Pinus flexilis E. James (limber pine) is a keystone five-needle pine species in mountain-top ecosystems of North America. White pine blister rust (WPBR), caused by a non-native fungal pathogen Cronartium ribicola (J.C. Fisch.), has resulted in mortality in this conifer species and is still spreading through the distribution. The objective of this research was to develop P. flexilis transcriptome-wide single nucleotide polymorphism (SNP) markers using RNA-seq analysis for genetic mapping of the major gene (Cr4) that confers complete resistance to C. ribicola.Linkage of DNA markers with phenotypic traits provides essential information to dissect clustered genes with potential phenotypic contributions in a target genome region. In silico SNP markers were uncovered by mapping the RNA-seq reads back to the de novo assembled transcriptomes. A total of 110,573 in silico SNPs and 2,870 indels were identified with an average of 3.7 SNPs per Kb. These SNPs were distributed in 17,041 unigenes. Of these polymorphic P. flexilis unigenes, 6,584 were highly conserved as compared to the genome sequence of P. taeda L (loblolly pine). High-throughput genotyping arrays were designed and were used to search for Cr4-linked genic SNPs in megagametophyte populations of four maternal trees by haploid-segregation analysis. A total of 32 SNP markers in 25 genes were localized on the Cr4 linkage group (LG). Syntenic relationships of this Cr4-LG map with the model conifer species P. taeda anchored Cr4 on Pinus consensus LG8, indicating that R genes against C. ribicola have evolved independently in different five-needle pines. Functional genes close to Cr4 were annotated and their potential roles in Cr4-mediated resistance were further discussed.Needle tissues of one resistant and two susceptible seedling families were subjected to RNA-seq analysis. in silico SNPs. This strategy may be applied to mapping any trait in non-model plant species that have complex genomes. Whole transcriptome sequencing provides a powerful tool for SNP discovery in conifers and other species with complex genomes, for which sequencing and annotation of complex genomes is still challenging. The genic SNP map for the consensus Cr4-LG may help future molecular breeding efforts by enabling both Cr4 positional characterization and selection of this gene against WPBR.We demonstrated a very effective, low-cost strategy for developing a SNP genetic map of a phenotypic trait of interest. SNP discovery through transcriptome comparison was integrated with high-throughput genotyping of a small set of The online version of this article (doi:10.1186/s12864-016-3079-2) contains supplementary material, which is available to authorized users. Pinus flexilis E. James (limber pine) is a keystone five-needle pine species of the subgenus Strobus in mountain-top ecosystems of North America. Its high elevation distribution ranges ~1,600 m to > 3,300 m, much wider than any co-occurring conifer tree species. Populations dominate in dry, rocky, exposed windswept slopes; and its latitudinal range extends from 33°N to 51°N [P. flexilis stands show slow growth in diverse environments across the landscape with longevity of some trees surpassing 1500 years, indicating that adaptation to different habitats has equipped this conifer species with a high capacity for physiological plasticity or broad physiological tolerances [P. flexilis to colonize extreme environments and withstand climate variability makes it ecologically important in high elevation ecosystems. However, white pine blister rust (WPBR), caused by a non-native fungal pathogen Cronartium ribicola (J.C. Fisch.), threatens the sustainability of this conifer species and other five-needle pines in North America [Dendroctonus ponderosae), limber pine dwarf mistletoe (Arceuthobium cyanocarpum), and climate change have caused widespread mortality in P. flexilis and reduced capacity for forest recovery throughout a significant portion of its range [P. flexilis is projected by 2030 in the absence of intervention [lerances . The abits range –6; a 40 rvention .C. ribicola, named Cr1, Cr2, Cr3, and Cr4 in P. lambertiana Dougl. (sugar pine) [P. monticola Dougl. ex D. Don (western white pine) [P. strobiformis Engelm. (southwestern white pine) [P. flexilis [Cr1, Cr2, and Cr3 target at different avirulent (avcr) alleles [Cr4 shows resistance to the virulent (vcr2) inoculum that overcomes Cr2 [avcr genes have been molecularly characterized.All native five-needle pines are highly susceptible to WPBR and their wild populations have been impacted to various degrees and at increasing rates . To datear pine) , P. montte pine) , P. strote pine) , and P. flexilis , respect alleles . Cr4 shoomes Cr2 . Althougomes Cr2  has beenCr1 and Cr2 were developed previously [P. monticola [P. lambertiana [P. flexilis, and genetic diversity and molecular mechanisms underlying P. flexilis resistance to WPBR remain poorly understood.Genetic maps of eviously –16, but eviously . For devonticola  and P. lbertiana . DNA marbertiana . In the bertiana –25. HowePinus species, P. flexilis has a huge genome with 1 C of 31.2 pg [P. taeda draft genome has been assembled [P. flexilis genome is still a challenging task due to its huge size. Identification of genetic determinants is critical for the development of disease resistant P. flexilis. Compared to agricultural plants, conifer breeding is a slow process, because identifying and pyramiding disease tolerance traits is far more challenging in these species with much longer life cycle, and larger but less well characterized genomes. DNA markers closely linked to R genes have the potential to strengthen the efficiency of breeding [P. flexilis and other five-needle pines, resistant parents can be identified relatively quickly if a marker-assisted selection (MAS) tool is available.Like other  31.2 pg ; the estssembled  and the breeding , 29. In P. flexilis transcriptome de novo assembled by RNA-seq analysis. SNPs were then applied to develop high-throughput genotyping arrays for genetic mapping of a Cr4 linkage group (LG) using populations selected from current breeding and conservation programs in North America. Cr4 was anchored on a Pinus consensus genetic map by comparison with P. taeda genetic maps.This research was undertaken to identify a large number of SNPs in the Cr4-mediated resistance for Cr4 genetic mapping. Their seed germination rates varied from 38-75 %, and seedling family GE-213, GE-214, LJ-112, and PHA-106 yielded 146, 145, 122, and 156 seedlings, respectively. The inoculation spore density delivered to the seedlings was measured at 11,670 spores/cm2 and C. ribicola basidiospores showed a germination rate of 97 %. Each seedling was phenotyped via inspections for signs and symptoms of disease and resistance; early inspections revealed C. ribicola needle infection spots (lesions) on all seedlings and 99 % of them presented with more than 10 needle infection spots, indicating a successful inoculation that challenged every seedling  revealed the transcriptome assembly contained 10,886 unique gene sequence highly conserved between P. flexilis and P. taeda . A total of 37,294 unique gene sequences showed significantly homology hits  to the P. taeda proteome. These 37,294 Pinus conserved unigene sequences covered a total P. flexilis exome sequence length of 42.9 Mbp, which accounts for 0.14 % of the 30.5 Gbp genome of P. flexilis. A tBLASTn search of the P. taeda protein database  against our P. flexilis transcripts revealed that 94.3 % of them had significant homology hits (E < 10e-6)  identified 2,654 transcripts encoding NBS-LRR proteins expressed from 792 unigenes, and BLASTn analysis  identified 2,219 transcripts encoding putative WPBR-responsive proteins expressed from 827 unigenes in the P. flexilis needles  between resistant seedling family (NR-3647) and susceptible seedling families (MRO-3501 and UT-3359A). A total of 527 transcripts were expressed from these DEGs, and 142 DEGs for 152 transcripts were commonly detected in both comparisons of transcriptomes: NR-3647 vs. MRO-3501, or NR-3647 vs. UT-3359A Fig. . As revede novo assembled transcriptome as a reference, a total of 110,573 SNPs and 2,870 indels were called in the three seedling families, and these polymorphisms were distributed among 21,561 transcripts expressed from 17,041 unique genes. On average, we detected 6.5 SNPs per unigene and 3.7 SNPs per Kb. Of 10,886 P. flexilis unigene sequences highly conserved between P. flexilis and P. taeda , 6,584  were polymorphic with SNP distribution. A BLASTn search against P. taeda mapped gene sequences [P. flexilis polymorphic genes with identical hits , providing anchored conserved genes for comparative mapping between P. taeda and P. flexilis.Using our equences , yieldedCr4-linked DNA markers at a next step.Nucleotide variation counts detected 31,740, 34,622, 37,959 SNPs; as well as 234, 273, and 515 indels in the seedling families MRO-3501, UT-3359A, NR-3647, respectively. The majority of nucleotide variations  were seed family-specific. Nucleotide variations shared among seed families were limited. Pair-wise comparison among the three seedling families revealed that only about 20 % of total SNPs from one seedling family were shared with another family  with high conservation among Pinus, and used to design Sequenom genotyping arrays. In addition to SNP coverages, gene expression was also considered in SNP selection. As shown in Fig. p < 0.05 with FDR correction). Others were included due to their high homologies with candidate groups. These SNP markers were screened in the seedlings with identified phenotypes following C. ribicola infection. Seventy-two SNPs were selected to genotype all megagametophyte samples from four maternal trees. After manually checking SNPs, we constructed individual linkage maps for each maternal tree separately . The SNP markers shared among the four maternal trees were aligned manually and a total of 32 SNP markers were found to reside in 25 unique genes  were shared across all four maternal trees. The same allele at each of these three shared SNP loci was linked in coupling to the resistant allele at the Cr4 locus in all four families used in the mapping experiments described here. Nine SNP loci  were mapped in repulsion on the Cr4-LGs in different maternal trees. These results indicated high genetic divergence among the maternal trees from different geographical regions.Among the 32 Cr4-LG was constructed by merging four individual maternal maps with a total length of 47 cM and an average density of 1.5 cM per SNP marker. The Cr4 region was highly saturated; and SNP markers snp290363Y and snp296815Y were closest to Cr4 with genetic distances at 0.35 cM and 1.00 cM respectively  were usually mapped at the same position on the LG. A consensus Cr4-LG to P. taeda consensus maps reported recently [Cr4-LG showed identical hits to P. taeda genes, with BLASTn e-values ranging from 7.66E-109 to 0   protein, benzyl alcohol o-benzoyl transferase (EC: 2.3.1.196), aleurain-like thiol protease (EC: 3.4.22.16), nodulation-signaling pathway 1 (NSP1), and galactinol synthase 1  , and de novo assembled transcriptomes and related NGS data from other five-needle pines [Pinus species [Pinus to generate a consensus genetic map for Cr4-LG, an important step towards P. flexilis molecular breeding and further genomics research on resistance mechanisms.Molecular breeding of five-needle pines for resistance to WPBR would benefit from recent advance in conifer genomics studies, including the assembly   are developed on random genomic sequences without knowledge of the causal genes. Marker discovery has been greatly improved as NGS technologies have advanced and whole genome or transcriptome sequences in non-model plants have become available. The majority of SNP markers discovered through a strategy of genotyping by sequencing were localized in the non-genic regions . RecentlCr4 using 32 SNP markers that represented 25 unique genes. These Cr4-linked genic SNP markers were selected from in silico SNPs by transcriptome comparison between Cr4-resistant and cr4-susceptible seedling families. Common SNP loci among four maternal trees that originated in three different geographical regions were used to build a consensus map for Cr4-LG. Comparison of the Cr4-LG maps among the four maternal trees revealed highly consistent marker orders for the 25 mapped genes, by which the consensus Cr4-LG map was constructed. We observed small discrepancies for the Cr4 position in four individual Cr4-maps, which may not suggest real chromosome rearrangements because different numbers of susceptible samples were calculated in each mapping population. As more recombination events are expected in the larger population resulting from the merging of four maternal trees, the Cr4 position on the consensus map is theoretically more reliable.We constructed a consensus map by genetically localizing Pinus conserved genes in the consensus Cr4-LG map gives us a solid framework for further comparative analysis of P. flexilis resistance with other pine species. By comparison with recently updated P. taeda consensus genetic maps [Cr4 to the Pinus consensus LG8 with a predicted location at 146.74 cM. The highly syntenous relationship between Cr4-LG and the Pinus consensus LG8 (R2 = 0.886) will guide fine mapping using larger populations to increase marker density in the genomic region of Cr4 in a future study. Application of consensus maps with syntenic markers localized P. lambertiana Cr1 on the Pinus consensus LG2 [P. taeda Fr1 for resistance to fusiform rust pathogen Cronartium quercuum sp. fusiforme (Cqf) was localized [P. monticola Cr2 was localized on Pinus consensus LG1 .Localization of 25 tic maps , we anchnsus LG2 , the samocalized , 39. P. Cr1, Cr2, and Cr4 on different LGs (or chromosomes) indicates that R genes against C. ribicola have evolved independently in five-needle pines. C. ribicola invaded into North America only about one hundred years ago, suggesting that occurrence of the five-needle pine R genes is not caused by pathogen selection pressure and that they may have already been present in wild populations at rare frequencies in North America before the arrival of C. ribicola. Furthermore, the maternal trees sampled in this study, except PHA106, are from sites not yet invaded by C. ribicola. A further genomic comparison of Pinus R genes against C. ribicola and C. quercuum f. sp. fusiforme would generate new insights into the evolution of the innate immune response within the genus [Localization of he genus .Cr4-LG facilitates Cr4 characterization at the molecular level. Genetic mapping of the R gene families is an effective strategy for identification of positional R-gene candidates, development of high-density R-gene genetic maps, and design of diagnostic markers for breeding selection of resistance genotypes and QTL [Cr4 were identified as functional R candidate genes and defence-related genes in the P. flexilis immune system.Localization of functional genes as well as their SNPs on the  and QTL . SeveralCr4, which indicates that it is not the Cr4 candidate. However, >60 % of NBS-LRR genes are organized in clusters in angiosperms, and R-genes families have been expanded by lineage-specific tandem duplications with high sequence similarity between duplicated gene copies [Cr4 positional candidates. P. flexilis contigs LP-296815, LP-290363, and LP-362068 were localized within about one cM from Cr4, providing landmark genes for searching for Cr4 candidates on the Pinus consensus maps. One cM genetic distance probably covers about 10 Mbp of genomic DNA sequence on the Pinus physical map [P. taeda and P. lambertiana genome draft assemblies are updated with improved completeness, this length of DNA sequence can be scrutinized for members of R gene families (such as NBS-LRR and RLK), which may include the targeted Cr4 gene itself.Contig_463406, encoding a NBS-LRR protein as a homolog of the tobacco N protein against tobacco mosaic virus (TMV), was mapped at a position of ~25 cM from e copies . Therefoical map . As P. tCr4 encoding proteins with putative physiological roles in disease resistance, including class IV chitinase, NOTUM homolog, C3H-type ZF protein, benzyl alcohol o-benzoyltransferase, aleurain-like thiol protease, and NSP1. Class IV endochitinaes are a group of pathogenesis-related (PR) proteins functioning in plant defence against pathogens [P. monticola class IV endochitinases were up-regulated in both resistant and susceptible seedlings post-C. ribicola infection [C. ribicola [Six functional genes were uncovered in a range of 3 cM from athogens . As glycnfection , and an ribicola .P. monticola C3H-type ZF family, expression of two members are upregulated in response to C. ribicola infection, one responsive in both resistant and susceptible genotypes and another responsive only in Cr2-resistant seedlings [GhZFP1) in transgenic tobacco increased resistance to a pathogenic fungus (Rhizoctonia solani) and enhanced tolerance to salt stress [C3H-type ZF proteins may have regulatory functions in mRNA processing during a series of plant developmental and adaptive processes, including plant defense responses to (a)biotic stresses . Most C3eedlings . A loss-eedlings . Overexpt stress .Arabidopsis resistance to microbial pathogens [Aspergillus nidulans PAE gene (AnRAE) showed reduction of pectin and xyloglucan acetylation and enhanced resistance to Botrytis cinerea, which probably is achieved by H2O2 accumulation, up-regulation of defense-related genes, and callose deposition [NOTUM-homologs encode pectinacetylesterase (PAE) in plants and fungi, catalyzing the deacetylation of pectin, a major compound of primary cell walls. Decreased pectin acetylation resulted in increased athogens –48, suggposition . DeacetyCladosporium fulvum contains a virulence factor (Avr2) that inhibits several host proteases  required for plant basal defense [A benzyl alcohol O-benzoyltransferase gene was up-regulated in tobacco leaves during a HR response to a pathogen infection . This en defense .Solanum tuberosum) with Phytophthora infestans [Cr4-linked candidate genes may have putative roles in the plant immune system at different levels. However, none of these candidates may encode for an R protein directly involved in recognition of the C. ribicola avirulence factor (termed as avcr4), and the physiological roles of these genes in Cr4-mediated resistance still has to be investigated.NSP1 is a member of the GRAS transcription factor family. GRAS transcription factors are proposed to have high functional diversity and may act as integrators of multiple growth regulatory and environmental signals in non-legume plants. GRAS transcripts accumulated in tomato during incompatible interactions and silencing one of them impaired host resistance to bacterial speck disease . The prenfestans . All of Cr4-LG provide targeted genomic DNA sequences to develop MAS tools for P. flexilis breeding and conservation programs. The match rate of SNP genotypes with the phenotypic traits is an important factor for considering application of SNP markers in practical selection of desired plant traits and prediction of related phenotypic development [Cr4-mediated resistant phenotype was not detected consistently in all four mapped seedling families in the present study. The numbers of populations and SNPs were still limited for discovery of NBS-LRR or RLK genes with tight linkage to Cr4. Due to complex genomes, long life cycles, and difficulty in phenotyping, similar limitations were encountered in genetic mapping of other forest species [Cr4 gene itself would be an ideal genomic tool for MGR selecting in P. flexilis, but positional cloning and functional determination of a conifer gene still requires considerable research effort. Nonetheless, following molecular characterization of R genes, R allele-specific sequences have been used to create marker-assisted tools for plant breeders to select resistance genotypes [SNP markers of the elopment , which d species , 30, 39.enotypes .Cr4 in the seed family GE-213 (with distance of ~ 0.3 cM), but it did not segregate in other three mapped seedling families. Searching additional SNPs from the gene contig_290363, or other Pinus conserved genes with physical linkage to this gene in the P. taeda genome sequence, may lead to identification of DNA markers for Cr4 selection with application in a wider range of P. flexilis populations.The present work identified SNP marker snp290363Y closest to Cr4 at genetic distance of 1.12 and 3.21 cM on the consensus Cr4-LG. The same allele at these two SNP loci was linked in coupling to the resistant allele at the Cr4 locus in all four mapped seedling families, suggesting there may be a potential to use the SNP markers in selection of resistant progeny in related seedling families. It waits further confirmation if these two SNP markers are valid or not in more geographic regions. Using genome-wide genetic markers, genomic selection has the potential to improve P. taeda breeding by shortening the current breeding cycle from 12 to 20 years to less than 7 years [Markers snp296815Y and snp204077R1 segregated in all four mapped seedling families and placed at loci to  7 years . CurrentP. flexilis, little breeding work is done. There will be selection, identification of resistant parents ; then management strategies will be developed accordingly [P. flexilis, genomic-based tools can assess the Cr4 status of younger non-reproductive trees in the field currently untestable with progeny tests, enabling timely assessments of the presence and frequency of Cr4 in populations that have sustained high mortality of mature trees as a result of disturbance such as the recent mountain pine beetle (Dendroctonus ponderosae) epidemic. Estimates of the frequency of Cr4 in stands and knowing how many MGRs there are in the species would help guide management actions [Cr4 status of a formerly disease-free tree with recent WPBR symptoms allows for rapid detection of the evolution of virulence (vcr4) in C. ribicola.For many of the high elevation five-needle pines, including ordingly . In many actions . In addiArabidopsis galactinol synthase is stress-inducible and plays a key role in the accumulation of galactinol and raffinose as osmoprotectants in drought-stress tolerance of plants [Cr4 seedling families constitutively exhibited greater cold hardiness and lower stomatal conductance than susceptible seedling families during moderate drought. These results suggest that Cr4 and drought-tolerance genes may be co-inherited and there is a potential to select P. flexilis genotypes for better fitness to both biotic and abiotic stresses under a warming climate.The SNP marker snp204077R1 results in an amino acid change (Lys/His) of galactinol synthase 1, suggesting it may be functional marker affecting enzymatic activity. f plants . Vogan af plants  found thC. ribicola reference transcriptome has been assembled and putative pathogenic effectors were identified [avcr candidates can be used as pathogenic baits for biochemical screening of their corresponding R products in the five-needle pine. Molecular elucidation of the gene-for-gene model during five-needle pine-blister rust interactions is another strategy that could lead to the identification of the R genes in the WPBR pathosystems.In addition to host markers for marker-assisted selection, pathogen effectors are now applied as molecular markers that accelerate and improve plant breeding of genetic resistance to various pathogens/pests . As the entified , the avcP. flexilis major gene (Cr4) for resistance against C. ribicola by genotyping SNPs of candidate genes discovered by RNA-seq analysis. The first SNP dataset with more than 100,000 novel SNPs were uncovered in the P. flexilis exomes from three seedling families. A large number of non synonymous SNPs for amino acid changes in the encoded proteins provide novel insight into the mechanisms underlying the genetic variability of pest/pathogen resistance and other adaptive traits.The present study represents the first research on genetic mapping of the Cr4 genetic mapping, seeds of four maternal trees GE-213, GE-214, LJ-112, and PHA-106, which have shown segregation of the Cr4-controlled canker-free trait in previous tests [Cr4 genetic mapping based on haploid segregation analysis. Seedlings were inoculated with C. ribicola September 8-15, 2014 at Dorena-GRC following a previously reported protocol [For For RNA-seq analysis, two susceptible seedling families (MRO-3501 and UT-3359A) and one MGR seedling family (NR-3647) were sown in April, 2010. The MGR seedling family was inoculated in Sept. 2010 and susceptible seedling families were not inoculated. Foliage from at least ten canker-free MGR-survivors or ten non-inoculated susceptible seedlings per seedling family was sampled using liquid nitrogen on July 21–22, 2014 at Dorena-GRC, and kept at -80 °C until RNA extraction.Needle samples of the three seedling families were used for total RNA extraction following a manual protocol as described previously . After rde novo assembly using Trinity (version: trinityrnaseq_r2013-02–25) with default k-mer length of 25 [Raw reads were first trimmed using Trimmomatic with default settings at ILLUMINACLIP:TruSeq3-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36 . Trimmedth of 25 .De novo assembled transcripts were annotated using the BLAST2GO program [P. taeda proteome  derived from genome v1.01 [Pinus highly conserved genes were identified in BLASTx at homology E values ≤ 10e-100. P. monticola resistance gene analogs of the NBS-LRR and RLK families and defense responsive genes to C. ribicola infection [P. flexilis transcriptome. program . The P. me v1.01  was usednfection  were usede novo assembled transcriptome and only paired reads (fragments) were counted in read mapping with a minimum length fraction of 0.9 and a minimum similarity fraction of 0.9. Expression values were calculated as FPKM (Fragments Per Kilobase of exon per Million fragments mapped) using CLC Genomics Workbench 5.5 . A resistant seedling family was used as reference group in comparisons. A Z-test was used for statistical analysis with FDR correction to identify differentially expressed genes (DEGs) with fold change > ±2.Transcriptome profiles were compared among three seedling families. Trimmed reads from each seedling family were mapped to the In silico SNP detection was performed in each seedling family by mapping trimmed RNA-seq reads back to the de novo-assembled transcriptome using CLC Genomics Workbench (v5.5) with the following parameters: a mismatch cost of 1, indel cost of 3, length fraction of 0.95, and similarity fraction of 0.95. SNPs were detected using quality-based variation detection at the following parameters: neighborhood radius = 5, maximum gap and mismatch count = 1, minimum neighborhood quality = 20, minimum central quality = 20, ignore non-specific matches = yes, ignore broken pairs = no, minimum coverage = 10, minimum variant frequency (%) = 35, maximum expected alleles = 2, require presence in both forward and reverse reads = yes. The number of shared and unique SNPs was calculated based on pairwise comparisons among three seedling families.in silico data set generated above for high-throughput genotyping based on a few criteria related to putative gene functions, transcript expression, and SNP types as outlined in a previous report with a little modification [Pinus highly conserved genes were included with a high priority for a comparative genetic mapping between different Pinus species. Of SNPs within candidate genes, we focused on those non-synonymous SNPs, which resulting in dramatic changes of amino acids  for a potential to include putative functional SNPs in the genotyping arrays.A subset of SNPs were selected from the fication . In genein silico SNPs were selected for array design and screened for marker segregation analysis using 96 megagametophyte samples from three MGR seedling families, each seedling family with 16 susceptible and 16 resistant samples. Genes and their SNPs with the expected Mendelian segregation ratio (1:1) and potential association with the susceptible phenotype were selected and genotyped in all megagametophyte samples of four seedling families using Sequenom iPLEX SNP genotyping technology.Megagametophyte tissues of individual seedlings were homogenized in liquid nitrogen using a FastPre-24 instrument . Genomic DNA was extracted using a DNeasy Plant Mini kit (Qiagen). SNP genotyping was performed using Sequenom iPLEX MassARRAY  at the Génome Québec Innovation Centre, McGill University as described previously . A totalCr4-LG as described previously [X2 (α = 0.05). Markers showing significant (P < 0.05) segregation distortion were initially eliminated from the map construction and were then added later as accessory markers. The polymorphic SNP markers were mapped to Cr4 using JoinMap version 3.0 software [P. taeda consensus maps [Cr4 in Pinus LGs. The Pearson correlation coefficient was used to measure the strength of the genetic map relationship between two Pinus species. The sequence and nucleotide variation has been submitted to GenBank dbSTS and dbSNP databases.Haploid segregation analysis was performed to map SNP markers for construction of eviously . Each SNsoftware . A LOD tsoftware . The latsus maps  were use


Bemisia tabaci Q, has been an important agricultural pest in China since 2008. In order to identify the invasion routes and to provide insight into its invasion success in China, we analyzed the composition, distribution, and genetic diversity of mitochondrial haplotypes of B. tabaci Q. Samples were obtained from 23 provincial level administrative units in 2011, and analyses conducted based on the mtCOI. Our results revealed five haplotypes (abbreviated as Q1H1-Q1H5) were present in the Q1 subclade based on 773-bp mtCOI fragment analysis. The diversity of haplotypes indicated the B. tabaci Q populations were derived from multiple invasion sources originating from the western Mediterranean region. Among the haplotypes, Q1H1 was dominant, followed by Q1H2. The whitefly populations were generally characterized by low levels of genetic diversity based on the 773-bp mtCOI fragment. Similar results were obtained when the 657-bp fragment was analyzed using the procedure in a previous report. Potential mechanisms contributing to the dominance of the Q1H1 in China are also discussed. These results will be helpful in revealing the mechanisms that enabled the successful invasion of B. tabaci Q into the country.Invasive genotypes may be associated with their ability to access the invasion habitat. The whitefly,  Studies involving the genotype and/or genetic diversity of the invasive species will help in determining the invasion mechanisms involved and possibly provide information that will be useful in the sustainable management of species invasions4. The invasive genotypes represented by the predominant haplotypes, for example, may be associated with the invasion route and a set of traits that facilitate their ability to access the invasion habitat7. Alternatively, molecular studies have shown genetic diversity in the invaded regions may contribute to the successful invasion of the alien species10. The analyses of genotype and genetic diversity of invasive species may be conductive to the selection of appropriate management measures such as the interception of the potential invasive individuals and introduction of their natural enemies.Biological invasions often inflict tremendous damage to agriculture, forestry, and natural ecosystems in the introduced regionsBemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae), is one of the most destructive plant pests of major great agriculture importance, causing damage to its hosts directly through feeding on plant sap or indirectly through virus transmission11. Most species and varieties of the B. tabaci complex are restricted to specific geographic regions, but several of them, such as the putative species of MEAM1 (hereafter referred to as B. tabaci B) and MED (hereafter referred to as B. tabaci Q), are highly invasive and have become distributed worldwide14. Since 2003, B. tabaci Q has rapidly spread throughout China in a variety of field crops, displacing the previously established invasive whitefly, B. tabaci B, which has been the globally dominant species for approximately two decades20. However, the invasion route(s) of B. tabaci Q and mechanism underlying its successful invasion into China remain poorly understood.The whitefly, mtCOI) gene has become a very useful tool in exploring the evolutionary aspects of species invasions ever since it was used to serve as the marker of choice in the context of the DNA barcode system21. De Barro and Ahmed3 used the mtCOI haplotype (657 bp fragment) to identify genetic networks of B. tabaci Q on a global scale. Eighty-five haplotypes of this pest have been described to date3. Three distinct mitochondrial variants of B. tabaci Q whiteflies identified by McKenzie et al.22 were widely distributed throughout North America. Nuclear data combined with the mtCOI data strongly support the inference of multiple, independent B. tabaci Q invasions into North America from at least three different introductions23. A previous study24 revealed that B. tabaci Q could be differentiated into two subclades based on the mtCOI gene sequence. These two subclades have been labeled as B. tabaci subclades Q1 and Q2. B. tabaci Q1 mainly distributes in the western Mediterranean region while Q2 mainly in the eastern Mediterranean region. Using mtCOI and nuclear (microsatellite) DNA, an investigation of genetic diversity by Chu et al.25 indicated that B. tabaci Q in China (Shandong Province) may have originated from the western Mediterranean region. The genotype and genetic diversity of B. tabaci Q, using the mtCOI gene, remain unknown from most regions across China. Identifying these will be helpful in tracing the invasion routes of B. tabaci Q and in determining the mechanism underlying its successful invasion into China.The mitochondrial cytochrome c oxidase I  as well as helping to define the likely mechanisms underlying the successful invasion(s) of B. tabaci Q into China.The present study involved sampling an extensive geographic area covering the majority of the known B. tabaci Q, 214 (33%) were B. tabaci B, and 38 (6%) were miscellaneous other “species” of the B. tabaci species complex (abbreviated as “others”). The other forms were mainly four B. tabaci “species” indigenous to China, including Asia II 2, Asia II 6, Asia I, and China126.In total, 648 individual whiteflies from 50 different collections were successfully sequenced and analyzed. Of those individuals, 396 (61%) were B. tabaci Q individuals (based on the 773 bp fragment), 371 individuals’ haplotypes have been determined: 280 (76%) were the Q1H1 haplotype, 79 (21%) were the Q1H2 haplotype, and 12 (3%) were other haplotypes (classified as Q1H3-Q1H5). The Genbank numbers of haplotypes Q1H1-Q1H5 were as follows: KY468416, KY468417, KY468418, KY468419, and KY468420. Using the 657 bp fragment, as defined by De Barro and Ahmed3, 386 individuals’ haplotypes have been determined: 306 (79%) individuals were identified as the Hap1 haplotype (Genbank No. KY468421), and 80 (21%) individuals were the Hap2 haplotype (Genbank No. KY468422).The haplotypes that were determined refers to Q haplotypes with at least two identical sequences. Among the 396 well-defined B. tabaci Q, whereas three collections from Heilongjiang and Taiwan contained only B. tabaci B, Other B. tabaci non-B/Q “species” were found in seven collections from Anhui, Fujian, and Hainan  in only five provinces out of 13 containing mixed species  contained only Based on the 773 bp length fragment, a total of six haplotypes, designated as Q1H1-Q1H5, were identified Fig. . Other t3, only two haplotypes, Hap1 and Hap2, were identified  in each of the remaining mixed-haplotypes provinces except Hainan, Guangxi, and Chongqing.Based on the 657 bp length fragment, as defined by De Barro and AhmedmtCOI gene varied among different geographical populations. Based on the 773 bp fragment, the haplotype diversity (Hd) values found in different provinces ranged from 0.000 to 0.639 with an average Hd value of 0.372  which were shared by all sampling regions based on the 657-bp length fragment as defined by De Barro and Ahmed3  or Q1H1-Q1H5 (based on the 773 bp gene fragment). The haplotype composition of the mtCOI gene can be very informative in affording clues to the likely origin of invading organisms. Previous studies28 involving analysis of the mtCOI gene showed that the B. tabaci Q1 subclade may be distributed primarily in the western Mediterranean countries (e.g. Morocco and Spain) and the Q2 subclade, in the eastern Mediterranean countries (e.g. Israel). Moreover, another previous study demonstrated that all of the B. tabaci Q in China were grouped into subclade Q124. De Barro and Ahmed3 determined that the 657-bp Hap1  and Hap2  haplotypes originated in the western Mediterranean regions. The phylogenetic analysis of NJ trees based on mtCOI sequences of ~650-bp revealed that all haplotypes and the sequences that were found only a single time (Genbank No. KY468408- KY468415) clustered to subclade Q1 25. Chu et al.25 identified four distinct haplotypes . Among these, their haplotype 1 is analogous to Hap 1 (based on 657 bp) or Q1H1 (based on 773 bp), and haplotype 2 analogous to Hap 2 (based on 657 bp) or Q1H2 (based on 773 bp), while their haplotypes 3 and 4 (belonging to the Q1 subclade) were not found in the present study. In addition, our previous study found that the introduced populations of B. tabaci Q in China have high spatial genetic heterogeneity possibly resulting from multiple introductions, rapid evolution following one or few introductions, or some combination of multiple introductions and rapid evolution29. Thus, the introduced haplotype as identified based on the 773-bp mtCOI gene, indicates the Q populations in China may have been introduced from multiple invasion sources.This study revealed that et al.30 stating that high gene flow occurred between B. tabaci Q populations in Shandong Province, China. Previous studies have suggested that genetic diversity is often associated with the ecological adaptation of a species33. Multiple introductions populations with a distinctive genetic composition, may contribute to the founding population and that the accumulation of diversity can act to facilitate the process of invasion34. However, the genetic paradox in species invasions which often experience bottleneck or founder events showed that many invasive species have a low genetic diversity36. Chu et al.25 indicated the loss in haplotype diversity of mitochondrial DNA does not necessarily mean that nuclear allelic diversity should also decline. Our study suggests that the genetic diversity based on mitochondrial DNA is not closely associated with the successful invasion of B.tabaci Q in China. Thus, we postulate that the nuclear diversity of Q in China might not be lower compared to their source or original population as indicated by Chu et al.25.Our results demonstrated that most of the genetic diversity found in our samples is similar except in individuals from Hubei, Jilin, Jiangsu, and Jiangxi provinces, where it is relatively low. The results suggest that the gene flow among most sampled areas across China is extensive, which is consistent with results published by Chu mtCOI haplotypes may be associated with a set of traits such as adaptability to adverse environments, host plant or due to some extent to other external factors, e.g. insecticide applications, that facilitate their ability to successfully colonize the invasion habitat. For B. tabaci Q, we believe insecticide application may have contributed to the prevalence of Q1H1 in China. Pan et al.20 demonstrated that B. tabaci Q was more tolerant of insecticides, and that the overuse of insecticides reversed the B. tabaci B and Q competitive hierarchy and allowed B. tabaci Q to displace B in managed landscapes in China. Notably, field populations of B. tabaci Q have developed high levels of resistance to imidacloprid and thiamethoxam, and the haplotype of B. tabaci Q was further identified to be the haplotype Q1H138. This phenomenon may likely be a major reason why haplotype Q1H1 is now the predominant haplotype in most agricultural regions in China.Surprisingly, we found that the Q1H1 is the dominant haplotype in most agricultural regions in China. The invasive genotypes represented by the predominant B. tabaci “species”39. The present study demonstrated that haplotype Q1H2 is especially widespread in southern China where temperatures, rainfall and humidity are considerably higher than in other parts of the country. Another factor that may be contributing to the prevalence of Q1H2 is the effect of inherited endosymbionts, which are common in many arthropods and are known to have major effects on the biology of their hosts. Our recent study showed that, compared to the Cardinium-uninfected B. tabaci Q, Cardinium-infected B. tabaci Q was less competitive. This would explain why Cardinium-uninfected B. tabaci Q have become the dominate form found in field populations in China41. It has been suggested that endosymbionts may have played a role in the B. tabaci Q invasions in southern Italy, acting as a sex-ratio manipulator and possibly benefiting the host fitness42. The haplotype-endosymbiont associations of B. tabaci Q, as well as other biological differences between Q1H1 and Q1H2 should be further explored to better understand the reasons behind the invasion success of B. tabaci Q in China. Identifying the precise factors may provide the basis contributing to ultimately controlling this harmful pest with appropriate strategies.High temperatures may be another factor in explaining the distribution and composition of B. tabaci in China during 2011. Collection details, host plants, and number of whiteflies are summarized in Table Specimens were collected from greenhouses and cultivated fields of cotton, vegetables, and soybeans from 23 provincial level administrative units, covering the majority of the known distribution of 43. The individual whitefly DNA extracts were stored at −20 °C and used for both species and haplotype determinations.DNA was extracted from individual whiteflies by placing a single whitefly into a plastic tube, adding 10 μl of DNA lysis buffer, and grinding with a sealed pipet. Extracts were first incubated at 65 °C for 15 min and then 95 °C for 10 min prior to homogenizing in a final volume of 50 μlmtCOI) gene, using the primer pair Cl-J-2195 (5′-TTBATTTTTTGGTCATCCAGAAGT-3′) and L2-N-3014 (5′-TCCAATGCACTAATCTGCCATATTA-3′) as described by Frohlich et al.43. All PCR reactions were performed in 13 μl solutions containing 1 × buffer, 0.16 mM of each dNTP, 0.5 mM of each primer, 0.5 unit of Taq DNA polymerase, and 2 μl of template DNA. Cycling conditions consisted of an initial denaturing at 95 °C for 5 min, followed by 35 cycles of 1 min at 94 °C for denaturation, 1 min at 52 °C, for annealing and 1 min at 72 °C for elongation, and final extension at 72 °C for 7 min. The resultant PCRs were electrophoresed with the negative control (sterile water instead of DNA) and positive controls (DNA from previous sequencing) on a 1.5% agarose gel and visualized by Gelview staining. All mtCOI PCR products (~820 bp) were directly sequenced bi-directionally by ABI 3730 DNA analyzer at Sangon Biotech in Shanghai. The sequences were assembled using the CAP3 sequence assembly program (http://doua.prabi.fr/software/cap3). Confirmations of the B. tabaci species were obtained using the 773 bp assembled sequence analysis.Specimens were identified based on the mitochondrial cytochrome oxydase I . The geographical regions were demarcated according to the standard as the under website. .The haplotype network of mtCOI using DnaSP v.5.032 included the number of polymorphic (segregating) sites (S)46, the total number of mutations (η)48, the average number of nucleotide differences (K)49, the number of haplotypes (H), the haplotype diversity (Hd)50, the nucleotide diversity (π)50, the nucleotide diversity with Jukes and Cantor correction (π (JC))51, and the number of net nucleotide substitutions per site between collections with Jukes and Cantor correction, Da (JC)50. Tajima’s D (D)49 and Fu ’s F test52 were conducted to detect deviation from neutrality.The genetic diversity indices of each collection which were analyzed based on Dataset 1


Short tandem repeats (STRs) and single nucleotide polymorphisms (SNPs) have been already used to perform noninvasive prenatal paternity testing from maternal plasma DNA. The frequently used technologies were PCR followed by capillary electrophoresis and SNP typing array, respectively. Here, we developed a noninvasive prenatal paternity testing (NIPAT) based on SNP typing with maternal plasma DNA sequencing. We evaluated the influence factors , the number of total SNP, fetal fraction and effective sequencing depth) and designed three different selective SNP panels in order to verify the performance in clinical cases. Combining targeted deep sequencing of selective SNP and informative bioinformatics pipeline, we calculated the combined paternity index (CPI) of 17 cases to determine paternity. Sequencing-based NIPAT results fully agreed with invasive prenatal paternity test using STR multiplex system. Our study here proved that the maternal plasma DNA sequencing-based technology is feasible and accurate in determining paternity, which may provide an alternative in forensic application in the future. The discovery of cell-free fetal DNA (cffDNA) in maternal blood in 1997 provides the possibility to develop novel noninvasive prenatal paternity testing, which can avoid the procedure-associated fetal loss as well as the restriction of sampling time . In theiHere, we combined massively parallel sequencing and SNP-based method together and developed a novel sequencing-based noninvasive paternity testing (NIPAT). We performed systematic evaluation of influencing factors, such as the SNP frequency, required number of effective SNP, sequencing depth, threshold of fetal fraction, as well as sequencing strategy. We validated the reliability of this sequencing-based NIPAT in real clinical samples, thus showing the feasibility of using NIPAT in a clinical setting.We designed a two-stage study to evaluate the performance of the noninvasive prenatal paternity testing (NIPAT) based on maternal plasma DNA sequencing. In Phase I, one case was recruited to initiate methodology development and described the characteristics such as several affecting factors. In Phase II, 16 clinical pregnancies were recruited to optimize and validate the feasibility in selected SNP panels and established the quality control system. Finally, we chose the best panel for further validation in real clinical pregnancies. An overview of the study workflow is showed in +6 weeks and the maternal age from 26 to 44 years old. Two-milliliter peripheral blood samples of pregnant women and their husbands were collected into EDTA-containing tubes. Maternal plasma was isolated with a two-step centrifugation protocol [th weeks and five milliliters amniotic fluid was obtained from the hospitals. 100 microliters blood samples from both parents and 500 microliters amniotic fluid from 5 families were sent to Shenzhen Municipal Public Security Bureau  for conventional prenatal paternity test using the AmpFlSTR®Identifiler® PCR Amplification Kit (ABI). Meanwhile, sequencing data of 90 unrelated healthy Han Chinese were downloaded from the 1000 Genomes Project (BioProject ID:298320). Detailed pedigree information of all families is listed in Table A in Seventeen families were recruited from the collaborative hospitals. The gestational age (GA) ranged from 13 to 30protocol . AmnioceInformed written consent was obtained from each participant. This study was approved by the institutional review board of BGI-Shenzhen and conducted in accordance with the Declaration of Helsinki.Genomic DNA (g-DNA) of parents and amniotic fluids was extracted with QIAamp DNA Mini Kit (Qiagen) following the manufacturer’s instructions. One microgram g-DNA was fragmented by sonication (Covaris). CffDNA in maternal plasma was extracted from 200-microliter maternal plasma by TIANamp Micro DNA Kit (Tiagen) and the DNA was already fragmented by nature. After end blunted, all fragments were added an “A” tail for the ligation with adaptors. Barcodes were introduced during PCR for multiplex sequencing.TM 2000 (Illumina) according to the manufacturer’s instructions.Three customer-designed panels were obtained from NimbleGen (Roche). DNA libraries were measured with Agilent 2100 bioanalyzer (Agilent) for insert size and quantified by real-time PCR. Three microgram DNA libraries were hybridized to the SNP panels according to the manufacturer’s instructions . CaptureThe paired-end sequencing reads were mapped to the human reference genome  using SOAP2 . The reaerror), where dmother and derror stand for the depth of allele from mother and error  respectively.When the locus was homozygous in both parents with the same genotypes, we defined error as the situation that the fetal genotype was unexpected heterozygote based on Mendel’s law. The formula dfather and dmother stand for the depth of allele from father and mother respectively.For locus homozygous in both parents but with different genotypes, the fetal genotype was an obligate heterozygote based on Mendel’s laws. Thus fetal fraction was calculated using the formula X = Pr, which stands for the probability that male candidate is the biological father; and Y = Pr, which stands for the probability of that a random man is the biological father. According to the Bayesian model, the PI was calculated as:We developed a novel algorithm to describe the paternity index (PI) of a male candidate in a random population based on the effective SNP (the SNP homozygous in mother) in maternal plasma sequencing data. This value was defined as the odds ratio in the formula: Thus the combined paternity index (CPI) was calculated as the product of PI: Fetus|Mother,Father)) was calculated by the Mendel’s Law and independent assortment. The probability of the depth distribution in maternal plasma corresponding to different combination of mother and fetus was calculated by quadrinomial distribution, described as:Moreover, the probability of every candidate fetal genotype  in WGS data of S01 plasma, the calculated Lg(CPI) were 9.88×104 and -8.53×104, the error rate were 3.79‰ and 3.20‰, respectively. This preliminary result based showed that for sequencing-based NIPAT, only HF SNPs worked well with not enough deep sequencing depth (40.06X for HF SNPs and 34.68X for LF SNPs). This outcome was consistent with previous study of invasive paternity test [First, we evaluated the influence of MAF to sequencing-based NIPAT. In our bioinformatics pipeline, the binomial distribution was suited to the frequency distribution of the effective SNP in NIPAT. According to the binomial distribution probability model, there was a positive correlation between MAF, the number of total SNP and the number of effective SNP . To furt5, and the fetal fraction from 1% to 30%; 2) with 10% fetal fraction, the number of effective SNP ranged from 10~1×105 and the effective sequencing depth from 10X~2×103X; 3) with 1×103 effective SNPs, the fetal fraction ranged from 1%-30% and the effective sequencing depth from 10X~2×103X.Based on above results, HF SNPs were chosen for NIPAT. To evaluate the other factors affecting NIPAT, simulating sequencing data with consistent error rate was generated using the sequencing data of S01 . These simulation data had three situations: 1) with 75X sequencing depth, the number of effective SNPs ranged from 10 ~1×103, thus showing significant positive correlation between calculated CPI and fetal fraction. Fetal fraction showed considerable influence to CPI when changed from 1% to 10%. However, once above 10%, fetal fraction had little effect to the calculated CPI  increased from 3.34 (-1.52~-10.74) to 91.84 (30.79~157.30) when the number of effective SNP was 1×10ated CPI . With thd CPI  was recommend  increased from -113.66 (-239.24~-21.91) to 90.95 (36.97~150.90) when the number of effective SNP was 1×10any more . Additioany more . For lowecommend .3~2×103 effective SNPs , MAF of the SNPs greater than 0.3, sequencing depth over 75X, and fetal fraction over 3%. In the condition of fetal fraction less than 3%, >125X sequencing depth was recommended. We firstly performed our bioinformatics pipeline in S01 plasma WGS data to validate the applicability of this method. The calculated Lg(CPI) for NIPAT based on our bioinformatics pipeline in S01 by using effective HF SNP was 9.88×104 and the p-value was < 10−4. Meanwhile, the calculated Lg(CPI)for amniotic fluid was 2.92×104 and the p-value was < 10−4. With Lg(CPI) > 4 and p-value <0.01, we defined the S01 alleged father as biological father, which was consistent with the results based on invasive paternity test  for further validation of our sequencing-based NIPAT method in clinical samples. Array-HLF663243 contained 3.330×103 (176.78~1.55×104) and -4.5534×103 (-2.87×104~ -153.72) respectively. 11.6% (4.2%~15.4%) of effective SNPs assigned wrongly from the pool of potentially informative SNPs for a tangible parents  and -6.31×104 (-3.39×105~-1.19×104) respectively. The error rate for biological father and unrelated males were 2.29‰ (1.33‰~4.36‰) and 41.55‰ (6.27‰~93.89‰), respectively (4 (818~9.17×104) and 1.97×104 (872~1.04×104), respectively  of the biological father and unrelated males were 5.51×10ectively . InteresSeventeen cases underwent sequencing-based paternity test using fetal DNA from amniotic fluid cells. The NIPAT results were 100% (17/17) consistent with the results from invasive paternity test based on amniotic fluid sequencing data. Additionally, 5 in total of 17 cases had conventional paternity test by CE STR, and also showed 100% consistency to NIPAT results (5/5) .Sequencing data from ten families tested by Array-HF8242 was used to study the four influencing factors to verify the findings from simulation data. We extracted sequencing data from this real clinical plasma sequencing data based on controlling variables method.8). We observed that when effective sequencing depth was relative low (40X), the number of effective SNPs should be larger than 1×105 for a highly accuracy (>99.99%) performance of NIPAT . There was no significant correlation between CPI or the number of effective SNPs and the fetal fraction with the same effective sequencing depth (75X) and fetal fraction>10% . Second,of NIPAT . This reCurrent STR-based paternity testing was applied for families with postpartum children  as well as prenatal fetus . For special cases of prenatal testing, such as cases before the 8th week of pregnancy or contraindicated patients with invasive procedures, a noninvasive prenatal paternity test would be useful and necessary to give out a result. Since plasma cffDNA is fragmented, it is hard to obtain sufficient effective loci of short tandem repeat (STR) to determine paternity by using commercial STR typing kit, especially in female fetus. Conside3~8×103, the sequencing depth 75-200X, which could reach over highly accuracy  by using maternal plasma. There is a need for perform noninvasive paternity test in first trimester, however the low fetal fraction (<3.5%) made it difficult to accurate determine paternity. We simulated conditions with 1% fetal fraction, 8×103 total SNPs and 200X effective sequencing depth, and we accurately determined paternity from maternal plasma, providing basic data for NIPAT in first trimester by using our bioinformatics method.Here, we developed a novel, robust and highly accurate maternal plasma DNA sequencing-based noninvasive prenatal paternity testing (NIPAT) and successfully determined paternity in 17 real clinical cases. Furthermore, we evaluated the influence of the number of effective SNPs , the MAF of SNPs, fetal fraction as well as effective sequencing depth. We observed that the number of effective SNPs had a significant relationship with MAF and the number of total SNPs. In addition, there are individual differences between pregnant women, so the effective SNPs was not similar even using the same capture array. Meanwhile, we systematically evaluated the influence of fetal fraction and effective sequencing depth to the power of NIPAT. Based on our results, we suggested that for NIPAT, the general design should be selecting SNPs with HF MAF of 0.3~0.5, the total number of HF SNP of 5×10th week) with relatively high fetal fraction (5.68%~29.74%). Further study should be performed with samples in the first trimester (<12th week) with lower fetal fraction. Second, the SNP was selected from db135 with MAF >0.3, validated only in Chinese population. Other ethnic groups need to be verified in the subsequent study. Last, a large-scale study should be developed to evaluate the accuracy of this method in clinical samples.There were some limitations in our study. First, all our recruited samples were in the second trimester Click here for additional data file.S2 FigIn the boxplots, the y-axis marks the logarithmic value of combined paternity index, and the x-axis stands for the concentration of cffDNA in the plasma. The plots of a, b, c, d, e and f are corresponded to different number of effective loci, which is predicted in the x-axis.(TIF)Click here for additional data file.S3 FigIn the boxplots, the y-axis marks the logarithmic value of CPI, and the x-axis stands for the sequencing depth of the plasma. The plots of a, b, c, d, e and f are corresponded to different number of effective loci, which is predicted in the x-axis.(TIF)Click here for additional data file.S4 FigIn the boxplots, the y-axis marks the logarithmic value of combined paternity index, and the x-axis stands for the number of effective SNPs.(TIF)Click here for additional data file.S1 FileDetail information of PI calculation formula was listed in the S1 file.(DOCX)Click here for additional data file.S2 FileDetail loci information of three arrays was listed in the S2 file.(RAR)Click here for additional data file.S3 FileAll supporting Tables were listed in the S3 File. Table A listed the clinical information of all samples. Table B listed the basic sequencing information of all samples. Table C listed the number of SNPs where assigned wrongly from the pool of potentially informative SNPs for a tangible parents. Table D listed the information of effective depth and the number of effective SNP of all samples. Table E listed the basic simulated information of clinical samples.(XLSX)Click here for additional data file.


After its domestication, rice cultivation expanded from tropical regions towards northern latitudes with temperate climate in a progressive process to overcome limiting photoperiod and temperature conditions. This process has originated a wide range of diversity that can be regarded as a valuable resource for crop improvement. In general, current rice breeding programs have to deal with a lack of both germplasm accessions specifically adapted to local agro-environmental conditions and adapted donors carrying desired agronomical traits. Comprehensive maps of genome variability and population structure would facilitate genome-wide association studies of complex traits, functional gene investigations and the selection of appropriate donors for breeding purposes.A collection of 217 rice varieties mainly cultivated in temperate regions was generated. The collection encompasses modern elite and old cultivars, as well as traditional landraces covering a wide genetic diversity available for rice breeders. Whole Genome Sequencing was performed on 14 cultivars representative of the collection and the genomic profiles of all cultivars were constructed using a panel of 2697 SNPs with wide coverage throughout the rice genome, obtained from the sequencing data. The population structure and genetic relationship analyses showed a strong substructure in the temperate rice population, predominantly based on grain type and the origin of the cultivars. Dendrogram also agrees population structure results.Based on SNP markers, we have elucidated the genetic relationship and the degree of genetic diversity among a collection of 217 temperate rice varieties possessing an enormous variety of agromorphological and physiological characters. Taken together, the data indicated the occurrence of relatively high gene flow and elevated rates of admixture between cultivars grown in remote regions, probably favoured by local breeding activities. The results of this study significantly expand the current genetic resources available for temperate varieties of rice, providing a valuable tool for future association mapping studies.The online version of this article (doi:10.1186/s12284-016-0130-5) contains supplementary material, which is available to authorized users. Oryza sativa L.) domestication occurred in southern China and, concomitant with human migrations, expanded to a wide range of geographical regions with diverse climates , for instance, has been generated as a result of re-sequencing 3000 rice genomes (3K RPG The characterization of genome diversity can be efficiently performed by using next-generation sequencing (NGS) technologies that enable the massive identification of single nucleotide polymorphism (SNP) markers. These markers have been successfully applied for this purpose previously in many major crops . Approximately 78 % of them corresponded to unique reads (Table Genome sequencing of the subset of 14 varieties generated a mean of 75 × 10A custom Infinium SNP genotyping array (Illumina) compiling 2697 SNPs selected out of the polymorphisms identified in the 14 rice cultivars was designed. The SNP panel was generated by selecting bi-allelic polymorphisms with a uniform distribution along the 12 chromosomes, but avoiding centromere regions where gene occurrence is scarce. SNPs that were detected in more than one cultivar were prioritized. These selected SNPs were manually curated using Integrative Genomics Viewer  software. The mean interval distance between adjacent SNPs was 137,525 pb. The number of SNPs per chromosome ranged from 163 in chromosome 9  to 321 SNPs in chromosome 1 . The distribution of these SNPs reflected their non-redundancy. The number of selected SNPs varied among cultivars, ranging from 761 SNPs in L-202 to 502 in Bahia, with an average of 607 SNPs per genome (Table The proportion of SNPs located within intergenic regions (54.8 %) was higher than those within genic regions 22.1 %) . The LD decay rate was calculated as the chromosomal distance where the correlation coefficient (r2) between SNP pairs dropped to half its maximum estimated value. LD estimation reflects the strong population structure of the japonica subpopulation in our collection, since r2 drops to 0.23 when chromosomal distance extends to approximately 368 kb while in the 3KRPG subpopulation we estimated a more rapid decay that extends to 174 kb for the same value of correlation coefficient  below 5 % were removed, then originating a panel of 1713 SNPs evenly distributed along the genome at a mean distance of 215,223 pb. Using this panel we estimated the extent of linkage disequilibrium (LD) in the japonica and indica varietal groups in our collection and in the temperate japonica 3KRPG subpopulation  indicating that the optimum number of subpopulations was 4 , which separated a new subgroup that included Spanish cultivars. Accessions showing higher than 80 % of membership to this cluster were released in the mid-twentieth century. A total of 12 accessions were classified as admixed and showed approximately 50 % of membership to the Spanish and American clusters. This admixed group includes accessions originated in Spain that were released approximately at the same time, during the second half of the twentieth century. The proportion of the genetic content belonging to these two clusters clearly reflects their breeding history, since these cultivars were obtained when American germplasm was introduced into the Spanish breeding programs in the early 20th century.A further maximum of ∆K was found for K = 5 (∆A subsequent division of long grain cluster was observed at K = 6 (∆K = 3.5), which separated in a new subgroup a set of long grain varieties, as Moroberekan, Agami and Honduras, cultivated in tropical regions.To deepen in the patterns of population structure we performed principal components analysis (PCA) with the set of 1713 SNPs selected from the Infinium analysis. The first PC, which accounted for 22.1 % of the variance, separated cultivars according to the grain size, which agrees with the four most probable groups described in the population structure analysis, since long-grain type cultivars included in cluster 2 are separated from the other groups Fig. .Fig. 6Prindica varieties appear in a highly supported cluster. Closely related to indica cluster, long grain type varieties diverge into a wider cluster that includes most of the varieties grouped in cluster 2 in the population structure analysis, such as japonica accessions from tropical regions (Azucena or Moroberekan) and temperate regions (Cormoran or Apolo). Aromatic rice cultivars, for instance Fragance or Giglio, can also be distinguished among them.The 1713 SNPs panel was used to determine the relationship of cultivars in our collection and to estimate genetic distances among them. Basically, the 217 cultivars were grouped into a dendrogram with clusters arranged similarly to the distribution obtained in the STRUCTURE analysis , U.S. National Plant Germplasm System , Rice Genome Resource Center  and Instituto Valenciano de Investigaciones Agrarias . Seeds of different cultivars were germinated and grown in pots in greenhouses (39° 28’ N) under controlled temperature (25 °C) and relative humidity (50 % RH), and in natural daylight conditions during summer in 2013 and 2014. Plantlets were manually transplanted in rows of 20 plants in May and harvested in September. Fields were irrigated by flooding. Heading time was measured as 50 % of panicle emergence in each row.japonica cultivars were previously selected to carry out the SNPs characterization (Table Fourteen varieties representative of on Table . Seven don Table . Genome http://www.ebi.ac.uk/ena/data/view/PRJEB13328).The libraries were sequenced using TruSeq SBS Kit v3-HS (Illumina Inc.), in paired end mode, 2x101bp, in ½ of a sequencing lane of HiSeq2000 flowcell v3 (Illumina Inc.) according to standard Illumina operation procedures with the yield of >14Gb and median coverage of 33-41x. Primary data analysis, the image analysis, base calling and quality scoring of the run, was processed using the manufacturer’s software Real Time Analysis (RTA 1.13.48) and followed by generation of FASTQ sequence files by CASAVA. Data have been deposited at the European Nucleotide Archive (ENA) in the European Bioinformatics Institute (EBI) with the accession number PRJEB13328 ) using the GEM toolkit (version 2) . The adaptability to the Illumina iSelect detection system of the SNPs and their neighbouring sequences was scored and SNPs with a score higher than 0.4 were selected for the multiplexing Infinium Assay (Illumina Inc.).A panel of 2697 SNPs representative of The 217 accessions were genotyped with the 2697 SNP array. Comparison of the called genotypes was performed using GenomeStudio software (Illumina). Low frequency alleles, considered when they were present in less than 5 % of the cultivars, were removed. Genotypes were missing data if they failed in more than 50 % of the cultivars.http://bioinfo.sibs.ac.cn/carmo/).Genomic variation analysis and classification of SNP according to the TIGR rice gene models was implemented in platform CARMO (Comprehensive Annotation of Rice Multi-Omics data)  for the 1713 SNPs panel in prcomp command in R version 3.2.3. We called genotypes for 1713 SNPs from the panel used in the LD analysis. Data were analyzed using ggbiplot (version 0.55).A Principal Component Analysis (PCA) was performed using the K) and calculated the membership proportion of cultivars to them. Analyses were based on the admixture ancestral model for a range of K values from 1 to 15. We performed 20 runs for each K, and removed those with extreme values of L(K) that were tagged as outliners according to Evanno et al. (K clusters was estimated with the ad hoc parameter (ΔK) of Evanno et al. (Population genetic structure of the cultivars was estimated using the Bayesian clustering method implemented in STRUCTURE 2.3.4 (Pritchard et al. o et al. . Each ruo et al.  in Struco et al. . We estio et al. , employiPairwise genetic distances between rice cultivars were calculated as implemented in Darwin 6.0 (Perrier et al. We also used the software STRUCTURE to estimate Fst values for the genetic clusters established for each group, the mean value of 20 runs was calculated. Finally, Fst pairwise values between the subpopulations defined by STRUCTURE analysis were calculated in Arlequin (Excoffier and Lischer


Aggressiveness in humans is a hereditary behavioral trait that mobilizes all systems of the body—first of all, the nervous and endocrine systems, and then the respiratory, vascular, muscular, and others—e.g., for the defense of oneself, children, family, shelter, territory, and other possessions as well as personal interests. The level of aggressiveness of a person determines many other characteristics of quality of life and lifespan, acting as a stress factor. Aggressive behavior depends on many parameters such as age, gender, diseases and treatment, diet, and environmental conditions. Among them, genetic factors are believed to be the main parameters that are well-studied at the factual level, but in actuality, genome-wide studies of aggressive behavior appeared relatively recently. One of the biggest projects of the modern science—1000 Genomes—involves identification of single nucleotide polymorphisms (SNPs), i.e., differences of individual genomes from the reference genome. SNPs can be associated with hereditary diseases, their complications, comorbidities, and responses to stress or a drug. Clinical comparisons between cohorts of patients and healthy volunteers (as a control) allow for identifying SNPs whose allele frequencies significantly separate them from one another as markers of the above conditions. Computer-based preliminary analysis of millions of SNPs detected by the 1000 Genomes project can accelerate clinical search for SNP markers due to preliminary whole-genome search for the most meaningful candidate SNP markers and discarding of neutral and poorly substantiated SNPs.Here, we combine two computer-based search methods for SNPs  {i} Web service SNP_TATA_Comparator  and {ii} PubMed-based manual search for articles on aggressiveness using heuristic keywords. Near the known binding sites for TATA-binding protein (TBP) in human gene promoters, we found aggressiveness-related candidate SNP markers, including rs1143627 (associated with higher aggressiveness in patients undergoing cytokine immunotherapy), rs544850971 (higher aggressiveness in old women taking lipid-lowering medication), and rs10895068 (childhood aggressiveness-related obesity in adolescence with cardiovascular complications in adulthood).After validation of these candidate markers by clinical protocols, these SNPs may become useful for physicians (may help to improve treatment of patients) and for the general population (a lifestyle choice preventing aggressiveness-related complications).The online version of this article (doi:10.1186/s12864-016-3353-3) contains supplementary material, which is available to authorized users. Ethologists define aggressive behavior as a hereditary behavioral pattern performing functions that are important for preservation of the species, namely, defense of the territory, progeny, and shelter and establishment of social-hierarchical relationships within society . MoreoveOne of the biggest modern scientific projects—1000 Genomes —involvesBiomedical SNP markers represent differences between an individual human genome and the reference human genome; these markers can help to improve a medical treatment , to prevWithin this mainstream approach, SNPs located in protein-coding gene regions  seem to http://beehive.bionet.nsc.ru/cgi-bin/mgs/tatascan/start.pl) , people with a minor allele of this SNP can modify their lifestyle to minimize their contact with materials containing lead.It should be emphasized that known SNP markers of monogenic diseases ese SNPs . For exaIn this study, we encountered a huge number of clinical cases, retrospective reviews, research articles, laboratory data, and empirical findings—on aggressiveness in various life situations—from clinicians, pharmacists, physiologists, geneticists, psychologists, bioinformaticians, pedagogues, sociologists, legal scholars, economists, and other relevant experts such as specialists on insurance, management, health care, law enforcement, and environmental protection. The gigantic scale, multidisciplinary nature, complexity, and disarray of this information pool may hinder the use of this vital knowledge for broad practical applications in the general population. As shown in Tables d) for binding of human TATA-binding protein to 26-bp synthetic duplex DNA identical to the SNP in question . After that, such SNP markers will become practically applicable. To facilitate the validation, for each predicted candidate SNP marker we show a quantitative parameter: the equilibrium dissociation constant  as well as for the general population (may help to choose a lifestyle preventing aggressiveness-related comorbidities and complications).2 until the desired level of confluence. All the experiments were performed at 80–85% confluence. Oligonucleotides corresponding to ancestral and minor alleles of the predicted candidate SNP marker rs201381696 (Table Cell lines HCT116 (human colon adenocarcinoma) and MCF-7 (carcinoma of the human mammary gland epithelium) were cultivated in a complete medium consisting of Dulbecco’s modified Eagle’s medium/Nutrient mixture F-12 Ham, supplemented with 10% (v/v) of fetal bovine serum (Sigma) and penicillin (100 U/mL) and streptomycin (100 mg/mL) (BioloT). The cultures were maintained at 37 °C in a humidified atmosphere containing 5% of CO96 Table  were clo0D) ± δ of the affinity of TBP for the  promoter region  using our Web service [We analyzed DNA sequences of the human gene promoters retrieved from the human reference genome using the standard BioPerl library  via our  service , 52 as d service –150.For each case of predicted significant overexpression or underexpression of the human genes , we conducted a manual two-step keyword search in the NCBI databases  as descrIn addition, in Additional file


Tuber yield and starch content of the cultivated potato are complex traits of decisive importance for breeding improved varieties. Natural variation of tuber yield and starch content depends on the environment and on multiple, mostly unknown genetic factors. Dissection and molecular identification of the genes and their natural allelic variants controlling these complex traits will lead to the development of diagnostic DNA-based markers, by which precision and efficiency of selection can be increased (precision breeding).Three case-control populations were assembled from tetraploid potato cultivars based on maximizing the differences between high and low tuber yield (TY), starch content (TSC) and starch yield . The case-control populations were genotyped by restriction-site associated DNA sequencing (RADseq) and the 8.3 k SolCAP SNP genotyping array. The allele frequencies of single nucleotide polymorphisms (SNPs) were compared between cases and controls. RADseq identified, depending on data filtering criteria, between 6664 and 450 genes with one or more differential SNPs for one, two or all three traits. Differential SNPs in 275 genes were detected using the SolCAP array. A genome wide association study using the SolCAP array on an independent, unselected population identified SNPs associated with tuber starch content in 117 genes. Physical mapping of the genes containing differential or associated SNPs, and comparisons between the two genome wide genotyping methods and two different populations identified genome segments on all twelve potato chromosomes harboring one or more quantitative trait loci (QTL) for TY, TSC and TSY.Several hundred genes control tuber yield and starch content in potato. They are unequally distributed on all potato chromosomes, forming clusters between 0.5–4 Mbp width. The largest fraction of these genes had unknown function, followed by genes with putative signalling and regulatory functions. The genetic control of tuber yield and starch content is interlinked. Most differential SNPs affecting both traits had antagonistic effects: The allele increasing TY decreased TSC and vice versa. Exceptions were 89 SNP alleles which had synergistic effects on TY, TSC and TSY. These and the corresponding genes are primary targets for developing diagnostic markers.The online version of this article (doi:10.1186/s12864-017-3979-9) contains supplementary material, which is available to authorized users. Solanum tuberosum L.), the dry matter content of the tubers ranges from about 18 to 26% of the total yield. The main component is the glucose polymer starch [Crop yield is essential in agriculture and therefore a major selection criterion in any crop breeding program. New cultivars with higher yield per area unit are required for producing enough food for a growing world population under limited availability of arable land. Other than in cereals, a high portion of the total yield of root and tuber crops  consists of water. In case of the cultivated potato  for tuber yield and starch content (or specific gravity or dry matter), using various types of DNA-based markers, has been performed in a number of diploid and tetraploid, in intra- as well as interspecific potato families comprising between 50 and 230 full sib clones –11. IrreAssociation mapping in populations of individuals related by descent increases the genetic resolution, as it takes advantage of recombination events over multiple meiotic generations in multiple parents . AssociaThe candidate gene approach was appropriate for association mapping of tuber starch content because the biochemistry and molecular genetics of starch metabolism is well understood in plants including the potato. Many genes encoding enzymes and transporters functional in carbohydrate metabolism have been cloned, sequenced and functionally characterized , 24. TheRecently, new potato genomic resources became available, which allow a more comprehensive approach to the dissection of QTL and possibly the identification of the causal genes. The annotated genome sequence provides physical maps of the twelve potato chromosomes, to which any QTL can be anchored by means of linked or associated sequence based markers , 32. ThiIn this paper we address the question of the number and physical position of QTL for tuber yield, starch content and starch yield based on genome wide SNP genotyping of tetraploid potato cultivars using both RAD sequencing and the 8.3 K SolCAP SNP array . We compTwo populations of tetraploid varieties and breeding clones were used for association mapping. The QUEST population consisted of 264 varieties and breeding cloness, which were evaluated in Northern Spain in two years at two locations for tuber yield (TY), starch content (TSC) and starch yield (TSY) . A subseKpnI. The 96 libraries were individually barcoded with a custom made P1 adapter, which was ligated to the KpnI cut sites. Adapter ligations were incubated at 16 °C overnight. The barcodes were designed to be separated from each other by at least six mutational steps. The 96 libraries were then pooled into two samples comprising 48 genotypes each. The two samples were sheared by ultrasound and DNA fragments of 300 to 400 bp were extracted after size separation from 1.5% agarose gels. An A-overhang was added to the blunt ends and the common P2 adapter was ligated as before to the template. P1 and P2 adapters were kindly provided by Anja Bus and Benjamin Stich (MPI for Plant Breeding Research). After a PCR (polymerase chain reaction) enrichment step, a second size selection of 300–400 bp fragments was performed. Sample quality was assessed on a 2100 Bioanalyzer . The two samples were custom sequenced in two lanes of an Illumina HiSeq2000 system at the Max-Planck Genome Centre Cologne using GAIIx chemistry.Ninety six RAD (Restriction site Associated DNA) libraries for paired-end sequencing were prepared from 90 QUEST genotypes and six duplicate genotypes according to Etter et al.  with modPaired-end reads were combined from both sequencing lanes and sorted according to the barcode, allowing maximally one mismatch. No mismatch was allowed at the restriction site. The sequences were mapped against the potato genome sequence  was assigned to each individual for each SNP. The quality of the genotype clusters of differential/associated SNPs was visually examined a posteriori with GenomeStudio software version 2011.1 (Illumina) and very low quality SNPs were discarded. To detect SNPs with differential allele frequency in the QUEST case-control populations, Pearson’s goodness-of-fit test (chi-square test) was applied to test the null hypothesis that the allele frequencies of a bi-allelic SNP in a pair of genotype pools with contrasting phenotypic means were equal. The null hypothesis was rejected at the significance level α < 0.01. The chi-square tests were performed using SAS software (version 9.1). Duplicates of six QUEST genotypes were included in the analysis. For each genotype duplicate Pearson’s correlation coefficient between all genotypic data was calculated to assess the technical repeatability of the genotyping method.Genomic DNA was extracted from freeze dried leaf tissue as described , 41. DNAPCR amplification of genomic fragments, amplicon sequencing, pyrosequencing, SNP detection and scoring were performed as described previously , 49. AmpIn the QUEST population, association analysis of SNPs in candidate genes was performed as described in Schönhals et al.  using a p < 0.001). The two groups selected for TSC differed also for TSY (p < 0.001) but not for TY (p = 0.732), the groups selected for TY differed also for TSY (p < 0.001) but not for TSC (p = 0.576), whereas the groups selected for TSY differed also for TSC and TY (p < 0.001).Based on the adjusted means for TSC, TY and TSY of 264 genotypes of the QUEST population , ninety http://www.ebi.ac.uk/ena/data/view/PRJEB10900). SNPs were identified by mapping the sequence reads to the potato genome sequence. SNP allele frequencies were estimated after pooling the data of the HIGH and LOW groups for TSC, TY and TSY. The results are summerized in Table The case-control populations for TSC, TY and TSY were genotyped by RAD sequencing (European Nucleotide Archive (ENA): Study accession No. PRJEB10900, Twenty three percent 6000) of the SNPs had differential allele frequencies in two case-control populations, in TSC and TY 3005), TSC and TSY (665) or in TY and TSY 2330) (FDR < 0.05) , transcriptional regulation (transcription factors), protein synthesis , protein degradation by the 26S proteasome pathway  or by proteases, chaperones , organelle biogenesis and function (Pentatricopeptide repeat containing proteins), transport , cell wall synthesis and modification , chromatin organization and modification (e.g. ‘chromo’ domain containing proteins), biotic stress (nucleotide binding site – leucine rich repeat (NBS-LRR) type resistance genes) and retrotransposons   that could be assigned to one of the five genotype classes n = 264), we selected eleven candidate genes for association analysis  in the PIN184 population is shown in Fig. Identity was low between differential or associated SNPs detected in the three experiments . Six of 306 SolCAP SNPs with differential allele frequency in the QUEST case-control populations were also detected by RAD sequencing in the same populations  . As in BThe majority of differential SNPs in the QUEST case-control populations showed differential allele frequency between the HIGH-TSC and LOW-TSC populations, based on SNP genotyping by RAD sequencing as well as by the SolCAP SNP chip. The same observation applied to the number of genes with differential SNPs. Possible reasons for this observation are that more genes control tuber starch content than tuber yield, or that many genes with small effect on tuber yield were below detection level. Alternatively, the phenotypic selection of the HIGH-TY and LOW-TY genoptypes might have been less effective due to the lower heritability of tuber yield compared with starch content , resultiMore SNPs were differential for both TY and TSY than for TSC and TSY, and the same was true for the corresponding genes. This indicates that tuber starch yield was more strongly influenced by genes controlling tuber yield than starch content.Eighty percent of the genes detected by RAD sequencing contained multiple differential SNPs, and the largest fraction of these contained different SNPs that were differential for TSC, TY, TSY and combinations thereof Table . Many ofKpnI restriction site used for generating the RAD libraries. The SolCAP SNPs represented a small fraction of the DNA variation among five US varieties and the European variety Bintje [The three QUEST case-control populations for TSC, TY and TSY were genotyped for genome wide SNPs on the one hand by RAD sequencing and on the other with the 8.3 K SolCAP SNP chip. The main difference between the two genotyping methods was the genome coverage, which was two orders of magnitude higher in RAD sequencing. The average physical distance between SNPs was approximately 1.25 kbp in RAD sequencing and 100 kbp between SolCAP SNPs. The average SNP frequency in the genome of tetraploid potatoes is one SNP in every twenty to thirty base pairs . Both gey Bintje , whereasy Bintje . The detThe numbers of genes with differential SNPs obtained with RAD sequencing were one order of magnitude higher than with the SolCAP SNP array, most likely due to inflation by extensive LD between differential SNPs in physically closely linked genes, only one of which might have a genuine effect on the phenotype. Although a direct estimate of LD was not possible in the case-control populations (see above), we assumed that LD caused the peaks observed in the frequency distribution of genes with differential RADseq SNPs on the physical chromosome maps Figs. . We furtPHO1a on chromosome III, a well characterized functional candidate gene that was also associated with TSC in two additional, independent populations, although with different SNPs [PHO1a is one of the causal genes for tuber starch content QTL [GWAS in the PIN184 population with the SolCAP SNP chip allowed a comparison of QTL for TSC in different genetic backgrounds, which were evaluated in different geographical regions. The QUEST population was evaluated in northern Spain  and the ent SNPs , 16, 18.tent QTL . On the tent QTL , particup values and analysed genomic sequences could increase the overlap between results of the candidate gene and the genome wide approach.Two additional populations of tetraploid varieties and breeding clones different from the QUEST and PIN184 populations studied here, have been previously phenotyped, among other traits, for tuber starch content and yield , 18. ThePHO1a locus was detected by the same SolCAP SNP in the QUEST case-control population for TSC as well as in the unselected PIN184 population (see above), whereas the marker for a specific PHO1a allele diagnostic for higher tuber starch content [Eight of the ten candidate loci associated with TSC, TY or TSY in the whole QUEST population  were not content , 17 was  content .These comparisons show that the results obtained with different study designs and genotyping strategies in different genetic backgrounds partially overlap. Such comparisons are novel in potato genome analysis and highly useful for selecting the most promising candidate genes for breeding applications as well as further functional studies.PR1 gene (DMG400037874) and a cluster of methylketone synthase genes possibly involved in insect resistance [RAD sequencing provided the highest genome coverage of the three experiments described in this study. RAD sequencing offered therefore the best chance that some among the 6664 genes with differential RADseq SNPs are causal for TSC-, TY- and TSY-QTL. Enriched for such genes should be the 2308 genes with differential SNPs causing amino acid changes, although we cannot exclude that some amino acid changes deduced from sequence alignment are artefacts caused by the algorithm used for mapping the sequence reads . Stringesistance  between This is the first study, which physically dissects and maps QTL for potato tuber starch content, yield and starch yield based on genome wide, high troughput genotyping methods and the potato genome sequence. Comparative genotyping of three case-control populations by RAD sequencing and the 8.3 k SolCAP SNP array showed that 8303 SolCAP SNPs were not sufficient for the comprehensive tagging of QTL for TSC, TY and TSY in the potato genome, whereas 600,000 RADseq SNPs tagged most QTL redundantly due to LD within genes and between physically linked genes. Neither method captured fully the DNA diversity present in tetraploid potato cultivars. Comparative RAD sequencing of the QUEST case-control populations identified, depending on the criteria for data filtering, between 450 and 6664 annotated genes with differential SNPs for TSC, TY, TSY and combinations thereof. Based on a small sample of eleven genes, approximately half of the differential SNPs in 80% of the genes could be confirmed by association analysis in the unselected QUEST population. The 450 genes which contained highly differential SNPs causing amino acid changes, are considered as primary functional candidates for controlling the natural variation of tuber yield and starch content. TSC and TY are partially controlled by identical genes with pleotropic effects. In the majority of cases, a positive effect on yield was confounded by a negative effect on starch content and vice versa. However, 89 SNP alleles had synergistic effects on TY, TSY and TSY. These SNPs and the corresponding 72 genes are primary targets for the development of diagnostic markers for breeding applications. The positions of the genes with differential SNPs on the physical chromosome maps of potato suggest that at least 200 QTL regions with one or more underlying genes on all chromosomes control the natural variation of tuber starch content and yield, and consequently starch yield, in the QUEST population. GWAS for tuber starch content in the independent, unselected PIN184 population using the SolCAP SNP chip identified SNPs associated with TSC in 117 genes. The comparison of the physical QTL maps of the QUEST and the PIN184 population identified genomic regions, which harbour QTL for TSC, TY and TSY across genetic backgrounds and environments.Additional file 1:Phenotypic values (adjusted means) of 90 cultivars of the QUEST population used for the assembly of case-control groups with high (1) and low (0) tuber starch content (TSC), yield (TY) and starch yield. (TSY) (XLSX 15 kb)Additional file 2:n = 264) for association with TSC, TY and TSY. (DOCX 23 kb)Sequences, primers, annealing temperatures, SNP positions and nucleotide alleles of eleven genes fragments analysed in the QUEST population  in 6664 annotated genes in the QUEST case-control populations. Trait TSC is highlighted blue, TY orange and TSY green. SNPs differential for two traits are highlighted yellow. SNPs differential for all three traits are highlighted red. (XLSX 8035 kb)Additional file 4:Alleles and allele counts of differential RADseq SNPs in 3144 annotated genes with at least one SNP at FDR < 0.001 in the QUEST case-control populations. Trait TSC is highlighted blue, TY orange and TSY green. SNPs differential for two traits are highlighted yellow. SNPs differential for all three traits are highlighted red. Loci highlighted dark green contain SNPs with FDR < 0.0001 and causing amino acid changes. (XLSX 2822 kb) Additional file 5:Distribution of 3144 annotated genes with differential RADseq SNPs on the physical chromosome maps at a resolution of 0.5 Mbp. Intervals with at least 10 genes per 0.5 Mbp are highlighted yellow. (XLSX 37 kb)Additional file 6:Alleles and allele counts of RADseq SNPs with differential allele frequencies (FDR < 0.05) in two case-control populations: TSC and TY, TSC and TSY or TY and TSY. Trait TSC is highlighted blue, TY orange and TSY green. (XLSX 1349 kb)Additional file 7:Alleles and allele counts of RADseq SNPs with differential allele frequencies (FDR < 0.05) in all three case-control populations: TSC, TY and TSY. Trait TSC is highlighted blue, TY orange and TSY green. SNPs with unidirectional or neutral effect on the three traits are highlighted dark green. (XLSX 610 kb)Additional file 8:Differential SolCAP SNPs in 275 genes in the QUEST case-control populations. Trait TSC is highlighted blue, TY orange and TSY green. (XLSX 84 kb)Additional file 9:SolCAP SNPs in 117 genes associated with TSC in the PIN184 population. (XLSX 38 kb)Additional file 10:Identical genes detected by differential SolCAP and RADseq SNPs in the QUEST case-control populations. (DOCX 24 kb) Additional file 11:Identity of genes detected in previous association studies using the candidate gene approach with genes detected in this study. (DOCX 18 kb)