Cucurbita maxima, C. moschata, and C. pepo are commonly cultivated worldwide. To identify genome-wide SNPs in these cultivated pumpkin species, we collected 48 F1 cultivars consisting of 40 intraspecific hybrids  and 8 interspecific hybrids (C. maxima x C. moschata). Genotyping by sequencing identified a total of 37,869 confident SNPs in this collection. These SNPs were filtered to generate a subset of 400 SNPs based on polymorphism and genome distribution. Of the 400 SNPs, 288 were used to genotype an additional 188 accessions  with a SNP array-based platform. Reliable polymorphisms were observed in 224 SNPs (78.0%) and were used to assess genetic variations between and within the four predefined populations in 223 cultivated pumpkin accessions. Both principal component analysis and UPGMA clustering found four major clusters representing three pumpkin species and interspecific hybrids. This genetic differentiation was supported by pairwise Fst and Nei’s genetic distance. The interspecific hybrids showed a higher level of genetic diversity relative to the other three populations. Of the 224 SNPs, five subsets of 192, 96, 48, 24, and 12 markers were evaluated for variety identification. The 192, 96, and 48 marker sets identified 204 (91.5%), 190 (85.2%), and 141 (63.2%) of the 223 accessions, respectively, while other subsets showed <25% of variety identification rates. These SNP markers provide a molecular tool with many applications for genetics and breeding in cultivated pumpkin.Three pumpkin species  Cucurbita spp.; 2n = 2x = 40) is a major crop in the Cucurbitaceae family including cucumber, melon, and watermelon. The Cucurbita genus consists of at least 12 diverse species; three major cultivated species are C. maxima Duchesne, C. moschata Duchesne, and C. pepo L.1. The cultivated varieties in these species produce thicker, more highly colored, and less fibrous fruit flesh relative to wild species2. Pumpkin provides an excellent source of pro-vitamin A, carotenoids, sugars, and minerals4. In addition, the world production of pumpkin including squash and gourd exceeded 27.6 million tons from 2.04 million ha in 20185. Due to the nutritional and economic value of pumpkin, breeders have made many efforts to develop new varieties in public and private breeding programs. Therefore, plant variety protection (PVP) is important to prevent unauthorized use of new varieties and support breeding activities6.Pumpkin  working group of UPOV have suggested models for the application of molecular markers in variety registration9. Recent advances in high-throughput genotyping technology have made molecular markers a more attractive option for supplementing or even replacing phenotype-based DUS testing10.The International Union for the Protection of New Varieties of Plant (UPOV) has harmonized PVP systems among 76 member countries and organizations (as of February 2020). Within this PVP system, a new variety must have distinctness, uniformity, and stability (DUS) to be eligible for registration and protection. UPOV provides a total of 331 guidelines for DUS tests in crop species (as of February 2020). Current DUS testing is mainly based on phenotypic evaluations during two growing seasons, and is labor-intensive, time-consuming, and environment-sensitive17. However, SSR markers are not suitable for high-throughput genotyping with a large number of markers. Single nucleotide polymorphisms (SNPs) are amenable to automation for high-throughput and cost-effective genotyping. Next-generation sequencing (NGS) technologies have accelerated the identification of genome-wide SNPs, making SNPs ideal to many applications in plant breeding18. In pumpkin, NGS-based transcriptome sequencing of C. pepo found over 9,000 SNPs19. A total of 8,660 SNPs were also identified from genotyping by sequencing (GBS) in the F2 population (n = 186), which were derived from two inbred lines of C. maxima20. These SNPs were used to construct high-density genetic maps and to detect QTL associated with a dwarf vine. In addition, the GBS-based SNP studies were conducted in the pumpkin species  and the resulting SNPs were used to map loci associated with powdery mildew resistance and fruit-related traits22. Recently, the Cucurbita genomes were assembled in C. maxima, C. moschata, and C. pepo24. For C. pepo, the genomes of seven morphotypes were also studied by resequencing with an average of 33.5x coverage25. These genome resources have accelerated genome-wide SNP discovery in cultivated pumpkin.Molecular markers, especially DNA markers, are an effective tool to explore genetic variations in crop species. Of these markers, simple sequence repeats (SSRs) have been commonly used for DNA fingerprinting and genetic diversity assessment due to advantages such as co-dominant and multi-allelic natures1 cultivars. Therefore, we generated genome-wide SNPs with a GBS approach in a collection of 48 commercial F1 cultivars representing intraspecific hybrids for each of three pumpkin species  and interspecific hybrids (C. maxima × C. moschata). Of these, 288 SNPs were used to genotype an additional collection of 188 accessions with the Fluidigm platform. The genetic variations between and within pumpkin populations were assessed based on these SNP markers. In addition, several subsets of SNP markers were generated for variety identification in commercial F1 cultivars. These SNP markers are a useful resource for developing a cost-effective and rapid DNA-based system for DUS testing and thus benefit breeders by protecting their ownerships of new pumpkin varieties.Although a large number of SNPs were previously identified in pumpkins, this genomic resource is limited to investigation of genetic variations and variety identification in cultivated pumpkin germplasm including commercial F1 cultivars generated a total of 389.9 million reads ranging from 2.4 million to 13.2 million per cultivar with an average of 8.1 million (Table C. maxima24. The 381.6 million reads (97.9%) showed expected barcodes and 953,780 tags were mapped to the C. maxima genome. The TASSEL-GBS pipeline detected a total of 232,256 variants including 202,722 SNPs (Table C. maxima × C. moschata) to 34,869 (C. pepo)  to 0.69 Mb (chromosome 19)  were derived from coding sequences, consisting of 126 non-synonymous and 161 synonymous SNPs (Table The polymorphic information content (PIC) values of 37,869 confident SNPs were calculated based on polymorphisms in the 48 F19) Fig. . Based oC. pepo (breeding line ‘Zhdana’) showed a call rate of 83.8%. Since the other 13 accessions (4 F1 cultivars and 9 landraces) showed call rates of 36.6–62.0%, these were excluded from further analyses. In the 174 accessions, 224 of 288 SNPs (77.8%) were polymorphic and 13 SNPs (4.5%) were monomorphic  were genotyped with call rates >90%. One accession of Most of the SNPs showed two or three clusters corresponding to two homozygous genotypes (XX and YY) and a heterozygous genotype (XY) Fig. . HoweverC. maxima, 63 C. moschata, 45 C. pepo, 31 interspecific hybrids (C. maxima × C. moschata), and 11 unknown accessions. In PCA, the 223 pumpkin accessions were divided into four major clusters using the first two principal components (PC1 and PC2), which explained 65.9% and 15.3% of the total variance, respectively  were grouped with two C. moschata , five C. pepo (one F1 cultivar and four breeding lines), and two unknown accessions  in cluster 1. We found 30 of 31 interspecific hybrids in cluster 2, which also included 10 intraspecific hybrids  and three unknown F1 cultivars. These accessions in cluster 2 were further divided into two sub-clusters based on PC1 , one interspecific hybrid, and four unknown  accessions. Cluster 4 consisted of 38 C. pepo, eight C. maxima (five breeding lines and three landraces), and two C. moschata (one F1 cultivar and one breeding line), and two unknown  accessions. Most of the pumpkin accessions were separated according to their predefined populations based on a priori knowledge using PC1 and PC2. However, we found no obvious genetic differentiation among F1 cultivars, breeding lines, and landraces  were differently clustered between the UPGMA and PCA methods, indicating a consistent result (Table Fst and Nei’s genetic distance (D). For this analysis, we excluded the 11 unknown accessions. The four populations were all significantly differentiated by pairwise Fst at P < 0.001 (Table C. maxima and C. moschata populations (Fst = 0.63 and D = 0.58). The C. pepo population was separated from C. maxima (Fst = 0.49 and D = 0.30) and C. moschata (Fst = 0.46 and D = 0.24) populations. The pairwise estimates of Fst and D suggested that the interspecific hybrids were more similar to C. maxima (Fst = 0.21 and D = 0.12) than C. moschata (Fst = 0.41 and D = 0.30) , while the C. pepo population showed the lowest estimates . Similar levels of genetic diversity were found in the C. maxima  and C.moschata  populations (Table Allelic richness (A), expected heterozygosity (He), and PIC were used to investigate levels of genetic diversity within each of the predefined populations Table . The C. ns Table .Table 6D1 cultivars . Interestingly, all 45 C. pepo accessions including F1 were distinct using this core set of SNP markers  of the 223 pumpkin accessions including all inbred accessions and 126 of 138 Fars Fig. . From thers Fig. . Four aders Fig.  and S3. ers Fig. . The 92 26. Therefore, GBS have been commonly used as a powerful tool for high-resolution genetic mapping, genome-wide association study, and genetic diversity analysis in crop species29. In this study, a total of 37,869 confident SNPs were generated using GBS in the collection of 48 F1 pumpkin cultivars representing three main Cucurbit species  and interspecific hybrids (C. maxima × C. moschata). In addition, 26,707–34,869 SNPs were found in each of these four populations. Previous studies in pumpkins reported relatively small numbers of SNPs using inbred accessions. Blanca et al.19 identified 9,043 filtered SNPs between two C. pepo subspecies using NGS-based transcriptome sequencing. The GBS study generated 8,660 SNPs in the F2 population of C. maxima20. We also found similar percentages of transition (~63%) and transversion (~37%) SNPs relative to those in the study of Blanca et al.19. The genome-wide SNPs from the present study may be biased to C. maxima because the GBS reads of 48 F1 cultivars were mapped to the C. maxima genome assembly for SNP discovery. Therefore, the GBS reads are deposited in the Sequence Read Archive (SRA) of NCBI (PRJNA633011) for a customized SNP identification with the C. moschata or C. pepo genomes. Our results contribute to developing a large SNP collection that is a useful resource to investigate genetic variations in three major pumpkin species.Next-generation sequencing (NGS) technologies have led to rapid SNP discovery and high-throughput genotyping. As an NGS-based method, genotyping by sequencing (GBS) is a cost-effective approach based on genome complexity reduction for identifying genome-wide SNPs1 cultivars used for GBS were separated into four clusters in both PCA and UPGMA dendrogram. Most of the accessions in each cluster were derived from C. maxima, C. moschata, C. pepo, or C. maxima × C. moschata populations. The pairwise Fst and Nei’s genetic distance also indicated significant genetic differentiation between these four populations. Similar genetic relationships between these Cucurbit species were also previously found using SSR markers31. In addition, the sub-division in the C. maxima × C. moschata accessions suggests that the interspecific hybrids used in this study are differentiated from two different genetic backgrounds. These results demonstrate that the SNP markers are a powerful tool to detect species-specific loci and/or alleles in discriminating Cucurbit species.A subset of 288 SNPs with ≥0.3 PIC values was used to genotype the 188 pumpkin accessions  in the Fluidigm assay. Of these, 224 SNPs (77.8%) showed clear polymorphism in this collection. With these SNP markers, the 223 pumpkin accessions including 48 FC. maxima (n = 73) and C. moschata (n = 63) populations displayed higher estimates of allelic richness, expected heterozygosity, and PIC than the C. pepo population (n = 45). In the study of Gong et al.30, the C. pepo accessions showed the highest levels of genetic diversity among these three species, despite having a smaller number of accessions . Cultivated C. pepo, which is known to have a great diversity of morphology, consisted of two subspecies: ssp.pepo  and ssp.ovifers 33. In the PCA analysis, our C. pepo accessions were tightly aggregated in a cluster relative to the other two species, suggesting that the C. pepo population represents few morphotypes including Zucchini. Therefore, it is possible that this discrepancy in genetic diversity of C. pepo is due to the sampling of accessions.Both 10. With NGS technologies and high-throughput genotyping platforms, genome-wide SNP markers have been widely used across numerous applications in crop species18. The core set of 192 SNP markers in this study were sufficient in detecting genetic variations for identification of all 85 pumpkin inbred accessions. For the collection of 138 commercial F1 cultivars, 119 accessions were identified using these SNP markers. The unidentified 19 F1 cultivars, which consist of 11 interspecific hybrids (C. maxima × C. moschata) and 8 intraspecific hybrids (4 C. maxima and 4 C. moschata), are likely to have narrow genetic bases. Kong et al.31 also found similar genetic backgrounds between the commercial F1 cultivars of C. maxima x C. moschata. A possible explanation for these reduced genetic bases is to use a few elite inbred lines as parents to develop different F1 cultivars in breeding programs. Therefore, variety identification in the F1 cultivars with high levels of genetic similarity is often challenging. Our study revealed that the 224 SNP markers identified six more interspecific hybrids relative to the 192 SNP markers, suggesting that increasing the number of SNP markers allows to us distinguish these F1 cultivars. Since we identified a total of 26,707 SNPs in the interspecific hybrid population using GBS, it is possible to find additional markers that are effective in detecting minimal genetic variations in unidentified F1 cultivars.To recognize breeder’s intellectual property rights, new varieties must satisfy three criteria in the plant variety protection (PVP) system: distinctness, uniformity and stability (DUS). The current DUS testing based on phenotypic evaluation involves laborious and time-consuming tasks. Therefore, a DNA-based system with molecular markers has been considered as an alternative to improve the efficiency and accuracy of DUS testingAlthough the core set of 192 SNP markers is a powerful tool for variety identification, their subsets can also be useful in providing additional options for genotyping with different platforms. Of the four subsets, the 96 and 48 SNP markers were able to identify 85.2% and 63.2% of the 223 pumpkin accessions, respectively. The other two subsets of 24 and 12 SNP markers showed low identification rates . This result indicates that the subsets of 96 and 48 SNPs are suitable for pre-screening tests using cost-effective genotyping platforms. The 24 marker set can also be sufficient when detecting species-specific genetic variations between intraspecific hybrids. Thus, these subsets of SNP markers are valuable resources for developing a DNA-based system for PVP in pumpkin.C. maxima, C. moschata, and C. pepo) using GBS and commercial F1 cultivars. These SNPs contribute to an expansion of genomic resources for both basic and applied researches in cultivated pumpkin. Our results also demonstrate that the core sets of SNP markers are useful for exploring genetic variations between and within the four pumpkin populations representing three species and interspecific hybrids (C. maxima × C. moschata). Furthermore, these SNP markers provide a rapid and accurate option for variety identification and facilitate development of a DNA-based system for DUS testing in the PVP system. Other applications of these markers include seed purity tests and background selection in breeding programs.In conclusion, a large collection of SNPs was generated for three major pumpkin species (1 cultivars was collected to identify genome-wide SNPs via genotyping by sequencing (GBS). This collection included 40 intraspecific hybrids of three species  and eight interspecific hybrids (C. maxima × C. moschata) derived from 25 seed companies (Table 1 cultivars and 94 inbred accessions (50 breeding lines and 44 landraces) for SNP validation . The final concentration of DNA was adjusted to 50 ng/μL for GBS and Fluidigm assay.Genomic DNA was extracted from fresh, young leaves using a modified cetyl trimethyl ammonium bromide (CTAB) method1 cultivars were prepared according to the protocol described by Elshire et al.26. The 200 ng of genomic DNA for each cultivar were digested using a methylation-sensitive restriction enzyme, ApeKI . After digestion, the DNA fragments were ligated to different barcode adapters that were assigned to each cultivar. These DNA samples were pooled and amplified by PCR to generate GBS libraries. The libraries were sequenced with the pair-end method in the HiSeq 2500 platform . For SNP calling, the filtered, high-quality sequencing reads were mapped to the C. maxima (Rimu) genome24 using the Burrows-Wheeler Alignment (BWA) method35 in the TASSEL-GBS pipeline36. The resulting bi-allelic SNPs with 5x of minimum depth were filtered based on >5% of minor allele frequency and <10% of missing data for further analysis.GBS libraries of 48 FTM system  in the 188 pumpkin accessions. The PIC value for each SNP was calculated using the following equation:n is the number of alleles and pi is the frequency of the ith allele37.A subset of SNPs was selected based on polymorphism information content (PIC) value and physical position on 20 chromosomes for SNP genotyping with the Fluidigm JunoFor the Fluidigm SNP genotyping, three types of primers were designed using the 300 bp flanking sequence of each SNP and the D3 Assay Design software . Both specific target amplification and locus specific primers were used for pre-amplification and two allele specific primers were used for PCR amplification in the Juno 96.96 Genotyping IFC (Integrated Fluidic Circuit). The resulting end-point fluorescence images were analyzed for SNP calling using the Fluidigm SNP genotyping analysis software v4.5.1.38 as implemented in R39. The Euclidean genetic distances were also calculated between pumpkin accessions with the dist function and hierarchical cluster analysis was then conducted using the unweighted pair group method with arithmetic mean (UPGMA) and the hclust function in R. The UPGMA dendrogram was visualized using the R package dendextent40. Pairwise Fst41 and Nei’s genetic distance were estimated between four predefined populations of the pumpkin collection using the Microsatellite Analyzer (MSA) software v4.0542. The P-value for the pairwise Fst was obtained from 10,000 permutations of genotypes and an applied Bonferroni correction. In addition, allelic richness (A)44 and expected heterozygosity (He)45 were calculated for the pumpkin populations using MSA.The genotypic data from both GBS and Fluidigm SNP genotyping were used to investigate genetic variations in the cultivated pumpkin germplasm Table . PrincipFigure S1Figure S2Figure S3Table S1Table S2


Solanum pimpinellifolium) and most distantly related (S. neorickii) species to cultivated tomatoes. Comparative analyses among the chloroplast genomes sequenced in this work and those available in GenBank allowed evaluating the variability of plastomes and defining phylogenetic relationships. A dramatic reduction in genetic diversity was detected in cultivated tomatoes, nonetheless, a few de novo mutations, which still differentiated the cultivated tomatoes from the closest wild relative S. pimpinellifolium, were detected and are potentially utilizable as diagnostic markers. Phylogenetic analyses confirmed that S. pimpinellifolium is the closest ancestor of all cultivated tomatoes. Local accessions all clustered together and were strictly related with other cultivated tomatoes (S. lycopersicum group). Noteworthy, S. lycopersicum var. cerasiforme resulted in a mixture of both cultivated and wild tomato genotypes since one of the two analyzed accessions clustered with cultivated tomato, whereas the other with S. pimpinellifolium. Overall, our results revealed a very reduced cytoplasmic variability in cultivated tomatoes and suggest the occurrence of a cytoplasmic bottleneck during their domestication.In various crops, genetic bottlenecks occurring through domestication can limit crop resilience to biotic and abiotic stresses. In the present study, we investigated nucleotide diversity in tomato chloroplast genome through sequencing seven plastomes of cultivated accessions from the Campania region  and two wild species among the closest ( Domestication of crops was one of the most complex and dynamic processes in plant evolution driven by humans, as it changed the distribution and frequency of plant species on the planet. Crop domestication, through natural or artificial selection, generally results in a reduction of genetic diversity and in the loss of many adaptive traits from wild relatives ,2. The aSolanum lycopersicum L.) is one of the most consumed vegetables in the world and belongs to the Solanaceae family, which includes species with a considerable economic importance  [Solanum is the largest and probably the most economically important genus, including both potatoes and tomatoes [S. lycopersicum is divided into two botanical varieties S. lycopersicum var. cerasiforme  and S. lycopersicum var. lycopersicum. Cherry tomato is native to the Andean region, but it also occurs in the subtropical areas and grows either as a true wild or cultivated species. For several years, cherry tomato has been considered an evolutionary intermediate between S. pimpinellifolium, the closest wild ancestor, and the cultivated S. lycopersicum. Recently, genetic studies [S. pimpinellifolium.Tomato (petunia) . Within tomatoes ,9. The otomatoes . The cul studies  found chS. lycopersicum var. lycopersicum derived from cherry tomato through a multiphases process of domestication [tication ,12. In ptication  assumed tication ,14,15,16tication ,18,19. Atication ,21,22.Although it has been widely demonstrated the potentiality of cytoplasmic markers to study crop evolution and assess cytoplasmic bottlenecks occurred during the domestication history of several crops  ,26,27,28S. pimpinellifolium and S. neorickii. Among Italian tomato accessions we selected the “Corbarino” landrace (processed tomato) characterized by obovoid fruits and moderate shelf-life, and six accessions belonging to the “Vesuviano” landrace (long shelf-life) characterized by hearth-shaped fruits with a pronounced pointed apex. Although they have the same place of origin, analysis based on nuclear single nucleotide polymorphisms (SNPs) showed a different clustering for some of these accessions [de novo mutations likely associated with the domestication and on the potential cytoplasmic bottleneck occurred during such a process.In this work, we reported the complete plastome sequences of seven Italian cultivated tomato accessions grown in the Campania region  and two wild species, namely cessions . The selSequencing of the nine tomato genotypes produced from 6.2 M (ves2001) to over 11.5 M (pol) high quality paired-end reads.S. neorickii 1  and smaller in S. pimpinellifolium 1 , with the exception of pgl (only one bp shorter), whereas plastome size in wild species was slightly larger in S. pimpinellifolium. All local accessions showed identical plastome sequences, with the exception of cor that differed for one point mutation in the exon 2 of the rpoC1 gene and pgl that was one bp shorter.Comparative analyses were performed in order to identify patterns of nucleotide variability among the tomato plastomes (the nine genotypes sequenced in this work and the twelve genotypes retrieved from GenBank). An overview of the nucleotide variability was shown in de novo mutations likely evolved before or after the domestication process, a comparative analysis was performed on the investigated tomato genotypes clustered into five groups: (1) the S. lycopersicum var. lycopersicum tomato commercial varieties IPA-6 and M82; (2) the seven local accessions from Campania region; (3) the S. lycopersicum var. cerasiforme cer1 and cer2; (4) the S. pimpinellifolium, S. pimpinellifolium 1 and S. pimpinellifolium 2, and (5) the wild including S. habrochaites, S. cheesmaniae and S. galapagense, phylogenetically closer to cultivated tomato than other wild species  and the S. pimpinellifolium groups, whereas other wild species showed either the reference or the alternative allele. It is very likely that these latter 90 SNPs have been fixed only in the phyletic lineage of wild S. pimpinellifolium and cultivated tomatoes. Only two SNPs distinguished the local accessions from the remaining ones, but 38 SNPs (invariable among the cultivated genotypes) were different between cultivated tomatoes and S. pimpinellifolium. Notably, the S. lycopersicum var. cerasiforme group (cer1 and cer2) showed either the reference or the alternative allele of these 38 SNPs with cer1 sharing the S. pimpinellifolium allele, whilst cer2 the cultivated one. This result suggests that these 38 SNPs evolved as de novo mutations after the separation of cultivated forms from wild S. pimpinellifolium but were already present in the ancestral domesticated gene pool (including the S. lycopersicum var. cerasiforme group) and only subsequently fixed in the cultivated S. lycopersicum var. lycopersicum and local accessions groups. The other five SNPs were common between local accessions, S. pimpinellifolium and wild groups, while the S. lycopersicum var. lycopersicum and the S. lycopersicum var. cerasiforme groups showed, respectively, the reference and either the reference or the alternative allele. By excluding cer1, in cultivated tomatoes seven SNPs  represent the only differences between plastomes of S. lycopersicum var. lycopersicum and local accessions.Notably, a high number of SNPs (271) was common between all five groups and different from distantly related wild species, thus being ancestral mutations evolved in the phyletic lineages including cultivated tomatoes. Ninety SNPs were common between the cultivated tomatoes , whilst the nucleotide variability observed in ycf1 was also reflected at the amino acid level. Interestingly, a SNP variation produced an amino acid change between the var. lycopersicum and the local accessions . As observed for SNPs, clustered heatmap of SSRs across grouped genotypes revealed a very low level of polymorphism (One hundred and fourteen simple sequence repeats (SSRs) were identified. The mononucleotide repeat (adenosine or thymine) was the most common type of microsatellite. Only four wild genotypes showed dinucleotide repeats . An exclusive number of repeat units in the local accessions group was detected in the ndhC-tRNA-Val (UAC) intergenic region, while a number of repeat units exclusive of S. lycopersicum var. lycopersicum group was found in the psbE-petL intergenic region , being shared by all the analyzed genotypes as compared with the most distantly related wild species not included in the wild group; six SSRs have the same number of repeat units both in c region . Interest number . A complt number b.ndhF-rpl32 intergenic region that allowed distinguishing local accessions group from other tomato landraces and that was also confirmed by the electrophoresis profiles (data not shown).Among the in silico identified microsatellites, eight SSR loci with small variation in the number of repeat units were experimentally tested to verify the correct estimation of their length. No variation in the number of repeat units was detected both in silico and in the electrophoresis profiles in a representation of the nine genotypes sequenced in this work and in a large dataset including additional local accessions and processed/fresh market tomatoes  confirming the absence of SSR variation within and among cultivated tomatoes. A notable exception was the one basis difference found in the microsatellite located in the rps16 and rps4 genes (de novo TRs could be identified. A TR located in the tRNA-Gln (UUG)-psbK intergenic region was the only one to be found variable among species , S. lycopersicum var. cerasiforme (cer1 and cer2), S. pimpinellifolium 2, and the remaining wild species had two copies (de novo duplication of four bases motif (ATAA)2, exclusive of the local accessions, was scored by MSA  were found, with cultivated species displaying a lower TR number when compared with wild species a. The ids4 genes b. The TRs4 genes c. TRs co species . Particuo copies . Interesd by MSA .S. tuberosum cv. Désirée, DQ386163) as an outgroup, showed two main clades with strong bootstrap support  with S. pennellii as the basal species. The other clade is further separated into several subclades. In particular, the group that included the seven local accessions from the Campania region was closely related to a cluster populated by other cultivated varieties . As expected, all cultivated genotypes were more closely related to the clade comprising the two S. pimpinellifolium accessions and cer1. The remaining wild species  were in a separate clade. Finally, the phylogenetic analysis confirmed the admixed nature of S. lycopersicum var. cerasiforme as cer1 was closely related to the wild species (S. pimpinellifolium 1 and 2), while cer2 was part of the cultivated genotypes clade (M82 and IPA-6).Phylogenetic tree inferred from the complete plastomes of the twenty-one tomato genotypes using the potato chloroplast genome  due to the domestication process . Indeed,Landraces are locally adapted cultivars that are gaining increasing attention considering their typical traits  ,36,37,38de novo TRs were identified in our survey.Comparative analyses of the nine plastomes sequenced in this work and of twelve plastomes retrieved from GenBank allowed both to evaluate the extent of the genetic bottleneck on the tomato chloroplast genome and define phylogenetic relationships among wild and cultivated accessions. For these aims, SNPs and SSRs were revealed to be more informative than TRs since no specific TR for cultivated tomato genotypes, or S. lycopersicum var. cerasiforme group is remarkable, namely, the two analyzed accessions showed a different behavior. Collected data and phylogeny clearly highlighted higher variability in cer1 compared with cer2 and suggest that although cer1 belongs to cerasiforme group, probably it was not subjected to the domestication process and can be considered as “wild” cultivated accession.Very low cpDNA variability was detected in tomato varieties with respect to that observed in wild species, thus indicating the occurrence of a very strong cytoplasmic bottleneck during domestication. The number of SNPs in wild species is 24-fold higher than in cultivated tomatoes (389 polymorphic SNPs out of 454 (86%)), while SSRs were slightly lower (49 polymorphic SSRs out of 114 (43%), 4-fold those observed in tomato varieties). The heterogeneous nature of the Detected levels of plastome variability are consistent with the extensive genetic erosion of cultivated tomato, especially in the light of the large diversity observed across wild relatives . SimilarOnly 16 out of 454 SNPs were found polymorphic among cultivated tomato genotypes (3.5%). Comparable results were found in pepper varieties, where only the 4% of the scored SNP loci were polymorphic in cultivated accessions .Similarly, only 12 out of 114 identified SSRs were polymorphic among cultivated tomato genotypes (11%). Comparable results were reported in cultivated barley showing one polymorphism out of seven analyzed SSRs (14%)  and peppAs previously argued, genetic bottleneck at the nuclear level may not be reflected at the cytoplasmic level. An extreme cytoplasmic bottleneck has been previously hypothesized in cultivated potato by the analysis of SSR markers but no decreased levels of nuclear SSR diversity were recorded ,39. On tSNP arrays on some tomato cultivars, partially shared with this work , revealed a reduced nuclear genetic diversity .S. pimpinellifolium  and only seven de novo SNPs and two de novo SSRs were different between S. lycopersicum var. lycopersicum and local accessions groups. All analyzed local accessions showed identical cpDNA sequences suggesting that these accessions have a unique domestication origin and that their cytoplasm has evolved monophyletically from the founder tomato gene pool, rather than representing an independent introduction. Still, the local accessions have distinctive sequences from the other commercial tomatoes  excluding multiple independent selections of the obovoid fruits (Corbarino) or the hearth-shaped fruits with a pronounced pointed apex (the remaining accessions).Concordantly, the cpDNA analyses suggest an extreme low cytoplasmic variability of the founding cultivated tomato population. Indeed, cultivated varieties shared 361 out of 454 SNPs (79%) and 74 out of 114 SSRs (65%) with the ancestor S. pimpinellifolium 1 and 2. These differences could be due to natural variability among accessions and/or possible errors in the sequencing/assembly procedure. The former hypotheses, however, is supported by differences also observed among other related wild species . Thus, the significant reduction in cpDNA variability found in the cultivated tomato gene pool can be directly ascribed as a consequence of the domestication process rather than to an already occurred loss of genetic variation in the closest wild relative, S. pimpinellifolium. Therefore, the present study suggests that a severe ‘cytoplasmic bottleneck’ occurred during the domestication of tomato, as has been reported in other crops: barley [In this work, we also detected plastome variability between wild : barley , lentil : barley , onion [: barley , and pot: barley .S. pimpinellifolium was supported by phylogeny. Species belonging to the Lycopersicon group  [A strict relationship between cultivated tomato varieties and the ancestor pagense)  form a wpagense) .S. lycopersicum var. lycopersicum and cer 2. On the contrary, some accessions  were grouped in different clusters based on nuclear SNP genotyping [S. pimpinellifolium accessions, thus plastome diversity analysis confirmed the mixed nature of S. lycopersicum var. cerasiforme as previously observed with the analysis of nuclear variability [In particular, all local accessions clustered together in a subgroup with notyping . Noteworiability ,11,50,51de novo mutation. Although low variation is the rule in tomato cpDNA, few plastid regions have been identified that might be exploited as diagnostic markers: two de novo SNPs, one SSR and a short sequence duplication (ATAA)2 were exclusive of all local accessions, whereas, one SSR was typical of all the var. lycopersicum group.The observed low variability of the cultivated tomatoes chloroplast genome can be explained by taking into account both the genetic bottleneck during their domestication and its low mutation rate. Notably, comparison of the plastome sequences of the two modern tomato varieties IPA-6 and Ailsa Craig, the former bred in South America and the latter in Europe, resulted in identical cpDNA sequences, thus demonstrating the stability of plastome in tomato cultivars over a period of at least a few hundred years of separation  without ycf1 (showing both synonymous and non-synonymous mutations) and ndhH. Both these genes have been proposed as tools to resolve the phylogenetic relationships among closely related genera and species [ycf1 was found variable even within cultivated plastomes leading to amino acid change  and S. neorickii  were sampled for chloroplast isolation, DNA extraction and sequencing. Drs. M.S. Grillo and S. Grandillo from the CNR, Institute of Bioscience and BioResources, Portici, kindly provided the seeds.Seven Italian cultivated tomato accessions grown in the Campania region , Corbarino (cor) landrace, and six accessions belonging to the “Vesuviano” landrace, Pollena (pol), PDS (pds), Vesuvio 2001 (ves2001), Vesuviano foglia riccia (vfr), Vesuviano pizzo (vpz), Piennolo giallo (pgl), and two wild tomato species, Plants were kept in the dark for 48 h before harvesting to reduce starch accumulation. Fresh leaves (15–25 g) were collected and used for chloroplast isolation with discontinuous sucrose gradient . Purifiehttp://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Then, the fastq_quality_filter utility from the FASTX-toolkit (http://hannonlab.cshl.edu/fastx_toolkit/) was used to remove sequences with a quality score equal or lower than 30 in more than 90% of the read length. Illumina technical sequences were removed by using Trimmomatic v.0.32 [S. lycopersicum cv. IPA-6 (AM087200) was used as reference. Contigs were ordered and oriented by using ABACAS [https://chlorobox.mpimp-golm.mpg.de/geseq.html). Gene annotations were manually curated using S. lycopersicum cv. IPA-6 (AM087200) annotations as reference. Chloroplast genome sequences and annotations produced in this study can be found in GenBank under accession numbers MT811790-MT811798.DNA samples were sequenced using the GA II Illumina sequencer (2 × 75 paired-end reads with an estimated inset size of 400 bp). Quality check on raw reads was performed using FastQC v.0.11.2 (c v.0.32 . Referenc v.0.32  with a kg ABACAS  for the g ABACAS  with defg ABACAS . The annhttps://github.com/sanger-pathogens/snp-sites). Such a tool extracts SNPs from a multiple sequence alignment using the cpDNA of S. lycopersicum cv. IPA-6 as reference sequence. SNP annotation was manually curated.Single nucleotide variants (SNVs) were identified using the snp-sites tool (http://pgrc.ipk-gatersleben.de/misa/) was run to identify microsatellites (SSR) using the unit_size/min_repeats parameters as follows: 1/8, 2/6, 3/5, 4/5, 5/5, 6/5. The Tandem Repeat Finder web tool accessible at https://tandem.bu.edu/trf/trf.basic.submit.html was used to detect perfect tandem repeats with default settings.The microsatellite (MISA) identification tool (http://frodo.wi.mit.edu/primer3/). The primer size was set from 18 to 25 bp, the Tm ranged from 51 to 59 °C and the other parameters were set as default  as reference. To highlight differences among nucleotide sequences of plastomes, MSA were visualized using the NCBI Multiple Sequence Alignment Viewer available at https://www.ncbi.nlm.nih.gov/projects/msaviewer/.Multiple sequence alignments (MSA) were generated using MAFFT version 7  with defS. tuberosum cv. Désirée (DQ386163) was used as the outgroup. The ML tree was visualized with FigTree v.1.4.2 (http://tree.bio.ed.ac.uk/software/figtree/).RAxML  was usedS. lycopersicum cv. IPA-6 (AM087200), eleven tomato genotypes available in GenBank: S. peruvianum (KP117026), S. chilense (KP117021), S. neorickii , S. pennellii (HG975452), S. habrochaites (KP117023), S. galapagense (NC_026878), S. cheesmaniae (NC_026876), S. pimpinellifolium , S. lycopersicum , and S. lycopersicum var. cerasiforme  were retrieved for comparative analyses. Heatmaps were generated using Morpheus (https://software.broadinstitute.org/morpheus). Single-linkage hierarchical clustering on both rows and columns was based on the metric “Euclidean distance”.In addition to


In genome-wide association studies, detecting high-order epistasis is important for analyzing the occurrence of complex human diseases and explaining missing heritability. However, there are various challenges in the actual high-order epistasis detection process due to the large amount of data, “small sample size problem”, diversity of disease models, etc. This paper proposes a multi-objective genetic algorithm (EpiMOGA) for single nucleotide polymorphism (SNP) epistasis detection. The K2 score based on the Bayesian network criterion and the Gini index of the diversity of the binary classification problem were used to guide the search process of the genetic algorithm. Experiments were performed on 26 simulated datasets of different models and a real Alzheimer’s disease dataset. The results indicated that EpiMOGA was obviously superior to other related and competitive methods in both detection efficiency and accuracy, especially for small-sample-size datasets, and the performance of EpiMOGA remained stable across datasets of different disease models. At the same time, a number of SNP loci and 2-order epistasis associated with Alzheimer’s disease were identified by the EpiMOGA method, indicating that this method is capable of identifying high-order epistasis from genome-wide data and can be applied in the study of complex diseases. A genome-wide association study (GWAS) involves the examination of genetic variations in a given genome to identify genetic variations associated with a phenotype. This type of study has become a powerful tool for detecting single nucleotide polymorphisms (SNPs) and has detected a number of single SNPs associated with complex diseases . HoweverHowever, there are several challenges associated with detecting high-order epistasis on a whole-genome scale. First, detection of high-order epistasis is the process of screening a high-dimensional search space constituted by SNP data, which entails an enormous computational burden. In particular, with the rapid development of high-throughput sequencing technology, a large amount of SNP data has been obtained, resulting in a geometric increase in the computational amount of the detection process. At the same time, the large amount of SNP data also creates a “small sample size problem”: the number of samples contained in a GWAS dataset is much smaller than the number of SNPs . In addiIn the face of these challenges, a variety of epistasis detection methods have been proposed in recent years, the theoretical basis of which presents the situation of a diversified development of statistics theory, informatics theory, etc. In statistics, logistic regression analysis is the most basic epistasis detection method . AlthougExhaustive methods verify all possible SNP combinations in the data set, which can effectively avoid the omission of epistasis detection, but require a massive amount of computation. BOOST is a classic multistage exhaustive approach that defines SNP epistasis through logistic regression and divides the entire detection process into search and filter phases . The effSearch algorithms include stochastic methods and heuristic searches. Stochastic methods are mostly realized by random sampling and a probability calculation. For example, BEAM detects suspected SNPs and their interactions via a Bayesian partitioning model and computes the posterior probabilities of the candidates belonging to true-associated SNPs and epistasis via Markov Chain Monte Carlo (MCMC) sampling . A heuriMachine learning methods are also able to detect the epistasis of SNPs, such as the support vector machine , random Most of these studies show the potential preference of disease models or perform poorly in small sample size problems. Therefore, this paper proposes a multi-objective epistasis detection method based on a genetic algorithm, called the multi-objective genetic algorithm (EpiMOGA). In this approach, multi-objective optimization is applied to the fitness function in the genetic algorithm and multiple candidate solutions are searched to solve the complex pattern optimization problem. We verified the performance of EpiMOGA in both simulation data and a real dataset and compared the results with some representative methods, including FDHE-IW , BOOST , Epi-GTBTo compare the performance of the EpiMOGA method in different disease models and characteristics, GAMETES  was used2), minor allele frequency (MAF) and prevalence (P(D)). The heritability h2 is the proportion of the population variance due to genetic differences  = TwoScore01),y); 05: end06: For each i, j = 1:t07: If<objvalue1(i))&&<objvalue2(i)))||<objvalue1(i)) &&==objvalue2(i)))||==objvalue1(i))&&<objvalue2(i))) 08:     objvalue(j) = objvalue(j) + objvalue(i); 09:     objvalue(i) = 1; 10:     break; 11:    end12: end01: In the optimized genetic algorithm, different individuals in the population (candidate solutions) can be divided into two sets without superposition according to the Pareto optimal method: a dominant solution set and a nondominant solution set. The fitness function generally uses the numerical form to evaluate the merits of a solution. The higher the fitness value is, the better the solution. Therefore, in the EpiMOGA method, the nondominant solution was regarded as the better individual and the fitness value was determined according to the number of solutions that it dominated. Furthermore, the fitness value of the dominant solution was uniformly set to 1. These is the pseudocode of the fitness function calculation in EpiMOGA in Algorithm 2.The screening of candidate sets was also based on the K2 score and the Gini score. According to the Pareto optimal method, the candidate sets obtained from multiple searches were divided into nondominant solution sets and dominant solution sets. Among them, the nondominant solution set was output as the elite set and became the final result of the EpiMOGA method.In a GWAS, we generally regard that disease-related SNP combinations are positive and disease-unrelated SNP combinations are negative, leading to an imbalanced problem in the GWAS dataset exemplified by the existence of a much larger number of negatives than positives. For example, a simulated dataset that is made up of 100 SNPs can produce In the simulation experiment, we choose 1. 2. The In the simulation experiment, to compare the performance of the methods in different situations, we designed 3 simulation experiment cases that included 26 simulation datasets. Taking 2-order epistasis detection as an example, EpiMOGA was compared with other comparative epistasis detection methods  in terms of detection efficiency given by We used the three DME introduced in the materials section to generate 15 datasets with sample sizes of 200, 400, 600, 800 and 1600 when MAF = 0.1, which were used to analyze the influence of the disease model and sample size on the performance of methods. In the DME1 dataset with a sample size of 400, the detection accuracy of EpiMOGA was 0.09 lower than that of the BOOST method, but the detection efficiency of EpiMOGA was more than ten times that of the BOOST method. Therefore, we can see that EpiMOGA is obviously more suitable for this dataset compared to the BOOST method.At the same time, EpiMOGA also performs well in datasets with large sample sizes. Campared with Epi-GTBN, we found that if two method have similar value on one objective, Epi-GTBN is more likely to be worse than EpiMOGA on the other. As an example, for the DME2 dataset with a sample size of 800, the two index values of EpiMOGA and Epi-GTBN are shown in In In summary, compared with other methods, EpiMOGA significantly optimized the detection performance on small datasets of three different disease models. In larger sample sizes, EpiMOGA maintained a good and stable detection performance.In the same disease model DME2, 12 simulation datasets with MAF set to 0.05, 0.1, and 0.2 and the sample size set to 200, 400, 600, and 800 were generated to compare the performance of different characteristics. In In Three simulation datasets with an MAF of 0.2 and heritability of 0.01, 0.05, and 0.1 were generated to compare the performance of different methods on DNME. In We also have a simulation experiments case about the number of SNP loci and the details about this experiments case can be found in p-values were less than 0.001, SNP combinations were retained. Subsequently, a total of 48 pairs of SNPs were obtained, including 58 SNP loci. The EpiMOGA method was used to perform 2-order epistasis detection on Alzheimer’s disease data. After searching and filtering by the EpiMOGA method, 89 2-order SNP pairs were output as the final result. In further analysis, Chi-squared tests were performed on 89 pairs of SNPs; if their In cAMP) response element binding protein (CREB) 5 gene. CREB signaling plays a major role in long-term memory formation. Defective CREB signaling underlies impaired hippocampal neurogenesis and cognitive deficits in Alzheimer’s disease [ARMCX5-GPRASP2 gene, which is also known as P60TRP. The protein coded by this gene was initially identified to be downregulated in the temporal lobe of brains in Alzheimer’s disease patients [DLGAP1 gene, which is also called GKAP or SAPAP1, and rs6530517 is a G/T SNV variation in the FRMPD4 gene, while encoded protein FRMPD4 (FERM and PDZ domain-containing 4) is a neural scaffolding protein [MIGA) 2 gene, which is a mitochondrial protein encoding gene. Experiments show that the loss of a MIGA leads to mitochondrial defects and neurodegeneration and results in fragmented mitochondria [CACNA2D3 gene. Studies have shown that CACNA2D3 is associated with cognitive ability and intelligence and is a predisposing risk factor for Alzheimer’s disease [Among the 58 SNP loci, 31 SNPs were in the coding region and 27 SNPs were in the noncoding region. Some of genes have been reported to be related to Alzheimer’s disease. For example, rs42733 is an A/G SNV variation in the coding cyclic adenosine monophosphate  protein complex, a main player for learning and memory in the brain [LARGE gene result in severe intellectual disability and affect learning and memory, which is consistent with the clinical symptoms of Alzheimer’s disease. rs12395602 is a variant loci of the gene CNKSR2. CNKSR2 is highly expressed in the brain and is known to play a role in synaptogenesis [CNKSR2 causes intellectual, attention, and language deficits [GRID2 gene. The coding protein GRID2 is considered to be a suppressor in neurodegeneration [Several genes are also related to the clinical symptoms of Alzheimer’s disease. rs7358822 is an SNV variation in the  neurons . Mutatiohe brain . Mutatioogenesis . Absent deficits , which adeficits . In addineration . TherefoMTUS2 gene, which is also called CAZIP. One study suggested a role of CAZIP in the development and function of the nervous system in vertebrates [TIAM-1 gene. TIAM-1 has been implicated in the development of the central nervous system [GTPBP6 gene. One study showed that the overexpression of GTPBP6 is negatively correlated with speech cognition and has a causal relationship with neural development and speech function [SHROOM2 gene. The influence and clinical phenomenon of SHROOM2 missense have not been found in the existing literature. In the test results, SHROOM2 showed an epistatic role with other Alzheimer’s disease-related genes. Therefore, we speculate that MTUS2, GTPBP6, TIAM-1 and SHROOM2 are likely associated with Alzheimer’s disease.In addition, rs12870203 is a variant on the tebrates . rs21863s system  and conts system . The decfunction . rs59337p-values from Chi-squared tests, including SNP loci information, p-value of a single SNP site, p-value of 2-SNP and the prediction accuracy of the support vector machine (SVM). By comparing the p-values, we can see that the p-value of 2-SNP was far less than that of a single SNP, indicating that epistasis does exist. Moreover, we can see from the last column that the SVM prediction accuracies of these 2-SNP combinations were approximately 70%, which may be worth study for biologists. (Additional information about the 3-order epistasis detection on Alzheimer’s disease data can be found in the In summary, the results of EpiMOGA detection in the dataset on Alzheimer’s disease were reliable and had a certain biological significance.A genome-wide association analysis is mainly used to detect the correlation between a single SNP locus and a phenotype . The resEpiMOGA is an epistasis detection method that completes a spatial search through a genetic algorithm and carries out screening based on a multi-objective function. The K2 score based on the BN criterion  and the Experimental results in simulated datasets and an Alzheimer’s disease dataset showed that EpiMOGA is an effective method for epistasis detection. Compared with other comparative methods, EpiMOGA can maintain good detection performance with balanced detection efficiency and detection accuracy in datasets with different characteristics and disease patterns. At the same time, EpiMOGA also shows a strong advantage in small datasets, in which both of the evaluation criteria are obviously optimized. Under the dilemma of a small sample size problem caused by a large number of SNP data, EpiMOGA has a more extensive application.EpiMOGA also has some limitations, such as the dependence on parameters. In EpiMOGA, increasing the number of searches can effectively reduce the impact of the initial population and the risk of local optimization. However, as the number of searches increases, the run time also increases. In simulation experiment case 2, we observed that the detection efficiency of EpiMOGA remained above 90% but showed a decreasing trend with the increase in MAF. One reason for this result is the effect of the parameter settings on the efficiency of the method. Therefore, to improve the performance of EpiMOGA, it is necessary to select different and appropriate parameters when detecting epistasis in different datasets.In the analysis of the dataset on Alzheimer’s disease, we found that a number of SNP sites in the noncoding genomic region were recognized. The function of SNP in the noncoding genomic region should be a part of future research, as it can be conducive to research on complex diseases. The other important future direction is determining how to add prior knowledge to the method to accelerate the search process.


Analyses of gut microbiome composition in livestock species have shown its potential to contribute to the regulation of complex phenotypes. However, little is known about the host genetic control over the gut microbial communities. In pigs, previous studies are based on classical “single-gene-single-trait” approaches and have evaluated the role of host genome controlling gut prokaryote and eukaryote communities separately.P < 0.05) and 738,913 connections (SNP-to-SNP co-associations). Our findings highlight the complexity and polygenic nature of the pig gut microbial ecosystem. Prominent within the network were 5 regulators, PRDM15, STAT1, ssc-mir-371, SOX9 and RUNX2 which gathered 942, 607, 588, 284 and 273 connections, respectively. PRDM15 modulates the transcription of upstream regulators of WNT and MAPK-ERK signaling to safeguard naive pluripotency and regulates the production of Th1- and Th2-type immune response. The signal transducer STAT1 has long been associated with immune processes and was recently identified as a potential regulator of vaccine response to porcine reproductive and respiratory syndrome. The list of regulators was enriched for immune-related pathways, and the list of predicted targets includes candidate genes previously reported as associated with microbiota profile in pigs, mice and human, such as SLIT3, SLC39A8, NOS1, IL1R2, DAB1, TOX3, SPP1, THSD7B, ELF2, PIANP, A2ML1, and IFNAR1. Moreover, we show the existence of host-genetic variants jointly associated with the relative abundance of butyrate producer bacteria and host performance.In order to determine the ability of the host genome to control the diversity and composition of microbial communities in healthy pigs, we undertook genome-wide association studies (GWAS) for 39 microbial phenotypes that included 2 diversity indexes, and the relative abundance of 31 bacterial and six commensal protist genera in 390 pigs genotyped for 70 K SNPs. The GWAS results were processed through a 3-step analytical pipeline comprised of (1) association weight matrix; (2) regulatory impact factor; and (3) partial correlation and information theory. The inferred gene regulatory network comprised 3561 genes (within a 5 kb distance from a relevant SNP–Taken together, our results identified regulators, candidate genes, and mechanisms linked with microbiome modulation by the host. They further highlight the value of the proposed analytical pipeline to exploit pleiotropy and the crosstalk between bacteria and protists as significant contributors to host-microbiome interactions and identify genetic markers and candidate genes that can be incorporated in breeding program to improve host-performance and microbial traits.Video AbstractThe online version contains supplementary material available at 10.1186/s40168-020-00994-8. The gut microbiota is a diverse ecosystem predominantly dominated by bacteria, but other microorganisms such as fungi and protists are also present. Influenced by the host immunity and environmental factors such as age, diet, and geography, the gut microbiota is known to be involved in many physiological functions as well as in disease pathogenesis  , 14 to gAnimal care and experimental procedures were carried out following national and institutional guidelines for the Good Experimental Practices and were approved by the IRTA Ethical Committee.).The pigs employed in this study are a subset of those reported in Ramayo-Caldas et al. (2020) . In brie1yijk corresponds to the microbiome phenotype under scrutiny of the i-th individual animal of sex j in the k-th batch; sexj and bk correspond to the systematic effects of j-th sex (2 levels) and k-th batch (7 levels), respectively; ui is the random additive genetic effect of the i-th individual, collectively distributed as u ~ N where G is the genomic relationship matrix calculated using the filtered autosomal SNPs based on the methodology of Yang et al. (2011) [sli is the genotype  for the l-th SNP of the i-th individual, and al is the allele substitution effect of the l-th SNP on the microbiome phenotype being analyzed. Following [The Porcine 70 K GGP Porcine HD Array  was used to genotype 390 out of 405 animals. We excluded single nucleotide polymorphisms (SNPs) with minor allele frequencies < 5%, rates of missing genotypes above 10%, as well as SNPs that did not map to the porcine reference genome (Sscrofa11.1 assembly). Then, to identify SNPs from the host genome associated with the alpha diversity as well as bacteria and protists relative abundances, a series of 39 GWAS were performed between 42,562 SNPs and the alpha diversity or the centered log ratio (clr) transformed genera abundance. For the GWAS, we used the GCTA software  using th. (2011) ; sli is ollowing  and withollowing , FDR wasP is the P value tested, A is the number of SNP that were significant at the P value tested, and T is the total number of SNP tested. For further analyses and in order to allow for direct comparison across phenotypes, estimated SNP effects were standardized by dividing them by the standard deviation of all SNP effects.Where We used the AWM methodology , 14 in cUsing a population of 20 European pig breeds, Muñoz et al. (2019)  reportedUsing the alpha diversity indexes for bacteria and protists as key phenotypes, we selected those SNP-Gene associated with alpha diversity and namely diversity-associated SNPs.NA, and selected the remaining SNP-Genes associated (P < 0.05) to more than NA phenotypes and referred to as pleiotropic SNPs.We captured the average number of phenotypes to which the diversity-associated SNPs were associated with, namely We used the RIF algorithm  to identTo undertake the RIF analysis, we considered the diversity-associated SNPs and the pleiotropic SNP as potential targets of all the SNP-Genes annotated as either transcription factors (TF) or microRNA genes (miRNA). The RIF algorithm is designed to detect loci with high regulatory potential in a set of loci, TFs, and microRNA genes in our case, while contrasting two biological conditions or groups, such as bacteria and protists in our case. The analysis makes use of two metrics: RIF1 and RIF2. While RIF1 prioritizes regulators that are consistently the most differentially co-associated with the highly associated potential target genes, RIF2 highlights regulators with the most altered abilities to predict the association of potential target genes.P value  used to ascertain significance, PCIT is an information theoretic approach. Its threshold is an informative metric; in this case, the partial correlation after exploring all trios in judging the significance of a given correlation, which might then become a connection when inferring a network. It thereby tests all possible 3-way combinations in a dataset and only keeps correlations between loci if they are significant and independent of the expression of another locus, whereas no hard threshold is set for the correlation strength. The significance threshold for each combination of loci depends on the average ratio of partial to direct correlations. Gene interactions were predicted using correlation analysis of the SNP effects across pairwise rows of the AWM. Hence, the AWM-predicted gene interactions are based on significant co-association between SNP. In the network, every node represents a gene (or SNP), whereas every edge connecting two nodes represents a significant gene–gene interaction (based on SNP–SNP co-association). Finally, the Cytoscape software [To identify significant gene–gene interactions, we used the partial correlation and information theory (PCIT) algorithm  which casoftware  was usedsoftware  was usedPRDM15) and the signal transducer and activator of transcription 1 (STAT1), a TF-binding motif search was implemented in the promoter region of the predicted target genes . Transcription factor binding motifs for PRDM15 and STAT1 genes were retrieved from JASPAR database v.2020 [P value below 10− 4 were considered significant and retained for further analyses.To further investigate the alleged role of the most relevant TFs, i.e., the PR/Set domain 15 . In order to obtain a putative list of targeted mRNAs by ssc-miR-371, the seed region of the miRNA (2nd to 8th 5′ nucleotides of the mature miRNA) was reverse complemented and miRNA binding sites comprising perfect sequence matches between the seed and the retrieved 3′-UTRs (7mer-m8 sites) were assessed by using the locate tool from the SeqKit toolkit [Following the rationale for detecting putative functional interactions among the set of key regulators identified by the AWM approach, we aimed at determining whether miRNA-mRNA significant co-associations found for relevant miRNA regulator genes were suggestive of an interaction between the miRNA and their co-associated mRNA transcripts. In this way, the ssc-miR-371 gene was among the top regulatory factors according to RIF metrics. The mRNA genes harboring SNPs significantly co-associated with the polymorphism within miR-371  gene were retrieved and their 3′-UTR sequences according to the Sscrofa11.1 assembly annotation in Ensembl repositories  were dow toolkit . The pot toolkit .P value thresholds  across the 39 phenotypes that were subjected to GWAS. The results highlight the trade-off that exists between significant SNP (the higher the better) and the FDR (the lower the better) as P values become more stringent. On average across the 39 phenotypes, the number of significant SNP (average FDR in brackets) at P values < 0.05, 0.01, and 0.001 was 4015.3 (FDR = 50.6%), 272.8 (FDR = 16.5%), and 87.6 (FDR = 5.8%), respectively. At the most stringent P value threshold of < 0.001, the highest number of significant SNP  was obtained for the abundance of Faecalibacterium. On the other extreme, the lowest number of significant SNP  was obtained for the abundance of Catenibacterium. Details of the genome map position and strength of the statistical association of the most significant SNP in each of the 39 phenotypes are given in Table SORCS2 (SNP rs320095924) and one in the coding region of TRIM2 (rs329143797).Table P < 0.05) with any of the phenotypes and their relevance would have been overlooked by GWAS alone. For the remaining 3551 genes, the number of associated phenotypes ranged from 1 to 13 and averaged 3.79. A total of 84.08% of the SNPs mapped within genes and the 15.92% were located upstream/downstream of annotated genes. Correlations between microbial traits were calculated using AWM columns (standardized SNP effects across bacteria and protists diversity and abundance) and were visualized as a hierarchical tree cluster, in which strong positive and negative correlations are displayed as proximity and distance, respectively . The network is characterized by a large central module where most of the bacteria vs. protists crosstalk takes place, surrounded by lots of smaller modules mostly phenotype specific. Reflecting the pleiotropic nature of the AWM, while the key phenotypes to capture SNP-Genes were the bacteria and protist alpha diversities, these only represent a minority of the genes: 241 for bacteria alpha diversity (dark blue nodes) and 231 for protist alpha diversity (bright red nodes). In contrast, other bacteria abundance was captured by 2568 genes (light blue nodes), while other protist abundance phenotypes were captured by 521 genes (orange nodes).PRDM15, STAT1, ssc-mir-371, SOX9, and RUNX2 which gathered 942, 607, 588, 284, and 273 connections, respectively . Regarding the microRNA ssc-mir-371, a total of 155 binding sites were identified (Supplementary Table 3), comprising a total of 71 different mRNA genes . The search for background random miRNA-binding sites in the reverse complemented 3′-UTRs gave a total of 115 different binding sites (Supplementary Table 3), comprising 48 different mRNA genes, i.e., the expected number of miRNA 7mer-m8 binding sites for ssc-miR-371 seed was increased by 1.48 folds compared with background random miRNA-mRNA interactions. When we assessed the putative structural consequences of the presence of the rs320008166 (n.59 T > C) mutated allele, a reduction in the minimum free energy (MFE) of the folding of the precursor miRNA hairpin was highlighted. More specifically, the presence of the alternative C allele at position 59th of the precursor region of the miRNA implied the stabilization of a G:U wobble pairing in the wild-type miRNA sequence, introducing a stable canonical Watson-Crick G:C pairing. While the miRNA hairpin carrying the T allele had a MFE = − 35.44 kcal/mol, the presence of the T allele implied an estimated MFE = − 37.74 kcal/mol. Overall, in agreement with AWM original publication [The RIF analyses identified 47 key regulators among genes selected in the AWM procedure, and these are listed in Table response . Similarresponse . On the se [SOX9  and RUNX9 [RUNX2 , respectsyndrome . It shoulication , here wer = 0.571; P value < 0.0001), suggesting that both metrics are indicators of the pluripotential capacity of the regulators. This finding is of relevance because while pleiotropy was computed from the number of significant phenotypes in the GWAS, the number of connections is a feature of the connectivity in a co-association network. Two vastly different concepts which, when pointing to the same outcome, underscore the relevance of, in this case, regulators. Similarly, while RIF1 and RIF2 scores are moderately correlated , only RIF2 is significantly correlated with pleiotropy  and more significantly with connectivity . This relationship, to our knowledge, never before documented, indicates the ability of RIF2 scores to prioritize regulators that, in our case study, have an uncanny ability to address the ‘bacteria vs. protists’ crosstalk by differentiating between genes associated with bacterial traits from those associated with protists phenotypes, i.e., the contrast used in developing RIF. To further explore ‘bacteria vs. protists’ crosstalk, Fig. A closer inspection of values in Table Prevotella and Mitsuokella clustered together and distantly of a second cluster that includes Treponema and Ruminococcus  as well as the known co-occurrence patterns of the pig gut microbiota . For inscus Fig. . We alsoPRDM15 was among the genes commonly identify by [PRDM15 was associated with the highest number of traits  revealed the identity of predicted target genes and the higher complexity and polygenic nature of the diversity and composition of the pig gut microbial ecosystem. It is worth noting that literature mining confirms the association between the pig host-genome and the relative abundance of six bacterial genus reported by Crespo et al. 2019 (Supplementary Table 4). Furthermore, our findings also confirm association of 27 of the 68 genes recently reported by  as linkentify by . As prevts Table  and the nd mouse –39, seveSLIT3, reported in the UK Twins Dataset [Clostridiaceae [SLC39A8, a gene with a pleiotropic missense variant related with Crohn’s disease and the composition of human gut microbiome [NOS1, for which a pleiotropic association with body fatness and gut microbiota composition in mice had been shown [CSMD1, ZFAT, FRMPD1, CLEC16A, IL1R2, BANK1, PRKAG2, LHFPL3, ST5, and NXN) and gut bacterial abundance  in mice [NEBL, ASAP3, ABLIM1, CUEDC2, PRRC2C, DENND1A, LAMC1, MAL2, ITGB1, CAST, A2ML1, IL7R, PCDH7, NFATC2IP, SORCS2, and DNM3) were also found in our study. Furthermore, genes linked to the functional profiles of the human gut ecosystem  were among the predicted target genes in our network. As previously mentioned, SORCS2 was reported as linked to a plant-derived steroid degradation pathway. Meanwhile, genetic variants located in ARAP1 were linked to the bile acid metabolism, and LRRC32 was associated to the profile of ‘cell–cell signaling’ GO term [We also noted that the list of target-genes contains a total of 200 candidate genes previously reported as linked to microbial traits in mouse or human studies (Supplementary Table 6). Among them, it is worth highlighting the following: (1)  Dataset  as associdiaceae , linked idiaceae ; (2) SLCcrobiome ; and (3)en shown . We also in mice –47. Gene in mice  as diffe GO term . It shouFaecalibacterium, Dorea, Blautia, Butyrococcus, and Coprococcus (Table Butyricicoccus (P = 2.43E− 15) and piglets body weight (P = 0.026)  and Faecalibacterium  that were suggestively associated with piglets body weight. It is noteworthy to highlight that in these three cases, the allelic effects were in the same direction: the same allele affects the relative abundance of Butyricicoccus, Coprococcus, Faecalibacterium and piglets body weight. In agreement with [The identification of host-genetic markers linked with the relative abundance of butyrate producer bacteria such as us Table  promptedus P = 2.3E− 15 anent with , our finThe functional analysis from the list of regulators reveals overrepresentation of immune-related pathways. As many as 64% (16 out of 25) of the pathways reported as overrepresented by IPA relate to the host immune response  [NFATC3 and IKZF2), cell-mediated immune response , humoral immune response , and the modulation of human B-cell differentiation  [The list of regulator includes other TFs-related with host immune system such as tivation , while Nelopment . SMAD4 rpression  and is epression . Interespression , experim2, KLF7) , 67, hem, STAT1) . Therefo, STAT1) , 39, 69,Faecalibacterium, Butyrococcus, and Coprococcus) and host performance. Placing emphasis on regulatory elements, a total of 47 regulators that enriched for immune-related pathways were identified. Among them, five regulators resulted prominent within the network: PRDM15, STAT1, ssc-mir-371, SOX9, and RUNX2. The list of predicted targets included 200 candidate genes previously reported as associated with microbiota profile in mice and human, such as SLIT3, SLC39A8, NOS1, IL1R2, DAB1, TOX3, SPP1, THSD7B, ELF2, PIANP, A2ML1, and IFNAR1. Taken together, our results highlight the value of the proposed analytical pipeline to exploit pleiotropy and the crosstalk between bacteria and protists as significant contributors to host-microbiome interactions.In the present study, we built and explored a SNP-gene co-association network comprising 3561 genes related to the diversity and abundance of 31 bacterial and 6 commensal protist genera in pigs gut microbiota. Besides identifying genes associated with alpha diversity in both bacteria and protist, our analytical approach takes advantage of the genetic contribution to related microbial traits and revealed genetic variants with pleiotropic effects on pig gut microbiota profile. We also identify SNPs with pleiotropic effect associated with the relative abundance of butyrate producer bacteria (Additional file 1: Supplementary Table 1. Microbial traits employed in the study.Additional file 2: Supplementary Fig. 1. Overview of the PCIT-inferred gene co-association network for 3561 SNP-Genes included in the AWM procedure.Additional file 3: Supplementary Table 2. List of predicted target genes with at least one TF binding site for STAT1 and PRDM15.Additional file 4:Supplementary Table 3. List of predicted target genes with at least one TF binding site for microRNA ssc-mir-371.Additional file 5: Supplementary Table 4. Overlappings QTLs confirmed in our study from the reported by Crespo et al., 2019.Additional file 6: Supplementary Table 5. Overlapping QTLs confirmed in our study from the reported by Bergamaschi et al. 2020.Additional file 7: Supplementary Table 6. List of candidate genes previously reported as linked to microbial traits in mouse or human studies.


Fusarium verticillioides, which causes ear, kernel and stem rots, has been reported as the most prevalent species on maize worldwide. Kernel infection by F. verticillioides results in reduced seed yield and quality as well as fumonisin contamination, and may affect seedling traits like germination rate, entire plant seedling length and weight. Maize resistance to Fusarium is a quantitative and complex trait controlled by numerous genes with small effects. In the present work, a Genome Wide Association Study (GWAS) of traits related to Fusarium seedling rot was carried out in 230 lines of a maize association population using 226,446 SNP markers. Phenotypes were scored on artificially infected kernels applying the rolled towel assay screening method and three traits related to disease response were measured in inoculated and not-inoculated seedlings: plant seedling length (PL), plant seedling weight (PW) and germination rate (GERM). Overall, GWAS resulted in 42 SNPs significantly associated with the examined traits. Two and eleven SNPs were associated with PL in inoculated and not-inoculated samples, respectively. Additionally, six and one SNPs were associated with PW and GERM traits in not-inoculated kernels, and further nine and thirteen SNPs were associated to the same traits in inoculated kernels. Five genes containing the significant SNPs or physically closed to them were proposed for Fusarium resistance, and 18 out of 25 genes containing or adjacent to significant SNPs identified by GWAS in the current research co-localized within QTL regions previously reported for resistance to Fusarium seed rot, Fusarium ear rot and fumonisin accumulation. Furthermore, linkage disequilibrium analysis revealed an additional gene not directly observed by GWAS analysis. These findings could aid to better understand the complex interaction between maize and F. verticillioides. Fusarium species responsible for diseases such as root rot, stalk rot, seedling blight and ear rot, but Fusarium verticillioides (Sacc.) Nirenberg  , has been studied in different surveys based on artificial inoculation in field (Fusarium researchers carried out Quantitative Trait Locus (QTL) mapping analysis and GWAS by testing several distinct population structures , plant seedling weight (PW) and germination rate (GERM). The panel was previously analyzed for resistance to Fusarium infection of seedlings  analysis were performed.In the present study, the Rolled Towel Assay (RTA) screening method was applied to a maize association panel (F. verticillioides inoculation (FIS) by The maize core diversity panel, sometimes referred as the “Goodman” association panel  phenotyping method was used  and sterilized distilled water for treated and control towels, respectively. Incubation was performed for 7 days in the dark at 25° as previously reported  from the tip to the end of the longest root, and fresh plant weight (PW) of maize genotypes in control (C) and inoculated (I) conditions were calculated. Germination percentage was evaluated as reported by www.panzea.org) was used. Monomorphic and multiallelic SNPs and INDELs were discarded. Imputation was performed using the software Beagle 4.1  = Genome Wide Association Analysis (GWAS) was performed in Tassel (version 5.2.25). The mixed linear model (MLM) fitted by Tassel was:P < 1x10−4 within at least 30% of data subsamples, i.e., a resample model inclusion probability (RMIP) threshold of 0.30, were considered as significant  and restricting the search to Zea mays (taxid:4577). For SNPs positioned inside genes the possible effect on protein sequence was inquired. Introns and translated regions were identified using GeneWise online tool (http://www.ebi.ac.uk), coding sequence were translated using ExPASy (http://web.expasy.org/translate) and amino-acid variations were detected aligning the reference protein retrieved from MaizeGBD, and the SNP derived protein using MultAlin .Genes containing or close to associated SNPs were detected using Maize GDB genome browser using the B73 RefGen_V3 as the reference genome to place SNPs and genes avoiding low confidence genes and transposable elements. The function and conserved domains of proteins encoded by genes identified were retrieved submitting sequences to NCBI-protein BLAST (r2) were estimated using TASSEL version 5.0 between each significant SNP and other SNPs within a surrounding window of 60 adjacent SNPs (r2 > 0.5 and the distance between the position and the SNP resulting from GWAS was greater than 2 Kb.Linkage disequilibrium (LD) measures (https://figshare.com/s/54bb47a6abf418c859b9. Table S1. (Excel file) List of germplasm and phenotypic values. Table S2. (DOCX file) Table of LD analysis.All supporting data are included as supplemental files and are available at Figshare: Seed germination was affected in different ways by artificial inoculation , previously evaluated in Furthermore, seedling traits  of both inoculated and control assays were significantly correlated each other . This fii.e., four SNPs for PL_I and PW_C; one SNP for PL_I and GERM_I and one SNP for PL_C and PW_C.GWAS analysis resulted in 42 SNPs, distributed across all chromosomes, significantly associated with the traits examined. 35 SNPs were located in 21 genes, while the remaining 7 were intergenic, but within 1.1 kb of known genes . A totalFor the trait PL_C only two SNP associations were discovered, both on chromosome 4, but inside two different genes . Eleven For the trait GERM_C only one significant SNP was identified and it was inside a gene on chromosome 8 . For GERThe highest number of genes was identified on chromosome 5, while chromosome 6 had only one gene associated to disease traits. Genes highlighted were generally at the end of the chromosome than in centromere or pericentromeric regions. No SNPs on chromosomes 7 and 10 were associated to traits.F. verticillioides, thaumatin gene had lower expression ratios in resistant lines compared to susceptible ones. In contrast, kernels of the resistant lines showed higher gene expression before fungal infection, highlighting the key role of constitutive resistance in limiting pathogen attack (F. verticillioides infection (Fusarium seedling rot resistance (qFSR4.2) and seedling weight (qSWT3.1) in inoculated kernels of a Multi-parent Advance Generation Intercross (MAGIC) maize population , was associated with PL_I. RLKs are surface transmembrane receptors able to perceive different signals and the presence of pathogens. The RLK family includes the BAK1 that is involved in brassinosteroid-mediated plant development. Interestingly, in previous studies this gene was highly expressed in maize coleoptile at V1 stage  analysis was performed in a window of 60 adjacent SNPs on the same chromosome to identify correlations between markers. Genome-wide, 4 SNPs or groups of SNPs were detected in LD of r2 greater than or equal to 0.5 to significantly associated markers (Table S2). Generally, for each trait-associated SNP examined, from 1 to 3 SNPs were detected to be in LD r2 > 0.5.Linkage disequilibrium Regarding the trait PW_I, only the gene GRMZM5G800586 was found both by GWAS and LD analysis; this gene encodes a protein of unknown function.For the trait GERM_I, three positions were found in LD in the gene GRMZM2G497710 that was also identified by GWAS; also GRMZM2G125432 already reported as containing a SNP was the closest to a position found by LD analysis.Genome-wide, three genes were identified by both analyses (GWAS and LD); while LD analysis exclusively revealed only one new gene.Fusarium seed rot (FSR) and seedling traits, including seedling length, weight and FIS, were investigated  on chromosome 2 and one (GRMZM2G141638) on chromosome 3 related to GERM_I overlapped two QTL regions reported for FER and fumonisin accumulation .The genes associated with PL_C, GRMZM2G058394 and GRMZM5G851807, were located at the ends of QTL reported for FER and fumonisin resistance on chromosome 4 .On chromosome 5, two genes (GRMZM2G125432 and GRMZM2G171801) related to GERM_I were located inside QTL reported for FER and fumonisin resistance. One more gene (GRMZM2G071970) associated with the same trait was located inside a known QTL involved in FSR resistance . MoreoveOne gene related to GERM_I (GRMZM2G129288) was located inside a QTL reported for fumonisin accumulation on chromosome 6 .Two genes associated with GERM_I traits (GRMZM2G126010 and GRMZM2G139460) overlapped with QTL intervals for resistance to fumonisin accumulation and FSR on chromosome 8, respectively, and one additional gene for GERM_C (GRMZM2G024799) was in the QTL for FER .On chromosome 9, three genes  were found associated with PL_I inside a QTL reported for fumonisin accumulation .No gene associations were observed on chromosomes 7 and 10.F. verticillioides infection limits germination of maize kernels and reduces seedling weight and vigor. Conversely, no correspondence was observed with QTL previously described by Fusarium seedling rot traits were evaluated by the same phenotyping method. This could be explained by the different genetic materials employed for mapping in the two researches (association mapping panel vs. multi-parent population-MAGIC) and the use of diverse genome assemblies.In the recent work by F. verticillioides resistance. GWAS analysis revealed 42 SNPs associated with three phenotypic traits: plant seedling length (PL), plant seedling weight (PW) and germination rate. In total, 25 genes were found associated with the SNPs and many of these genes had a role in disease response.In this work, a bioassay was applied to the maize association panel tested for seedling related traits to Comparison between genes found in our GWAS analysis and previous QTL mapping studies reported for resistance to FER, fumonisin accumulation and seedling traits revealed that the majority of the genes, associated with the SNPs, localized inside QTL for these traits.F. verticillioides in order to improve genomic selection for Fusarium resistance at the seedling stage. Functional validation of the most recurring candidate genes will be required to verify their role in the pathways to hamper fungal infection in maize.These findings will help us to better understand the existing complex interaction between maize and