Solanum pimpinellifolium, Solanum lycopersicum var. cerasiforme and Solanum lycopersicum var. lycopersicum, was selected to represent the genetic and morphological variability of tomato at its centers of origin and domestication: Andean regions of Peru and Ecuador and Mesoamerica. The collection is enriched with S. lycopersicum var. cerasiforme from the Amazonian region that has not been analyzed previously nor used extensively. The collection has been morphologically characterized showing diversity for fruit, flower and vegetative traits. Their genomes were sequenced in the Varitome project and are publicly available (solgenomics.net/projects/varitome). The identified SNPs have been annotated with respect to their impact and a total number of 37,974 out of 19,364,146 SNPs have been described as high impact by the SnpEeff analysis. GWAS has shown associations for different traits, demonstrating the potential of this collection for this kind of analysis. We have not only identified known QTLs and genes, but also new regions associated with traits such as fruit color, number of flowers per inflorescence or inflorescence architecture. To speed up and facilitate the use of this information, F2 populations were constructed by crossing the whole collection with three different parents. This F2 collection is useful for testing SNPs identified by GWAs, selection sweeps or any other candidate gene. All data is available on Solanaceae Genomics Network and the accession and F2 seeds are freely available at COMAV and at TGRC genebanks. All these resources together make this collection a good candidate for genetic studies.A collection of 163 accessions, including  Solanum lycopersicum var. lycopersicum L. (SLL), is one of the most consumed vegetables all over the world with a production that exceeds 180 million tonnes . Its cultivation has become highly efficient thanks to the introduction of technological advances and the development of modern varieties. These modern varieties are the result of intensive plant breeding programs since the beginning of the 20th century, and the natural biodiversity of tomato wild species has been key in this success.Tomato, 1 proposed that SLL evolved from S. lycopersicum var. cerasiforme  Spooner, G.J. Anderson & R.K. Jansen (SLC). Recently, Blanca et al.3 proposed a two-step domestication process from SLC to SLL based on molecular and morphological evidence. The first step involves the pre-domestication of SLC in the Amazonian region of Southern Ecuador and Northern Peru. Subsequently, SLC would have migrated to Mesoamerica where it would be domesticated to SLL. Razifard et al.4 proposed that many traits considered typical of cultivated tomatoes arose in South America. However, these domestication traits were lost or diminished once these partially domesticated forms spread to Mesoamerica, where it was finally morphed into the SLL6. This domestication and diffusion process was accompanied by a selection of alleles related to fruit color, size and shape and also changes in plant architecture10. This process also included various genetic bottlenecks that progressively narrowed the genetic diversity of modern tomato, compared to its wild species11. The main loss of variability occurred during the migration to Mesoamerica from the Peruvian and Ecuadorian Amazon region. Most of the allelic variants present in european vintage tomato are already present in these Amazonian SLC populations3.The cultivated tomato and its wild relatives came from the Peruvian and Ecuadorian regions of South America. According to allozyme variation, Rick and FobesSolanum pimpinellifolium L. (SP) is the closest wild relative to SLC and SLL. It is also a red-fruited species and native to coastal areas from Ecuador to Southern Peru. According to its distribution, this species presents varying degrees of genetic variation15 and morphological differences such as flower and inflorescence size, style exertion, or fruit color12. This fact and its capacity to hybridize with tomato, make this species a valuable source of desired traits in tomato breeding. For instance, SP has been used as a genetic source for quality improvement related to solid content, firmness, fruit color17, volatile compounds19, or resistance against fungi or viruses such as Tomato leaf curl virus20, Alternaria solani, Fusarium oxysporum, and Phytophthora infestans21 or Cladosporium fulvum22. SLC has a worldwide distribution in tropical regions, but it is native to the Andean region of Ecuador and North of Peru1. This species is found over a vast range of environmental conditions such as tropical or arid regions, sea level or high altitudes23, and it has also been collected at native markets24. It usually bears red and small fruits, but Rick and Holle25 described a remarkable morphological variability in fruits, plant habit, or leaf size and shape. A higher genetic variability has been described in Ecuadorian and Peruvian accessions2 due to the development of morphological diversity during a pre-domestication phase. In fact, tomatoes collected in local markets of Ecuador were morphologically classified as vintage tomato; but they have been genetically classified as SLC3. These studies and data show that SLC from Northern Peru are very close to Mexican and vintage tomatoes. Despite that, Amazonian SLC has not been used frequently for tomato improvement as opposed to SP. Furthermore, SLC has been characterized as a valuable genetic source for abiotic and biotic stresses, such as moisture-tolerance26 or resistance to root rot caused by Phytophthora27; traits related with the global climate change and sustainability challenges currently facing agriculture.28, plant habit and adaptation to machine harvesting in processing cultivars or traits related to fruit appearance for fresh market28. However, nowadays, the new objectives of tomato breeding focus on sustainable production or adaptation to unfavorable environmental conditions due to climate change and nutritional quality. The genetic variation of exotic germplasm collections has been used in tomato breeding to bypass the limited genetic diversity of SLL. These germplasm collections have mainly included S. pimpinellifolium, S. chilense  Reiche, S. peruvianum L. s. str., S. habrochaites S. Knapp & D.M. Spooner and S. pennellii Correl. Thus, the maintenance and characterization of germplasm collections are essential in order to achieve these breeding goals. Germplasm is a good source of natural allelic variants, useful for genetic analyses and subsequent breeding applications. Consequently, the creation of genebank collections characterized at genetic and phenotypic level is a primary objective for a sustainable breeding. In addition, it is crucial that these data and genetic resources are easily available to the scientific community to exploit this extensive amount of information.Most modern breeding programs have usually focused on resistance, yield and quality traits, such as firmness, color, or texture29 that can be useful for improving breeding cultivars30. For instance, the availability of its genomes in association with its characterization at phenotypic and molecular level allows the development of genome-wide association studies (GWAS). GWAS studies have already identified regions of the genome related to morphological and metabolic diversity32. For example, Bauchet et al.33 detected associations for traits such as fruit weight, flowering time, early fruit development, malate, and phenalyacetaldehyde/phenylethanol content. Finally, the first meta-analysis of GWAS has revealed numerous candidate genes involved in tomato flavor34. Full genome sequences have been published in several studies and more than 725 genome sequences of tomato accessions are available39. A pan-genome analysis of tomato including SLL, SLC, and SP has discovered 4873 genes that are not present in the reference genome37 thus increasing the interest of these populations for tomato breeding. Once a candidate region of the genome, gene or SNP has been characterized as significantly associated with a trait, it is necessary to validate its role in the control of the trait by using segregating families or mutants. However, this latter step sometimes becomes limiting as the development of such populations is time consuming and costly.The advent of NGS technologies has created a huge amount of available genetic information about germplasm held in genebanks37. These accessions include SP, SLC, and SLL and represent the diversity at the center of origin and domestication of tomato. We have annotated the identified SNPs within our collection using SnpEff. We have performed GWAS analysis for all of our morphological descriptors with the aim of detecting candidate regions. In addition, a collection of segregating families has been developed by crossing the complete set of accessions with a representative accession for each of the three species. These populations could help to speed up the validation of candidate genes and SNPs. The combination of passport, phenotypic, genetic information, and germplasm with easy accessibility converts this collection into a powerful instrument for genetic studies and breeding.In the present study, we have morphologically characterized the variability of fruit, flower, and vegetative characters from a collection of 163 tomato accessions of the Varitome project, for which the full genome is available3. Plants were evaluated for a total of 54 morphological traits . The SNPs were annotated and their putative impacts were predicted by using SnpEff. The number of effects were classified by the impact of these variants, type of effect, and region. The lowest number of variants was detected in our SLL group. SLC groups had a variation between SLL and SP groups with lower levels of variants in SLC Mexico. A total of 37,974 out of 19,364,146 SNPs detected in this collection have been designated as high impact in the SnpEff analysis. The number of variants per type and the number of effects by impact for each group are summarized in Table The SNPs identified in this collection are available at Solanaceae Genomics Network (The whole collection was crossed with BGV007109 (SP), LA2278 (SLC), and Money Maker (SLL). The 163 accessions were used as female parents to obtain the F1 generations, except for some accessions, mainly SP, where flowers were too difficult to emasculate due to their small size. F1 plants were self pollinated to obtain the F2 generations. A collection of 485 F1 populations and 457 F2 population were achieved  has been suggested to have arrived in Europe from Mexico in the 16th century9. These changes in fruit size and shape have been described to be a consequence of derived alleles of fas, sun, ovate, and lc genes9. According to the study of Blanca et al.3, some accessions from our SLC group carry fas and ovate and some of our SLL also carries derived alleles. These results support our observations and these derived alleles could be an explanation for the diversity that we have detected.The high variation related to fruit color and shape was an interesting source of variation for breeding and genetic purposes. Some SP and SLC accessions, collected in Peru and Mexico respectively, exhibited colors ranging from yellow to orange. In fact, yellow fruits have been previously described in these sites40, as well as the variation observed in SLC from South America25. This insertion process is related with the migration from the center of origin and resulted in the increasing of the autogamy levels. Interestingly, two different subgroups can be discerned in SLC Mexico, accessions collected as wild that exhibited inserted styles, and accessions with fruit size similar to cultivated tomato that were characterized as exerted ones. This presence of exertion is also detected in SLL and probably related to fasciated and big sized fruits.Changes in flower complexity and style exertion could be other interesting changes related to domestication and further selection processes. For example, the number of petals and sepals tended to increase in SLC and SLL groups, although not in SLC Mesoamerica. The style position was also altered from highly exerted in SP Peru to slightly exerted or even inserted in SP Ecuador. In SLC, the degree of style exertion tended to decrease from Amazonian SLC to SLC Mexico, whereas in the SLL group it tended to be inserted. This correlation between stigma exertion and SP geographical origin had been previously described3. Rick and Fobes1 described that variation in SLC depended on its geographical origin, being SLC from other countries less variable than SLC from these regions. The analysis within the SLC group also revealed a considerable decrease in the number of SNP variants detected in SLC Mexico. This is in agreement with the loss of variability that took place during the migration to Mesoamérica41. The detected 37,974 SNPs labeled as high impact show that this collection may be an interesting source of new alleles. This is supported by the SNPs with a high putative effect that were detected for some of the candidate genes from GWAS analysis (Supplementary Table Solyc01g087260), 14 of them correspond to a protease-like protein (Solyc03g081260), and the last 2 correspond to a homeobox leucine-zipper protein (Solyc05g015030). Also, a high impact SNP was detected in the genomic region where lc gene is located. Besides, the genomic region associated to fw2.2 presented two allelic variants with high effect. These variants are related with a nodulin MtN21 family protein (Solyc02g087050) and with an uncharacterized protein (Solyc02g091330).As expected, the highest level of diversity was found in Peru and Ecuador for both SP and SLC groups42, our analysis identified a possible novel genomic region on chromosome 11 which carries genes encoding for Agenet and cellulose synthase proteins. Agenet has been described to be involved in flower development43 and the expression of cellulose synthase has also been detected in flowers of Arabidopsis thaliana44. The association identified for forked inflorescence on chromosome 9 corresponds to a SNP in ARGONAUTE 1 gene (Solyc09g082830). This gene is a member of AGO gene family, which is known to regulate vegetative and reproductive development and stress response45. The expression of these genes have been detected in flower and fruit of tomato46. A significant association with uniparous inflorescence was identified on chromosome 11 and was located approximately 5 Mb away from a mapped region which is considered to be involved in branched inflorescences of fin mutants47. Six associations for dark green leaves were detected on chromosome 9. An annotated gene as chloroplast FLU-like protein was located 2 kb away from this region. FLU is a nuclear-encoded plastid protein that interacts with enzymes involved in chlorophyll synthesis48. Besides that, some new identified SNPs lacked in functional annotation, for example SNP associated to the total number of inflorescences. Finally, another several traits were associated with SNPs located on genes that were not apparently related to the trait they are associated with, such as the association between leaf length and a RING-finger protein-like which could regulate ubiquitination processes49 or the association between fruit longitudinal stripes and heat shock proteins. All these novel detected regions would require further experiments for validation and identification of candidate genes suitable for tomato breeding.GWAS analysis revealed a total number of 107 SNPs associated to eight quantitative traits and 30 SNPs associated to seven qualitative traits. This analysis has allowed the identification of known and novel genes for these traits. In addition of the QTLs for flowers per inflorescence previously described on chromosomes 2, 3, and 5loci associated to fruit size such as fw2.250, fw9.251, locule number (lc)9, and fas9. For instance, we detected an association between fruit weight and SNPs close to fw2.2 and also close to SNPs that have already been identified in other GWAS analysis52. In case of lc and fas genes, our study revealed associations between the trait number of locules and SNPs located genetically close to both QTLs, which are located on chromosome 2 and 11, respectively. Sacco et al.52 detected lc gene and also one of our annotated candidate genes on chromosome 11, Solyc11g071840. This detected region on chromosome 11 was located in a region previously described as fas and really close to the annotated fw11.3. The fw11.3 is a QTL controlled by cell size regulator, which regulates weight by the control of cell size in the pericarp53. Strikingly, no association has been found between the qualitative trait fruit fasciation and chromosome 11 (where fas gene is located). However, two regions previously undescribed that could be associated to fasciated phenotype on chromosomes 1 and 4 were revealed. Despite that fas gene is not located in these detected regions, this result suggests the involvement of new genome regions. In fact, the SNP located on chromosome 1 at 3,717,866 bp was also associated with the number of locules in our analysis.As expected, our analysis has identified several SNPs located close to genes or candidate regions previously characterized. GWAS analysis has allowed the identification of previously described t), delta (Del) and beta (B) or the yellow flesh (r) mutant54. The genome region involved in this r mutant is located on chromosome 3 at 9 Mbs from our associated region. For the pink color, associations were located in a genomic region with an annotated gene as colorless fruit epidermis (y gene). This association between the pink fruit color and this gene had already been detected by GWAS analysis and a deletion in this region has been hypothesized to control this trait52.An association signal for fruit color was identified on chromosome 1 and located in a region with a candidate gene described as carotenoid cleavage dioygenase 1B. Carotenoids are important factors implied in fruit color and modifications or absence of their syntheses are the reason for the orange color of mutants such as tangerine  and the other in America (TGRC). The characterized and sequenced plants came from a double round of self-pollination of a single plant, so they are quite homozygous and it is possible to use the genotype data to do other GWAS analyses with other traits.Different segregating families have been developed and have led to the creation of a powerful tool to speed up genetic studies based on this collection. The use of three different parents in crosses with all accessions, allows the testing of the same alleles in different genetic backgrounds. The F2 populations will facilitate the analysis of the segregation of any variant in this collection. Studies can be conducted starting from SNP alleles, presence or absence of a determinate gene or phenotypic variants. By choosing an accession carrying a selected allele and one of the three parental accession which carries the alternative allele, the F1 and F2 segregation families are available for the genetic study of this variant. The availability of these segregating families allows to speed up research to confirm possible candidate genes. Besides sparing the effort of developing segregating families, researchers could analyze different natural mutants of the same gene or study the mutation effect in different genetic backgrounds.The usefulness of this collection is based on the fact that all these resources are freely and easily available. Seeds of the original and self-pollinating accessions and F2 families are available at COMAV and TGRC genebanks. Passport and characterization data, pedigree information, genome sequences, SNPs and GWAs results are available and integrated at Solanaceae Genomics Network (solgenomics.net). All these resources build up a powerful platform for tomato genetics and breeding that could be reinforced with new studies performed on it.Solanum pimpinelifollium (SP), from Ecuador and Peru. Accessions were grouped according to their geographical origin and previous genetic results3, as is shown in Fig. A germplasm collection of 163 accessions was selected with the aim of representing a broad range of geographical, morphological, and genetic diversity. These plant materials consisted of 15 accessions of SLL from Mexico; 121 accessions of SLC coming from Ecuador, Peru, Mexico, and different countries of Mesoamerica and 27 accessions of Plants used for the morphological characterization were cultivated in a greenhouse at Universitat Politècnica de València  during the spring-summer seasons of 2016. Plants were grown in 12-l pots with coconut fiber and fertirrigated under standard dosages for tomato in our area. A completely randomized experimental design was conducted with two plants per accession, each replicate in a different greenhouse.55, mainly related to plant architecture, inflorescences and flowers, leaves and fruit size were evaluated. Some descriptors were modified for a better representation of the morphological variability exhibited in the collection. The descriptors and their definitions are listed in Supplementary Table https://solgenomics.net/search/traits).Twenty-six quantitative and 27 qualitative traits based on the descriptors developed by IPGRIt test and Mann–Withney-Wilcoxon test for quantitative traits depending on whether the data was normally distributed. Fisher’s exact test was conducted on qualitative data. As no differences between the two greenhouses were found, data from both greenhouses were joined, and the mean value was calculated for quantitative traits. For qualitative data, a new level for each qualitative trait (named as 10) was created to include the accessions which presented different scale values for this qualitative trait. Robust ANOVA and Fisher test were conducted to detect significance differences between species, depending on whether the data was quantitative or qualitative. A Bonferroni correction of p values was conducted.Prior to any analysis, all traits were manually curated to detect possible errors. Differences due to a greenhouse effect were assessed using Student’s 35 and the SNPs identified in these accessions are publicly available in Solanaceae Genomics Network (https://solgenomics.net/projects/varitome). Using these data, the collection of SNPs has been annotated to detect the localization and possible impact of changes using SnpEff56, and statistics were calculated for each geographical group.Genome sequences of the accessions of this collection have been published previously57. A total number of 1,479,141 high quality SNPs were used for the GWAS analysis. A PCoA has been done to visualizate genetic structure . The accessions, BGV007109 of SP, LA2278 of SLC and Money Maker of SLL were selected as parents. Each fruit from each individual cross was maintained separately in order to facilitate the detection of possible mistakes. Two different F1 plants of each combination were self pollinated to obtain the set of two independent F2 breeding populations. The culture for the F2 family generation was done during the years 2017–2019 in the Centro de experiencias Cajamar de Paiporta . Plants were grown in greenhouses in soil and fertirrigated under standard dosages for tomato in our area.A complete list of these available materials is recorded in Supplementary Table Figure S1Figure S3Figure S4Table_S1Figure S2Table S2Table_S6Table_S3Table_S4Table_S5


Agricultural products that are biofortified with α-linolenic acid (ALA), the precursor of n-3 LC PUFA, have been launched to the market, but their impact on BAT function is unknown. This study aimed to evaluate the effects of ALA-biofortified butter on lipid metabolism and thermogenic functions in the BAT. C57BL/6 mice were fed a high-fat diet containing ALA-biofortified butter  for ten weeks in comparison with the isocaloric high-fat diets prepared from conventional butter or margarine. The intake of n3Bu significantly reduced the whitening of BAT and increased the thermogenesis in response to acute-cold treatment. Also, n3Bu supplementation is linked with the remodeling of BAT by promoting bioconversion into n-3 LC PUFA, FA elongation and desaturation, and mitochondrial biogenesis. Taken together, our results support that ALA-biofortified butter is a novel source of n-3 PUFA, which potentiates the BAT thermogenic function.Supplementation with  Adult humans contain a significant amount of metabolically active brown adipose tissue (BAT), which is estimated to be responsible for ~20% of total energy expenditure through non-shivering thermogenesis (adaptive thermogenesis) ,2,3. Then-3 long-chain (LC) polyunsaturated fatty acids (PUFA), such as eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), potentiates the cell-autonomous brown adipocyte differentiation from murine brown precursor cells [Several bioactive compounds derived from our foods have shown to modulate brown thermogenesis ,11. Dietor cells ,14. Addior cells . Moreoveor cells ,13.n-3 PUFA replacing fish products. Numerous attempts have been made to generate the n-3 PUFA-containing agricultural products, either through the incorporation of fish oil into dairy products via enzymatic inter-esterification [n-3 PUFA-rich feed [n-3 PUFA  is frequently used for feedstuff, as plant-based n-3 PUFA is absent of fish odor and inexpensive compared to fish-based n-3 LC PUFA . Dairy products produced by bio-fortified cows have been launched to the market, such as n-3 PUFA-fortified milk and butter. However, its efficacy on brown thermogenesis is largely unknown.Despite the various metabolic benefits, including thermogenic energy expenditure, fish oil has limited application as a routinely consumed food, due to unpleasant fish odor. Hence, there is a growing demand for a new source of fication ,17,18 orich feed . Insteadn-3 LC PUFA [Recently, we reported that ALA-enriched butter is effective in attenuating HF diet-induced insulin resistance and hepatic steatosis by promoting the endogenous bioconversion of ALA into  LC PUFA . In that LC PUFA . Here, wn = 8/group)—chow group (chow) or isocaloric high-fat diet groups  from conventional butter , n-3 enriched butter , or margarine —and fed for 10 weeks ad libitum. The diets were formulated based on the AIN-93M purified rodent formula  at the University of Nebraska–Lincoln. C57BL/6 mice were purchased at 6 weeks of age from Jackson Laboratory. The mice were randomly assigned into four groups  acutely (3 h). The core body temperature was detected using a rectal probe for mice . An infrared (IR) camera  was used to detect thermal release and capture images of the surface body temperature. FLIR Research IR program software was used to display surface heat release via a color palette representing temperatures between 10 and 34 °C. To determine the fatty acid profile change during cold stress, the mice were housed either at room temperature  or a cold temperature  for 48 h. At the time of necropsy, brown adipose tissue (BAT) was collected, snap-frozen in liquid nitrogen, and kept at −80 °C until analysis.To determine the FA profile in the BAT, the total lipids were extracted, as we previously described . Brieflyn = 4 per group) and qPCR reactions were performed in triplicate. The relative gene expression was calculated based on the 2−ΔΔCT method with the normalization of the raw Ct values to Hprt (hypoxanthine-guanine phosphoribosyltransferase) . Then, the RNA was reverse transcribed for cDNA synthesis . Real-time PCR was carried out on a QuantStudio 6 Flex  using SYBR Green (Fisher scientific). Equal amounts of cDNA prepared from each individual animal were pooled (sferase) . The priβ-actin. Chemiluminescence from ECL solution (Western Lightning) was detected using an ODYSSEY FC Imaging System (LI-COR). The details of antibody information are available in The protein was extracted from BAT using a RIPA buffer containing protease and phosphatase inhibitors . Proteins were fractionated using 10% SDS-PAGE, transferred to PVDF membranes, and incubated with antibodies agonist uncoupling protein 1 (UCP1), PR-domain containing 16 (PRDM16), CD11c, F4/80, stearoyl-Coenzyme A desaturase 1 (SCD-1), elongation of long-chain fatty acid-like family member 6 (Elovl6), voltage-dependent anion channel 1 (VDAC1), pyruvate dehydrogenase (PDH), respiratory oxidative phosphorylation protein (OxPhos), and The BAT samples were fixed in 10% buffered formalin, embedded in paraffin, and cut into 5 μm sections. After deparaffinization, BAT sections were stained with hematoxylin and eosin (H&E) staining as previously described .t-test, * p < 0.05 and *** p < 0.001. All analyses were performed using Graph Pad Prism (Version 6.02).All data were analyzed using one-way ANOVA followed by Tukey′s multiple comparison tests or Student′s n-3 PUFA fortified butter. Made out of vegetable oil (~80%), margarine contained a lower amount of saturated FA (both C16:0 and C18:0) and palmitoleic acid (C16:1n7), but possessed a 10-fold higher amount of linoleic acid  than conventional butter or ALA-biofortified butter. In all samples, other PUFA levels, such as arachidonic acid , DHA, and EPA, were negligible (Fatty acid (FA) analysis by GC/MS revealed that butter made out of biofortified milk (refer to as bio-fortified butter) contained approximately ~4% of ALA (C18:3), similar to margarine, while conventional butter was nearly absent of ALA. Except for ALA content, FA composition was identical between conventional butter and gligible A.n-6/n-3 FA ratio in the BAT by ~4-fold compared to Bu or Ma , ALA-biofortified butter (n3Bu), and margarine (Ma). HF feeding with Bu or Ma, but not n3Bu, significantly increased the BAT weight compared to chow B,C. H&E Bu or Ma E. These n-6/n-3 FA ratio in ALA-biofortified butter alters BAT thermogenic activity. When mice were placed to a cold temperature (4 °C) acutely (3 h), n3Bu-fed mice were able to maintain a higher core-body temperature than Bu- or Ma-fed mice, as comparable to chow-fed mice , a NADogenesis . Consist-fed BAT C,D. Colln-3 LC PUFA, reduced inflammation in the metabolic tissues , and systemic production of anti-inflammatory oxylipins [n-3 LC PUFA conversion, by increasing ELOVL6 and SCD activity and by stimulating mitochondrial biogenesis  A levels , and (3)A levels ,28. Heren-3 LC PUFA requires a series of reactions by elongases and desaturases. In general, LC n-6 PUFA synthesis is the favored pathway over n-3 PUFA formation due to the overabundance of LA to ALA [n-3 LC PUFA content in response to thermogenic stimuli [n-3 LC PUFA content upon cold treatment [n-3 LC PUFA levels in basal as well as cold-stimulated conditions  is dependent on the reduced dietary n-6/n-3 PUFA ratio. Despite having the same ALA content, margarine supplementation was unable to increase n-3 LC PUFA content, presumably due to high LA content  among the isocaloric dietary groups (Cardiolipins (CL) are unique phospholipids that are predominantly found in the mitochondrial inner membrane. CL have critical functions in the formation of the respiratory supercomplex ,34. Lipiy groups . Nonethen-3 PUFA.In addition, there is growing evidence showing that conjugated linoleic acid (CLA), either trans-10 or cis-12 isomer alone, or the mixture with cis-9 and trans-11 isomer, increases the thermogenic activity in the adipose tissue ,36,37. Tn-3 PUFA is widely provided to farm animals such as pigs, chicken, and cows by supplementing the feed with a plant source of n-3 PUFA, i.e., flaxseed oil or algae [n-3 PUFA is very low in ruminant animals. The most significant barrier is suppressing the bio-hydrogenation of ruminal microbes, which revert dietary unsaturated FA into SFA in the rumens. Without the modulation of gut microbes, the dietary unsaturated FA are unable to reach the small intestine for absorption and incorporation [n-3 PUFA content in cattle in the hope of yielding n-3 PUFA-enriched dairy products such as milk, cheese, and butter [Fat-1 gene, the n-3 FA desaturase derived from C. elegans [n-6/n-3 ratio) and accumulates the ALA content by modulating the bio-hydrogenation capacity [n-6/n-3 FA ratio at the systemic levels, including meats, fats, and milk (data not shown). We cautiously speculate that our cattle feed may trigger the bypassing of bio-hydrogenation or the suppression of the bacterial population that have bio-hydrogenation capacity. Currently, it is under investigation whether our cattle feed induces a population shift of ruminal bacteria and suppresses the bio-hydrogenation capacity in rumens.Bio-fortification of or algae . Howeverporation . Numeroud butter . For exa elegans . However elegans . Severalcapacity . In our n-3 PUFA other than fish oil. The ALA-enriched butter can recapitulate the enhanced thermogenic energy expenditure, similarly to fish oil supplementation. The thermogenic activation of BAT was associated with the improved biosynthesis of n-3 LC PUFA, FA elongation/desaturation, and mitochondrial biogenesis. By thoroughly evaluating the effectiveness of ALA bio-fortified butter on thermogenesis in comparison with conventional butter and margarine, our study opens a new research avenue for designing health-promoting (or therapeutic) dairy products via manipulation of animal nutrition, metabolism, and presumably ruminal microbiome.This study provides a novel insight that ALA-biofortified agricultural-products could be an alternative source of


Arachis hypogaea L.) forms root nodules to enable a symbiotic relationship with rhizobia for biological nitrogen fixation. To understand the genetic factors of peanut nodulation, it is fundamental to genetically map and clone the genes involved in nodulation. For genetic mapping, high throughput genotyping with a large number of polymorphic markers is critical. In this study, two sets of sister recombinant inbred lines (RILs), each containing a nodulating (Nod+) and non-nodulating (Nod-) line, and their Nod+ parental lines were extensively genotyped. Several next generation sequencing (NGS) methods including target enrichment sequencing (TES), RNA-sequencing (RNA-seq), genotyping by sequencing (GBS), and the 48K Axiom Arachis2 SNP array, and various analysis pipelines were applied to identify single nucleotide polymorphisms (SNP) among the two sets of RILs and their parents. TES revealed the largest number of homozygous SNPs  between the original parental lines, followed by the Axiom Arachis2 SNP array , RNA-seq , and GBS (312). Among the five SNP analysis pipelines applied, the alignment to A/B genome followed by HAPLOSWEEP revealed the largest number of homozygous SNPs and highest concordance rate (79%) with the array. A total of 222 and 1,200 homozygous SNPs were polymorphic between the Nod+ and Nod− sister RILs and between their parents, respectively. A graphical genotype map of the sister RILs was constructed with these SNPs, which demonstrated the candidate genomic regions harboring genes controlling nodulation across the whole genome. Results of this study mainly provide the pros and cons of NGS and SNP genotyping platforms for genetic mapping in peanut, and also provide potential genetic resources to narrow down the genomic regions controlling peanut nodulation, which would lay the foundation for gene cloning and improvement of nitrogen fixation in peanut.Cultivated peanut ( Arachis hypogaea L.) is one of the most important oilseed crops grown worldwide. As a legume species, peanut can form a symbiotic relationship with rhizobia to biologically fix nitrogen, thus reducing the amount of synthetic nitrogen fertilizers applied in the growing season. The symbiotic process and molecular mechanisms have been extensively studied in two model legume species Lotus japonicas and Medicago truncatula, where rhizobia enter into the host plant via an intracellular root hair pathway (3 population from a cross between two nodulating (Nod+) genotypes 487A-4-1-2 and PI 262090  with two sub-genomes, A and B, derived from A. duranensis and A. ipaensis, respectively .As the A and B genomes of peanut are highly similar with a median identity of 93.11% , it has Arachis2 array was also used for genotyping and served as a SNP cross-validation platform for identified SNPs. This is the first study to compare different SNP calling and filtering pipelines for various NGS data sources in peanut. Results and suggestions from this study provide insights into SNP identification and genotyping in peanut. The polymorphic genomic regions between the sister RILs revealed candidate genes controlling peanut nodulation, which will be beneficial for future genetic mapping studies.In this study, to explore the genetic factors and genetic regions controlling nodulation in peanut, SNPs were identified between the two original Nod+ parental lines as well as between two sets of RILs. Three NGS approaches, including TES, RNA-seq, and GBS were applied and compared for SNP identification. To summarize and compare different SNP analysis methods, we have applied and compared two alignment methods (to A + B genome or to A/B genome) and various SNP calling and filtering pipelines using the sequencing data. In addition, the Axiom 6 lines, which were deived from the cross between PI 262090 and UF 487A. They are also parental lines for two F2 mapping populations (E4 × E5 and E6 × E7) for genetic mapping of nodulation genes. The morphological and genetic characterizations of the RILs were previously described  & E5 (Nod+), and E6 (Nod+) & E7 (Nod−), as well as their parental lines, PI 262090 (Nod+) and UF 487A (Nod+) were included in this study. The pedigree information of these six lines was introduced previously . In brieescribed . The genescribed . DNA conTo preferably target peanut genes potentially related to nodulation, a series of genes were included for probe design. Firstly, the putative orthologous nodulation-related genes and differentially expressed genes (DEGs) upon infection of rhizobia from the previous report  were incA probe could capture or hybridize with the DNA fragments if they share sequence similarity with each other. The genomic regions sharing sequence similarities with probes were considered as probe target regions. However, the capture efficiency would be different for target regions with different similarities. Thus, the number of target regions was investigated for the probes under different alignment identity cutoffs when they were mapped to the genome. The uniqueness and distribution of the designed probes were further evaluated.To evaluate uniqueness of the designed probes in the genome, the probe sequences were mapped back to the diploid genomes of peanut (A + B) using Blat . A hit wThe synthesized probes were used to capture the DNA fragments of the six genotypes. The captured DNA fragments were sequenced using the Illumina HiSeq 3000 platform (100 bp paired-end reads). The probe design, synthesis, library preparation, target enrichment, and sequencing were performed by Rapid Genomics LLC .To evaluate the probe target regions, the sequences of designed probes were aligned to the A + B genomes using Blat following the same criteria as above. The read coverage for probe target regions was assessed. In addition, the relationship between read coverage and target regions’ sequence similarities with probes was investigated, which could indicate the influence of alignment identity of probes on capture efficiency. To achieve this, different alignment identity cutoffs were applied to define a hit, including 96, 90, 84, 78, 72, 66, and 60, which correspond to 80, 75, 70, 65, 60, 55, and 50% match of probe sequences to the genome. The coordinates of those hits in the genome were extended 100 bp from both directions (in BED file), which subsequently served as target regions. Bedtools v2.24.0 (intersect) was used for assessing read coverage for target regions. The alignment files for both overall and uniquely mapped reads generated from BWA-mem , as descSRP093688, BioProject PRJNA354154, and BioSample SAMN06041692-SAMN06041727). Each genotype had six cDNA libraries, for a total of 36 cDNA libraries for the six samples. In total 403,245,464 read pairs (150 bp) were included for analysis. The raw reads were trimmed with Trimmomatic  were obtained (data deposited in the Sequence Read Archives at NCBI under accession number of SRP154150). Raw reads from GBS data were trimmed to 64 bp using Stacks (The GBS data were obtained for each genotype previously as described by g Stacks .in silico concatenated (A + B) tetraploid genome (concatenated from diploid genomes), and only uniquely mapped reads were used for subsequent analysis  was used to align reads to A and B genomes separately (for the First method) followed by SNP filtering, which was extensively applied previously in peanut . Due to Finally, SNPs called from methods M1, M2, and M3 were filtered based on read depth. A homozygous genotype was called if there were at least four reads supporting either the reference or alternate allele. A heterozygous genotype was called if there were at least two reads supporting the reference and alternate allele, respectively.Arachis2 array. The genotype calling was performed as previously described  by Blat, a total of 230,730 probes had a single unique hit  to the genomes. To avoid any redundancy due to genome sequence duplications, CD-HIT-EST was applied and a total of 219,850 single-hit probes remained. Among the single-hit probes, a total of 20,212 probes corresponding to 2,072 Class I genes, and 9,582 probes covering 907 resistance genes were first selected . In addiTo select the remaining probes covering Class II gene models, the genome sequences were chopped into 44.3 Kb fragments using EMBOSS and a total of 56,296 fragments were obtained. By excluding the 2,783 fragments that were already covered by the previously selected probes, a total of 24,922 fragments were covered by the remaining single-hit probes. Thus, three fragments were randomly excluded and one probe was selected from each of the remaining 24,919 fragments so that all the selected probes were basically evenly distributed throughout the genome. Finally, a total of 55,000 probes  were selOn average, there were 14,211,850 paired-end reads (100 bp) per sample obtained from TES, 67,207,577 paired-end reads (150 bp) per sample from RNA-seq, and 2,901,440 single-end reads (100 bp) per sample from GBS . After tAfter mapping probe sequences to the genomes, under the alignment identity cutoff of ≥96, there were 50,580 and 48,275  probe target regions covered by reads according to overall and uniquely mapped reads, respectively . By decrArachis2 array, the randomly overlapped SNPs between the ones identified from the five pipelines and those placed on the array were used for SNP calling cross-validation. For TES data, the largest number of SNPs  was from M2, followed by M4 , M1 , M5 , and M3   . However  . Most hoArachis2 array revealed 23,060 SNP loci with high quality genotypes called for PI 262090 and UF 487A  of them were called as heterozygous SNPs from sequence data, but as homozygous SNPs from the array. This result showed that M1 was able to identify true polymorphic loci but may not assign a correct genotype due to the alignment of homoeologous reads while the sub-genome specific haplotype cannot be differentiated. In contrast, for the HAPLOSWEEP-based approaches M4 and M5, most of the genotype calls from sequence data matched those from the array . For theThe overall called and cross-validated SNPs among the five pipelines from TES and RNA-seq were further compared . For botArachis2 SNP array were compared , RNA-seq , and GBS (312)  . The perArachis2 array, 1,859  were monomorphic between E4 and E5; 1,519  were monomorphic between E6 and E7. By combining the filtered SNPs identified from the three NGS methods as well as those from the Axiom Arachis2 array, a total of 19,607 non-redundant homozygous SNPs between PI 262090 and UF 487A were obtained. Among those homozygous SNPs, a total of 222 and 1,200 were further obtained between E4 & E5 and E6 & E7, respectively, after filtering. Thus, they were placed on the graphical genotype maps  allopolyploid nature of the cultivated peanut and (2) near-isogenic nature of the two pairs of sister lines. Therefore, in this study, we implemented multiple NGS-enabled SNP genotyping methods and SNP calling pipelines to identify reliable and sufficient number of SNP markers.Arachis2 array  The concordance rate of heterozygous SNPs was always low between TES and RNA-seq. This could be caused by false positive SNPs derived from the misalignment of reads from homoeologous regions on the genome. (2) The alignment to A/B genome followed by SWEEP and SNP-ML filtering (M1) revealed a considerably smaller proportion of homozygous SNPs than the alignment to A + B genome followed by traditional filtering (M2), and HAPLOSWEEP approaches M4, and M5. As SWEEP was not able to differentiate haplotypes, by using A/B genome as the reference, a lot of true homozygous SNPs could be called as heterozygous SNPs due to misalignment. (3) M2 revealed a decent concordance rate (67.61%) of homozygous SNPs and could identify new and true polymorphisms that were not found by the HAPLOSWEEP approach. (4) When using HAPLOSWEEP, the alignment to A/B genome (M4) revealed more homozygous SNPs than alignment to the A + B genome (M5), however, M5 could also identify new and true polymorphisms that were not covered by M4. In summary, none of the pipelines above could cover all possible polymorphisms between the genotypes. However, the best option among the five analysis pipelines was to align the reads to A/B genome followed by HAPLOSWEEP, which can yield the highest amount of homozygous SNPs with a high concordance rate with the SNP array, similar to the rate reported in the recent study (74%) . AlternaArachis2 SNP array, the tool used for cross-validation of the SNP callings was designed based on the wild diploid genome and the probes designed for TES were also referred to the diploid genomes. Therefore, in this study, a concatenated A + B genome from wild diploid peanut was used for alignment to achieve a good consistency in comparison.In this study, we used the concatenated A + B genomes from the diploid wild peanut species  as the r6 generation, derived from the cross between PI 262090 and UF 487A (The two sets of sister RILs used in this study were selected at the F UF 487A . The NodArachis2 array were mostly discovered from 21 peanut genotypes, which may not be representative enough to cover all the genome polymorphisms. Axiom Arachis2 array would be a good choice for genotyping populations developed from or related to the genotypes used for the initial SNP discovery. However, if the populations to be genotyped are not related with the initial genotypes for the development of the Axiom Arachis2 array, then other NGS approaches should be considered. If genes or genomic regions of interest are to be focused, TES should be preferably considered, since the potential candidate regions can be specifically included for SNP identification. Among the SNP calling pipelines to be used for NGS data analysis, the best performing pipeline is to align the reads to A/B genome followed by SNP filtering using HAPLOSWEEP. To identify a larger number of true homozygous SNPs, other pipelines, such as the alignment to A + B genome with traditional SNP filtering, can be combined with HAPLOSWEEP.Based on the findings from this study, several suggestions were made for future SNP identification studies in peanut. SNPs included in the Axiom PRJNA354154, BioSample SAMN06041692–SAMN06041727, and NCBI SRP154150.The datasets generated for this study can be found in the NCBI SRP093688, BioProject JW conceived the experiments and secured the funding. ZP performed the experiments. ZP and ZZ analyzed the data and drafted the manuscript. JC and DP helped with data analysis. YC and PO-A provided the SNP array data. All authors read and approved the draft.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


This study investigated the informative regions and the efficiency of genomic predictions for backfat thickness, days to 90 kg body weight, loin muscle area, and lean percentage in Korean Duroc pigs. The several regions of the genome were identified and a significant marker was found near the MC4R gene for growth and production-related traits. No differences in genomic accuracy were identified on the basis of the Bayesian approaches in these four growth and production-related traits. The genomic accuracy is improved by using deregressed estimated breeding values including parental information as a response variable in Korean Duroc pigs.Genomic evaluation has been widely applied to several species using commercial single nucleotide polymorphism (SNP) genotyping platforms. This study investigated the informative genomic regions and the efficiency of genomic prediction by using two Bayesian approaches (BayesB and BayesC) under two moderate-density SNP genotyping panels in Korean Duroc pigs. Growth and production records of 1026 individuals were genotyped using two medium-density, SNP genotyping platforms: Illumina60K and GeneSeek80K. These platforms consisted of 61,565 and 68,528 SNP markers, respectively. The deregressed estimated breeding values (DEBVs) derived from estimated breeding values (EBVs) and their reliabilities were taken as response variables. Two Bayesian approaches were implemented to perform the genome-wide association study (GWAS) and genomic prediction. Multiple significant regions for days to 90 kg (DAYS), lean muscle area (LMA), and lean percent (PCL) were detected. The most significant SNP marker, located near the MC4R gene, was detected using GeneSeek80K. Accuracy of genomic predictions was higher using the GeneSeek80K SNP panel for DAYS (Δ2%) and LMA (Δ2–3%) with two response variables, with no gains in accuracy by the Bayesian approaches in four growth and production-related traits. Genomic prediction is best derived from DEBVs including parental information as a response variable between two DEBVs regardless of the genotyping platform and the Bayesian method for genomic prediction accuracy in Korean Duroc pig breeding. Genomic selection (GS) has been widely applied to several species, for example, pigs, chickens, beef, and dairy cattle, using commercial single nucleotide polymorphism (SNP) genotyping platforms from Illumina, GeneSeek-Neogen, and Affymetrix. These arrays estimate genomic-enhanced estimated breeding values (GE-EBVs), which are blended with classical estimated breeding values (EBVs) from classical genomic best linear unbiased prediction (BLUP) and molecular breeding values (MBVs) from summation of single nucleotide polymorphism (SNP) marker effects for genotyped animals. The most important parameter in genomic prediction modeling is the accuracy of genomic prediction for the estimation of GE-EBVs because the weights are determined on the basis of that parameter when blended with traditional EBVs and MBVs in a “correlated traits” approach . Two terGenetic improvements are achieved by reducing the generation interval and increasing the accuracy through genomic selection modeling in dairy and beef cattle. However, in pigs, genetic improvements with the generational interval parameter are limited by rapid generational turnover. Therefore, increased accuracy of genetic predictions may be the largest parameter impacting genomic selection in pig breeding . These aThe objectives of this study were to (1) identify informative genomic regions through a genome-wide association study (GWAS) and (2) to investigate and compare the accuracy of genomic prediction of two genomic evaluation methods using two Bayesian methods (BayesB and BayesC) under two medium-density, SNP genotyping platforms with two response variables (DEBVexcPA and DEBVincPA). This is the first study to assess the accuracy of genomic prediction for growth and production-related traits in Korean Duroc pig populations.A total of 1026 Duroc pigs were genotyped. These animals were genotyped with two medium-density SNP genotyping platforms, including 487 genotyped by the Illumina PorcineSNP60 version 2  and 539 genotyped by GeneSeek-Neogen PorcineSNP80 , respectively. They consisted of 61,565 and 68,528 SNP markers, respectively. The quality control measures in SNP markers and animals were performed by excluding 7849 and 7758 unmapped SNPs, 1458 and 3273 SNPs on sex chromosomes, 6399 and 1613 SNPs with a poor call rate (<0.90), 17 and 18 SNPs with a poor call rate for a duplicate SNP map-position for SNP markers, 7 and 5 animals with poor call rates (<0.90), 8 and 34 animals that did not match with phenotypes for each Illumina PorcineSNP60 version 2 and GeneSeek-Neogen PorcineSNP80 genotyping platform, and 3 animals with a poor call rate for duplicate genotypes between two SNP genotyping platforms. Consequently, the number of available SNP markers was 45,840 and 55,866 for 60K and 80K SNP panels, respectively, leaving 472 and 500 animals for 60K and 80K SNP panels, respectively, for use in further genome-wide association studies, and genomic prediction modeling.The imputation processes of these two medium density SNP panels (Illumina60K and GeneSeek80K) were separately performed using the following two steps: (1) FImpute version 2.2  imputed A multitrait animal model with 46,305 phenotypic data recorded from 2005 to 2017 and 72,781 pedigree records was applied to estimate the variance components and genetic parameters .h2 is the heritability, This was required as a priori information for the genomic prediction model and for EBVs and corresponding reliabilities for the genotyped animals and their sires and dams. These analyses used the ASReml version 4.1 software  for fouri et al. . After rTwo methods  in this analysis. The theoretical rate of the genetic variance could be assumed approximately 0.04% (100% /2454), but the stringent threshold of 0.8%, which is twenty times higher than the theoretical proportion was considered as the small reference set in Korean Duroc pigs. The Bayes factor (BF) was used to determine SNPs with a significant association within this region using Equation (4): Raftery  for the maxa and ija, and their standard deviations  was lower than for those with a lower MAF for both SNP genotyping platforms .These results are consistent with the results of Badke et al. , who shoGWAS for growth and production traits was performed using two commercially developed Porcine SNP genotyping platforms (Illumina60K and GeneSeek80K) to identify the most informative window regions and significant SNP markers based on the Bayes factor within these regions. GWAS analyses using BayesB with a high value of π (0.99) and a DEBVincPA response variable for growth and production-related traits in Duroc pigs were chosen because the informative window region and significant SNP markers were similarly distributed across the three response variables and Bayesian methods. The results of these associations are shown in RNF152 and MC4R. For DAYS, ASGA0004988, which was positioned on SSC1 at 177 Mb, was detected as an informative SNP marker, but the nearest gene was MC4R. The MC4R gene is a major determinant of the nervous system and plays a substantial role in the regulation of food intake, energy balance, and body weight in mammals [Three and four informative windows (1 Mb) were detected for BFAT using the Illumina60K panel and GeneSeek80K panel, respectively. The most significant window was identified on Sus scrofa chromosome (SSC)1 at 62 Mb using the Illumina60K panel and on SSC1 at 178 Mb using the GeneSeek80K panel, which explained 1.26% and 1.88% of genetic variance, respectively. Significant SNP markers, based on the Bayes factor, common to both two panels were ALGA0003581 and ALGA0003587, which were located on SSC1 at the 62 Mb position nearby the CGA gene. For DAYS, we detected five informative quantitative trait loci (QTL) using the Illumina60K panel and three informative QTLs using the GeneSeek80K panel. The regions of SSC7 at 124 Mb (1.58%) and SSC18 at 29 Mb (1.19%) were the most informative 1 Mb window regions in GWAS for DAYS using the Illumina60K and using GeneSeek80K, respectively. The common significant SNPs were ALGA0097693 (located on SSC18 at the 29 Mb position between TSPAN12 and CFTR) and ASGA0004988 (located on SSC1 at the 177 Mb position between RNF152 and MC4R) in both panels. We identified six significant regions using the Illumina60K panel and five significant regions using the GeneSeek80K panel for LMA. The most significant region using the Illumina60K was detected on SSC5 at 87 Mb (2.36%), and SSC1 at the 178Mb (3.56%) region was detected when using GeneSeek80K. Two informative QTLs were detected using the Illumina60K and GeneSeek80K panels for PCL. In addition, common significant SNPs were not identified using either panel. The GeneSeek80K genotyping panel contained more SNP markers in major genes than the Illumina60K genotyping panel . As a result, the informative SNP markers not included in the Illumina60K panel were detected using the GeneSeek80K panel. Interestingly, the WU_10.2_1_178188861 SNP located by the GeneSeek80K panel was associated with all growth and production traits except DAYS, which is not included in the Illumina60K panel and is located on SSC1 at the 178 Mb position between  mammals ,28,29. P mammals ,31,32 haA similar trend was observed with BayesC when using the GeneSeek80K, with a range of accuracies from 0.176 (BFAT) to 0.246 (LMA) and from 0.250 (BFAT) to 0.331 (LMA) for DEBVexcPA and DEBVincPA, respectively . These rThe average accuracies of genomic prediction across the growth- and production-related traits ranged from 0.177 (BFAT) to 0.244 (LMA) for the Illumina60K and GeneSeek80K when using DEBVexcPA as a response variable, and from 0.252 (BFAT) to 0.327 (LMA) for the Illumina60K and GeneSeek80K when using DEBVincPA as a response variable with 10-fold cross validation . In the An advantage of excluding the parent average (PA) was to avoid double counting. Otherwise using PA would shrink the individual EBV toward to the parent average . HoweverIn this study, we identified candidate genes for growth- and production-related traits in purebred Korean Duroc pigs, and evaluated and compared the accuracy of genomic prediction between two genotyping platforms, response variables, and two Bayesian methods (BayesB and BayesC). A total of 15 and 12 informative 1 Mb window regions for growth- and production-related traits were identified using the Illumina60K and GeneSeek80K panels, respectively. The genomic accuracy when using DEBVincPA as the response variable was of higher value than other response variables. We suggest that a fine-mapping study is necessary to pinpoint the causal variant of the informative genomic region , and that the genomic accuracy for growth- and production-related traits will be improved by adding a pinpoint for the causal variant of the informative genomic region. Furthermore, a genomic selection model for growth- and production-related traits could be useful for future genomic evaluation in purebred Korean Duroc pigs.


Meloidogyne incognita cause significant crop losses worldwide. Although RKN are polyphagous, with wide host ranges, races with differing host compatibilities have evolved. Associations between genotype and infection phenotype in M. incognita have not yet been discovered. In this study, 48 M. incognita isolates were collected from geographically diverse fields in Japan and their genomes sequenced. The isolates exhibited various infection compatibilities to five sweetpotato (SP) cultivars and were assigned to SP races. Genome‐wide association analysis identified 743 SNPs affecting gene coding sequences, a large number of which (575) were located on a single 1 Mb region. To examine how this polymorphic region evolved, nucleotide diversity (Pi) was scanned at the whole genome scale. The SNP‐rich 1 Mb region exhibited high Pi values and was clearly associated with the SP races. SP1 and 2 races showed high Pi values in this region whereas the Pi values of SP3, 4, and 6 were low. Principal component analysis of isolates from this study and globally collected isolates showed selective divergence in this 1 Mb region. Our results suggest for the first time that the host could be a key determining factor stimulating the genomic divergence of M. incognita.Plant parasitic root‐knot nematodes (RKN) such as  Pi) of the 1 Mb SNP‐concentrated region segregated with the sweetpotato races. The majority of SP1 and 2 genotypes were heterozygotes, whereas SP3, 4, and 6 genotypes were mostly homozygotes.Genomic diversity level ( RKN penetrate root cells and induce the production of giant cells close to the vascular bundle, from which the nematode obtains nutrients via an injected stylet. RKN exhibit sedentary lifestyles and reproduce asexually with the production of egg masses on the surface of plant galls. The four most damaging Meloidogyne species are considered to be M. incognita, M. javanica, and M. arenaria  and M. hapla (temperate)  is an important food crop that is highly nutritious and a good source of dietary fibre. Sweetpotato is also used as a bioenergy crop via processing of starch obtained from the tuberous root. In Japan, the major area for sweetpotato cultivation region is found in the south‐west. Fields in this area are often infested with M. incognita isolates that can infect sweetpotato, causing substantial crop loss. Previous research showed that M. incognita isolates collected from geographically different fields showed distinct infection phenotypes against five sweetpotato cultivars, Norin‐1, Norin‐2, Tanegashimamurasaki‐7, Elegant Summer, and J‐red  analysis or genome‐wide association study (GWAS). However, association techniques can be limited. For example, QTL analysis requires a mapping population, genetic maps, and DNA markers to allow association of genotype with phenotype. M. incognita isolates was used to increase the possibility of identifying genetic markers associated with host range. Forty‐eight isolates were sequenced and phenotyped using five SP cultivars according to the SP race method. This resulted in the identification of 743 candidate single nucleotide polymorphisms (SNPs) with effects on gene sequences. Surprisingly, more than 77% of the SNPs were located on a single contig. Diversity within this region was clearly associated with SP race. The presence of a high number of SNPs within a single region suggests the presence of a hotspot region in the M. incognita genome that diversified during the host adaptation process. To our knowledge, this is the first report associating genotype with infection phenotype in M. incognita.In this study, a large number of 22.1M. incognita isolates that were assessed, phenotyping was successful for 32 isolates  were obtained and assembled into 193.1 Mb primary contigs consisting of 374 sequences with N50 of 974.8 kb and 10.7 Mb haplotigs (167 sequences with N50 of 72.3 kb). The primary contigs were designated as MINJ2. In the subsequent gene prediction, 31,206 genes were predicted in the MINJ2 sequences , and five isolates from the Americas . The other two groups contained relatively few isolates. One group contained two African isolates (SRR4242456 and SRR4242469), one isolate from Anguilla (SRR4242479), and one isolate from Brazil (SRR7504310). The remaining group contained two Brazilian isolates (SRR7504322 and SRR7504330)  implemented in TASSEL 5  regions of ≤500 kb were found on contigs MINJ2_001F.1 and MINJ2_0012F.1, and longer regions of approximately 1 Mb were found on contigs MINJ2_005F.1 and MINJ2_021F.1  was detected in SP1 and 2, but not in SP3, 4, and 6 (mean: 0.19)  of the 743 SP‐related SNPs that affected gene sequences were located on the 1 Mb high‐s Figure .Pi region to examine the evolution of the race‐associated genomic region , moderate‐impact (11), and low‐impact (39) SNPs, were identified Table . RemovalM. incognita transcriptome data set (SRX2919273) encompassing several developmental stages  among isolates. Several regions were found that exhibited extended high‐Pi values, including the 1 Mb region with 575 SP‐related SNPs  is estimated to be more than 43 million years ago  proteins during the infection process of cyst nematodes (Heterodera spp.) is well understood. CLE proteins were necessary for the establishment of syncytium  was processed into a 13 amino acid peptide of sequence GKKPSGPNPGGNN, similar to the CLE motif , but it remained unknown whether the MAP protein was processed to produce functional CLE peptides  in A. thaliana  at 2,150 × g for 15 min at 20 °C using a high‐speed refrigerated centrifuge (CR22N) equipped with a swinging bucket rotor (R4SS) (Koki Holdings Co. Ltd). The J2 juveniles on the 40% sucrose layer were collected using a glass Pasteur pipette and rinsed twice with 2 ml sterile water in an Ultrafree‐CL centrifuge filter (Merck). The J2 juveniles were then pelleted by centrifugation at 15,300 × g at 20 °C for 10 min in an antistatic 1.5 ml tube. The pelleted J2 juveniles were stored at –20 °C until used for DNA isolation. Genomic DNA was isolated using Isohair (Nippongene) according to the manufacturer's instructions, with the exception that DNA was treated with RNase (Takara Bio Inc.) prior to extraction with phenol:chloroform:isoamyl alcohol (25:24:1 vol/vol/vol). DNA was ethanol precipitated, resuspended in 20 μl TE buffer, and stored at –20 °C.Genomic DNA of 4.4et al., et al., et al., Caenorhabditis training preset. Gene ontologies and domains of the predicted genes were annotated using BLAST searches against non‐redundant protein sequences and InterProScan, respectively. Genome structures were compared using D‐GENIES , and was then used for library preparation as described previously  with minor modifications. Genomic DNA samples (50–100 ng) were fragmented with NEBNext dsDNA Fragmentase (New England BioLabs) and purified with Agencourt AMPure XP (Beckman Coulter) to eliminate short (<300 bp) DNA fragments. Purified DNA was ligated to index adapters (Illumina), and amplified by PCR in accordance with the manufacturer's instructions (Illumina). Genomic libraries were sequenced using Illumina HiSeq4000 and NextSeq500 instruments to obtain 101 or 76 bp paired‐end reads, respectively. Low‐quality reads were trimmed using Trimmomatic v. 0.36 , trimmed, and mapped on our reference genome as described above. The relatedness of the M. incognita genome data generated in the two published studies and the current study was assessed by PCA implemented in TASSEL 5 . The effects of SNPs on gene sequences were determined using SnpEff v. 4.3T  implemented in TASSEL 5  The fold coverage shown in Table S1 did not affect mapping rate. (b) Comparison of mapping rate with previously reported Meloidogyne incognita reference genomeClick here for additional data file.FIGURE S2 Moving average of Pi for each contig with window size of 50 bp. The mean value of Pi was 0.15 over the whole genome. Extended high‐Pi (>0.3) regions of ≤500 kb were found on contigs 001 and 012, and longer regions of approximately 1 Mb were found on contigs 005 and 021Click here for additional data file.FIGURE S3 Comparison of nucleotide diversity (Pi) between sweetpotato races on contigs MINJ2_001F.1, MINJ2_012F.1, and MINJ2_021F.1. In each panel, black line shows the Pi plot of races 1 and 2, and red line shows the Pi plot of races 3, 4 and 6. These contigs did not show different Pi values in association with the racesClick here for additional data file.FIGURE S4 Phylogenetic tree of Meloidogyne incognita isolates of global origins. For isolates from Japan, no relationships between geographic origin and sweetpotato race were observedClick here for additional data file.FIGURE S5 A coverage plot for the contig MINJ2_005F.1 together with the Pi. Values in sliding windows after dividing the entire contig into 50 blocks were indicated by bold line for the Pi, and by broken line for the read coverage. The result showed no clear difference of the mapping depth between the first 1 Mb region with the concentrated single nucleotide polymorphisms (SNPs) and the following regions, suggesting that the concentrated SNPs region in this contig did not result from a collapsed artefact of the assemblyClick here for additional data file.FIGURE S6 Collection sites of Meloidogyne incognita isolates used in this study. Forty‐eight isolates were collected from sweetpotato and other crop fields in Japan. Isolates were collected over a wide geographical range, from Mori, Hokkaido (42°06′17″N) in the north to Ishigaki, Okinawa (24°20′26″N) in the south. Red dots indicate cities where fields are located. Orange dots indicate sample locations where the precise field locations are unknown. One isolate (Unknown_unknown) is not shown in this Figure, because its record of collected site was lostClick here for additional data file.TABLE S1 Summary of Illumina short read sequences accumulated for each isolateClick here for additional data file.TABLE S2 List of significant single nucleotide polymorphisms (SNPs) affecting coding sequences, related to the infection traitClick here for additional data file.FILE S1 Coding sequences of the predicted genes in MINJ2Click here for additional data file.FILE S2 Genome positions of the predicted genes in MINJ2Click here for additional data file.FILE S3 Protein translations of the predicted genes in MINJ2Click here for additional data file.


High-throughput genotyping boosts genome-wide association studies (GWAS) in crop species, leading to the identification of single-nucleotide polymorphisms (SNPs) associated with economically important traits. Choosing a cost-effective genotyping method for crop GWAS requires careful examination of several aspects, namely, the purpose and the scale of the study, crop-specific genomic features, and technical and economic matters associated with each genotyping option. Once genotypic data have been obtained, quality control (QC) procedures must be applied to avoid bias and false signals in genotype–phenotype association tests. QC for human GWAS has been extensively reviewed; however, QC for crop GWAS may require different actions, depending on the GWAS population type. Here, we review most popular genotyping methods based on next-generation sequencing (NGS) and array hybridization, and report observations that should guide the investigator in the choice of the genotyping method for crop GWAS. We provide recommendations to perform QC in crop species, and deliver an overview of bioinformatics tools that can be used to accomplish all needed tasks. Overall, this work aims to provide guidelines to harmonize those procedures leading to SNP datasets ready for crop GWAS. High-throughput genotyping, which leads to the identification of a large number of single-nucleotide polymorphisms (SNPs) is boosting the implementation of genome-wide association studies (GWAS), linking DNA variants to phenotypes of interest . In cropSeveral genotyping methods are available  procedures are of pivotal importance to minimize false-positive or false-negative associations, referred to as type I and type II errors, respectively. QC includes filtering out poor-quality or suspected artifactual SNP loci, filtering out individuals in relation to missing data, anomalous genotype call and genetic synonymies, and the characterization of ancestral relationships among individuals of the GWAS population. Excellent reviews focused on QC of human SNP data ; howeverThis review aims to provide recommendations on how to plan genotyping experiments and best practices on how to perform QC in crop species.per sample, and these two parameters are directly proportional. Given this premise, choosing the correct option for GWAS requires to have a clear idea on two key aspects, i.e., the number of SNPs that is sufficient/desirable to fulfill the GWAS goals and the cost associated with each genotyping alternative. In addition, genotyping methods come with different technical specifications that should be evaluated in relation to the particular GWAS experiment.Genotyping methods differ with respect to the number of identifiable SNPs and the cost of the analysis r2) between pairs of loci may take several Mb to decay to values indicating substantial linkage equilibrium (0.2 or 0.1)  .WGR is especially desirable for GWAS populations displaying rapid LD decay. Indeed, in this case, low marker density may result in missing genomic regions associated with phenotypic traits. Extremely rapid LD decay (in the range of a few base pairs) has been reported for highly heterozygous populations of open-pollinating species , in which recombination is effective in breaking up haplotypes . In thisWGR genotyping has been so far generally performed using paired-end Illumina technology e.g., , which, RRS consists in sequencing only a small fraction of the genome, thus reducing the cost of the analysis with respect to WGR . GenotypThe number of SNPs identified by RRS genotyping typically varies from a few to several thousands , dependiA major technical limitation of RSS is that the genomic distribution of SNPs depends on the specific combination of REs used . In addiSNP arrays for agrigenomics have been developed for over a decade to meet the needs for single research groups or consortia and are still widely used for GWAS in crops despite the decreasing cost of NGS-based technologies ; Table 1From a technical standpoint, SNP array genotyping has a series of advantages. First, genotype calls are generally accurate, even for highly heterozygous species . In addiA main disadvantage of SNP arrays is that they suffer from ascertainment bias , i.e., tQ) . As for array genotyping, these mainly consist in applying a clustering algorithm on fluorescence measurement data of ASO probes to distinguish samples into genotype clusters , and in assessing a set of QC scores on the goodness of cluster separation and signal-to-background ratio.Genotyping companies apply QC procedures depending on the method used. For NGS genotyping, these consist in removing loci with low sequencing depth  and loci with low PHRED-like quality score . Filtering for MAF ≥1–5% has been commonly applied for crop GWAS involving populations of a few hundred individuals , howeverAs for loci, the presence of individuals with high rates of missing data is also suggestive of technical issues, often related with poor quality and/or quantity of DNA samples. This can generate inaccuracies and bias in downstream analyses. We emphasize that filtering for SNP missingness should normally precede filtering for individual missingness, as the opposite procedure may result in unnecessary removal of individuals. In literature, very different cutoff thresholds for individual missingness have been reported . Our sugSNP loci characterized by excessive heterozygosity should be filtered out, as they are indicative of technical artifacts or paralogous/repetitive regions that could not be distinguished through the genotyping procedure . TherefoIT inbreeding coefficient  to filter SNPs in homozygous populations, and applied a minimal FIT threshold of 0.8 in case of a large population of maize inbred lines. The identification of gaps in the distribution of FIT across all loci may help to set up a threshold that allows the elimination of most of the genotyping errors while retaining the highest possible number of loci   can be performed to remove excessively heterozygous loci. In accordance with GWAS on human genotypic data, the HWE filter in open-pollinating crops has been generally applied using a threshold termelon . We strertichoke ; (ii) aprtichoke .In the case of crops, GWAS populations might contain several genetically identical samples. This is often caused by the occurrence, in germplasm collections, of unintended duplication of anonymous accessions and/or the occurrence of synonymous accessions. For example, genotyping with the 9K SNP array of the USDA grapevine collection revealed that 568 out of 950 accessions (58%) were genetically identical to at least another accession .The identification and removal of duplicated samples is usually performed on the basis of pairwise identity-by-state (IBS) or identity-by-descent (IBD). Pairwise IBS refers to the proportion of alleles shared by two individuals, whereas pairwise IBD refers to the proportion of two individuals’ genome tracing back to the same recent common ancestor . The latAncestral relationships generate LD between unlinked loci, so they are considered in the GWAS model to limit spurious associations . TherefoStarting from genotypic data, the analysis of population structure can be carried out through different approaches. Parametric methods, such as those implemented in the popular software STRUCTURE  and ADMINon-parametric methods such as principal component analysis (PCA) and multidimensional scaling (MDS) can be used to account for population structure, using coordinates of each individual along the main PCA/MDS axes as covariates in association models . While nKinship ultimately depends on the proportion of the genome that is identical-by-descent. Therefore, in order to account for kinship, the GWAS model can use IBD estimates from pedigree notes. However, it is clear that pedigrees of crop species might be in several cases unknown or inaccurate. As mentioned above in this paragraph, methods to estimate pairwise IBD from genotypic data have been also developed. These yield a kinship matrix, also referred to as K-matrix, which has been widely used together with the Q-matrix or PCA/MDS covariates to implement the so-called Q + K and P + K GWAS models .We finally highlight that several works showed that a simple pairwise IBS matrix could efficiently capture both remote and recent ancestry . Therefo1  performed and the file requested as input. Therefore, the investigator is often called to the conversion of genotypic data among different formats, the most common being variant call format (VCF), haplotype map (hapmap), pedigree/map (ped/map), binary (bed/bim/fam), Affymetrix chip (chp), Illumina sample map and final report, and structure. PGDSpider1  is a ded1 , which dradiator , acceptiFIT filter. In relation to the study of genetic ancestry, it has options for LD pruning and MDS, and for the estimation of pairwise IBS and IBD.Several open-source software suites are available for QC. Among the most widely used, PLINK , startinFIT filter. As for the genetic ancestry options, it can perform PCA/MDS and estimate pairwise IBS. While PLINK is based on command lines, thus requiring specific training by the user, TASSEL also implements a graphical user interface. Another important feature of TASSEL is the possibility to easily build histograms for SNP and individual missingness and SNP heterozygosity, which, as discussed above, are useful to set up cutoff thresholds specific for each GWAS experiment.Compared with PLINK, the abovementioned TASSEL  accepts Investigators with some bioinformatics skills may be interested in QC tools also enabling filtering procedures depending on the genotyping method, which, as stated above, are commonly performed through external services. For NGS genotyping, we cite VCFtools , a commaFinally, concerning the study of genetic structure, besides the above mentioned STRUCTURE  and ADMIThis work is thought to provide researchers, who mainly focus on the biology and breeding of crop species, with essential technical and economic aspects required to plan and carry out cost-effective and accurate GWAS. To the best of our knowledge, this is the first work specifically addressing the issue of QC in crop species, so we expect that it may contribute to the future harmonization of the procedures leading to the obtainment of high-quality SNP datasets ready for GWAS.SP, ND’A, and EC conceived the review. SP, ND’A, EC, and CD wrote the manuscript. CL and LR critically revised the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.