.99, thus indicating a very reduced reordering of the 67 breeders.Finally, with EcoRI or TaqI, based on standard deviations, the results obtained for the top 150 individuals for each trait were significantly lower than those obtained for the 67 breeders with the exception of AH with EcoRI and ESS with TaqI. For AvaII, PstI and ddRADseq, the results for the top 150 individuals for each trait were also lower than those obtained for the 67 breeders, but the differences were not significant.The impact on the relative accuracy of genomic evaluation was studied using Pearson correlations to compare the results of the “Full_HD” genomic evaluation and the results of the different genomic evaluations based on ancestry with imputed HD genotyping of the selection candidates of G1. Pearson correlations were calculated for the 67 breeders of G1 having at least 10 offspring in the next generation G2  occurs in a restriction site, the methylation-sensitive enzyme will not be able to cleave the DNA. This will lead to the creation of a longer fragment that may not be sequenced if the size exceeds 500 bp. From the enzymes used in this study, only PstI was insensitive to methylation, whereas EcoRI was sensitive to CpG methylation, TaqI to dam methylation and AvaII to CpG and dcm methylation. This sensitivity to methylation events was not simulated in this study but has to be taken into account with real data since this would lead to more variability between the reads for the different individuals.Finally, sensitivity to methylation must be taken into account in choosing the restriction enzyme. Indeed, if a methylation event . For AvaII, PstI, and the association of TaqI and PstI, the results were significantly greater than those obtained with an equidistant low-density SNP chip of 15 K SNPs, with fewer SNPs than on the low-density SNP chip. In detail, the equidistant methodology used in Herry et al.  resulted− 1 for each type of chromosome, and the mean correlations decreased with chromosome size. With EcoRI, the ratio was only 1.6 SNP. Mb− 1 for macro-chromosomes, 2.1 SNP. Mb− 1 for intermediate chromosomes and 2.3 SNP. Mb− 1 for microchromosomes, and the mean correlations decreased with the size of the chromosomes. However, compared to the close and expected ratio of 2 SNP. Mb− 1 for 2Kequi, the significantly lower results obtained with EcoRI could be explained by the distribution of the SNPs on chromosomes that were not equidistant. In contrast, with PstI, the ratio was 8.9 SNP. Mb− 1 for macro-chromosomes, 18.8 SNP. Mb− 1 for intermediate chromosomes and 42.7 SNP. Mb− 1 for microchromosomes, and the mean correlations were rather stable according to the type of chromosome. For 15Kequi, the ratio was expected to be approximately 15 SNP. Mb− 1 for each type of chromosome with a decrease in mean correlations with chromosome size. The use of PstI enabled a large increase in the number of SNPs detected on microchromosomes. Thus, in addition to the nonuniform distribution of the SNPs, there was an optimization in the number of SNPs on macrochromosomes and a densification on microchromosomes. At an equivalent SNP density, this densification on microchromosomes was even higher than that observed with SNP chips based on linkage disequilibrium. All these factors can explain the higher results obtained for enzymes enabling optimization of the number of SNPs on macrochromosomes and densification of this number on microchromosomes compared to an equidistant methodology. The use of a restriction enzyme that enabled densification of the Gallus gallus microchromosomes was thus of major importance to obtain high imputation accuracy.To understand these differences, the results need to be studied according to the type of chromosome. With the equidistant methodology used in Herry et al. , the SNPThe case of sexual chromosome Z was particular for each enzyme tested with a lower imputation accuracy (except for EcoRI) and a lower SNP distribution per Mb than that observed for the other type of chromosome. With the HD SNP chip, among the 26,867 SNPs of the Z chromosome, only 10,113 SNPs were informative for the line studied. Among the 10,113 SNPs, only 63, 113, 314, 316 and 360 SNPs were in common with those obtained with EcoRI, TaqI, TaqI and PstI and AvaII and PstI, respectively. However, by looking at the ratio of the number of fragments of interesting sizes per Mb  to AvaII and PstI . This increase in mean correlations was consistent with the literature. Aliloo et al. , where sWith EcoRI, the results were lower than those obtained with a low density of 2 K SNPs for all traits studied. The differences were not significant except for ESC of the top 150 individuals, for which the correlations were 0.6481 with EcoRI and 1,797 SNPs and 0.7956 with an equidistant low-density SNP chip of 2013 SNPs. The results using TaqI were greater but not significantly different from the results obtained with an equidistant low-density SNP chip of 4 K SNPs. Finally, the results for AvaII and PstI and the association of TaqI and PstI were also greater but not significantly different from the results obtained with an equidistant low-density SNP chip of 15 K SNPs. However, the number of SNPs used with AvaII or PstI and with TaqI and PstI was lower than the 14,963 SNPs of the 15Kequi SNP chip. Thus, similar results were obtained with SNPs lower than the 15Kequi SNP chip with these two enzymes. In addition, at an equivalent SNP density, SNP chips based on linkage disequilibrium yielded lower results than those based on equidistant methodology. Except for EcoRI, this was no longer the case for the results obtained using RADseq approaches. These differences can be explained by the SNP distribution according to the type of chromosome. As seen previously, the gene density is greater on microchromosomes than on macrochromosomes . CombineThe impact on the relative accuracy of genomic evaluations of comparing the results of the “Full_HD” genomic evaluations and genomic evaluations based on ancestry with imputed HD genotyping yielded lower results for EcoRI, except for ESC, compared to the results obtained using the other enzymes or the HD SNP chip. The differences observed in relative accuracy were not significant. The results concerning the other enzymes were closer to the maximum relative accuracy attainable with HD information, but the differences observed were not significant. These results were consistent with previous results showing a low impact of imputation errors on genomic evaluations of the 67 breeders. Indeed, except for EcoRI, Spearman correlations were greater than 0.98 for each trait studied by comparing the GEBVs based on ancestry obtained with true HD genotyping or imputed HD genotyping. These results could be different for the 150 best individuals for a specific trait. Indeed, among these 150 best individuals, only a portion are selected as breeders and have offspring. The best individuals for a specific trait are not necessarily chosen as breeders because this choice is based on a multitrait index. All these results were in agreement with the literature. Chen et al.  showed iTherefore, the use of restriction enzymes in the GGRS or ddRADseq approaches and the use of the SNPs detected in common with the SNPs of the HD SNP chip are an interesting alternative to low-density SNP chips without a decrease in the relative accuracy of genomic evaluation of the selection candidates.As seen previously, the use of different restriction enzymes led to different numbers of fragments of interest and finally a different number of SNPs detected in common with the SNPs of the high-density SNP chip. The sequencing costs depend on the number of individuals and the expected read depth of each fragment for each individual. Thus, it depends on the number of fragments to be read. In a previous study using the GGRS approach in chickens , 37, a sFinally, the availability of sequencers with higher performance, allowing the sequencing of more individuals in a single lane of a flow cell with increasing read density, is now causing the sequencing costs for RADseq approaches to plummet. However, the cost of DNA library preparation cannot be reduced, and this step can still be expensive.Among the total number of SNPs detected using the GGRS and ddRADseq methods, only 2.64% for AvaII to 3.85% for EcoRI were common to the SNPs of the HD SNP chip. However, the remaining SNPs can be useful for imputation and genomic prediction. Indeed, Brouard et al.  showed tLikewise, Torkamaneh and Belzile  focused Finally, Poland et al.  and ElbaThus, when applied to chickens, combining SNP chips and RADseq panels could be helpful to accurately impute missing data among RADseq datasets. However, combining SNP chips and RADseq panels to impute untyped loci would require a reference population with higher SNP density than that obtained with RADseq methods and SNP chips separately to ensure the overlap of both panels. This is currently still too expensive for routine use in a selection scheme. Finally, the use of RADseq techniques instead of SNP chips on reference and candidate populations may be an interesting approach to obtain higher genomic evaluation accuracy. However, large changes in genotyping strategies are currently needed by creating reference populations genotyped with a RADseq approach. By doing so, unmapped SNPs on the reference genome and not included on SNP chips can be used for genomic evaluations. Gains in the accuracy of genomic evaluations can be expected for traits with QTLs located in poorly known chromosomal regions.Our studies have demonstrated the value of using genome complexity reduction methods with restriction enzymes for genotyping purposes. By comparing the SNPs detected by these methods with SNPs from low-density chips with an equidistant distribution, we were able to observe a better distribution and densification of SNPs according to chromosome type. In the context of using these genotypes for genomic evaluation, the GEBVs from imputed RADseq genotypes compared to those obtained with HD genotypes show very high correlations, suggesting an interesting alternative to low density SNP chips.


Lactuca sativa L.) is an important leafy vegetable cultivated in moderate climates around the world. Based on phenotypic characteristics, such as formation and size of ‘heads’ (leaves arranged in a dense rosette), size, and texture of leaves, length of stems, and size and composition of seeds, lettuce cultivars can be classified into several distinct horticultural types . L. serriola, a wild progenitor of cultivated lettuce, is frequently utilized in breeding programs to introgress desirable genes and alleles  into lettuce gene pool. A diversity panel of ∼500 lettuce accessions was genotyped with tunable genotyping-by-sequencing (tGBS) method to identify single nucleotide polymorphism (SNP) sites. 115,261 SNPs were positioned on lettuce genome using the reference genome of cultivar Salinas. The described diversity panel together with the set of SNP markers can be used for mapping quantitative trait loci (QTL) and to develop marker assays for marker assisted selection (MAS). Identified SNP sites can also be used to identify F1 hybrids, genotype gene bank collections, and in other areas of lettuce genetics and breeding.Lettuce ( Specifications Table•The tGBS markers can be used in linkage and association mapping studies, analyses of population structure, and developing assays for marker-assisted selection (MAS).•These data will benefit researchers studying inheritance and genetics of complex traits, lettuce breeders utilizing marker-assisted breeding, and gene bank curators analyzing inventories.•1 hybrids, construct haplotypes, capture diversity in lettuce horticultural types, predict the breeding value in genomic selection, and determine genetic differences within and among populations.The tGBS markers can further be used to identify F•L. indica accesions can provide an introductory insight into the genetic similarity with L. sativa and L. serriola.SNP data from two 1The main objective for generating tGBS markers using the diversity panel of ∼500 lettuce accessions was to apply genome-wide association studies (GWAS) to map quantitative trait loci (QTL) for resistance to Impatiens necrotic spot virus (INSV) 220 that keeps only SNPs which were genotyped in at least 20% of all sequenced samples (≥ 103 samples), maintained 115,261 SNPs 100) were available for analysis  cut-off. MCRanalysis .Table 1E20 determined that the mean heterozygous rate per SNP site was 7.4% and the mean minor allele frequency (MAF) was 10.6% (L. indica samples (GBS-329A and GBS-330A) had the highest rate of missing data (> 85%). Four samples  showed heterozygous rate > 15%, indicating that they are genetically substantially less uniform than other tested samples , the diversity panel will contain data for 496 unique accessions. Depending on a project objective, data for four L. serriola accessions  could be kept or eliminated.When this diversity panel is used for GWAS, it is recommended to combine tGBS data from samples that were replicated  or use only one of the replicated samples as a representative for the respective genotype. In addition, it is recommended to eliminate 11 samples that combine seeds from two phenotypically distinct lines . For example, the combined sample GBS-385A should be eliminated, but two individual, distinct phenotypes GBS-385A_B (black seeds) and GBS-385A_W (white seeds) should be kept. After eliminating data from two 3Lactuca sativa L.) is a diploid (2n = 2x = 18) autogamous species with highly homozygous and homogenous commercial cultivars. A lettuce diversity panel consisting of 498 accessions was developed that includes commercial cultivars, advanced breeding lines, plant introductions, and selected individuals from recombinant inbred lines (RIL). The panel comprised 492 accessions from eight lettuce horticultural types  though most of the accessions phenotypically resembled romaine type. In addition, four accessions of L. serriola, a close relative of cultivated lettuce used in breeding programs, were included in the diversity panel. Two accessions of L. indica were also genotyped but should not be used in GWAS due to their sexual incompatibility with cultivated lettuce.Cultivated lettuce , therefore a total of 514 seed samples were sent for DNA extraction and sequencing to Data2Bio .Genotyping was performed using tGBS technology that mitigats the high rate of genotypic errors and missing data that occurs at the conventional GBS approach Confidently mapped reads were filtered and used for SNP discovery. SNP calling was conducted using the uniquely aligned reads after examining 3,438,949 bases that had ≥ 5 reads in at least 50% of the samples . Polymorphism were ignored if detected in the first 3 bp and the last 3 bp of each read. SNP was determined to be homozygous if the most common allele was supported by at least 80% of all the aligned reads covering that position, at least five unique reads supported the most common allele, and each polymorphic base had the Phred quality score ≥ 20 (error rate ≤ 1%). SNP was determined to be heterozygous if the two most common alleles were supported by at least 30% of all aligned reads covering that position, at least five unique reads supported each of the two most-common alleles, sum of reads of the two most common alleles accounted for at least 80% of all aligned reads covering that nucleotide position, and each polymorphic base had the Phred quality score of ≥ 20 (error rate ≤ 1%).The current work meets the ethical requirements for publication in Data in Brief. The current work does not involve human subjects, animal experiments, or any data collected from social media platforms.Ivan Simko: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition.The author declares no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Osteoporosis is amultifactorial disease influenced by geneticand environmental factors, which contributes to an increased riskof bone fracture, but early diagnosis of this disease cannot be achievedusing current techniques. We describe a generic platform for the targetedelectrochemical genotyping of SNPs identified by genome-wide associationstudies to be associated with a genetic predisposition to osteoporosis.The platform exploits isothermal solid-phase primer elongation withferrocene-labeled nucleoside triphosphates. Thiolated reverse primersdesigned for each SNP were immobilized on individual gold electrodesof an array. These primers are designed to hybridize to the SNP siteat their 3′OH terminal, and primer elongation occurs only wherethere is 100% complementarity, facilitating the identification andheterozygosity of each SNP under interrogation. The platform was appliedto real blood samples, which were thermally lysed and directly usedwithout the need for DNA extraction or purification. The results werevalidated using Taqman SNP genotyping assays and Sanger sequencing.The assay is complete in just 15 min with a total cost of 0.3€per electrode. The platform is completely generic and has immensepotential for deployment at the point of need in an automated devicefor targeted SNP genotyping with the only required end-user interventionbeing sample addition. Generic platformusing isothermal solid-phase primer elongationfor cost-effective, rapid electrochemical SNP genotyping from a fingerprickblood sample. As demonstrated by clinical trials,there was no evidence of a causal effect for calcium or vitamin Dsupplementation.27 Out of the 15 identifiedfracture risk loci,27 we selected 4 SNPsthat explained the largest proportion of fracture risk and mappedto genes implicated in bone biology which included WNT16 (rs2908007),RSPO3 (rs10457487), FAM210A (rs4635400), and SOST (rs2741856) as wellas the well-established lactose intolerance marker (LCT(C/T-13910)polymorphism; rs4988235) to be implemented in an electrochemicalplatform for the detection of SNPs from a fingerpick blood sample,which could be employed at the point of care and used as a cost-effectivescreening tool, coupled with subsequent DXA analysis, for the earlydiagnosis of osteoporosis.The discovery of geneticvariation loci and the elucidation oftheir biological functions are critical to enable a further understandingof the etiology of osteoporosis and to thus facilitate the developmentof new approaches to screen for osteoporosis.28 and SNPs are the most significant contributorsto genomic variation among individuals.29 A variety of techniques have been developed for SNP genotyping,including electrophoresis systems such as the cleaved amplified polymorphicsequence (CAPS), derived CAPS (dCAPS), and allele-specific (AS-PCR).30 High-throughput SNP-genotyping technologieshave been developed and are employed, including the gene chip microarrayand the competitive allele-specific PCR-based KAPS platform,31 and genotyping can also be achieved by sequencing.32 The first SNP array was developed by the WhitesideInstitute together with Affymetrix and was designed to simultaneouslydetect almost 1500 SNPs,33 and there arenow hundreds of customizable SNP chips available. However, the costof these instruments is significant, and this upfront investment andsubsequent maintenance costs cannot be met by a majority of laboratories.Furthermore, the slow turn-around times in obtaining results fromcentralized sequencing or microarray facilities is not compatiblewith rapid clinical decision making, and the possibility to detectSNPs at the point of care would facilitate prompt and informed treatmentdecisions. A cost-effective, robust, rapid, easy-to-use, and reliabletechnology for target-specific low- to middle-scale genotyping ofgenome-wide SNPs is desirable.A single nucleotide polymorphismrepresents a variation in a singlenucleotide that occurs at a specific position in the genome,34 as well as an approach using loop-primer endonuclease cleavage(LEC)-LAMP for the detection of SNPS in Neisseria meningitidis.35 Further examples include a competitivefluorophore-labeled probe hybridization assay following LAMP, whichwas applied to the detection of an SNP associated with the clinicalresponse of personalized peptide vaccination in saliva samples.36 In another report, the use of loop-primer LAMPwas used for the detection of SNPs in Salmonella entericaserovar Gallinarum biovars Pullorum and successfully applied in realsample testing of embryos, livers, and anal swabs from chickens inpoultry farms.37 LAMP has also been usedfor the duplex detection of Factor V Leiden and Factor II G20210Avariants in whole blood samples38 in amolecular beacon LAMP format for the detection of BRAF V600E,39 and further examples of the use of LAMP forthe detection of SNPs has been comprehensively reviewed.40 Isothermal recombinase polymerase amplification(RPA) has also been exploited for the detection of SNPs, using allele-specificligation to discriminate genetic variants related to cardiovasculardiseases,41 which also has been used forthe parallelized detection of the genotyping of four SNPs relatedto the treatment of tobacco addiction.42 A forward primer SNP detection approach using RPA has also beenreported, where removing the reverse primer enhanced discriminationwith single, double, and three scattered mismatches, and this approachwas combined with lateral flow detection of an SNP that results inpyrethroid resistance in Aedes aegypti mosquitoes.43 Giant magnetoresistive (GMR) nanosensors usingRPA have been applied to the genotyping of four SNPs  along the catechol-O-methyltransferasegene (COMT) in salvia samples.44 Allelle-specific RPA has been used for the real-time fluorescencedetection of the point mutation encoding hemoglobin S (HbS) in capillaryblood, with the entire assay complete in <30 min at a cost of <$5.45Isothermal amplification hasbeen exploited for the detection ofSNPs, including approaches exploiting loop-mediated isothermal amplification(LAMP) for the genotyping of blood and buccal cells49 Fluorescently labeled ddNTPs were also used in approachesusing surface-tethered primers51 and in array-basedprimer extension (APEX)53 as well as alternatearray-based technologies exploiting primer elongation54 and solid-phase polymerase chain reactions.55 As an alternative to fluorescence detection,SNP detection technologies based on electrochemical detection havebeen detailed, including an approach based on DNA-functionalized Cd-MOFs-74as a cascade signal amplification probe under enzyme-free conditionsfor the detection of an SNP in the p53 tumor suppressor gene56 as well as the SNP detection based on siliconsemiconductors57 and the use of electroactiverather than fluorescent labels for ddNTPs.58A wide range of diverse SNP detectionmethods have been developed,including single base extension using fluorescently labeled chain-terminatingdideoxynucleotides (ddNTPs).59 This platform is based on isothermal solid-phase recombinase polymeraseamplification, where four identical 5′-thiolated primers differingonly at the terminal 3′ base are self-assembled onto individualgold electrodes of an array. The terminal base is designed to bindspecifically to the base at the SNP site under interrogation, andfollowing direct hybridization with genomic DNA, using carefully optimizedconditions, only the primer with the complementary base is elongated.To facilitate electrochemical detection, ferrocene-labeled deoxynucleotides62 are incorporated during primer elongation, and square wave voltammetryis used to directly detect which primer has elongated and thus identifythe allele present at the SNP site. In both differential pulse voltammetry(DPV) and square wave voltammetry (SWV), the faradaic to nonfaradaiccurrent ratio is drastically increased and there is a very effectivediscrimination against the charging background current, achievingvery low limits of detection compared to cyclic voltammetry.63 However, SWV has the added advantage that itprovides currents up to 4 times higher and considerably faster responsesthan DPV. The scan rate of SWV (above 1 V/s) markedly decreases theanalysis time, when compared to the commonly used DPV scan rate (aroundmV/s) where faster scan rates decrease the sensitivity and resolutionof signals. This is of particular importance for multiplexed electrochemicaldetection.Electrochemical devices are cost-effective, portable, andrapid,and we recently exploited these advantages to develop an approachfor the electrochemical detection of an SNP directly from a fingerprickblood sample, without the need for DNA extraction or purification,which was completed in less than 20 min.Mycobacterium tuberculosis to detect an SNP associated withrifampicin resistance64 and then in a fingerprickblood sample to identify an SNP linked with cardiomyopathy.59In first demonstrations of the proof of concept,single SNPs wereidentified in DNA extracted and purified from sputum samples containing 15 This system was first optimizedusing synthetic DNA and later tested using extracted genomic DNA.The effect of blood on the performance of the solid-phase primer elongationwas evaluated, and the results were finally successfully validatedwith biobanked blood samples that had previously been genotyped usingSNP-qPCR and further confirmed using Sanger sequencing. This platformcan be facilely expanded to a plethora of further applications, andthe number of SNPs can be vastly increased according to the end-userrequirements, with the complete analysis from blood lysis to finalread-out completed in less than 20 min.In the present paper, we have extendedour generic platform forthe simultaneous detection of the five SNPs identified in the GEFOS/GENOMOSstudy.65 and directlyused without the need for any further purification  that can help to identifyindividuals at the extremes of the fracture risk distribution  as well as an establishedCaucasian lactose intolerance marker were chosen to be detected.66 The subsets of SNPs are included in For the present work, the SNPsidentified by GEFOS/GENOMOS in previousGWAS analysesFigure S1). As schematically depictedin Since humansare diploid organisms, two sets of homologous chromosomeshave the same loci, one allele from the paternal and the other fromthe maternal parent. If both alleles are the same, then the organismis homozygous at that locus or SNP , and if they aredifferent, then the organism is heterozygous at that locus  S1. As scin silico withthe aim of obtaining similar melting temperatures (Tm) to facilitate equivalent amplification efficiency andavoid cross-reactivity between the primers. Two different softwareprograms were used for the primer design: Primer Blast software toobtain primers with similar Tm valuesand GC content  and to checkfor cross-reactivity with nonspecific sequences found in the genomeand Multiple Primer Analyzer software to screen for any potentialself-dimer/primer-dimer formation . Finally, Table S1 summarizes the primersets and DNA sequences used for SNP detection.The primers were primarily designed Table S1) was then confirmedusing PCR. Different PCR master mixes were prepared using the combinedfive forward primers with the individual reverse primer specific foreach SNP to amplify the synthetic DNA sequences (Table S1). In all cases, a single band was observed via gelelectrophoresis , indicatingthat each synthetic DNA was amplified only with its specific reverseprimer. When the target sequences were tested using the noncorrespondingreverse primers, no band was observed in gel electrophoresis, confirmingthe specificity of the primers. Furthermore, no other bands were observed,demonstrating that no self-dimers or primer-dimers had formed duringamplification . This primerspecificity was again achieved when these tests were performed usingisothermal liquid-phase RPA, where only a single band was observedwhen each synthetic DNA was amplified with its specific reverse primer.The specificityof the primers  not only facilitates true simultaneous,parallelized detection of the SNPs from a single sample but also reducesthe number of washing steps and avoids the use of sensitive reporterenzymes and substrates. Furthermore, the volume of the mixture ofRPA reactants and the sample was decreased by ca. 27-fold, from approximately4 mL to 150 μL . Theinterelectrode reproducibility in the signal is excellent as evidencedby the signal obtained at each electrode of a 64-electrode array functionalizedwith 6-(ferrocenyl)hexanethiol .The specificity of primers wassubsequently tested by using solid-phaseRPA with colorimetric detection. An activated maleimide microtiterplate was modified with the 5′-thiolated reverse primers forthe detection of five SNPs, including the β-globin housekeepinggene as a positive control. A reaction mixture containing the RPAreagents, the five forward primers, and the synthetic dsDNA targetswas added to the wells, and primer elongation was achieved using biotinylateddNTPs. Following completion of the isothermal solid-phase primer elongation,streptavidin-modified HRP was added to bind to the incorporated biotinylateddNTPs, followed by addition of the substrate 3,3′,5,5′-tetramethylbenzidine.As can be seen in Figure S8. Each set of primers is composedof two 5′-thiolated reverse primers complementary to the specificpart of the genome containing the SNP under interrogation and differingonly in the base at the 3′-OH end, with the terminal basesdesigned according to the SNP to be detected. Two additional negativecontrols of electrodes functionalized with 5′-thiolated reverseprimers with terminal bases not specific to the SNP were also employed.The thiolated primers were self-assembled on the surfaceof individual gold electrodes of an array following the pattern outlinedin Additionally, two positive controls were implemented. The firstpositive control was an electrode functionalized with a 5′-thiolatedreverse primer specific for the housekeeping β-globin gene,and a positive signal obtained at this electrode indicates correctcell lysis and solid-phase isothermal amplification. For the secondpositive control, an electrode was functionalized with the 5′-thiolatedpoly-A sequence that should simply hybridize with an Fc-poly T, whichis added together with the forward primers. A positive signal obtainedat this electrode indicates correct functioning of the electrodes,connectors, and fluidics.Figure S6B), with the RPA carried out with synthetic sequences using differentparameters and the electrochemical signal measured in the setup shownin Figure S6D.In the assay reported here, the genomicDNA interacts with 20 surface-tethered primers, 4 primers for eachof the 5 SNPs, requiring a careful optimization of the reaction time,temperature, and concentrations of the solution-phase forward primers.For optimization of the assay, several electrode arrays were functionalizedand housed inside the microfluidic cell S6B, withFigure S8). As can be seen in test 1 , the kinetics of the solid-phase primer elongationreaction are not equal for every SNP. Using a 0.5 μM forwardprimer concentration, the positive primers for SNP 10 and 49 are fullydiscriminated from negative primers but not for SNP 27, SNP 29, andSNP 46. The differentiability was improved for SNP 27 and SNP 29 bydecreasing the concentration of their forward primers to 0.25 μM(test 2) while maintaining the concentration of the forward primersof SNP 10 and 49 at 0.5 μM, and the concentration of the forwardprimer for SNP 46 had to be further decreased to 0.125 μM (test3). The optimum concentration of forward primers , highlightingthat there is negligible matrix effect of the whole blood on the solid-phaseisothermal primer elongation reaction and electrochemical detectionof the surface-tethered amplicon.The effect of the blood matrix was primary evaluated. The genomicDNA was extracted and purified using a NucleoSpin blood kit to eliminatethe complex blood matrix from a panel of five representative wholeblood samples containing different SNPs. The signal obtained followingsolid-phase primer elongation was compared to that obtained with theoriginal blood sample, which had been thermally lysed and used withoutpurification. The results obtained fully agreed with each other interms of SNP identification, and the intensity of the signals wasvery similar .Having demonstrated that the whole blood matrixhas a negligibleeffect on the signal and in no way affects the identification of theSNP, the remaining whole blood samples were processed using rapidthermal lysis, followed by isothermal solid-phase primer elongation,and the elongated primers were detected electrochemically .The electrochemical platform for the detection of SNPs was primarilyvalidated using the SNP-specific TaqMan fluorogenic 5-exonucleaseassay, an assay routinely used in clinical laboratories for the detectionof SNPs, and was the assay employed at the Medical University of Graz.This assay requires prior cell lysis, DNA extraction, and purification,and individual assays need to be carried out for each SNP to be studied.The assay mix contains the TaqMan probes specific for each SNP, whichwere labeled with 2′-chloro-7′-phenyl-1,4-dichloro-6-carboxy-fluorescein(VIC) for allele 1 and 6-carboxyfluorescein (FAM) for allele 2 andthe allelic discrimination plots used for genotyping , while Blastnsoftware was used to align the obtained sequences with the referencesequence from the synthetic DNA, demonstrating greater than the 90%similarity between both sequences .Bioedit software was used to check the resultingsequences andthe chromatograms  with automated Sanger sequencing,the TaqMan fluorogenic 5-exonuclease assay, and electrochemical detection.As can be seen, there is a 100% correlation among the results obtainedusing the three techniques, thus validating the results obtained withthe electrochemical platform developed for SNP genotyping.68 The four SNPs selectedfrom the GWAS studies have been shown to be associated with up toa 10% increase in fracture risk and have been mapped to genes clusteringin pathways known to be relevant to bone biology.69 For instance, SOST and WNT16 are two of the key regulatorsof bone remodeling,71 have been robustly associatedwith low bone mineral density, and have led to 10 and 6% increasesin fracture risk, respectively.72 RSPO3 has been shownto regulate vertebral trabecular bone mass and bone strength in miceand fracture risk in humans73 and was associatedwith up to a 5% increase in fracture risk. FAM210A as a novel bonepathway has been shown to influence the structure and strength ofboth muscle and bone74 and has been shownto be associated with up to a 5% increase in fracture risk. Furthermore,the well-established lactose intolerance marker (LCT(C/T-13910)) isof immediate diagnostic as well as therapeutic value for the potentialusers, as clinical congruence with lactose malabsorption is very high.75 Therefore, peroral supplementation with lactose-freecalcium in individuals with lactose intolerance can be immediatelyrecommended to avoid gastrointestinal malabsorption by common milkproducts containing lactose in patients at risk for osteoporosis.Osteoporosisis highly polygenic and heritable, with heritabilityranging from 50 to 80%; however, only a useful combination of geneticvariants will be able to demonstrate clinical efficiency in the predictionof patients at risk.76 Althoughthe sum of trait-associated alleles was not weighted for their effectivesizes, a simple quantitative index of genetic predisposition to bone-relatedrisks was established. Figure S14).Finally, the use of genotyped SNPs is maximized when constructinga so-called polygenic risk score.3, capable of simultaneous detection of all 64 electrodes inless than 7 s has been developed by Labman Automation, which has theability to scale up to 128, 256 electrodes, etc. and will be employedin future work for the implementation of the assay at the point ofneed.Ageneric platform for the simultaneous electrochemical detectionof multiple single nucleotide polymorphisms (SNPs) via solid-phaseisothermal primer elongation has been successfully implemented. FiveSNPs associated with an increased risk of developing osteoporosisand a risk of fracture were detected in 15 blood samples, with theresults validated using both a TaqMan SNP-specific fluorogenic 5-exonucleaseassay and Sanger sequencing. It has been demonstrated that the bloodmatrix has no effect on the assay performance or the electrochemicaldetection. The generic platform is robust and can be used directlywith thermally lysed blood samples with no need for DNA extractionor purification, with the entire assay from the addition of the lysedsample to the readout of the results being complete in just 15 min,with the cost per SNP, on a laboratory scale, including the cost ofthe electrode array, microfluidics, and all reagents being ca. 0.3€.The heterozygous versus homozygous nature of the SNP is robustly determined,with results further confirmed by negative and positive controls.While in the work reported here, the assay was carried out in triplicatefor each of the immobilized primers, the use of the negative controlswas demonstrated to be robust enough to eliminate the need for triplicatereadings, and thus 4 electrodes per SNP is adequate, with 2 positivecontrols per array and using the 64 electrode array 15 SNPs couldbe simultaneously detected. The reported platform is completely genericin nature and can be used for any application requiring SNP genotyping.The number of SNPs can be facilely expanded to a higher number ofSNPs simply by increasing the number of electrodes per array, whichcan be defined by the end-user requirements, with no impact on cost/SNPor assay time, and is currently limited by the number of channelsavailable with commercial potentiostats. A potentially battery-operatableportable potentiostat, measuring L 146 × W 85 × H 88 mm


In this study, 371 Holstein cows from six herds and 26 Holstein bulls, which were used in these herds, were genotyped by the Illumina BovineSNP50 array. For runs of homozygosity (ROH) identification, consecutive and sliding runs were performed by the detectRUNS and Plink software. The missing calls did not significantly affect the ROH data. The mean number of ROH identified by consecutive runs was 95.4 ± 2.7, and that by sliding runs was 86.0 ± 2.6 in cows, while this number for Holstein bulls was lower 58.9 ± 1.9. The length of the ROH segments varied from 1 Mb to over 16 Mb, with the largest number of ROH having a length of 1–2 Mb. Of the 29 chromosomes, BTA 14, BTA 16, and BTA 7 were the most covered by ROH. The mean coefficient of inbreeding across the herds was 0.111 ± 0.003 and 0.104 ± 0.004 based on consecutive and sliding runs, respectively, and 0.078 ± 0.005 for bulls based on consecutive runs. These values do not exceed those for Holstein cattle in North America. The results of this study confirmed the more accurate identification of ROH by consecutive runs, and also that the number of allowed heterozygous SNPs may have a significant effect on ROH data. Inbreeding in dairy cattle is an inevitable phenomenon of artificial selection. Traditionally, the inbreeding coefficient is calculated based on ancestry . With the advent of SNPs arrays , it became possible to investigate autozygosity at a previously unattainable level . In fact, due to the runs of homozygosity (ROH) approach, animal genome analysis for long continuous homozygous stretches is still ongoing. The primary cause of autozygosity in livestock measured by ROH is assumed to be inbreeding  or consanguineous marriage in humans . For identifying ROH, software based either on identity by descent (IBD) GERMLINE , or on Hidden Markov Model (HMM) Beagle  and BCFtools  has been elaborated. In addition, software based on scanning by SNPs window Plink , overlapping sliding window SNP101 , or both consecutive and sliding runs detectRUNS , as well the software based on other scripts , cgaTON  have been provided. The commercial software SNP & Variation Suite(Golden Helix SNP & Variation Suite) is also widely used.It has been shown that software based on HMM and IBDis inferior to other software mentioned above . The main challenge facing scientists is the lack ofconsistent criteria among studies regarding a threshold valueof each parameter analyzed to determine ROH . The most crucial parameters that are used in anysoftware are the number of heterozygous or missing SNP callsallowed in ROH. There is an inconsistency between thresholdsthat should be applied in studies. Some authors disallowed anynumber of heterozygous SNPs in ROH , others allowed one, two and more heterozygous SNPsdepending on the length of the ROH segments . Anyway, M. Ferenčakovićet al. (2013) suggested that allowing a certainamount of genotype errors in a long ROH could minimize theunderestimation of these segments. Although S. Mastrangeloet al. (2016) showed different values of the inbreeding coefficient,if heterozygous genotypes were allowed.There are relatively few studies assessing which set of theseparameters is optimal for detecting ROH, in order to betterunderstand their effect on identified autozygosity. M. Ferenčakovićet al. (2013) have shown that SNP array density andgenotyping errors introduce patterns of bias in the assessmentof autozygosity. These authors observed that allowing heterozygousSNPs in ROH can lead to the merging of adjacentROH segments which resulted in biased estimates of the ROHnumber. Based on simulation data, D. Howrigan et al. (2011)recommended disallowing existence of any heterozygousSNPs in ROH. Summarizing, there is currently no consensuson a reasonable number of heterozygous SNPs in ROH toavoid bias in the ROH data.When planning this study, special attention was paid to assessingthe impact of allowed missing SNPs and heterozygousSNPs in ROH runs on the results using commonly appliedconsecutive and sliding runs. Another goal of the study wasto evaluate the distribution of ROH in the chromosomes,and the effect of allowed heterozygous SNPs on inbreedingscores.The following main objectives of the study were: (i) to assessthe number and length of ROH segments in the cows andbulls genome, as well as their proportion in the chromosomes,(ii) to calculate the inbreeding coefficient, (iii) to assess thedata bias resulting from an allowance of missing and heterozygousSNPs in ROH, (iv) to use the sliding windows andconsecutive runs to obtain ROH data.Animal resources and SNPs genotyping. Data and genotypeswere obtained from Committee on Agro-Industrial Complexof the Leningrad region. This study analyzed Holstein cowsborn from 2010 to 2013 in six herds located in the Leningradregion (Russia). More information on breeding our local Holsteincattle can be found in the article .Animals for genotyping were selected by farmers withregard to the pedigree structure of the herd. The sampled animalsaccounted for 8–15 % of the total number of dairy cowsin herds. Altogether, 371 cows from six herds and 26 bullsfrom the Netherlands, North America, Germany and Canadaused in these herds were genotyped by BovineSNP50 v. 2.0array . Quality control was carried out byPlink. (i) SNPs calls with a quality score of less than 0.7 wereremoved. (ii) Only autosomal chromosomes were considered.(iii) 5 % of missed SNPs and 1 % MAF were allowed, whichresulted in 48,108 SNPs for cows and 43,441 for bulls. Totalgenotyping rate was > 0.99.Identification of ROH. The ROH segments were identifiedusing detectRUNS  implementedin the R environment (http://www.r-project.org/index.html),and Plink tool . The parameters appliedto define ROH by detectRUNS for consecutive runs methodwere: (i) the minimum number of SNPs required to definesegments as ROH, 15 and 20, (ii) the number of missingcalls allowed in a ROH segment, 0–4, (iii) the number ofheterozygous calls allowed in a ROH segment, 0–2, (iv) theminimum length of ROH segments, 250 Kb, (v) the maximumgap between ROH segments, 1 Mb.For sliding window method in detectRUNS the parametersand thresholds were: (i) window size 15 and 20 SNPs, (ii) thethreshold 0.05, (iii) the minimum number of SNPs required todefine segments as ROH, 15 and 20, (iv) the number of missingcalls allowed in a ROH segment, 0–4, (v) the number ofheterozygous calls allowed in a ROH segment, 0–2, (vi) theminimum length of ROH, 250 Kb, (vii) the maximum gapbetween ROH segments, 1 Mb, (viii) the minimum alloweddensity of SNPs, 1 SNP per 1 Mb.The parameters applied to define ROH by Plink were (i) thesliding window, 20 SNPs, (ii) the proportion of homozygousoverlapping windows, 0.05, (iii) the minimum number ofSNPs in ROH, 20, (iv) the density was one SNP per 60 Kb,(v) the number of missing SNPs was zero, (vi) the number ofheterozygous SNPs was zero.Inbreeding coefficients (FROH) were calculated as the sumof the animal’s ROH lengths divided by the total length of theautosomes covered by SNPs (2508.706681 Mb).Impact of missing SNPs on ROH data. Primarily, the effectof missing SNPs allowed in ROH on the data was evaluatedby consecutive and sliding runs. No impact on ROH datawas found for either method if one to four missing SNP callswere allowed in ROH. Therefore, to further evaluate the ROHresults, this value was set to zero.Effect of heterozygous SNPs on ROH data based on consecutiveruns. To evaluate the number of ROH segments inthe cow genome, 15 SNPs 1 and 20 SNPs(Table 1) consecutive runs were carried out. When ROHsegments were not interrupted by heterozygous SNPs, themean number of ROH was 1.9 times greater at 15 SNPs runs( p ≤ 0.03). In fact, the average number of ROH across theherds was 182.1 ± 3.4 at 15 SNPs runs compared to 95.4 ± 2.7at 20 SNPs runs. To avoid overestimation of the autozygousROH due to short ROH segments, 20 SNPs runs were usedfurther.https://vavilovj-icg.ru/download/pict-2023-27/appx17.pdfSupplementary Materials are available in the online version of the paper:For an adequate understanding of the results, it is necessaryto define the term ROH further used. ROH is a contiguoushomozygous SNP sequence uninterrupted by heterozygousSNPs, except for the allowed number of heterozygous SNP.Descriptive data statistics are given in Table 1. The meannumber of ROH varied across herds. However, the differencesbetween them are insignificant (t-test). It should be noted thatthere is considerable variation in the ROH number among thefourth herd cows. This result was due to a large number ofROH in one cow (757 ROH segment). The exclusion of thiscow resulted in the mean ROH of 85.9 ± 2.1 in the fourth herd.However, this did not lead to significant differences betweenthe herds (t-test). The effect of allowed heterozygous SNPson the number and length of the ROH segments was assessedwhen their values ranged from 0 to 2. Initially, the averagenumber of ROH increased more than 1.6-fold from 95.4 ± 2.7to 151.3 ± 2.7 when one heterozygous SNP in ROH was allowed(see Table 1). Then the mean increased to 249.6 ± 2.6with an increase in the number of allowed heterozygous SNPsin ROH to two. Thus, the allowance of heterozygous SNPsleads to a significant ( p ≤ 0.02) increase in the number of ROH.The length of the ROH segments has been classified intofive categories . Themost abundant in the number of ROH was the 1–2 Mb class. The largest proportion of the ROH numberhad the same class, up to two allowed heterozygous SNPs.A particularly noticeable increase in the number of ROH inthis class occurred with the use of 15 SNP runs . These data indicate the presence in the genomeof cows of a large number of short (less than 1 Mb) ROHsegments, which are more effectively detected when scanningfor 15 SNPs.ROH identification based on sliding runs. As for 15 SNPsand 20 SNPs, the sliding runs were used (Table 2). Interestingly, the data for 15 SNPs runs identified by both consecutiveand sliding runs were largely the same ), while for 20 SNPs consecutive and20 SNPs sliding runs, the data differ somewhat, but insignificantly(t-test) (see Tables 1 and 2). To obtain a comparableresult with consecutive runs, 20 SNPs window was usedfurther. Descriptive statistic for 20 SNPs sliding data is givenin Table 2. The mean number of ROH between herds wasinsignificant (t-test). But, similar to consecutive runs afterexclusion of the most deviated cow (it included 731 ROHsegments) among the fourth herd, the mean number of ROHbecame 77.6 ± 2.0. However, this value still did not significantlydiffer from those for other herds (t-test). The averagenumber of ROH increased by 1.7 times, from 86.0 ± 2.6 to146.7 ± 1.7, when one heterozygous SNP was allowed in ROH,then by 3 times when two heterozygous SNPs were allowed.The observed increase in the number of ROH was significantp ≤ 0.02 (t-test).The length of the ROH segments for sliding runs has beenclassified into the same five categories  as it has been carried out for consecutive runs. The most numerous in the number ofROH has occurred in the same class of 1–2 Mb, in whicha considerable increase in ROH segments was observed withan increase in the number of allowed heterozygous SNPs inROH. This indicates the proximity of numerous ROH segmentsshorter than 1 Mb in the cow genome.ROH identification based on Plink. Plink software iswidely used in ROH studies. Therefore, it is necessary tocompare the data obtained by Plink and detectRUNS. Themean number of ROH obtained with Plink was 74.9 ± 1.9and this value was no different from the value calculated bydetectRUNS based on sliding runs 86.0 ± 2.6 (t-test). Thefact that Plink identified fewer ROH segments mainly in the1–2 Mb class than detectRUNS detected  indicates Plink’s lesser ability to identify shortsegments less than 1 Mb. Thus, the data obtained for theshortest ROH length class can be highly dependent on thesoftware and parameters used.Comparative analysis of consecutive and sliding runs.Comparative analysis of the consecutive and sliding dataled to the following conclusions. When heterozygous SNPswere disallowed, the consecutive runs showed a slightlybigger mean number of ROH than sliding runs (94.4 ± 2.7vs. 86.0 ± 2.6) and even bigger for sliding windows (Plink)74.9 ± 1.9, but the difference between them was insignificant(t-test). The fewer SNPs were used in consecutive runs, themore 1–2 Mb ROH segments there were (Table 3). Summarizingthe comparative analysis of the applied methods, one cancome to the conclusion that there are some differences in theresults obtained by these methods.Distribution of ROH in the cow chromosomes. Toevaluate the chromosomes with the largest number of ROHsegments taking into account their length, the following calculationwas carried out. For each chromosome, the proportionof ROH in it was divided by the share of its size in the cattlegenome. The rank chromosome calculation is shown in Suppl.Material 4. For both runs, the list of chromosomes ranked inthis way is shown in Table 3. Out of 29 chromosomes, thetop chromosomes covered with ROH were BTA 14, BTA 16and BTA 7, not BTA 1 (seventh position in the list), BTA 2(20th position in the list) and BTA 3 (16th position in thelist). Thus, the number of ROH in the chromosomes was notproportional to their length. Spearman’s correlation betweenconsecutive and sliding runs data in Table 3 was r = 1.0( p ≤ 2.0E–07). Whether this fact is a result of drift or/andselection requires further study.Inbreeding. To assess the level of inbreeding in the herds,the mean inbreeding coefficient was calculated across all herds(Tables 4 and 5). When heterozygous SNPs were disallowed,the mean inbreeding coefficients across the herds amountedto 0.111 ± 0.003 and 0.104 ± 0.004 for consecutive and slidingruns, respectively, and the difference between them wasinsignificant (t-test). The mean inbreeding coefficient estimatedby Plink was 0.105 ± 0.004, which is consistent withthose for sliding runs. A greater variability in inbreedingoccurred for the fourth herd. This result is mainly associatedwith a highly inbred cow in this herd. Exclusion of this cowresults in the average inbreeding coefficient of 0.096 ± 0.005and 0.089 ± 0.005 for consecutive and sliding runs. It shouldbe noted that in this herd the cows were inseminated only fromthe Netherlands bulls, while in other herds the bulls’ semenfrom North America, Germany, Canada, and the Netherlandswas used. The proportion of the bulls from these countriesused in the herds was published in the article . After excluding the highly inbred cow, the averageinbreeding coefficient in the fourth herd decreased comparedto other herds. This result indicates the correct selection of thebulls even if their semen was imported from the same country.The fourth herd deviated significantly from the other herds when variability was measured by the Wright’s fixation indexor PCA . When one heterozygousSNP was allowed in ROH, then the mean inbreeding coefficientacross all herds was 0.145 ± 0.003 and 0.148 ± 0.003based on consecutive and sliding runs. Thus, the allowanceof even one heterozygous SNP resulted in an increase in theinbreeding coefficient ( p ≤ 0.06). Therefore, to assess inbreedingin the herds, heterozygous SNPs should be disallowed inROH due to sizable bias.Confirmation of results obtained on cows with data onbulls. To validate the results obtained on the cows, the bullsthat have been used two generations ago in the same herdswere analyzed for ROH. The mean number of the ROH segmentsfor the bulls, 58.9 ± 1.9, turned out to be significantlyless than for the cows, 95.4 ± 2.7 ( p ≤ 0.05) (Tables 1 and 6).The mean inbreeding coefficient for the bulls was 0.078 ± 0.005and did not differ significantly from the cows (t-test). The coefficientof inbreeding did not significantly increase when oneheterozygous SNP was allowed (t-test) (Table 7).Over the past decade, the runs of homozygosity approach hasbeen widely used both in humans  andfarm animals . A distinctive feature ofROH studies is the variety of software and threshold criteriaused in them. The most widely applied software tools for identifyingROH segments are either sliding window or consecutiveruns. We preferred detectRUNS, where both approacheshave been implemented .The consecutive runs resulted in the average number ofROH 94.4 ± 2.7, while sliding runs, 86.0 ± 2.6. These valuesfor North American , Italian , European Holstein , andPolishHolstein Black-and-White variety  are 82.3 ± 9.8 (SD), 81.7 ± 9.7 (SD), 74.6 ± 2.3 (SE), and53.3 ± 7.3 (SD) respectively. The first three values do not differsignificantly from ours, while the value for Polish cattle differsconsiderably. It should be noted that the allowance of even oneheterozygous SNP in ROH significantly increases the numberof ROH by 55.9 and 60.7 points for consecutive and slidingruns, respectively (see Tables 1 and 2). A limited number ofstudies have analyzed the effect of allowed heterozygousSNPs on ROH data. D. Howrigan et al. (2011) recommendeddisallowing the use of any heterozygous SNPs in ROH, whileM. Ferenčaković et al. (2013) suggested that the number ofallowed heterozygous SNPs should be determined separatelyfor each ROH length of interest and for each SNPs density.Moreover, the allowance of heterozygous SNPs in ROH leadsto a sizable bias in the inbreeding coefficient . My results confirm this conclusion.The relative frequency of the ROH number in differentlength classes obtained from the cows data for consecutiveruns were 61.4 % (1–2 Mb), 19.8 % (2–4 Mb), 11.3 %(4–8 Mb), 5.5 % (8–16 Mb) and 1.9 % (longer than 16 Mb),while for sliding runs these values were 60, 19.8, 12.1, 5.8, and2.1 %. Thus, the largest number of ROH was identified in theshortest 1–2 Mb class. Plink-running of the cows genome revealedthe following ROH frequencies in five categories 52 %(1–2 Mb), 25 % (2–4 Mb), 14 % (4–8 Mb), 7 % (8–16 Mb)and 2.5 % (longer than 16 Mb), the distribution of which isslightly different from those defined by detectRUNS. ForNorth American Holstein animals, these values were 43.5,23.9, 17.7, 10.5, and 4.7 % . The correspondingvalues for Italian Holstein bulls were 56.9, 20.8,11.9, 7.2, and 3.7 %  and Polish Holstein,23, 19, 9.8, 4.4, and 1.3 % . Thus, whenwe used detectRUNS to scan the genome of our local Holsteincows, we obtained an abundant number of short ROH as aresult of haplotypes reflecting the ancient relationship withinbreeding animals. But, when we used Plink, the values weresimilar to the American and Italian data. It should be notedthat the authors of the article  used thecgaTOH software and their data differ considerably from otherdata. Whether this result was due to the cgaTOH software or/and selection requires further analysis. Estimationof the true number of short ROH is important, since 0.1–3 MbROH segments have the more number of deleterious variantsthan segments longer than 3 Mb . Forevaluation of the genomic estimated breeding value (GEBV),short ROH is essential for genomic construction of ROHbasedrelationship matrix (GROH) .According to my data, the largest number of ROH falls intothe 1–2 Mb class. As the number of allowed heterozygousSNPs in ROH increases, the number of ROH segments in theshortest 1–2 Mb class increases as well . This fact indicates a close location of a large numberof short, less than 1 Mb, ROH segmentsThe same conclusion was reached in a study of ten sequenced(WGS) breeds of cattle . Then,the results of the animals ROH genome scanning can substantiallydepend not only on the selection but also on thegenotyping method and the software used to identify shortROH segments. This fact should be taken into account in thecomparative analysis of the ROH dataEstimated by detectRUNS, the mean inbreeding coefficientfor six herds was 0.111 ± 0.003 and 0.104 ± 0.004 for consecutiveand sliding runs, respectively, and for bulls, 0.078 ± 0.005for consecutive runs. It was equal to 0.105 ± 0.004 based onthe sliding window runs evaluated by Plink. It should be notedthat cows from six herds did not differ in the mean inbreedingcoefficient (see Tables 4 and 5), while according to PrincipalComponents Analysis, the fourth herd differed significantlyfrom all other herds . Therefore,this difference is not due to inbreedingThe accurate knowledge of inbreeding in the herds thatoccurred several decades in the past is necessary both forcalculating the inbreeding trend and for evaluating selectionstrategies. To solve this problem, high-density arrays or wholegenome sequencing (WGS) should be used. Comparison of50k and HD panels provides evidence that the data from the50k panel lead to imprecise determination of short ROH segments. However, it has been shownthat ROH detection based on high-density or 50k array datamight give the estimates of current inbreeding most similar toROH values obtained from the sequence data . M. Bhati et al. (2020) provided comprehensive WGSdata for Braunvich cattle. Medium-sized ROH (0.1–2 Mb)were the most frequent class (50.46 %) and made the largestcontribution (75 %) to total genomic inbreeding, while short,50–100 Kb, ROH occurred almost as frequently (49.17 %) asmedium-sized ROH, they contributed only 19.52 % to totalgenomic inbreeding. These findings provide an accurate estimateof short ROH in the cattle genome and their contributionto total inbreeding. The average FROH estimated from the WGSdata was 0.14 in Braunvich cattle. This value is less than WGSFROH in Holstein, 0.18 . Summarizing, the50k panel cannot accurately capture ancient inbreeding thatoccurred a few decades in the past. The inbreeding coefficientof American Holstein measured with ROH in 2011 was 0.12and after applying genomic selection, it increased to 0.15in 2018 . For European , Italian , and Polish Holstein, these values were 0.108 ± 0.006 (SE),0.116 ± 0.001 (SE), and 0.118 ± 0.027 (SD), respectively. It isimportant to note that in the above studies, ROH data werebased on the 50k array; thereby, ROH segments not shorterthan 1 Mb were identified. Once again, we have to admit that,according to our data, an increase in the number of mostlyshort ROHs (1–2 Mb) by 395 points identified during consecutive runs compared to sliding runs leads to only a slight increase in the inbreeding coefficient(Tables 4 and 5).It can be assumed that there should be an event horizon fora herd or population, beyond which it is impossible to obtainvalid information about inbreeding events in history of theirbreeding. I hypothesize that in our local population, a reducedeffective population size, ongoing admixture and inbreedingthroughout its history, accompanied by recombination, shouldlead to the largest number of short ROH less than 1 Mb inthe herds currently studied. These short ROH can be consideredas ancient ROH segments formed by some populationevents, such as drift, bottleneck, and inbreeding that occurredmany decades ago. The bottleneck in our local herds has notpreviously been proven by Principal Component Analysis. An accurate interpretation ofthese short ROH can be troublesome without knowledge ofthe herd management history. In addition, it is very importantto know the true number of short ROHs in the analyzed animalsresulting from inbreeding (see above-mentionedWGSdata). Thus, the event horizon can depend on both pedigreeinformation, ROH length profile (SNPs array or WGS used)as well as on the algorithm-defined approach to ROH identification.However, ROH segments shorter than 500 Kb can beconsidered to be beyond the event horizon due to strong LDand inconsistency with autozygosity. The short ROH characterizedby strong LD among markers are not always consideredautozygous, but nevertheless they may have formed due tomating with distantly related animals .Summarizing, it should be assumed that inbreeding data canbe only relatively correct based on ROH larger than 1 Mb (nomore than 50 generation back)A number of studies have noted an uneven distribution ofROH in the bovine genome, e. g. . Giving the numberof ROH in the chromosomes, we calculated their rank takinginto account the proportion of chromosome length in thegenome of the cattle (see Table 3). Out of 29 chromosomes,the most covered with ROH segments were BTA 14, BTA 16and BTA 7 for both approaches used. D. Purfield et al. (2012)noticed that among the breeds studied, BTA 14 and BTA 16had the highest degree of ROH segments overlap. The regionsof the genome with the highest frequency of occurrence ofROH in the genome of the studied animals were called “ROHislands” . TheROH islands on BTA 14 and BTA 16 were identified amongPolish Holstein-Friesian animals .In Holstein cows in our study, ROH islands were localizedin BTA 7 and BTA 14 (unpublished results). In AmericanHolstein, ROH distribution was more variable among thegenomes of the selected animals, compared to a relativelyeven ROH distribution in unselected animals . Regions with a high proportion of ROH for Americanand New Zealand Jersey cows and bulls were revealed onBTA 3 and BTA 7 . On BTA 14 andBTA 16, one strongest ROH region was found common forKholmogor and Holstein breeds and one region common forYaroslavl and Holstein breeds . Extremelynon-uniform ROH patterns among bovine populationsof Angus, Brown Swiss, and Fleckvieh breeds were mainlylocated on BTA 6, BTA 7, BTA 16, and BTA 21 . The highest number of ROH islands among allNeilore breed lineages was found on BTA 7 . In addition, an enrichment of genes affecting traits ofinterest for dairy breeds was shown on BTA 14 in dairy Gyrbreed (Bos indicus) . D. Goszczynskiet al. (2018) analyzed ROH >16 Mb (three generations froma common ancestor) in highly inbred Retinta bulls. Amongother chromosomes, the highest occurrence of ROH was foundon BTA 7. Summarizing the above studies, it can be suggestedthat BTA 7 is outstanding regarding ROH islands occurrencein the cattle genome but in general there is no overall directrelationship between the proportion of ROH segments in thechromosomes and ROH islands identified there.As discussed above, the number of identified short ROH ishighly dependent on the software used and also on the genotypingmethod. Moreover, it can be suggested that consecutiveruns more accurately identified the ROH pattern in the cowgenome. However, both methods coincide in assessing thedistribution of ROH segments on chromosomes (see Table 3).Taking the findings together, it should be assumed that unevendistribution of ROH segments in the cow genome is a resultof different inbreeding events that have occurred in theirhistory.Analysis of ROH data showed that consecutive runs mostaccurately identified ROH in the cattle genome. It has beenshown that missing SNPs did not have a noticeable effect onthe number of ROH, while an allowance of even one heterozygousSNP in the ROH segments had a significant effect.Therefore, care should be taken to allow any heterozygousSNPs in the ROH. The average number of ROH across herdswas 95.4 ± 2.7 and their length varied from 1 Mb to more than16 Mb. The class with the length of 1–2 Mb was the most numerousin the number of ROH. This confirms the long historyof inbreeding in herds for many decades in the past. Moreover,the number of ROH in the chromosomes does not depend ontheir length. ROH segments mainly cover BTA 14, BTA 16,and BTA 7. The average inbreeding coefficient for our localHolstein herds was 0.111 ± 0.003, which is not much differentfrom the Holstein cattle inbreeding coefficient worldwide.This value indicates competent management of the studiedherds. 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