Brassica napus  by resequencing many accessions has become feasible, due to the availability of reference genomes of Brassica rapa (2n = AA) and Brassica oleracea (2n = CC), which are the progenitor species of B. napus (2n = AACC). Although many SNPs in B. napus have been released, the objective in the present study was to produce a larger, more informative set of SNPs for large-scale and efficient genotypic screening. Hence, short-read genome sequencing was conducted on ten elite B. napus accessions for SNP discovery. A subset of these SNPs was randomly selected for sequence validation and for genotyping efficiency testing using the Illumina GoldenGate assay.Single nucleotide polymorphisms (SNPs) are the most common type of genetic variation. Identification of large numbers of SNPs is helpful for genetic diversity analysis, map-based cloning, genome-wide association analyses and marker-assisted breeding. Recently, identifying genome-wide SNPs in allopolyploid B. napus genome. A total of 36,458 putative amino acid variants were located in 13,552 protein-coding genes, which were predicted to have enriched binding and catalytic activity as a result. Using the GoldenGate genotyping platform, 94 of 96 SNPs sampled could effectively distinguish genotypes of 130 lines from two mapping populations, with an average call rate of 92%.A total of 892,536 bi-allelic SNPs were discovered throughout the B. napus, nearly 900,000 simple SNPs were identified by whole genome resequencing. These SNPs were predicted to be effective in high-throughput genotyping assays . Hence, the development of a much larger genotyping array of informative SNPs is feasible. SNPs identified in this study to cause non-synonymous amino acid substitutions can also be utilized to directly identify causal genes in association studies.Despite the polyploid nature of  Brassica napus , Triticum aestivum (wheat), Solanum tuberosum (potato), Gossypium hirsutum (cotton), Avena sativa (oat) and Saccharum officinarum (sugarcane). Brassica napus is an allopolyploid species originating from interspecific hybridization between diploid progenitors Brassica oleracea and Brassica rapa[B. napus[B. napus[It is estimated that approximately 70% of angiosperms have experienced one or more chromosome doubling events during their evolutionary history . Many crsica rapa, which asica rapa, 4. Thes[B. napus. Hence, [B. napus.Oryza sativa (rice) [Helianthus annuus (sunflower) [Zea mays L. (maize) [Triticum aestivum (wheat) [Manihot esculenta (cassava) [Arabidopsis thaliana  [SNPs (single-nucleotide polymorphisms) are single-nucleotide substitutions of one base for another in DNA sequences. SNPs are quite abundant throughout the entire genome of most organisms and every SNP in low copy DNA is a potentially useful marker . SNP mara (rice) , 13, Helnflower) , Zea may (maize) , Triticu (wheat) , Manihotcassava)  and Arabe cress) . Mass SNe cress) , 19–21, B. napus[B. oleracea[B. napus cultivars. They obtained 23,330-41,593 (two accessions) putative SNPs through alignment to a publicly available set of approximately 94,000 Brassica species unigenes [B. napus linkage maps from 21,323 and 1,714 SNP markers, but discovered that the first map comprised 16,800 (78.8%) hemi-SNP types and only 4,124 (19.8%) simple SNP types, and that the second map comprised 1,266 hemi-SNPs and only 409 simple SNPs [B. napus germplasm types to identify genome-wide restriction-site associated DNA (RAD) fragments, and obtained over 20,000 SNPs [B. napus is still limited, and development of a large set of simple SNP markers is highly desirable.In recent years, many SNPs have been discovered in B. napus, 23 and  oleracea. Howeverunigenes  used traple SNPs . Later, B. rapa and B. oleracea) of B. napus have been sequenced. The B. rapa A genome was released in 2011 [B. oleracea C genome has been sequenced by a collaboration between the OCRI  and other research communities and hence could also be used for BLAST analysis using the BRAD database [http://brassicadb.org/brad/), although the C genome sequences have not yet been released. Based on these available reference sequences, large-scale identification of simple SNPs in B. napus could be implemented. The objective of the present study was to develop a set of genome-wide and evenly spaced SNPs through genome re-sequencing of ten B. napus varieties, and to validate the use of these SNPs on high-throughput genotyping platforms.At present, both the A and C progenitor genomes  paired-end reads of 75-bp or 100-bp read length  were retained. The sequencing depth for each variety averaged 10.7 ×, ranging from 5.3 × to 37.5 × depth . This step served to minimize the data set to low-copy DNA. SNPs were then extracted from SOAP2 alignments after a filtering scheme that (i) excluded 521 million reads with redundant hits to the reference genomes, retaining 585 million reads uniquely matched to the reference sequences; (ii) excluded 78 million SNPs supported by less than four reads in each line ; (iii) excluded 6,331,887 SNPs that were heterozygous in at least in one individual and (iv) excluded 7,224,690 SNPs with a minor allele frequency more than 0.2. After applying the filters described above, the total number of remaining SNPs was 892,803, including 892,536 bi-allelic SNPs and 267 tri-allelic SNPs. The SNP flanking sequences and the mutated sites have been deposited in NCBI dbSNP databases (ss647660101-ss657954846) [Samples for resequencing were chosen from e B. rapa and B. o7954846) .Table 1rd nucleotide upstream, or a ‘C’ or ‘A’ base in the 3rd nucleotide downstream: these SNPs can be efficiently genotyped using the allele-specific PCR method [Brassica rapa and Brassica oleracea. On average, 119 SNPs were found per 100 kb in the A genome, and 89 SNPs were found per 100 kb in the C genome. SNP frequency in the A genome was about 1.3-fold SNP frequency in the C genome  which may further affect phenotype. A total of 36,458 non-synonymous SNPs were identified and were located in 13,552 predicted genes. A total of 479 non-synonymous SNPs transformed stop codons to amino acid codons, whereas 505 non-synonymous SNPs transformed amino acid codons to stop codons. GO enrichment analysis predicted that the genes containing non-synonymous SNPs were involved in binding and catalytic activity more often than predicted by chance . The 96-plex OPA included 42 A-genome SNPs and 54 C-genome SNPs that were evenly distributed genome-wide.2) comprising a total of 130 B. napus lines were genotyped using the GoldenGate assay. Clustering of Cy3 and Cy5 normalized intensities in a polar coordinate plot was used to infer the SNP genotypes. Genotype calls for all SNPs could be grouped into one or more groups depending on whether a SNP site was monomorphic or polymorphic. There were 49 SNPs which were polymorphic in at least one population. In the DH population, there were 32 polymorphic SNPs between the two parents. Six of these 32 SNPs segregated in a 1:1 ratio . In the F2 population, there were 44 polymorphic SNPs between the two parents. Twelve of these 44 SNPs segregated in a 1:2:1 ratio .Two mapping populations . It is also possible that for some of these SNPs genotyping using the GoldenGate assay resulted in theincorrect grouping of multiple genotype clusters together (e.g. AAAB and AABB), which would result in distorted segregation ratios. However, multi-locus genotypes are usually clearly identifiable in Genome Studio by the presence of additional separate clusters, so it is more likely that the segregation distortion observed was due to selective pressure for one or the other parental allele under population growth conditions. Future work could include validation of the genomic location of these SNPs by designing and using arrays in large mapping populations originating from diverse B. napus parent genotypes.Although 892,803 SNPs have been developed, there are still some limitations to this work. Ten accessions was a productive number for the SNP discovery. However, increasing the number of resequenced accessions will enhance efficient, polymorphic SNP discovery. As well, eight of the accessions used were semi-winter-type B. napus. The average number of SNPs per 100 kb was 119 and 89 in the A genome and C genome respectively. Transition-type SNPs accounted for 57.5% of all SNPs, and transversion-type SNPs accounted for 42.5%. A subset of developed SNPs was tested through sequencing of PCR amplification products and the GoldenGate genotyping technique, and it is predicted that the majority of the SNPs identified in this study  can be applied in the development of much larger arrays of informative SNPs, such as Infinium II assays.A total of 892,536 bi-allelic SNP markers were developed for allopolyploid B. napus populations were used to validate the SNPs for high-throughput genotyping via GoldenGate Array. The first set comprised 92 lines of a DH population generated from crossing parents ‘zy036’ and ‘51070’ [2 population were genotyped using the GoldenGate Array.A total of ten representative accessions were chosen for SNP marker development. These comprised eight semi-winter type accessions: ‘Zhongshuang11’, ‘73290’, ‘08-806-2’, ‘09CB01’, ‘Xiangyou15’, ‘09CB03’, ‘PY-1’ and ‘PY-2’; one winter-type accession, ‘Tapidor’; and one spring-type accession, ‘Westar’. Two  ‘51070’ , and theB. napus accessions were germinated at 25°C on MS medium in a dark chamber. After five days, etiolated seedlings were collected for genomic DNA extraction using a standard CTAB (cetyl trimethylammonium bromide) protocol [Seeds of ten protocol . SequencBrassica rapa (Brassica rapa Release v1.01) [Brassica oleracea assembled scaffolds (unpublished) were combined and repeat sequences were masked using RepeatMasker software. Short Oligonucleotide Alignment Program 2 (SOAP2) [The e v1.01)  and Bras (SOAP2)  was used (SOAP2) , 25. Fin (SOAP2)  for fraghttp://www.ocri-genomics.org/bolbase/). Gene families were annotated using hmmer3 software [B. rapa and B. oleracea as the background. All genes with non-synomyous SNPs were extracted via custom Perl script. The GO annotations of these genes were extracted from the Brassica Genome Database. The p-value was approximated by Pearson’s chi-squared test. Fisher’s exact test was used when the expected value of any count was below 5.The localization of SNPs in coding regions was based on annotation of gene models as provided by the Brassica Genome Database  using the repeat databases. Repeats in the SNP-containing sequences were replaced with lowercase letters prior to submission to Illumina Inc. to undergo a preliminary design phase of the custom oligo pool assay (OPA), which contains the allele-specific oligoes and locus-specific oligoes for all SNPs included in the assay. A designability rank score was given to each SNP by Illumina. Scores ranged from 0 to 1.0, where a rank score of <0.4 indicated a low success rate, 0.4 to 0.6 indicated a moderate success rate, and >0.6 indicated a high success rate for the conversion of a SNP into a successful GoldenGate assay. The GoldenGate assay was performed according to the manufacturer’s protocol and as described in Fan et al. [A total of 110 SNPs were randomly selected from the identified SNP set. SNP-containing sequences were extracted and screened with RepeatMasker software (n et al. .Genomic DNA was extracted from leaf tissues of 130 individuals. A NanoDrop spectrophotometer was used to ascertain that DNA quality and quantity met the requirements for the genotyping assay. Genotyping was performed using the Illumina GoldenGate Assay platform  and the


A main goal of cattle genomics is to identify DNA differences that account for variations in economically important traits. In this study, we performed whole-genome analyses of three important cattle breeds in Korea—Hanwoo, Jeju Heugu, and Korean Holstein—using the Illumina HiSeq 2000 sequencing platform. We achieved 25.5-, 29.6-, and 29.5-fold coverage of the Hanwoo, Jeju Heugu, and Korean Holstein genomes, respectively, and identified a total of 10.4 million single nucleotide polymorphisms (SNPs), of which 54.12% were found to be novel. We also detected 1,063,267 insertions–deletions (InDels) across the genomes (78.92% novel). Annotations of the datasets identified a total of 31,503 nonsynonymous SNPs and 859 frameshift InDels that could affect phenotypic variations in traits of interest. Furthermore, genome-wide copy number variation regions (CNVRs) were detected by comparing the Hanwoo, Jeju Heugu, and previously published Chikso genomes against that of Korean Holstein. A total of 992, 284, and 1881 CNVRs, respectively, were detected throughout the genome. Moreover, 53, 65, 45, and 82 putative regions of homozygosity (ROH) were identified in Hanwoo, Jeju Heugu, Chikso, and Korean Holstein respectively. The results of this study provide a valuable foundation for further investigations to dissect the molecular mechanisms underlying variation in economically important traits in cattle and to develop genetic markers for use in cattle breeding. Native cattle have been raised across the Korea peninsula since 2000 B.C th century. However, those cattle breeds are currently being revaluated to conserve local genetic resources and to pioneer new niche markets to meet demands for safe meats from Korean native breeds in Korea. Holstein has been the most widely used dairy breed in Korea since its introduction there in 1885. Since the initiation of the official dairy herd improvement program in 1979, Holsteins have been intensively selected for Korean environments. As a result, milk traits have dramatically improved in the past 30 years; for example, yields of 4,681 kg of milk per cow per lactation can be achieved A selective breeding program for Hanwoo was initiated in 1979, and it has contributed to significant increases in economically important traits, such as carcass yield and marbling scores Thanks to the international bovine genome sequencing and HapMap projects, substantial numbers of single nucleotide polymorphisms (SNPs) are known throughout the cattle genome Bos taurus cattle, these studies clearly demonstrated that there remain substantial numbers of SNPs to be discovered . In this article, we present WGS analyses of three Bos taurus cattle breeds, two Korean native breeds (Hanwoo and Jeju Heugu) and a Korean Holstein, using the Illumina HiSeq 2000 sequencing platform. In addition, a recently sequenced Chikso genome was included for comparison Despite these recent achievements, there is a drastic shortage of whole-genome sequencing (WGS) investigations for Asian cattle breeds and of their comparisons with European-origin cattle breeds that are widely used for beef or dairy For sampling cattle breeds in this study, the study protocol and standard operating procedures were reviewed and approved by the National Institute of Animal Science's Institutional Animal Care and Use Committee.Bos taurus bull genomes from two Korean native cattle breeds, Hanwoo and Jeju Heugu, and a Korean Holstein as a representative dairy breed. These bulls were accessed at the Hanwoo Experiment Station, National Institute of Animal Science, Rural Development Administration, Pyongchang; the Jeju Provincial Livestock Research Institute, Jeju; and the Dairy Cattle Improvement Center, National Agricultural Cooperative Federation, Goyang respectively. Each bull was an influential sire for artificial insemination, with extensive phenotypic records and proper pedigree information. In particular, the Korean Holstein  had been ranked in the first percentile of elite sires in the international bull evaluation service database. Genomic DNA from each animal was isolated from the EDTA-blood, using the PAXgene Blood DNA Kit . The quality and quantity of the DNA were evaluated by the Qubit fluorometer  and Infinite F200 microplate reader (TECAN), according to the manufacturer's instruction. The status of the DNA was visually checked by 0.8% agarose gel electrophoresis.We sequenced three Purified genomic DNA was randomly sheared to yield DNA fragments of 400∼500 bp in size, and the average fragment size was determined using an Agilent Bioanalyzer 2100 . The fragments were ligated with index adapters using the Illumina TrueSeq End Repair-Kit and the AMPure XP Beads purification kit . After size selection of the ligation products using a 2% agarose gel, successfully ligated fragments were enriched via PCR with adapter-specific primer sets. The DNA was further isolated using AMPure XP Beads, and the resulting libraries were assessed on a 2100 Bioanalyzer  and then sequenced by 100 bp paired-end sequencing using the Illumina HiSeq 2000 platform. Further image analysis and base calling were performed with the Illumina pipeline using default settings. Additionally, previously published data for Chikso was included for comparison with the three newly sequenced genomes obtained in this study. The genomic data of Chikso was generated using the same library construction and sequencing procedures as used in the current study http://picard.sourceforge.net). Variants were called using Samtools-0.1.18 mpileup For each sample, sequence reads were removed if they failed the Illumina chastity filter or if the average Phred quality score was less than 20. Next, reads were trimmed to 90 bp to omit the error-prone ends. The remaining reads were mapped against the bovine genome assembly UMD 3.1 P = 0.001 and log2 threshold = 0.7) with a window size of 5 to generate a list of CNVs. Additionally, we used a minimum-window-required setting of 10 to specify a CNVR by ten consecutive sliding windows showing a significant read depth difference.Putative CNV regions (CNVRs) were detected for all 29 bovine autosomes and the X chromosome using the CNV-seq application, which compares two sets of mapped reads and reports genomic regions with significantly different read depths A previously described method was followed to identify regions of homozygosity throughout all 29 bovine autosomes NGS-SNP was used to assess the gene content of each CNVR by comparing its coordinates to the positions of genes in the Ensembl database (release 68) Each sequenced animal was genotyped using the Illumina BovineSNP50 v2 BeadChip array , which includes markers spanning the entire reference sequence (UMD 3.1). Approximately 1% of the panel SNPs were excluded from further concordance analysis because their genomic coordinates could not be determined or because their alleles were incompatible with those detected by WGS. We calculated the concordance rate based on SNPs that were successfully genotyped by the chip array and that were not homozygous for the reference allele. Subsequently, we evaluated the genotype concordance by two measures: genotype concordance at variant sites, and non-reference sensitivity. Genotype concordance at variant sites is calculated by dividing the number of concordant non-reference genotypes by the total number of non-reference chip array genotypes. Non-reference sensitivity refers to the rate at which non-reference sites (heterozygous or homozygous non-reference) detected by the genotyping panel are recovered in the WGS-derived data and is calculated by dividing the number of non-reference genotypes identified by the chip array and classified as non-reference using WGS by the number of non-reference genotypes identified by the chip.All SNPs and InDels detected in this study have been submitted to dbSNP under the handle “AGL_CJW’ with the accession number: from 99630286 to 1026504451. The complete lists of CNVRs detected are provided in Genomic DNA from Hanwoo (HAN), Jeju Heugu (JJH), and Korean Holstein (HOL) were sequenced at a high depth of coverage using the Illumina HiSeq 2000 sequencing platform using 100-bp paired-end libraries. To the best of our knowledge, this study provides the first whole-genome sequence of Jeju Heugu. We applied rigorous custom filtering to the resulting sequence reads to detect high-confidence variants; including removing error prone regions at read ends and redundancy occurring in the library preparation step see . CompareA total of 10,471,178 SNPs were identified throughout the genomes from all three breeds sequenced in this study . Of these SNPs, 5,667,367 (54.12%) were found to be novel  using dbThe homozygous-to-heterozygous ratios of the breeds were 1∶1.6 for HAN , 1∶1.5 for JJH , and 1∶1.6 for HOL  . The facTo assess SNP quality, transition-to-transversion (TS/TV) ratios were computed as indicators of possible random sequence errors. The ratios all approximated the empirical human TS/TV ratio >2.1   [39]. FoThe quality of the SNPs from this study was further assured by experimental validation using the commercial Bovine SNP array to genotype the same individuals. The Illumina BovineSNP50K BeadChip exhibited high concordance rates computed by both genotype concordance at variant sites, which measures the overall accuracy of variant genotype calls, and non-reference sensitivity, which evaluates the rate at which non-reference sites in the SNP panel were recovered in the WGS-derived SNPs see . The resBos taurus.In addition, we systematically compared all the detected SNP sets, including SNPs from the recent WGS effort for the native Korean Chikso InDels remain less explored than SNPs in cattle genomics. In this study, we also investigated InDel events and found a total of 1,063,267 InDels , of which 568,069 were deletions. The InDels ranged in length from 31 (insertion) to –49 (deletion). Most InDels were short; approximately 76.5% were less than 4 bp . The disOf the InDels, 224,125 (21.09%) were found in the dbSNP database, while the remaining 78.91% were novel. Of the InDels, 707,901 (66.57%) and 279,622 (26.30%) were located in intergenic and intronic regions, respectively , and 36,CNVRs were identified across all 29 bovine autosomes and the X chromosome using CNV-seq with the same strict criteria that achieved a high CNVR validation rate (∼82%) in a recent study We observed distinctly more CNVR gains (more copy numbers) in Korean Holstein than in Hanwoo and Chikso, with 755  and 1,639 (85.9%) gains in the Holstein in the HANvsHOL and CHSvsHOL comparisons, respectively, but not in the JJHvsHOL comparison . Such differences could reflect subtle variations in the preparation of the samples and libraries or different selection histories applied to each breed. Because Holsteins have had a longer and more intensive artificial selection history than Korean native cattle breeds, the greater abundance of CNVR gains in the Holstein may be caused partly by recent strong selection. This result is well coincided with a previous report showing the high copy number gains in Holstein We identified numerous nonsynonymous SNPs (nsSNPs), some of which may account for genetic variation in economically important traits in cattle. Including SNPs from Chikso, we extracted breed-specific nsSNP sets that did not overlap among breeds; we found 3,264, 3,563, 3,459, and 3,573 nsSNPs among 2,080, 2,209, 2,191, and 2,327 genes in Hanwoo, Jeju Heugu, Chikso, and Korean Holstein, respectively. GO enrichment analyses of the 100 genes containing the most nsSNPs for each breed were performed using agriGO The GO enrichment analysis was also applied to the genes that overlapped with the genic CNVRs. Each CNVR in this work represented a gain of sequence dosage in one animal relative to the other. Some of the GO terms were commonly enriched in the genic CNVR gains, such as “immune system process”, “cellular component organization”, and “response to stimulus” –10. ThisMC1R)  and is responsible for the dominant black phenotype MC1R were detected only in Jeju Heugu and Korean Holstein, which both have black coloration (D leading to black colour from the Hereford (without black) used to construct the bovine reference sequence assembly. The SNP detected in Holstein should be corresponded with ED locus as well. The brindle pattern in Chikso requires at least one wild-type MC1R in the absence of a dominant allele MC1R in the Chikso sequence. Because coat color involves multiple genes and remains incompletely understood in cattle, the information provided here should be a useful resource for clarifying its underlying genetic mechanisms in cattle.By identifying numerous genetic variants, we could instantly locate several promising candidates for further investigations into how the genes are associated with traits of interest in cattle. For example, several nsSNPs occurred in pigmentation-related genes, such as tyrosinase, tyrosinase-related protein 1, dopachrome tautomerase, and melanocortin 1 receptor (MC1R) . Coat coMC1R)  MC1R is loration . The SNPNOS2). NOS2 acts as a mediator in several biological processes, such as growth, development, and involution of the mammary gland NOS2 knockout mice showed a delay in involution along with increased levels of prolactin, which is required for alveoli differentiation in pregnancy and milk protein expression during lactation NOS2 consistently had fewer copies in Holstein than in the other three Korean native cattle breeds. The estimated differences in copy number were 2.9 (Chr19_CNVR_7-11), 3.6 (Chr19_CNVR_2-4), and 2.4 (Chr19_CNVR_11-13) fewer in Holstein than in Hanwoo, Jeju Heugu, and Chikso, respectively  using massively parallel sequencing, leading to the identification of numerous SNPs and InDels. Both the high-depth coverage and strict custom filtering applied here allowed us to detect reliable variants; in particular, SNPs were estimated to be highly accurate, as indicated by the approximately 99% concordance rate using the BovineSNP50 BeadChip. Including data from the Chikso breed, we showed that a substantial portion of SNPs overlapped among the cattle breeds and that, while a lower but still significant number of SNPs were breed specific and may contribute to the unique breed characteristics. In addition, compared with Korean Holstein, genome-wide CNVRs were identified in the three Korean native cattle breeds. The Holstein has much higher copy number gain abundance than Hanwoo or Chikso, which may reflect its stronger selection history than the Korean native breeds. However, a distinct difference in abundance was not observed between Jeju Heugu and Holstein. Because so few studies have attempted to associate CNVRs with characteristics of each breed, our results clearly highlight the need for further elucidation of how CNVRs relate to selection history in cattle. Furthermore, ROHs were identified in each of the breeds using genotypes derived from the whole-genome sequence data. Particularly long ROHs were more frequently detected in Jeju Heugu and Holstein possibly due to the reduced population size and the recent inbreeding in Jeju Heugu and Holstein respectively.We annotated the potential functional role of each variant, which allowed us to identify several candidate variants of functional significance, such as nsSNPs in coat color-related genes as well as CNVRs overlapping with a gene potentially implicated in milk traits in cattle. Because only one animal of each breed was used in this whole-genome sequencing study, further studies are required to elucidate the detailed dynamics of each gene–trait combination. Nonetheless, the results from this study can be used as a valuable resource for further genetic investigations in cattle aimed at finding DNA polymorphisms that account for genetic variations in economically important traits and for the development of accurate genomic tools for cattle breeding.Figure S1Distribution of mapped reads on the bovine reference assembly UMD3 for the Hanwoo, Jeju Heugu, and Korean Holstein samples.(PDF)Click here for additional data file.Figure S2Histograms of InDel characteristics. (A) Read depth. (B) Number of alternative allele reads.(PDF)Click here for additional data file.Figure S3Venn diagram showing the overlap of all detected ROH in the Hanwoo, Jeju Heugu, Chikso, and Korean Holstein genomes.(PDF)Click here for additional data file.Table S1CNVRs detected from HANvsHOL.(PDF)Click here for additional data file.Table S2CNVRs detected from JJHvsHOL.(PDF)Click here for additional data file.Table S3CNVRs detected from CHSvsHOL.(PDF)Click here for additional data file.Table S4Annotated genic-CNVR detected from HANvsHOL.(XLSX)Click here for additional data file.Table S5Annotated genic-CNVR detected from JJHvsHOL.(XLSX)Click here for additional data file.Table S6Annotated genic-CNVR detected from CHSvsHOL.(XLSX)Click here for additional data file.Table S7Gene Ontology terms enriched among the top 100 genes containing the highest number of nsSNPs in each of breed-specific nsSNPs. HAN, JJH, CHS, and HOL indicate Hanwoo, Jeju Heugu, Chikso, and Korean Holstein respectively.(PDF)Click here for additional data file.Table S8Gene Ontology terms enriched among the genic-CNVRs from HANvsHOL.(PDF)Click here for additional data file.Table S9Gene Ontology terms enriched among the genic-CNVRs from JJHvsHOL.(PDF)Click here for additional data file.Table S10Gene Ontology terms enriched among the genic-CNVRs from CHSvsHOL.(PDF)Click here for additional data file.Table S11The nsSNPs identified in Hanwoo, Jeju Heugu, Chikso, and Korean Holstein that overlap with pigmentation-related genes. TYR, TYRP1, DCT, and MC1R indicate tyrosinase, tyrosinase-related protein 1, dopachrome tautomerase, and melanocortin 1 receptor respectively.(PDF)Click here for additional data file.Table S12Regions of homozygosity (ROHs) detected from Hanwoo, Jeju Heugu, Chikso, and Korean Holstein in this study.(PDF)Click here for additional data file.


Totally, 23 loci were detected, and 383 candidate genes were identified, and four of these candidate genes,Os01g0392100, Os04g0630000, Os01g0904700andOs07g0615000, were regarded as promising targets.Oryza sativa L.)-planting countries because it is labor- and time-efficient. However, low seedling establishment and slow seedling emergence have restricted the application and popularity of the technique. Mesocotyl elongation and shoot length are two important traits that can enhance rice seedling emergence. A single nucleotide polymorphism (SNP) is a genome sequence variation caused by a single base within a population, and SNPs evenly distributed throughout the genomes of plant species. In this study, a genome-wide association study (GWAS), based on 4136 SNPs, was performed using a compressed mixed linear model that accounted for population structure and relative kinship to detect novel loci for the two traits. Totally, 23 loci were identified, including five loci located known QTLs region. For the mesocotyl elongation, 17 major loci were identified, explaining ~19.31 % of the phenotypic variation. For the shoot length, six major loci were detected, explaining ~39.79 % of the phenotypic variation. In total, 383 candidate genes were included in a 200-kb genomic region (±100 kb of each locus). Additionally, 32 SNPs were identified in 30 candidate genes. Relative expression level analyses indicated that four candidate genes containing SNP variations, Os01g0392100, Os04g0630000, Os01g0904700 and Os07g0615000, represented promising targets. Finally, eight elite accessions with long mesocotyl and shoot lengths were chosen as breeding donors for further rice direct-seeding variety modifications.Direct-seeding cultivation is becoming popular in rice (The online version of this article (doi:10.1007/s00425-015-2434-x) contains supplementary material, which is available to authorized users. In recent years, as tight labor markets have continued, direct seeding, which is a simple, convenient and resource-efficient cultivation technique that addresses the conflict between rice production and the labor shortage, has become an inevitable trend in rice-growing regions. In particular, deep direct seeding improves not only plant lodging resistance but also rice drought tolerance because roots absorb the deep soil water , based on the historic recombination in a large natural population, is considered an excellent new strategy to analyze genetic variation and identify valuable genes (or QTLs) for complex traits in genomes. Over the past several years, GWAS was successfully applied in rice  were used to conduct association mapping. The goals are (1) to analyze genomic variation; (2) to uncover the correlation among direct-seeding emergence, mesocotyl elongation and shoot length; (3) to detect novel loci controlling mesocotyl elongation and shoot length; (4) additionally, to identify elite parental materials for direct-seeding cultivar improvement.To reduce spurious associations caused by population structure, a set of 469 global diverse indica accessions with rich genetic diversity (supplementary Table S1), representing the major rice-growing regions, was collected to construct a large association mapping population. All the samples were genotyped using a custom-designed array containing 5291 single nucleotide polymorphisms (SNPs) following the Infinium HD Assay Ultra Protocol , with the minor allele frequency <5 % eliminated. Finally, 4136 SNPs were used in our GWAS analyses (supplementary Table S2). These accessions were planted in a randomized complete block design with three replications in a six-column × six-row area in Lingshui  and Hangzhou  in 2014.The mesocotyl elongation and shoot length were systematically evaluated in LS and HZ in 2014. Seeds were harvested at 30–40 days after flowering and air-dried under natural conditions. For each accession, 50 plump grains with three replications were treated at 45 °C for 3 days to eliminate dormancy. Then, the grains were wrapped in 20 × 20 cm wet absorbent filter paper, placed vertically, and cultivated at 30 °C in the darkness. During germination, tap water was sprayed to keep the filter paper moist. After 10 days, the mesocotyl and shoot lengths were measured.In total, 24 accessions with long, and 28 with short, mesocotyl lengths were selected to evaluate the difference in seedling emergence at 2 and 5 cm sowing depths and to analyze the effects of different mesocotyl lengths on the speed of emergence (SOE) and final percentage of emergence (POE) in direct-seeding cultivation. For each accession, 50 plump grains were selected manually and soaked in tap water for 2 days, and then pre-germinated at 30 °C for 1 day in the darkness. Germinated seeds were sown in a 12 × 4 cm square, covered with cohesive, continuously moistened fine sand at 2- and 5-cm depths, and then cultivated in a greenhouse at 30 °C. After sowing, the number of emerged seedlings was recorded until the seedling emergence of all accessions remained stable. Then, the mesocotyl and shoot lengths were measured after being carefully cleaned. The experiment was performed with two replications.https://cran.r-project.org/web/packages/corrgram/), and the best linear unbiased prediction (BLUP) was carried out in R “lme4” package (https://cran.r-project.org/web/packages/lme4/) for the estimation of mesocotyl and shoot length phenotypic values in two environments.The mean, standard error (SE) and broad-sense heritability (FST) among different subpopulations were calculated using PowerMarker version 3.25 (Liu and Muse r2) for estimating the degree of LD between pairwise SNPs was calculated under TASSEL version 4.0  as covariates was used to reduce false-positive associations , the candidate genes were predicted within a 200-kb genomic region (±100 kb of each significant locus). The genes and annotations were obtained from the Rice Haplotype Map Project Database (http://202.127.18.221/RiceHap2/).A GWAS was performed in TASSEL version 4.0  using two groups of accessions with completely different phenotypes, and each group contained three typical accessions. For each accession, 50 plump grains were germinated at 30 °C for 10 days in the darkness. Total RNA was extracted from the mesocotyl and shoot tissues using a MiniBEST Plant RNA Extraction kit . First-strand complementary DNA (cDNA) was synthesized by PrimeScript RT Master Mix . The reaction mixture was run on an Applied Biosystems 7500 Real-Time PCR system . Three replicates were performed for each sample. Rice P < 0.001) across the two environments, indicating that environment effect should not be ignored. For the mesocotyl length, the phenotypic variation explained by the population structure , which was also supported by the neighbor-joining tree . The PCA results showed that PC1, PC2 and PC3 accounted for 17.8, 8.7 and 6.4 % of the genetic variation, respectively  indicated that the indica panel can be classed into four subpopulations  with moderate differentiation  (supplementary Table S8). In addition, the distributions of the 4136 SNPs were analyzed and 32 SNPs were located in 30 candidate genes for the two traits (supplementary Table S9). Moreover, the average phenotypic values of accessions carrying each SNP allele were examined. Interestingly, the phenotypic values of the two genotypes of the 13 different candidate genes reached significant (P = 0.05) or highly significant (P = 0.01 or 0.001) levels (Supplementary Table S9). We speculated that the phenotypic variation may be caused by these SNP changes. More importantly, four SNPs were discovered to be located in the coding sequence (CDS) region in four of the 13 different candidate genes. For example, Os01g0392100, encoding a conserved hypothetical protein, was located near seq-rs303 (P = 9.65E-04) that was significantly associated with the mesocotyl length, and SNP seq-rs305 (G/A) was located in the second CDS region , which was significantly related to the mesocotyl length, and SNP seq-rs2340 (G/A) was located in the first CDS region , which was a shoot length QTL. The SNP seq-rs609 (G/A) was located in the second CDS region , which correlated with the shoot length. The SNP, seq-rs3540 (G/T), was observed in the first CDS region  from the Rice Haplotype Map Project Database   Table . Moreove1) Table .qMel-1 and qMel-3, which were fine mapped to 3799- and 6964-kb intervals, respectively  and the seq-rs4192 (P = 1.69E-04), to a 200-kb genomic region, and 25 and 17 candidate genes were obtained, respectively (supplementary Table S8). However, on chromosomes 2, 3, 8 and 10, there was no significant association for mesocotyl length, possibly due to the QTL effects being too small or the marker density being too low to detect any QTLs. For the shoot length, the seq-rs609 locus, which was identified in previous study  and CH290 (Zaobai), showed a higher expression level Supplementary material 2 (PDF 44 kb)Supplementary material 3 (PDF 122 kb)Supplementary material 4 (PDF 309 kb)Supplementary material 5 (PDF 75 kb)Supplementary material 6 (XLSX 27 kb)Supplementary material 7 (XLSX 150 kb)Supplementary material 8 (XLSX 15 kb)Supplementary material 9 (XLSX 34 kb)Supplementary material 10 (XLSX 15 kb)Supplementary material 11 (XLSX 74 kb)Supplementary material 12 (XLSX 15 kb)Supplementary material 13 (XLSX 30 kb)Supplementary material 14 (XLSX 17 kb)Supplementary material 15 (XLSX 18 kb)Supplementary material 16 (XLSX 358 kb)


With its simple library preparation and robust approach to genome reduction, genotyping-by-sequencing (GBS) is a flexible and cost-effective strategy for SNP discovery and genotyping, provided an appropriate reference genome is available. For resource-limited curation, research, and breeding programs of underutilized plant genetic resources, however, even low-depth references may not be within reach, despite declining sequencing costs. Such programs would find value in an open-source bioinformatics pipeline that can maximize GBS data usage and perform high-density SNP genotyping in the absence of a reference.Actinidia arguta), GBS-SNP-CROP yielded on average three times as many SNPs as TASSEL-GBS analyses (32 and 64 bp tag lengths) and over 18 times as many as TASSEL-UNEAK, with fewer genotyping errors in all cases, as evidenced by comparing the genotypic characterizations of biological replicates. Using the published reference genome of a related diploid species (A. chinensis), the reference-based version of GBS-SNP-CROP behaved similarly to TASSEL-GBS in terms of the number of SNPs called but had an improved read depth distribution and fewer genotyping errors. Our results also indicate that the sets of SNPs detected by the different pipelines above are largely orthogonal to one another; thus GBS-SNP-CROP may be used to augment the results of alternative analyses, whether or not a reference is available.The GBS SNP-Calling Reference Optional Pipeline (GBS-SNP-CROP) developed and presented here adopts a clustering strategy to build a population-tailored “Mock Reference” from the same GBS data used for downstream SNP calling and genotyping. Designed for libraries of paired-end (PE) reads, GBS-SNP-CROP maximizes data usage by eliminating unnecessary data culling due to imposed read-length uniformity requirements. Using 150 bp PE reads from a GBS library of 48 accessions of tetraploid kiwiberry  contains supplementary material, which is available to authorized users. Digitaria exilis), leafy vegetables and seed crops , legumes (e.g. Sphenostylis stenocarpa), tuber crops (e.g. Plectranthus rotundifolius), corm crops (e.g. Colocasia esculenta), fruit trees , oil nut trees (e.g. Vitellaria paradoxa), and herbs (e.g. Hibiscus sabdariffa). Though historically under-researched, orphan crops are now recognized as germane to the issue of future global food security due to their potential to diversify the food supply .


Inbreeding reduces the fitness of individuals by increasing the frequency of homozygous deleterious recessive alleles. Some insight into the genetic architecture of fitness, and other complex traits, can be gained by using single nucleotide polymorphism (SNP) data to identify regions of the genome which lead to reduction in performance when identical by descent (IBD). Here, we compared the effect of genome-wide and location-specific homozygosity on fertility and milk production traits in dairy cattle.Genotype data from more than 43 000 SNPs were available for 8853 Holstein and 4138 Jersey dairy cows that were part of a much larger dataset that had pedigree records . Measures of inbreeding were based on: (1) pedigree data; (2) genotypes to determine the realised proportion of the genome that is IBD; (3) the proportion of the total genome that is homozygous and (4) runs of homozygosity (ROH) which are stretches of the genome that are homozygous.A 1% increase in inbreeding based either on pedigree or genomic data was associated with a decrease in milk, fat and protein yields of around 0.4 to 0.6% of the phenotypic mean, and an increase in calving interval (i.e. a deterioration in fertility) of 0.02 to 0.05% of the phenotypic mean. A genome-wide association study using ROH of more than 50 SNPs revealed genomic regions that resulted in depression of up to 12.5 d and 260 L for calving interval and milk yield, respectively, when completely homozygous.Genomic measures can be used instead of pedigree-based inbreeding to estimate inbreeding depression. Both the diagonal elements of the genomic relationship matrix and the proportion of homozygous SNPs can be used to measure inbreeding. Longer ROH (>3 Mb) were found to be associated with a reduction in milk yield and captured recent inbreeding independently and in addition to overall homozygosity. Inbreeding depression can be reduced by minimizing overall inbreeding but maybe also by avoiding the production of offspring that are homozygous for deleterious alleles at specific genomic regions that are associated with inbreeding depression.The online version of this article (doi:10.1186/s12711-014-0071-7) contains supplementary material, which is available to authorized users. Inbreeding depression is the reduction in fitness of offspring that result from the mating between individuals that share at least one common ancestor. Inbreeding reduces fitness by increasing the number of homozygous deleterious recessive alleles that affect traits related to fitness, such as survival, disease resistance, predation and birth weight in avian and mammalian populations . Some puGenetic markers can be used to estimate inbreeding as the realised proportion of the genome that is identical by descent (IBD), for example by calculating a genomic relationship matrix (GRM) among individuals -11. The The diagonal elements of the GRM and the proportion of the genome where SNPs are homozygous are both examples of genomic measures that can be used to estimate the level of inbreeding across the genome. A limitation of both of these measures is that they do not distinguish between identity by state and identity by descent. One possibility to resolve this is to use runs of homozygosity (ROH), which are regions of the genome where the copies inherited from the two parents are IBD . ROH canDairy cattle are potentially a good model to investigate the effects of genome-wide and genome location-specific inbreeding depression, since phenotypes of traits associated with fitness  and quantitative performance traits (such as milk yield) are measured on a large scale. In addition, intense selection of males to be the sires of the next generation has made it almost impossible to find dairy cattle without multiple common ancestors , so therEstimate inbreeding depression for milk production and fitness traits using pedigree-based inbreeding coefficients.Compare these estimates to inbreeding depression estimated from genome-wide estimates that were derived from SNP data.Investigate whether there are specific regions of the genome that are associated with inbreeding depression in fertility and milk production.In this study, we used data on Holstein and Jersey dairy cows with pedigree data and phenotypes for milk production and fertility traits and for which a subset had SNP genotypes to:Pedigree data that traced back to the 1950s were available on 2 070 219 Holstein and 293 588 Jersey cows . AnimalsC is the pedigree completeness, d is the number of generations and ai is the proportion of known ancestors in generation i. Selection of cows was restricted to animals born in or after 1994 to be able to compare the dataset with only pedigree information with the genotyped population. After editing, pedigree-based inbreeding coefficients were calculated using the algorithm of Meuwissen and Luo [Only cows with at least two generations of complete pedigree and born after 1994 were retained. The proportion of cows with complete pedigree over the first four generations was approximately 0.75 in both the Holstein and Jersey populations and was derived using the following equation ,22:\docu and Luo  for 338 Bos taurus build UMD 3.1 . The average spacing between SNPs retained for this study was 58 kb. Homozygous SNPs were coded as 2 or 0 and heterozygous SNPs as 1.The genotyped cows that were included in this study were part of an initiative of the Dairy Future’s Cooperative Research Centre . Briefly, cows were selected to become part of the Australian genomic reference population on the basis of completeness of their phenotypic data. They were also selected to have sires that were genotyped and part of the national Australian genomic reference population. For each cow in the dataset, the validity of its sire was verified by checking for excessive “opposing homozygotes”, which is when an individual’s alleles at a given locus are homozygous, but for different alleles than its sire . The pedThe genomic relationship of an individual with itself relative to a base population was calculated as the diagonal of the genomic relationship matrix minus 1 (GRM_F) using ,11:\docuN is the number of SNPs, pm is the allele frequency of SNP m, and xm is the genotype code at SNP m . Allele frequencies pm were calculated separately for the Jersey and Holstein populations, which included genotyped males of the same breed, which amounted to a total of 12 649 and 5240 Holstein and Jersey individuals, respectively. Thus, GRM_F was relative to the current population, as represented by the sample of genotyped females and bulls that are part of the Australian genomic reference population.where The proportion of the genome that was homozygous .n SNPs in length, where n ranged from 5 to 100. If the ROH at SNP position i exceeded n, then ROHi was coded as 1 or else 0. ROH_F was the sum of ROHi divided by the total number of SNPs (nSNP): The proportion of the genome that consists of runs of homozygosity (ROH_F) that were at least The following measures of inbreeding were calculated using the genomic data:For the Holstein breed, 887 561 lactation records for milk, fat and protein yields were available for 338 696 cows (i.e. each cow had on average 2.6 lactation records) and 755 618 calving interval records  were available for 299 590 cows. In Australia, calving interval is available on more cows than measures of fertility calculated from insemination and pregnancy diagnosis data , which iyijklmn is the nth record of the phenotype , HYSi the ith herd-year-season of calving, parityj the jth parity, monthk the kth month of calving, and b1 the regression coefficient on ageijklmn , which was the age at first calving, b2 is the regression coefficient on Fijklmn, which was the measure of inbreeding , perml is the lth random permanent environmental effect to account for multiple records on each individual, and cowm was the random genetic effect for the mth animal, assumed to follow the distribution N, where A is the numerator relationship matrix, and eijklmn is the random error associated with the observation. The quadratic effects of inbreeding were also tested in preliminary analyses but are not presented since none were significant. ASReml version 3.0 was used for the analysis [All analyses were conducted separately for the Holstein and Jersey datasets. Following previous research using a very similar dataset by Haile-Mariam et al. , we usedanalysis . FinallyThe genome-wide association study (GWAS) was designed to investigate the effect of overlapping ROH at consecutive positions across the genome using sliding windows, so the number of analyses was equal to the number of SNPs. Since the effect of inbreeding estimated using ROH, was of interest in this analysis, the model also included the SNP present at the start of the ROH to correct for its additive effect. In this analysis, milk yield was used to represent milk production traits, and calving interval to represent fertility traits. The model used for the GWAS was:F was replaced by two new covariates: (1) the regression coefficient (b3) on SNPijklmn, which was to correct for the additive effect of the presence of additional copies of the allele, and (2) the regression coefficient (b4) on ROHijklmn, where, at each SNP position, ROHijklmn was coded as 1 when a run of homozygosity of at least 50 SNPs was present at SNPijklmn for that animal, and as 0 otherwise. A ROH was selected as having a statistically significant association with the trait analyzed if the regression coefficient had a p-value less than 0.001.This model was similar to that defined above, except that http://asia.ensembl.org/biomart/martview/). QTL regions were also compared to QTL previously identified for the analysed trait through the Cattle QTLdb [http://www.animalgenome.org/cgi-bin/QTLdb/BT/index).To identify possible candidate genes, QTL regions were defined based on SNP positions with significant associations (P <0.001) of ROH with milk yield or fertility. A list of annotated genes between the positions of the first and last SNP in the ROH or cluster of ROH was obtained from Ensembl BioMart MartView  were lower than correlations among the genomic measures of inbreeding Table . The corFor Holstein cows, a 1% increase in pedigree-based inbreeding or in GRM_F was associated with a reduction in milk yield of 21 and 28 L/lactation, respectively Table , which rFor most other traits, the effects of increasing GRM_F were similar for the Holstein and Jersey populations Table . HoweverA 1% increase in homozygous SNPs resulted on average in a 1% and 1.5% reduction in milk, fat and protein yields for Holstein and Jersey cows, respectively  and fertility was not significant for either breed (results not shown). In fact, for the Holstein breed, the effect of ROH_F on fertility was almost entirely captured by the overall homozygosity.After correcting for the additive effect of SNPs, some regions of the genome contained stretches of ROH of 50 or more SNPs that were significantly associated with both milk yield and fertility for the Holstein and Jersey breeds. Significant ROH frequently formed clusters at overlapping positions on the genome, which may indicate that they were in linkage disequilibrium with the same QTL.Negative associations of ROH with milk yield were found on chromosomes 11, 14, 16, 17, 20, 26, 28 for the Holstein breed and on chromosomes 8, 17, 20 and 24 for the Jersey breed  of the 63 ROH was estimated at 72.5% using the method of Bolormaa et al. . The mosSince the Jersey population was smaller, there was less power to detect ROH that had an association with either milk yield or fertility. For this breed, only 44 and 17 ROH were significantly associated with milk yield and fertility, respectively, with FDR of 100% or more indicating that all these associations were probably identified by chance. However, 14 ROH on chromosome X had an effect on fertility in this breed Table , with anCorrelations of 0.26 and 0.29 were found between inbreeding coefficients based on pedigree and GRM_F for the Jersey and Holstein datasets, respectively Table . These vCorrelations of the proportion of homozygous SNPs with pedigree-based inbreeding coefficients were stronger than the equivalent correlations using GRM_F for both Holstein and Jersey populations. The lower correlations obtained with GRM_F are probably due to the effect of allele frequencies in the sub-population on the diagonal elements of the GRM, which results in artificially elevated “inbreeding coefficients” in minority sub-populations. To overcome this problem, several authors ,30,33 haIn accordance with other studies, the correlations between pedigree and genomic measures of inbreeding in cows were lower than the equivalent estimates in bulls ,31, whicThe correlation between ROH and pedigree-based inbreeding was strongest for ROH that consisted of 10 SNPs (0.58 Mb) Figure a and strA 1% increase in inbreeding in the population with pedigree information only, or in the population with GRM_F resulted in a reduction in milk production traits of around 0.3% of the phenotypic mean and a deterioration in fertility for both the Holstein Table  and the There have been relatively few studies on inbreeding depression using genomic measures of inbreeding. Recently, Bjelland et al.  estimateThe proportion of homozygous SNPs was associated with inbreeding depression in all traits analysed, with the exception of fertility in the Jersey breed, which could be due to a lack of statistical power and bias in calving interval caused by culling. However, the association between homozygosity and milk production was not stronger than that between GRM_F and milk production Tables  and 5. TLong ROH (>60 SNP or 3.5 Mb) were associated with a decrease in milk yield after correcting for average homozygosity, but short ROH were not. This could be due to selection eliminating deleterious mutations before inbreeding occurs if the inbreeding is due to an ancient common ancestor. Inbreeding arising from recent common ancestors has also been found to be associated with reproductive performance and body weight in mice  and withTo account for recent and older inbreeding simultaneously in breeding programs that aim at reducing inbreeding, longer ROH (>50 SNPs or 3 Mb) should be explicitly considered in addition to other measures of genomic inbreeding. In this study, the Illumina BovineSNP50 BeadChip  was useFor the GWAS analysis, a ROH length of at least 50 SNPs (~3 Mb) was chosen because, in this case, the ROH frequency was large enough to detect statistically significant differences and the ROH length was sufficient to detect recent inbreeding. Other studies  have repThe regions that were identified here were not close to the recessive lethal haplotypes reported by VanRaden et al.  and FritHowever, the ROH that were identified as significant could be associated with genes that, if impaired, can lead to loss-of-function. In addition, when the presence of a ROH has a positive effect on a trait under selection, such as milk yield or fertility, it can be indicative of selection signatures .glutamate-ammonia ligase (GLUL), which is involved in alanine, aspartate, and glutamate metabolism, arginine and proline metabolism and nitrogen metabolism. This gene has previously been reported to control fat to protein ratio [For the Holstein population, a region on chromosome 16 contained 22 ROH that had a negative effect on milk volume. This region included 18 genes  gene has been proposed as the likely candidate gene [prolactin receptor (PRLR) [PRLR nor GHR aligned with the genes identified in our scan. One hypothesis is that intense selection for GHR or PRLR has made this genomic region globally more homozygous and while homozygosity of specific alleles may be beneficial, overall homozygosity is detrimental to production because of unfavourable alleles that are in linkage disequilibrium with the favourable alleles of GHR/PRLR.On chromosome 20, two ROH were detected that, when homozygous, had a negative effect on milk yield in both the Holstein and Jersey breeds. Selection signatures close to this region have been identified in the genomes of US and German Holsteins ,45 and tr (PRLR) ,47. Neitinsulin receptor substrate 4; IRS4) has been reported to be associated with mild defects in reproduction and growth in mice [For the Jersey breed, there was a cluster of 14 ROH on the X chromosome, located between 60.4 and 62.1 Mb , with an unfavourable association with fertility, that was validated in both breeds. No candidate genes were identified in this region  capture recent inbreeding, which had an effect independent of overall homozygosity on milk production. Using a GWAS with ROH of 50 or more SNPs, genomic regions were detected that had an effect of up to 12.5 d and 260 L for calving interval and milk yield, respectively, when completely homozygous. Breeding programs could exploit whole-genome data, as well as these site-specific regions.


Perca fluviatilis) have been observed to coincide with high densities of sticklebacks (Gasterosteus aculeatus). Mechanisms behind this shift between piscivore and stickleback dominance were studied both experimentally and in field. Results showed that predation by sticklebacks has a strong negative effect on perch larvae survival, but this effect rapidly decreases with increasing perch size, likely due to gape limitations and digestion constraints in sticklebacks. Large spatial and temporal variations in patterns of stickleback migration into perch spawning sites were observed. Whether or not high density of sticklebacks will cause declines in coastal piscivore populations is suggested to depend on the availability of spawning sites in which sticklebacks do not migrate into or arrive late in the reproduction season of coastal piscivores.Intraguild predation interactions make fish communities prone to exhibit alternative stable states with either piscivore or prey fish dominance. In the Baltic Sea, local declines of coastal piscivores like perch (The online version of this article (doi:10.1007/s13280-015-0665-5) contains supplementary material, which is available to authorized users. Life history omnivory (shifts in resource use from invertebrates to fish over ontogeny) is common in fish and may involve both habitat shifts and interactions where species both prey on and compete with each other, i.e., intraguild predation (IGP)  has resulted in predation release of small planktivores, and a planktivore dominated offshore ecosystem  and pike (Esox lucius) , medium (45–55 mm), and large (65–75 mm) placed individually in aquarium and six size classes of YOY perch as prey . Prior to experiments, sticklebacks were starved for 24 h, and thereafter either ten YOY perch (size classes 11 and 14 mm) or five YOY perch  were introduced to each aquarium. Number of consumed perch was recorded after 4 h.−2) first feeding perch larvae  were introduced at 28th of May into each of the enclosures. Thereafter, six  adult three-spined sticklebacks (62.5 ± 4.7 mm) were introduced either 1, 8, 17, or 24 days after the introduction of perch larvae. The stickleback density chosen is low compared to natural densities where up to 30 individuals m−2 could be found in some coastal areas  with eight enclosures each . We used a design with four treatments with temporal variation in stickleback presence and one control (no sticklebacks) replicated 3 times each. Three hundred  for perch larval densities and stickleback abundances. Larval perch were sampled weekly or every second week, approximately from hatching (Table www.ellafishing.com) were set over night approximately 10 m apart along the shoreline at a depth of 1–2 m to obtain a relative measure of stickleback abundance in each bay. Captured sticklebacks were counted and thereafter released back to the bay after a subsample of sticklebacks was collected and frozen for later diet analyses. Zooplankton abundance was sampled at three pelagic stations in each bay using a 100-µm mesh net (diameter 250 mm). Samples were preserved in Lugol’s solution. In this study, we report data on YOY perch abundances and stickleback catches from only two of the sampling sites, Yttre Spelgrundet and Västra Stadsviken, which were sampled in both 2011 and 2012 as the complete data set is used in another article , whereas we use the whole dataset to study variation in stickleback catches at perch the spawning sites.In order to study relationships between changes in perch larvae and three-spined stickleback density over time, we sampled perch spawning sites along the Bothnian Sea coast in the years 2011 and 2012  according to Gape size = 0.01 × L1.457  that a stickleback can consume increases with stickleback length (SL) according to PL = 0.515 × L1.214. According to the experiment, sizes of YOY perch that sticklebacks could consume were slightly lower than predicted above. The maximum size of YOY perch that an adult stickleback 60–70 mm in length can consume varied between 20 and 25 mm  .Survival of perch was substantially higher 72.7 ± 13.1 %) in the absence of sticklebacks (controls) compared to the survival of perch in enclosures where sticklebacks were introduced at day one . Stickleback densities at, and up to 21 days after, hatching of perch larvae varied strongly between spawning sites along the coast of the Bothnian Sea. Four out of ten sites had CPUE of stickleback above 30 individuals trap−1 at or within eight days from hatching, while others had almost none (Table Densities of perch larvae varied little between trawling occasions up until an increase in Catch-per-Unit-Effort (CPUE) of sticklebacks took place, when a strong decrease in perch larval density was observed Fig. a–d. Zoop−3 on the 29th of May and at the 6th of June, respectively. No sticklebacks were captured in the lake at either of the two sampling dates. All of the perch individuals from the coastal area were according to the Sr:Ca ratio in the core of their otoliths determined to have been recruited from freshwater systems.In total 194 , perch were recorded migrating from the coastal Gaviksfjärden towards the lake from the 2nd to the 29th May, with a peak in migration around the 6–8th of May. Estimated densities of perch larvae in the lake were 177 ± 104 and 189 ± 72 individuals mBoth our experimental results and field data support the suggested negative impact of sticklebacks on coastal piscivore recruitment


A better understanding of non-additive variance could lead to increased knowledge on the genetic control and physiology of quantitative traits, and to improved prediction of the genetic value and phenotype of individuals. Genome-wide panels of single nucleotide polymorphisms (SNPs) have been mainly used to map additive effects for quantitative traits, but they can also be used to investigate non-additive effects. We estimated dominance and epistatic effects of SNPs on various traits in beef cattle and the variance explained by dominance, and quantified the increase in accuracy of phenotype prediction by including dominance deviations in its estimation.Bos taurus, Bos indicus and composite breeds were used. A genome-wide association study was performed by fitting the additive and dominance effects of single SNPs. The dominance variance was estimated by fitting a dominance relationship matrix constructed from the 729 068 SNPs. The accuracy of predicted phenotypic values was evaluated by best linear unbiased prediction using the additive and dominance relationship matrices. Epistatic interactions (additive × additive) were tested between each of the 28 SNPs that are known to have additive effects on multiple traits, and each of the other remaining 729 067 SNPs.Genotype data  and phenotypes on up to 16 traits of 10 191 individuals from The number of significant dominance effects was greater than expected by chance and most of them were in the direction that is presumed to increase fitness and in the opposite direction to inbreeding depression. Estimates of dominance variance explained by SNPs varied widely between traits, but had large standard errors. The median dominance variance across the 16 traits was equal to 5% of the phenotypic variance. Including a dominance deviation in the prediction did not significantly increase its accuracy for any of the phenotypes. The number of additive × additive epistatic effects that were statistically significant was greater than expected by chance.Significant dominance and epistatic effects occur for growth, carcass and fertility traits in beef cattle but they are difficult to estimate precisely and including them in phenotype prediction does not increase its accuracy. Mutations of large effects often show non-additive effects on the phenotype such as dominance and epistasis and one well-known example is the coat colour of mice . HoweverGenome-wide dense single nucleotide polymorphisms (SNPs) have been widely used in cattle for association studies -12 and gTherefore, the objective of this study was to estimate dominance and epistatic effects in beef cattle using high-density SNP genotypes and phenotypic data on growth, carcass and reproductive traits.Bos taurus breeds , one Bos indicus breed (Brahman cattle), three composite breeds , and one recent Brahman cross population . The structure of the populations used is described in Bolormaa et al. [B. indicus, 3296 B. taurus, and 3506 B. taurus × B. indicus) with SNP genotypes and phenotypes for at least one trait were used.Animals originated from nine different populations of three breed types. They included four a et al. . A totalPhenotypes for 16 different traits (Table http://res.illumina.com/documents/products/datasheets/datasheet_bovinehd.pdf) that contains 777 963 SNPs; the BovineSNP50K BeadChip  version 1 (2) and version 2 (3) that contain 54 001 and 54 609 SNPs, respectively; (4) the Illumina SNP7K panel that comprises 6909 SNPs; and (5) the Parallele SNP10K chip  with 11 932 SNPs. All SNPs were mapped to the UMD 3.1 assembly of the bovine genome sequence provided by the Centre for Bioinformatics and Computational Biology at University of Maryland (CBCB) (ftp://ftp.cbcb.umd.edu/pub/data/assembly/Bos_taurus). Procedures for stringent quality control of the SNP data and for imputation to 729 068 SNPs are described in Bolormaa et al. [http://res.illumina.com/documents/products/technotes/technote_topbot.pdf).Data on 729 068 SNPs were imputed from genotypes from five different SNP panels: (1) the Illumina HD Bovine SNP chip , μ is the overall mean, b is a vector of fixed effects, X is a design matrix relating observations to the corresponding fixed effects, Z is a design matrix relating observations to random animal genetic effects, a is a vector of polygenic breeding values sampled from N ~ , where σa2 is additive genetic variance and A is the additive relationship matrix constructed from the pedigree of the animals and their 5-generation-ancestors; si is a vector of additive genotype codes of each animal at the i-th SNP, with genotypes AA, AB and BB coded as 0, 1 or 2, respectively, i.e. according to the number of B alleles present, αi is the additive effect of the i-th SNP, wi is a vector of dominance genotype codes at SNPi with the heterozygote AB coded as 1 and the homozygotes AA and BB coded as 0, δi is the dominance effect of i-th SNP, and e is the vector of random residual effects. Vectors si and wi were fitted as covariates. In this parameterization, significance of the dominance effect was tested after fitting the additive effect. This analysis was carried out once for each SNP, i.e. 729 068 times.where The model included dataset, breed, cohort and sex as fixed effects for all traits, except for the two female fertility traits. Other fixed effects varied by trait, as detailed in ,22-26. TFollowing Bolormaa et al. , the falP is the P-value used to declare the additive or dominance effect of a SNP to be significant , A is the number of SNP effects that were declared significant at the stated P -value and T is the total number of SNPs tested. Although the true FDR cannot exceed 100%, the estimated FDR can exceed 100% if the number of significant SNPs is smaller than expected by chance, but the interpretation is simply that all SNPs discovered are false positives.where In order to validate statistically significant SNP effects in an independent population, the animals in the complete dataset were split into five sets by allocating the offspring of randomly selected sires to one of the five datasets. Then, one of the five sets was used as a validation population and the four other sets as the reference population. In this way, no animal used for validation had paternal half-sibs in the reference population. GWAS for each trait was performed in the reference population. For SNPs with a significant dominance effect in the reference population, the analysis was repeated in the validation population. We counted the number of times that the direction of the estimated SNP effect was the same in the validation and discovery populations.The dominance variance was estimated by restricted maximum likelihood (REML) using the following model:y is the vector of observed phenotypic values of the animals, 1n is a vector of 1′s, μ is the overall mean, X is a design matrix relating observations to the corresponding fixed effects where the fixed effects were the same as used in the GWAS, b is vector of fixed effects, het is a vector containing the average heterozygosity over all SNPs for each animal, ß is the regression of each trait on heterozygosity, g is a vector of genomic breeding values distributed as N ~ , where σg2 is additive genetic variance explained by SNPs and G is the genomic relationship matrix (GRM) [d is a vector of dominance deviations distributed as N ~ , where σd2 is dominance variance explained by the SNPs and D is the dominance relationship matrix (DRM), and e is the vector of random residual effects. The GRM was constructed using the genotypes of all 10 191 animals in the combined datasets according to [where ix (GRM) , d is a rding to . More derding to . The DRMj-th animal at the i-th SNP  was coded as Hij = −p/q, +1, and –q/p, respectively. Then, in a base population in Hardy-Weinberg equilibrium:Let the allele frequency of A be q and that of B be p = 1-q. The genotype of the andj-th animal (Tij) is coded as -2p/ The additive contribution of the genotype of the HH’/m and GRM = TT’/m, where m is the total number of SNPs, H is a matrix of dimension of the number of animals x number of SNPs, where Hij is present at each position in the matrix; in the same way, T is a matrix containing Tij. With this model, σd2 is the classical definition of dominance variance. Model (2) is equivalent to a model in which g is replaced by Tu, Var(u) = Iσa2/m, and d is replaced by Hv, Var(v) = Iσd2/m where u and v are vectors containing the additive and dominant effects of each SNP.This shows that allele substitution effects and dominance deviations that are implicit in the model are orthogonal contrasts. Therefore, DRM = This definition of the DRM is similar to that used by Vitezica et al. , except The significance of the dominance variance was tested by comparing twice the difference in log-likelihood between additive genomic model (AM) and additive and dominance genomic model (ADM) to a chi-squared distribution with degrees of freedom of 1.ĝ is a predicted total genetic value, â is the estimated genomic breeding value, and ĝ were obtained using ASReml software [For validation purposes, predictions of additive and dominance genetic values were made using the best linear unbiased prediction (GBLUP) analysis with variance estimated by REML. A five-fold cross validation scheme was carried out. The data were split into five parts of approximately equal size, by allocating the offspring of each sire to one of the five datasets. In this way no animal used for validation had paternal half sibs in the discovery population. The analysis was performed five times using each dataset in turn as a validation group and the four other sets as the discovery population. Validation animals were included in the GRM and DRM but had missing phenotypes in the calculation of genomic estimated breeding values (GEBV). For each validation population, the genetic and phenotypic value of each animal in the validation population was predicted as software . The pheĝ and the corrected phenotype within each breed. Accuracies were reported only when the number of records per breed was greater than 200. When combining accuracies across breeds, the overall accuracy was the mean accuracy within breeds weighted by the number of records of each breed. The accuracy when including dominance effects was compared to the accuracy obtained using the same model but without the dominance effects.The accuracy of this prediction was calculated as the correlation between P < 10−5) in a multi-trait analysis of the same traits as those used here. To minimise multiple-testing, we tested only additive × additive interactions between each of the 28 lead SNPs and all other 729 068 SNPs. Therefore, for each trait and for each lead SNP, we performed a GWAS in which we fitted the same model as for the dominance GWAS but, instead of a dominance effect, we fitted the additive effects of the lead SNP and one other SNP and the interaction between the two of them. The statistical model used for each trait was:Bolormaa et al.  identifiy is the vector of observed phenotypic values of the animals, 1n is a vector of 1′s, μ is the overall mean, b is a vector of fixed effects, X is a design matrix relating observations to the corresponding fixed effects, Z is a design matrix relating observations to the random animal effect, a is the vector of polygenic breeding values, e is the vector of random residual effect, lj is a vector containing the coded genotypes for the j-th lead SNP , uj is the additive effect of the j-th lead SNP, si is a vector containing coded genotypes for the i-th SNP , ai is the additive effect of the i-th SNP, lj × si is a vector of the interactions between the lead SNPj and SNPi, and mji is the effect of the interaction between the lead SNPj and SNPi. The effects of the j-th lead SNP, the i-th SNP, and their interaction (lj × si) were fitted simultaneously as covariates.where 10 of SNP dominance effects is in Figure P < 10−4) dominance effect in the discovery population for each trait is in Table P < 10−4) dominance effects. Since 729 068 SNPs were tested, 70 significant SNPs are expected by chance alone and therefore the FDR was 36%. Among these 191 SNPs, 174 had a positive and 17 a negative dominance effect. In the validation population, only 185 of the 191 SNPs could be tested for dominance because, for the other six SNPs, not all three genotypes were represented in the validation population. Among the SNPs that had a positive effect in the discovery population, 66% also had a positive effect in the validation population and among those that had a negative effect in the discovery population, 56% also had a negative effect in the validation population. If the significant SNPs in the discovery population were all false positives, we would expect 50% of the effects to be in the same direction in the validation population. Based on a chi-squared test, 66% differs significantly (P < 0.001) from 50% but 56% does not differ significantly from 50%. For the trait, weight measured at feedlot exit (X_lwt), similar results were observed (Table A Manhattan plot of the –logTable P < 0.05) for PW_hip, PW_lwt, X_lwt, PWIGF, CIMF, CRBY and AGECL (Table D/VP) varied from 0 to 0.42 but the standard errors were large in some cases . For seven traits, VD/VP was estimated to be 0. Therefore, the average VD/VP across traits was biased upwards but the median, which was 0.05, should not be biased. The only fertility trait that had a significant dominance variance was AGECL (18%). Dominance variance was not significant for both male fertility traits (SC12 and PNS24) and for PPAI (Table Estimates of genetic variance using the additive genomic model (AM) and the model fitting both additive and dominance genetic effects  are in Table CL Table . EstimatAI Table , which mHe) for each trait is in Table The effect of the overall observed heterozygosity  epistatic interactions between one lead SNP and one of the other remaining 729 067 SNPs was calculated for the 16 traits (Table P < 10−5) interactions were found between the lead SNP BTA14_25 (close to the PLAG1 gene) and other SNPs. Two examples of these interactions are in Table P-values of epistatic effects for PW_lwt between lead SNP BTA14_25Mb and all other SNPs are shown in Figure P < 10−5) epistatic effects for PW_lwt on BTA2, 5, 8, 9, 19, and 29 were observed  on BTA1, FAT/CD36 (fatty acid translocase/cluster of differentiation 36) on BTA4, GRN (granulin), FASN (fatty acid synthase), and ITG (platelet glycoproteins (A2B and B3)) on BTA19, and INS (insulin) and IGF2 (insulin-like growth factor 2) on BTA29.The number of significant P < 10− epistatiAnalysis of epistatic effects shows that the number of significant interactions between SNPs was greater than expected by chance and varied widely between traits Table . As descFive lead SNPs  had an allele that increased meat tenderness and fatness, as described by Bolormaa et al.  and showTraits related to fitness, including growth rate, are commonly found to show inbreeding depression and heterosis ,31, whicDirectional dominance can cause a significant regression of trait phenotype on average heterozygosity, as we observed. In fact, the effect of average heterozygosity on trait phenotype could be considered as an estimate of inbreeding depression based on SNP genotypes. However, significant inbreeding depression can occur without generating much dominance variance if it is due to small dominance deviations at very many loci. The fact that we observed significant dominance variance for some traits implies that some individual loci have moderate dominance effects. This is supported by the finding of significant dominance effects at individual SNPs in the GWAS.Across traits, the proportion of phenotypic variance that was estimated to be due to dominance varied widely (from 0 to 42%) but much of this variation could be due to large standard errors of the estimates. The median values across traits for the dominance variance as a proportion of total phenotypic variance is 0.05 compared with a median value for the additive variance which was 0.4 of the total phenotypic variance. The results for post-partum anoestrous interval (PPAI) are somewhat surprising for a fertility trait for the following reasons: (1) the heritability for PPAI was equal to 0.39, which is higher than many heritability estimates of fertility traits in beef cattle ; (2) theThe model that we used to estimate dominance variance is similar to that of Vitezica et al. , except There are a few publications e.g., -5,34) th,34 th5,3Su et al.  estimateg) and the dominance value (d) are replaced by the sum of all the effects of individual SNPs, that is g = Tu and d = Hv. Thus, our model can be considered as estimating the combined variances due to all SNPs. T and H are defined using the allele frequencies over the whole dataset and thus correspond to a model in which the current breeds have descended from a base population with these allele frequencies, and the current breeds are regarded as inbred relative to this base population. Consequently, the diagonal elements of the GRM and DRM are not expected to be 1. Diagonal elements of the GRM are greater than 1 when calculated by TT’/m, as expected. Diagonal elements of the DRM calculated from HH’/m are also typically greater than 1, which reflects the effect of inbreeding. The estimated variance components refer to the variances in the hypothetical base population. A potential problem is that the additive (T) and dominance (H) values are not necessarily orthogonal when genotype frequencies deviate from Hardy-Weinberg equilibrium. However, the results in Table We used a set of animals that originated from multiple breeds. The extension of the traditional model with additive and dominance variance components to multiple breeds has been done in two ways. Either the breeds are regarded as separate populations  or the P < 10−5). This could be due to the additive × additive epistatic variance being small and/or to the epistatic effects being difficult to estimate. There were no significant epistatic interactions among the 28 lead SNPs, for which the power for detecting such interactions was expected to be greatest, since the lead SNPs had highly significant additive effects on multiple traits. By focusing on these 28 SNPs, we may have missed many important epistatic interactions. However, testing interactions between all pairs of SNPs would have greatly increased the multiple-testing problem. Therefore, caution is warranted in interpreting the significant interactions that we identified. Still, an interaction between PLAG1 and IGF2 is biologically plausible, since PLAG1 is a transcription factor that regulates many genes and pathways, including the IGF2 and IGF1R pathways [Although there were more significant epistatic effects than expected by chance, the FDR was high  and significant variance explained by dominance deviations for some traits. Despite this, including dominance in the prediction model did not increase the accuracy of prediction of genetic and phenotypic values for any trait, which is probably because the dominance variance is much smaller than the additive genetic variance, and dominance effects are difficult to estimate accurately. The number of additive × additive epistatic effects was greater than expected by chance but their FDR was high.Across all SNPs, we observed dominance effects for growth and fertility traits in the direction that is expected to increase fitness. The dominance effects were not equal for all SNPs, which resulted in some dominance effects reaching significance (


One of the goals of genomics is to identify the genetic loci responsible for variation in phenotypic traits. The completion of the tomato genome sequence and recent advances in DNA sequencing technology allow for in-depth characterization of genetic variation present in the tomato genome. Like many self-pollinated crops, cultivated tomato accessions show a low molecular but high phenotypic diversity. Here we describe the whole-genome resequencing of eight accessions (four cherry-type and four large fruited lines) chosen to represent a large range of intra-specific variability and the identification and annotation of novel polymorphisms.The eight genomes were sequenced using the GAII Illumina platform. Comparison of the sequences with the reference genome yielded more than 4 million single nucleotide polymorphisms (SNPs). This number varied from 80,000 to 1.5 million according to the accessions. Almost 128,000 InDels were detected. The distribution of SNPs and InDels across and within chromosomes was highly heterogeneous revealing introgressions from wild species and the mosaic structure of the genomes of the cherry tomato accessions. In-depth annotation of the polymorphisms identified more than 16,000 unique non-synonymous SNPs. In addition 1,686 putative copy-number variations (CNVs) were identified.This study represents the first whole genome resequencing experiment in cultivated tomato. Substantial genetic differences exist between the sequenced tomato accessions and the reference sequence. The heterogeneous distribution of the polymorphisms may be related to introgressions that occurred during domestication or breeding. The annotated SNPs, InDels and CNVs identified in this resequencing study will serve as useful genetic tools, and as candidate polymorphisms in the search for phenotype-altering DNA variations.The online version of this article (doi:10.1186/1471-2164-14-791) contains supplementary material, which is available to authorized users. Currently next generation sequencing facilitates SNP discovery and allows deeper analysis of genome variation , 2. In pSolanum lycopersicum) and an accession of the closest wild relative, S. pimpinellifolium, revealed more than 5.4 million SNPs representing a divergence of 0.6%.The tomato genome has recently been sequenced and the international Tomato Genome Consortium has released a high-quality reference sequence . The avaS. lycopersicum cv. cerasiforme), which are intermediate between wild and modern cultivated accessions [Tomato is a model species for fruit development and composition and is also a vegetable of high economic importance. It is grown all over the world, and its production has continuously increased over the last 50 years. Tomato originated in South America where all the wild species related to cultivated tomato grow in the Andean region. Domestication probably started in Peru or Ecuador followed by diversification in Mexico or alternatively domestication directly took place in Mexico . Tomato cessions , 16. In cessions . This wacessions .in silico mining of EST [As a genetic model for fruit crops, tomato has been used in many QTL mapping and gene cloning studies. Due to the lack of molecular polymorphism, most of the gene and QTL mapping experiments were performed on inter-specific progeny involving a cultivated and a wild species . The useg of EST –27 and ag of EST , 28, 29.g of EST –32.In this article we present the polymorphisms detected from the resequencing of eight tomato accessions chosen to represent a large range of intraspecific variation. While characterizing the diversity of 360 tomato accessions with 20 SSR and later 275 SNPs, we developed nested core collections representing a maximum of molecular and phenotypic variation . In ordeS. lycopersicum and S pimpinellifolium[S. pimpinellifolium, cherry and cultivated accessions), we showed that 76% of the 275 SNPs identified in the collection were detected in at least one of these eight lines [S. pimpinellifolium accessions. We can thus predict that a large fraction of the SNPs present in any accession of the cultivated species were detected in this sample.We analysed two groups of accessions: a group of four cherry-type tomato accessions whose genomes consist in an admixture between the genomes of llifolium and a grllifolium. Followiht lines . FurtherGenome sequencing of the eight tomato lines yielded 970 million reads, most of them being 101 bp paired-end reads. After cleaning, 82 to 90% of the reads remained and were mapped to the high-quality genomic reference sequence of Heinz 1706 . A totalGenome coverage was equivalent for all accessions and chromosomes except for one long region of chromosome 9 from the Levovil accession, which corresponded to an introgression from a distant species Figure . The depA total of 4,290,679 unique SNPs and 127,913 InDels were detected when comparing each genome separately to the reference sequence, with the parameters defined in the Materials and Methods. For detecting homozygous polymorphisms, we applied two filters: a minimum of 4 reads and a maximum of 128 reads had to be mapped at any position and a minimum allele frequency of 0.9 was required. If we increased the minimal depth to 8, the number of SNPs dropped to 3,173,618 but several polymorphisms previously detected by Sanger sequencing were no longer detected, in particular in the three lines with a depth of coverage lower than 10x .S. l. cerasiforme accessions and from 180,000 to 350,000 in the four S. lycopersicum lines  to 1.75×10-3 (for Cervil) and in the coding sequences from 1.90×10-4 to 1.29×10-3 for the same lines  and chromosome 5 the highest (9.5×10-3). The range of variation in π among the lines was higher than 100-fold for chromosome 5 while it was lower than 10-fold for chromosomes 1 and 6. Within the lines, the range varied from 8-fold for LA 0147 to 63-fold for Cervil.The nucleotide diversity π (average number of SNPs per nucleotide) varied among the lines from 2.49×10S.l. cerasiforme accessions, more than 75% of the SNPs detected in Cervil were on chromosomes 2, 4, 5, 8 and 9, while chromosomes 4 and 7 contributed to more than half of the SNPs of Criollo. The S. lycopersicum Levovil accession presented an excess of SNPs on chromosome 9 (52% of the SNPs for this accession were found on this chromosome), while this was evident on chromosome 11 for Ferum (50% of the SNPs) and on chromosome 12 for Stupicke (53% of the SNPs).The contribution of each line to the overall number of SNPs was also highly variable. For instance, for the four The distribution of the SNPs along each chromosome also showed high variation as illustrated in Figure In order to validate the SNPs detected, we compared the genotypes obtained from the SolCAP SNP array for 7720 SNPs  with theS. lycopersicum lines  was higher than for SNPs (0.097%) as an InDel may rapidly cause a frame shift in the sequence  and we S. lycopersicum and S pimpinellifolium[S. lycopersicum were on average lower than 10% with the exception of chromosome 12, for which Stupicke provided 65% of the unique SNPs. This is in agreement with the distances among the lines , one half to two-thirds of the SNPs remained specific to each line. Even the chromosomes with the lowest SNP number exhibited more than 3,000 SNPs. It is thus now possible to build genetic maps of almost any cross and address genetic questions at the intraspecific level, which was not possible before the availability of resequencing approaches.New rapid and low-cost techniques based on next-generation sequencing platforms have been proposed to identify SNPs among lines. They consist either in a first genome reduction before sequencing or in low coverage whole genome resequencing such as Genotyping by Sequencing (GBS) . In tomaS. lycopersicum lines also showed some regions poor in SNPs compared to the four cherry-type tomato lines, notably on chromosome 1, 5, 7 and 8. The most striking feature is the occurrence of large regions covering more than 10 Mb, present in one or two lines, and carrying large number of SNPs. This kind of pattern appeared on chromosome 2 and 8 for Cervil, on chromosome 3 for Plovdiv, on chromosome 9 for Levovil, on chromosome 11 for LA 1420 and Ferum and on chromosome 12 for Stupicke. Cervil and Plovdiv presented the same profiles for chromosome 4 and 5, with regions of low SNP density spread over regions of higher SNP density. Charles Rick, a pioneer in tomato genetics, underlined the role of natural hybridization in tomato, particularly in South America where cultivated accessions may grow close to wild relatives [The SNPs and InDels appeared non-randomly distributed between different chromosomes, but also within each chromosome Figure . For inselatives : this phS. pimpinellifolium[Verticilllium (Ve gene on the top of chromosome 9) and Fusarium (I2 gene on the bottom of chromosome 11). Other introgression events from S. pimpinellifolium in the Heinz 1706 genome have been reported, particularly a large one on chromosome 4 [Ve gene, Ferum and Criollo carried the I2 resistance gene, but it was not possible to relate the presence/absence of these genes with variations in polymorphism rate. Cervil carried the resistance gene to Fusarium radicis on chromosome 9 (position not yet identified). Chromosome 9 of Levovil carried the TMV resistance gene introgressed from S. peruvianum . This introgression from a distant species reduced the coverage depth in the region, but the number of SNPs detected with the mapped reads was higher than in the rest of the genome for this line. For the other regions it is more difficult to identify any known introgressed gene. These regions often cover the centromeric regions where the recombination rate is lower [Since the early 20th Century, tomato breeders have crossed cultivars with wild species in order to transfer resistance genes . This hallifolium, notablyis lower  and thusis lower .S. pimpinellifolium, 3,423 genome regions were lacking when compared to Heinz 1706 with large regions missing on chromosome 1 and 10 [In 1 and 10 . We deteArabidopsis thaliana[S. lycopersicum and S. pimpinellifolium[Annotation of SNPs and InDels in the eight lines showed that less than 5% of the polymorphisms occurred in coding regions. The 55,337 unique polymorphisms with significant effects  affected 20,959 genes. Non synonymous to synonymous ratios ranged from 1.34 on average in the four cherry tomato lines to 1.48 on average in the four cultivated lines. These values are close to those detected in soybean  with large fruits and four cherry-type accessions, S. l. var cerasiforme lines . LA 0147 and LA 1420 were kindly provided by the Tomato Genetics Resource Center, Davis, California. Cervil and Levovil were provided by Vilmorin Seed Company. The other lines are conserved in the Genetic Resource Center in INRA, Avignon (France). Genomic DNA quality control, Illumina libraries construction and sequencing on GAIIx  were performed at Unité Etude du Polymorphisme des Génomes Végétaux, INRA, using the Bank service and Illumina sequencers facilities of CEA-Institut de Génomique/CNG, Evry (France). All the DNA samples went through quality control successfully. Non-indexed paired-ends (PE) libraries were carried out with an initial input DNA of 3 μg by following the Illumina Paired-End DNA Sample Prep protocol  with some modifications: 3 μg of Genomic DNA were submitted to fragmentation by using Adaptive Focused Acoustics (AFA) process from Covaris technology (S2 Focused-Ultrasonicator). After end-repairing and adapters ligation, a 400-bp size selection of DNA fragments was performed by band excision after gel electrophoresis. The steps of fragmentation, ligation and PCR were validated on Agilent 2100 BioAnalyser. One lane per library was originally loaded on several flow cells, Clusters amplification was performed either on a Clustering Station or a Cbot, then sequencing was performed as a PE 76b/101b run length on GAIIx, following technological improvements. Data from a total of 14 sequencing runs were collected, 3 single 101 bp, (not paired-end because sequencing failed for the read-2), one 76-bp long and all others 101 bp-long. A first analysis was conducted by applying the process of quality control and cleaning for validation of the sequencing data.DNA was extracted from young leaves of four S. pimpinellifolium LA 1589 draft genome [Before the mapping step, sequences were cleaned and filtered with Python home-made scripts (available upon request to the authors). First, duplicated sequences were removed. Then low quality regions (phred score lower than 28) were cleaned, and sequences shorter than 30 nucleotides, or containing more than two N were removed. After the cleaning step, single and paired-end sequences were kept in different files. Cleaned reads were mapped onto the total Tomato reference genome  with tht genome .For the identification of copy number variation regions, the BAM files were analysed with the cn.Mops bioconductor package . Only thThe VarScan2 output files (VCF) containing the homozygous SNPs and InDels were annotated based on their genomic location with the SnpEff software . A tomaTo validate the identified homozygous SNPs, we compared the predicted genotypes and the genotypes obtained using the Infinium SolCAP’s Illumina Bead Chips  for six http://www.ebi.ac.uk/ena/data/view/PRJEB4395). Raw sequences, i.e. 11 fastq files, have been deposited in ENA with accession numbers ERR327646 to ERR327656. Files containing the SNPs and INDELs identified for the eight accessions, i.e. 16 vcf files, have been deposited in ENA with accession numbers ERZ015686 to ERZ015701. BAM files and SNP characteristics are available upon request to the corresponding author and on the SolGenomics ftp site (ftp://ftp.solgenomics.net/projects/causse_tomato_snp8lines). Detailed information on CNV is available in Additional file This study is recorded in the European Nucleotide Archive (ENA) with the project number PRJEB4395 (Table listing the number of homozygous SNPs per chromosome and line. (DOC 51 KB)Additional file 1: Figure showing the distribution of genes, homozygous and heterozygous SNPs and InDels along each chromosome over the 8 accessions and for each accession (using a window size of 100 kb). (PDF 14 MB)Additional file 2: Table listing the number of homozygous InDels per chromosome and line. (DOC 52 KB)Additional file 3: Table listing the number of heterozygous SNPs in genomic DNA of the eight accessions . (DOC 52 KB)Additional file 4: Figure showing the distribution of the number of heterozygous SNPs. (DOC 74 KB)Additional file 5: Table listing the classification of InDels in coding sequences according to their effects (snpEff version 2.1b). (DOC 60 KB)Additional file 6: Table listing the number of regions showing a significant copy number variant . (DOC 54 KB)Additional file 7: Phylogenetic tree representing the 8 accessions, Heinz 1706 and LA 1589, constructed with the set of 7200 SNP positions common to the SolCap array. (PDF 6 KB)Additional file 8: Table listing the position and characteristics of all the CNVs. (ODS 56 KB)Additional file 9: