Bos indicus) using data from 2,016 animals and 503,088 single nucleotide polymorphism (SNP) markers. The strongest signal (p = 1.01 × 10-9) was found on chromosome 5 spanning positions 47.9–48.2 Mbp. This region contains introns 3 and 4 and exons 4 and 5 of the high mobility group AT-hook 2 gene (HMGA2). Further inspection of the region with whole genome sequence data of 21 Nellore bulls revealed correlations between counts of the significant haplotype and copy number gains of a ∼6.2 kbp segment of intron 3 of HMGA2. Analysis of genome sequences from five African B. indicus and four European Bos taurus breeds revealed that the copy number variant (CNV) is indicine-specific. This intronic CNV was then validated through quantitative polymerase chain reaction (qPCR) using Angus animals as copy neutral controls. Importantly, the CNV was not detectable by means of conventional SNP-based GWAS or SNP probe intensity analyses. Given that HMGA2 affects the expression of the insulin-like growth factor 2 gene (IGF2) together with the pleomorphic adenoma gene 1 (PLAG1), and that the latter has been repeatedly shown to be associated with quantitative traits of economic importance in cattle, these findings highlight the emerging role of variants impacting the insulin-like growth factor pathway to cattle breeding.Navel injuries caused by friction against the pasture can promote infection, reproductive problems and costly treatments in beef cattle raised in extensive systems. A haplotype-based genome-wide association study (GWAS) was performed for visual scores of navel length at yearling in Nellore cattle ( Bos indicus) animals compose the majority of the herds and approximately 88% of the females in reproductive age are subjected to natural mating (Navel length at yearling (NY) is an economically important trait in beef cattle raised in extensive production systems. A pendulous navel increases the risk of injuries caused by friction against the pasture, leading to infection, reproductive impairment and treatment expenses . This trl mating . Consequl mating .PLAG1), which is a transcription factor for the insulin-like growth factor gene 2 (IGF2), contributed significantly to the genetic variance of all traits analyzed. Indeed, the PLAG1 chromosomal domain has been shown to affect a wide range of economically relevant traits in both Bos taurus  for body weight and carcass visual scores in Nellore cattle revealed pleiotropic loci sheltering genes that are known to modulate the insulin-like growth factor (IGF) or somatomedin pathway . In parts taurus  and B. i indicus  cattle. HMGA2, another transcription factor for IGF2 that was not detectable from conventional GWAS or SNP probe intensity analyses. Moreover, validation of the CNV through quantitative polymerase chain reaction (qPCR) showed that the genome-wide significant haplotype performed well as a predictor of CNV genotype in our samples. Finally, analysis of a panel of whole genome sequences of African B. indicus and European B. taurus breeds revealed the CNV to be of B. indicus origin.Here we report a haplotype-based GWAS for visual scores of navel length at yearling (NY) in Nellore cattle, which resulted in the discovery of a copy number variation (CNV) at intron 3 of Illumina BovineHD BeadChip assay  genotypes of 953 Nellore bulls and 1,278 Nellore cows were available for analysis from previous studies . These aPseudo-phenotypes were based on estimated breeding values (EBVs) obtained from an animal model fitted to records of 745,466 animals. Navel length at yearling (NY) was recorded based on visual evaluation of animals by trained technicians. The navel length scores were assigned considering an absolute scale, ranging from 1 to 5, so that the largest scores were attributed to animals with longer or more pendulous navels. For males, the penile sheath was considered part of the navel. Animals pertaining to the same contemporary group  were evaluated by the same technicians, so that the fixed effect of contemporary group fitted in the animal model was expected to account for any systematic effects associated to technicians. The distribution of NY phenotypes is presented in the Supplementary Figure The GWAS model used here was essentially the leave-one-chromosome-out (LOCO) procedure proposed by n is a vector of ones , μ is the intercept, × is a n × 1 vector of standardized haplotype counts, b is the fixed effect of the haplotype, u is the n × 1 vector of accumulated contributions of random effects of all SNPs except those located in the same chromosome as the haplotype being tested , and e is the n × 1 vector of random residual effects. It was assumed that y ∼ MVN , where G is the SNP-based genomic relationship matrix (GRM) excluding all SNPs pertaining to the same chromosome where the tested haplotype is located, σu2 is the marked additive genetic variance, I is an identity matrix and σe2 is the residual variance. The model above was fitted in two steps: first, variance components were estimated using the average information restricted maximum likelihood (AI-REML) algorithm; then, estimates of fixed effects were obtained using generalized least squares equations. In order to reduce computational burden, variance components were estimated only once per chromosome using a reduced model without haplotypes. By conditioning all analyses on the accumulated effects of SNPs, associations presumably reflected significant haplotype effects not captured by random SNP effects. Moreover, the SNP-based GRM helped controlling the analyses for putative confounding effects of cryptic relationships and population substructure. Haplotypes were constructed with GHap v1.2.2  birth weight, (2) weight gain from birth to weaning, (3) weight gain from weaning to yearling, (3) scrotal circumference, (4) age at first calving, (5) gestation length, and visual scores for (6) conformation, (7) precocity, and (8) muscling at yearling. These traits are routinely recorded and used as part of the genetic evaluation system of the commercial breeding program, and the models used to obtain deregressed EBVs for these traits were previously described by B. taurus assembly  of the significant haplotype and nucleotide coverage was averaged within groups. A last normalization step was adopted by taking the median coverage of 1 kbp windows. This procedure was intended to smooth out outlying values and increase the signal-to-noise ratio in the data. Candidate variants and alignment data were also inspected with the Integrative Genomics View (IGV) v2.3 software (n = 13) from five African zebu B. indicus cattle breeds, namely Kenana, Butana, Sudanese Baggara, Ogaden, and Kenyan Boran (n = 20) of four B. taurus breeds   . Per sammmental)  and Ensemmental)  genome bR = X + Y. The normalized total signal intensity (LRR) was calculated as log2 (Robserved/Rexpected), where Rexpected was computed from linear interpolation of the canonical genotype clusters. Since LRR is prone to genomic “waves,” supposedly produced by differences in GC-content (%GC) and intensified as the sample deviates from optimal DNA quantity  and 5′ GTGTCAGCTGCCTGAGTCCT 3′ (reverse) targeting the tumor suppressing subtransferable candidate 4 gene (TSSC4) were used as a single copy reference. The assay was performed using a 96-samples plate. The qPCR analysis was then conducted using samples available from our DNA bank, and comprised 4, 4 and 3 animals with 0, 1, and 2 copies of the significant haplotype, respectively. Additionally, 4 Angus animals were used as copy neutral controls. All samples were assessed in triplicate and no-DNA templates were included for each pair of primers as a control. Each assay consisted of a reaction with a total volume of 20 μl, which contained 1 μl of genomic DNA, 0.8 μl of each primer (10 μM), 7.4 μl of pure water and 10 μl of QuantiTect® SYBER® Green RT-PCR Master Mix (QIAGEN®). Relative increase of copy number was calculated for each sample using the 2-ΔΔCt method  and 5′ TTTCCTATGGTCCCAAGCG 3′ (reverse) was designed considering the limits of the CNV region using the NCBI Primer-BLASTt method . The 2-Δp = 1.01 × 10-9), which formed the consensus allele TCCTCCAAC. This consensus haplotype overlapped introns 3 and 4 and exons 4 and 5 of HMGA2.A total of 2,048,168 six-marker haplotypes were tested for association with NY in Nellore cattle. The minimum, median and maximum number of observed haplotypes per six-marker window was 1, 4, and 15, respectively. Association results pointed to a single leading QTL region on chromosome 5 mapping between 47.9 and 48.2 Mbp  associations for visual scores of precocity and muscling at yearling , regardless of haplotype count, suggesting a baseline copy gain instead of a copy neutral sequence background, and co-segregation of the relevant haplotype with additional copies of the ∼6.2 kbp DNA segment. Inspection of alignments of the bovine HMGA2-CNVR sequence against the human reference genome assembly (GRCh38.p12) showed that the region spanned a HMGA2 antisense RNA (accession numbers AC090673.2 and ENSG00000256083). The UCSC LiftOver tool further predicted that the antisense RNA would map to positions 48,070,427–48,085,449, which substantially overlapped with the detected CNV. The existence of AC090673.2 in the human genome was well supported by evidence from expressed sequence tags (ESTs) obtained from pooled human melanocyte, fetal heart and pregnant uterus samples (accession number AA773790.1). Therefore, the HMGA2-CNVR stood as the most plausible candidate variant underlying the NY associations.Assuming that the TCCTCCAAC haplotype tagged an unobserved causal variant, we used haplotype counts to predict causal variant genotypes in a sample of 21 Nellore bulls with whole genome sequence data. From a total of 1,185 sequence variants detected by SAMtools within the chromosome 5 range 47.9–48.2 Mbp, only 6 presented genotype correspondence with haplotype counts, namely: rs209832737 , rs521422509 , rs525140201 , rs521794670 , rs519732918 , and rs516281664 . While we could not exclude causality of any of these variants, none of them were novel or intragenic, making it difficult to select any specific variant for further scrutiny. However, analysis of coverage data revealed an additional variant comprising a ∼6.2 kbp CNV spanning positions 48,074,233–48,080,443 Figure . This CNHMGA2-CNVR was a recent derived mutation private to Nellore cattle or an ancient variant, we inspected the candidate chromosomal region in sequence data from five African B. indicus and four European B. taurus breeds  and 10 markers  , a transcription factor for IGF2, also presented marked growth retardation and decreased fertility . Regulation of IGF2 by HMGA2 has been recently demonstrated to occur directly or through increased expression of PLAG1 (HMGA2 that was associated with navel length at yearling (NY) in Nellore (B. indicus) cattle. This CNV was detected through a combination of haplotype-based GWAS and whole genome sequence analysis. We also showed that this candidate variant was difficult to identify via conventional GWAS and state-of-the-art CNV detection methods. Moreover, we found that other B. indicus breeds apart from Nellore cattle also carry copy gains of this HMGA2 intronic segment.The discovery of multiple quantitative trait loci (QTL) mapping to the bovine of PLAG1   binding to its 3′-UTR region, has been known for at least a decade. For instance, one of the first characterized miRNAs, let-7, is a major negative regulator of HMGA2 and explains the suppression of this gene in later stages of development , which is yet to be validated and observed in the bovine species. Nevertheless, given the major regulatory role of this intron on HMGA2 transcript abundance, we propose an effect of variation of copy number at this CNVR on HMGA2 expression and navel size in B. indicus cattle. This is also supported by associations between penile sheath and SNPs close to HMGA2 in Brahman (B. indicus) and Tropical Composite (B. indicus ×B. taurus) cattle  cattle , indicatHMGA2-CNVR on navel size, the candidate variant is potentially involved with other heritable traits considering the pleiotropic nature of HMGA2. For instance, HMGA2 variants have been associated with floppy ears in dogs  and the pendulous navel are phenotypic marks that morphologically differentiate B. indicus from B. taurus cattle, future analyses of the HMGA2-CNVR should address the question of whether this variant had a role in the evolutionary history of the B. indicus lineage.Apart from the observed effects of the  in dogs , ear siz in dogs , fat dep in dogs  and stat in dogs , cattle  in dogs , horses  in dogs , and dog in dogs . Mice hoertility . In the enturies , whereasenturies . AltogetPRJNA312138 and PRJNA238491, respectively. The Nellore datasets are not publicly available because they comprise part of the genomic selection reference population from a commercial breeding program ran by an alliance of cattle breeders from Brazil. Data are, however, available for academic use from the authors upon request .Whole genome sequences of African and European cattle are available through the NCBI SRA Archive under BioProjects B. taurus sequence data analyses. TA and YU wrote the manuscript. All authors revised and agreed with the contents of the manuscript.YU and JG conceived and designed the study. AdC and TS coordinated genotyping and sequencing of Nellore animals. AT, HM, and OH provided and analyzed African cattle sequence data. RC and HN analyzed phenotypes and pedigree data and generated deregressed EBVs. AU performed genotype phasing. TA and YU performed association analyses. MM, YU, and PA-M designed the qPCR experiments. TA, BT, RT, and FL performed the qPCR assays. TA, RT, and MM performed Nellore and HN is employed by Gensys Consultores Associados and TS is employed by Recombinetics, Inc. TS and YU are Associate Editors at Frontiers in Genetics, Livestock Genomics. 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. Mention of trade name proprietary product or specified equipment in this article is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the authors or their respective institutions.


Discorea alata L.), 4,593 good quality SNPs were identified in 40 accessions. One hundred ninety six of these SNPs were selected to represent the overall dataset and used to design a competitive allele specific PCR array (KASPar). This array was validated on 141 accessions from the Tropical Plants Biological Resources Centre (CRB‐PT) and CIRAD collections that encompass worldwide D. alata diversity. Overall, 129 SNPs were successfully converted as cost‐effective genotyping tools. The results showed that the ploidy levels of accessions could be accurately estimated using this array. The rate of redundant accessions within the collections was high in agreement with the low genetic diversity of D. alata and its diversification by somatic clone selection. The overall diversity resulting from these 129 polymorphic SNPs was consistent with the findings of previously published studies. This KASPar array will be useful in collection management, ploidy level inference, while complementing accurate agro‐morphological descriptions.Using genome‐wide single nucleotide polymorphism (SNP) discovery in greater yam ( The high importance of D. alata for food security has prompted the establishment of several international and national ex situ collections. Due to the limited shelf‐life of stored tuber, yam genetic resources are conserved in vitro or/and in the field. All of these repeated manipulations are time‐consuming and may affect long‐term conservation. Quality control of genotype purity and general collection management is mainly based on morphological descriptors  genomic resources  to identify genome‐wide polymorphic SNP markers, (b) to develop a cost‐effective SNP genotyping array using KASPar technology and (c) to test its use as a tool in managing yam ex situ collections.22.1D. alata diversity was selected and genotyped to identify polymorphic SNPs and design KASPar markers. Then, for the purpose of validating these markers, 141 landraces from the Tropical Plants Biological Resources Centre (CRB‐PT) and CIRAD ex situ collections maintained in the West French Indies (Guadeloupe) were used.Based on a previous microsatellite markers study . First, DNA extractions were performed with dried leaves from the 48 accessions as described by Risterucci et al. . The genD. alata contigs (http://www.ebi.ac.uk/ena/data/view/PRJEB10904) using Bowtie2 v2.2.6  at 22°C for 1 hr, and the ligase was then inactivated, prior to sample pooling, by heating at 65°C for 20 min. Pooled samples were PCR‐amplified in a single tube. Single‐end sequencing was performed on a paired‐end lane of an Illumina HiSeq3000 . The Tassel 5.2 pipeline  were selected using SNiPlay3  on a validation panel of 141 landraces from the CRB‐PT and CIRAD ex situ collections. From raw fluorescence data, allele calling was performed using LGC Kluster Caller software by defining fluorescence clusters. Some accessions with known ploidy level were used as reference to identify fluorescence clusters and assess allelic dosage.2.4To identify duplicate accessions and compare accessions with different ploidy levels, a matrix of dissimilarity between each accession pair was computed as the percentage of shared alleles based on the allele presence/absence.Then, to refine the kinship assessment, similarities between accessions with the same ploidy level were computed in the same way but using the allelic dosage. For diploid accessions, genotypes were coded as 0, 1, and 2 where the number represents the number of nonreference allele. Heterozygous genotypes assessed as polyploid during allele calling were converted to 1. Moreover, for triploid accessions, genotypes were coded as 0, 1, 2, and 3 with allelic dosage score as 1:1 during allele call converted to 1.5. For tetraploid accessions, genotypes were thus coded as 0, 1, 2, 3, or 4 and no correction was needed.Diversity analysis was conducted in two steps. During the first step, groups of duplicate accessions (redundancy groups) were defined by grouping accessions having up to one allele mismatch. Then, in the second step, the diversity analysis focused on the similarity between those groups. Clustering based on allele frequencies within redundancy groups followed by a bootstrap approach  or did not align to any contig (35%). From these sequence tags, SNP calling produced a raw vcf file of 158,695 SNPs. This raw vcf file was then filtered resulting in a dataset of 40 accessions  produced more than 344 million reads resulting in 521,918 sequence tags out of which 207,810 (39.82%) aligned exactly once on cessions ppendix AAmong the 192 SNPs, 26 (13%) SNPs failed as they did not produce any amplification signal. From the remaining 166 SNPs (87%), 129 SNPs Appendix  with lesD. rotundata reference genome  coexisted  for diploids and high for triploids (0.83 ± 0.05). In addition, the three distributions of this ratio corresponding to the three ploidy levels did almost not overlap Figure .We were thus not able to differentiate all allelic dosage from each other when looking at one SNP. However, ploidy level could be deduced when taking all the KASPar array into account and considering the proportion of genotypes scored as “polyploid‐like” per accession. This KASPar assay thus differentiated the accession ploidy level and allowed us to assign it for 12 accessions originally of unknown ploidy. Nine were set as diploid and three as triploid.3.3Overall, 141 accessions from CRB‐PT and CIRAD ex situ collections in Guadeloupe were used to validate the KASPar assay .The allele presence and/or absence was used to assess the similarity between accessions and thus to identify duplicate accessions Figure . Indeed,These groups of genetically similar accessions were partially expected based on the accession vernacular names. For example, the second biggest group . This group consisted of 67% (24/36) of the triploid accessions present in the CRB‐PT and CIRAD collections.More generally, redundancy groups only consisted of accessions with the same ploidy level Figure . MoreoveThe diversity analysis was based on these 43 redundancy groups to avoid bias. After clustering, the bootstrap procedure detected five significant gene pools, named “cluster” here, represented in the kinship network Figure . Only onGenotype permutations and network analysis gave a more detailed view of kinship between redundancy groups and Clusters. This approach revealed a low number of significant links between the diversity clusters D or E and the others Figure  revealin44.1KASPar technology is based on competitive allele‐specific amplification followed by allele‐specific fluorescence assessment  already identified in previous studies. For example, using DarT markers, a low varietal richness was revealed by Vandenbroucke et al.  exhibited diversity in tuber shape and tuber flesh color in agreement with previous genetic diversity studies that already pooled these accessions together and highlighted this intragroup variability in tubers  and (c) the presence of real duplicates within collections. This tool is thus efficient for attributing accessions to a genetic lineage , but a good complementary agro‐morphological and ecophysiological characterization of collections should also be done to completely differentiate somaclonal mutant clones from duplicates .The ability of KASPar assay developed in this study to differentiate duplicates in collections from genetically close accessions was related: (a) to the low number of studied loci (129), but also (b) to the 4.3D. alata diversity  to assess complementarity/redundancy between the different collections, (b) to identify under represented genetic groups, and (c) to plan future collects to fill gaps in collections.The CRB‐PT collection has been shown to be representative of worldwide u et al.  and the 5D. alata and validated on a subset of accessions representative of worldwide D. alata diversity. This tool will allow users to estimate accession ploidy levels and genetic lineages. The results showed a good correlation between the diversity assessed by this KASPar array and the findings of previous studies. This KASPar array is a robust and cost‐effective tool for diversity assessment and collections management. Regarding the importance of vegetative reproduction and somaclonal selection in D. alata, it is a good tool to complement agro‐morphological description in collections.This is the first SNP array designed for The authors declare that they have no conflict of interest.C.P., F.C., H.C., and P.M. designed the study. C.P., F.C., E.M., G.A., and R‐M.G. contributed to collecting materials and sample preparation. P.M. and S.C. developed GBS protocol, carried out DNA extraction, and GBS library preparation. H.C. and P.M. performed SNP discovery. F.C. and H.C. designed the KASPar assay and performed its analysis. C.P., F.C., and H.C. wrote the manuscript with the input of all authors.


PAV) in crop genomes, and there is increasing evidence that PAV associates with important traits. Today, high‐resolution genetic analysis in major crops frequently implements simple, cost‐effective, high‐throughput genotyping from single nucleotide polymorphism (SNP) hybridization arrays; however, these are normally not designed to distinguish PAV from failed SNP calls caused by hybridization artefacts. Here, we describe a strategy to recover valuable information from single nucleotide absence polymorphisms (SNaPs) by population‐based quality filtering of SNP hybridization data to distinguish patterns associated with genuine deletions from those caused by technical failures. We reveal that including SNaPs in genetic analyses elucidate segregation of small to large‐scale structural variants in nested association mapping populations of oilseed rape (Brassica napus), a recent polyploid crop with widespread structural variation. Including SNaP markers in genomewide association studies identified numerous quantitative trait loci, invisible using SNP markers alone, for resistance to two major fungal diseases of oilseed rape, Sclerotinia stem rot and blackleg disease. Our results indicate that PAV has a strong influence on quantitative disease resistance in B. napus and that SNaP analysis using cost‐effective SNP array data can provide extensive added value from ‘missing data’. This strategy might also be applicable for improving the precision of genetic mapping in many important crop species.Evolutionary processes during plant polyploidization and speciation have led to extensive presence–absence variation ( Vastly increased production, shorter crop rotations and global warming have led to particularly strong disease pressure in major growing areas for the fungal pathogens B. rapa  genotyping array  were selected for SNP‐based genomewide association studies (GWAS). In a refined filtering approach, a three‐step method was used to select SNaP markers that could indicate structural presence–absence polymorphisms.Many of the 36.5% SNP markers removed during this standard filtering procedure showed a low frequency of failed calls in the 5 nested association mapping (NAM) subpopulations. However, separate investigation of each subpopulation revealed SNaP frequencies that were frequently close to the expected segregation pattern of a biparental homozygous population (50%). Figure B. napus chromosomes most substantially affected by structural rearrangements . The deletion was further validated by PCR using specific primer pairs targeting the corresponding chromosome region. As expected, DH5ON showed no amplification of specific primer pairs for four consecutive regions (containing six genes) in the estimated deleted interval, whereas the expected PCR products that indicate presence were visible in the other parental lines and positive controls Figure . The resB. napus parent H165, consistent with whole‐genome resequencing data and read coverage analysis. This deletion segregates in the DH5ON x H165 subpopulation in our study. Chromosome C01 also shows large deletions in the parents H149 and MOY4. In general, the natural B. napus parent DH5ON exhibits the lowest frequency of genome restructuring and SNaPs, but these segregate in all subfamilies.Physical location of SNP probes corresponding to SNaP markers confirmed small, medium and large deletions up to chromosome‐size in the five segregating subpopulations Figure . On chroP‐value)( ≥3) with p‐values between 7.88E‐05 and 8.53E‐04 ( ≥3). After correction for false discovery rate , however, no SNPs remained in either trial with confirmed associations to blackleg disease resistance.Genomewide SNP‐trait association analyses were first conducted using the 18 076 quality‐filtered SNP markers that were polymorphic in the NAM subpopulations. In order to reduce the rate of false‐positive marker–trait associations, a mixed linear model was applied that controls population substructure using the first two principle components and the kinship matrix. Phenotype data from blackleg disease screening in field trials in France revealed 12 significant SNP‐trait associations . SNaP markers revealing deletions inherited from the common elite parent DH5ON were associated with resistance to Sclerotinia stem rot resistance for the QTL present on A03 at position 21 Mb. The absence of these alleles in DH5ON was reconfirmed by SNP analysis in all parents and the BnNAM population by resequencing of parents and by PCR analyses  led to extensive structural genome variation a widespread phenomenon in the pangenomes of many crop plants .Using standard filtering quality procedures to select 60K Brassica Illumina SNP array markers facilitated detection of a number of regions corresponding to previously identified loci conferring disease resistance. For blackleg, studies using biparental crosses and association mapping on diverse germplasm sets have identified a small number of major loci with monogenic inheritance , which is involved in wax accumulation on the leaf surface. A homozygous mutation was found to hinder the germ tubes of the Asian soybean rust and two other fungal pathogens, preventing them from developing successfully on the leaves of Medicago truncatula. Association of mutations or deletions with resistance in natural populations is a rare phenomenon, as loss of function is typically recessive and in heterozygous genotypes resistance can be masked by the dominant allele or by a dosage effect.It is known that PAV and other structural variation in plants affect stress response genes, particularly genes involved in disease resistance  population was used in this study. The BnNAM population consists of 50 genetically diverse winter B. napus accessions  crossed with an elite doubled haploid winter‐type line (DH5ON). Each of the 50 subpopulations is composed of ≥50 doubled haploid lines per cross  or ≥50 single‐backcross recombinant inbred lines (BC1‐RILs) for crosses with one resynthesized B. napus parent . Approximately 25 plants were scored for each of the 200 tested BnNAM lines. Using the lesion lengths recorded at the three dates, an area under the disease progress curve (AUDPC) was calculated according to Obermeier et al.  and KWS SAAT SE  in 2015/2016. The area of necrosis at the plants base stem was evaluated for 30 plants per genotype using a 1–6 scale at crop maturity in late June. This procedure is also known as the G2 index, where a score of 1 corresponds to complete absence of affected tissue, while a score of 6 corresponds to 100% area affected, a broken stem or a dead plant , indicating a possible deletion. If a SNP exhibited a presence–absence segregation pattern ratio in at least one of the subfamilies, the presence SNP allele was recoded as AA and the failed SNP call as BB to enable inclusion in the marker matrix for the GWAS.The entire BnNAM panel was genotyped with the 60K Illumina Infinium Brassica SNP array containing 52 158 SNP probes. Using the Darmor‐, et al. . Initialet al., et al., et al., 10(1/n), where n represents the number of SNP markers. To reduce the type II error rate, we also captured the SNP‐trait associations for disease resistance using an arbitrary threshold of −logP‐value)10 between pairs of SNPs. Only markers with MAF ≥ 0.05 were included in the analysis. Haplotype patterns were assessed for SNPs and SNaPs that showed significant marker trait association. Haplotype blocks were defined using the confidence interval method described by Gabriel et al.  were designed and included in a multiplex PCR to ensure that no technical errors occurred during the tests. Additionally, we included for PCR the B. napus reference genotype Darmor‐bzh as a positive control for PCR amplification. PCR primer information, reaction setup and conditions are listed in Table Specific primers were designed for all genes present in QTL intervals that showed significant associations between SNaP markers and traits on chromosomes A03 and C04. Additionally, primers specific for an B. napus Darmor‐bzh reference genome assembly v. 4.1 . (a) Anchoring of consensus Sanger reads to the reference genome Darmor‐bzh; (b) anchoring of Sanger reads to individual NAM parents of five targeted genes; (c) PCR amplification of genes.Click here for additional data file.Figure S2. Manhattan plots resulting from genome‐wide association analysis (GWAS) for blackleg resistance in the NAM panel using (a) SNP markers, (b) SNP and SNaP markers. The x‐axis represents the marker positions along each chromosome anchored to the Darmor‐bzh reference; the y‐axis shows the −log10P‐value) only SNP markers, and (a2) SNP plus SNaP markers. Haplotype patterns reveal two blocks at the beginning of the chromosome, one (BnPAV_C04_1) spanning 40k and harbouring 3 SNaP markers (b1), and 13 genes (c1), and a second (BnPAV_C04_2) spanning 200k and harbouring 2 SNaP markers (b2), and 19 genes (c2), respectively.Click here for additional data file.Table S1. Mean genomic distances and haploblock numbers between SNP markers and between SNP and SNaP markers, respectively.Table S2. Summary of SNP‐trait associations with a −log10P‐value)( ≥3.Table S4. Common QTL regions with literature.Table S5. Information on PCR primer sequences, reaction setups and cycling conditions.Click here for additional data file.


Felis silvestris silvestris) populations can be locally threatened by introgressive hybridization with free-ranging domestic cats. Identifying pure wildcats and investigating the ancestry of admixed individuals becomes thus a conservation priority. We analyzed 63k cat Single Nucleotide Polymorphisms (SNPs) with multivariate, Bayesian and gene-search tools to better evaluate admixture levels between domestic and wild cats collected in Europe, timing and ancestry proportions of their hybrids and backcrosses, and track the origin (wild or domestic) of the genomic blocks carried by admixed cats, also looking for possible deviations from neutrality in their inheritance patterns. Small domestic ancestry blocks were detected in the genomes of most admixed cats, which likely originated from hybridization events occurring from 6 to 22 generations in the past. We identified about 1,900 outlier coding genes with excess of wild or domestic ancestry compared to random expectations in the admixed individuals. More than 600 outlier genes were significantly enriched for Gene Ontology (GO) categories mainly related to social behavior, functional and metabolic adaptive processes (wild-like genes), involved in cognition and neural crest development (domestic-like genes), or associated with immune system functions and lipid metabolism . These kinds of genomic ancestry analyses could be reliably applied to unravel the admixture dynamics in European wildcats, as well as in other hybridizing populations, in order to design more efficient conservation plans.The survival of indigenous European wildcat ( Hybridization between free-ranging domestic animals and their wild conspecifics may spread artificially-selected maladaptive variants causing fitness declines, outbreeding depression and gradual alterations of locally adapted gene complexes, thus increasing the risk of extinction of wild populations or entire species8. However, recent studies documented cases of beneficial introgression of domestic mutations in wild populations of North American wolves (a melanic deletion at the β-defensin-103 locus9) and Alpine ibex (a domestic goat MHC haplotype10).Anthropogenic hybridization, the cross-breeding of genetically differentiated Felis silvestris catus), together with the demographic decline and fragmentation of European wildcat populations (F. s. silvestris11), offers a remarkable case-study of anthropogenic hybridization13. The widespread diffusion of stray or feral cat populations14 likely promoted reproductive interaction between the two subspecies. Moreover, the full fertility of their hybrid offspring15 due to the recent origin of the domesticated cat17 has likely increased the risk of genetic introgression. Despite the active role of ecological barriers found to limit hybridization in some Mediterranean regions19, variable degrees of admixture have been detected throughout the European continent and across habitat types as a consequence of human pressures26, leading to a complete hybrid swarm in wild-living cats in Scotland12. Such geographical heterogeneity in admixture levels might be explained by different environmental conditions and ecological barriers19, population histories and proportions27, or by the choice of markers and sampling design26.Cross-breeding between wild and domestic cats, intensified by the human-mediated worldwide dispersal of domestic cats  and short mitochondrial DNA (mtDNA) sequences. The high variability of these markers, analyzed using Bayesian and phylogenetic statistical tools, has radically improved our knowledge of the European wildcat population genetic structure37, but showed a limited power to investigate the ancestry of admixed individuals. The wild and domestic cats used as parental references for multivariate and Bayesian assignment analyses were often regionally sampled, and the different applied marker panels were seldom comparable39. Consequently, standardized and more powerful panels of molecular markers to be applied at a large scale are required to lower the risk of underestimating the prevalence of introgressive hybridization in natural wildcat populations.Variations in coat color patterns and morphological traits between wildcats and domestic cats are not always diagnostic40.Recent next-generation sequencing platforms can offer solutions allowing the assemblage of extensive and cost-effective panels of ancestry-informative markers (AIMs) constituted by Single Nucleotide Polymorphisms (SNPs), which represent the most widespread source of genome-wide variationet al.25 because of their fixed allelic differences between domestic and wild reference cats and applied for European wildcat admixture analyses. Furthermore, an increased set of SNPs (n = 158) has been identified by Oliveira et al.30 from the 1.9 × genome sequence of an Abyssinian domestic cat41 because of their informativeness and variability in domestic cat breeds42. Both marker panels were applied on a wide cat sampling and proved to be able to successfully assign hybrid categories up to the second admixture generation . However, these SNPs showed a limited power to detect old-generation backcrosses resulting from a repeated cross-breeding between admixed individuals and parental species.A promising set of 96 nuclear and mitochondrial SNPs has been recently selected by Nussberger 44. Additionally, the availability of efficient AIMs widely distributed across the entire genome can allow to identify patterns of introgressed linkage blocks hosting candidate genes that may underlie introgressed functional traits that are still unknown46, further help to disentangle historical and contemporary admixture47 by analyzing the distribution of haplotype block lengths48 and associate anomalous phenotypes with their genetic bases44. These approaches allow researchers to better understand the dynamics and consequences of anthropogenic hybridization compared to previous studies, helping to face specific management and conservation issues46.Recent studies showed how the employment of thousands of markers might help to reveal previously undetectable backcrosses (older than two–three generations in the past) and estimate the timing from the admixture events49. This array offers a suitable molecular tool to further investigate the ancestry of European wildcat populations in conservation and monitoring projects49.The recently released Illumina Infinium iSelect 63k DNA cat array contains 62,897 variants that are mostly polymorphic within the domestic cats and includes 4,240 wildcat-specific markers52, (3) quantify and localize domestic and wildcat-derived genomic regions, (4) search for genes significantly deviating from random inheritance patterns, possibly due to selective pressures, (5) define a reduced panel of AIMs to routinely apply for population structure and hybridization monitoring projects.Here we genotyped a wide sampling of European wildcats, domestic cats and known or putative admixed cats from a large part of the European wildcat home range distribution with the Illumina Infinium iSelect 63k DNA cat array by applying multivariate, Bayesian and gene-search analysis tools to: (1) improve the identification of admixed genotypes older than the first few generations of backcrossing, (2) estimate their times of admixture37, successfully typed for 57,302 autosomal SNPs, hereafter referred to as the 57k SNP panel set .Quality-control and filtering procedures yielded a final sample set consisting of 80 presumed European wildcats (WC), 44 domestic cats (DC) and 22 known or presumed WC x DC admixed individuals , previously identified from STR assignment and multivariate analyses37, were scattered along the first axis (74%) between the parental cats, mainly closer to the wildcat group, except for the known hybrids , which were intermediate  performed in SVS using the 57k SNP panel set Fig. , which cdmixture tests performed with the 35k LD-pruned SNP panel set that showed the main decrease in the cross validation error (CV) at the optimal genetic clusters K = 2 . Therefore, we considered as admixed all cats showing an intermediate assignment value 0.090 > qw < 1.000 (n = 45). For K > 2, the genetic substructure of European wildcats31 progressively took shape, with the initial split of the southern European wildcat populations  from the Central-Northern cats  observed at K = 3  and confirmed by analyzing the five European wildcat macro-areas separately .A highly significant admixture rate between domestic cats and European wildcats was corroborated by the Tdmix which identified an average 17% of domestic regions  with those estimated in Admixture at K = 2 (mean qd = 0.141). None of the 40 randomly-selected reference individuals reanalyzed as hybrids for comparison in PCAdmix showed any switch from domestic to wildcat blocks (or viceversa) along their genomes, confirming the reliability of the reference populations selected for the admixture timing analyses and the possibility to exclude any ascertainment bias from the SNP array.The observed widespread signals of genomic admixture were individually estimated with PCA54 in Alder. Significant admixture was detected in the five European wildcat biogeographic areas identified with Admixture , although with inconsistent decay rates in all the cohorts considered, except for the Dinaric macro-area. The admixture midpoint in the European wildcats was generally estimated in Alder to have occurred about 5.02+/−0.37 generations before sampling, which correspond to about ten years considering a cat generation time of two years. The most ancient hybridization events were detected in the Italian Peninsula (6.62+/−0.58 generations) and in the Central European area (8.60+/−1.56 generations), respectively corresponding to about 14 and 18 years before sampling, whereas a more recent admixture time of about six years was estimated in the Dinaric region (3.15+/−0.24 generations).We inferred the time in generations during which the admixture events between domestic and European wildcat populations took place by analyzing patterns of linkage disequilibrium decaydmix in single individuals identified a number of switches from the reference European wildcat ancestry blocks to domestic cat blocks ranging from 31 to 231 , revealing that all the admixture events within the European wildcats occurred at least six generations before sampling , while the most recent events in the Dinaric Alpine populations . This pattern dated the first case of hybridization in the Italian Peninsula to 1962 (corresponding to 44 years before sampling), whereas the last admixture event likely dated to 1994 (ca. 14 years before sampling) in the Dinaric region  correlated, the average admixture timing estimated with PCAdmix resulted approximately twofold more ancient compared to the midpoints estimated by Alder.The local ancestry inferred with PCAing Fig. , with thdmix. Therefore, 138 regions with high frequency of wildcat alleles (wildcat-like regions) were identified, including 1,045 annotated genes, 577 of which were significantly enriched for specific Gene Ontology (GO) categories and are known to be involved in several biological and cognitive processes related to communication and elusive behaviors  and Molecular Function (MF) categories correlated to cell adhesion molecular binding  that were highly correlated one another , and even to the HE values  had also the lowest average FST (0.002) and average IN (0.003) values. Based on these results, we selected the top 192, 96 and 48 SNPs showing the highest FST values and evaluated their performance using a PCA visual summary of their observed genetic variation.A reduced panel of SNPs was selected based on estimates of WC-DC divergence , grouping most admixed individuals intermediately, with the exceptions of about ten genotypes that plotted more closely to the wildcat group , although a portion of putative admixed cats (qw < 1.000 in PCAdmix), ranging from 9% to 22% , were misclassified and confused as parental wildcats  and 48  informative SNPs, selected based on WC divergence (FST) and graphically plotted in a PCA , mostly confirmed Admixture results  and 22%  putative admixed individuals were misassigned and confused as reference wildcats, despite their high PCAdmix qw values ranging from 0.929 to 0.982 , see Fig. The combined panel of SNPs, selected based on both WC-DC and WC divergence , we performed a genome-wide assessment of admixture patterns and timing in a number of European wildcat populations. A preliminary genomic screening, based on pairwise FST values, multivariate and assignment procedures, showed that wild and domestic cats remain highly differentiated and well-distinguished. All the analyzed putative admixed cats confirmed to bear admixture signals and were well-identifiable, though some of them were very close to the wildcat group, ranging from c. 50% domestic-derived ancestry to almost complete wildcat assignments. Considering a neutral perspective, these patterns clearly indicate that ~40% of the analyzed admixed individuals would fell within the first three hybrid generations, whereas ~60% could represent more ancient backcrosses in which the domestic legacy would have been diluted through time. Genome-wide assignment procedures were also highly efficient in detecting population substructure, since five main biogeographic European wildcat macro-populations were clearly identified, consistent with previous findings based on data generated using 31 autosomal microsatellite loci27.Human-mediated processes, such as habitat destruction, human persecution and anthropogenic hybridization, can directly or indirectly threat global biodiversity because of their unpredictable consequences on the fitness of natural populationsPCAdmix results showed that admixed animals mostly originated from 7 to 14 generations in the past, with some individuals older than the twentieth generation of backcrossing, thus detecting hybridization events occurred between 1962 and 1994 considering a generation time of two years. In particular, the most ancient admixture traces were detected in individuals which had been misclassified as pure in previous microsatellite-based analyses, confirming the deeper diagnostic power of genomic data in detecting past backcrossing events58. Alder results estimated the midpoint of hybridization at approximately five generations in the past, compared to the mean value of 12 generations extrapolated from ancestry switches. Such discrepancies between these admixture-dating methods might be attributable to PCAdmix algorithms that are more efficient in detecting more ancient hybridization traces in the genome of the hybrids by identifying their residual domestic blocks. Conversely, Alder algorithms are more suitable to identify the major admixture event (if a main one occurred), the midpoint (in case of continuous admixture events) or the latest events (if these were punctuated). Such timing patterns, even if preliminary, can provide additional information about the context and the period during which hybridization occurred in our analyzed samples, and thus can be useful to better understand causes and dynamics of the phenomenon at local scale. However, we cannot exclude that more ancient hybridization events have remained undetected in the European cat populations we analyzed since (1) the 63k SNPs still offer a moderate snapshot on the whole Felis genome, (2) our sampling design was not homogeneous neither in time nor in space, and (3) data about the collection year were not available for all the analyzed individuals. Future analyses based on a wider sampling and the comparison of entire genomes might shed more light on patterns and histories of admixture.21, had assignment values ranging from 0.347 to 0.673, although they actually showed admixture traces dating back from 9 to 11 generations in the past. Such findings would suggest these animals might represent the product of repeated crosses among F1 or F2 individuals, rather than true second generation hybrids, whose domestic components could be detected only through the haplotype block analyses. These results highlight the power of such methods in improving the assessment of admixture proportions and timing from genomic data by detecting domestic-like and wild-like local genome ancestry better than the assignment procedures or morphology alone59.Interestingly, the six known hybrids, coherently with their possible F2 origins declared in previous analysesThe employment of thousands of SNPs, in fact, allowed us to distinguish backcrossed individuals with small proportions of domestic genome introgressed from wildcat parental populations, by accounting for the number of generations since secondary contact, which can occur when two (or more) species that have been in allopatry come back into sympatry.19), variable levels of admixture were detected in all the sampling pools representative of five European wildcat macro-populations we analyzed. Such evidence suggests that, in absence of strong ecological barriers, hybridization can potentially threat some of the extant wildcat populations, including those living in the Italian Peninsula, as previously described not only for the wildcat37 but also for other mammalian species such as the wolf44, the roe deer60 and the wild boar61.Even if some studies hypothesized that ecological barriers can play a key role in limiting hybridization in some areas  through extensive country-wide sampling programs and genetic analyses of wounded or found-dead wildcats and (2) by well-planned local intensive non-invasive genetic and camera-trapping monitoring projects in hot spots of known or suspected hybridization throughout the entire wildcat distribution range. This approach should avoid the risk that carcasses of introgressed individuals might be confused with feral domestic cats and thus not analyzed, and it would allow to simultaneously obtain detailed phenotypical and genetic information at a European scale, as well as potential capture-recapture data to provide more reliable estimates of population size and hybrid prevalence.The reduced panels of AIMs we selected from the Illumina Infinium iSelect 63k DNA cat array, based on multiple criteria of genetic differentiation and linkage independence, appeared to be particularly suitable for future large-scale monitoring projects in territories with high conservation priority and in areas of supposed or documented hybridization since (1) they allowed to distinguish individuals, even strictly related, without ambiguity (probabilities of identity <0.001), (2) clearly identified the geographic and genetic European wildcat macro-population structure and variability, (3) were highly concordant with the 35k LD-pruned SNP panel set and more accurate than the small number of previously used microsatellite loci in the identification of admixed individuals.30 and with markers that will hopefully emerge from coordinated ongoing genomic studies26. Routinely, SNP genotyping of both invasively and non-invasively collected samples could be carried out through innovative analysis methods such as quantitative PCR62 or microfluidic66 techniques, which allow the cost-effective genome-wide characterization of dozens of samples and markers at a time, even starting from low DNA quality or quantity materials. Such approaches turned out to be highly reliable both for multilocus DNA fingerprinting reconstructions and for the correct identification of admixed individuals until the second backcross generation for a number of taxa such as the brown bear66, the wolf63 and the wildcat65.These reduced SNP panel sets could be easily integrated with other AIMs previously identified in cat admixture studiesFelis_catus_8.0 genome assembly49 to search for specific genes hosted in both domestic and wildcat-inherited regions, which might be associated with specific biological and phenotypical ontology processes as adaptive response to selective pressures.We also capitalized the availability of the domestic cat SNP array dataset remapped on the MORN3, NKD1), the maintenance of pregnancy  and the likelihood of survival (RNPEP), that might contribute to increase the fitness of the admixed individuals living sympatrically with wildcats.The significantly enriched CC wildcat-like genes we identified in the admixed cats were mainly related to brain development and cognitive processes which also regulate aggressiveness and elusive behaviors typical of wild species. A variety of wildcat-like genes included in enriched CC categories were further found to be related with morphological features (such as genes regulating body size and hair coats) and development processes (such as muscle development and the energy metabolism), which might influence the body growth and composition resulting from adaptive pressures. Interestingly, we also identified a few significantly enriched CC genes correlated with fertility  or even in quasi-natural contexts, as demonstrated for the domestic goat MHC haplotypes in the Alpine ibex10 and the dog-derived black coloration in wolves9. The possibility that similar patterns result from an ascertainment bias linked to the original SNP chip design, mostly based on the domestic cat variation, is very unlikely for closely related taxa diverging less than one million years68.Nonetheless, we also found four domestic-derived genes significantly enriched for GO categories mostly related to cognition and behavior, physiological adaptations and neural crest development, whose cellular deficit during embryonic development has been demonstrated to directly or indirectly modify several morphological and physiological traits, as well as to influence tameness during cat domestication, in agreement with the domestication syndrome hypothesis70 in which strong pressures operated by breeding strategies selecting for specific physical features occurred only recently and with limited effects on behavioral traits. Therefore, their gene pool has been poorly isolated from their wild counterparts, and the number of genomic regions with strong signals of selection and differentiation since cat domestication appeared modest71 compared with those reported in another domesticated species, the domestic dog72.Cats have experienced a self-domestication historyST outliers, which are a more direct estimate of deviations from neutrality at a given marker. Such lack of selection could rely on the limited samples analyzed, or reflect the actual absence of differential selection for wild-derived or domestic-derived alleles in the admixed individuals. Therefore, all these data will need to be integrated in the future with systematic studies on fitness of the admixed individuals, including survival and breeding rates, in order to better understand the adaptive patterns of wild-living admixed individuals.However, though gene enrichment analyses can provide a broad sense of the type of functions that are common to a significant number of genes , our gene-search approach only allowed us to gain a preliminary insight on the inheritance patterns of domestic and wild ancestry blocks. Indeed, no significant evidences of selection signatures were detected by tests based on F27. On average 17% of domestic ancestry were detected in the genomes of most analyzed putative admixed cats, which were all classified as backcrosses more ancient than six generations in the past.In conclusion, in this study we provide a comprehensive genome-wide approach to detect the occurrence and infer the timing of admixture events in the European wildcat populations investigated, improving the reliability of old-generation backcross identification and pinpointing a number of outlier genes possibly influenced by natural and artificial selection in samples collected from the main genetic macro-populations in EuropeObtaining additional information on the admixture levels, timing and inheritance patterns can improve our understanding on the underlying factors favoring hybridization and its possible consequences, thus supporting the identification of the most appropriate conservation needs.Consequently, management actions should be mainly aimed at reducing the high number of free-ranging cats within the current wildcat distribution, deserving particular attention to those areas where ecological barriers are not so strong to limit hybridization. Furthermore, priority management actions, such as captivation or sterilization, should be primarily addressed to recent generation hybrids, which carry significant portions of domestic genome ancestry, and eventually further extended toward more ancient backcrosses when they locally occur at high prevalence, thus increasing the probability of interbreeding and retaining domestic variants.Future genome-wide scanning of a larger number of individuals from the whole European wildcat distribution range and the application of the optimized small marker panels in non-invasive genetic monitoring projects will contribute to (1) assess the hybrid frequencies and the current rates of domestic introgression in the wild populations, (2) provide information on the health status of wild-living individuals , (3) identify areas with high conservational priority where try to limit the occurrence of hybridization and support appropriate local management practices.No ethics permit was required for this study, and no animal research ethics committee prospectively was needed to approve this research or grant a formal waiver of ethics approval.x DC admixed , 46 domestic cats (DC) and 36 known or presumed WC The vast majority (97%) of the samples used in this study were collected from found-dead cats by specialized technician personnel for scientific purposes. The blood samples (n = 5) were collected with permission from owners from domestic cats by veterinarians during their routine health examinations. Additionally no anesthesia, euthanasia, or any kind of animal sacrifice was applied for this study and all blood samples were obtained aiming at minimizing the animal suffering. No ethics permit was required for this study, and no animal research ethics committee prospectively was needed to approve this research or grant a formal waiver of ethics approval.49. Considering the alignment of the markers to Felis_catus_8.055 , 704 SNPs (including 7 insertion/deletion makers) did not map to any chromosomes or anchored contigs and were excluded from the analyses. X-linked SNPs  were further excluded. The remaining 59,469 autosomal SNP genotypes were then filtered for individual missingness rates (GENO > 0.2), individual missing call rate (MIND > 0.2) and number of invariant SNPs in Plink73, resulting in a starting dataset of 146 samples genotyped at 57,302 SNPs (the 57k SNP panel set). This panel included 92% of the wildcat-specific variants38 . The initial dataset was also pruned for Linkage Disequilibrium (LD), filtering for r2 > 0.5 in a 50-SNP sliding windows, shifted and recalculated every five SNPs. LD-filtered loci resulted in a dataset of 146 samples genotyped for 35,228 SNPs (the 35k LD-pruned SNP panel set). Based on the analysis undertaken, the most appropriate SNP panel set was utilized.Genomic DNA was extracted using the Qiagen DNAEasy Blood and Tissue kits  according to the manufacturer’s instructions, quantified using the Infinite200 PRO NanoQuant  and visually-controlled for DNA degradation by standard 1.5% agarose gel electrophoresis. An initial panel of 182 samples, showing no DNA degradation and at least 50 ng/ul DNA, was genotyped using the Infinium iSelect 63k Cat DNA Array  including 62,897 SNP positions of which 4,240 were wildcat-specific74 (PCA) in the SNP&Variant Suite v.8.0.1  using the 57k SNP panel set. Each sample was then reassigned to its population of origin running the 35k LD-pruned SNP panel set in Admixture v.1.2375 assuming K values from 1 to 20. The most likely number of clusters was identified based on the lowest cross validation error75 and results were plotted in R v.3.5.0 . Individual ancestry components assessed with Admixture (at K = 2) were then used to select the reference wildcats, reference domestic cats and admixed individuals for all the subsequent analyses (see results).Patterns of genetic differentiation among samples was explored by a preliminary non-model Principal Components AnalysisThreePop program implemented in TreeMix v.1.12, using blocks of 20 adjacent SNPs to estimate standard errors, and Z-score values < −3 to significantly indicate admixture in the target population53.The occurrence of admixture events in the European wildcat populations was formally tested on the 57k SNP panel set with the F3-statistics running the PCAdmix v.1.056. Each chromosome was analyzed independently, running blocks of 20 consecutive, non-overlapping SNPs, and local-ancestry assignment was based on loadings from principal-component (PC) analysis on the two putative ancestral populations’ panels (the reference wildcats and the reference domestic cats). For each admixed individual, we then calculated the average genome-wide proportion of blocks assigned to each reference population.The 57k SNP panel set was further used to infer local ancestry along individual chromosomes and to calculate genome-wide proportions of admixture, through a PCA-based approach implemented in the PCAdmix.The reliability of the selected reference populations to detect admixture signals and the absence of any possible ascertainment bias linked to the original SNP chip design based on the domestic cat variation were tested by reanalyzing 20 baseline wildcats and 20 baseline domestic cats, randomly chosen, as putative hybrids in Shapeit v.2.83776 using the 57k SNP panel set with default parameter settings and considering domestic cat recombination maps55. The phased haplotypes were then used to estimate the average time of admixture events between the reference populations (domestic and wild cats) in Alder v.1.0354, which models the signature of decay in Linkage Disequilibrium (LD) between a pair of sites located on the same chromosome as the distance between these sites increases. The putative admixed individuals were first analyzed as a unique cohort and then grouped into cohorts representative of each European wildcat macro-population. Significant admixture events were assessed at P-values < 0.01 and then compared to those estimated using the number of ancestry switches inferred with PCAdmix with the formula developed by Johnson et al.77, and converted into years since sampling assuming a generation time of two years78.We reconstructed chromosomal haplotypes in PCAdmix was finally used to adaptively search for introgressed alleles in the domestic x wildcat admixed individuals. We first selected the genome-wide regions showing an excess of domestic or wild cat contributions in the admixed individuals identified by Admixture. Chromosomal haplotype blocks of 20 SNPs were thus ranked according to their relative proportion of “domestic cat” or “wildcat” assignment detected by PCAdmix  in order to subsequently identify within them only the top and bottom 1% of the genome-wide frequency distribution that is expected to be enriched for genes bearing signature of positive selection after admixture79.The admixture mapping reconstructed by ST outlier SNPs at a significant P < 0.05 in BayeScan80 for evidence of selection signatures by comparing admixed individuals versus wildcats and admixed individuals versus domestic cats. The analysis was performed using default parameter values of 100,000 iterations after an initial burn-in of 50,000 steps, setting a maximum False Discovery Rate (FDR) = 0.05 , and a q value = 10% (the minimum FDR at which a locus may become significant). Outlier regions were obtained including 100 Kb on each side of the FST outlier SNPs detected by BayeScan, assuming an average LD in domestic cats of 96 Kb81.We additionally identified FBiomart82 (http://www.ensembl.org/biomart/martview/), and checked them for their possible enrichment for any Gene Ontology (GO), Biological Processes (BP) and Human Phenotypes (HP) categories available in G-profiler83. Enrichment was tested retaining those categories that were significant at P < 0.05 after Benjamini–Hockberg correction.Finally, we recovered the genes included in each domestic-like and wildcat-like outlier region obtained from both methods based on the Ensembl gene annotation 92 in Admixture qw = 1.000, and confirmed by PCAdmix, see Results) and ranked the SNPs for decreasing wild x domestic cat FST values in SVS and for informativeness of the assignment index (IN) in Infocalc84. The Spearman Rank correlation, r, was estimated among FST and IN ranks and its significance was tested with a Student’s t test. Three panel sets of 192, 96, and 48 SNPs were finally selected for assignment procedures performed in Admixture and PCA analyses in SVS to estimate their power to clearly identify reference parental populations (wild and domestic cats) and their admixed individuals.The 35k LD-pruned SNP panel set was examined to identify a reduced set of ancestry-informative SNPs (AIMs). We first identified the most divergent SNPs between domestic cat and wildcat genotypes (showing 27, ranking the markers for decreasing macro-pop average FST values in SVS and selecting two reduced panel sets of 96 and 48 SNPs that were reanalyzed in a PCA plot.Subsequently, we identified the most informative SNPs to distinguish the main European wildcat macro-populationsFinally, all the reduced panel sets of markers were combined to develop the most affordable panel set informative for both admixture and genetic structure analyses.Supplemetary InformationSupplemetary Table S1


Glycine max (L.) Merrill] and is especially critical for various soybean food types. In this study, a representative sample consisting of 185 accessions was selected from Northeast China and analysed in three tested environments to determine the quantitative trait nucleotide (QTN) of HSW through a genome-wide association study (GWAS).The hundred seed weight (HSW) is one of the yield components of soybean [A total of 24,180 single nucleotide polymorphisms (SNPs) with minor allele frequencies greater than 0.2 and missing data less than 3% were utilized to estimate linkage disequilibrium (LD) levels in the tested association panel. Thirty-four association signals were identified as associated with HSW via GWAS. Among them, nineteen QTNs were novel, and another fifteen QTNs were overlapped or located near the genomic regions of known HSW QTL. A total of 237 genes, derived from 31 QTNs and located near peak SNPs from the three tested environments in 2015 and 2016, were considered candidate genes, were related to plant growth regulation, hormone metabolism, cell, RNA, protein metabolism, development, starch accumulation, secondary metabolism, signalling, and the TCA cycle, some of which have been found to participate in the regulation of HSW. A total of 106 SNPs from 16 candidate genes were significantly associated with HSW in soybean.The identified loci with beneficial alleles and candidate genes might be valuable for the molecular network and MAS of HSW.The online version of this article (10.1186/s12864-019-6009-2) contains supplementary material, which is available to authorized users. The seed weight (SW) of soybean (often denoted by hundred seed weight (HSW)) is an important yield component and positively correlates with seed yield , 2. HSW www.soybase.org), which are distributed on 20 chromosomes ) from more than 40 different genetic populations and 50 parental materials. Most of these identified QTL were found in F2 or recombinant inbred line (RIL) populations [http://www.soybase.org), which have a relatively low accuracy, limiting their application in MAS efforts.Marker assisted selection (MAS) can increase the efficiency of the traditional selection method for HSW by improving the allele frequencies of desirable HSW quantitative trait loci (QTL) . Presentulations , 16–26. ulations , 27. Onlulations . Among tGenome-wide association studies (GWAS) have more extensive recombination events and shorter linkage disequilibrium (LD) blocks due to high density of SNP markers used for mapping and wider phenotypic variation available in germplasm. Therefore, GWAS could significantly improve the resolution and accuracy of marker-phenotype associations compared with linkage analysis, based on bi-parental mapping populations. Presently, GWAS have begun to effectively analyze the genetic basis of HSW in soybean. Yan et al. identified 17 HSW QTL on six chromosomes through 166 samples with SoySNP50K BeadChip based on two tested environments. Zhang et al. found a total of 22 HSW QTL with minor effects based on 309 germplasm accessions and 31,045 polymorphic SNPs , 30. HowIn the present study, we performed a GWAS of soybean HSW in Northeast China based on 185 tested accessions and 24,180 SNPs. The aim of the present study is to identify QTLs associated with HSW and to screen candidate genes located in peak SNP regions.The phenotypic value was evaluated in three locations for two years to analyse the variation distribution  were observed among different chromosomes. The average marker density was approximately 1 SNP per 40.29 kb (Table A compressed mixed linear model (CMLM) was used to identify association signals using the R package GAPIT. Principal component and kinship analyses of the association panel were evaluated using the whole set of SNPs, which were also considered in the CMLM model. The first three principle components (PCs) accounted for 12.48% of the total genetic variation . Of these inferred genes, fifty-four had no functional annotations and were derived from protein families with unknown function. Another 183 genes were related to plant growth regulation, hormone metabolism, cell, RNA, protein metabolism, development, starch accumulation, secondary metabolism, signalling, and the TCA cycle  and Glyma.08G317300 (located near HSW8–2 of Chr.5), a MYB-domain protein, play key roles in regulating seed development and determining seed weight in plants [Glyma.05G196400 (located near HSW5–2 of Chr.5), one Leucine-rich repeat protein kinase family protein, has been proven a regulator of seed weight in plants [Glyma.06G201700 (located near HSW6–1 of Chr.6), regulates seed development in plants [OsPP2C51 positively regulates rice seed germination and affects seed yield [OsPP2C51 and Glyma.12G109500 (located near HSW-12-2 of Chr.12) are members of protein phosphatase 2C.Genes located in the 200-kbp flanking regions of each peak SNP were considered candidate genes. A total of 237 genes, derived from 31 QTNs, were located near peak the SNPs from the three tested environments in 2015 and 2016  were significantly associated with HSW in soybean  has been reported to be associated with HSW in crops [To identify the possible roles of candidate genes in the HSW of soybean, gene-based associations were conducted using the GLM method. A total of 3057 SNPs in 237 candidate genes (MAF > 0.10) were obtained among twenty lines (ten higher/lower HSW lines) through genome re-sequencing. A total of 106 SNPs from 16 genes , Glyma.05G005400 (located near HSW5–1 of Chr.5), Glyma.08G317300 (located near HSW8–2 of Chr.5), Glyma.05G196400 (located near HSW5–2 of Chr.5), Glyma.06G201700 (located near HSW6–1 of Chr.6), and Glyma.12G109500 (located near HSW-12-2 of Chr.12) have been reported to play key roles in regulating seed development and determining seed weight in crops, specific mechanisms of which still need to be analysed.Presently, only a few genes that control HSW were identified. Lu et al. (2017) discovered that a phosphatase 2C-1 (PP2C-1) allele from wild soybean ZYD7 contributes to the increase in HSW . GWAS coGlyma.03G192300 was previously reported to be important in seed development and the other 15 gene were novel genes for HSW in soybean. The present study provides an invaluable resource and new QTL/genes for further study on the HSW of soybean in the molecular network and molecular assistant selection.By performing a GWAS of soybean HSW in Northeast China based on 185 tested accessions and 24,180 SNPs, the results showed that 34 QTNs located on 15 chromosomes are associated with HSW in three environments for two years. Among these 34 association signals, fifteen QTNs overlapped with or near the known HSW QTL. Gene based association showed 106 SNPs from sixteen candidate genes were significantly associated with HSW in soybean. Of them, One hundred and eighty-five samples were selected and collected from the Chinese National Soybean GeneBank to analyse the HSW variation and for subsequent reduced-sequencing. Among these accessions, there were 97 elite varieties, 38 elite lines and 28 landraces from the soybean production areas between 36.23° N and 61.50° N of China belonged to six maturity groups  , represeMseI (EC 3.1.21.4) and HaeIII (EC: 3.1.21.4) , were selected to produce more than 50,000 sequencing tags (approximately 300 bp to 500 bp in length) in each tested sample. In each accession, the sequencing libraries were defined based on the obtained sequencing tags, which spanned unique genomic regions in soybean. The barcode method and Illumina Genome Analyzer II System  were utilized to obtain the 45-bp sequence reads at both ends of the sequencing tags from each accession library. The alignment between the obtained raw paired-end reads and the reference genome was conducted with BWA software (Version: 0.6.1-r104) [Genomic DNA of tested samples was isolated via the hexadecyl trimethyl ammonium bromide (CTAB) method and genotyped through a reduced-sequencing method (the specific locus amplified fragment sequencing (SLAF-seq) methodology) . Two res.1-r104) . The rawpicard.sourceforge.net, Version: 1.87). The BEDtools (Version: 2.17.0) coverage Bed program was applied to compute the coverage of sequence alignments. A sequence was defined as absent when coverage was lower than 90% and present when coverage was higher than 90%. SNP detection was performed by the Genome Analysis Toolkit  and SAMtools. Only the SNPs detected by both methods could be analyzed further. SNPs with allele frequencies lower than 1% in the population were discarded  with BWA (Version: 0.6.1-r104) using the default parameters. SAMtools48 (Version: 0.1.18) software was used to convert the mapping results into the BAM format and to filter the unmapped and non-unique reads. Duplicated reads were filtered with the Picard package (2) in TASSEL version 3.0 [The principal component analysis (PCA) programs were used to analyze the population structure of the association panel through GAPIT software . The squsion 3.0  were useP value was calculated with the Bonferroni method with α ≤ 0.05 (≤2.70 × 10− 4) and was used as the threshold to declare whether a significant association signal existed [Association signals of HSW were identified based on 24,180 SNPs from 185 tested samples with the compressed mixed linear model (CMLM) with default parameters in GAPIT . The P v existed .P < 0.01.Candidate genes, located in the 200-kb flanking genomic region of each peak SNP, were classified and annotated underlying the reference genome. The variation present in exonic regions, splicing sites, 5′UTRs and 3′UTRs, intronic regions, upstream and downstream regions of candidate genes in ten higher HSW lines and ten lower HSW lines was identified from genome re-sequencing data. A gene-based association analysis was conducted using the General Linear Model (GLM) method in TASSEL version 3.0  to identAdditional file 1:n = 185). (XLS 16 kb)Basic genetic parameter statistics for 100-seed weight in the tested soybean population Additional file 3:Gene-based association study on 100-seed weight of soybean with 281 candidate genes. (XLS 35 kb)Additional file 4:The information of soybean association panel. (XLSX 16 kb)Additional file 5:SNP data of 185 soybean accessions for GWAS. (XLSX 1490 kb)Additional file 6:SNP data of 20 soybean accessions generated from genome resequencing. (XLSX 299 kb)


Identifying single nucleotide polymorphism (SNP) interactions is considered as a popular and crucial way for explaining the missing heritability of complex diseases in genome-wide association studies (GWAS). Many approaches have been proposed to detect SNP interactions. However, existing approaches generally suffer from the high computational complexity resulting from the explosion of candidate high-order interactions. In this paper, we propose a two-stage approach  to detect high-order genome-wide SNP interactions based on significant pairwise SNP combinations. In the screening stage, to alleviate the huge computational burden, ClusterMI firstly applies a clustering algorithm combined with mutual information to divide SNPs into different clusters. Then, ClusterMI utilizes conditional mutual information to screen significant pairwise SNP combinations in each cluster. In this way, there is a higher probability of identifying significant two-locus combinations in each group, and the computational load for the follow-up search can be greatly reduced. In the search stage, two different search strategies (exhaustive search and improved ant colony optimization search) are provided to detect high-order SNP interactions based on the cardinality of significant two-locus combinations. Extensive simulation experiments show that ClusterMI has better performance than other related and competitive approaches. Experiments on two real case-control datasets from Wellcome Trust Case Control Consortium (WTCCC) also demonstrate that ClusterMI is more capable of identifying high-order SNP interactions from genome-wide data. Genome-wide association study (GWAS) has become a popular and powerful tool for studying human complex diseases . Many GWThere are two challenges in SNP interactions’ detection. The first is the intensive computational burden caused by the exponential growth of the search space. For moderate genome-wide data, even only considering pairwise SNP combinations, more than ten billion combinations need to be evaluated. The second is developing statistical tests that can effectively detect SNP interactions. To attack the first challenge, some computationally-efficient approaches have been proposed ,7,8,9,10Currently, there are four categories of SNP interactions’ detection approaches: exhaustive search, stochastic search, machine learning search and stepwise search. The exhaustive search-based approaches enumerate all SNP combinations and performs the chi-square test (or logistic regression) for each combination. Ritchie et al.  proposedStochastic search-based approaches apply random sampling procedures to search the space of SNP interactions. BEAM   firstly screen an SNP subset based on some low-order statistic tests, then conduct multi-locus detection based on the selected SNP subsets. EDCF   to detect high-order SNP interactions based on two-locus combinations. In the screening stage, considering there is a higher probability of identifying significant two-locus combinations in a high-associativity SNP group, ClusterMI firstly utilizes clustering to divide SNPs into several clusters and applies the mutual information as the association measurement. Then, ClusterMI applies conditional mutual information to screen significant pairwise SNP combinations in each cluster. This strategy avoids exhaustively analyzing all two-locus combinations and greatly reduces the search space and computation load. In the search stage, to be adaptive to different data sizes and to obtain high detection accuracy, ClusterMI provides two alternative strategies to search high-order SNP interactions. For a small candidate set, ClusterMI employs the exhaustive search. For a large candidate set, ClusterMI employs an improved ant colony optimization (ACO) algorithm . ExtensiD is the number of all datasets. Power can be seen as a measurement of accuracy in simulation experiments to compare the performance of different approaches. Since there was no training set and test set throughout the detection process, cross-validation was not suitable for ClusterMI. In actual fact, almost all SNP interaction detection approaches adopt power as the measurement. We firstly analyze the parameter sensitivity of ClusterMI in To evaluate the performance of ClusterMI quantitatively, the power and runtime were used as the evaluation metrics. We adopt the same measurement of power suggested by Wan et al. :(1)Powerk) and cMI  threshold λ=pAag et al.  to simulg et al. , SNPHarvg et al.  and HiSeg et al. . Particui et al. . For ModSince ClusterMI grouped high-associativity SNPs into clusters and screened two-locus combinations from these clusters, ClusterMI(E) had a higher power than other approaches on the five models (Model 1–Model 5), except a few cases. In such cases, ClusterMI(E) had lower power than DCHE and EDCF. That was because DCHE and EDCF measured the significance via the chi-square test with a lower degree of freedom than ClusterMI(E), and they could report more interactions in such cases. For all models, when ClusterMI(E) had better performance than DECMDR, HiSeeker(A) and ClusterMI(A), since ClusterMI(E) employed exhaustive search to find optimal solutions without considering the time complexity, while DECMDR, HiSeeker(A) and ClusterMI(A) applied heuristic search. ClusterMI(A), HiSeeker(A) and DECMDR performed poorly when The high-dimensional simulation dataset with 8000 samples (4000 cases and 4000 controls) and 3000 SNPs for Model 5 was further used to test ClusterMI and other comparison approaches. The parameter settings including MAF and LD were the same as above described. We also used the same simulation program as Zhang et al.  to simulp-value (Hardy–Weinberg equilibrium) <0.0001 in controls or a sample with a call rate of <98%. Then, SNPs with MAF <0.1 were further excluded. This process produced a BC dataset containing 1045 cases and 3893 controls with 5607 SNPs.It has been reported that breast cancer is caused by a combination of genetic and environmental risk factors . A real p-value was below the chi-square threshold. In this way, 3812 two-locus combinations were retained for the next stage. Many retained two-locus combinations were significant, and several representative ones are reported in ClusterMI took 10 min to analyze the BC dataset. In the screening stage, ClusterMI employed conditional mutual information combined with the chi-square test following igenesis . This gerognosis . rs37851t cancer .B, which belongs to HLA class I heavy chain paralogues. Antigen expression of HLA class I is associated with the aggressiveness and prognosis of breast cancer [In the search stage, ClusterMI also identified some significant three-locus combinations, and a representative one is reported in the last row of t cancer . These sFurthermore, we also ran HiSeeker, EDCF and DCHE on the BC dataset. HiSeeker took 15 min to analyze this dataset, and selected 2576 two-locus combinations in the first stage. SNP interactions identified by HiSeeker were  on chromosome 3,  on chromosome 16 and  on chromosome 6. rs1108842 in gene GNL3, rs3785181 in gene GAS11, rs879882 in gene POU5F1 and rs2523608 in gene HLA-B are reported to have a high probable association with tumor or breast cancer. All of these SNPs can be identified by ClusterMI. In addition, ClusterMI also identified another SNP interaction  on chromosome 16. rs17822931 is in gene ABCC11, which is reported to be associated with breast cancer. EDCF and DCHE heavily suffered from the marginal effects, and they did not identify interactions associated with breast cancer in the BC dataset.Celiac disease (CD) is a common heritable chronic inflammatory condition of the small intestine induced by dietary wheat, rye and barley in susceptible individuals . After tClusterMI took 15 min to analyze the CD dataset. In the screening stage, ClusterMI also employed conditional mutual information combined with the chi-square test to screen two-locus combinations. For the threshold of cMI, since the values of conditional mutual information between two SNPs for CD dataset were generally above 2.1, we selected a larger value 2.19 as the cMI threshold. For the threshold of the chi-square, we used the same value as the BC dataset  on chromosome 6, in which rs542441 in gene N and number of SNPs M. The recorded runtimes of these approaches are shown in Computational efficiency is another key performance index that needs to be considered in high-order SNP interactions’ detection. The runtime of ClusterMI was compared to those of the other four approaches under varying sample sizes N individuals , heterozygous genotype (Aa) and homozygous variant genotype (aa). Generally, they are coded as {1, 2, 3}, respectively. The whole framework of ClusterMI is illustrated in Given a genotype dataset consisting of W. The following two subsections elaborate on these two stages.ClusterMI is a two-stage approach. In the first stage, it screens candidate two-locus combinations using clustering and mutual information. In the second stage, it detects high-order SNP interactions based on two alternative search strategies (exhaustive search and improved ACO search) for different sizes of candidate set We assume that SNPs with high associativity usually have a higher probability of being identified as significant SNP interactions. Given this, ClusterMI clusters SNPs into several groups and then selects two-locus combinations from each group. Since only two-locus combinations in each group need to be analyzed, clustering SNPs can greatly reduce the search space and computation complexity. For a GWAS dataset containing 1 million SNPs, the number of two-locus combinations that need to be analyzed is D with N samples and M SNPs, the clustering process is described as follows:(a)k SNPs are randomly selected from M SNPs as initial centroids of k clusters k is the preset number of SNP groups.Initialization: (b)u (or v) = {1,2,3} denotes the three genotypes of m-th Clustering: Mutual information can measure the dependency or associativity between two variables ,41. Give(c)k clusters. Suppose m-th cluster; ClusterMI measures the sum of mutual information of centroid Update centroids: In each iteration, ClusterMI updates each centroid after each SNP has been divided into one of the Then, for each SNP If Given a dataset k clusters no longer change or the maximum number of repetitions is reached. In this way, ClusterMI obtains k SNP groups. Each cluster has a disjoint subset of all the SNPs, and two-locus interactions with high-associativity SNPs are more likely to be significant and placed into the same subset. As such, ClusterMI can efficiently screen significant pairwise SNP combinations from a reduced number of SNP combinations, instead from all SNP combinations.Steps (b) and (c) are repeated until the centroids of k clusters, ClusterMI then applies conditional mutual information to screen two-locus combinations in each cluster as follows:(a)m-th cluster, the association between a two-locus combination and the disease can be measured by conditional mutual information [y = 1) can be calculated as:For the ormation ,41. The (b)ClusterMI takes a two-locus combination After dividing all SNPs into W.For all two-locus combinations in each cluster, ClusterMI applies the same two-step procedure (a) and (b) described above to screen significant SNP combinations. Next, it places all the significant two-locus combinations of each cluster into the candidate set W.In the search stage, ClusterMI provides two alternative strategies (exhaustive search and improved ACO search) to search high-order SNP interactions based on the size of W is small, and it has a larger chance to detect high-order SNP interactions than heuristic search. To exhaustively search K-SNP  algorithm [W obtained in the first stage. Here, we take K-SNP (i)W is initialized as Initialization: The pheromone value of all two-locus combinations in (ii)i-th two-locus combination t can be defined as [q is a randomly-generated number with a uniform distribution in  and R and T can be described as:i-th two-locus combination t and a at iteration t, and a randomly selects a two-locus combination from Combinations selection: ACO introduces fined as :(8)Pai((iii)u-th joint genotype for the three-locus combination and the v-th disease status, u-th joint genotype under the case i-th two-locus combination i-th two-locus combination t, a at iteration t and Pheromone update: In each iteration, each ant selects two two-locus combinations, the corresponding pheromone of each combination is updated as:When the cardinal of lgorithm  to searclgorithm ,42,43,44K-SNP combinations of all these reported combinations. It finally takes K-SNP subsets, the p-values of which are below the Bonferroni-corrected significance level K-SNP interactions.The improved ACO algorithm introduces a memory strategy to save the optimal solutions in each iteration and speed up the convergence . The aboIn this paper, we developed a two-stage approach called ClusterMI to detect high-order SNP interactions from genome-wide case-control data. Considering SNP groups with high associativity usually have a higher probability to encompass high-order SNP interactions and the high computational complexity with the exponential growth of high-order SNP combinations, ClusterMI employs a clustering algorithm that utilizes mutual information as the association measurement, to divide all SNPs into several clusters. Next, it applies conditional mutual information to screen significant two-locus combinations in each group. As such, ClusterMI avoids exhaustively analyzing all two-locus combinations and greatly reduces the computational complexity. In the search stage, ClusterMI provides two alternative search strategies (exhaustive search and improved ACO search) to detect high-order SNP interactions based on the size of candidate two-locus combinations. Exhaustive search is used for a small candidate set, and improved ant colony optimization (ACO) is used for a large candidate set. Extensive simulation experiments compared to representative approaches show that ClusterMI has a better performance. Experiments on two real case-control datasets also demonstrate that ClusterMI can identify high-order SNP interactions from genome-wide data.The clustering algorithm utilized in the screening stage of ClusterMI can place SNPs with high-associativity into a cluster, in which the true SNP interactions can be more easily identified. In addition, it greatly reduces the computational complex by avoiding analyzing the whole set of two-locus combinations.The conditional mutual information-based evaluation strategy in each high-associativity cluster can effectively screen two-locus combinations and reduce the search space of ACO; it also can improve the power of SNP interactions’ detection and make high-order SNP interactions’ detection on genome-wide data more efficient.ClusterMI can be an effective methods for detecting high-order interactions, and its main contributions are:Although ClusterMI shows good performance on both simulated and real datasets, it still has some limitations and can be improved. In the future work, we will investigate more efficient and effective clustering algorithms ,46,47, d