As a model, we chose experimental chicken lines divergently selected for only one trait, the abdominal fat weight, in which several QTL were previously mapped. Using new haplotype-based statistics exploiting the very high SNP density generated through whole-genome resequencing, we found 129 significant selective sweeps. Most of the QTL colocalized with at least one sweep, which markedly narrowed candidate region size. Some of those sweeps contained only one gene, therefore making them strong positional causal candidates with no presupposed function. We then focused on two of these QTL/sweeps. The absence of nonsynonymous SNPs in their coding regions strongly suggests the existence of causal mutations acting in cis on their expression, confirmed by cis-eQTL identification using either allele-specific expression or genetic mapping analyses. Additional expression analyses of those two genes in the chicken and mice contrasted for adiposity reinforces their link with this phenotype. This study shows for the first time the interest of combining selective sweeps mapping, coding SNP annotation and cis-eQTL analyses for identifying causative genes for a complex trait, in the context of divergent lines selected for this specific trait. Moreover, it highlights two genes, JAG2 and PARK2, as new potential negative and positive key regulators of adiposity in chicken and mice.Very few causal genes have been identified by quantitative trait loci (QTL) mapping because of the large size of QTL, and most of them were identified thanks to functional links already known with the targeted phenotype. Here, we propose to combine selection signature detection, coding SNP annotation, and  This is especially the case for species such as chicken, for which the individual production is inexpensive; in this case, for each phenotype of interest, divergent lines are usually created to maximize QTL heterozygosity after crossing parental lines. To date, 4714 QTL have been referenced in the Mouse Genome Database  mapping approaches is to gain a better understanding of biological mechanisms involved in these phenotypes to offer molecular tools for medical diagnosis or animal selection. Over the past three decades, several thousand QTL have been mapped for different traits in many species. In this context, whereas genome-wide association study is developing as the reference QTL mapping method for human populations  annotation in coding regions by using DNA-seq, to improve identification of causal genes underlying QTL regions with no presupposed function. The model we used is based on two chicken lines divergently selected for abdominal fat weight (AF), for which six QTL responsible for AF were previously mapped with an average size of 12 Mbs, and containing tens to hundreds of genes.We attempted to combine selection signatures in divergent lines for one complex trait, ST approaches underpinned by 60,000 SNP markers , but we use millions of SNPs. Several approaches exist to detect selective sweeps at the population level. They are based on allelic frequency patterns within populations  or GWAS (mice). These observations therefore strengthen positional and also functional status of those genes. By combining these complementary approaches, summarized in In our experimental design, the analysis of colocalization of QTL and selective sweeps markedly shortened the list of candidate genes in each region, sometimes even down to one, making the latter a strong causative positional candidate. We therefore focused on two genes, each located in two distinct QTL colocalizing with different selective sweep patterns. The absence of missense and nonsense SNP on those genes strongly suggested that their expression should be regulated in th generation were sequenced: seven LL, four FL, and nine F1  including the five F1 sires used for previous QTL study. Blood was collected from all the animals for DNA analyses. All procedures were conducted under License No. 37-123 from the Veterinary Services, Indre-et-Loire, France, and in accordance with guidelines for Care and Use of Animals in Agricultural Research and Teaching .The two experimental meat-type chicken lines were divergently selected for seven generations using the abdominal fat weight-to-animal weight ratio at 9 wk as an index of fattening, maintaining approximately similar live body weight  according to the manufacturer’s instructions. The libraries were quantified using QPCR Library Quantification Kit (Agilent), checked on a High-Sensitivity DNA Chip (Agilent), and sequenced in pair end 2 × 101 bp on a HiSequation 2000 (Illumina) using a TruSeq v3 Kit. Sequencing produced, on average, 92% of raw reads correctly aligned against the reference genome per animal, Gallus gallus WASHUC2.1 genome reference obtained from Ensembl 58 using BWA v0.7.0  with the following primers: F 5′-ACCAGCAGATTCCAGTGCC-3′ – R 5′-GCCCCACTAAAACATGAGGG-3′. Amplicons (1817 bp) were then purified using a NucleoFast 96 PCR kit (Macherey Nagel) following the manufacturer’s protocol, and they were finally diluted at 50 ng/μL; 5 μL of each sample was then mixed with 5 μL of specific resequencing primers  and sent to GATC-Biotech  for Sanger resequencing. Sanger traces were analyzed using Mixed Sequence Reader  from hapFLK analyses. To speed-up the computations, we calculated the kinship matrix using only 1% of the high-frequency SNPs (estimating this matrix does not require a large number of SNPs) and computed hapFLK (with the modification mentioned above) using 30% of the high-frequency SNPs. When estimating haplotype clusters, we assumed six clusters, because this corresponds to the number of ancestral lines in the founder population, and estimated them using 10 expectation maximization iterations. We computed the ob/J mice (n = 8), null for the gene encoding leptin, and mice from the same genetic background C57BL6/J (n = 8). B6.V-Lepob/J and HMDP mice were fed a standard chow diet and slaughtered at 14 and 16 wk old, respectively. The mice from the high-fat HMDP panel were fed high-fat diet during the last 8 wk.To explore implications of highlighted candidate genes in adiposity, we focused on two mouse models, including strains highly variable for obesity-related phenotypes. The first, developed by i.e., genetically similar strains have similar phenotypes), and any SNP that correlates with these strain relationships will be falsely associated with the phenotype. The Factored Spectrally Transformed Linear Mixed Models (FaST-LMM) algorithm has been shown to effectively reduce this confounding . The cis regulation indicates a potential functional genomic variation within or near a gene that significantly influences its expression.Population structure is a major confounder for genome-wide association analyses in the HMDP. This is due to the fact that many phenotypes correlate with the phylogeny of HMDP strains . The gene expression level was normalized relative to GAPDH expression level for chicken samples and relative to HPRT expression level for mouse samples using a ΔCt approach. Reference and target genes had similar PCR efficiencies. The difference in expression for a gene was assessed using a Student t-test to compare 2−ΔCt means between genotypes.Total RNA from chicken  and mouse liver and WAT were extracted using TRIzol reagent. RNA quality was assessed for each sample on an Agilent 2100 Bioanalyzer (Agilent Tech.). Reverse transcription was performed using the high-capacity cDNA archive kit (Applied Biosystems) according to the manufacturer’s protocol. Specific reverse and forward RT-qPCR primers are described in 18S rRNA expression level. Pancreatic total RNA was prepared by the guanidinium thiocyanate extraction procedure developed by t value above 30 were excluded from subsequent expression analysis, and related genes were considered as not expressed. Results are given as the gene expression fold change in each tissue relative to the tissue in which this gene was less expressed.Procedures described for differential analysis were also used for tissue expression profiles in chicken, normalizing the target gene expression by using PARK2, two marker SNPs, located on chromosome 3 at 46,581,638 bp and 46,581,695 bp on the reference genome WASHUC2.1, were tested on a Qiagen PyroMark Q24 sequencer in 8 F1 LL × FL chickens. Primers were designed using PyroMark Assay Design software (Table S3). gDNA and cDNA runs were analyzed by PyroMark Q24 1.0.10 software with default analysis parameters.To investigate the allele-specific expression of 1 samples were further considered because the remaining three individuals were homozygous at both marker SNPs. For each sample, analyzed in duplicates, we standardized cDNA ratios according to the gDNA ratios for which a perfect balance is theoretically expected. We then divided frequency of the reference allele by the frequency of the alternative allele for each SNP and each individual to obtain the allelic ratio. The significance of the allelic imbalance was assessed using a Mann-Whitney nonparametric test to compare the observed allelic ratio with the expected one .Only five FSNP annotations were performed using Ensembl Variant Effect Predictor. With this application, the location of a SNP within a gene can be defined as outside of the gene, in the coding sequence, or in untranslated regions (UTR). For the SNP that is localized in the coding sequence, the functional impact can be determined . For further analyses on coding SNPs of special interest, the tool NGS-SNP was used, which enabled us to add meta-information about conservation between orthologous sequences  animals for genome resequencing.The first goal of this study was to identify selective sweeps using high-density SNP data generated by whole-genome resequencing in two meat-type chicken lines divergently selected for abdominal fat (AF) weight. The Fi.e., call rate of 100%). Sanger resequencing was used to validate a set of 17 SNPs. The insertions and deletions (indels) were also detected: a total of 1,090,316 indels were identified, of which 810,754 (74.4%) had genotype information for all the animals.Reads were aligned to the reference genome WASHUC2.1. In all, 92% of the total raw reads were correctly aligned and the raw coverage was, on average, 19.7 X ± 3.6 X. After the removal of PCR duplicates, and to limit the number of false positives due to sequencing errors or read multi-mapping, we retained only SNPs detected from reads that could be uniquely mapped with mapping quality scores higher than 30. After these filtering steps, 86% of the genome was covered, and the sequencing depth was 12.6 X ± 4.6 X. Subsequently, the SNP calling was performed using the GATK software, which includes modules for sequence realignment and recalibration reducing biases due to sequencing or sample preparation. A total of 11,847,150 SNPs were identified, of which 9,422,311 (79.5%) had genotype information for all the 20 animals , an average of 2,729,525 SNPs were identified within a single bird, corresponding to a density of 2.6 ± 0.5 SNP/kb. These results are in agreement with previous studies  were set apart.Among the 9,422,311 SNPs observed in the whole population (n × ρ × T) = 95 kb, where n = 10 is the haploid sample size, ρ = 3 × 10−8 is the bp per generation recombination rate in chicken .The 9.4 million SNPs generated by whole-genome resequencing were used to detect genomic regions showing outstanding haplotypic differentiation between LL and FL. To do so, we used the hapFLK statistical test recently developed by This approach, based on outstanding haplotypic differentiation between FL and LL, revealed the genomic regions being most impacted by the selection pressure applied on the common ancestral line. Because it is possible that some false-positive signals remain due to genetic drift among these 129 selective sweeps, we proposed to overlap those selective sweeps with AF QTL previously detected by linkage analysis to focus on the most compelling genomic regions.P < 0.05) and four suggestive QTL (P < 0.1) for AF weight on chromosomes 1, 3, 5, and 7 using a F0-F1-F2 design generated by crossing the FL and LL lines . This clearly indicates that the relatively large number of sweeps detected genome-wide are not due to an excess of false positives, but rather to other factors related to trait architecture. As shown in In previous genetic studies of the LL and FL lines, we reported two QTL  . We did not find any indel or nonsynonymous SNPs suggesting the existence of causal polymorphism acting on the expression of this gene. Because liver and adipose tissue are major actors in lipid metabolism, we therefore focused on those tissues. We did not observe any significant differential expression between FL and LL in liver and adipose tissue , which are KO for the gene encoding leptin and have severe obesity compared with mice from the same genetic background C57BL6/J (n = 8), and the Hybrid Mouse Diversity Panel  in HMDP fed a chow diet. In addition, the difference of JAG2 expression between chow and high-fat/high-sucrose diet was negatively correlated to the difference of fat mass between those diets . These results suggest that a mutation acting in cis on JAG2 expression could be the causal mutation responsible for adiposity variation in this mice panel.To further question the implication of se diets , highligt top of , whereasn the FL , and becMLLT4 (from 1 kb upstream of the gene to intron 6), and a fraction of PARK2 (the whole exon 3 and the flanking intronic regions) (File S2). We first focused on the sweep AF3.II-a containing no gene and established that there was no unannotated gene on this region that was expressed by visualizing, at the sweep position, all tissues RNA-seq reads available in Ensembl. Also, this sweep was located quite far from the closest annotated genes, i.e., 80 kb upstream of SLC35F3 and 100 kb downstream of KCNK1. Taken together, these results were strong enough to obviate a deeper exploration of the AF3.II-a sweep, with no clear evidence that this sweep was actually carrying a variant with an impact on AF. We then explored both sweeps AF3.II-b and AF3.II-c containing, respectively, a fraction of MLLT4 and a fraction of PARK2. Focusing on polymorphisms in coding regions, we observed no indels or nonsynonymous SNPs, suggesting the existence of a causal polymorphism acting in cis on the expression of these genes. Regarding JAG2, we therefore focused on the expression of these two genes in hepatic and adipose tissues in both FL and LL lines. First, we verified that these genes are expressed in these tissues . We then generated F1 birds by crossing FL and LL to investigate the ASE using pyro-sequencing focusing on two marker SNPs for which assignment of the line-of-origin was possible. We found that PARK2 was a cis-eQTL; it exhibited an ASE profile in liver at both marker SNPs   , suggestPARK2 and adiposity in another animal model, we compared its expression profile between the two same mice models used for JAG2 study. No significant correlation was observed between adiposity and PARK2 expression in liver and WAT of the HMDP mice fed a chow diet. However, RT-qPCR analysis showed a two-fold increase in PARK2 expression in liver and WAT of obese B6.V-Lepob/J mice compared with wild-type mice . This analysis revealed that 3 SNPs and 16 SNPs had an impact on 4 PTFBS and 22 PTFBS for PARK2 and JAG2, respectively. This illustrates that, in regulatory regions, it remains difficult to identify causative mutation among all candidates.When there are nonsynonymous mutations in a positional candidate gene, it is often possible to target only a few candidate polymorphisms to deeply study e.g., breast muscle weight in our case), which explains why some sweeps—and not all—should overlap with QTL mapped for the first phenotype. This study is, to our knowledge, the first to investigate the genetics of complex traits using a combined QTL mapping approach and high-throughput-based selective sweep mapping in divergently selected lines. Whereas previous selective sweep studies in chicken focused on the characterization of the genome-wide effects of divergent selection on a specific phenotype (vs. 7), marker density (60 k vs. 9.4 M SNPs), and methods used to detect selective sweeps , the number of sweeps was quite similar .Although selective sweep analysis revealed genomic regions affected by selection, QTL mapping approaches allow detection of genomic regions impacting a specific complex trait. Thus, in the context of single trait divergently selected lines, some selective sweeps should overlap with QTL regions for this trait. Although the divergent selection is performed on one specific trait, it could also occur on other traits that are in balance with the trait on which the selection is applied  and another in which adiposity variation is due to the diet (high fat vs. chow). These results observed in chicken and mouse HMDP and based on genetic and expression approaches reveal that JAG2 is a highly confident positional and functional candidate for regulating adiposity as a negative regulator of this trait.Focusing on the QTL AF5, the only gene present in the sweep was PARK2 as an outstanding candidate gene. As stated in the literature, this gene belongs to the E3 ubiquitin-protein ligase family that promotes polyubiquitination with subsequent proteosomal degradation and/or mono-ubiquitination and multi-ubiquitination with post-translational regulation of protein function and stability . Otherwise, in response to HFD feeding, the PARK2+/+ mice showed a robust increase in PARK2 mRNA level, in parallel with elevated lipid transport protein levels, increased hepatic insulin resistance, and steatohepatitis. In addition to this unique study reporting a link between PARK2 and adiposity, our study shows that PARK2 is: a strong positional candidate for adiposity in chicken, because it alone was found in a sweep colocalizing with an AF QTL; a functional candidate, because it is differentially expressed in liver and WAT between the FL and the LL chicken lines as well as between the obese B6.V-Lepob/J mice and wild-type mice from the same genetic C57BL6/J background; and a probable positive regulator of adiposity because it is systematically overexpressed in the fat animals. In addition, we clearly showed by the ASE approach in F1 FL × LL chickens that the hepatic overexpression in fat line is due to a polymorphism acting in cis on its expression. Taken together, these results for mouse and chicken strengthen the hypothesis that PARK2 is a positive regulator of fat metabolism.Finally, for the QTL AF3.II, we revealed  disease , and its biology  showed tcis-eQTL and selective sweep mappings in divergent lines selected for a specific trait for identifying causative genes. As shown in i.e., affecting the protein sequence if nonsynonymous SNPs/indels are identified in coding regions vs. acting in cis on gene expression and thus located in regulatory regions when no nonsynonymous SNP was found in coding regions. In this latter case, cis-eQTL analysis can help to reinforce the causative status for adiposity of those genes, as shown for JAG2 in mice and PARK2 in chicken. Analyses of expression variations in the different fat and lean models clearly show that the two causative candidates, JAG2 and PARK2, act on adiposity as negative and positive regulators, respectively. However, it remains difficult to identify the causal variant among all potential cis-acting SNPs of the two investigated candidate genes. In conclusion, in a research field where the identification of causal genes remains exceptional, our strategy led to the identification of two highly confident causal candidates, one of which was never related to the trait of interest (JAG2) and another one (PARK2) that was linked to this trait in a single study.This study shows for the first time the interest to combine


SLC2A9 and ABCG2). Here we examined whether gene–gene interactions had any roles in regulating SUA using two large GWAS cohorts included in the meta-analysis [the Atherosclerosis Risk in Communities study cohort (ARIC) and the Framingham Heart Study cohort (FHS)]. We found abundant genome-wide significant local interactions in ARIC in the 4p16.1 region located mostly in an intergenic area near SLC2A9 that were not driven by linkage disequilibrium and were replicated in FHS. Taking the forward selection approach, we constructed a model of five SNPs with marginal effects and three epistatic SNP pairs in ARIC—three marginal SNPs were located within SLC2A9 and the remaining SNPs were all located in the nearby intergenic area. The full model explained 1.5% more SUA variance than that explained by the lead SNP alone, but only 0.3% was contributed by the marginal and epistatic effects of the SNPs in the intergenic area. Functional analysis revealed strong evidence that the epistatically interacting SNPs in the intergenic area were unusually enriched at enhancers active in ENCODE hepatic  and precursor red blood  cells, putatively regulating transcription of WDR1 and SLC2A9. These results suggest that exploring epistatic interactions is valuable in uncovering the complex functional mechanisms underlying the 4p16.1 region.Human serum uric acid concentration (SUA) is a complex trait. A recent meta-analysis of multiple genome-wide association studies (GWAS) identified 28 loci associated with SUA jointly explaining only 7.7% of the SUA variance, with 3.4% explained by two major loci ( SLC2A9 and ABCG2 (Human serum uric acid concentration (SUA) is the outcome of balancing production (primarily in the liver) against excretion (mostly in the kidney) . High SUnd ABCG2 . The metnd ABCG2 ,8 but rend ABCG2 . Gene–gend ABCG2 . Tools fnd ABCG2 –16 and tSLC2A9 were detected but could not be robustly replicated (In contrast to the great success in genome-wide association studies (GWAS)  , the genplicated .P-value (Pint) of <1.0E−05  based on a specific significance threshold adjusted for a much reduced number of tests ,21,26–29<1.0E−05 . Such lo<1.0E−05 ,31, and <1.0E−05 ,32 and p<1.0E−05 ,34. Both<1.0E−05  and thus<1.0E−05 .Here we used two large cohorts included in the GWAS meta-analysis , i.e. thSupplementary Material, Table S1). SNP positions quoted in this study are based on the human genome build (UCSC hg19/NCBI 37.3). Conventional GWAS identified 166 genome-wide significant (P < 5.0E−08) SNP associations in ARIC  and 75 in FHS , allocated mostly to the SLC2A9-WDR1 (4p16.1) and ABCG2 regions (4q22) in both cohorts. These results are in line with the meta-analysis .After careful data scrutiny and quality control , 514 662 SNPs and 9172 samples  in ARIC and 410 947 SNPs and 5538 samples  in FHS were used in subsequent data analyses  and had no LD between the two SNPs. All interacting SNPs were located in an intergenic area between WDR1 and ZNF518B within the 4p16.1 region, where the top four pairs of SNPs fell into a small window of <30 kb implicating a common epistatic signal upstream of rs3733588  in FHS . The 48 directly replicated SNP pairs , we further identified 83 significant pairs of SNPs all mapped to the 4p16.1 region, of which 45 pairs of interactions were directly replicated (i.e. both SNPs were genotyped with Pint < 1.0E−05) found the whole 4p16.1 region was enriched with interaction signals (917) in ARIC . Outside of this region, we also observed a strong local interaction between rs2622621 and rs1564481 , which, however, was not replicated in FHS. Another SUA-associated gene tagged by local interactions in ARIC was BCAS3 on chromosome 17: rs9914370 (BCAS3)–rs758596 (TBX4)  that was not replicated in FHS either.A further assessment of local interactions  passed the conditional tests, suggesting they did not simply mirror the marginal effects of rs3433588. Similar conditional tests of the remaining marginal SNPs within the region also suggested multiple independent associations (P < 0.05) that were generally in low LD with the lead SNP rs3433588, except for several SNPs in relatively long range (e.g. >100 kb) LD (0.5 < r2 < 0.6) .We then performed conditional tests of the 917 local interaction pairs observed in the 4p16.1 region in ARIC by fitting the lead associated SNP rs3433588 (additive effect only) in the background and found 27% of them with SLC2A9 and the latter two are intergenic between WDR1 and ZNF518B. Conditioning on the five selected marginal SNPs, we still found ∼10% (88 out of 917) of the 4p16.1 local interactions remained significant (Pint <0.05), including only one genome-wide significant pair  . A forward selection of the 10% significant local interactions led to three independent SNP pairs all located between WDR1 and ZNF518B  capturing most marginal effects of the region, where the first three are within Supplementary Material, Table S7). Using the forward selection approach described earlier, six independent imputed SNPs were selected: rs938558, rs4428284, rs4697695 , rs10489074, rs4481233  and rs16895984 , where rs10489074 and rs16895984 are intergenic between WDR1 and ZNF518B and the rest are within SLC2A9. Conditioning on the six selected imputed SNPs, 45 local interactions remained significant (Pint <0.05) , most also appeared in Supplementary Material, Table S6 including the genome-wide significant rs4697708–rs16895984. These results suggested there were local interactions independent to marginal effects.We further imputed the 4p16.1 region (i.e. 9 900 000–10 400 000) in ARIC using the 1000 Genomes Project reference panel and tested whether the observed local interactions reflect a single untyped variant. Fourteen imputed SNPs had associations stronger than rs3733588, but the associations were not substantially different  were analysed for enrichment of active regulatory regions using an online tool HaploReg (P = 4.7E−5) and K562  cell lines  and FHS .The genome-wide significant local interactions in the 4p16.1 region , including several strong enhancers located in the two intergenic areas flanking WDR1 in the HepG2 cell line. Consistent with this, transcription factor binding-site clusters and various other features of functionally active HepG2 chromatin were also found in these intergenic areas . The intergenic area between WDR1 and ZNF518B appears to be bound by transcription factors and RNA polymerase II in cell lines including HepG2 and K562 . Similarly, the intergenic area between SLC2A9 and WDR1 is actively transcribed in multiple cell lines including HepG2 and K562, again consistent with active enhancers in this region . Near the 30-kb window marked by the top five SNP interactions . To investigate whether ChIA-PET interactions are commonly enriched in other GWAS regions, we sorted 17 680 marginal SNPs currently available from the GWAS Catalog . This empirical analysis places the observed SLC2A9 enrichment for interactions within a small proportion of known complex trait loci identified by GWAS.Closer examination of the chromatin states predicted using the software chromHMM  also sug Catalog  into 881Supplementary Material, Fig. S9). Local interactions in the 30-kb window appeared relatively consistently in both genders suggesting a common enhancer activity. The most striking difference was that females had very strong (−log10Pint > 14) long range interactions between two WDR1 SNPs (rs4604059 and rs12498927) and two intergenic SNPs (rs7681212 and rs11943276) near ZNF518B, which did not appear in males .We further examined local interactions in the 4p16.1 region in ARIC female and male samples separately following the same analysis procedure . The significant epistatic signals identified in ARIC were well replicated in FHS, either exactly as the same SNP pair if both SNPs were genotyped   –25,39.Supplementary Material, Fig. S3). The conditional analysis results based on the lead SNP rs3733588 showed that a number of local interactions and marginal SNPs were statistically independent, supporting the hypothesis of multiple variants residing in the region . This differs from the report of only one associated SNP within SLC2A9 from the meta-analysis  , suggesting there might reside some regulatory elements. We showed that these local interactions did not reflect the effects of a single imputed SNP. The ‘final’ forward selection model of the five marginal SNPs and three SNP pairs further emphasized the intergenic area between WDR1 and ZNF518B, covering the two marginal SNPs and all the three epistatic SNP pairs.To fully assess the impact of marginal effects on local interactions in the region, we forward-selected five marginal SNPs as the additive genetic background and still found a substantial number of local interactions significant in the new conditional tests, most involving at least one epistatic SNP located in the intergenic areas flanking the WDR1-ZNF518B intergenic area, statistical interactions, enhancers, chromatin interactions between TSS of WDR1 and the enhancers collectively suggest complex mechanisms regulating SLC2A9 function, which may potentially contribute to the SLC2A9-mediated effect on gender difference in SUA levels (WDR1 are actively transcribed with many transcripts overlapping or adjacent to each other lead to the hypothesis that SLC2A9 and WDR1 may be co-transcribed or share transcription regulatory machinery. The hypothesis is intriguing as SLC2A9 gene expression may be regulated by enhancers directly targeting SLC2A9 and/or indirectly regulated by other enhancer(s) via WDR1 transcription. Further work is needed to test these hypotheses and dissect the regulatory mechanisms.Functional analyses provide strong evidence that the epistatically interacting SNPs are unusually enriched at enhancers active mainly in hepatic and precursor red blood cell types implicated in SUA Table . DespiteA levels . The obsGCKR was the only locus other than SLC2A9 and ABCG2 detected significantly in ARIC (suggestively in FHS). Hence, at the level of single-GWAS populations, searching for marginal SNP interactions and local interactions under relaxed significance thresholds are perhaps more realistic approaches. For example, local interactions also captured ABCG2 and BCAS3 despite not being directly replicated in FHS. In fact, a suggestive local interaction pair of rs2725227 and rs2725222  was near ABCG2, with both SNPs were located in PKD2, a candidate causal locus of polycystic kidney disease : individual call rate at 97%, SNP call rate at 95%, minor allele frequency at 2%, P-value for deviation from Hardy–Weinberg equilibrium at 1.0E−10, false discovery rate for unacceptably high individual heterozygosity at 0.01. SUA in ARIC was corrected for sex, age, body mass index (BMI), serum creatinine, hypertension treatment and sample centre. SUA in FHS was corrected for sex, age, BMI, creatinine, hypertension treatment, renal disease status and generation . To control relatedness, individuals that were outliers of the first three principal components computed from the identity-by-state matrix constructed using GenABEL were removed. In addition, subjects younger than 18 years old, or with BMI >50, or with creatinine beyond the range of 3 SD of the population mean were removed from the study. After quality control, 9172  and 5538  samples, 514 662 and 410 947 autosomal SNPs were analysed in ARIC and FHS, respectively .A common protocol was used to perform quality control of the genotype data in both cohorts using the GenABEL package  implemenrntransform function and then adjusted for polygenic effects and the first ten principal components to account for relatedness using the mixed model-based polygenic function where the polygenic heritability was computed  and the resultant environmental residuals  were used as the actual trait values for association tests (mmscore function (P = 5.0E−08)  in ARIC, the thresholds were 3.8E−13 (P =0.05/(514662 × (514662–1)/2)) for SNP pairs identified from the full pairwise genome scan and 5.9E−10 (P = 0.05/((514662–1) × 166)) for SNP pairs involving at least one marginal SNP. We adopted the threshold of 1.0E−05 for local interactions derived previously based on permutation (Genome scans were performed for each cohort as follows: (a) the identity-by-state matrix was reconstructed and the first ten principal components were calculated and stored; (b) SUA was adjusted for covariates correspondingly and normalized using the GenABEL on tests ; (c) confunction  and the 5.0E−08)  was used5.0E−08) . Genome-5.0E−08) ,21, i.e.mutation .Pint < 0.05 in FHS , fit into the background and test the remaining, repeating until no more significant conditional associations were found. Variance explained was calculated using the polygenic function with marginal SNPs or SNP pairs fitted as fixed effects.Significant epistatic SNP pairs were tested for replication in FHS at the SNP level only for simplicity, i.e. claiming a replication of an epistatic pair only if both SNPs were genotyped and with 5 in FHS . Conditiwww.cog-genomics.org/plink2/) to take the best genotypes of the imputed SNPs and then performed forward selection and conditional tests in R as described earlier.We imputed the 4p16.1 region (from 9900 to 10400 kb) based on 9172 samples and 260 typed SNPs in ARIC using IMPUTE2  and the http://compbio.mit.edu/HaploReg) that tests enrichment based on a rigorously defined genomic background  (r2>0.8) from the 1000 Genomes Project and a background set of Affymetrix 6.0 SNPs. ANNOVAR (http://enlight.usc.edu) was used to visually inspect the relationship between LD and regulatory signals.GWAS marginal SNPs and genome-wide significant epistatic SNPs within the 4p16.1 region were analysed for enrichment of ENCODE  cell-typnotyped) , with LD ANNOVAR  and UCSC ANNOVAR  were useHMG online.Supplementary Material is available at Higher Education Funding Council for England (HEFCE). Y.G. and K.W. are supported by NIH Grant R01 HG006465. BBSRC Travel Grant (BB/K004964/1) initiated the collaboration with K.W. Funding for open access charge: the UK Medical Research Council Core Fund. Funding to pay the Open Access publication charges for this article was provided by the UK Medical Research Council University Unit Strategic Partnership Funding to MRC Human Genetics Unit, University of Edinburgh.W.H.W., C.A.S. and C.S.H. are supported by the UK Medical Research Council University Unit Strategic Partnership Funding to MRC Human Genetics Unit, University of Edinburgh. W.H.W. is partially funded by


ASIP gene region, well known for its association with coat color phenotypes. Nine significant signals were also found for both traits. The highest signal for each trait was detected by both single SNP and haplotype approaches, whereas the smaller signals were not consistently detected by the two methods. Altogether these results demonstrated a strong genetic control of the “pink” and “pink neck” phenotypes in French Saanen goats suggesting that SNP information could be used to identify and remove undesired colored animals from the breeding program.This paper reports a quantitative genetics and genomic analysis of undesirable coat color patterns in goats. Two undesirable coat colors have routinely been recorded for the past 15 years in French Saanen goats. One fifth of Saanen females have been phenotyped “pink” (8.0%) or “pink neck” (11.5%) and consequently have not been included in the breeding program as elite animals. Heritability of the binary “pink” and “pink neck” phenotype, estimated from 103,443 females was 0.26 for “pink” and 0.21 for “pink neck”. Genome wide association studies (using haplotypes or single SNPs) were implemented using a daughter design of 810 Saanen goats sired by 9 Artificial Insemination bucks genotyped with the goatSNP50 chip. A highly significant signal  was associated with the “pink neck” phenotype on chromosome 11, suggesting the presence of a major gene. Highly significant signals for the “pink” phenotype were found on chromosomes 5 and 13 . The most significant SNP on chromosome 13 was in the  This trThe coat color of French goats is phenotyped by Capgenes, the organization in charge of the breeding program. Two distinct phenotypes for coat color defects have been registered in Saanen flocks: “PINK” (completely pink coat) and “PINK NECK” see . Males cASIP) and Extension (i.e. MC1R) are often highlighted because they balance the type of pigment produced [No fewer than 127 loci have already been identified to be associated with color in mammals  and at lproduced –10: the produced ,12The goat genetics context has changed since 2010 following sequencing of the goat genome  and the Our aim in this work was first to determine the genetic parameters of the two coat color traits, and then to perform a GWAS, based on goatSNP50 chip data, to identify the genomic regions responsible for “pink” and “pink necked” animals.The distributions and frequencies of each phenotype are presented in The average frequencies of PINK and PINK NECK animals in families with more than 100 daughters per sire were similar to those of the entire population , and werIn our sample for the GWAS, the frequencies of PINK and PINK NECK were 9.5%  and 10.3%  respectively, similar to the national population frequencies.The estimated heritability for PINK NECK was 0.21 (standard error = 0.03) and a little higher for PINK i.e. 0.26 (s.e. = 0.03).The possible genetic correlation between the two traits was then explored by calculating the correlation between the PINK and PINK NECK frequencies for families with more than 100 daughters. The resulting correlation of 0.03 suggested independence.Results from a principal component analysis (PCA) based on SNPs information were then used to construct and plot the population structure and investigate the possibility of improving the subsequent analyses. As illustrated in The quantile-quantile plots (QQplots) were then examined to determine the validity of the P value . For the10 of 10.2. One other chromosome-wise significant SNP was detected on CHIR 21 (see 10(P) = 4.5).R 21 see , at a lo10(P) = 7.3.In the haplotype-based association, the same peak was detected on CHIR 11  with a hThe haplotype-based GWAS highlighted another signal on CHIR 6 but consisting of only one significant position and at the chromosome-wise level see .The distribution of the animals, regarding their performance and genotype for the most significant SNP (snp15376-scaffold1630-140951), is presented in 10(P) = 7.7 for both. The other genome-wise significant SNP (located on chromosome 5) had a lower P-value (-log10(P) = 7.2).10(P) = 5.2 and 5.6). The Manhattan plot revealed another peak on chromosome 11 with two chromosome-wise significant SNPs in the same position as the signal found for PINK NECK.In the haplotype-based association, eight SNPs on five chromosomes  were significant at the chromosome-wise level see  but noneThe two significant SNPs on CHIR 13 exhibited a perfect LD and gave redundant information. The effect explained by the SNPs was high (36%) but due to the low frequency of the associated allele (2%), may have been overestimated. The association between genotypes and phenotypes for the snp6524-scaffold1231-179272 can be seen on http://www.ncbi.nlm.nih.gov/genome?term=capra%20hircus, were scrutinized for the most significant positions for the two traits.The goat genome map and annotation, available in ASIP gene (position 61.7 Mb on CHIR 13), a strong functional candidate. This gene is well known for its action in coat color control and has already been shown to be associated with coat color phenotypes in various mammals [For the PINK trait, the two SNPs with the highest signal, on CHIR 13, were located near the Agouti Signaling Protein,  mammals ,16, incl mammals . Molecul mammals –20 in whASIP gene, although some probably overlap [A large number of alleles (about 20) have been described in goats for the  overlap . Adalste overlap  proposed overlap ,12. The  overlap ,22 as al overlap .http://www.espcr.org/micemut/). This result emphasizes the need for further detailed analyses of the region to allow fine mapping and identification of the causal gene and mutation.The main region on CHIR 11 associated with PINK NECK has a large confidence interval: 20Mb and 30Mb in width according to the SNP GWAS and haplotype GWAS, respectively. When we concentrated on the 9Mb region around the most significant positions obtained with the two methods (between 57 and 65Mb), it was found to include no less than 44 genes. These genes are listed in Although the causal mutations of the two traits have not yet been identified, breeders can use associated SNPs as predictors and this option would be feasible for PINK NECK. The highest SNP would indeed provide a good predictor for avoiding PINK NECK animals and could be used to choose between young male twins at the beginning of the selection process or to avoid matings with females at risk.For the PINK trait, the negative predictive value indicates that the SNP on CHIR 13 would not be a good predictor and the proportion of PINK animal would remain around its current level (8%) even after selection on this marker. Further investigations need to be done at the molecular level to find the possible causal mutation or at least a better predictor.As mentioned previously, the main loci involved in the PINK NECK and PINK traits, and thus the genetic determinism, are different. However, completely pink animals may carry the pink neck allele or genotype, so some confusion is possible. Indeed, we can assume that the significant SNPs for PINK on the CHIR 11 according to the haplotype-based association (and strongly associated with PINK NECK) are due to the presence of animals identified as completely pink but also carrying pink neck alleles.Besides the confusion due to this partial inclusion, defining the animal’s coat colors is not always straightforward. The color intensity in PINK NECK and PINK animals is highly variable. Also, depending on the light in the barn or if the animals are dirty, animals with a slight coloration might be identified as white. Also the color can often be more intense on the neck than on the rest of the body, so slightly PINK animals may be phenotyped as PINK NECK. Some genetic markers would be useful for breeders to avoid possible confusion. It would be interesting in future to record color intensity for further and complementary analyses.One assumption in SNP GWAS is that the markers are independent and not accounting for the potential linkage disequilibrium (LD) between SNPs. In comparison, the Haplotype GWAS is based on phased haplotype clusters in association and accounts for the potential linkage disequilibrium between markers.Based on both simulated and real barley data, Lorenz et al. (2010) concluded that superiority of one over the other method to detect QTLs depends on the population history and architecture of traits and recommend use of haplotype methods as a complement to SNP GWAS. Strength of the SNP methods include the fact that errors in map order would have no effect on the single SNP analysis but may lead to improper inference of haplotype alleles. Also, Haplotype GWAS tests are based on chi-square distribution of more degree of freedom than for single SNPs, so that for the same p-value the likelihood ratios need to be higher to reach the significance thresholds . The mulOur results showed that a few significant regions were detected by both association methods, but that each method found additional SNPs which were not detected by the other method. Some could be false positives, but in view of the QQplots, the haplotype-based association was not obligatorily more accurate in our case than the SNP based one. Moreover, the robustness of haplotype-based association is highly affected by the accuracy of haplotype reconstruction, which depends on the family structure of the sample.The observed differences in the results are more likely to stem from the variation in linkage disequilibria between significant SNP and their neighbors. The SNPs on chromosome 11 seem to be closely linked with each other, which is why the signal for PINK NECK appeared denser and had a more highly significant position according to the haplotype-based analysis. Conversely, the two significant SNPs for PINK were not as highly significant in the haplotype-based analysis because they were the only two associated with the phenotype and their significance was diluted by neighboring SNPs. In fact, these two SNPs are located just before a large gap of 300,000 bp in the coverage of chromosome 13 by the chip (probably due to a CNV), so that, at least on one side, their neighbors are unusually distant. As different types of signals were more easily detected by one or other method, we recommend using both.ASIP), the evidence of a major region for PINK NECK is new with regards to known color loci. This may be due to the different origins of the two phenotypes: the PINK coloration might be derived from an ancient inclusion of Alpine blood in the Saanen breed whereas PINK NECK seems to be specific to the Saanen breed. The mode of inheritance of these two traits is not straight forward. They are not monogenic dominant as at least one parent of each colored animal would then itself be colored, but this was not the case. They are not monogenic recessive either because this would imply that all the sires carried at least one copy of the mutant allele, there being at least one colored daughter in every family, which is very unlikely as the frequency of the phenotypes was not high. Their determinism is probably due to reduced penetrance and an effect of additional loci, other than those mentioned here, cannot be excluded. The results of this work can be used, short-term, for SNP-based selection to avoid PINK NECK. In view of the heritabilities of the two traits, the results can also be used to calculate the estimated breeding values and efficiently avoid both PINK and PINK NECK. This work emphasizes the need for further detailed analyses of the genomic regions identified to evidence the causal mutations and/or offer better genomic predictors.In summary, this first heritability estimation provided evidence of substantial genetic variation for two undesirable coat color phenotypes in Saanen goats and the large amount of genotyping data was used to localize significant genomic regions associated with PINK or PINK NECK. Whereas the main signal found for PINK is probably due to an already well-known gene  and the EU "3SR" (www.3srbreeding.eu) projects. The design consisted of 936 goats from 88 herds that were sired by nine different artificial insemination (AI) bucks. The nine bucks were selected to be both representative of the genetic diversity of the whole population and to maximize the genetic diversity between families, from the widely used AI bucks that had large numbers of daughters in commercial farms between 2009 and 2010.The GWAS data came from the subset of Saanen goats involved in a daughter design implemented in 2009 and 2010 as part of the national "Phénofinlait" . The information about coat color is qualitative: totally white (in agreement with the breed definition), pink neck  denotes the probability of goat n having the colored phenotype with the vector of fixed effects i, including flock of birth and year of birth; a is the random genetic animal effect; W and Z are the incidence matrices and e is the residual effect.Variance components for the two binary variables (PINK NECK and PINK) were estimated with the following animal logistic model with mixed effects using ASReml 3.0 Software :Logit FR CC 860. Sperm collection was done by Artificial Insemination stations, and we used extra doses from this collection. Blood samples were taken at commercial farms. The animals were not part of any experimental design but were sampled by veterinarians and/or under Veterinarian supervision for routine veterinary care; extra samples were requested when blood sampling took place.www.labogena.fr). Quality control included SNP call rate (>99%), animal call rate (>98%), minimum allele frequency (>2%), Hardy Weinberg equilibrium  and pedigree inconsistency removal. A total of 125 goats were discarded because of pedigree inconsistencies or low animal call rates. After these controls, 810 Saanen goats, including 83 pink neck and 77 pink animals, and their nine fathers  remained. After editing, a total of 49,574 out of 53,347 synthesized SNPs were validated for further analyses. The marker order and positions were based on the caprine Assembly CHIR_1.0 (http://www.ncbi.nlm.nih.gov/assembly/GCF_000317765.1) and can be downloaded from the following link: http://www.goatgenome.org/data/capri4dbsnp-base-CHI-OAR-UMD.csv.All 936 goats and nine bucks were genotyped using the Illumina goatSNP50 chip . DNA extPopulation stratification was assessed by calculating the genetic relationship matrix from all SNPs, and the two principal components, using the GCTA software . The firGWAS were carried out for each trait  with two models.n = 1) denotes the probability of goat n having the colored phenotype; b is the vector of fixed SNP effect ; W is the incidence matrix of b corresponding SNP effect to individuals; a is the random genetic animal effect (polygenic effect) where a is assumed to follow a ~ N and A is the relationship matrix based on pedigree information. Z is the incidence matrix of a accounting for pedigree relationship structure among individuals and e is the random residual effect following e ~ N where I is the identity matrix. Only the four most recent generations of the pedigree were used in this analysis.The first model was a SNP GWAS: the effect of each SNP was tested individually from the following logistic model with mixed effects:10. Because of the test multiplicity, a Bonferroni correction of α = 5% was applied for both genome-wise and chromosome-wise thresholds (Significance threshold = − log10(α/number of SNPs)). SNPs with P<1.04 x 10−6 were considered to be significantly associated at the genome-wise level.These calculations were done with restricted maximum likelihood using ASReml Software . After cThe second model used haplotypes and data were phased using PHASEBOOK . The PHAn = 1) denotes the probability of goat n having the colored phenotype; β is the vector of haplotype effect (hidden cluster effect); W is the incidence matrix of β corresponding haplotype effect to individuals ; a is the random genetic animal effect (polygenic effect) and Z, a and e are the same as in model [The model used for this haplotype-based association was identical to the first model except that the haplotype effect (or hidden cluster effect) was fitted instead of a SNP in the model.in model . The pedRestricted maximum likelihood (REML) was applied to solve the model using ASReml . The hapQuantile-Quantile plots (QQ-plots) were produced for each trait and each model to check the distribution of the test-statistics and detect the degree of fit of the model for this trait. In the case of deviation on the QQ-plot , the tests were regressed from the 90% less deviated SNPs to obtain the theoretical P distribution so that the regression coefficient on those SNPs was 1.The value of the best SNPs found by GWAS analyses as a predictor was evaluated from the positive predictive value , the negative predictive value , the sensitivity  and the specificity .S1 Fig(TIF)Click here for additional data file.


In this study, we designed and demonstrated a workflow for in silico and empirical ddRAD-Seq analysis in tomato, as follows: (i) in silico prediction of optimum restriction enzymes from the reference genome, (ii) verification of the prediction by actual ddRAD-Seq data of four restriction enzyme combinations, (iii) establishment of a computational data processing pipeline for high-confidence single nucleotide polymorphism (SNP) calling, and (iv) validation of SNP accuracy by construction of genetic linkage maps. The quality of SNPs based on de novo assembly reference of the ddRAD-Seq reads was comparable with that of SNPs obtained using the published reference genome of tomato. Comparisons of SNP calls in diverse tomato lines revealed that SNP density in the genome influenced the detectability of SNPs by ddRAD-Seq. In silico prediction prior to actual analysis contributed to optimization of the experimental conditions for ddRAD-Seq, e.g. choices of enzymes and plant materials. Following optimization, this ddRAD-Seq pipeline could help accelerate genetics, genomics, and molecular breeding in both model and non-model plants, including crops.Double-digest restriction site-associated DNA sequencing (ddRAD-Seq) enables high-throughput genome-wide genotyping with next-generation sequencing technology. Consequently, this method has become popular in plant genetics and breeding. Although computational  Microarray-based SNP chip technologies, e.g. GoldenGate and Infinium , and Axiom , have enabled high-throughput SNP genotyping and thereby contributed to statistical genetic approaches, e.g. quantitative trait locus analyses and genome-wide association studies.3 However, SNP microarrays have a disadvantage, namely, the lack of flexibility in experimental design. Progress in next-generation sequencing (NGS) technology has enabled the development of huge numbers of SNPs in both model and non-model plant species, including crops.4 Correspondingly, SNP genotyping by NGS, e.g. genotyping by sequencing (GBS) and restriction site-associated DNA sequencing (RAD-Seq), have recently become popular due to their flexibility and relatively low cost.5DNA markers are essential tools for molecular genetics and genomics. Simple sequence repeats  and single nucleotide polymorphisms (SNPs) are the most powerful and widely used DNA markers. SSRs have the advantages of being both co-dominant and multi-allelic in nature, but they require time-consuming gel or capillary electrophoresis analyses. On the other hand, SNPs, most of which are co-dominant but bi-allelic, can be analysed using time-saving gel-free techniques, e.g. TaqMan assays,6 and subsequently applied to other crop species.7 In the original GBS protocol, genomic DNA is digested with restriction enzymes, and adapters are ligated to the restriction ends.6 Sequencing data of single-end reads are always obtained from sites associated with the restriction ends, which is a great advantage in sequencing of identical loci across multiple samples. The RAD-Seq method,8 which is similar to GBS, has been applied to several plant species.7 In the original RAD-Seq protocol, genomic DNA is fragmented twice by different methods: first by a restriction enzyme, and second by physical shearing. The resultant DNA fragments, with restriction sites on one end and sheared ends on the other, are targeted for single-end sequencing analysis from the restriction ends. On the other hand, paired-end sequence reads can be more accurately mapped onto the reference genome than single-end reads, especially in plants, which often have large and complex polyploid genomes.9 Double-digest restriction site-associated DNA-Seq (ddRAD-Seq), in which a second restriction enzyme is employed for digestion of genome DNA to reduce cost and time to prepare the sequencing libraries, enables paired-end sequencing of identical loci across multiple samples.10 Therefore, from the point of view of high accuracy read mapping even in the complex plant genomes, ddRAD-Seq technology has the advantage over GBS and RAD-Seq. Along with the great advances in the sequencing technology, several data processing pipelines for GBS and RAD-Seq have been reported.12 However, as mentioned, plants have complex genomes due to many types of ploidy, reproduction systems as well as various genome sizes. Therefore, data processing methods with flexibility in manipulation would be required.GBS was initially developed in maize13 This situation makes it possible to simulate ddRAD-Seq in silico, allowing prediction of the numbers, sizes, and genome positions of digested fragments. Based on in silico analysis, the optimal restriction enzymes for ddRAD-Seq analyses are chosen.10 However, few reports have evaluated the in silico predictions by comparative experiments using several combinations of restriction enzymes and multiple samples with different SNP density. Moreover, it remains unclear what fraction of the SNPs in the whole genome can be detected by ddRAD-Seq. In this study, we performed in silico simulation of ddRAD-Seq analysis in tomato (Solanum lycopersicum) and validated the predictions by empirical ddRAD-Seq data using an optimized protocol. We selected tomato for this demonstration because of the richness of available genome information14 and the diversity of available tomato lines.15 In addition, we investigated the numbers of SNPs detected by ddRAD-Seq in six inbred tomato lines with different densities of genome-wide SNPs. To evaluate the quality of the SNPs, we performed linkage analyses of the SNPs identified in an F2 mapping population and constructed genetic linkage maps. Finally, we proposed an analytical workflow for the ddRAD-Seq procedure including a pipeline for data processing.Whole-genome sequencing (WGS) analysis of several plant species has been accelerated by NGS technology; as of June 2015, genome sequence data are available from >100 plants.2.2.1.in silico ddRAD-Seq and establishment of computational data processing pipelines. Published WGS data for Micro-Tom (accession number of DRX020765: Illumina data)16 and Regina (accession numbers of DRX011585 and DRX011586: SOLiD data)15 were used to generate a genome-wide SNP dataset. The WGS reads of the two lines were treated to remove low-quality reads and to trim adapters as described below , and mapped onto the tomato (cultivar Heinz 1706) reference genome sequences, version SL2.50, with Bowtie2 17 and Bowtie ,18 respectively. Subsequent SNP calling was also performed as below .Two tomato lines, Micro-Tom and Regina, were used as controls for empirical and https://www.sgn.cornell.edu) as well as those of Arabidopsis thaliana , Lotus japonicus , and Oryza sativa  were in silico treated with five restriction enzymes, e.g. EcoRI (recognition at site G↓AATTC), HindIII (A↓AGCTT), MspI (C↓CGG), PstI (CTGCA↓G), and SalI (G↓TCGAC): the genome sequence was digested into restriction fragments at the points of the recognition sites of the enzymes, and information on sizes of each fragment was retained.The genome sequence of tomato , and quantitated using a Qubit fluorometer .Six inbred tomato lines  were used for ddRAD-Seq analysis. All lines except for Regina were obtained from the National BioResource Project through the University of Tsukuba, Japan  and the Tomato Genetic Resource Center, University of California, Davis, USA . Regina was commercially available from Sakata Seed Corporation . An F2.3.SalI and PstI, PstI and EcoRI, EcoRI and HindIII, or PstI and MspI ; ligated to adapters (Table 2O and amplified by PCR with indexed primers (Table Supplementary Table S1). The PCR mixture (50 µl) contained 0.4 ng of DNA, 0.2 µM of each indexed primer (one pair per mixture), 1× PCR buffer for KOD –plus– Ver. 2 , 160 µM dNTPs, 1 mM MgSO4, and 1 U DNA polymerase . Thermal cycling conditions were as follows: a 3 min initial denaturation at 95°C; 20 cycles of 30 s of denaturation at 94°C, 30 s of annealing at 55°C, and a 60 s extension at 72°C; and a final 3 min extension at 72°C. Amplicons were pooled and separated on a BluePippin 1.5% agarose cassette , and fragments of 300–900 bp were purified using the QIAGEN Mini Elute Kit (Qiagen). Concentrations of the resultant libraries were measured using the KAPA Library Quantification Kit  on an ABI-7900HT real-time PCR system (Life Technologies). Nucleotide sequences of the libraries were determined on a MiSeq (Illumina) in paired-end, 250 bp mode.A total of 250 ng of genomic DNA for each line was double digested with rs Table  using th2.4.http://hannonlab.cshl.edu/fastx_toolkit; version 0.10.1). The filtered reads, or subsets of the reads randomly selected using seqtk (https://github.com/lh3/seqtk), were mapped onto the reference sequences of either contigs generated by assembly of the filtered reads using Newbler  or the tomato genome sequence (SL2.50) using Bowtie 2 .17 The resultant sequence alignment/map format (SAM) files were converted to binary sequence alignment/map format files and subjected to SNP calling using the mpileup option of SAMtools 19 and the view option of BCFtools (parameters: -vcg). Lengths of genome regions covered with more than one read at least were calculated with genomeCoverage option of BEDtools .20 Furthermore, variant call format (VCF) files were filtered with VCFtools .21 Missing data were imputed using Beagle4.22 The locations of SNPs in genic and intergenic regions were predicted using SnpEff ,23 and those in repetitive sequences and non-repetitive were classified in accordance with the annotation by International Tomato Annotation Group . Similarity searches of marker-associated sequences against the SL2.50 tomato genome sequence were carried out using BlastN with default parameters.24In ddRAD-Seq data analysis as well as WGS ( Processing data for whole-genome sequence of tomato), low-quality sequences were removed and adapters were trimmed using PRINSEQ  and fastx_clipper (-a AGATCGGAAGAGC -l 100 -M 10 -n) in FASTX-Toolkit : SNP quality, >10, and depth of coverage, ≥4. The SNP loci were unevenly distributed over the genome, with numbers ranging from 10,170 SNPs on chromosome 6  to 277,708 SNPs on chromosome 4  (Supplementary Table S2). Only 13.5 and 39.1% of the 1,187,941 SNPs were found on genic regions and non-repeat sequences , respectively, both of which are biologically important sequences in the genome (see Distribution of SNPs in genic/intergenic regions and repeat/non-repeat sequences for details).WGS data for Micro-Tom3.2.in silico restriction digestion. For this analysis, we selected five enzymes  with different frequencies of recognition sites in the tomato genome, low in SalI and PstI, middle in EcoRI and HindIII, and high in MspI  were used for in silico digestion of the genome sequence. The numbers of fragments with 300–900 bases, our target for experimental ddRAD-Seq experiment, covered the entire tomato genome evenly , but varied from 5,082 for SalI/PstI to 65,104 for EcoRI/HindIII . The distributions of SNPs on each chromosome corresponded to those obtained from WGS data, although the total numbers of the SNPs decreased drastically, to only 3,553 (0.3%) for SalI/PstI and 47,768 (4.0%) for EcoRI/HindIII . While proportions of SNPs on genic regions to the detected SNPs were ranging from 16.3% (EcoRI/HindIII) to 29.0% (PstI/EcoRI), those of SNPs in non-repeat sequences to whole genome were from 26.7%  to 45.0% (PstI/EcoRI) . The in silico analysis was applied to other plant species, e.g. A. thaliana, L. japonicus, and O. sativa. The result indicated that the tendency was similar to those of A. thaliana and L. japonicus except for O. sativa in which number of PstI/MspI fragments were predominant .To identify the optimal restriction enzymes for experimental ddRAD-Seq analysis, we performed spI Fig. A. Four c3.3.PstI/MspI combination (PM libraries). Briefly, sequence reads were processed by removing low-quality reads and trimming adapters, and then mapped onto the reference sequence to detect SNP candidates . When 1.9 and 2.2 M paired-reads for Micro-Tom and Regina, respectively, were analysed using this pipeline, 1.2 and 1.4 M high-quality reads were obtained, and 83,011 SNP candidates, including 20,689 homozygous and 62,322 loci with genotypes called as ‘heterozygous’, were detected prior to filtering. Because the two lines are inbred, the ‘heterozygous’ SNPs were excluded because they were likely to reflect sequencing or alignment errors. Of the 20,689 homozygous SNPs, 19,969 SNPs with quality values >10 were selected as high-confidence SNP loci. Out of them, 15,746 SNPs (78.9%) were identical to those from WGS data, whereas the remaining 4,223 SNPs (22.1%) were not found due to sites of insufficient read coverage in the WGS data.A data processing pipeline for SNP discovery was established using actual MiSeq reads of Micro-Tom and Regina ddRAD-Seq libraries generated using the 3.4.in silico predictions that the numbers of SNPs detected in ddRAD-Seq would be depending on choice of restriction enzymes, additional MiSeq reads were obtained from Micro-Tom and Regina ddRAD-Seq libraries generated using three more restriction enzyme combinations, SalI/PstI (SP), PstI/EcoRI (PE), and EcoRI/HindIII (EH) as well as PM as above. After removal of low-quality sequences and trimming of adapters, a subset of 100k to 900k high-quality paired-end reads were generated for all four libraries. Each subset was mapped onto the reference genome sequence, SL2.50, and high-quality SNP candidates were selected by filtering using the criteria described above. As expected, the number of SNPs in each dataset increased as the number of reads increased . Furthermore, the PM and SP libraries were enriched for genic SNPs. In contrast, the EH library had SNP frequencies comparable with those obtained from the prediction. The proportions of the SNPs in the repeat/unique sequences were also markedly different among the libraries : SNPs from the SP, PE, and PM libraries were enriched in the unique sequences in comparison with the prediction, while the proportion of the EH library was comparable with the prediction. We concluded that the PM and PE libraries had advantages to detect SNPs in gene regions and non-repetitive sequences in the tomato genome.Since SNPs in genes and non-repetitive sequences are biologically meaningful in comparison with those in intergenic and repetitive regions. Not only proportions of genic and intergenic SNPs detected in the empirical ddRAD-Seq but also those of unique and repeat sequences in the tomato genome were investigated. The result indicated remarkable differences of the proportions among the restriction enzyme combinations Fig. A. In the3.6.28 To assess the numbers of SNPs in genetically diverse samples, the six lines, i.e. Ailsa Craig, M82, Moneymaker, and San Marzano as well as Micro-Tom and Regina, were further analysed with ddRAD-Seq. The PstI/MspI combination was employed in accordance with the results of the validation test, expecting to gain as many SNPs in genes and unique sequences of the tomato genome as possible. The high-quality sequence data from the six PM libraries were divided into subsets of 100k–900k paired-end reads and mapped onto the reference genome. As expected, the numbers of SNPs with respect to Heinz 1706 (SL2.50) detected by experimental ddRAD-Seq in Regina and Micro-Tom increased linearly up to ∼25,000, whereas those in the other four cultivars reached 5,000 or less .14 The SNP densities relative to SL2.40, a previous version of the tomato genome sequence with the same base compositions to the SL2.50,27 were estimated to be one SNP per 651 bp in Regina,15 803 bp in Micro-Tom,16 1,011 bp in M82,15 3,105 bp in Moneymaker,29 4,347 bp in San Marzano,30 and 8,387 bp in Ailsa Craig.15 The percentages of SNPs detected by ddRAD-Seq analysis per genome-wide SNPs by the WGS were almost even in these six lines: 2.0% genome-wide SNPs on average, ranging from 0.5% in M82 to 3.6% in San Marzano, and proportion of the SNPs in gene regions and repeat sequences from the six lines were similar to those from a combination of Micro-Tom and Regina , RMF2, derived from a cross between Micro-Tom and Regina. Ninety-six libraries of RMF2 with index tags distinguishing each line (Table Supplementary Table S1) were pooled and sequenced on an Illumina MiSeq, yielding an average of 268k paired-end reads  per line. After removal of low-quality sequences and trimming of adapters, 226k high-quality reads on average in each line were mapped onto SL2.50 along with the reads from the parental lines. Mapping rates of Micro-Tom and Regina were 94.3 and 92.9%, respectively, while that in the F2 population was 91.0% on average. Of 155,992 SNP candidates between the parental lines, 60,512 loci with quality of ≤10 and depth of <4 were eliminated; furthermore, 89,241 ‘heterozygous’ SNPs, probably resulting from sequencing and/or alignment errors as noted above, were also removed. Ultimately, 6,239 loci were selected. By allowing 20% missing data for each SNP locus across the 96 F2 lines, 1,845 positions (depth of coverage of 13.5 on average) of the 6,239 loci were selected as high-confidence segregating SNPs in the F2 population. Prior to linkage analysis, the missing genotypes were imputed in accordance with genotype data from the parental lines. Subsequently, 528 genetic loci similar to others were eliminated. Of the remaining 1,317 non-redundant SNP loci, 1,297 (98.5%) were classified into 13 groups, each of which corresponded to one tomato chromosome . Linkage analysis generated a genetic map consisting of 13 linkage groups, with 1,257 loci (95.4%) covering a total of 1,693.2 cM . Therefore, this point as well as the number of SNPs should be considered to select optimum restriction enzymes. It seems likely that the strong enrichment of euchromatic genes in the libraries is correlated to the methylation sensitivity of restriction enzymes.5 Whole-genome bisulphite sequencing analysis would be helpful to verify this hypothesis.Gene-associated SNPs located on non-repetitive sequences would be biologically meaningful, being beneficial for functional genomics, molecular genetics, and marker-assisted selection in breeding. Interestingly, the rate of gene SNPs detected by the empirical ddRAD-Seq was higher than the predicted rate when Genome complexity, i.e. ploidy and zygosity, is another important factor that influences the choice of restriction enzymes. For inbred lines and haploids without any heterozygous loci, SNP loci can in principle be correctly genotyped with coverage of at least one high-quality read. In such cases, to obtain as many SNPs as possible, a combination of enzymes should be selected that yields SNP numbers that increase linearly with the number of sequence reads  to 4.6% (EH) of SNPs in the genome were theoretically detectable using a single sequencing library, this fraction reached a maximum of 7.6% when four libraries  were analysed simultaneously. For plants with ultra-low SNP density in the genomes from which few SNPs are expected, alternatively, the SNP chip technologies and/or target capture or target amplicon sequencing technology,31 which tags SNPs regardless of their distances from restriction sites, might be useful; however, this approach would be more costly than ddRAD-Seq. Therefore, prediction of the expected number of SNPs based on SNP density throughout the genome would be helpful to maximize the efficiency of ddRAD-Seq analysis.The numbers of SNPs detected by ddRAD-Seq varied depending on SNP density in the genome Fig. B. In oth10 The ddRAD-Seq analytical workflow and the pipeline for the data processing developed in this study .32All sequence data obtained in this study are available from the DDBJ Sequence Read Archive under accession number DRA003569 and Kazusa Tomato Genomics DataBase (KaTomicsDB: Supplementary Data are available at www.dnaresearch.oxfordjournals.org.Funding to pay the Open Access publication charges for this article was provided by the Kazusa DNA Research Institute.


Large epidemiologic studies have the potential to make valuable contributions to the assessment of gene-environment interactions because they prospectively collected detailed exposure data. Some of these studies, however, have only serum or plasma samples as a low quantity source of DNA.We examined whether DNA isolated from serum can be used to reliably and accurately genotype single nucleotide polymorphisms (SNPs) using Sequenom multiplex SNP genotyping technology. We genotyped 81 SNPs using samples from 158 participants in the NYU Women’s Health Study. Each participant had DNA from serum and at least one paired DNA sample isolated from a high quality source of DNA, i.e. clots and/or cell precipitates, for comparison.We observed that 60 of the 81 SNPs (74%) had high call frequencies (≥95%) using DNA from serum, only slightly lower than the 85% of SNPs with high call frequencies in DNA from clots or cell precipitates. Of the 57 SNPs with high call frequencies for serum, clot, and cell precipitate DNA, 54 (95%) had highly concordant (>98%) genotype calls across all three sample types. High purity was not a critical factor to successful genotyping.Our results suggest that this multiplex SNP genotyping method can be used reliably on DNA from serum in large-scale epidemiologic studies. Single nucleotide polymorphisms (SNPs) have been found to be associated with risk of many complex diseases. For instance, more than 70 susceptibility SNPs have been identified for breast cancer –3. AssesDNA isolated from serum has been used by our group and others to achieve high call frequencies with single SNP Taqman assays (without performing whole genome amplification prior to PCR amplification) in epidemiological studies –7. HowevBetween 1985 and 1991, the NYU Women's Health Study (NYUWHS) enrolled 14,274 healthy women aged 35–65 years at a breast cancer screening center. Enrollment in the cohort required donation of 30mL of blood. Blood collection was performed without anti-coagulant; tubes were kept covered at room temperature (25°C) for 15 minutes and then at 4°C for 60 minutes. Tubes were then centrifuged and the serum supernatant was collected and divided into several 1 mL aliquots. Serum samples were stored in polypropylene screw capped tubes at -80°C within 2 hours of collection. For blood samples collected in or after 1988, the cellular precipitates were also partitioned off and stored in polypropylene tubes at -80°C. The remaining blood clots from each sample were stored starting in 1989. Clots were stored immediately at -40°C after serum and cell precipitate removal in two sealable plastic-lined aluminum bags. The NYU Medical Center Institutional Review Board approved this study. Participants completed written informed consent.i.e., serum, clots, and cell precipitates.This study included samples from 158 NYUWHS participants: 48 women with both serum and clots, 48 with both serum and cell precipitates, and 62 women with all three types of samples, DNA was isolated from 200 μL of serum, clots (a section of clot was digested in streptokinase for at least 8 hours prior to DNA isolation), and cell precipitates manually using QIAamp Blood Mini kits from  according to manufacturer protocols. A nanodrop spectrometer was used to estimate DNA concentration and purity using the A260/A280 ratio. Three repeat readings were taken for each DNA sample and the mean of the three measurements was used.We genotyped 81 SNPs with minor allele frequencies ranging from 2% to 40%. SNPs genotyped in this study were selected for a separate study. Among the 81 SNPs, 57 (70%) were tag SNPs in three vitamin D-related gene regions on three chromosomes, and 24 were SNPs in 19 gene regions on 10 other chromosomes. Approximately 10% of the SNPs were in coding regions, 10% in UTRs, 4% in non-coding regions or upstream of gene regions, and 75% were intronic. Genotyping was performed using multiplex Sequenom technology, using 2 μL per reaction of DNA isolated from serum, clots, and cell precipitates and following standard manufacturer protocols (without whole genome amplification). The genotyping design software generated three multiplex reactions: 29 SNPs (plex1), 28 SNPs (plex 2), and 24 SNPs (plex 3). All genotyping was performed with the MassARRAY iPLEX platform . Laboratory personnel were blinded to the identity and source of the DNA sample.Call frequencies, expressed as percentages, were calculated for each SNP. Genotype concordance was assessed by examining groups of paired samples  separately. SNPs that could not be called were excluded from concordance calculations. A call frequency across samples ≥95%, a commonly used standard in epidemiologic studies ,9, and cAs shown in Our study demonstrates that Sequenom MassARRAY technology can be used to genotype SNPs with a multiplex approach using a small volume of serum DNA. For the SNPs we examined, 74% had call frequencies of 95% or higher  in DNOnly one other study examined the utility of multiplex Sequenom for genotyping DNA isolated from serum. It concluded that the genotyping using Sequenom was not reliable, contrary to genotyping using Taqman methods . In thatA DNA sample with an A260/A280 ratio of 1.8–2.0 is considered to have low contamination (e.g. by proteins and carbohydrates). Only 29% of clots and 16% of serum DNA had values within this range. It is possible that different DNA isolation protocols/kits, and, for serum, a volume larger than 200 μL, would have led to better results. We were not able to compare different approaches for DNA isolation because the serum samples collected in the NYUWHS (and in other long-term cohorts) are very precious, and the minimal necessary volume is allocated for each assay. It should be noted though that we achieved successful genotyping calls and high concordance for over 70% of SNPs, despite the low percentage of samples within the 1.8–2.0 range. Further, excluding DNA samples with A260/A280 ratios below 1.8 or above 2.0 did not result in appreciable differences in the percentage of SNPs with acceptable call frequencies (data not shown). Since exclusion of samples (and therefore of participants) results in loss of power and possibly bias in epidemiologic studies, it does not appear that it would be a good strategy to exclude from the statistical analysis of these studies samples falling outside the 1.8–2.0 range for the A260/A280 ratio. High purity of DNA does not appear to be a critical factor to successful genotyping using Sequenom iPLEX.Genome wide association studies (GWAS) and other large targeted genotyping scans, apply relatively strict criteria for exclusion of samples with low genotype call frequencies across all SNPs ,11. ExclWith the exception of one, all the SNPs with a high call frequency (≥95%) in serum DNA samples were also highly concordant (≥98%) with DNA from cell precipitates . This suggests that applying a stringent call-frequency criterion leads to the exclusion of SNPs with unreliable genotype calls. This result is consistent with the high correlation between miscalls and no calls observed by others  and suggIsolation of DNA from 200 μL of serum  provides a sufficient amount of DNA to genotype up to 1,000 SNPs using Sequenom . Assuming our results are broadly applicable to other SNP loci, a similarly conducted genotyping experiment from serum DNA would yield approximately 75% of SNPs having >95% call frequencies .Considering that for many diseases, GWAS and other large targeted genotyping studies have identified susceptibility SNPs which are expected to number at most in the hundreds, and that many SNPs can be tagged by other SNPs in high LD should the assay not work for certain SNPs, our study shows that the iPLEX Sequenom platform appears to be a useful tool for SNP genotyping in epidemiologic studies that have serum samples as the only source of DNA for their participants.S1 Table(DOCX)Click here for additional data file.


Eucalypts known to suffer considerable inbreeding depression from related matings. A single nucleotide polymorphism (SNP) marker panel consisting of 106 markers was selected for pedigree reconstruction from the recently developed high-density Eucalyptus Infinium SNP chip (EuCHIP60K). The performance of this SNP panel for pedigree reconstruction in open-pollinated progenies of two Eucalyptus nitens seed orchards was compared with that of two microsatellite panels with 13 and 16 markers respectively. The SNP marker panel out-performed one of the microsatellite panels in the resolution power to reconstruct pedigrees and out-performed both panels with respect to data quality. Parentage of all but one offspring in each clonal seed orchard was correctly matched to the expected seed parent using the SNP marker panel, whereas parentage assignment to less than a third of the expected seed parents were supported using the 13-microsatellite panel. The 16-microsatellite panel supported all but one of the recorded seed parents, one better than the SNP panel, although there was still a considerable level of missing and inconsistent data. SNP marker data was considerably superior to microsatellite data in accuracy, reproducibility and robustness. Although microsatellites and SNPs data provide equivalent resolution for pedigree reconstruction, microsatellite analysis requires more time and experience to deal with the uncertainties of allele calling and faces challenges for data transferability across labs and over time. While microsatellite analysis will continue to be useful for some breeding tasks due to the high information content, existing infrastructure and low operating costs, the multi-species SNP resource available with the EuCHIP60k, opens a whole new array of opportunities for high-throughput, genome-wide or targeted genotyping in species of Eucalyptus.Pedigree reconstruction using molecular markers enables efficient management of inbreeding in open-pollinated breeding strategies, replacing expensive and time-consuming controlled pollination. This is particularly useful in preferentially outcrossed, insect pollinated  Eucalyptus nitens (Dean et Maiden) Maiden has shown promise as a vigorous species for planted forests, well-suited to many New Zealand sites. Early introductions of this species to New Zealand showed spectacular early growth, good form characteristics and cold hardiness. Performance of this species in Australia indicated promise for both pulp and sawn-timber production. Provenance testing in NZ began in 1979 [Paropsis charybdis Stål (Coleoptera: Chrysomelidae: Paropsini). Until the 1990’s there was little confidence in planting E. nitens because of periodic severe defoliation by P. charybdis (hereafter referred to as Paropsis). In the 1990s a biological control agent was successfully introduced, and interest in the species was re-kindled. A second round of progeny testing was completed and the best material grafted into seed orchards. The biological control was itself overcome by a hyperparasite, and currently the growing of E. nitens is only carried out in cooler areas where Paropsis does not thrive [t thrive .Eucalyptus nitens is insect-pollinated across an estimated mean distance of 42 m [Eucalypts open-pollinated progenies are known to include up to 20–30% selfed individuals which can suffer from considerable inbreeding depression, both early and late-acting [E. nitens . It was decided to establish the next generation of progeny tests using seed collected from orchards and archives, rather than pursue an expensive and difficult controlled crossing programme, and to split the population into two smaller trials, separated in time to spread costs . A key approach .Microsatellite markers have been the major tool for a number of genetic analyses, including verification of clonal identity, pedigree reconstruction, monitoring genetic diversity, inbreeding, population structure, and detection and quantification of linkage disequilibrium . This waRecent studies have compared the performance of microsatellites and SNPs. In bread wheat, analyses of population structure and genetic diversity performed using both microsatellites and SNPs gave different results and analysis of linkage disequilibrium required greater numbers of SNPs to ensure adequate chromosomal coverage . AnalysiEucalyptus genomics has now entered the post-genome era [Eucalypts are extremely high; as high as one SNP every 16 base pairs (bp) in one study [Eucalypts [Eucalypts; from early application of microsatellites for parentage reconstruction in E. grandis [nome era . Estimatne study , and onene study  dependinucalypts ,24. The  grandis ,26,27, D grandis , and fin grandis .Eucalyptus SNP chip platform (EuCHIP60K) that allows high-throughput, genome-wide SNP genotyping in 14 different eucalypt species using Illumina’s Infinium technology [Eucalyptus breeding programs could include precise parentage assignment and reconstruction of pedigrees. Correia et al. [Eucalyptus grandis, matching the performance of commonly used 17-microsatellite marker sets, while Thavamanikumar et al. [Initially, small numbers of SNP markers were examined in candidate genes, such as in Külheim et al.  study inchnology . Applicaa et al.  showed tr et al.  suggester et al. ,33.Eucalyptus nitens. While a few studies have looked at this topic in wild animals [Eucalyptus SNP chip EuCHIP60K [E. nitens breeding program.In this study we examined the efficacy of the two commonly used DNA markers, SNPs and microsatellites, for parentage reconstruction in a highly heterozygous forest tree species,  animals ,35, no s animals  and a se animals  with a suCHIP60K . We utilEucalyptus nitens samples were collected from privately owned seed orchards or progeny trials planted on private land. The Tinkers and Fortification Rd samples were owned by Southwood Export Ltd. and owner Graham Manley was involved in sample collection. The Alexandra samples were collected from Conroys Rd. Nursery, with the permission and support of owner Mike Olsen. The progeny collected from the Scion nursery were our own samples.All Open-pollinated seeds were collected in 2004 from two clonal seed orchards: Tinkers (46°16.1’ S 169°1.6’ E), and Alexandra (45°15.6’ S 169°24.3’ E). Both orchards were established with grafted ramets from the previous generation of trials.et al. [E. nitens is insect-pollinated across an estimated mean distance of 42 m [Open-pollinated seedlings were raised at the Scion nursery in 2005 (38°9.5’ S 176°16.1’ E) and a trial was established at Fortification in November 2005 (46°30.5 S 168°59.9 E). In February 2011 this trial was assessed for diameter, straightness, and stem malformation and parental breeding values were estimated for all traits . Selections for the next generation were made based on selection of the best tree in each family. Leaves were collected for downstream DNA analysis. As discussed in Gea, E. nitens offspring, gDNA was extracted using a high-throughput method developed from the commercially available NucleoSpin Plant II kit  [From the Alexandra orchard, 9 open-pollinated (OP) offspring and 18 putative parents were sampled. From the Tinkers orchard, 17 OP offspring and 29 putative parents were sampled. The seed parents of these offspring were recorded but without 100% confirmation. The putative parents included pollen parents and the recorded seed parents. Genomic DNA (gDNA) from the parental trees was extracted from leaf tissue using a CTAB (cetyltrimethyltetraammonium bromide) buffer as described by Gea et al. [en, GER) .Eucalyptus nitens microsatellite markers were developed into a 13-marker multiplex panel and genotyped as described in Gea et al. [xl Genetic Analyzer using the following Life Technologies consumables: POP-7 Polymer, GeneScan 600 LIZ size standard. Alleles were detected automatically using the GeneMapper v4.1 software package (Life Technologies). Only microsatellite genotypes obtained post-2007 have been used for the pedigree reconstruction analysis; however, we have compared the pre- and post-2007 genotypes of the 13 microsatellite markers to highlight how changes in technology have affected allele calling over time.The Scion Eucalyptus EMBRA microsatellites developed by Brondani et al. [xl Genetic Analyzer using POP-6 Polymer, using a custom-made ROX-labeled size standard [A set of 16 i et al.  was speci et al. . Allelesstandard  and dataEucalyptus SNP chip [Genomic DNA samples (500ng) from 47 putative parents, including two pairs of replicate samples, and 26 progeny from two seed orchards were genotyped by GeneSeek, Inc.  using the Illumina Infinium EuCHIP60K SNP chip . Call raSNP chip  GenomeStFollowing genotyping, 9 and 10 markers from the Scion microsatellite marker set with call rate greater than 70% were used for parentage reconstruction analysis in the Alexandra and Tinkers seed orchards respectively. For the EMBRA microsatellite marker set, 14 markers with call rates greater than 70% were used for parentage reconstruction analysis in both seed orchards.Eucalyptus SNP chip genotypic dataset for parentage reconstruction. The criteria for selecting SNPs were as follows: the observed minimum allele frequency (MAF) between 0.45 and 0.55, GC score average > 0.85, and sample call rate >0.95. In addition, selected SNPs had to adhere to Hardy-Weinberg equilibrium (HWE) as assessed using a Chi-square goodness-of-fit test, which tested the significance of the differences between the observed and expected frequencies [p-value of this test was larger than 0.10. Finally, selected SNP markers had to show no evidence of linkage assessed by a squared correlation of allele frequencies r2< 0.11 used to measure linkage disequilibrium [A set of 106 SNP markers was selected from the EuCHIP60K quencies . SNP marilibrium .Hexp), polymorphism information content (PIC) and exclusion probability (EP) were calculated for SNP and microsatellite markers. Expected heterozygosity was calculated as pij is the observed frequency of allele i on locus j, and PIC was estimated as in Botstein et al. [pi and pj are observed allelic frequencies of locus i and j, n is the number of loci.We used exclusion probability analysis to determine the most likely two parents of each progeny individual using both microsatellite markers panels and the select set of SNP markers. Expected heterozygosity  was calculated as in [Exclusion probability is the probability of excluding a random individual from the population as a potential parent of an individual based on the genotypes of two parents and one offspring. The probability of excluding a parent given two parents and one offspring (ed as in :EPA = 1The probability of excluding both parents given a trio of two parents and one offspring (S Taylor :EPB = 1EPC) was calculated as in Jamieson and C S Taylor [The probability of excluding a parent and offspring relationship when one parental genotype is unavailable (S Taylor :EPC = 1P) over k unlinked markers in any of above formulae gives:Combining exclusion probability , the post-2007 genotypic dataset had a fail rate of 25% . Across all samples with a genotype generated in both the pre-2007 and post-2007 microsatellite datasets, the mistyping rate was 29% and included missing data points, shifts in allele size and loss of second allele in a number of previously heterozygous loci. An example from a single genotype showing changes in allele calls over time is given in For the 16 microsatellite markers genotyped at EMBRAPA, the fail rate across all genotypes was 10% . Replicate samples genotyped at EMBRAPA had a mistyping rate of 3% or 1/32 alleles genotyped . A singlExpected heterozygosity, polymorphic information content and combined exclusion probabilities of SNPs and microsatellites in Alexandra and Tinkers are listed in In the Alexandra orchard, the recorded seed parent was returned as the most likely parent for 8 out of the 9 progeny individuals tested with both the 106 SNP marker dataset  and the In the Tinkers orchard, the recorded seed parent was returned as the most likely parent for 16 out of the 17 progeny tested using the 106 SNP marker dataset . The SciOverall the SNP marker set and the EMBRA microsatellites performed the best, with 24 of the progeny having the recorded seed parent supported by both of these marker sets. In 20/24 of these progeny, the same pollen parent was predicted as well .As determined by the SNP markers, the total number of parental contributions from the Alexandra seed orchard, with a marker-supported recorded seed parent is shown in We generated three different sets of genotypic data for the same set of parents and progenies using the EuCHIP60K , the micE. nitens was not one of the main sequenced species wherefrom SNPs were derived for chip development. Nevertheless the high SNP transferability and polymorphism observed across eucalypts of subgenus Symphyomyrtus showed that over 18,000 SNPs were polymorphic in a small sample of 12 trees [Eucalyptus species by simply genotyping with the EuCHIP60K. Equivalent exclusionary power and marker robustness are expected, providing future-proof security for genotypic datasets that are collected over time. Furthermore, the absence of mistyped data in the SNPs data obtained from DNA extracted with different extraction methods, also indicates that the EuCHIP60K is a very robust platform with respect to variation in input DNA quality and generates highly reproducible results.A robust SNP marker genotyping platform, such as the EuCHIP60K, on the other hand provided ample opportunity for choosing extremely robust, informative and independent SNPs. Interestingly, 12 trees . TherefoWe successfully showed marker support for the recorded seed parent in 8 out of 9 offspring from the Alexandra clonal orchard, and 16 out of 17 offspring from the Tinkers clonal orchard, using a subset of 106 SNP genotypes generated using the EuCHIP60K. In the case of the single unassigned offspring in each of Alexandra and Tinkers orchards, it is highly likely that this arises from a labelling error as there are many places along the chain where these samples may have been mislabelled. The subsequent correct assignment of tree 897.153 as the seed parent of Tinkers progeny 913 indicates this mislabelling occurred in the laboratory, not the nursery or seed orchard. The “chain of identity” for a given sample includes: the parental clone that was placed in the orchard, the progeny that was grown, the samples collected from both putative parent and progeny, and the DNA sample that was extracted and stored. Whilst it may not be practical or possible to identify where a sample was mislabelled, it appears to be happening at a reasonably low level (7%). Mislabelling in other seed orchards ranges from none detected , low 5050, to 15With the successful identification of male parentage for selected OP offspring, and a desire to limit inbreeding in the next breeding cycle, results show that some parents occur as males up to 4 times , decreasEucalyptus is important, as inbreeding levels of up to 36% have been recorded in open-pollinated populations [Eucalyptus including pedigree analysis, linkage and QTL mapping, clone identification, association studies and genomic selection allowing a considerable improvement beyond the current status of genomic research in species of the genus [The control of inbreeding in ulations . Ignorinulations  and a loulations . While tulations . SNP subhe genus .Eucalyptus. With the progressive adoption of genomic data by breeding programs and the continued competition among SNP genotyping platforms, it is expected that SNP genotyping will become increasingly cost-competitive and eventually substitute microsatellite genotyping for most breeding applications.In conclusion, this study has reported a case study where the power and robustness of microsatellite and SNP genotype data were compared in an operational breeding setting. Besides comparing the performance of the two marker technologies, the data generated were used to inform the management of inbreeding in forward selections of open-pollinated progeny. The 106 SNP markers genotypes used for pedigree reconstruction out-performed the microsatellite marker genotypes for accuracy, reproducibility and robustness. Although specific sets of microsatellites can provide equivalent resolution for pedigree reconstruction, they require considerably more experience and time-consuming analyses to deal with the uncertainties of allele calling, while presenting challenges for data transferability across labs and over time. Still, microsatellite analysis will continue to be useful for breeding applications such as identity analysis and parentage verification due to the high information content, existing infrastructure and low operating costs. The multi-species SNP resource now available with the EuCHIP60k, however, clearly opens a whole new array of opportunities for high-throughput genome-wide or targeted genotyping in species of S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.