Petunia ×hybrida) is derived from the progenitor species P. axillaris and P. integrifolia. The hybridization dates back only to the 1830s, though intensive breeding efforts have yielded cultivars exhibiting incredible diversity for many traits, including growth habit, flower color, and flower size. Until now, little is known about the genetic diversity and genomic background of modern cultivars. Here we selected a panel of 13 cultivars with contrasting growth habits and three wild species (the progenitors and P. exserta) to estimate the genomic contribution from the ancestral species and to study whether the variation of the genetic origin could be associated with different breeding programs or morphological variability. Transcriptome sequencing identified 1,164,566 SNPs representing 98.4%  of the transcripts that cover 99.2%  of the P. axillaris transcriptome. Cultivars with an upright growth habit had more homozygous alleles and more P. axillaris-derived alleles than trailing cultivars, while mounded cultivars had intermediate heterozygosity. Unlike previous studies, we found the proportions of alleles derived from each progenitor species varied across cultivars but overall were not biased toward one progenitor species, suggesting diverse selection during cultivar development. For trailing cultivars, alleles potentially introgressed from other wild species  were enriched. The “out” alleles were clustered in particular regions of chromosomes, suggesting that these regions may be hotspots of introgression. Transcripts in these regions were enriched with gene ontology terms associated with growth habit. This study provides novel insight into the contributions of progenitor species to the genomic background of modern petunia cultivars and identifies genome regions that may harbor genes conferring the trailing growth habit for further exploration.The cultivated petunia ( Petunia ×hybrida, consistently ranks among the top selling annual bedding plants in the U.S. and Europe. In 2018, petunia ranked first among annual bedding plants in the U.S., with a wholesale value of $141.7 million1. The genus Petunia contains 14 species found in Argentina, Bolivia, Brazil, and Uruguay2. Petunia is derived from crosses between P. axillaris and one or more species within the P. integrifolia clade4.The garden petunia, 3. Since then, commercial breeders have released hundreds of cultivars exhibiting considerable diversity for traits like flower color, pattern and size, and growth habit5. The breeding history has sometimes involved hybridization with additional crop wild relative species. For example, petunia cultivars traditionally exhibited an upright growth habit3. However, a major new form of plant architecture for petunia, a strongly prostrate (trailing) and radial growth habit, was introduced with the release of the cultivar ‘Wave Purple’. This novel growth habit was likely the result of introgression of the species P. altiplana into commercial germplasm, as this is the only Petunia species exhibiting this growth habit2. Following the release of ‘Wave Purple’ and a second cultivar with a prostrate growth habit, petunia ‘Surfinia Red’, a large number of cultivars exhibiting growth habits intermediate to the upright and trailing types (mounded and mounding/trailing types) began to appear on the market.Domestication of the cultivated petunia is quite recent, with first mention of the hybrid occurring in 1835Petunia spp., including genome sequences for P. axillaris and P. inflata (a close relative of P. integrifolia)5, and transcriptome sequences for P. axillaris, P. exserta, P. ×hybrida, and P. integrifolia8. In addition, we have generated high-density single-nucleotide polymorphism (SNP)-based genetic maps for two interspecific Petunia recombinant inbred line (RIL) populations, including a P. integrifolia × P. axillaris population12. While these resources will greatly facilitate the elucidation of the genetic control of important traits, sequence resources representing the diversity of commercial petunia cultivars are currently lacking.In recent years, there has been a considerable increase in sequence availability for 5 utilized the genome sequences of P. axillaris and the close P. integrifolia relative P. inflata and transcriptome data from three petunia laboratory strains, ‘Mitchell’, R27, and R143, to determine that the contribution of P. axillaris varied from 49.8 to 62.1%. However, these laboratory strains are not very representative of commercial cultivars, as R27 and R143 have been inbred for many generations, and ‘Mitchell’ is a doubled haploid derived from a cross between P. axillaris and petunia ‘Rose of Heaven’13. The contributions of P. axillaris and P. integrifolia to the genetic background of modern commercial cultivars are largely unknown.Bombarely et al.P. axillaris and P. integrifolia to the genomic composition of these cultivars, identify genomic regions that may be derived from other (“outgroup”) crop wild relative species, and to further identify potential associations between prostrate growth habit and outgroup-derived genomic loci.The objectives of this study were to generate transcriptome-derived SNPs from a broad panel of petunia cultivars to evaluate the genetic diversity of petunia cultivars representing several breeding companies and a broad range of phenotypes, assess the relative contributions of P. axillaris, P. integrifolia, and P. exserta were previously described6.Paired-end sequencing of three petunia cultivars  contained 85–109 million high-quality reads. The average depth of coverage for uniquely mapped reads ranged from 37.1 to 53.3. The 10 other cultivars yielded 12.2 to 25.8 million high-quality single-end reads (Table P. axillaris), which left 1,164,566 SNPs (entire dataset). These SNPs were located on 32,451 transcripts with a total length of 52,697,361 bp, which was 98.4% of the total transcripts and covered 99.2% of the P. axillaris transcriptome assembly length6. A total of 74,218 SNPs with no missing data (no missing) were detected among the 16 genotypes. This set of no missing SNPs were located on 7815 transcripts with a total length of 16,790,590 bp. They comprised 23.7% of the P. axillaris transcripts and covered 31.6% of the reported P. axillaris transcriptome6.A total of 1,353,141 SNPs were detected among the 16 genotypes (3 wild species and 13 cultivars). The SNPs were further filtered to exclude those where no polymorphisms were detected between the cultivars and the reference genotype  and P. axillaris and P. exserta had similar and the lowest heterozygosity. The cultivars had a range of heterozygosity rates sitting between the three wild species. In general, the trailing cultivars had higher transcriptome heterozygosity than the upright cultivars, and the mounded cultivars had heterozygosity rates in between. The low rates of transcriptome heterozygosity for the upright types  are particularly surprising, as most of these cultivars are F1 hybrids.The transcriptome heterozygosity for each genotype was estimated using both the entire SNP dataset 1,164,566) and the no-missing SNP dataset   the method of Bombarely et al.5 (approach 1); and (2) a two-model thresholds approach (approach 2)  according to similarity with the progenitor species  1); and  a two-mo.3% Fig. .Fig. 2GeP. axillaris/P. integrifolia (7.4–27.7%), followed by heterozygous SNPs with one nucleotide from the common allele of P. axillaris and P. integrifolia and another nucleotide other than the current P. axillaris or P. integrifolia, which were named “out” genotypes. The “out” genotypes (3.3–14.3%) could come from another species or from other P. axillaris and P. integrifolia genotypes than those used in this study.Most of the alleles in the cultivars were derived from the progenitor species. The majority of heterozygous SNPs in the cultivars were heterozygous alleles of P. axillaris alleles (34.7–45.2%) were detected in the cultivars than P. integrifolia alleles (15.5–26.9%) when the number of P. axillaris and P. integrifolia alleles in the heterozygous P. axillaris/P. integrifolia SNPs were set as 50% of those in homozygous SNPs (P. axillaris/P. axillaris or P. integrifolia/P. integrifolia), and the loci where P. axillaris and P. integrifolia showed no polymorphisms were excluded (25.1–31.1%). The percentage of P. axillaris alleles was higher in the upright cultivars compared to the trailing types, while the percentage of P. integrifolia alleles was higher in the trailing cultivars compared with the upright cultivars.A higher percentage of P. axillaris/out, P. integrifolia/out, and (P. axillaris = P. integrifolia)/out] had 50% fewer “out” alleles compared to homozygous “out” SNPs (out/out or out1/out2). ‘Wave Purple’ and ‘Surfinia Red’ had the largest percentage of “out” alleles among these cultivars (~9.9%).Interestingly, alleles not derived from either progenitor species  were detected across all cultivars, which indicates that there could be allele introgression events from other species during cultivar development Fig. . Those aP. axillaris genotype used in this study, approximately 59% of the SNPs showed “P. axillaris only” alleles, and for the P. integrifolia sample, about 35% SNPs showed “P. integrifolia only” alleles. In addition, P. axillaris and P. integrifolia shared approximately 35% common homozygous alleles.For the 16. The exact genotypes of the wild species from which the current petunia cultivars were derived are unknown. In this study, both P. axillaris and P. integrifolia accessions had some heterozygosity (the heterozygosity of P. integrifolia was high). Bombarely et al.5 assumed that both parental accessions were 100% homozygous, which probably will not accurately represent the true genetic components of the cultivars. Therefore, a “two-threshold” method was implemented to estimate the range of the genetic proportion in the cultivars derived from the progenitor species. To further estimate the range of genomic components from the progenitor species, we developed a model assuming two “extreme statuses” of the P. axillaris and P. integrifolia genotypes: (1) the hypothetical progenitor species are 100% homozygous, with no common alleles ; and (2) the P. axillaris and P. integrifolia samples used in this study (with the current heterozygosity) are the exact progenitor genotypes from which these cultivars were developed . Under the second assumption, 21.6–41.6% of the P. axillaris SNPs and 8.8–27.8% of P. integrifolia SNPs were detected in the cultivars. In addition, 16.8–28.9% of the SNPs were “out” alleles and 19.8–30.5% of the SNPs shared common alleles between P. axillaris and P. integrifolia. Under both assumptions, P. axillaris SNP alleles were estimated to comprise a higher percentage for the upright cultivars than the trailing cultivars while the P. integrifolia and “out” alleles were higher in the trailing than the upright cultivars. Also, under the second assumption, the estimated percentage of P. axillaris and P. integrifolia shared alleles was lower in the trailing cultivars than the upright types. The estimated ancestral alleles contribution from approach 1  is within the estimated progenitor proportion threshold using the hypothetical ancestor genotype model (approach 2).It has been reported that the first petunia hybrids were produced by multiple crosses from different accessions of the two parent cladesped Fig. . Under tP. axillaris following Bombarely et al.5. The similarity ratio was then projected onto the high-density genetic linkage map according to the corresponding locations of the transcripts on the P. axillaris scaffolds and the genotyping-by-sequencing (GBS)-SNP locations on the AE (P. axillaris × P. exserta) genetic linkage map that we generated previously11. Overall, 1345 unique transcripts were placed on the AE map corresponding to 1978 GBS-SNP loci, which was 31.4% of the total SNPs on the AE map  were mapped to multiple locations on the AE map, yet most of these locations could be combined because of their short genetic distances (located within 1 cM). A few transcripts (<10) were mapped to locations >20 cM apart on the same linkage group. In this case, both locations were kept and the percentage of similarity with P. axillaris were compared with the adjacent mapped regions to gain a comprehensive understanding of the overall progenitor background distribution on the linkage map.To dissect the genome-wide distribution of the ancestor origins for the cultivars, each locus was calculated for the percentage of similarity with the genotype of P. axillaris  species or genotypes. We detected regions across the cultivar panel where both P. axillaris and P. integrifolia samples had high similarity with P. axillaris (red color), while all cultivars had low similarity (blue color). These could be the regions where the “out” alleles were introgressed.Some consistent patterns could be detected from the genome-wide distribution of genetic similarity with the hypothetical progenitor species ris Fig. . P. axilP. axillaris was lower in the trailing cultivars compared with upright cultivars. Consistent with the principal component analysis (PCA) analysis  with exclusive genetic similarity with either of the progenitor species was detected for the cultivars. The percentage of similarity with P. axillaris transcriptome . For all the cultivars in the representative growth habit group, the proportion of each genotype category did not seem to change significantly in comparison with their proportions in the entire SNP set  were category 6 and 7 SNPs. When comparing the proportion of “enriched SNPs”  in each genotyping category for each cultivar, the proportion of category 7  were enriched in the representative trailing cultivars and thus added a piece of evidence that the trailing phenotype might be introduced from a third species. Considering all 14 petunia species are freely crossable with P. ×hybrida30, and P. altiplana has been reported to be “widely spreading and often forming a mat-like structure”31, unique within the genus2, we suspect that the trailing alleles in the cultivars were introgressed from P. altiplana.The trailing cultivars have a short breeding history, dating back only to the 1990sP. axillaris and P. integrifolia genotypes, which may have biased the genotype category estimation. It is also possible that the proportion of “out” alleles were overestimated; (2) the complex breeding history of petunia cultivars might have introduced alleles from other species than the two main progenitor species. Nevertheless, the most likely alleles for the trailing growth habit should be the “out” alleles that were only present in the trailing cultivars. Genome-wide comparisons of representative trailing cultivars with the compact cultivars showed that the introgressed “out” alleles were not restricted to a few discrete regions, instead, they were widespread and mostly heterogeneous loci across the genome.However, if the trailing cultivars were the only type that had introgressed alleles from other species, the “out” alleles should be present only in these cultivars, which is inconsistent with the current findings. This could be due to: (1) the fact that exact progenitor genotypes for the petunia cultivars are unknown, therefore, some of the “true progenitors” alleles were not present in the current P. ×hybrida. For these cultivars, tissues were collected from combined stages of floral development (from ca. 5 mm long buds through early fruit development) and leaves at different developmental stages  pooled from five 10-week-old plants per cultivar and immediately frozen in liquid N2. For each of these cultivars, RNA extraction was carried out on pooled flower and leaf samples separately. To investigate the genomic background and genetic diversity of petunia cultivars, an additional ten cultivars were sequenced. For these ten cultivars, only leaf tissues were collected (same stages as above) and pooled for RNA extraction, again from five plants per cultivar. Of these cultivars, ‘Supertunia Yellow’, ‘Supertunia Vista Bubblegum (Vista Bubblegum)’, ‘Supertunia Vista Silverberry (Vista Silverberry)’, and ‘Tidal Wave Silver’ have a mounding/trailing habit; ‘Avalanche Salmon’ has a mounded habit; and ‘Fantasy Rose’, ‘Madness Red’, ‘Orchid Daddy’, ‘Storm Blue’, and ‘Super Cascade White’ have an upright habit (Table A panel of 13 petunia cultivars with diverse growth habits was selected for transcriptome sequencing. Three of the cultivars—‘Wave Purple’ (trailing type), ‘Surfinia Red’ (trailing type), and ‘Madness Yellow’ (upright type), were selected to generate a reference transcriptome for http://rtsf.msu.edu/; East Lansing, MI). For the other ten cultivars, the samples were barcoded and run on one lane of SE50 Illumina High Output flow cell at the Michigan State University RTSF. Sequences were filtered and trimmed with Trimmomatic with the corresponding functions for paired- and single-end reads, respectively32. Both raw and cleaned reads were assessed with FastQC v0.11.3 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/).RNA was extracted using the Trizol® reagent (Life Technologies) following the manufacturer’s instructions. Forty micrograms of RNA was treated for DNA contamination with RNase-free DNase set (Qiagen). DNase-treated RNA was purified with the RNeasy® MinElute Cleanup Kit (Qiagen). RNA yield and quality were determined by Agilent 2100 BioAnalyzer RNA 6000 Pico chip (Agilent Technologies) with threshold RNA integrity number values ≥8.0. A TruSeq RNA Sample Preparation kit (Illumina) was used to construct the cDNA libraries. The six libraries of the leaf samples and flower samples for ‘Wave Purple’, ‘Surfinia Red’, and ‘Madness Yellow’ were pooled in one lane and sequenced on the Illumina HiSeq 2500 sequencer with 100 nt paired-end at the Michigan State University Research Technology Support Facility  reported previously6 were mapped to the P. axillaris transcriptome assembly using the Burrows–Wheeler Aligner program with default values for both single- and paired-end reads33. Duplicate reads were removed after the initial alignment to eliminate reads that mapped to the same position of the transcriptome. Duplicate removal was performed for the aligned reads using picardTools/1.89 (broadinstitute.github.io/picard). The subsequent local realignment to correct misalignments due to the presence of indels was performed using the Genome Analysis Toolkit (GATK)34. Initial variant calling was performed by HaplotypeCaller from GATK with a Phred-scaled confidence threshold of 30. After the initial variant calling, SNP filtering was performed using previously described criteria except with a modification to retain clustered SNPs (SNP clustering filter was removed) and with a minimum total depth of coverage of five for each genotype6. Afterwards, SNPs that showed no polymorphisms within the panel were excluded using the filters as previously described6.Sequencing reads for the cultivar samples and wild species 35 with the following criteria: (1) AA: SNPs were called homozygous and assigned to have the same genotype as the reference genotype (P. axillaris) when the reference reads were ≥80% of the total reads; (2) BB: SNPs were called homozygous alternative alleles if the alternative reads were >80% of the total reads; (3) AB: SNPs were called heterozygous if both the ratios of reference SNP reads to total reads at the loci and the ratio of the first alternative allele reads to entire reads at the loci were >0.3, and the sum of both ratios is ≥0.8. After the genotyping correction, SNPs were filtered to only keep the polymorphic loci.SNP validation was carried out on 22 randomly chosen SNPs with no missing data on the genotype calling to ensure the maximum genotyping validation efficiency. The primers were designed around SNP loci where at least two species were polymorphic and on unigenes with exons >600 bp. Primer design, PCR conditions, and sequence visualization were as previously describedFST outliers  were detected by BayeScan v2.1 with default parameters37. BAYESCAN v. 2.163 implemented the Bayesian model decomposes FST values into locus-specific components (α) and population-specific components (β51). The method is based on a reversible jump MCMC algorithm and calculates the posterior probability of each under selection locus by assuming two alternative models, selection-based model and neutral model. A locus that is significantly more divergent than average is likely been affected by positive or direct selection. BayeScan can perform analyses on very small sample size without introducing bias37. The alpha values generated by BayeScan indicate the strength and direction of selection, with a positive value suggests diversifying selection and negative value suggests balancing or purifying selection38.To detect the presence of a breeding signature among the cultivars, the level of differentiation at a locus was compared with the level of differentiation across the transcriptome with 74,218 SNPs that had no missing data across the 16 genotypes. The Heterozygosity rate was calculated as the proportion of heterozygous SNPs for each individual (cultivars and the three wild species). Both the entire SNP dataset  and the set of SNPs with no missing data  were used for the heterozygosity rate estimation.5 with a slight modification. In brief, the cultivar genotypes were classified into 12 categories . Except that, at loci where P. axillaris and P. integrifolia had the same genotype calling, the cultivars with the same genotype as P. axillaris and P. integrifolia would be called homozygous P. axillaris = P. integrifolia, and any other alleles were categorized as from species other than P. axillaris and P. integrifolia . Unknown: Allele origin could not be determined due to the missing genotyping data in either P. axillaris or P. integrifolia. (Supplementary Table P. axillaris and P. integrifolia genotype as used in this study (where the P. integrifolia has >50% heterozygosity), therefore any cultivar genotypes that were the same as P. axillaris or P. integrifolia would be counted as derived from P. axillaris or P. integrifolia, and alleles not found in P. axillaris or P. integrifolia were assumed derived from other species (“out”). The second boundary assumed that both P. axillaris and P. integrifolia are homozygous, P. axillaris had the “0/0” genotype, and P. integrifolia had the “1/1” genotype. In such circumstances, the cultivars were dissected according to their similarity with “0/0” and “1/1”, and consequently the cultivars category classification was independent of the P. axillaris and P. integrifolia genotype calling.With the entire dataset , two genotype classification approaches were used to understand the genetic background of petunia cultivars and their relationship with the ancestor species. The first approach (approach 1) followed the previous publication of Bombarely et al.P. axillaris SNPs for each transcript was projected to the genetic linkage map generated from an RIL population developed from the crossing of P. axillaris with P. exserta (AE map)11. The percentage of homozygous P. axillaris SNPs for a transcript was calculated by first assigning all SNPs for that cultivar on the transcript to a similarity rate based on their genotype categories  had three cultivars: ‘Wave Purple’, ‘Surfinia Red’, and ‘Vista Bubblegum’; and the upright cultivar group (Group B) included ‘Orchid Daddy’, ‘Storm Blue’, and ‘Madness Yellow’. To inspect what genotype categories have been “enriched” in the trailing-type cultivars, the SNPs that were polymorphic between two groups were dissected into the 12 categories (approach 1), and the proportion of each category in the polymorphic SNP set and the original set were compared for each cultivar. In addition, the percentage of each category in the current polymorphic SNP set compared to the original SNP set were plotted for the cultivars, along with the average proportion of each category of polymorphic SNP in the entire SNP set plotted for comparisons.40 in R version 3.6 to identify if genes with particular molecular function and biological processes were overrepresented in these transcripts. The GO enrichment employed Fisher’s exact test for statistical significance. The enriched GO terms at p value of ≤0.05 were retained. Treemaps were produced to visualize enriched GO categories. Metabolic and the functional biological pathways were identified by mapping transcripts under the significantly enriched Go terms to the KEGG41 (http://www.genome.jp/kegg/) by KEGG Automatic Annotation Server42 (http://www.genome.jp/kegg/kaas/) with the available manually annotated plant databases and single-directional best hit.After the candidate transcripts were identified , we performed a GO enrichment analysis using topGO packageSuuplemental information fileSupplemental Table 4


Classifying a target population at the genetic level can provide important information for the preservation and commercial use of a breed. In this study, the minimum number of markers was used in combination, to distinguish target populations based on high-density single nucleotide polymorphism (SNP) array data. Subsequently, a genome-wide association study for filtering target-population-specific SNPs between the case and control groups and principal component analysis with machine learning algorithms could be used to explore various combinations with the minimum number of markers. In addition, the optimal combination of SNP markers was able to produce stable results for the target population in verification studies, in which samples were analyzed.A marker combination capable of classifying a specific chicken population could improve commercial value by increasing consumer confidence with respect to the origin of the population. This would facilitate the protection of native genetic resources in the market of each country. In this study, a total of 283 samples from 20 lines, which consisted of Korean native chickens, commercial native chickens, and commercial broilers with a layer population, were analyzed to determine the optimal marker combination comprising the minimum number of markers, using a 600 k high-density single nucleotide polymorphism (SNP) array. Machine learning algorithms, a genome-wide association study (GWAS), linkage disequilibrium (LD) analysis, and principal component analysis (PCA) were used to distinguish a target (case) group for comparison with control chicken groups. In the processing of marker selection, a total of 47,303 SNPs were used for classifying chicken populations; 96 LD-pruned SNPs (50 SNPs per LD block) served as the best marker combination for target chicken classification. Moreover, 36, 44, and 8 SNPs were selected as the minimum numbers of markers by the AdaBoost (AB), Random Forest (RF), and Decision Tree (DT) machine learning classification models, which had accuracy rates of 99.6%, 98.0%, and 97.9%, respectively. The selected marker combinations increased the genetic distance and fixation index (Fst) values between the case and control groups, and they reduced the number of genetic components required, confirming that efficient classification of the groups was possible by using a small number of marker sets. In a verification study including additional chicken breeds and samples (12 lines and 182 samples), the accuracy did not significantly change, and the target chicken group could be clearly distinguished from the other populations. The GWAS, PCA, and machine learning algorithms used in this study can be applied efficiently, to determine the optimal marker combination with the minimum number of markers that can distinguish the target population among a large number of SNP markers. Chicken is a rich source of protein in the human diet. The consumption of chicken has increased globally due to increased consumer interest in health; consumption is also rising annually in Korea . It has Generally, the methods used to identify chicken breeds are based on morphological features, but meat products already on the market cannot be identified by their morphological characteristics. A precise identification method allowing for verification at the genetic level is required. Animal genetic information can be used for the maintenance and improvement of livestock varieties, based on phenotypes and heritable genetic characteristics. The Korean government is currently developing genetic markers that can distinguish cattle, pig, and chicken breeds ,4,5. HowSingle nucleotide polymorphism (SNP) markers could be an important marker-based verification system, with the potential to replace MS markers. With the release of the draft genome sequence of the chicken in 2004, genome-wide SNPs have become available for various research applications. However, many restrictions have been placed on their use, due to the expense and the fact that the technology required to customize the desired SNPs is highly specialized ,9. In adSelecting SNP markers that can distinguish among livestock breeds is not easy. Unlike MS markers, SNPs are the same in all varieties/breeds, but normally possess one of three genotypes . The process of reducing the number of SNP markers for a particular population can affect the results. A fast and accurate SNP marker verification system running on an automated platform is needed as an alternative to existing verification systems based on MS markers . AttemptMachine learning is a type of artificial intelligence in which algorithms are developed that allow computers to make predictions through learning via training data ,19. The The Korean chicken industry developed rapidly after the Korean War and became industrialized. However, most of the native chicken genetic resources were lost during the war, and the remaining populations were maintained only in small breeding families in backyards. They had low productivity and made little contribution to the overall development of the industry . Thus, aThe purpose of this study was to identify the minimum number of SNP markers needed to identify and verify a target chicken population from among other populations, using information obtained from a 600 k SNP genotyping array for chicken.Two sets of samples were used in this study. The initial set included a total of 283 samples  that were used for a high-density SNP array analysis. This analysis was performed to identify a combination of SNP markers capable of distinguishing the new chicken breeding stock. Samples in this set were divided into four groups: purebred Korean native chicken (KNC), commercial native chicken, commercial broiler, and commercial layer . The purAll samples used in this experiment were collected according to guidelines issued by the Institutional Animal Care and Use Committee of Chungnam National University, who approved this study . Genomic DNA (gDNA) was extracted from whole blood samples taken from the wing vein of birds, using an EDTA (Ethylenediaminetetraacetic acid)-coated tube, to prevent coagulation. Muscle tissue samples were obtained from chicken meat purchased from a market. The gDNA extraction was performed according to the manufacturer’s protocol, using a PrimePrep™ genomic DNA isolation kit for blood and tissue . The quality and concentration of the extracted gDNA were verified with electrophoresis, using 1% agarose gel, and spectroscopic analysis, using a NanoDrop spectrophotometer .http://pngu.mgh.harvard.edu/~purcell/plink/download.shtml). A total of 545,563 SNPs were obtained from the merged common SNPs from the PLINK binary data, and this result was subjected to a QC procedure, with the two main criteria of genotype error  and minor allele frequency  used for the selection of SNP markers in genetic diversity analyses. After the QC process, 517,120 SNPs from 20 chicken populations were accepted and used for further analysis. The genetic distances in the chicken populations were calculated by using Nei’s equation, and fixation index (Fst) values were estimated. The formulas for these calculations are as follows:u is the total number of alleles, l is the total number of loci, pop1 is the allele frequency of population 1, and pop2 is the allele frequency of population 2. This value was calculated by using R software’s “poppr” package [High-density SNP genotyping of the entire genome was performed by using an Axiom 600 k SNP genotyping array for chicken . A total of 580,954 genotypes were analyzed, and the data were transformed into a binary file format, using PLINK software  plot and admixture analysis, to identify similarities and differences between the target population and the other chicken populations. The MDS plot obtained with PLINK was used to analyze information on pair-wise genetic distances via a four-dimensional scale . The genp-values were derived for each SNP [p-values in a genome-wide association study (GWAS) association test were selected. In the second scenario, 1 SNP per LD block was selected. In the third scenario, 50 SNPs per LD block were selected. An MDS plot of each scenario was constructed to show the degree of separation of the target group from the other populations. In addition, custom SNP assays were designed for verification, using additional chicken samples: A total of 182 samples from 12 populations were collected and genotyped by a Fluidigm array   is provided in each SNP . SNPs weUSA) see .The 96 selected SNPs, which were identified with the 600 k SNP genotyping array, were used as the training dataset. The data obtained via a verification study with the Fluidigm assay were used as the test dataset, with the target population identified by using classification algorithms of machine learning techniques. We applied eight models to classify varieties/breeds: Random Forest , AdaBoost (AB), quadratic discrimination analysis (QDA), naïve Bayes, nearest neighbor classification (nine neighbors), linear discriminant analysis (LDA), and Decision Tree (DT) classification. We used the “carret” machine learning package in R software to build a classification model . The eigEach machine learning model had its own criterion for determining whether the target population was consistently classified ,37,38,39TP is the number of true-positive outcomes, TN is the number of true-negative outcomes, FN is the number of false-negative outcomes, and FP is the number of false-positive outcomes [The specificity refers to the proportion of negative values that were accurately determined, i.e., the true-negative rate (TNR):outcomes .The HH, HF, and HY populations were shown by crossbreeding tests to be the best combinations for producing new chicken breeding stocks (data not shown). We sought the minimum number of marker combinations required to classify these three founder populations, and the great grandparent (GGP), grandparent (GP), and CCs produced through their mating. The overall procedure for this is shown in To identify the target chicken population among the 20 populations included in this study, genetic clustering was performed. The genetic components of each population were confirmed through the MDS plot and admixture analysis. The MDS plot showed that PC1 and PC2 explained 44.414% of the total variance. The HH and HF founder groups were clustered together, directly under the clusters of commercial broiler groups (Cobb broiler (Cobb), Arbor Acre (Ab), and Ross broiler (Ross); The results of a genetic distance analysis and the fixation index (Fst), calculated based on the 96 SNPs selected from the 600 k SNP genotyping array, are shown in The admixture results for the 20 chicken populations were used to compare the genetic components among the groups. The lowest cross-validation (CV) error was found at K = 13 . HH, HF,A GWAS was performed to identify the founder populations of the new chicken breeding stock and the other chicken populations: A total of 47,303 SNPs were used to distinguish between the populations . These mWe used the eight machine learning classification models to classify the founder group based on the 96 selected markers: More than 98.5% of the case and control samples were distinguished in all models . All modAdditional samples were collected to verify the ability of the selected marker combinations to distinguish the founder population and their offspring. Samples of founder groups, commercial native chickens , Yelim Farm commercial chicken (Yelim K), and Hyunin commercial chicken (HI)), commercial broilers , and commercial layers (LO) were collected from the Korean chicken market. To confirm the discrimination ability of CCs produced by crossing with the founder population, CCs from the HH, HF, and HY populations were used for verification , the delta statistic, the F statistic, and PCA were used for genetic classification, and breed identification, using low-density SNP arrays, has also been demonstrated ,23,24,42New chicken breeding stocks produced by three-way crossing require a combination of shared markers that can be used to clearly distinguish them from other chicken populations. Twenty chicken populations were used in this study . Of thesThe populations to be classified had HH, HF, and HY as their parental lines. It was difficult to distinguish HH, HF, and HY from the other chicken populations by using a limited number of SNP markers. In terms of genetic distance, HH and HF were very close (0.09), but HY was relatively distant from those two breeds on the MDS plot . The HY population was more closely related to the other chicken populations than HH and HF. Therefore, it was difficult to identify a marker shared by all three founder populations. The same approach was used to classify breeds by population-specific alleles, similar to the existing mtDNA and MS marker classification approaches. However, different results were obtained from using the different marker combinations when the verification samples were added (data not shown) because the SNPs extracted from the array were not conserved in each population. On the other hand, mutations in mtDNA or MS markers do not affect the function of genes and are selected based on the mutation occurring from the maternal origin of the population (mtDNA marker), or the specific allele (MS marker) of the population is used as an identification point for classification. It is, therefore, difficult to identify populations with a small number of samples by using population-specific SNPs.Classification analysis was performed to overcome the limitations of the population-specific markers mentioned above. The HH, HF, and HY populations were set as the case group, and the remaining 17 populations were set as the control group. The 96 markers selected by the GWAS were strongly related to the case group. The case and control groups tended to form distinct clusters but were not clearly distinguished by using only the GWAS with significant SNPs. Therefore, LD pruning was performed and confirmed that SNPset3, which selected 50 SNPs per LD block, could clearly distinguish the two chicken groups due to the removal of the sharing of LD blocks between markers, or the relationship between adjacent LD blocks.Regarding SNPset1, individuals with high genetic similarity had a high degree of clustering. Several samples overlapped in the MDS plot. When using SNPset2 and SNPset3, which selected SNPs based on the LD block, the clusters were separated according to their relationships. If the SNP markers were selected based on their p-values in the GWAS, those having a strong correlation with the case group were affected by the LD relationships based on marker distances. It was confirmed that 95.8% of SNPset1 (92 of 96 SNPs) shared 39 LD blocks on GGA1 . AdditioIn previous studies, methods for selecting the minimum number of high-density SNP markers for breed identification by using the delta statistic and Fst were reported. More than 300 and 591 breed-specific SNPs were selected by Judge et al. 2017)  and Kuma  and KumOur GWAS and LD analysis was not performed to identify markers capable of distinguishing among all of the populations included in the study, but rather to distinguish only the target population from the others. It is therefore difficult to directly compare the results with those of previous studies. Comparing the Fst, the genetic distance, and the genetic component of the research population before and after marker selection, it was confirmed that the changes of genetic distance and genetic composition as calculated by the selected markers were significant for the target population, including the Fst value. The genetic distances were calculated based on allele frequencies, and the results were similar to those obtained by using the delta score. The explanatory power of the principal component in the PCA analysis increased when using case-associated markers. The validation study results remained consistent after adding samples from other populations that were not used for marker selection. The 96 selected markers (SNPset3) well explained the genetic components of the target chicken group.Machine learning is a supervised learning approach for classifying new observations that can be used to classify bi-class or multi-class data. Machine learning can be used for voice and handwriting analysis, and document classification. In recent years, machine learning and deep learning algorithms have been used to determine phenotypic associations  in “omics” research, and to establish classification models ,44.In this study, eight machine learning classification models were used to efficiently identify target chicken populations. PCA was conducted with a machine learning algorithm to confirm whether case and control groups could be distinguished, based on the 96 markers in the SNPset3. All classification algorithms showed 100% breed classification accuracy except the naïve Bayes model 98.5%; . The AB,With use of feature selection, 36, 44, and 8 SNP markers were selected by the AB, RF, and DT models, which had classification accuracies of 99.6%, 97.9%, and 98.0%, respectively . Thus, tIn the validation study including additional samples, both founder group and non-founder group chickens could be classified. The added samples included PL and CC samples from the founder population, and various samples obtained from the Korean chicken market . The discriminatory power was excellent, even when samples from a group of chickens with a completely different genetic composition to that used in the initial marker selection process were added . Our SNPThe AB and RF models showed similar clustering results with and without the additional verification samples, while the DT model produced relatively diffuse clusters. Overall, the accuracy was similar among the classification models. The SNP marker combinations selected by the three models can be used for classifying the target chicken population. However, the DT model showed changes in clustering with additional samples, so it requires further verification.This approach can provide useful information for the development of the best SNP marker combination for use in forensic science, conservation genetics, and livestock traceability systems ,47,48. TA genetic marker capable of distinguishing among breeds, at the genetic level, is required to protect intellectual property rights and ensure consumer confidence, but the development of conventional mtDNA and MS markers requires large amounts of time and money, as well as expertise. A marker combination with the minimum number of SNPs required for distinguishing the target chicken population could be used to overcome these shortcomings. In this study, the minimum number of SNPs that could identify target chicken populations was determined by using their LD relationship, case/control GWAS, PCA, and machine learning algorithms. As a result, these methods increased Fst and genetic distance values for the selected marker combinations, when comparing target populations to other populations, thereby increasing the identification power. In addition, the feature selection of machine learning models suggested the most effective marker combinations by minimizing redundant marker information. The SNP selection methods used in this study to distinguish target populations at the genetic level can be used to efficiently select a minimal number of genetic markers. These results can be applied to a variety of livestock, as well as chicken populations, and will also be useful in the field of conservation genetics.


Furthermore, the proposed approach is compared with the precise point positioning (PPP) time transfer method. The results show that, for different types of receivers, the agreement between the two methods is between −6.7 ns and 0.2 ns.Since the observation precision of the Global Navigation Satellite System (GNSS) carrier phase is on the order of millimeters, if the phase ambiguity is correctly solved, while calibrating the receiver inter-frequency bias, time synchronization on the order of tens of picoseconds is expected. In this contribution, a method that considers the prior constraints of the between-receiver inter-frequency bias (IFB) and its random variation characteristics is proposed for the estimation of the between-receiver clock difference, based on the uncombined GNSS carrier phase and pseudorange observations of the zero and short baselines. The proposed method can rapidly achieve the single-difference ambiguity resolution of the zero and short baselines, and then obtain the high-precision relative clock offset, by using only the carrier phase observations, along with the between-receiver IFBs being simultaneously determined. Our numerical tests, carried out using GNSS observations sampled every 30 s by a dedicatedly selected set of zero and short baselines, show that the method can fix the between-receiver single-difference ambiguity successfully within an average of fewer than 2 epochs . Then, a clock difference between two receivers with millimeter precision is obtained, achieving time synchronization on tens of picoseconds level, and deriving a frequency stability of 5 × 10 Time synchronization plays an important role in our daily life. In particular, communication systems, power grids and financial networks all rely on precise timing to achieve synchronization and operation efficiency. Furthermore, in the maintenance of the spatial-temporal reference frame, the precision of time synchronization is also crucial. In the early 1980s, when the radio signals from the satellites of the Global Positioning System (GPS) began to be used for timing services, time keeping changed dramatically ,3, GNSS Based on the observation model, that is, whether to make a difference between the observations of the two stations, the CP time transfer method can be divided into two types: the zero-difference (ZD) method and the single-difference (SD) method. The ZD method is mainly based on the precise point positioning (PPP) technology, to estimate the receiver clock offset, which does not require any observation synchronization. This method is flexible, convenient, and highly accurate ,11,12. CTo summarize, using the carrier phase observation for time synchronization can achieve higher precision, but there are generally problems of poor timeliness due to the difficulty of solving the phase ambiguity, and easily being affected by the receiver hardware delay. In order to improve the precision and efficiency of the receiver clock offset estimation, two key issues need to be resolved: one is to deal with the receiver hardware bias reasonably, and the other is to fix the phase integer ambiguity rapidly and successfully. In this study, we assessed the precision and efficiency of the CP time-frequency synchronization using the uncombined GNSS carrier phase observations, considering the prior constraints of the between-receiver inter-frequency biases (IFBs). The mathematical principle and the data processing strategies for estimating the between-receiver clock difference were addressed. First, we analyzed and modeled the temporal variation characteristics of the between-receiver IFBs. Then, a method that took the prior constraints of between-receiver IFB and its random variation characteristics into account was proposed to obtain the between-receiver clock difference, by using only the uncombined GNSS carrier phase observation, which pays special attention to the resolution of the between-receiver single-difference ambiguity.In This section begins with the general observation models of GNSS signals. Then, the CP time synchronization model based on the uncombined carrier phase observations of the short baseline are developed in detail, which considers the prior constraints of the between-receiver IFB and its random variation characteristics. Finally, the method of parameter estimation is given.For a short baseline, the between-receiver single-difference equations of GNSS carrier phase and pseudorange observations can be described as :(1)Lur,jribed in . c is thAs is known, the spatial correlation between the observation error sources of two neighborhood stations are highly related to the length of the baseline. Since the length of baseline studied in this research is tens of meters and the heights of the two stations are basically the same, the single-difference tropospheric and ionospheric delays in (1) were ignored. Because the satellite-dependent part is assumed to be equal for every receiver tracking the corresponding signal, the terms for the satellite clock offset and the relativistic correction for the satellite clock due to noncircular orbits are dropped out of (1). Besides, for signals, e.g., BeiDou Navigation Satellite System (BDS) signals and GPS signals, that are based on code division multiple access (CDMA) technology, the frequency is identical for all channels, and the satellite hardware bias of a single signal can thus be eliminated by making a difference between two receivers .L1 carrier phase is lumped to the receiver clock offset parameter based on (1). Then, we can obtain the re-parameterized equations of the dual-frequency carrier phase and pseudorange observations, as shown below:L1 carrier phase, Due to the receiver phase or code hardware bias of a single signal linearly corelated to the receiver clock offset, the absolute quantities of the two cannot be separated without external observation information. Thus, for the dual-frequency band  included the between-receiver clock difference, between-receiver IFBs, and between-receiver single-difference ambiguities. Among them, the receiver hardware bias is closely related to the hardware characteristics of the receiver itself. For two receivers of the same type, their correlators and font-end designs are essentially the same, so the hardware bias of the two receivers for the same observation is roughly the same. It is reasonable to assume that the mathematical expectation of the relative deviation, after differencing values from two receivers of the same type, should be close to zero. Furthermore, the existing studies of the GLONASS receiver IFB have shown that the quantity of receiver inter-frequency phase bias is in the range of centimeters to decimeters ,24. Evenrs level .Above, we described the establishment of the observation equations and the addition of prior constraints to the receiver IFBs, to estimate the interest parameters. After that, the state model of the estimated parameters should be determined correctly. In a short time, a linear model (estimating the velocity of the relative clock offset) can be used to describe the state change of the receiver clock error. In continuous arcs without cycle slips, the ambiguity was usually treated as a constant. However, the between-receiver phase-to-phase IFB and code-to-phase IFB, especially the state change characteristics of the latter, is relatively lacking. Therefore, it is necessary to analyze the temporal variation characteristics of the between-receiver IFBs, and then model their state change process well .Usually, the researchers constructed the geometry-free phase/code combination to estimate the receiver differential phase/code bias (DPB/DCB) based on (1). Then, the variation characteristics of the receiver DCB/DPB were analyzed, and the factors that affected the variation were investigated. The retrieval of between-receiver DCB and modeling its variation characteristics have been explored using the zero and short baselines . It was They are biased because the phase ambiguities of the reference satellite in the first epoch are contained in the estimation. Since the ambiguity remains constant in continuous arcs without cycle slips, the actual epoch variation of the between-receiver IFBs can be obtained by performing time difference on the biased estimates, which read as:ented in , and theIn conjunction with the observation equation and the state change process of the parameters to be estimated, the matrix form of the parameter estimation for Kalman filtering can be expressed as:A Kalman filter was deployed to estimate the parameters on a satellite-by-satellite and epoch-by-epoch basis. In each epoch, the float solution mbiguity , which yfunction . Once itfunction . Using tIn order to verify the precision and reliability of the proposed method, the GNSS carrier phase and pseudorange observations recorded by the zero and short baselines composed of different types of receivers were dedicatedly selected. The strategies of data processing were then described in detail.The details of the selected stations, including the site names, receiver and antenna types, observations, and time period of the observations, were summarized in In this section, the numerical tests were carried out, and the experimental results were illustrated. Prior to our analysis, proper handling of the between-receiver IFBs had to be in place. Thus, we first analyzed and molded the temporal variation characteristics of the between-receiver IFBs, and then the ambiguity fixed solutions of the between-receiver clock difference and between-receiver IFBs were presented and discussed. In the end, we assessed the consistency of our proposed method with the PPP solution.In order to better constrain and describe the change process of the receiver IFBs and to set a reasonable configuration for parameter filtering, we first used the measured data to analyze and model the temporal variation characteristics of the between-receiver IFBs. The BDS carrier phase and pseudorange observations, collected from the zero baseline CUT0–CUT2 and the short baseline YARR–YAR3, were processed to obtain the biased estimation of the between-receiver IFBs, as demonstrated in (5). Since the between-receiver IFB is principally the same for all satellites of one constellation, the carrier phase and pseudorange observations of the satellite with the highest elevation angle were selected for the parameter estimation in each epoch, for the purpose of reducing the influence of the measurement noise.As shown in e) and standard deviations (σ) of the epoch-difference between-receiver IFBs of the zero baseline CUT0–CUT2 and the short baseline YARR–YAR3, on days 12–14 in 2018. The three-fold sigma (3σ) was expressed as a function of time, and the coefficient (w) of the process noise variances of the between-receiver IFBs were then obtained. Based on −10–5 × 10−10 m2/s, and that of the between-receiver code-to-phase IFB is approximately 1 × 10−6–5 × 10−6 m2/s. These statistical results will be treated as empirical reference values for the following data processing.The probability distribution histograms of epoch variation of the between-receiver IFBs were plotted in According to the above data processing strategies summarized in Three-day time series of the fixed solutions of the between-receiver clock difference and the between-receiver IFBs, that were derived from the BDS phase and code observations of the zero baseline CUT0–CUT2 and the short baseline YARR–YAR3, are shown in −14 of the short baseline YARR–YAR3, and that of the zero baseline CUT0–CUT2 is also around 5 × 10−14. The stability between days is basically the same, which demonstrates the stability and repeatability of this method for time-frequency synchronization.Furthermore, the frequency stability of the time link is a crucial indicator of the time and frequency transfer. In order to further analyze the stability of the receiver clock offset from the perspective of frequency stability, the Allan variance of the fixed solution of the relative clock offset at different time scales was statistically analyzed . Since tIn order to confirm the validity of the proposed method, the results derived from our proposed method were compared with the PPP solution. The online PPP tool of Canadian Spatial Reference System (CSRS-PPP) was employed to estimate the receiver clock offset, using the ionosphere-free code and phase combinations in a station-by-station manner. Then, the clock difference between two receivers were formed. The clock differences derived from three solutions, i.e., our method using GPS observations (solution A), our method using BDS observations (solution B), and PPP method using GPS ionosphere-free combinations (solution C), were shown in It can be seen that solution A and solution B have a good agreement. However, a systematic bias was observed. This bias is the inter-system frequency bias between GPS L1C carrier phase and BDS L2I carrier phase. From In this contribution, we investigated the mathematical principle of time and frequency synchronization, based on the uncombined GNSS carrier phase observations. In view of the issue that the precision and efficiency of time and frequency transfer being easily affected by the receiver hardware inter-frequency bias (IFB), a method that took the priori constraints of the between-receiver IFB and its random variation characteristics into account, was proposed, to determine the relative clock offset between two receivers with the single-difference ambiguity resolution. First, we analyzed and modeled the temporal variation characteristics of between-receiver IFBs. Then, considering the priori constraints and the state change model of the between-receiver IFBs, the ambiguity fixed solution of the between-receiver clock difference was achieved, and the between-receiver IFBs were calibrated based on the zero/short baseline mode.−14. Furthermore, comparative experiments show that, for the type of receiver tested, the agreement between our approach and the PPP method is between −6.7 ns and 0.2 ns, and this deviation is mainly caused by the receiver hardware bias of carrier phase and receiver hardware bias of ionosphere-free code combination.The validity and reliability of the proposed method were analyzed by using the BDS/GPS dual-frequency carrier phase and pseudorange observations, that were dedicatedly collected from the zero and short baselines on several consecutive days. The experimental results show that the between-receiver single-difference ambiguity can be fixed successfully within an average of fewer than 2 epochs . After the integer solution of the single-difference ambiguity was determined correctly, the between-receiver clock difference with a millimeter precision was obtained by using only the uncombined carrier phase observations. Additionally, the fixed solution of the between-receiver IFBs was obtained. Three-day time series of the fixed solution of between-receiver clock difference indicates that this method can achieve the time synchronization on tens of picoseconds level precision, and it has good stability. Since the single-difference ambiguity is resolved successfully, the daily boundary of time transfer is also continuous. Besides, for the tested short baseline, the average stability of 30,000 s for the proposed CP time and frequency method is better than 5 × 10


Detecting SNP-SNP interactions associated with disease is significant in genome-wide association study (GWAS). Owing to intensive computational burden and diversity of disease models, existing methods have drawbacks on low detection power and long running time. To tackle these drawbacks, a fast self-adaptive memetic algorithm (SAMA) is proposed in this paper. In this method, the crossover, mutation, and selection of standard memetic algorithm are improved to make SAMA adapt to the detection of SNP-SNP interactions associated with disease. Furthermore, a self-adaptive local search algorithm is introduced to enhance the detecting power of the proposed method. SAMA is evaluated on a variety of simulated datasets and a real-world biological dataset, and a comparative study between it and the other four methods  that have been developed recently based on evolutionary algorithms is performed. The results of extensive experiments show that SAMA outperforms the other four compared methods in terms of detection power and running time. The development of high-throughput sequencing technology makes it possible to analyze single-nucleotide polymorphisms (SNPs) from thousands of individuals , 2. WithIn the past few years, many methods have been raised for detecting two-locus disease models. These algorithms can be categorized into exhaustive search, stochastic search, heuristic search, and swarm intelligent optimization algorithms . The exhThe random search uses probabilistic methods to find the optimal solution , 16. TheIn the recent years, swarm intelligent optimization algorithms arising from natural phenomena and biological system have held high attention in the detection of disease-associated SNP-SNP interactions –21. For One promising approach for tackling the drawbacks mentioned above is to use a fast local search in the evolutionary algorithm. Hybridization of genetic algorithms (GAs) with local search (LS) has already been studied in various optimization problems –32. SuchThe paper is organized as follows. In S = {r1, r2, ⋯, rL}, where r is an SNP and L is the number of SNPs. For detecting two-locus disease models, there are L(L − 1)/2 combinations that can be selected. The value of each SNP is 0, 1, or 2, which represent the homozygous major genotype, the heterozygous genotype, and the homozygous minor genotype, respectively. A dataset contains n samples (nd cases and nu controls), and each sample has a set of SNPs. If the genotype distribution of a two-locus SNP-SNP interaction is significantly different between cases and controls, it may lead to an increase in the risk of the disease.A set of SNPs is represented by It is a time-consuming task to detect SNP-SNP interactions associated with disease if all possible two-locus interactions from hundreds of thousands of SNPs are considered in a genome-wide scale. In this paper, a fast self-adaptive memetic algorithm (SAMA) is proposed to enhance the detection power of two-locus SNP-SNP interactions in an efficient way.Memetic algorithm (MA)  is inspiM individuals. An individual is expressed as xi = {rp, rq} where rp and rq are SNPs, and the individual xi is generated byL is the number of SNPs in a dataset. After initialization, SAMA finds the current optimal solution xbest with the best value of fitness function. In SAMA, the χ2 test is used as the fitness function to measure the association between two-locus SNP-SNP interactions and the disease.The SAMA algorithm randomly generates a initial population with The crossover operator, a fundamental genetic search operator, takes advantage of the information available in the search space. In the SAMA algorithm, we use a hybrid crossover (HS) to cross two individuals. HC can be considered the hybrid between the current best individual and the individuals in the current iteration. The pseudocode of HC is shown in xbest and the individual xi in the current iteration are selected as two parents. If the random number r1 between 0 and 1 is less than the crossover probability pc1, the first SNP rp in xi is replaced by the first SNP rpbest in xpbest. If the random number r2 is less than the crossover probability pc2, the second SNP rq in xi is replaced by the second SNP rqbest in xqbest. If the conditions of r1 < pc1 and r2 < pc2 are satisfied at the same time, xi is replaced by xbest.In the algorithm, the current best individual zi is calculated by the following equation:δ is calculated from a distribution which prefers a small value, and the “+” or “−” is chosen with a probability of 0.5. Thus, rp is mutated in the interval between [rp − range · δ] and [rp + range · δ], and rq is mutated in the interval between [rq − range · δ] and [rq + range · δ].The mutation operator is used to randomly create the diversity of individuals in a population. We use a mutation called distributed breeder mutation (DBM) in the SAMA algorithm. DBM, inspired by the breeder genetic algorithm proposed by Muhlenbein and Schlierkamp-Voosen , is a roi is outside the specified range , zi will be reinitialized. δ is computed according to the following equation:If the mutated individual zαi is set to 0 before the mutation operation. Then, each αi is mutated to 1 with a probability of 1/16. The minimum step size is produced with a precision of rangei · 2−15. iz, and the piz is defined byξ is the switch parameter, and zi is an individual after HC and DBM. The initial piz of each individual is 1; hence, each individual will be selected at least once for SLS. If the fitness value of the individual zi is improved, the probability piz that zi is selected is still 1. Otherwise, piz is changed to ξ · piz. If the fitness value of zi is not improved after being selected n times, this value is ξn · piz. The pseudocode of SLS is shown in Local search (LS) is a simple iterative method for finding approximate solutions. If a candidate solution has better or equal fitness, LS moves the search from the current solution to the candidate solution. If LS is applied to every solution many times, the running time is very long because the additional functional evaluations required for LS is expensive. Thus, a self-adaptive LS (SLS) is introduced, which uses a probability to reduce the number of times that are used for local search. The probability that each individual is selected to allpy the SLS operation is pIn the SAMA algorithm, an elitist selection is introduced to select individuals that evolve to the next iteration. After HC, DBM, and SLS, the ES operation is performed according toi is greater than that of the previous individual xi, xi is replaced by wi. Otherwise, xi is unchanged.If the fitness value of the individual w1 = , x2 = , x3 = , x4 = , and x5 = . Among them, x4 obtains the highest fitness value, i.e., fit(xi) = 62.8, and hence, x4 is the current optimal solution xbest (rbestp = 121 and rbestq = 82).In this subsection, we give a running instance of SAMA in 1≥pc1 and r2≥pc2 for x1 and x4, r2 < pc2 for x2, r1 < pc1 for x3, and r1 < pc1 and r2 < pc2 for x5. According to 1 and x4 are not changed and assigned directly to y1 and y4, whereas the other three individuals are changed. One SNP in x2 and x3 is replaced; hence, x2 is changed to y2 =  and x3 is changed to y3 = . x5 is changed to y5 =  because both SNPs in x5 are replaced.First, we perform the HC operation. Suppose rδ of y1 is 0, the range · δ of y2 and y3 is 10, and the range · δ of y4 and y5 is 15. y2 and y4 get “−”, whereas y3 and y5 get “+.” Thus, y1 is not changed and assigned directly to z1 = , y2 is changed to z2 = , y3 is changed to z3 = , y4 is changed to z4 = , and y5 is changed to z5 = .Next is the DBM operation. We assume that range · 1, z2, and z5 are not changed and assigned directly to w1, w2, and w5 due to r3 ≥ pz. For z3 and z4, SLS is operated cyclically because of r3 < pz. z3 is changed to w3 =  and z4 is changed to w4 =  after the DMB operation in SLS.After completing HC and DBM, the SLS operation is executed. z1) ≤ fit(x1), fit(w2) ≤ fit(x2), fit(w3) ≤ fit(x3), fit(w4) > fit(x4), and fit(w5) > fit(x5). Thus, x1, x2, and x3 are retained to the next generation. For x4 and x5, the two individuals are replaced and assigned to the next generation.Finally, the selection operation is performed. We suppose that fit data .2) is 0.005 in Model 1, and h2 is 0.02 in Models 2 and 3. According to the combination of these values, 12 penetrance tables are obtained  . Model 1ined see . 200 datIn the experiments, we set the same maximum number of iterations for the five algorithms, that is, the maximum iteration number for datasets with 200 SNPs is set to 50 and the maximum iteration number for datasets with 2000 is set to 500. The maximum number of iterations is less than the number of iterations using an exhaustive algorithm. Furthermore, the other parameters of the five compared algorithm are shown in With the purpose of conducting the experiments comprehensively, we introduce two measurements: detection power and running time. The detection power is defined below:Figures Tables According to the results of the simulated experiments, SAMA performs well for detecting two-locus SNP-SNP interactions. In this section, we conduct experiments on a real-world biological dataset . The purIn the paper, we propose the SAMA algorithm to detect two-locus SNP-SNP interactions associated with disease. The global search ability of SAMA is greatly increased by using HC, DBM, and EC. The self-adaptive behavior of SLS enhances the local search ability of SAMA without significantly increasing the running time. When using simulated datasets, the experimental results indicate that SAMA is more effective than FHSA-SED, AntEpiSeeker, IEACO, and DESeeker in terms of detection power and running time. When utilizing the real-world biological dataset, the experiments show that the proposed algorithm successfully detected known disease-associated SNP-SNP interactions and some new suspected interactions. However, the SAMA algorithm still has some limitations. First, the detection power of SAMA is low for the disease models with small MAF. Furthermore, the current version of SAMA cannot detect high-order SNP-SNP interactions (SNPs > 2). As far as we know, there does not exist a powerful method for detecting high-order SNP-SNP interactions in GWAS. Therefore, detecting high-order SNP-SNP interactions associated with disease has many rooms to explore in the future.


Copy number variation is an important class of genomic variation that has been reported in 75% of the human genome. However, it is underreported in African populations. Copy number variants (CNVs) could have important impacts on disease susceptibility and environmental adaptation. To describe CNVs and their possible impacts in Africans, we sequenced genomes of 232 individuals from three major African ethno-linguistic groups: (1) Niger Congo A from Guinea and Côte d’Ivoire, (2) Niger Congo B from Uganda and the Democratic Republic of Congo and (3) Nilo-Saharans from Uganda. We used GenomeSTRiP and cn.MOPS to identify copy number variant regions (CNVRs).p < 0.0001) and included loci where CNVs have previously been associated with HIV, Rhesus D and preeclampsia. When integrated with 1000 Genomes CNV data, we replicated their observation of population stratification by continent but no clustering by populations within Africa, despite inclusion of Nilo-Saharans and Niger-Congo populations within our dataset.We detected 7608 CNVRs, of which 2172 were only deletions, 2384 were only insertions and 3052 had both. We detected 224 previously un-described CNVRs. The majority of novel CNVRs were present at low frequency and were not shared between populations. We tested for evidence of selection associated with CNVs and also for population structure. Signatures of selection identified previously, using SNPs from the same populations, were overrepresented in CNVRs. When CNVs were tagged with SNP haplotypes to identify SNPs that could predict the presence of CNVs, we identified haplotypes tagging 3096 CNVRs, 372 CNVRs had SNPs with evidence of selection (iHS > 3) and 222 CNVRs had both. This was more than expected (Novel CNVRs in the current study increase representation of African diversity in the database of genomic variants. Over-representation of CNVRs in SNP signatures of selection and an excess of SNPs that both tag CNVs and are subject to selection show that CNVs may be the actual targets of selection at some loci. However, unlike SNPs, CNVs alone do not resolve African ethno-linguistic groups. Tag haplotypes for CNVs identified may be useful in predicting African CNVs in future studies where only SNP data is available. Copy number variants are defined as duplications or deletions of genomic segments greater than 1 kb in length . HoweverAfrican populations have the highest genomic diversity globally . The fouAfrican populations are under-represented in genomic studies , but aren = 50); Niger-Congo-B speaking populations from Uganda  and the Democratic Republic of Congo ; and Niger-Congo A speaking populations from Côte d’Ivoire  and Guinea . We aimed to discover novel CNV region (CNVR) variants, investigate population differences associated with CNVs and identify SNP haplotypes which tag CNVs and may predict such CNVs in future genome wide association studies (GWAS). The CNVs identified may also be important in understanding African CNV diversity and allowing inference of CNVs from population specific SNP-chip data.The populations surveyed and their respective countries were: Ugandan Nilo-Saharans of Lugbara ethnicity  [p = 7.8e-05.Figure ient (F) , F = 0.1After removing the outliers, predicted CNVR retained for further analysis were 11,725 from GenomeSTRiP and 2115 from cn.MOPS. We defined as high confidence CNVRs those called by both GenomeSTRiP and cn.MOPS. This identified 7608 GenomeSTRiP CNVR that overlapped or were within cn.MOPS loci  were much shorter than those discovered by cn.MOPS (median length 32 kb)  than cn.MOPS (1691) and there were multiple GenomeSTRiP CNVRs within each cn.MOPS CNVR. The total lengths of CNVRs were 108 Mb and 1145 Mb in GenomeSTRiP and cn.MOPS, respectively. We found that 81 Mb (75%) of the GenomeSTRiP CNVRs were within cn.MOPS CNVRs, almost twice as much as the 43 Mb (40%) that was expected from random placement of the GenomeSTRiP CNVRs by simulation. Given that the GenomeSTRiP CNVRs conformed most closely in size to those described in DGV we used the GenomeSTRiP CNVRs for subsequent analysis. Amongst the 7608 CNVRs, there were 2172 CNVRs with only deletions, 2384 with only insertions and 3052 with both insertions and deletions. Counts of each class of CNV for each population are shown in Additional file 2 = 0.38), and least correlated between Niger-Congo and Nilo-Saharan (r2 = 0.17). Individuals of Nilo-Saharan origin had the lowest proportion of private CNVRs (20%) whilst the Niger-Congo A and Niger-Congo B populations shared more with each other than with the Nilo-Saharans, consistent with their closer linguistic relationship.24% of CNVRs were common to all three major linguistic groups represented in the data, 55% were unique to single linguistic groups and 21% were shared between pairs of major populations  between the five populations   numbers of CNVRs per Mb compared with other chromosomes (~ 14 CNVRs Mb− 1)  had not been previously reported, and were defined as novel CNVRs. Unique CNVR boundaries in the DGV cover 75% of the genome and much of the rest could be repeat regions where reads cannot be mapped with certainty and therefore CNVRs cannot be detected. CNVs in novel CNVRs were 10 times less frequently observed compared with CNV in known CNVR (mean frequency of novel CNVs was 0.74% compared with 7.4% for known CNVs). The novel CNVs were annotated using BEDTools intersect  against r2 is calculated for the regression line and also the p value that the slope is zero. Haplotype blocks were defined using linkage disequilibrium (r2 > 0.8), which has been shown to tag shorter haplotypes in African American genomes compared to West Eurasians [SNP haplotypes that tag CNVRs in our populations were identified to assist the interpretation of SNP based GWAS studies. We assumed that if a haplotype is associated with a CNV then the number of alleles 0, 1, 2) of that haplotype will be correlated with the observed number of copies reported in samples in the dataset. Therefore, copy number is plotted against haplotype count for each sample and the value of urasians . Allelesm r2 > 0., which h of that m r2 > 0., which hp > 3.0), of these 1805 were within CNVRs, more than twice as many as would be expected by chance   in the UNL population in a separate study of the same data . There w.0 in the556 of the 1805 SNP with significant iHS scores were within 548 genes (+/− 5 kb flanks), including 146 protein coding genes  was twice that of CNVRs without SNPs under selection 8.5%) (χ% (χ2 = 1p = 2.8− 15) .There were 2693 CNVRs with SNPs that tag haplotypes in the UNL population and 372 CNVRs with SNPs with evidence of selection. Given that there was a total of 7608 CNVRs, 132 CNVRs would be expected to have both tag SNPs and SNPs with evidence of selection. However, 222 CNVRs were observed with both tag SNPs and SNPs with evidence of selection, more than 50% as many as expected  of combined 1000 Genomes and TrypanoGEN populations showed population structure at the continental level  Fig. ST analyses of CNVs showed little difference (FST < 0.05) between populations  between populations. High FST loci (> 3sd) intersected selected loci (iHS > 3) within our data. CNVR regions with the highest FST difference between populations are annotated in Additional file UGT2B17 (UDP Glucuronosyltransferase Family 2 Member B17) associated with the bone mineral density quantitative trait locus and IRGM (Immunity-related GTPase family M protein) associated with inflammatory bowel disease 19.Although the mean FWe identified 7608 consensus CNVs, using GenomeSTRiP and cnMOPS in five African populations. We only retained CNVRs that were called in more than one sample and were identified both by cn.MOPS and GenomeSTRiP. The cn.MOPS CNVRs were much larger, with a mean of 4.5 GenomeSTRiP CNVRs overlapping each cn.MOPS CNVR Table . Given t224 of the 7608 CNVR were defined as novel since they have not been previously submitted to the DGV. All the novel loci had low frequencies of < 10% Fig. a. The lor2 = 0.71), suggesting that CNVR density may be an intrinsic property of chromosomes. The shorter chromosomes tended to have the higher densities of CNVRs in both our data and the 1000 Genomes data and a probe based study of CNVR distribution also found relatively high CNVR density on shorter chromosomes  [There was a threefold variation in CNV and CNVR frequency per Mb between chromosomes in our dataset and a nearly twofold variation in the 1000 Genomes data, even after correction for chromosome length Fig. . The den and 22) . AlthougHPR (Haptoglobin related protein) locus have been tagged by haplotypes [CCL4 (Cysteine-Cysteine Ligand 4) chemokine gene [CNVs may be the functional variant underlying some QTLs discovered by genome wide association studies using SNP chips. In order to identify SNPs that could predict the presence of CNVs at a locus, we discovered haplotypes with alleles that were associated with CNVs at a locus. The haplotypes we predicted are not only associated with the presence or absence of the CNV locus but also the likely copy number. Previous studies have shown correlations between SNPs or haplotypes and CNVRs. High copy numbers of CNVs at the plotypes . There iine gene .In the current study, SNP haplotypes tagged 41% of CNVRs. The mean frequency of CNVs at tagged CNVRs (12%) was nearly twice that of untagged loci (7%). This may reflect a lower power to detect associations with rarer CNVs. Longer CNVRs tended to contain more haplotypes and a higher proportion of these were associated with copy number. The relationship between SNP haplotypes and CNVs could be confounded by the same CNV recurring on different haplotypes or by clustering overlapping non-homologous CNV into a single CNVR. The weak association between CNV genotype and population structure in the PCA analysis was consistent with both these hypotheses. Therefore, the number of CNVRs associated with SNP haplotypes may be an indicator of the proportion of stable, non-recurrent homologous, high frequency CNVRs.10 p iHS > 3) suggests that the CNV may be the genomic feature that is under selection at these loci. CNVs have been found to be the structure under selection by other methods [ST. However, it should be noted that although there was a highly significant excess of CNVRs with SNPs that tagged CNVs and SNPs that had evidence of selection, the 32% excess of SNPs that were both Tag SNP and had evidence of selection was not significant. Therefore, it is possible that the tag SNP and the SNP with evidence of selection may both be correlated with some third factor other than CNV.The excess of CNVRs with both tagged haplotypes and SNPs with signatures of selection (−log methods  but thisC1orf63 (Chromosome 1 Open Reading Frame 63), Human Leukocyte Antigen (HLA), Killer-cell Immunoglobulin-like Receptor (KIR). The complete deletion of the RhD gene is the commonest cause of Rhesus negative status. Given the severe consequences of the interaction between Rhesus negative (Rh-ve) mothers with Rhesus positive (Rh+ve) foetus it has been assumed that the null allele might be maintained by some yet unknown selective advantage. Genetic studies have found evidence for heterozygous advantage at the RhD locus in an ecological regression study [Among the loci that had CNVR overlapping selected loci were Rhesus D (RhD), on study  and an aon study . Howeveron study . These sHLADQB1) has been associated with preeclampsia in Iranian women [HLA locus interacts with the locus for KIR which has been associated with preeclampsia in Ugandan Bantu women [KIR3DL1 is also associated with risk of HIV [HLADQB1 and KIR in preeclampsia and infectious disease which may impact infant birth and survival, they may be the actual targets of positive selection, resulting in the signatures of selection which have been seen in these loci. These observations generate useful hypothesis for testing. If QTL are discovered in these regions, then CNV should be high priority candidates for the functional variant.Variants in the Human Leucocyte Antigen, class II, DQ beta 1  among premature infants [We found that CNV distinguish major continental populations, when we included Asians, South Asians, Americans, Europeans and Africans from the 1000 Genomes in the same PCA plot. Similarly, the 1000 Genomes project found CNV data resolve population clustering at continental scales . Consequ infants , 38. In  infants . This raST showed that African CNV frequencies were similar across populations. However, there were 3–5-fold more CNVRs than expected by chance in regions where previous studies have found evidence for selection (p < 0.00001). The higher than expected number of CNVRs in regions under selection suggests either the CNVRs are under selection or that selection signatures are more likely to arise in CNVRs regions. Consistent with the hypothesis that selection drives the enrichment of CNVRs in particular populations, the alpha thalassaemia deletion reveals signatures of selection [Plasmodium [KIR locus had both SNP signatures of selection and SNP haplotypes tagging CNV. This suggests that CNVs are the polymorphism under selection in at-least some of these regions.Consistent with the PCA analysis, global Felection  and is sasmodium  and in oWe have presented a CNVR landscape of populations representing the Niger-Congo A, Niger-Congo B and Nilo-Saharan African ethnic groups. These include known CNVRs that have been described in the DGV, and novel ones (3%), that are not reported in the DGV, reflecting the diverse nature of these African populations. Some of the CNVRs described may have medical significance as they occur in Mendelian disease-causing genes and overlap SNPs significantly associated with various traits in the GWAS catalogue. We have used haplotypes to tag CNVRs. Haplotypes tagging CNVRs are useful in imputing CNVs from SNP genotyping data in future studies, especially in African populations known to have low linkage disequilibrium. We found overrepresentation of CNVRs in regions showing signatures of selection in SNP based studies, and an excess of CNVRs with both haplotypes tagging CNVs and SNP haplotypes with signatures of selection, suggesting a possible role of CNVR in selection and adaptation. Finally, we show that CNV distinguish between continental populations but do not stratify within the continent, such as the Africans in the current study.n = 50) from Northern Uganda and Ugandan Niger-Congo B speakers (Basoga) (n = 33) whereas Central African populations were Niger-Congo B speakers (n = 50) from the Democratic Republic of the Congo. West African populations were Niger-Congo A speakers (n = 49) from Guinea and Cote D’Ivoire (n = 50). The samples in the current study are a subset of those described in the TrypanoGEN bio-bank [. The summary of the population, linguistic group and sampling foci are in Table The study was conducted in the context of the TrypanoGEN project , which abio-bank . Sample bio-bank . The sumP-value < 0.01.We used the Illumina Truseq polymerase chain reaction (PCR) free kit to prepare WGS libraries. Sequencing was done at the Centre for Genomic Research at the University of Liverpool using the Illumina Hiseq2500 system at 10X coverage. We used Burrow Wheeler Alignment (BWA) to map sequenced reads onto the 1000 Genomes project human_g1k_v37_decoy reference genome. The Genome analysis tool kit (GATK v3.4) was used for SNP calling following GATK best practice guidelines. Quality control measures of SNPs included filtering by a) removing loci with > 10% missing SNP, b) removing individuals with > 10% missing SNP loci and c) removing loci with Hardy Weinberg We use CNVR to refer to a locus at which one or more samples may have a CNV; the overlapping CNVs at a CNVR may each have different boundaries. To select methods to identify CNVR we reviewed the literature and found 4 major approaches to CNV discovery: 1) Paired end methods (PE) estimate insert size between the paired ends but is limited by the size of the fragment sequenced; 2) Split Read methods (SR) are focused on identifying exact break points in reads that do not map but have a mate pair that maps perfectly, it works well with deletions but can only identify small (< 50 bp) insertions; 3) Read Depth (RD) methods count the number of reads at each genomic location and 4) De Novo assembly methods create new genome assemblies and compare them with the reference and are best suited to high coverage with long read data . Read DeWe sought evidence for population structure due to CNVs using PCA in PLINK . CNV datST between CNVs in the different populations. We used multi-allelic data format as described above for population differentiation (FST) analysis. FST were calculated using PLINK v 1.9 [We investigated population differentiation by comparing FNK v 1.9  using onr2 is calculated for the regression line and the p value for the null hypothesis that the slope of the regression line is zero. We used a custom Java program; TagCNV available from Github [2) between alleles in 50 kb windows. Sets of alleles within 5 kb of CNV boundaries and with r2 > 0.8 with at least one other SNP in the region were assembled into haplotypes. The counts of each allele were plotted against the fractional copy number for the CNV for each sample and the correlation (r2) between haplotype count and copy number was obtained using R and the association was considered significant if p < 0.05 for the null hypothesis that the slope of the regression line is zero. A Bonferroni correction was applied for the number of haplotypes at the CNVR that were tested for association. Haplotypes which were only present in samples with identical copy numbers were considered uninformative and excluded from all calculations.We used the method described by Handsaker et al. , implemem Github  to find Additional file 1: Table S1. List of samples in the study.Additional file 2. Correlation of GenomeSTRiP and cn.MOPS and supplementary figures.Additional file 3: Table S2. GenomeSTRiP CNVR that intersect cn.MOPS CNVR after QC.Additional file 4: Table S3. Counts of CNV types detected by both algorithms.Additional file 5: Table S4. Genes intersected by novel CNV.Additional file 6: Table S5. Novel CNVR intersecting the GWAS catalogue SNPs.Additional file 7: Table S6A. All CNVR intersecting OMIM genes B. Novel CNVR intersecting OMIM genes.Additional file 8: Table S7. Haplotypes that tag CNVR in each of the populations.Additional file 9: Table S8. Counts of CNVR and SNP with tagged SNP and SNP with Signatures of Selection (iHS > 3).Additional file 10: Table S9. CNVR tagged by SNP haplotypes and also containing SNP signatures of selection.Additional file 11: Table S10. CNVR with high FST between populations.Additional file 12: Table S11. Effect of Prior Impact and Minimum width on concordance with DGV in cn.MOPS.