Solanum pennellii for tomato and S. macrocarpon for eggplant). Maximum Likelihood phylogenetic trees and PCA outputs obtained from the whole dataset highlighted genetic relationships among accessions and species which were congruent with what was previously reported in literature. Better discrimination among domesticated accessions was achieved by using the target SNPs, while better discrimination among wild species was achieved using the whole SNP dataset. Our results reveal that SPET genotyping is a robust, high-throughput technology for genetic fingerprinting, with a high degree of cross-transferability between crops and their cultivated and wild relatives, and allows identification of duplicates and mislabeled accessions in genebanks.Single primer enrichment technology (SPET) is a new, robust, and customizable solution for targeted genotyping. Unlike genotyping by sequencing (GBS), and like DNA chips, SPET is a targeted genotyping technology, relying on the sequencing of a region flanking a primer. Its reliance on single primers, rather than on primer pairs, greatly simplifies panel design, and allows higher levels of multiplexing than PCR-based genotyping. Thanks to the sequencing of the regions surrounding the target SNP, SPET allows the discovery of thousands of closely linked, novel SNPs. In order to assess the potential of SPET for high-throughput genotyping in plants, a panel comprising 5k target SNPs, designed both on coding regions and introns/UTRs, was developed for tomato and eggplant. Genotyping of two panels composed of 400 tomato and 422 eggplant accessions, comprising both domesticated material and wild relatives, generated a total of 12,002 and 30,731 high confidence SNPs, respectively, which comprised both target and novel SNPs in an approximate ratio of 1:1.6, and 1:5.5 in tomato and eggplant, respectively. The vast majority of the markers was transferrable to related species that diverged up to 3.4 million years ago ( Single nucleotide polymorphisms (SNPs) are the most abundant type of sequence variation in eukaryotic genomes and have emerged as the most widely used genotyping markers . Genotyp® developed the single primer enrichment technology , which is a customizable solution for targeted sequencing at an affordable price. SPET requires a priori genomic or transcriptomic information and identification of SNPs for probe design. SPET probes are around 40-bases long and are designed adjacent to a region containing a sequence variant, thus enabling detection of both the SNPs and the discovery of additional ones, surrounding the target one  and eggplant (S. melongena L.) are amongst the economically most important vegetables, and the diversity and genetic relationships of their genepools has been extensively studied. Several studies have applied sequence-, PCR- or microarray-based genotyping in tomato and eggplant, to analyze the genetic diversity and population structure of a limited number of cultivars, breeding lines, landraces or cultivated, and wild relatives , the wild species genetically closest to cultivated tomato are those of the “Lycopersicon” group  Fosberg and S. galapagense S.C. Darwin & Peralta. While the genetic diversity of the latter two species is limited  D.M. Spooner, G.J. Anderson & R.K. Jansen, and S. neorickii D.M. Spooner, G.J. Anderson & R.K. Jansen, followed by the five species of the “Eriopersicon” group  Reiche, S. corneliomulleri J.F. Macbr, S. peruvianum L. and S. habrochaites S. Knapp & D.M. Spooner), and S. pennellii Correll, the only species included in the monotypic “Neolycopersicon” group  and gboma eggplant (S. macrocarpon L.) and their respective wild ancestors S. anguivi Lam. and S. dasyphyllum Schumach. & Thonn. as well as other species of potential for breeding such as S. tomentosum L.  only positions with alternative cohort-wise allele count greater than 8 (summing two from homozygous loci or one from heterozygous loci), (ii) SNPs within introns and UTRs had to be at least 15 kbp apart from each other or with SNPs in CDS, (iii) SNPs within CDS had to be at least 5 kbp apart from other selected SNPs, and (iv) SNPs have to reside on anchored chromosomes.S. melongena and one S. incanum accessions , were aligned against the “67/3” eggplant reference genome  homozygous states called with a minimum of 10 reads; (ii) MAF  >0.04; (iii) heterozygosity comprised between MAF 0.25–0.5; (iv) a minimum genotyping ratio of 80% . This allowed to retain 7,662 out of 11,625 SNPs (72%).From the eligible polymorphic sites previously identified in both the species, a randomly selected panel of SNPs were forwarded for probe design to NuGen . These filtered tomato and eggplant panels were then tested for their sequencing performances and reproducibility, and to identify a final set of about 5k probes for genotyping via SPET, commercialized under the name of AllegroBesides cross-species genomic sequence conservation and the frequency of expected SNP detection, in both crops, probes were first filtered based on their ability to hit the target SNPs in the first 25 bp after 3′-end of the probe. After the first pilot run with about twice the number of probes, a coverage analysis was used to select the final set of 5,000 and 5,082 probes in tomato and eggplant respectively. Sites showing an average coverage ranging from 46× to 90× and with less than four samples having five or less mapped reads were retained for tomato, while sites with a coverage range of 79–130× and excluding all those probes with three or more individual showing a coverage below 5× were selected for eggplant. Furthermore, 82 extra probes were added to the final eggplant set for specific functional purposes. Sequencing yields, coverage analysis, and filtering for probe selection are provided in 3 accessions maintained at Universitat Politècnica de València  were included in the study. They comprise 361 accessions of S. lycopersicum, 20 of S. pimpinellifolium, the closest wild ancestor of the cultivated tomato , two to the “Eriopersicon” group  and S. pennellii belonging to the monotypic “Neolycopersicon” group , together with S. melongena, are part of the “Eggplant” clade, and two other (S. anguivi L. and S. tomentosum L.), which together with cultivated S. aethiopicum and S. macrocarpon, are part of the “Anguivi” grade, while three are from American origin   and at the CREA-GB , comprise 362 accessions of l groups , 36 and elatives . The lat torvum) . As contDNA was extracted using the Qiagen plant mini-prep, the LGC Sbeadex kit or by a modified CTAB method. Libraries were prepared according to the Ovation Rapid Library Systems (Nugen) specifications. The streamlined workflow consists of six main steps, as shown in PRJNA542237 for tomato data and BioProject ID: PRJNA542231 for eggplant data).For the pilot test, sequencing was performed with the Illumina HiSeq 2500 platform , following the manufacturer protocol and using 75SE chemistry. For the genotyping of the whole set of accessions with the custom 5K probe sets, sequencing was performed with Illumina HiSeq 2500 platform , following the manufacturer protocol and using 150SE chemistry. The sequencing raw data are available at NCBI SRA  per-sample variants calling on target regions using HaplotypeCaller  of each SNP was evaluated by applying the following equation, as suggested by Genetic relationships were described by constructing a phylogenetic tree by maximum likelihood (ML) method using the IQ-TREE software ; data arS. lycopersicum (including S. lycopersicum var. cerasiforme) and S. pimpinellifolium using the data from the “150 Tomato Genome Resequencing Project” and the “AGIS Tomato 360 Resequencing Project”, of which 14,566 sites were selected for probe design. Of these, 40% were localized in coding regions and the rest in introns or UTRs. In eggplant, 72,739 eligible SNPs were identified, using a resequencing panel including eight S. melongena and one S. incanum accessions , of which 11,928 were selected for probe design, being 60% localized in coding regions and the rest in introns or UTRs.In tomato, 344,373 eligible SNPs were identified within A pilot study was run on 24 genotypes for each species using 75-bases long sequencing, of which 40 bp corresponded to the probe sequence. After sequencing, 13,615 (93%) and 11,265 (94%) probes were found to target SNPs within the first 25 bp after the probe 3′ end in tomato and eggplant, respectively.Based on sequencing coverage and missing data , a final set of 5,000 probes for tomato , and 5,082 for eggplant  were chosen . The proTo assess the robustness and transferability of the SPET method, the numbers of reads obtained with the final probe panels and their mapping efficiency were assessed using different DNA preparations . As can Using the final panel, around 215 million 150-bp single reads were produced in tomato, yielding 110 bp of useful sequence after probe trimming. After quality filtering, 198 high-quality million reads were retained (8% discarded) for the alignment to the “Heinz 1706” tomato genome sequence version 2.5  with an S. lycopersicum accessions with a read depth >10 showed very low levels of missing data (1.2%), high identity with the Heinz 1706 reference sequence (96.5%) and a low level of heterozygosity (0.65%), compatible with the autogamous reproduction of cultivated tomato (S. pimpinellifolium to 4.2% in S. neorickii. Only S. habrochaites displayed high missing data (average of 10.4%). Conversely, the identity with the Heinz 1706 reference sequence was lower in wild species, ranging on average from 66.0% in S. pimpinellifolium to 43.2% in S. huaylasense. The heterozygosity level of wild species was higher than S. lycopersicum, ranging from 4.6% in S. pimpinellifolium to 27.5% in S. pennellii on average, consistent with the partial or total allogamy reported for these species , corresponding to about 600K reads per sample on average. The latter were then aligned to the recently produced reference “67/3” eggplant genome  with an S. melongena accessions with a read depth >10 showed, on average, extremely low levels of missing data (0.02%), high identity with the “67/3” reference sequence (93.6%) and a low level of heterozygosity (0.67%), compatible with the autogamous reproduction of cultivated eggplant . The identity with the “67/3” reference genome was lower in the eggplant relatives, ranging from 85.9% in S. incanum to 50.6% in S. sisymbriifolium. The heterozygosity level of wild species and cultivated relatives S. aethiopicum and S. macrocarpon was higher than that of S. melongena, ranging from 1.7% in S. tomentosum and S. aethiopicum to 9.4% in S. sisymbriifolium, consistent with the partial allogamy reported or suggested for these species (The 360  eggplant . As for  species . As expeS. lycopersicum and S. pimpinellifolium) cluster together in a main branch, in which S. pimpinellifolium accessions are basal to the monotypic S. lycopersicum cluster. The S. pimpinellifolium accessions do not intermingle with the ones of S. lycopersicum, and they are divided in two branches, one which contains only Peruvian accessions, and another one basal to all S. lycopersicum that contains all accessions from Ecuador and three accessions from Peru. In the dendrogram, a second branch includes the other wild species and two main sub-clusters, of which one contains the three “Arcanum” group accessions , with S. arcanum basal to the two other species, and the other accessions of S. huaylasense and S. habrochaites from the “Eriopersicon” group, and S. pennelli from the “Neolycopersicon” group, with S. huaylasense basal to the two other species.The maximum likelihood (ML) dendrograms , 4 show S. pimpinellifolium is basal to the rest of accessions which are split in the two major clusters, one containing the S. lycopersicum accessions and one accession of S. pimpinellifolium, and the other containing the rest of wild species. In this latter, the major branch S. pimpinellifolium is spread in different branches which are basal to the rest of wild species, which largely display the same topology than in the dendrogram obtained with all the SNPs  are the closest to the “Eulycopersicon” (S. lycopersicum and S. pimpinellifolium), the species S. habrochaites (“Eriopersicon”), and S. pennellii (“Neolycopersicon”) are the most genetically distant, while S. huaylasense accessions occupy an intermediate position , and the other includes the rest of species native to the Old World , and one including the three species of the “Eggplant” clade , plus the two S. paniculatum accessions. In the cluster of the “Anguivi” grade, the four species are separated in different sub-branches, except S. aethiopicum and S. anguivi, which are intermingled. In the other cluster, a branch contains the “Eggplant” clade cluster, in which all the accessions of S. linnaeanum are separated in a sub-branch and S. melongena and S. incanum accessions in another sub-branch, with the latter basal to the monotypic eggplant cluster. The two accessions labeled as S. paniculatum are basal to the “Eggplant” clade branch. Three S. melongena accessions initially mis-labeled as S. aethiopicum in the germplasm bank list clustered correctly with the rest of S. melongena accessions.In eggplant, the ML-based dendrograms , 7 displld World  togetherS. aethiopicum and S. anguivi. Discrepancies, confirmed by the Normalized Robinson-Foulds distance equal to 0.53, were observed in the topology and branch lengths compared to the dendrogram obtained with the whole SNPs dataset. The dendrogram based on target SNPs identifies two main branches, one of which includes only S. melongena accessions, while the other contains the rest of S. melongena accessions, and in a sub-cluster the other cultivated and wild species. In this latter, the two American species S. sisymbriifolium and S. torvum cluster together and are basal to all the others (S. linnaeanum accession GPE003740 (for which a low number of reads was obtained) shows an odd position, and it appears genetically differentiated  from the others.The eggplant dendrogram based on target SNPs  confirmse others , while, S. sisymbriifolium and S. torvum are clearly separated from the rest of entries. The species of the “Eggplant” clade and “Anguivi” grade are clearly separated by the first component of the PCA, with two exceptions: the S. linnaeanum accession GPE003740 as well as the entries of S. aethiopicum and S. anguivi clustering together. The closest species to S. melongena are the other “Eggplant” clade species S. incanum and S. linnaeanum, as well as the two accessions labeled as S. paniculatum, followed by the “Anguivi” grade S. aethiopicum and S. anguivi, the entries of S. macrocarpon and the ones of S. tomentosum.The PCA analysis based on the whole eggplant SNPs dataset , largelyS. melongena and, surprisingly, S. linnaeanum accession GPE003740 from the other species. Solanum melongena is spread over a large area of the PCA graph with a wide range of values for both the components. The rest of species of the “Eggplant” clade and the “Anguivi” grade cluster together and in some cases are overlapped. Among this group of species, the closest to S. melongena are S. macrocarpon, followed S. tomentosum, S. linnaeanum, S. incanum, and finally S. aethiopicum and S. anguivi. Surprisingly, the American species S. sisymbriifolium and S. torvum plot in an intermediate area between S. melongena and the rest of Old World species  lines and in a natural black poplar (P. nigra) population  was observed when genotyping an intra-specific diversity panel, and a still acceptable (<5%) level was observed when genotyping species such as ectively .Single primer enrichment technology combines in a single approach both targeted analysis of SNPs, thus being comparable with genotyping arrays, and complexity reduction typical of GBS approaches . FurtherS. lycopersicum and S. melongena, respectively, while 7,130 and 24,892 were found to be shared in the remaining species, belonging to the tomato and eggplant genepools, respectively. This indicates that the technique enables the discovery of high numbers of novel polymorphisms, even in gene pools that came across severe bottlenecks during domestication, migration and selection.Compared to other crop species, both tomato and eggplant are known to have experienced a dramatic reduction of the genetic variability due to anthropogenic selection , and henS. lycopersicum and S. melongena  level of heterozygosity between S. lycopersicum and the other wild species, some of which are self-incompatible  due to the low number of reads obtained following sequencing. However, this did not affect their clustering with the other accessions of S. lycopersicum when ML tree and PCA were based on both the whole SNPs dataset or only the target SNPs. Indeed, several studies have explored how missing data may impact phylogenetic analyses using both empirical and simulated data, and suggest that it is possible to include taxa that have large amounts of missing data without ill effects  of missing data were detected; in the other cultivated eggplant (S. macrocarpon) their frequency was on average 4.5%. The missing data varied in entries of the wild species and reached the highest values in the New World native species S. torvum (21.2%) and S. sisymbriifolium (22.7%), characterized by greater evolutionary divergence  closely related to the eggplant clade or to the closely related “Anguivi” grade , as reported by other studies (ositions . Among t studies .S. melongena and the other two cultivated eggplants: S. aethiopicum (scarlet eggplant) and S. macrocarpon (gboma eggplant). In recent studies, it has been reported that the latter is genetically closer to eggplant  and some S. pimpinellifolium, while the eggplant panel was based on resequencing of eight S. melongena and one S. incanum accessions. Thus, in S. lycopersicum a clear assessment of the genetic relationships among species of the tomato genepool was obtained when SPET was based on both the whole dataset and target SNPs, although the genetic differentiation within tomato accessions was to a certain extent increased by the use of target SNPs. Vice versa, in eggplant, the whole SNP dataset was more informative for phylogenetic studies, while the use of target SNPs allowed a huge increase in the detection of genetic variation within accessions of S. melongena, as required for GWA studies.The differences observed when using the target vs. whole SNP datasets are attributable to the number and the genetic distance of the resequenced genotypes used for the initial identification of the polymorphic sites for SPET set up. In tomato, the SPET panel was based on a majority of resequencing data from S. lycopersicum and S. melongena germplasm accessions and of their cultivated and wild relatives maintained in genebanks. A high number of both target and non-target polymorphisms were analyzed by ML dendrograms and PCA analyses and the two approaches were complementary in the interpretation of data.Recently, a targeted genotyping approach (SPET) was released, but very limited information of its performance for high-throughput genotyping in plants is presently available. We assessed the efficiency and robustness of this technique in analyzing the genetic diversity in hundreds of Our results demonstrate that SPET represents a valid alternative to random complexity reduction methods and arrays, and it allows users to customize the panel of target markers and provides reliable fingerprinting of accessions maintained in genebanks. Our results also demonstrate the transferability of the panels to closely related species, and that the number and genetic distances of the resequenced genotypes used for identifying the target SNPs play a significant role. We designed panels enriched in intra-specific SNPs reasoning that additional inter-specific ones would be more likely to be discovered in the surrounding sequenced regions, which was indeed the case. The use of the whole SNP dataset is more appropriate for broad phylogenetic studies, while the use of target SNPs, boosting the intra-specific discrimination, for domestication and genome-wide association studies. The SPET technology made it also possible to clearly separate the different species, confirmed established phylogenetic relationships among different species and taxonomic groups, resolved the mislabeling of entries, and demonstrated its high reproducibility when applied on replicates of the same accession, proving its usefulness for the high-throughput genotyping, management, and enhancement of genebank collections.PRJNA542237 for tomato data and BioProject ID: PRJNA542231 for eggplant data.The datasets generated for this study can be found in the BioProject ID: GG, SL, JP, and GLR conceived and designed the research. LT, LaB, GLR, DA, PG, SV, MD, OD, PM, and PF provided the plant material and extracted the DNA samples. DS performed the SPET panel design. LoB, GA, EP, and AA analyzed the data. LoB, GG, JP, and SL wrote the manuscript. PG, SV, AA, EP, MD, GLR, and LT reviewed and edited the manuscript. All authors read and approved the manuscript.DS was employed by the company IGA Technology Services Srl. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


On the other hand, using haplotypes in the statistical analysis may increase the extent of linkage disequilibrium (LD) between haplotypes and causal variants and may also potentially capture epistastic interactions between variants within a haplotyped locus, providing an increase in the power and robustness of the association studies. We performed GWAA  using haplotypes based on variable-sized sliding windows and compared the results to a single-SNP GWAA using Warner-Bratzler shear force measured in the The GWAA using single SNPs identified eight variants influencing meat tenderness on BTA 3, 4, 9, 10 and 11. However, thirty-three putative meat tenderness QTL were detected on BTA 1, 3, 4, 5, 8, 9, 10, 11, 15, 17, 18, 24, 25, 26 and 29 using variable-sized sliding haplotype windows. Analyses using sliding window haplotypes of 3, 5, 7, 9 and 11 SNPs identified 57, 61, 42, 39, and 21% of all thirty-three putative QTL regions, respectively; however, the analyses using the 3 and 5 SNP haplotypes, cumulatively detected 88% of the putative QTL. The genes associated with variation in meat tenderness participate in myogenesis, neurogenesis, lipid and fatty acid metabolism and skeletal muscle structure or composition processes.GWAA using haplotypes based on variable-sized sliding windows allowed the detection of more QTL than traditional single-SNP GWAA. Analyses using smaller haplotypes (3 and 5 SNPs) detected a higher proportion of the putative QTL.The online version of this article (10.1186/s12863-019-0713-4) contains supplementary material, which is available to authorized users. Following the completion of the first draft bovine reference genome assembly, a high-density single nucleotide polymorphism (SNP) genotyping assay was developed , enablinThe use of haplotypes in GWAA analysis has not been widely exploited because there is no consensus on how many adjacent SNPs should be haplotyped, or on what is the best methodology for the definition and analysis of the haplotype blocks . DiffereWe performed a GWAA using haplotypes defined by variable-sized sliding windows and compared the results to single-SNP GWAA to ascertain the optimal window size for identifying QTL regions that influence meat tenderness in Nelore cattle. The results of this study provide a better understanding of the genetic basis of meat tenderness in zebu cattle.The 3161 Nelore bulls used in this study were born between 2008 and 2013 and were sourced from three different animal breeding programs. These animals were raised on pasture, finished in a feedlot for approximately 90 days, and then slaughtered in commercial slaughterhouses at a mean age of 691 ± 102 days. Contemporary groups (CG) were defined by the combination of farm of origin, year of birth, management group as long-yearlings and month and year of slaughter. The animals belonged to 116 CG with at least three animals per group.post-mortem. Steaks of 2.54 cm thickness were then collected from the longissimus thoracis muscle between the 12th and 13th ribs from the left half of the carcasses. The steaks were vacuum sealed and aged in a cold chamber for 150 h at 1 °C and then were frozen at − 20 °C. Next, the steaks were cooked in an oven to an internal temperature of 71 °C as proposed by  re reSUCLG1f cattle . ECHDC2  in pigs . SCP2 wa content  and was  content .TBC1D21, MSANTD3, MRPL49, SYVN1, FAU, ZYG11A or BACH2 genes to meat tenderness. These genes may play a role in the regulation of transcription or translation, or may interact with important genes for meat tenderness as shown in Fig. TBC1D21 encodes a GTPase-activating protein (GO:0090630) for Rab family proteins, which are involved in spermatogenesis [BACH2 and PTPRK. MSANTD3 is a member of the MSANTD3 family which contains DNA binding domains for Myb proteins and the SANT domain family. [MSANTD3 may be a transcription factor. In addition, MSANTD3 interacts with BCL11A, NIN, NOS1AP, SCP2 and TWIST1. A QTL region for meat tenderness in pigs harbors MRPL49, SYVN1 and FAU [ZYG11A encodes a protein that plays an important role in the regulation of ubiquitin-protein transferase activity (GO:0051438). A ZYG11A family member has been related to marbling in beef cattle [BACH2 is a transcription repressor and plays essential roles in the regulation of B cell development. B cells function in the humoral immunity component of the adaptive immune system by secreting antibodies [BACH2 has been related to intramuscular fat content in bulls [There was no evidence of a biological link or biological mechanism connecting ogenesis . Its ass family.  speculat and FAU . ZYG11A n GO:00900 for Rabtibodies . BACH2 hin bulls  and was n GO:009030 for RaCAPN1, which is responsible for the postmortem breakdown of myofibrillar proteins and seems to be the primary enzyme involved in the postmortem tenderization process [CAPN1 interacts with TM7SF2, ZNHIT2, MRPL49, SYVN1, VPS51 and FAU genes that are located in a QTL region for meat tenderness in this study. In addition, all of these genes are located near CAPN1 on BTA29 and their SNPs are in strong LD with SNPs located in CAPN1 in this population (with maximum r2 = 0.86).The gene network analysis Fig.  revealed process . The CAPThis study demonstrates that GWAA using haplotypes based on variable-sized sliding windows provides substantially more power to detect QTL than does single-SNP analysis, suggesting that this methodology should be considered for genomic predictions for WBSF and other traits. Analyses performed with smaller haplotype windows (3 and 5 SNPs) detected a higher proportion of QTLs than the analyses that used larger SNP windows. However, no single sliding window analysis identified all of the QTL that were found in the analyses using window sizes from 3 to 11 SNPs. This suggests that haplotype-based GWAA should employ several window sizes in order to detect the largest number of putative QTL. Likewise, the single-SNP analysis found two putative QTL that were not found by the haplotype-based analyses. While these may be type I errors, they may also be regulatory variants.We identified thirty-seven candidate genes influencing meat tenderness that participate in myogenesis, neurogenesis, lipid and fatty acid metabolism and skeletal muscle structure or composition processes. These findings contribute to a better understanding of the biological mechanisms underlying meat tenderness in Nelore cattle. Further validation of these genes and polymorphisms in different populations would contribute to their use in breeding programs for Nelore cattle.Additional file 1:Manhattan plot for single-SNP genome-wide association analysis. (PDF 55 kb)Additional file 2:QQ plot for single-SNP genome-wide association analysis. (PDF 14 kb)Additional file 3:Manhattan plots for haplotype-based association analyses for each number of alleles at haplotyped loci. (PDF 8648 kb)Additional file 4:QQ plots for haplotype-based association analyses for each number of alleles at haplotyped loci. (PDF 2144 kb)


Quercus. In this study, we utilized restrictionsite‐associated DNA sequencing (RAD‐seq) to develop an economical single nucleotide polymorphism (SNP) DNA barcoding system that suffices to distinguish eight common, sympatric eastern North American white oak species. Two de novo clustering pipelines, PyRAD and Stacks, were used in combination with postclustering bioinformatic tools to generate a list of 291 potential SNPs, 80 of which were included in a barcoding toolkit that is easily implemented using MassARRAY mass spectrometry technology. As a proof‐of‐concept, we used the genotyping toolkit to infer potential hybridization between North American white oaks transplanted outside of their native range  into a horticultural collection surrounded by natural forests of locally native trees  in the living collection at The Morton Arboretum . Phylogenetic and clustering analyses suggested low rates of hybridization between cultivated and native species, with the exception of one Q. michauxii mother tree, the acorns of which exhibited high admixture from either Q. alba or Q. stellata and Q. macrocarpa, and a hybrid between Q. stellata that appears to have backcrossed almost exclusively to Q. alba. Together, RAD‐seq and MassARRAY technologies allow for efficient development and implementation of a multispecies barcode for one of the more challenging forest tree genera.DNA barcoding has proved difficult in a number of woody plant genera, including the ecologically important oak genus  Existin2.2PstI as the restriction enzyme. RAD libraries were barcoded by individual and multiplexed on an Illumina Genome Analyzer IIx or on Illumina HiSeq 2000. Sequencing reads were 100 bp in length; after removal of the barcode, analyzed sequences were 85 bp long. Quality, read lengths, and base composition of FASTQ data were assessed in R v. 3.3.1 .RAD‐seq DNA extraction, library preparation, and sequencing were conducted as presented previously in oaks . In this pipeline, sequences are clustered first by individual using USEARCH . Any locus possessing more than two haplotypes within individuals after correcting for sequencing errors was discarded, under the assumption that it included one or more paralogous sequences. For each individual, each locus was summarized as a consensus sequence, and consensus sequences were clustered among individuals to generate a data matrix for each locus. Due to variation in sequencing coverage and mutation at the restriction site defining RAD loci, the resulting data matrix was not complete for all loci in all individuals. The following parameters were specified for PyRAD: minimum depth of reads per within‐sample cluster: 10; maximum number of sites in a read that can have a quality score of less than twenty: 4; clustering threshold (percent similarity): 0.85; minimum number of samples in each across‐sample cluster: 20; maximum number of individuals with a shared heterozygous site in an across‐sample cluster: 3. All other settings used default values.RAD‐seq fastq data were analyzed using PyRAD version 1.64 Eaton,  ; distance between two stacks to be assembled to loci was set to two (M); and the distance between loci to be merged was set to four (n). Filtering criteria were set to 75% of individuals in a population required to be present in a loci (r) and 10 as the minimum number of populations in which a locus must be present to be considered (p).For comparison, RAD‐seq data were also clustered using 2.3ST was calculated between all pairs of oak species using the RADami (version ≥1.1‐2)  between any pair of white oak species, identified the location of SNPs within the locus, and identified how many pairwise comparisons were at or above the threshold FST level (of 0.95). This approach generated a list of more than 1,090 loci containing 4,020 potential SNPs of FST ≥ 0.95, all of which were exported as FASTA files. Loci were then filtered under both stringent criteria and relaxed criteria. The stringent approach excluded SNPs with ambiguity codes of any kind and resulted in 1,068 potential SNPs  were obtained and further processed with custom bash scripts and manually selected in Excel  to create a list of potential SNPs. The corresponding position of a SNP within a RAD‐seq stack was identified by a custom script from the output files  was used to compare PyRAD loci to a Stacks loci database followed by reciprocal BLASTN of Stacks loci against a PyRAD loci database. The e‐value of 1E−05 was selected as the threshold for recognizing loci as orthologous; this is a commonly used value and is relatively arbitrary, but given the restricted nature of the dataset provides a framework for comparing loci. The output was saved as tabular format. Loci that matched between the PyRAD and Stacks outputs with alignment length exceeding more than 95% were treated as duplicates and excluded from downstream analyses.Stand‐alone NCBI‐BLASTN version 2.2.30  was used to design primers for MassARRAY analysis  SNPs selected as diagnostic for each of the 13 species and two species groups targeted  was used to visualize results.After SNP screening, SNPs present in all individuals of a species were marked as fixed SNPs, whereas those present in the majority of individuals were marked as nearly fixed; 11.6 ± 7.7  was placed in 96‐well plates and stored in a −20°C freezer. Prior to DNA extraction, plates were placed overnight into a vacuum desiccator. Dried leaves were ground using metallic beads for 1 min (twice) at 1,500 rpm  on a Geno Grinder . DNA was isolated using Plant Prep Adem‐Kit  according to manufacturer's instructions. DNA concentrations were estimated using Nanodrop spectrophotometer , and concentrations were set to 10 ng/μl using STARlet 8‐channel robot .Leaf tissue  were used to root the ML tree for purposes of visualization. Dataset templates retrieved from the iTOL help page were used for color‐coding species  and to highlight mother trees and learning samples  given the data, mean ΔK over replicates was calculated  followed by Q. muehlenbergii/prinoides (6) (Table Quercus montana exhibited the most nearly fixed SNPs (13). The majority of Q. stellata SNPs (15) were found to be heterozygous or overlapping with another species. The most false positives were determined for Q. muehlenbergii/Q. prinoides and representatives of the Eurasian white oaks. This is somewhat remarkable given that Q. muehlebergii/prinoides was represented by six individuals in the initial RAD‐seq screening, comparable to the number we had for Q. alba (4) and Q. michauxii (5).The Assay Design Suite was used to design suitable primers pairs for more than 844 potential SNPs, 395 from the stringent selection approach and 449 from the relaxed selection approach. The primer design software rejected 59% of these due to the presence of ambiguous nucleotides close to the SNP site, potential primer‐dimer formation, or placement of SNP sites too close to the 5′ or 3′ end of the amplified region. Primers were designed for the remaining 344 (40%) potential SNPs, of which we combined 291 SNPs into eight multiplexes for screening (Table The remaining 187 65%) of the screened SNPs showed the dinucleotide pattern indicated during SNP identification using the RAD‐seq data and were maintained in this study. We reduced the list of verified 187 SNPs to 119 by selection for a minimum of five to six SNPs per species , and 59 learning samples drawn from the 63 samples originally sequenced using RAD‐seq and used to identify SNPs. Maximum‐likelihood analysis of the SNP dataset from parent trees and learning samples recovers a topology comparable to that described in previous studies  and Q. muehlenbergii (704‐46*2 sd T)  and Q. stellata‐74 (OAK‐MOR‐000074). Morphologically, the former sample also had the appearance of being intermediate between Q. macrocarpa and Q. stellata. Phylogenetically, Q. macrocarpa‐616 was placed either outside of Q. oglethorpensis or wedged between clade 1  and clade 2 (Q. austrina and Q. chapmanii and Q. macrocarpa/Q. gambelii). Quercus stellata‐74 was placed next to the clade of Q. alba, suggesting hybrid origin  rather than Q. stellata  appears to be misclassified as i Figure a. As the4Advances in next‐generation sequencing technologies allow researchers to generate high amounts of reduced‐representation data by RAD‐seq, GBS, anchored enrichment, and other methods at relatively low per‐base‐pair cost. Such methods reduce the risk of ascertainment bias, as the primary criteria for developing genotyping strategies using these method are generally associated with marker number rather than variability per se. Moreover, although the choice of the restriction enzymes in RAD‐seq, GBS, and related methods may introduce genome sampling bias . PyRAD is expected to outperform Stacks in phylogenetic samples  to detect loci whereas t Eaton, . In our s Eaton, , making 4.2The RAD‐seq loci we used to design primers were approximately 85 bp in length (ignoring indels), which introduces a strong constraint on primer design. The quality of flanking sequences of a SNP contributes strongly to its suitability for multiplexing, although the MassARRAY primer design software does accept ambiguities within the flanking region. Our short loci constrained us to a 60‐bp maximum length region in the middle of each locus where we could amplify SNPs; any SNPs outside this region were not relevant to the study, reducing by roughly 75% the amount of the genome readable in our SNP assay relative to the amount genotyped using RAD‐seq. This limitation could easily be circumvented using longer sequencing reads. In our study, primer design and multiplex suitability cut our list of potential SNPs by half: Of more than 800 potential SNPs, only 187 reached the final set of SNPs that we considered in detail for the final reactions. The high rate of attrition expected during any such project makes increasing sequence read length at the outset of the project—thus increasing the percentage of the genotype sequenced that is suitable for SNP amplification—an efficient way of increasing the number of potential SNPs.4.3FST to identify markers that minimize variation within species while maximizing differences among species, with the expectation that at least some outlier loci would exhibit strong pairwise differentiation for all species pairs. We had a relatively small sampling of numerous species to begin with, so we expected that our initial screening would discover false positives. We did find that by screening more stringently, we could correct for some false positives: Our stringent screening approach yielded 83 SNPs, whereas the relaxed approach exhibited a higher rate of false positives.In most traditional genotyping projects, marker discovery aims to identify loci that harbor the greatest possible variation, with an eye toward capturing both within‐ and among‐population variation. In our study, we used pairwise Quercus stellata and its relatives; Table Q. stellata and Q. macrocarpa, probably for two reasons: in Q. macrocarpa, we started with a larger candidate pool in the RAD‐seq dataset , which may have rendered our FST threshold for SNP discovery too stringent; and both species are wide in geographic range and known to hybridize with numerous other white oak species . While this portion of the study was designed to demonstrate the barcode's potential for hybridization studies in mixed‐species collections of cultivated oaks, and our sample size was small, the preliminary findings are suggestive that both landscape position and possible reproductive barriers at the individual level play a role in hybridization patterns. For example, the mother tree that demonstrated the largest amount of hybridization was a Q. michauxii growing in close proximity to another mature Q. michauxii. In such a case, we might expect very limited hybridization . Scripts and auxiliary data utilized in this article are available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.dg5hv06.FASTQ (RAD‐seq) files are available under NCBI BioProject PRJNA376740 ( Click here for additional data file.


Nyssorhynchus darlingi (Root). We used nextera-tagmented, Reductively Amplified DNA (nextRAD) genotyping-by-sequencing to genotype 164 Ny. darlingi collected from 16 collection sites with divergent forest cover levels in seven municipalities in four municipality groups that span the state of Amazonas in northwestern Amazonian Brazil: São Gabriel da Cachoeira, Presidente Figueiredo, four municipalities in the area around Cruzeiro do Sul, and Lábrea. Using a dataset of 5,561 Single Nucleotide Polymorphisms (SNPs), we investigated the genetic structure of these Ny. darlingi populations with a combination of model- and non-model-based analyses. We identified weak to moderate genetic differentiation among the four municipality groups. There was no evidence for microgeographic genetic structure of Ny. darlingi among forest cover levels within the municipality groups, indicating that there may be gene flow across areas of these municipalities with different degrees of deforestation. Additionally, we conducted an environmental association analysis using two outlier detection methods to determine whether individual SNPs were associated with forest cover level without affecting overall population genetic structure. We identified 14 outlier SNPs, and investigated functions associated with their proximal genes, which could be further characterized in future studies.The relationship between deforestation and malaria in Amazonian Brazil is complex, and a deeper understanding of this relationship is required to inform effective control measures in this region. Here, we are particularly interested in characterizing the impact of land use and land cover change on the genetics of the major regional vector of malaria,  In the Amazon region, there is an increasing understanding that land use and land cover (LULC) changes caused by agricultural activity, logging, and road construction are modifying the risk of human malaria infection –6. In AmNyssorhynchus darlingi . Deforested areas have been associated with an increased Ny. darlingi human biting rate [Ny. darlingi [Ny. darlingi abundance generally highest in human-modified and deforested landscapes [Anopheles gambiae Giles s.l. has been associated with agricultural activity and the degree of urban/built environment landscapes [Ny. darlingi collected from two Brazilian settlements separated by 60 km with very different forest cover levels [Ny. darlingi among areas with different forest covers has implications for malaria transmission, as subpopulations of Ny. darlingi may differ in vector competence or vectorial capacity, or in their response to vector control activities. Differences in Plasmodium infection rates have previously been identified across genetically distinct populations of Anophelinae species [In the Amazon, the major vector of malaria is ation in  to elevaing rate , 13 and darlingi –16. LULCndscapes –20. Defondscapes , 22. Simr levels . The pos species –26.Plasmodium vivax, these states also have a persistently and disproportionately high proportion of cases caused by Plasmodium falciparum, which is associated with higher morbidity and mortality [Brazil accounts for ~25% of malaria cases reported from the Americas, and has reported 100,000–200,000 cases annually since 2013 . Over 99ortality . Within ortality , 31, witortality .Ny. darlingi into three groups by biogeographical region  [COI sequencing [Ny. darlingi across Western and Central Amazonian Brazil. At the microgeographic level, low levels of genetic differentiation have been detected between Ny. darlingi collected in different seasons [Ny. darlingi [Ny. darlingi in four municipality groups in Amazonas and Acre states in Amazonian Brazil.In Brazil, nextera-tagmented Reductively Amplified DNA (nextRAD) genotyping-by-sequencing detected genetic clustering of  Amazon) . These r Amazon) , 35 and quencing  that hav seasons  and habi seasons  using midarlingi , this quNy. darlingi were collected in 2015–2017 from 16 sites spread across seven municipalities in Amazonas and Acre States . Specific permission was not required for these locations as permission to collect was granted under the permanent permit. The collection locations were not privately owned or protected, and the field studies did not involve protected or endangered species.Ny. darlingi [ij corresponds to the forest area and A corresponds to the total area of the landscape.For each collection site, forest cover in a 1 km radius around the site was calculated; this radius was selected to reflect the approximate maximum flight range of darlingi . Forest darlingi . MaximumNy. darlingi were collected were selected to cover the maximum possible range of forest cover percentage within each municipality group. Collection sites were split approximately into quintiles by forest cover percentage . Nyssorhynchus darlingi were available from all 5 forest cover levels for two municipality groups (Lábrea and Cruzeiro do Sul-area), and from 3 levels for the remaining two municipality groups , for a total of 16 collection sites across the four municipality groups. Genomic DNA was extracted from individual Ny. darlingi using the Qiagen DNeasy Blood & Tissue Kit , and DNA concentrations measured using a Qubit Fluorometer . For genotyping, 15 Ny. darlingi with DNA concentration ≥0.5 ng/μL were selected from each level 1 and level 5 collection site, and 10 from each level 2, 3, and 4 site, for a total of 190 Ny. darlingi . All sequencing reads were uploaded to the NCBI SRA database (BioProject ID: PRJNA545461).Genomic DNA from the 190 individuals was converted into nextRAD genotyping-by-sequencing libraries by SNPsaurus, LLC as in . Genomicprocess_radtags program, and ustacks was used to align reads into stacks, with the minimum depth of coverage required to create a stack set to 3, the maximum distance allowed between stacks set to 4, and the maximum distance allowed to align secondary reads to primary stacks set to 6. A catalog of putative loci was built using 24 representative Ny. darlingi individuals . All 24 individuals were sequenced, and the reads processed in ustacks, using the methods described above. The catalog was built using the STACKS cstacks program, allowing 4 mismatches between stacks, with gapped alignments enabled. The catalog loci were mapped to the Ny. darlingi genome (AdarC3) [ustacks, stacks from the 190 individuals in the current study were searched against the catalog, and SNPs called, with the STACKS sstacks, tsv2bam, and gstacks programs using default settings.Raw sequence reads were analyzed using STACKS v2.3b , 43. Briividuals  collecte(AdarC3)  using BW(AdarC3) ; only lopopulations program was used to select the first SNP from each RAD locus found in all 4 municipality groups, and in at least 50% of the individuals in each municipality group, with the minimum minor allele frequency set to 0.02 and the maximum observed heterozygosity set to 0.7. A bash script including the STACKS parameters used is included as To control for quality and sequence coverage variation among individuals, only individuals that matched to at least 10,000 catalog loci were included in the analysis  was performed using the R package  v1.7–13  dudi.pcaa v1.0.5  fviz_pcaa v1.0.5  was perft v2.1.1 .poppr v2.8.2 [FST values with confidence intervals using 999 bootstrap samples were calculated using the stamppFst function in the R package StAMPP v1.5.1 [adegenet) and calculating a Mantel test to compare the two distance matrices using the mantel.randtest function in ade4.A hierarchical Analysis of Molecular Variance (AMOVA), with individuals grouped into forest cover levels within municipality groups, was calculated using the poppr.amova function in the R package r v2.8.2 . PairwisP v1.5.1 . IsolatiLEA v2.4.0 [LEA was run with ten repetitions of K = 1 through 10. The optimal number of populations, where the cross-entropy curve was at a minimum, was four. The sNMF results were used to impute missing genotypes using the LEA impute function. Five repetitions of LFMM were run on the imputed dataset, with a burn-in of 5000 and 10000 iterations for each, adjusting for four latent factors , with forest cover percentage as the explanatory variable. The z-scores from the five runs were combined, and the p-values adjusted for multiple testing using the Benjamini-Hochberg procedure as recommended by the authors [p-value of 0.05 were considered outliers.Two methods were used to identify SNPs associated with forest cover. First, latent-factor mixed modelling (LFMM)  was run A v2.4.0 . In prepA v2.4.0  analysis authors . SNPs wipsych v1.8.12 [ρ values were considered outliers. The final set of outlier SNPs includes only SNPs identified using both LFMM and bayenv2.As a second method to identify SNPs associated with forest cover, bayenv2  was run, v1.8.12 , the firNy. darlingi genome scaffolds [Drosophila orthologs of all genes were investigated. In addition, a gene ontology (GO) enrichment analysis was performed using the R package topGO v2.34.0 [p-value of 0.01.Genes in the annotated caffolds  located  v2.34.0  to deterNy. darlingi . From these, an average of 30,025 stacks per individual  made up of 643,458 total reads  matched to the catalog ) started leveling off at Fig A in . STRUCTUk-means clustering algorithm used in preparation for DAPC indicated that the optimal number of clusters was one, the Akaike Information Criterion (AIC) indicated that the optimal number of clusters was three or four  to 0.075   . There wNy. darlingi genome scaffolds, 150 genes are located within 100 kb of these 14 SNPs. These genes have a variety of functions in numerous structural and cell signaling pathways . Our redarlingi , that foNy. darlingi, as this species has been shown to display a high degree of plasticity in life history traits [An. gambiae [An. arabiensis [An. minimus [Ny. darlingi populations not reflected in our SNP dataset.Anthropogenic changes in forest cover may produce adaptive phenotypic changes in mosquito populations not reflected in genomic studies, particularly among y traits  and bitiy traits . Defores gambiae , An. araabiensis , and An. minimus . Additio minimus , 77 coulFST = 0.026–0.075) among four municipality groups across Amazonian Brazil separated by 800–1,600 km. This is consistent with a previous study using microsatellites that found similar FST values comparing Ny. darlingi collected from localities in central and western Amazonian Brazil separated by comparable geographic distances to the current study [Ny. darlingi from Amazonian Brazil belong to a single genetic population [Ny. darlingi at both continental and regional scales is needed.We identified weak to moderate genetic differentiation  between the two most geographically separated municipality groups , in combination with a significant Mantel test, is suggestive of IBD. Several previous population genetics studies of Ny. darlingi using nuclear and mitochondrial markers detected IBD [Ny. darlingi at eight microsatellite loci from municipalities in Amazonian Brazil at a similar geographic scale to the current study [Ny. darlingi have not found evidence of IBD [Ny. darlingi populations.The differentiation that we identified between the municipality groups could be the result of isolation by distance (IBD), isolation by barrier (IBB), isolation by resistance (IBR), or isolation by environment (IBE). It is not possible to differentiate between these possibilities with the sampling scheme of the current study because of the lack of intermediate sampling points between these municipality groups. The overall low level of differentiation between municipality groups, with the highest cted IBD , 78, 79,nt study . Howevere of IBD , 80, 81.Ny. darlingi genome. We acknowledge that this type of analysis is limited both by the highly fragmented nature of the current Ny. darlingi genome assembly [We identified 14 SNPs associated with forest cover percentage using two outlier detection methods. These SNPs were located within 100 kb of 150 genes, among which 6 GO terms were over-represented compared with the rest of the assembly , and by assembly . TherefoNy. darlingi to deforestation in the Amazon, but indicate that such an adaptive response was not associated with genome-wide differentiation. Additional studies using whole genome sequencing and an improved Ny. darlingi genome assembly should be undertaken to further explore this topic.Using nextRAD genotyping-by-sequencing, we report weak genetic structure among four municipality groups in the Amazonian Brazil, but a lack of microgeographic structure across forest cover levels within these municipality groups. These results do not preclude an adaptive response of S1 FileContains Figures A-G and Table A:• Fig A. Selection of optimal number of clusters for STRUCTURE analysis using the Evanno method.• Fig B. Results of fastSTRUCTURE analysis of 5561 SNP dataset.• Fig C. Principal components analysis of 5561 SNP dataset, with shapes and colors indicating the locality and forest cover level of Ny. darlingi collection sites.• Fig D. Selection of optimal number of clusters for DAPC.• Fig E. Isolation by distance plot depicting Prevosti’s absolute genetic distance vs. geographic distance in km for pairs of 16 collection sites.• Fig F. Selection of candidate SNPs using LFMM and bayenv2.• Fig G. Frequency of major allele for each candidate SNP within each collection site, plotted by the forest cover percentage at each site.• Table A. GO terms significantly enriched (Fisher’s p<0.01) within 100kb of candidate SNPs in Ny. darlingi genome.(PDF)Click here for additional data file.S2 File(XLSX)Click here for additional data file.S3 File(XLSX)Click here for additional data file.S4 File(TXT)Click here for additional data file.S5 File(STR)Click here for additional data file.S6 File(CSV)Click here for additional data file.


Solanum pimpinellifolium as a diverse panel of genome-wide association study through the restriction site-associated DNA sequencing technique. Previous attempts to conduct genome-wide association studies using S. pimpinellifolium were impeded by an inability to correct for population stratification and by lack of high-density markers to address the issue of rapid linkage disequilibrium decay. In the current study, a set of 24,330 SNPs was identified using 99 S. pimpinellifolium accessions from the Tomato Genetic Resource Center. Approximately 84% of PstI site-associated DNA sequencing regions were located in the euchromatic regions, resulting in the tagging of most SNPs on or near genes. Our genotypic data suggested that S. pimpinellifolium were divided into three single-ancestry subpopulations and four mixed-ancestry subpopulations. Additionally, our SNP genotypic data consistently confirmed the genetic differentiation, achieving a relatively reliable correction of population stratification. Previous studies utilized the 8K tomato SNP array, SolCAP, to investigate the genetic variation of S. pimpinellifolium and we performed a meta-analysis of these genotypes. The result suggested SolCAP array was less appropriate to profile the genetic differentiation of S. pimpinellifolium when more accessions were involved because the samples belonging to the same accession demonstrated different genome patterns. Moreover, as expected, rapid linkage disequilibrium decay was observed in S. pimpinellifolium, especially in euchromatic regions. Approximately two-thirds of the flanking SNP markers did not display linkage disequilibrium based on 2r = 0.1. However, the 18-Kb linkage disequilibrium decay indeed reveals the potential of single-gene resolution in GWAS when markers are saturated.To mine new favorable alleles for tomato breeding, we investigated the feasibility of utilizing  Solanum pimpinellifolium is a native perennial shrub in Ecuador and Peru, ranging along the western Andean slopes to the coastal regions. It is believed that S. pimpinellifolium originated in northern Peru and then diversified into several subpopulations after it migrated to Ecuador and southern Peru  . GWAS utilizes linkage disequilibrium (LD), the non-random association between marker alleles and alleles conferring targeted phenotypes in a given collection of germplasm, to map quantitative trait loci (QTL) . In compcessions .PstI is a methylation-sensitive restriction enzyme and recognizes the sequences “CTGCAG”  through the RADseq approach. Second, the population differentiation was examined by different methods to ensure a stable estimation. Moreover, a meta-analysis of SolCAP array was performed to infer the population differentiation in a scenario involving in more accessions. Third, the LD decay was assessed to estimate the required marker number and potential resolution in GWAS.The objective of the current study was first to develop genome-wide high-density SNP markers for a subset of http://tgrc.ucdavis.edu/). A total of 12 accessions from Ecuador and 87 accessions from Peru were utilized in this study. According to their mating types, 43 accessions were facultative self-compatible (FSC), and 56 accessions were autogamous self-compatible (ASC). Seeds were propagated by self-pollination for two generations using the method of single-seed descent in a greenhouse. Young leaves collected from plants of these single-seed descendent seeds were used for DNA extraction.All plant materials and their information were obtained from TGRC  as 10, and the populations command set the parameter –p (minimum number of populations a locus must be present) as 75. SNPs with a minor allele frequency of less than 0.05 were further excluded, and a set of 24,330 SNP markers was obtained. This set of 24,330 SNP markers was utilized for the analyses of genetic variation, LD, stF and AMOVA. Another SNP set without ‘redundant SNP markers’ was used to conduct the principal component analysis (PCA) and ADMIXTURE because these two matrices are expected to correct the structure in GWAS. To remove ‘redundant SNP markers’, we defined a sequencing unit as a sequencing region surrounding a PstI site, usually 186 bp long, which has at least one SNP with a minor allele frequency greater than 0.05 in the S. pimpinellifolium population. If more than one SNPs are located in a sequencing unit and they are in complete LD (2r = 1), only the first SNP is kept. This process resulted in a total of 19,993 SNP markers. ITAG2.4 gene model from SGN was used as the reference gene annotation.Reads were analyzed with Stacks version 1.37  using the R package hierfstat . Pairwishttps://doi.org/10.25387/g3.7730369.Data available: NCBI SRA BioProject Number PRJNA358110. The authors affirm that all data necessary for confirming the conclusions of this article are represented fully within the article and its tables and figures. Supplemental material available at Figshare: S. pimpinellifolium accession and contained at least 3.7 million DNA reads, except for LA2647 (Table S1). Among the 82,814 PstI sites in the tomato reference sequence SL2.50, only 23,988 PstI sites were covered by the sequenced DNA reads (Table S2). The sequenced regions included 0.54% of the SL2.50 reference sequences and 12,790 annotated genes (PstI sites were located in the euchromatic regions (Table S2). Besides, the proportion of sequenced genes in euchromatin (43.13%) were about twice as that in heterochromatin (19.75%) (Table S2).A total of 655,973,270 short DNA reads were obtained from four lanes of the Illumina HiSeq2000 flow cell and were divided into 99 parts according to barcode sequences. Each part was derived from the DNA of a ed genes . InteresS. pimpinellifolium accessions, and 24,330 of them had the minor allele frequency higher than 0.05. In the genotypic dataset of the 24,330 SNP markers (Table S3), the missing proportion of each accession ranged from 0.72 to 15.92%, except for LA2647 of which the value was 65.68% due to a low number of sequencing reads (Table S1). Regarding the location of these 24,330 SNPs, 16,365 SNPs were found in 7,383 annotated genes  and those in heterochromatin (60.59%) (Table S2). Meanwhile, the genotypic data of the LA0411 accession was dropped because the observed heterozygosity of LA0411 was inconsistent with its mating type (Table S1).Two criteria were set to ensure the accuracy of SNP calling and genotype calling: one was that the read depth aligning to the reference sequence was equal to or greater than 10, and the other was that at least 75% of the accessions showed genotypes associated with a defined SNP marker. A total of 67,804 SNPs were identified in the sequenced regions of 99 ed genes , and thePstI restriction enzyme in the euchromatic regions: only 28.97%  of PstI sites were found in the deep sequencing regions, and 83.55%  of the deep sequencing regions were located in the euchromatic region (Table S2). It is worth noting that the current RADseq protocol did produce low coverage of sequencing reads in some PstI sites (with a read depth less than 10), and these PstI sites were filtered by the criteria of SNP and genotype calling; therefore, the deep sequencing regions indicated that their read depths were no less than 10. Incidentally, because SNPs can be identified only in the sequenced regions, it is a reasonable deduction that most SNPs found in the current study are located in the euchromatic regions. PstI sites in the deep sequencing regions (C layer), and identified SNPs (D layer) are mainly located in the euchromatic regions.The observation that 67.26%  of the SNPs were located in the annotated gene regions  implied S. pimpinellifolium accessions was divided into three single-ancestry subpopulations and four mixed-ancestry subpopulations by the ADMIXTURE software (stF confirmed the genetic differentiation (Table S4), and AMOVA revealed that the variance between subpopulations was 41.96% .The collection of 98 software . Meanwhisoftware . POP S1,software . MoreoveS. pimpinellifolium.The within-population gene diversity was calculated to compare genetic variation within each subpopulation. POP S2 and POP M1 showed the highest genetic variation among the single-ancestry subpopulations and the mixed-ancestry subpopulations, respectively . Both suInterestingly, most accessions in the same subpopulation were in the same vicinity of their collection sites . Also, P2r value was set at 0.1 . Following the non-linear regression curve, the overall LD decay was within 18 Kb when the baseline of the t at 0.1 . The fas2r value of flanking sequencing units that contained at least one SNP marker. We observed two main trends: marker density in the heterochromatic regions was lower than that in the euchromatic regions , the genetic differentiation of S. pimpinellifolium is observed evidently with the aid of RADseq SNP markers.Previous studies suggested that genetic differentiation of ariation . The anaariation . Since tIsolation by distance (IBD) is a common tool to access genetic differentiation that expect a positive correlation between genetic variation and geographic distance . We condS. pimpinellifolium. This meta-analysis implied an unclear structure; especially the cross validation error has an ambiguous minimal value . It is possible that genetic diversity between wild tomatoes are underestimated because the polymorphisms of SolCAP array are selected between cultivars and wild tomatoes (S. pimpinellifolium, especially when more samples are involved. Also, for the same reason, we cannot validate the genetic differentiation in the SolCAP meta-analysis by stF or AMOVA nor achieve a stable estimation of genetic differentiation in a scenario of more accessions via the SolCAP meta-analysis.Our meta-analysis concluded that the genetic compositions of 214 samples came from 15 ancestry populations. This conclusion is different from the conclusion of S. pimpinellifolium was detected with up to a 40% outcrossing rate (S. pimpinellifolium population. Here, we proposed three possible approaches to increase markers. One is to increase the sample size evenly for each subpopulation (in silico to balance sequencing resource between sample sizes and sequencing coverage (The observed and expected heterozygosity of this population were 0.0761 and 0.2786, respectively, slightly higher than those in previous researches . Since Scoverage . Based o


Genome-wide association study (GWAS) on 923 maize lines and validation in bi-parental populations identified significant genomic regions for kernel-Zinc and-Iron in maize.P ≤ 5.03 × 10−05) with kernel-Zn and Fe concentrations with some of these associated SNPs located within previously reported QTL intervals for these traits. Three double-haploid (DH) populations were developed using lines identified from the panel that were contrasting for these micronutrients. The DH populations were phenotyped at two environments and were used for validating significant SNPs (P ≤ 1 × 10−03) based on single marker QTL analysis. Based on this analysis, 11 (Zn) and 11 (Fe) SNPs were found to have significant effect on the trait variance  in at least one bi-parental population. These findings are being pursued in the kernel-Zn and Fe breeding program, and could hold great value in functional analysis and possible cloning of high-value genes for these traits in maize.Bio-fortification of maize with elevated Zinc (Zn) and Iron (Fe) holds considerable promise for alleviating under-nutrition among the world’s poor. Bio-fortification through molecular breeding could be an economical strategy for developing nutritious maize, and hence in this study, we adopted GWAS to identify markers associated with high kernel-Zn and Fe in maize and subsequently validated marker-trait associations in independent bi-parental populations. For GWAS, we evaluated a diverse maize association mapping panel of 923 inbred lines across three environments and detected trait associations using high-density Single nucleotide polymorphism (SNPs) obtained through genotyping-by-sequencing. Phenotyping trials of the GWAS panel showed high heritability and moderate correlation between kernel-Zn and Fe concentrations. GWAS revealed a total of 46 SNPs (Zn-20 and Fe-26) significantly associated (The online version of this article (10.1007/s00122-018-3089-3) contains supplementary material, which is available to authorized users. Advances in agricultural research and technology have resulted in the increase of food grain production to meet the needs of an increasing human population. Though the increase in food grain production helped to meet the calorie requirement, low levels of micronutrients, zinc (Zn), iron (Fe), and pro-vitamin A among others, are a global cause for malnutrition related health impairments which could lead to socio-economic losses, reduced work performance and productivity (Diepenbrock and Gore http://www.HarvestPlus.org).Maize alone contributes over 20% of total calories in human diets in 21 countries and over 30% in 12 countries that are home to more than 310 million people  of 1860 µg/day of Zn and 1460 µg/day of Fe, the target level of Zn and Fe was established as 33 and 52 µg/g, respectively, in maize kernels  and Fe (9.6–159.43 mg/kg) have been identified in temperate  and 2013 (AF13A) and at Celaya in 2012 (CE12B). Agua Fria is located at 20°32′N, 97°28′W, 110 m above sea level (masl), and has average annual temperature of 22 °C with average annual precipitation of 1200 mm. Celaya is located at 20°26′N, 103°19′W; 1750 masl; average annual temperature 19 °C; average annual precipitation 700 mm. The trials at AF12A and CE12B were laid out in a randomized complete block design (RCBD) with two replications, whereas at AF13A, in alpha lattice design with two replications. The rows were of 2.5 m length and 75 cm apart and each entry was grown in a single row plot. Two to six plants from each plot were self-pollinated, hand-harvested and hand-shelled to avoid any metal contamination. Kernels were bulked for subsequent micronutrient analyses. Soil samples were also taken in at least five distal points in the field trials and at 0–30 cm deep. Samples were dried and sent for analysis at Fertilab commercial laboratory in Mexico.From the association mapping panel, three lines with high Zn and three lines with moderate or low Zn lines were selected based on the micronutrient analysis to form the bi-parental populations. Three double-haploid (DH) populations were derived from the crosses between high Zn lines with low or moderate Zn lines Table . The thr®. Five grams of flour was placed in the polypropylene capsules and closed with a Poly-4® Oxford Instruments™, and readings were recorded. About 10% of the samples were also analyzed by inductive coupled plasma (ICP) as described by Galicia et al. , using a Retsch™ miller (model MM400) and 35 mL grinding milling jar of zirconium. Milling time was 90 s at 30 Hz. Flour was collected in 15 mL plastic tubes and analyzed by X-ray fluorescence using X-ray fluorometer (XRF) Oxford instruments™, model X-Supreme 8000a et al.  to confihttp://www.panzea.org). From this, a smaller dataset of 347,765 SNPs which met the filtering criteria of call rate (CR) ≥ 0.7 and minor allele frequency (MAF) ≥ 0.03 was used for GWAS. For principal component and kinship analyses, 69,830 SNPs with filtration criteria of CR ≥ 0.9 and MAF ≥ 0.1 were used.DNA was extracted from leaf samples of 3–4-week-old seedlings using the standard CIMMYT laboratory protocol Cimmyt . The assσ2G, σ2GE and σ2e, for the multi-environmental phenotypic data were estimated from analysis of variance (ANOVA) using multi environment trial analysis with R (METAR)  of the trails was estimated as:σ2G is the genotypic variance, σ2GE is the genotype × environment variance, σ2e is the error variance, l is the number of environments, and r is the number of replications. Correlation coefficients between environments and traits, summary statistics  were also generated using standard procedures implemented in METAR. Best linear unbiased estimators (BLUEs) used for GWAS was estimated as Yijkl is the response value of observed trait, µ the overall mean, Geni is the treatment fixed effect , Repj is the replicate effect , Blockk is the block effect, Locl is the location effect and εijkl is the error term.Variance components, http://www.goldenhelix.com) was used for the analysis. A three-dimensional plot of the first three principal components was drawn to visualize the possible population stratification among the samples. A kinship matrix was also computed from identity-by-state (IBS) distance matrix as executed in SVS V_8.6.0:The principal component analysis (PCA) method as described by Price et al. , implemer2 values   was quantified as adjacent-pairwise U), corrected for population structure (Q), and corrected for population structure and kinship (Q + K) using SVS V_8.6.0. In the uncorrected analysis, associations were tested in an additive model without correcting for any of the confounding variables. In the Q model , the associations were corrected using population structure through principal component analysis, in which ten principal components (PC) were included. In the Q + K model (MLM—mixed linear model), associations were corrected using both PCs and kinship matrix. All the three models  involved testing one variant at a time. Manhattan plots were plotted using the − log 10 P values of all SNPs used in analysis. The appropriateness of the different models was evaluated through Q–Q plots that were obtained by plotting ‘expected − log10 P values’ on the x-axis and ‘observed − log10 P values’ on the y-axis. Multiple testing correction was performed to determine the significance threshold, where instead of 345,767 independent tests, the total number of tests were estimated based on the average extent of LD at r2 = 0.1  are greater than 4.3. The variance component based on the kinship analysis employing 347,765 SNPs was computed using the efficient mixed model analysis (EMMA)  is variance of observed phenotypes, For each trait, three different association analyses were carried out: uncorrected (http://www.maizegdb.org) annotations were obtained from http://ensembl.gramene.org/Zea_mays.Genes co-localized with associated SNPs were identified from the maize GDB genome browser . In the GWAS panel, there were 57 lines (Table S2) which had Zn concentration of 33 µg/g DW or more, the current breeding target for HarvestPlus. Among those 57, there are several elite CIMMYT maize lines (CMLs), 4 QPM lines, several lines susceptible or tolerant to drought, heat or a combination of drought and heat as well as lines susceptible or tolerant to low nitrogen (low N).Soil characteristic and composition were slightly different between samples from Agua Fria (AF) and Celaya (CE). Soil from CE was of clay type with a pH of 8.03, Zn content of 2.32 µg/g dry weight (DW), Fe 7.84 µg/g DW and N 6.15 µg/g DW. AF soil was of clay loam type with pH of 8.28, Zn content of 1.24 µg/g DW, Fe 17.2 µg/g DW and N 7.57 µg/g DW. ANOVA and other descriptive statistics for both the traits showed significant variability for kernel-Zn and-Fe concentrations among the genotypes of the association mapping panel (Table r2) between adjacent pairs of markers versus distance in kb between adjacent pairs, and showed that the average LD decay was 17.5 kb at r2 = 0.1 and 5.99 kb at r2 = 0.2 . Chromosome-wise LD analyses showed the slowest LD decay on chromosome 8 , followed by chromosome 1 .Principal component analysis using genome-wide markers revealed only a moderate population structure with the first three principal components . The temperate lines and the drought tolerant donor germplasm including both La Posta Sequia and drought tolerant population (DTP) groups clearly separated in different axes from the rest of the CIMMYT tropical and sub-tropical lines. The first three principal components explained 41.86% of the total variance. The genome-wide LD decay was plotted as LD (U) and the method corrected only for population structure (Q) showed significant genomic inflation as observed in the Q–Q plots  more than 0.7 and minor allele frequency (MAF) more than 0.03. 347,765 SNPs which formed this subset were tested against kernel-Zn and Fe concentrations from 923 inbred lines across environments. In the three models used for GWAS, SNPs were tested independently against the phenotypes for Zn and Fe. The uncorrected method  to 90.91% (S2_225529232). Similar analysis with high and low kernel-Fe lines showed an allele enrichment of favorable alleles in the high kernel-Fe lines ranging from 52.31% (S10_136070835) to 93.33% (S1_64238509).P values lower than the panel-determined threshold, 381 SNPs were selected for kernel-Zn and 345 SNPs for kernel-Fe under a reduced threshold limit of P value ≤ 1.0 × 10−03, for single marker QTL analysis. From these, the polymorphism between the respective parents of each DH population reduced the number of SNPs tested to 232 and 231 SNPs for Zn and Fe, respectively. These SNPs were tested for linkage to kernel-Zn and Fe concentration independently in the three bi-parental populations by analysis of variance due to each allele class in the DH populations. This analysis identified 11 SNPs each for kernel-Zn and Fe that had significant effect on the trait variance , in one or more populations in one or more environments . An above-average concentration of kernel-Zn was reported in the QPM germplasm as compared to non-QPM/normal maize germplasm , which was similar to some earlier reports  across the whole genome close to 6 kb in the panel. This is in accordance with several previous studies  were found to have a significant effect on these traits in at least one population. However, it should be noted that about one-third of the SNPs that were selected for testing were not polymorphic in any of the parental combinations, limiting the ability of them being tested or validated in the present study. Some of the SNPs that were tested explained large proportion of phenotypic variance in individual bi-parental populations, though these could have been slightly over-estimated in single-marker QTL analysis. SNPs S7_173181688 and S7_173181689, located at physical coordinates chr 7: 173,181,688 and chr 7: 173181689, respectively, were shown to have the largest proportion of variance explained for kernel-Zn in the bi-parental populations studied. These will be further tested in breeding populations for their usefulness in selecting lines with high Zn. Similarly, SNPs on chromosome 8, around 164 and 167 Mb were found to explain a large proportion of variance for kernel-Fe. Considering the SNPs that were tested to be significant in trait expression, approximately five genomic regions, represented by one to many SNPs were identified. A region on chromosome 7 within 1 Mb between 173 and 174 Mb was found to be having significant effect for both kernel-Zn and Fe, and will be closely followed in later studies towards using them as breeding targets in Zn and Fe improvement.In total 11 SNPs each for kernel-Zn and Fe  was found to be common for both kernel-Zn and Fe. The Zn-associated SNP S8_80619983 near GRMZM2G311974 gene model possesses No Apical Meristem (NAC) domain transcriptional regulator super family protein. Molecular studies have shown that NAC family transcription factors regulates Fe and Zn remobilization from source organs to developing seeds associated with senescence  using high-density genomic data conducted for detecting marker-trait associations for kernel-Zn and Fe in maize. The study identified about 20 and 26 SNPs, respectively, for kernel-Zn and Fe, respectively. A subset of the marker-trait associations was validated using single marker QTL analysis in three bi-parental populations. Whereas some of the genomic regions identified in this study were novel, others were located in already reported QTL intervals. Some of the identified SNPs were located within many known genes involved in uptake, transport and localization of Fe and Zn in plants. More studies are being carried out to validate the utility of the markers identified in this study in the breeding lines and populations, as a precursor to marker-based breeding for bio-fortification of tropical maize for increased kernel-Zn and Fe contents.Supplementary material 1 (PDF 85 kb)Supplementary material 2 (PDF 136 kb)Supplementary material 3 (PDF 70 kb)Fig. S1 Three dimensional (3D) plot representing population structure based on the first three Eigen values of principal components analysis (PCA) of GWAS panel using 69,830 SNPs. The color codes are displayed with figure (PNG 149 kb)2 and the X-axis represents the genetic distance in kilobases (kb) (PNG 1932 kb)Fig. S2 Linkage disequilibrium (LD) decay plot representing the average genome-wide LD decay of GWAS panel using 34,420 genome-wide SNP markers. The values on the Y-axis represents the squared correlation coefficient rBelow is the link to the electronic supplementary material.


SU) value for each SNP is calculated. Then, the top-k SNPs are isolated as guiders to identify 2-way SNP combinations with significant interaction weight values. Next, a forward search is employed to detect high-order SNP combinations with significant interaction weight values as candidates. Finally, the findings were statistically evaluated using a G-test to isolate true positives. The developed algorithm was used to evaluate 12 simulated datasets and an age-related macular degeneration (AMD) dataset and was shown to perform robustly in the detection of some high-order disease-causing models.Detecting high-order epistasis in genome-wide association studies (GWASs) is of importance when characterizing complex human diseases. However, the enormous numbers of possible single-nucleotide polymorphism (SNP) combinations and the diversity among diseases presents a significant computational challenge. Herein, a fast method for detecting high-order epistasis based on an interaction weight (FDHE-IW) method is evaluated in the detection of SNP combinations associated with disease. First, the symmetrical uncertainty ( In recent years, genome-wide association studies (GWASs) have played an important role in identifying single-nucleotide polymorphisms (SNPs) associated with complex human diseases. This approach is non-candidate-driven, and investigates the entire genome, thus offering a more comprehensive method when compared to gene-specific candidate-driven studies . Genome-Nevertheless, detecting and identifying the disease-causing SNP combinations on a genome-wide scale is met with several challenges. First, there is an enormous computational burden that is associated with the examination of SNP combinations due to multiple testing. Second, it is challenging to develop a method that is able to reliably identify disease-causing SNP combinations from those that are not given the diversity that exists among disease models , especiaTo tackle these challenges, some algorithms were developed to detect synergistic SNP combinations associated with complex diseases. The majority of these methods can be classified into three categories: exhaustive methods ,9,10,11,In the exhaustive method, all SNP combinations from the original data set are verified, which ensures that disease-causing SNP combinations are seldomly missed, but it bears an enormous computation burden. To speed up the calculations, some lightweight scoring methods are employed. However, they usually prefer a small part of a disease model. The classical exhaustive methods include Boolean operation-based screening and testing (BOOST) , graphicThe filtering method applies data-driven and biological knowledge filters. The data-driven filter reduces SNP numbers by calculating the correlation between individual SNPs and disease status. Single-nucleotide polymorphisms below a given threshold are filtered out, and some disease-causing SNPs with a very low effect may potentially also be removed. The biological knowledge filter groups genes in the light of their biological functions, and detects the epistatic interactions within a group, but introduces a bias towards well-known genes, and against less well-characterized genes ,12,13.Artificial intelligent algorithms, such as bayesian epistasis association mapping (BEAM) , Ant colk-way epistasis interactions are detected, with the detection process divided into two stages: searching and testing. During the searching stage, an improved method is utilized to calculate the symmetrical uncertainty (SU) of each SNP locus with a given phenotype. Next, several SNPs with high SU values are chosen as guiders to isolate 2-way, 3-way, and k-way SNP combinations that are significantly associated with disease status. These SNP combinations are recorded as candidate solutions. During the testing stage, the G-test statistical method was employed to test the association significance of the candidate solutions. An example schematic for detecting 3-way SNP combinations can be seen in Recently, information theory has focused on selection from high-dimensional data sets ,26,27. IiX is the genotype variable of the ith SNP locus with values of k-way SNP combination 3-way SNP combination I denotes the number of genotype combinations (I = 3k for k-way SNP combination), J is the number of phenotype states (C), with J = 2 for a given disease phenotype . The number of samples in a dataset is defined as in, with the SNP loci taking the value of the ith genotype combination (1 ≤ i ≤ I), and ijn representing the number of samples of the ith genotype combination being associated with the phenotype state (cj).Sets of SNP variables are defined by “Information entropy” has been defined as the average amount of information that is produced by a stochastic source of data that can be used to measure the data distribution diversity, and to measure the uncertainty of random variables . To intrEntropy: Let ith genotype of a SNP variable (X), with the entropy of X being expressed as:Joint entropy: Let k SNP variables, with k-way SNP combination The JE is a measure of the uncertainty that is associated with a set of variables and can be used to measure the genotype distribution of a Mutual information: To measure the mutual dependence between a SNP locus (X) and phenotype (C), the mutual information was defined as follows:X.Joint mutual information: The joint mutual information between k variables  and the phenotype (C) was defined as follows:X and C), or between a SNP (X) and the disease status (C).The mutual information can also be regarded as a measure of the interaction strength between two SNPs  between X and Y. Based on a previous proposed interaction gain with three variables X, Y, and C [Y, and C , the folX and Y can be regarded as SNP loci, and C can be either a SNP variable or phenotype variable. In IG, a 2-way interaction between SNP X and SNP Y for phenotype C can be depicted, or a 3-way interaction between X, Y, and C can be indicated. If X, Y, and C are a feature set, such as in X and Y.In GWASs, X and Y together yield a more synergistic effect on phenotype than would be expected from their sum individually. Thus, X and Y interacting with each other (interaction effect), and the presence of Y will increase the ability of predicting the phenotype (C). Conversely, X and Y.In Equation (5), if X, Y) and phenotype C is very important but difficult. In this study, a new interaction weight (IW) factor was employed.In GWAS, measuring the interaction between genotype variables (Interaction weight factor: The interaction weight factor (IWF) between X and Y has been previously [IWF has the following properties:(1)IWF ≤ 20 ≤ (2)IWF ≤ 2 if X interacts with Y.1 ≤ (3)IWF ≤ 1 if X is redundant to Y.0 ≤ eviously  defined Evaluating the association of all SNP combinations with specific phenotypes may be time-consuming, and the computational evaluation of high-order SNP combinations is generally difficult to perform. To speed up the process of detecting epistasis from high-dimensional data sets, the present study employed Symmetrical uncertainty in identifying search seeds.Symmetrical uncertainty (SU): Mutual information has been widely adopted for data mining from high-dimensional data. However, it tends to favor features with more values while ignoring interactive features [features . In thisfeatures ,30, and X) and the disease status (C). H(X) and H(C) measure the diversity of the SNP genotype distribution and the phenotype, respectively. Nevertheless, SU cannot effectively measure low associations. To enhance the ability to detect lower SNP and phenotype associations, the SU equation was modified as follows:In Equation (7), H(X) + H(C) is replaced with H because we have found that the joint distribution of variables X* and C (let X* be the disease locus) usually have a smaller degree of dispersion than those of the other variables, namely, X and C. This improved Equation (8) thus enables the identification of some susceptibility loci with low marginal effects. This proposed algorithm is further described in Algorithm 1.In Equation (8), Algorithm 1: FDHE-IWInputs: D —the given data set with N + 1 columns; is denotes the values of the ithSNP locus for all samples. T—the candidate size; θ—the threshold of the G-test p-value; k—the number of SNPs in a k-way SNP combination; and K—the number to find the SNP combinations based on a seed SNP.Outputs: SNP combinations (SC)—the k-way SNP combinations that are associated with disease status.(1)Initialize: (2)Calculate the i = 1 to N doFor    Calculate   End For (3) k-way SNP combination based on the interaction weight.Search a (3.1) Select a SNP locus with a maximumvalue. (3.2) Search SNP combination based on interaction weightm = 1m < K || // to find K k-way SNP combinations based onWhile       Whilek //< S.    i = 1 to |F| doFor  // |F| denotes the SNPs number in F.     is and aF:        weight coefficient.        // calculate relevance between C)                End For     C in F.     F.     End While   S into SC as a candidate solution.   m = m + 1   End While  (4) If the size of SC is less than Tk-way SNP combination that are associated with disease status.go to step (3) to find new EndIf(5) Statistical testG-test statistic for each SNP combination in SC.Perform k-way SNP combinations with a p-value < θOutput the SU value for each SNP (Step (2)). Then the SNP SU value is chosen (see step (3.1)) to find K k-way SNP combinations that are associated with the status of the diseases. In step (3.2) of the FDHE-IW algorithm, the IW and weight coefficient (W) for each SNP in F are updated iteratively, and a new SNP having a maximum relevance with the phenotype is combined with SNP The FDHE-IW algorithm first calculates the Time Complexity: In the FDHE-IW algorithm, the time complexity is defined as O(N + N*(k!)*K). Generally, the values of k and K are very small, and the value of (k!)*K is much less than N. Therefore, the time complexity of FDHE-IW is less than O(N2), which is a feasible computation amount for current computers to detect high-order SNP epistasis from a data set with thousands to millions of SNPs.G-Test: A G-test is a maximum likelihood statistical significance test [G-test will lead to the same test results for samples of a rational size. However, for some cell cases, it is always better than the Chi-squared test [nce test . Comparered test .G-test method [k-way SNP combination model, the formula for calculating the G value is as follows:In this study, an improved t method  was empld (d = (I − 1)(J − 1)) is modified correspondingly, as follows:jy, and the genotype takes the ith k-combination. ln denotes the natural logarithm function. The observed number ijE) is obtained according to Hardy–Weinberg principles [The degree of freedom inciples . ξ is a To evaluate the performance of the proposed algorithm, equations for the power Equation (10), the F-measure Equation (11), the recall Equation (12), and the precision Equation (13) were utilized.#S is the number of disease-causing models from all #T datasets (there are 100 data matrices for each disease model).Power is a measure of the capability to detect disease-causing models in all datasets, where k-way SNP combination that is associated with disease status, and FNs  are defined as a non-discovery of a SNP combination that is associated with disease. TNs (true negatives) indicate no discovery, and FPs  are defined as a k-way SNP combination that is falsely associated with a disease status [True positives (TPs) are defined as the discovery of a e status .recall, precision and F-measure were used to evaluate the statistical precision of this hypothesis testing method (G-test in our method) for finding disease models in the screening stage. #TP is equal to the number of disease-causing SNP combinations that have passed the threshold p-value, while #FN is the number of the disease-causing SNP combinations that failed to pass the threshold. #FP is the number of non-disease-causing combinations that passed the threshold, while #TN equals the number of non-disease-causing combinations that failed to pass the threshold.In this experiment, Twelve disease loci with marginal effects (DME)-simulated disease models  see  that areTo further validate the algorithm, real AMD data containing 103,611 genotypes SNPs for 50 controls and 96 cases  were utiG-test p-value was set equal to MAF). The candidate size set to T = 2*k for simulation data sets and T = 200 for AMD. K = k for simulation data sets and K = 5 for AMD data. All experiments were performed using a Windows 10 operation system with Intel(R) Core(TM) i7-4790 CPU@3.6GHz and 8 GB memory, and all program codes were written in MATLAB R2015b . The source code is in In the DME simulation experiments, the threshold θ of the The detection power of FDHE-IW was first investigated by comparing it with four state-of-the-art algorithms  using a DME dataset with 100 SNPs  and a daThe recall, precision and F-measure were also determined when using FDHE-IW for all 12 DME models , with thMAF and low hereditary, MACOED fails to identify them in the screening stage. Therefore, the MACOED has higher precision than FDHE-IW in the testing stage, but the detection power of MACOED is much lower than that of FDHE-IW , 18 3-way SNP-combinations . The obtained findings were further examined using Cytoscape (http://www.cytoscape.org/) [2-way SNP interaction network and a 2-way gene interaction network were constructed  , and a 2structed , where tCFH gene were also identified. The CFH gene has been commonly association with AMD [CFH had six connections due to six SNP pairs within the SNP network being mapped to a CFH–NA gene-pair. CFH was also found to have a novel connection with the JMJD2C gene, a histone lysine demethylase. JMJD2C has been reported to play a crucial role in the progression of breast cancer, prostate carcinomas, osteosarcoma, and blood neoplasms, thus indicating that JMJD2C represents a promising anti-cancer target [JMJD2C may have an important role in AMD.Within the SNP network , two widwith AMD ,18,19. Fr target ,40,41. T3-way gene combinations containing the CFH gene, and four 3-way gene combinations containing the JMJD2C gene were identified. Only one 3-way combination did not involve the CFH and JMJD2C genes, suggesting that these are important to AMD.Eight 4-way SNP combinations in 2-way, and 3-way) were computed and then compared to the AUC relative to the 4-way SNP combinations  and rs3776652 (in the JMJD2C gene) were also reported by FHSA-SED and NHSA-DHSC. SNPs rs6598991 and rs10507949, both in NA, have not been reported to date.Recent investigations have utilized AMD data sets, which include algorithm IOBLPSO , epiACO k-way SNP epistasis that is associated with disease. When utilizing simulation data sets, the method developed herein was shown to be more powerful than other comparable algorithms in detecting the disease-causing SNP combinations at the searching stage. However, in the testing stage, the balance between type I and type II errors is very difficult to manage, due to the p-value threshold for distinguishing between true and false disease-causing combinations being very different for different disease models. Therefore, multiple statistical tests, such as the Chi-square test, t-test, and G-test, were evaluated, with the G-test being shown to be the most robust. When utilizing the newly developed algorithm to evaluate an AMD dataset, almost all of the well-known disease-causing SNP loci associated with AMD and some new SNP combinations were identified. However, the method developed herein identified many SNPs that are in non-coding genomic regions, which will require further examination in future studies. The detection of disease-associated SNPs in high-order disease models is a very difficult problem.In this study, a fast-search method based on the interaction weight was proposed to detect k-way SNP combinations to detect k-way disease-causing SNP combinations. It is capable of detecting some higher-order epistasis on a whole genome scale with a time complexity that is much less than O(N2).The FDHE-IW algorithm does not need to evaluate all The proposed algorithm can effectively detection nested epistasis , suggestAt present there is still no fast or effective approach for detecting various disease-causing models with multi-loci in GWAS, due to the enormous computational burden. Therefore, detecting high-order disease models has room to be explored using high-performance methods and cloud computing. In future research, we will also focus on non-coding genomic regions, we will continue to focus on rare variants in GWASs, and we will develop new methods to aid in the identification of the causes of complex diseases. With the rapid development of high-performance cloud computing techniques, these abilities should continue to improve.