The specifics of breeding and selection significantly affect genetic diversity and variability within a breed. We present the data obtained from the genetic analysis of 21 thoroughbred and warmblood horse breeds. The most detailed information is described from the following breeds: Arabian, Trakehner, French Trotter, Standardbred, and Soviet Heavy Horse. The analysis of 509,617 SNP variants in 87 stallions from 21 populations made it possible to estimate the genetic diversity at the genome-wide level and distinguish the studied horse breeds from each other. In this study, we searched for heterozygous and homozygous ROH regions, evaluated inbreeding using FROH analysis, and generated a population structure using Admixture 1.3 software. Our findings indicate that the Arabian breed is an ancestor of many horse breeds. The study of the full-genome architectonics of breeds is of great practical importance for preserving the genetic characteristics of breeds and managing breeding. Studies were carried out to determine homozygous regions in individual breeds and search for candidate genes in these regions. Fifty-six candidate genes for the influence of selection pressure were identified. Our research reveals genetic diversity consistent with breeding directions and the breeds’ history of origin. Horse breeding is a branch of animal husbandry in which genotypic variability has a significant range compared with other types of farm animals. Historically, humans have used horses for different purposes, which has led to the genetic diversity of horses. Initially, they served as a source of food, and as they became domesticated, they began to be used as a means of transport . In a nuThe variety of modern horse breeds includes breeds that have existed for a long time, as well as relatively young breeds, which were established in the last 100–150 years. At the same time, the intra-breed diversity of horses is not significant. The principles and approaches used in the selection of a particular breed of horse are determined not only by the purposes of breeding but also by geographical and environmental (climatic) conditions. There are different reasons for breeding horses of various uses, both in the past and at present. While economically useful qualities such as productivity and efficiency were of primary significance, in a number of breeds, selection was carried out according to traits and for fixing a certain gait. In many horse breeds, selection has traditionally been carried out according to a set of characteristics. The economic aspects of horse breeding in Russia involve four main areas in which horses are currently being used: working purposes, production, breeding, and sports. In each of these areas, distinct technologies have been developed for maintenance and breeding, as well as the organisation of production; different goals are established; and different strategies are used to increase economic efficiency .The use of molecular genetic technologies has been an important advancement that can increase the efficiency of animal genome evaluation, making it possible to study the genetic architecture of horse breeds. The analysis of microsatellite loci has become widespread in horse breeding, becoming an essential feature of the horse breeding practice for the control of origin and pedigree. It continues to be used to study the genetic diversity and phylogeny  of breedThe aim of this work is to reveal the features of the whole-genome architecture obtained using data from more than 500,000 SNPs in horses of various origins and areas of use.The principles of laboratory animal care were followed, and all procedures were conducted according to the ethical guidelines of the L.K. Ernst Federal Science Center for Animal Husbandry. The protocol was approved by the Commission on the Ethics of Animal Experiments of the L.K. Ernst Federal Science Center for Animal Husbandry (Protocol Number: 2020/2) and the Law of the Russian Federation on Veterinary Medicine No. 4979-1 (14 May 1993).DNA was isolated from the sperm of stallions stored in the Cryobank of Genetic Resources . DNA was collected from 87 stallions of 21 breeds. For this study, samples were obtained from the following breeds: Arabian, Akhal-Teke, Terek, Hanoverian, Trakehner, Holstein, Don, Budyonnovskaya, Thoroughbred, Rhine, Oryol, French Trotter, American Standardbred Trotter, Vladimir Draft, Russian Draft, Soviet Draft, Welsh Pony, French Sel, Draft Crossbreed, and Half-breed. DNA was isolated from stallion sperm according to the standard method using mercaptoethanol to destroy the membranes of spermatozoa, proteinase K to remove proteins, and phenol extraction. The quality of the obtained DNA samples was determined in several stages: (1) by measuring the concentration of double-stranded DNA on a Qubit™ (1.0) fluorimeter ; (2) by determining the ratio of the degree of absorption of DNA preparations at 260 and 280 nm using a NanoDrop 8000 device ; and (3) by determining the degree of DNA degradation via electrophoresis in agarose gel with a concentration of 1%.DNA genotyping was performed on Axiom™ Equine Genotyping Array chips . DNA samples with a genotyping quality of more than 98% for SNP loci were selected. SNP selection was performed using the PLINK 1.9 program . After qThe genomic architecture of horse breeds was calculated using the program Admixture 1.3  and grapGenetic diversity was assessed based on the expected (He) and observed (Ho) heterozygosity values calculated using the PLINK 1.9 program .The calculation of inbreeding based on FROH was carried out using the R package detectRUNS. The minimum ROH length size was 500 kb to minimise the number of false-positive results .The analysis of over 50,000 SNP variants revealed the genomic architecture of 21 horse breeds. The cross-validation error is shown in At K = 4, the Trakehner breed was identified as a cluster with similarity to the Thoroughbred breed. This commonality persists even as the number of probable ancestors increases. The French Trotter (FR), with a significant number of ancestral populations, stood out from the rest, demonstrating commonality with the American Trotters (AMST) and the Thoroughbred (ENG). A highly informative picture of the horse population structure is displayed at K = 5 .Since some horse breeds were represented by a low number of individuals in the total sample, five populations were selected for further calculations, from which at least six samples were obtained for analysis. Heterozygosity values for the selected horse breeds are presented in For each of the studied breeds, the maximum number of homozygosity runs was observed on different chromosomes . Thus, fFurther annotation of genes was carried out in homozygous regions. In horse breeding, the use of information provided in studbooks to describe the origin of animals is widespread . UnfortuThe development of genetic technologies, including genome-wide screening using SNP data, has provided a good alternative to methods based on pedigree analysis. Using custom-designed chips, inbreeding levels can be compared both between breeds  and withIn our study, it was possible to analyse representatives of different breeds. These are breeds for which using producers of other breeds is not allowed, the so-called purebred and half-breeds. The analysed populations also differed in the type of occupancy.The population structure and genetic homogeneity of the studied sample of horses were calculated using the Admixture 1.3 program. . The graThe Arabian breed was characterised by low heterozygosity compared with other horse groups considered in this study, as evidenced by the values calculated for all SNPs (E (hom) = 0.290, O (hom) = 0.293). This is slightly lower than the values reported by Cosgrove et al., (2020) , who anaFROH revealed a significant range of values for the group of Arabian horses . Since tBased on the results of ROH analysis, the inbreeding of the Standardbred breed had high values compared with other breeds, despite their unrelated origin. The slightly scattered values and consolidation of FROH on chromosomes 11, 14, 21, 24, 28, and 31 indicate a non-random distribution of the islands of homozygosity and the effect of selection pressure on the breed . FollowiIn our study, DNA samples of Arabian horses from different regions were analysed. All animals were categorised into the same cluster, which indicates the same trend of breeding practices as those in different countries.In the course of analysing the structure of horse populations, it was found that, already at K = 3, horses of the Trakehner breed have many genomic features in common with Thoroughbred riding horses. Historically, the Thoroughbred has been a major influence in Trakehner breeding for 250 years. In the 1940s, the Trakehner breed underwent a significant reduction in livestock, and the current animals of this breed are descendants of a limited number of producers. In a survey conducted on four German warmblood breeds , ROH seaHanoverian and Holstein horses were bred for use in agriculture and later for military purposes. Therefore, despite a certain influence of the Thoroughbred horse breed, they retained traces of the genomes of the Arabian breed and trotting breeds to a greater extent than the Trakehner breed . Along with the Arabian, Thoroughbred, and Standardbred breeds, heavy horses also play a significant role. Cluster analysis showed a high differentiation of horses of the Soviet Heavy Draft breed from other groups of horses. This is well explained by the origin of the breed, which was officially registered in 1952. When creating the breed, mainly local Russian draft-type horses of various origins, bred via absorptive crossbreeding with Brabansons, were used as a maternal basis. Representatives of other draft breeds in our study were used as individual specimens but have traces of common origin with the Soviet Heavy Draft.For the study of breeds, it is important to identify specific areas of homozygosity associated with selection.Two homozygous regions were found on chromosome 3, the occurrence of which in the Soviet Heavy Draft population was 100%. The loci included the genes presented in The PIEZO1 gene, which has been studied in humans and mice, contributes to iron metabolism . Other sIn our study, the ROH islet, including the QDPR and FAM184B genes, was found in the Soviet Heavy Draft horse breed, while in the work of Szmatoła et al., it was observed only in draft horses . A homozAnother gene, namely CFAP299 (from the same region), has also been associated with the coat length of white yaks . More reGenomic regions were found to be accumulated homozygous regions on chromosome 4 in all studied American Standardbred horses (n = 8). Part of the genes annotated at these sites perform tasks related to general cellular function. Thus, the NUP42 gene is responsible for the synthesis of the protein nucleoporin 42, which promotes the export of protein molecules from the nucleus . The STKAMIGO1 gene has been previously linked to the genetic basis of cognitive function in humans [Interleukins act as intracellular communication signals in haematopoiesis, stress, inflammation, immunity, and tissue repair . Studiesn humans .Chromosome 7 exhibited a large extended homozygous region in American Standardbred horses . It inclIL-27 has an important metabolic function in humans and is a promising target for immunotherapy against obesity . In AmerThe CACNA1A gene is involved in muscle contraction in many horse breeds . In our The ACP5 gene protein participates in osteogenesis and is associated with selection for bone strength in trotting horse breeds . The ECSThe RIOK3 gene located at ECA8 in AMST is responsible for the innate immune response in humans . The preOne homozygous region was found on chromosome 11 in the Soviet Heavy Draft, which includes a group of HOXB family genes . They play a fundamental role in animal morphological diversity and the control of axial morphology along the anteroposterior axis of the body .The SNX11 gene affects body thermoregulation in mice and humans . The preThe NFE2L1 gene on chromosome ECA11 is associated with lipid metabolism and obesity in humans and mice . A largeThe CBX1 and ABI3 genes in humans are related to neurocognitive development , and theThe CALCOCO2 and NXPH3 genes, which are involved in degenerative joint disease in humans, have been discovered in a Soviet heavyweight at ECA11 . The preA gene belonging to the ZNF gene family, which plays a key role in the regulation of muscle growth and differentiation, was also found in ECA11. The discovered gene may be associated with selection pressure for strength, endurance, and a high percentage of muscle tissue, especially in draft breeds .A homozygous region of length 21,063,873–22,280,900, including the genes associated with muscle, ligament, and tendon development , was found at ECA17 in horses of the Trakenen breed , which iThe presence of annotated genes in the homozygous state in the Trakenen breed is probably related to selection for strong and athletic qualities in this horse breed. In French trotting horses, ROH was found in ECA22 with a frequency of occurrence of 100%. The genes included in this homozygous region are mainly associated with the development of different muscle groups (GPCPD1), different metabolic processes in the body   and behaThe accumulation of homozygous variants of these genes is probably related to the history of the origin of the French trotting breed (purebred breeding) and selection for endurance and persistence.The analysis of 509,617 SNP variants in 87 stallions from 21 populations allowed us to assess the genetic diversity at the genome-wide level and differentiate the studied horse breeds from each other. Thus, the whole-genome architecture of breeds is of great practical importance for preserving the genetic characteristics of breeds and managing selection. ROH islet analysis has proven to be a valuable approach to identifying differences between highly specialised breeds (such as trotting and draft breeds) and riding breeds. Our studies revealed genetic diversity corresponding to the direction of breeding and the history of the origin of the breeds. The heterogeneity of the occurrence of homozygous regions, and the low overall heterozygosity of horses of the Arabian breed with a slight level of inbreeding, were noted. Horses of the Trakehner breed showed genetic similarity to Thoroughbreds. Representatives of the Soviet Heavy Draft breed formed a separate cluster while showing their influence on other heavy draft breeds included in the analysis.Studies were carried out to identify hotspots of homozygosity associated with the breed characteristics of horses. Notably, 14 homozygous regions were found, occurring in all the studied representatives of four of the five breeds of horses. The American Standardbred had a high level of inbreeding and a significant occurrence of regions of homozygosity on chromosomes 4, 7, 8, and 18. “Traces of selection” were identified, and 56 candidate genes significant for selection were annotated. In horse breeding, it is important to consider the accumulation of homozygosity to reduce the effects of inbreeding.


Thrips tabaci is the most important, but not the only thrips species causing damage. We investigated which thrips species affects onion and related species worldwide, how much genetic variation there is within T. tabaci populations, and how this evolves. Furthermore, we determined the reproductive mode and the correlation between the genetic and geographic distances. Thrips samples from infested onions or related species were obtained from 14 different locations worldwide. Species and haplotypes were determined through DNA barcoding with the mitochondrial Cytochrome Oxidase subunit I (COI) gene. Thrips tabaci was the most commonly observed species, but Scirtothrips dorsalis, Thrips palmi, Frankliniella intonsa, Frankliniella occidentalis and Frankliniella tenuicornis were also found, especially at the beginning of the growing seasons and depending on the location. The Nei’s genetic distance within T. tabaci was less than 5% and the haplotypes were clustered into two phylogenetic groups, each linked to a specific mode of reproduction, thelytokous or arrhenotokous. Thelytokous thrips were more common and more widely distributed than arrhenotokous thrips. A high percentage of heteroplasmy was detected in the arrhenotokous group. Heteroplasmic thrips were only found in populations where thelytokous and arrhenotokous were present in sympatry. Some T. tabaci haplotypes were present in high frequency at several sampled locations. No correlation was found between the genetic and geographic distances, which points to anthropic activities spreading thrips haplotypes throughout the world.Thrips are a serious pest in many crops. In onion cultivation,  Thrips are small insects of around 1 mm long that belong to the order Thysanoptera. This order contains 6,200 species [Thrips tabaci Lindeman (Thysanoptera: Thripidae), commonly known as onion thrips [Thrips tabaci is the major pest in onions worldwide [Thrips tabaci has a high reproductive rate [n thrips , is polyn thrips . Thrips orldwide . It feedorldwide , 6. Thriive rate , shows cive rate , 8, and ive rate , 10, incive rate . All theive rate .Thrips tabaci comprises three phylogenetic groups associated with different host plants or reproductive modes [ve modes . One grove modes . The othve modes , 15. Theve modes  and pyreve modes .T. tabaci corresponds to an irregular mode of reproduction of the arrhenotokous thrips on tobacco [In the case of thelytokous thrips, reproduction is asexual by parthenogenesis in which the embryos develop into female offspring without fertilization. Arrhenotokous thrips can reproduce both sexually and asexually, producing female offspring from fertilized eggs , and hap tobacco . The mos tobacco , but cro tobacco . Such cr tobacco .Thrips tabaci originates from the Mediterranean region where onion is the main host plant [T. tabaci was distributed over the world is unknown. In a study with Chinese T. tabaci populations, no correlation was observed between genetic and geographic distances [T. tabaci are not evenly distributed around the world [st plant . From thst plant . How T. istances . This mahe world .Genetic diversity has different hierarchical levels, between and within species. The within-species variation can be subdivided into intra and inter-population diversity. How the variation is distributed over populations is highly influenced by reproductive mode, gene flow, and selection . GeneticThrips genus showed that the COI gene is ideal for the simultaneous detection of intra- and interspecific genetic diversity, as the range of diversity within and between species does not overlap; between thrips species about 20% of genetic diversity is detected in the COI gene sequence, while within T. tabaci the genetic diversity is below 5% [Different genotypes of thrips can be discriminated by sequencing (part of) the Cytochrome Oxidase subunit I (COI) gene , 24, 26.T. tabaci reproductive modes (or lineages), which are impossible to discriminate morphologically, either by using the COI sequence directly or by using reproductive-mode specific primers [T. tabaci the presence of heteroplasmic individuals has been reported. Heteroplasmic thrips contain mitochondria with copies of the COI gene from different lineages [The COI gene can also be used to distinguish the different  primers . The modlineages , and in lineages .T. tabaci is the most important pest in onion [T. tabaci, and relate these with the haplotypes reported in the NCBI sequence database; 3) compare the genetic variation within and between sampled thrips per location; 4) analyze the geographical distribution of the T. tabaci haplotypes as well as the correlation between the genetic and geographic distances of populations; and 5) analyze the seasonal variation in the composition of thrips populations at three locations.Although in onion , other tin onion . It is iin onion . This reThrips samples were collected from onion or onion-related species present in experimental fields aimed to identify thrips resistance at 14 locations around the world . Adult t® Blood & Tissue Kit , according to the suppliers’ recommendations.Thirty-three individual thrips (adults or larvae) were randomly chosen from each sample. In one case (Konan 08.08.2019) only 16 adult thrips were collected. DNA was extracted from individual thrips using the DNeasyATTAGGAGCHCCHGAYATAGCATT and MTD9.2R CAGGCAAGATTAAAATATAAACTTCTG [DNA from individual thrips was used as the template for PCR amplification, using the universal COI primer pair, MTD7.2F AACTTCTG . Twelve T. tabaci reproductive mode-specific PCR reaction was performed using three PCR primers, one universal (TCOR-ATTGCGTAAATTATTCCTAAAAGTCCA) and two reproduction-mode-specific primers: 1) mtCOA-SSP TCOS-AACAGCTATTCTCCTTCTTTATCTC, amplifying a fragment of 261 nucleotides in arrhenotokous reproducing thrips, and, 2) mtCOT-SSP TCOC-GAACAGTATATCCACCTTTATCAACG, amplifying a fragment of 451 nucleotides in case of thelytokous reproduction [The oduction . In the oduction .T. tabaci haplotypes was used as the template for PCR amplification. Twelve μl of PCR solution was prepared according to the manual of the Qiagen Kit . The PCR amplification protocol used was 95°C for 15 min, 35 cycles of 98°C for 10 s, 60°C for 1 min, and 68°C for 1 min, followed by a final extension at 72°C for 10 min. The size of PCR products was determined by 1.5% agarose gel electrophoresis, with a 100 bp DNA Ladder (Thermo Fisher Scientific) as a reference, and stained with RedSafe™. All the reproductive mode-specific DNA fragments obtained were purified and sequenced as described above. All the consensus sequences from the different T. tabaci haplotypes were aligned using CLUSTALW [DNA from individual thrips of the different CLUSTALW  in MEGA CLUSTALW  and grouT. tabaci haplotypes collected in our research plus the ones reported in the NCBI database for which the reproductive mode was indicated in the passport data or published papers. One F. occidentalis haplotype from NCBI was used as an outgroup. The congruence between the reproductive mode derived from mode-specific primers and the position in the phylogenetic tree was confirmed for non-heteroplasmic individuals. The frequency of thelytokous and arrhenotokous T. tabaci per location was calculated. A Chi-square test was carried out to determine if the thelytokous and arrhenotokous T. tabaci were evenly distributed over locations.The molecular determination of the reproductive mode  is not cA COI gene database was generated containing all the consensus sequences obtained from the individual thrips sequenced. All consensus sequences were ordered and grouped in MEGA 7 by location or location-time for those locations that were sampled more than once  . FinallyDifferent haplotypes were identified with the Pop Art program  and blasTo depict which haplotypes were present at the sampled locations or locations-time, a stacked bar chart was made with InfoStat  using thT. tabaci using the COI sequence.Nei’s genetic diversity was calculated as the number of base substitutions averaged over all pairwise comparisons in the thrips COI gene fragment by location or location-time using MEGA 7 . Within T. tabaci populations the 545 T. tabaci sequences were grouped by location or location-time in MEGA 7 and the number of base substitutions per site was averaged over all sequence pairs between samples [To calculate the Nei’s genetic distance between  samples . The gen samples  implemenT. tabaci populations at the different locations, was calculated using the matrix of Nei genetic distances and the matrix of geographic distances among locations. For locations with more than one sampling time, each sample was considered a population, and the geographic distance between them a zero. The matrix of Nei genetic distances for the T. tabaci populations was calculated in MEGA 7. The matrix of geographic distances, among the different sampling locations, was calculated using the Diva Gis program [The correlation between the genetic and geographic distances, of the  program . The cor program . The geoT. tabaci available in the NCBI database on November 25, 2022 were retrieved (https://www.ncbi.nlm.nih.gov/nuccore/?term=thrips+tabaci+coi). This resulted in a total of 504 sequences. Sequences were trimmed to obtain a fragment between positions 341 and 775 of the mitochondrial COI gene, that coincides with the fragment of our study. Sequences that missed more than five nucleotides at the beginning or end of the sequence compared to our COI gene fragment, were discarded. The T. tabaci haplotypes obtained in the present study were named H followed by a number, or HU in the case a haplotype was unique in our dataset . A Neighbor-Joining tree [T. tabaci and one of F. occcidentalis (accession JQ082479.1 from the NCBI database) as an outgroup  and Krabbendam (The Netherlands) two species were found, T. tabaci and F. occidentalis, whereas in De Kwakel (The Netherlands), one individual of F. tenuicornis was found next to T. tabaci. Thrips species different from T. tabaci were found in Allium spp. fields only, while in the onion fields all the thrips were T. tabaci.Five hundred and eighty-six individual thrips from 14 locations were sequenced for the COI region. All data can be found in uicornis . Thrips ocations . In SansT. tabaci in the first or second sampling, while later in the season (last sampling) only T. tabaci was found   and T. palmi (26%) were found. In the third sample, taken 20 days later, only T. tabaci was found  . After 2as found .T. tabaci sequenced, 58 COI haplotypes were found , it was present at 10 of the 14 locations. In Tulancingo de Bravo, haplotype H1 was the only haplotype found. In Huron, H1 was the most important with a genetic diversity close to zero , all with a different haplotype  followed by H1 (25%)  . In the H1 (25%) .T. tabaci haplotypes clustered into two main groups, except for one haplotype (HU58) which did not belong to any group. Of the 58 haplotypes, 20 had the thelytokous fragment (451 bp) and were present in group 1 of the NJ tree, 11 haplotypes had the arrhenotokous fragment (261 bp) and were present in group 2, and 26 haplotypes had both fragments. Haplotype HU58 did not amplify in the reproductive mode PCR-specific reaction. Among the haplotypes having both fragments, three belonged to group 1, and 23 to group 2  bands  showed tT. tabaci analyzed belonged to nine common and widely spread haplotypes   or both bands (3), while 31 belonged to Group 2 of which 11 had only the arrhenotokous band and 20 had both bands.The majority (72%) of individual 10, H28) , of whic10, H28)  and had 10, H28) , while t10, H28)  with bot10, H28) . The othT. tabaci populations collected by us and the geographic distances between the locations where they were collected  (T. tabaci reproductive mode per location is presented in Χ2) indicated significant differences between the observed thelytokous and arrhenotokous T. tabaci frequencies. The null hypothesis, assuming that both reproductive modes are evenly distributed per location around the world was rejected  = 333.05, p = 2.83 E-63).No correlation was found between the matrixes of Nei’s genetic distances for the : 0.428) . The freT. tabaci haplotypes  (T. tabaci accessions from the NCBI collected in Greece in 2004 from tobacco plants.Group 1 consists of 49 haplotypes of which we collected 23 and 26 were reported in the NCBI database . Among tr study) . Thirty-r study) . The thiT. tabaci from other species. The genetic distance between T. tabaci haplotypes was in all cases below 5%, which is similar to other studies that used the same COI primers set. Furthermore, the average genetic distance between T. tabaci haplotypes and the other species’ haplotypes was around 20%, also in agreement with previous studies [This research aimed to study thrips diversity in fields with onion or its related species around the world. We set out a comprehensive study in which we sequenced 33 randomly chosen individual thrips per location or location-time, allowing us to identify with a 95% certainty, all genotypes (haplotypes) present in the population with a frequency of at least 0.1 . Some lo studies , 30, 40.Thrips tabaci, which is in agreement with previous studies [Thrips palmi, Scirtothrips dorsalis, Frankliniella tenuicornis, Frankliniella intonsa, and Frankliniella occidentalis, were present at specific locations. The presence of F. occidentalis has previously been reported in onions in Tanzania [Frankliniella fusca, was the most important species in onion [T. tabaci in onions in Georgia is increasing [T. tabaci outcompetes F. fusca on onion [F. intonsa in A. cepa and A. fistulosum has also been reported in Poland [Thrips palmi and S. dorsalis are commonly reported in melon and pepper, respectively [Allium spp. [F. tenuicornis in Allium. Until now, F. tenuicornis was reported as specific for wild and cultivated Gramineae [We found that the main species of thrips that affects onion and its related species is  studies , 41, 42.Tanzania  and GeorTanzania . In Georin onion . Howevercreasing , which mon onion . The occn Poland . Thrips ectively  and bothium spp. , 47. Finramineae .T. tabaci were found at certain locations only. Which thrips species are found is likely to be determined by the climatical conditions and centers of origin of the species. Sansai is located in the tropics, which may explain the presence of thrips species like S. dorsalis and T. palmi which are typical for these regions [S. dorsalis, commonly known as chili thrips. Pepper is the main host of S. dorsalis [S. dorsalis to remain in an area during intercropping [F. intonsa at Sansai might be associated with the presence of Allium fistulosum in the Allium spp field since it has been reported as the common host plant for F. intonsa [Allium field in Sansai was surrounded by several (crop) species, including banana, bamboo, and herbs such as marigold and weeds, from which thrips may have moved into the field. In the case of De Kwakel, the field was surrounded by wild Gramineae grasses which are the typical host of F. tenuicornis [Thrips species other than  regions , 50. In  regions . The predorsalis , but itscropping . The pre intonsa . Apart fuicornis , which muicornis , 55 and T. tabaci as the unique species present at the end of the season. Phenomena that may be explained by a combination of factors, including a higher competition capacity [Our results also show that the change in species composition at certain locations can be fast and dynamic, with capacity , higher capacity , and migcapacity .T. tabaci was found. In fields planted with Allium spp., other thrips species were found as well, although not all Allium spp. fields sampled had thrips species other than T. tabaci. Whether the more diverse set of host plants in the Allium spp. fields has played a role in shaping the thrips communities and populations remains to be established. Our setup does not allow us to draw any firm conclusions on this.In the sampled onion fields only T. tabaci haplotypes obtained from onion and related species clustered into two phylogenetic groups. The haplotypes within group 1  in populations that contained individuals of both phylogenetic groups (sympatric distribution), suggesting that they are the result of crossings between members of these groups. Such crossings have already been reported , 21. TheT. tabaci haplotypes not reported before in the NCBI database of which the majority (32) belongs to the group 2 (arrhenotokous lineage).Novel variation seems to be generated. We found clear examples of mutations. One individual at Huron (haplotype HU40) is only one base pair different from H1, and therefore a likely descendant of that haplotype. Also, recombination between different COI haplotypes that coexist in heteroplasmic individuals may be a source of novel variants, a mechanism likely to occur in group 2, where the percentage of heteroplasmy is high. We found 51 The low frequency of most arrhenotokous haplotypes is in agreement with their limited presence in the NCBI database. Many arrhenotokous haplotypes might have been undetected in former studies due to limited sampling efforts . Since wIn our study, six out of the nine more frequent haplotypes were thelytokous and showed a global distribution, which might be expected from their generalist nature . Of the Thrips tabaci is reported in more than 120 countries, but only for 29 countries, there is molecular data related to reproductive mode. Thelytokous thrips were found in all 29 countries while the arrhenotokous were reported in 16 countries only [T. tabaci for the first time in Argentina, Uruguay, France, Spain, and Thailand, and we confirmed their presence in Israel [n Israel , Japan [n Israel , and Then Israel .T. tabaci were not evenly distributed around the world. The thelytokous thrips (group 1) were present at all sampled locations  have higher fitness compared to others. For example, H8 in Konan (Japan), H1 in Sansai (Thailand), and H7 in Pulpi (Spain) were the haplotypes most frequent across the season at these locations. In the case of H8 a positive interaction between haplotype and location might be expected since this haplotype is exclusively present in Konan. For the haplotypes H1 and H7 an overall higher fitness might be inferred since they were found at several locations. It will be interesting to see if the most frequent haplotypes across the season at certain locations are maintained across different years.Based on our results the diversity of T. tabaci populations from different locations or years [T. tabaci populations not only varies within a season but also among locations. The absence or nearly zero genetic diversity in Tulancingo de Bravo (Mexico) and Huron (USA) is not commonly reported and might point to a relatively recent invasion of T. tabaci or a poor adaptation of other haplotypes. On the other hand, highly diverse T. tabaci populations were also detected, distant from each other and composed of several haplotypes from different phylogenetic groups.Different levels of genetic diversity are reported for or years , 43, 60.T. tabaci populations both close to the center of origin [T. tabaci is larger than originally thought [Genetic diversity in pests is expected to be high in the regions that coincide with the center of origin of the pest, and lower in places that were invaded recently . High dif origin  (Pulpi)  thought , or thatT. tabaci populations [F. occidentalis populations [Bemisia tabaci [T. tabaci haplotypes  are present both in Uruguay  and Konan (Japan) can most probably be explained as the result of commercial trade of garlic and/or onion as a cause of the gene flow between these distant locations.The distribution pattern of the haplotypes throughout the world and their frequencies at different locations explain the lack of correlation between the geographical and genetic distances. This lack of correlation has also been reported in a study within China with ulations  and F. oulations . The faculations . Plant ta tabaci . Human aa tabaci . The finThrips tabaci was the main species present in Allium fields. Nevertheless, other thrips species were also found in small numbers. Thrips other than T. tabaci were found at specific locations with diverse surroundings, mainly in the tropics and particularly frequent at the beginning of the growing season. In T. tabaci the genetic distance among haplotypes was below 5% and two different phylogenetic groups were found, which can be linked to the reproductive modes thelytokous (group 1) and arrhenotokous (group 2). Within group 2 there was a remarkable presence of heteroplasmic individuals. The thelytokous thrips were more frequent and worldwide distributed, while arrhenotokous thrips have a more restricted distribution and were always found in sympatry with the thelytokous thrips. The level of genetic diversity within populations differed among locations. Some T. tabaci haplotypes were present in a high number at different locations worldwide. No correlation was found between the genetic and geographic distances, suggesting that human activities may have spread thrips haplotypes throughout the world.S1 FigT. tabaci populations sampled at 14 different locations and the geographic distances in kilometres between the locations. There was no correlation between the Nei’s genetic distances among T. tabaci populations at the different locations and its geographical distances .Nei’s genetic distances between the (TIF)Click here for additional data file.S2 FigT. tabaci sequences in the phylogenetic tree was 0.027. Next to the accession or haplotype name, the number of times a specific fragment was found among the 112 NCBI accessions numbers and our 58 haplotypes is shown between brackets, and further detailed in The COI gene fragment used consisted of 434 nucleotide positions. Branch lengths is based on the number of base substitutions per site. The average genetic distance among all (TIF)Click here for additional data file.S1 TableIn the last two columns the total sites and coverage of the NCBI accessions are depicted in comparison with the haplotypes found in our study after all sequences aligned and trimmed before and after position 1 and 434 respectively of the fragment amplified in our study.(XLSX)Click here for additional data file.S2 TableFor each individual thrips the haplotype and species are presented. The latter is based on a BLAST against the sequences in the NCBI database.(XLSX)Click here for additional data file.S3 TableThrips tabaci haplotype frequencies per sample considering only the T. tabaci in the samples that were originally composed by multiple thrips species. In all cases the frequencies are colored by conditional formatting from red to white in descendent order respectively.a) Thrips haplotypes frequencies considering all the thrips sequenced per sample. b) (XLSX)Click here for additional data file.S4 TableThe different COI fragments are grouped per reproductive type.(XLSX)Click here for additional data file.


However, whether corals can adapt to ocean acidification and how remains unclear. We addressed these questions by re-examining transcriptome and genome data of Acropora millepora coral holobionts from volcanic CO2 seeps with end-of-century pH levels. We show that adaptation to ocean acidification is a wholistic process involving the three main compartments of the coral holobiont. We identified 441 coral host candidate adaptive genes involved in calcification, response to acidification, and symbiosis; population genetic differentiation in dinoflagellate photosymbionts; and consistent transcriptional microbiome activity despite microbial community shifts. Coral holobionts from natural analogues to future ocean conditions harbor beneficial genetic variants with far-reaching rapid adaptation potential. In the face of climate change, these populations require immediate conservation strategies as they could become key to coral reef survival.Ocean acidification, caused by anthropogenic CO 2 seeps that are exposed to future ocean acidification conditions are acclimatized and adapted to low pH conditions, which could be critical to the survival of coral reefs on a global scale.Corals living near CO Over this period, oceans absorbed around a third of the total anthropogenic CO2 emissions3, resulting in a pH reduction and a decrease of carbonate saturation in surface waters commonly referred to as ocean acidification4.In the last 250 years, atmospheric partial pressure of carbon dioxide , depend on carbonate to build their calcium carbonate structures8. Examples of poleward range expansions of tropical coral species have been reported in southern Japan in response to rising sea temperatures9, while mechanisms of rapid acclimatization of corals are still being unraveled. Rapid acclimatization usually takes place at the holobiont level and may include photosymbiont shuffling, bacterial community shifts and epigenetic modifications transmitted to the progeny through transgenerational plasticity10. Although acclimatization mechanisms will surely play an important role on coral long-term survival, the amount of standing genetic variation and the presence of adaptive alleles in coral metapopulations will be decisive in future coral reef persistence12.According to these predictions, many reefs will degrade within the following decades unless corals are able to keep pace with the foreseen global environmental changes. Corals’ responses are limited to two main processes: poleward range expansions or persistence through adaptation and/or acclimatization2 seeps act as natural analogues of a future acidified ocean and provide unique opportunities to study long-term acclimatization and adaptation to ocean acidification14. Coral populations occurring in these in situ natural laboratories could harbor adaptive alleles conferring them with high resilience and/or resistance to ocean acidification. These adaptive alleles may have been selected by natural selection like in coral colonies living in naturally high-temperature microclimates15. For instance, temperate azooxanthellate solitary Mediterranean corals from a CO2 seep system of Italy exhibit high genetic differentiation in genes involved in calcification16. However, no study to date has used CO2 seeps to study genomic signals of local adaptation to ocean acidification in tropical reef-building corals.Underwater volcanic CO17) together with recent high-quality reference genomes to (i) unveil genomic targets of natural selection in the coral host, (ii) outline algal symbiont population differentiation and (iii) assess microbiome functionality changes in the reef-building coral Acropora millepora from the CO2 seep system of Milne Bay Province in Papua New Guinea . Kenkel and coauthors17 compared gene expression profiles of Acropora millepora and their algal symbionts from control and seep environments in Dobu and Upa-Upasina reefs, identifying core transcriptomic responses involved in long-term acclimatization to ocean acidification. Here, we called Single Nucleotide Polymorphisms (SNPs) from Kenkel and coauthors’17 transcriptomic data using a recently sequenced high-quality reference genome of A. millepora20 to uncover genomic signals of natural selection and local adaptation to high pCO2 conditions. SNPs associated with environmental variables and SNPs showing signals of natural selection provide insight into the particular genomic regions that are under selective pressure, and have commonly been used to inform about the adaptive potential of coral species21. We hypothesize that these signals exist in genes involved in calcification and skeleton formation processes, on top of changes in gene expression. Symbiodiniaceae community composition in A. millepora does not differ between coral colonies from CO2 seeps and control environments in the seep system of Milne Bay Province, with Cladocopium goreaui  being the dominant species found in all coral colonies21. However, we hypothesize that intraspecific genetic differences exist between CO2 seep and control C. goreaui populations due to environmental or coral host selection of specific C. goreaui genotypes. To test this hypothesis, we called SNPs using C. goreaui transcriptomic data from Kenkel and coauthors17 with a pool-seq approach, and performed population genetic analyses. Finally, Morrow and coauthors22 identified microbial community changes driven by a reduction of symbiotic Endozoicomonas in A. millepora from CO2 seeps at Upa-Upasina reef. We hypothesize that these microbial community changes also drive changes in microbial transcriptional activity and extracted microbiome metatranscriptome from Kenkel and coauthors’17 transcriptome data to unveil specific functions that might differ between A. millepora microbiomes from CO2 seep and control sites.Here, we used a publicly available transcriptomic dataset  and the migration network analysis revealed high levels of connectivity among the four sites . A total of 2,707 SNPs were retained after SNP calling and filtering.A total of 25.8 million reads mapped the C. goreaui pool-seq SNP dataset showed genetic differentiation between reefs and environments   Table .Fig. 5PoA total of 702,876 reads were taxonomically classified with Kraken2 using the NCBI taxonomic information, all of them belonging to Bacteria. The non-metric multidimensional scaling (NMDS) plot on the Bray-Curtis dissimilarities among colonies showed no clustering of samples, indicating similar abundances of metatranscriptomic reads from each taxonomic group regardless of reef and environment Fig. . In factA total of 318,320 reads were functionally classified with Kraken2, all of them belonging to Bacteria. As for the taxonomic classification, the NMDS plot based on the functional classification showed no clustering of samples, indicating similar abundances of metatranscriptomic reads from each GO term regardless of reef and environment Fig. . SignifiAcropora millepora in the CO2 seep system of Milne Bay Province. For instance, carbonic anhydrases play major roles in the physiology of coral calcification24 by catalyzing the interconversion of CO2 to bicarbonate ions and protons. Endothelin-converting enzyme, and CUB and peptidase-domain containing protein are part of the coral skeleton proteome26 and are involved in membrane processing, extracellular matrix/transmembrane and protein modification processes, as well as vesicle/secretion and metal binding26. Alongside these three enzymes, a relevant amount of genes encoding for coral skeletal proteins such as α-collagen, Protocadherin, Coadhesin, and Complement C3, also presented signals of adaptation to seep environments , which is involved in the innate immune response and the coral stress response40, was a top discriminatory gene for seep environment17. Interestingly, signals of selection and associations with seep environments at the TNF receptor-associated factor were found in the present study, confirming that changes in its expression are part of an adaptation mechanism to long-term elevated pCO2. Signals of environmental-driven adaptation were also found in eight additional differentially regulated loci from Kenkel and coauthors17, suggesting cis-regulation of gene expression in key components of the coral response to acidification, including calcification, transcription processes, and the immune system 42. We found that some of the candidate adaptive genes here were shared with genes locally adapted in these studies, suggesting common targets of natural selection across environmental stressors. For instance, we found seven genes in common between our candidate adaptive genes and those conferring heat resistance in Acropora hyacinthus15 . Since the 26 S proteasome degrades damaged ubiquitin-conjugated proteins after any type of cellular stress46, different stressors may induce a similar selective pressure on the regulation of this protease machinery. Finally, many ribosomal proteins seem to be frequently targeted by natural selection under different long-term environmental stressors41. Signals of adaptation in ribosomal proteins may be due to the role of translational reprogramming during the stress response48 or to their extra-ribosomal functions as p53 regulators during cellular stress49.Previous studies scanning for genomic signals of local adaptation and genotype-environment associations on reef-building corals were mostly focused on disentangling their adaptive responses to elevated temperature50. Hence, our dataset could be missing other primordial gene regions bearing signals of adaptation that might have been detected with whole transcriptome or whole genome sequencing approaches. However, high linkage and low probability of recombination events within a single gene ensure that signals of selection found in tag-based RNAseq transcripts are representative of selection in that whole gene. In fact, because of the high chance of linkage within neighboring genomic regions, it is common practice in whole genome selection scans to produce thinned SNP datasets filtering physically linked SNPs within a few kbp, resulting in a similar exclusion of putative adaptive SNPs52.Unlike whole transcriptome sequencing, tag-based RNAseq only produces short fragments of the transcriptome, complementary to the 3’-end of the transcriptspCO2. In fact, the pool-seq approach implemented in this study yielded more informative results than other commonly used methods  about the effects of ocean acidification on algal photosymbionts. Here, we detected the presence of a highly differentiated seep-specific genetic cluster of C. goreaui in Dobu Seep site, and a combined effect of both space and environment in structuring C. goreaui populations, suggesting that symbiont shuffling may well be a long-term strategy for adaptation to acidification in A. millepora53.Besides revealing signals of selection in the coral host genome, our results also showed how other compartments of the coral holobiont  respond to long-term elevated 2 seep colonies55 . Finally, two additional genes, known to be differentially expressed between symbiotic and apo-symbiotic Aiptasia anemones, were under selection in the CO2 seep corals57 . Kenkel and coauthors17 (2018) sequenced tag-based RNAseq libraries for a total of 59 Acropora millepora colonies originating from two reefs in this system, Upa-Upasina and Dobu reefs. At each site, they sampled coral fragments from both a CO2 seep site and an adjacent control site <2.5 km apart: 14 colonies from Dobu CO2 seep site , 15 from Dobu control environment , 15 from Upa-Upasina CO2 seep site , and 15 from Upa-Upasina control environment 17  from tag-based RNAseq data were called following GATK best practices and Rogier and coauthors69. First, raw reads were adapter and quality trimmed using specific perl scripts for tag-based RNAseq with default parameters (available at https://github.com/z0on/tag-based_RNAseq), followed by a quality check using fastqc 0.11.970. Clean reads were then mapped against the A. millepora high-quality reference genome71 using bwa-mem 0.7.1772, and sorted with samtools 1.1673. MarkDuplicates 2.18.25 in Picard tools (http://broadinstitute.github.io/picard/) was used to mitigate biases introduced during library preparation and minimize gene expression variations. Finally, SplitNCigarReads in GATK 4.2.4.174 was applied as the last post-processing step as recommended by GATK best practices.Raw reads were downloaded from the NCBI SRA BioProject PRJNA36265275, sample names were added to each original individual BAM file and merged into one single stream with bamaddrg (https://github.com/ekg/bamaddrg). Only variants belonging to reads with mapping quality >20 were retained, using the FreeBayes flag -m 20. VCFtools 0.1.1676 was used to filter and retain biallelic SNPs  with a minimum quality score of 20 (--minQ 20) and present in at least 70% of the individuals (--max-missing 0.7). A minimum allele frequency filter of 0.05 (--maf 0.05) was set to avoid calling singleton SNPs. Our final A. millepora coral host dataset consisted of 79,273 SNPs.Prior to calling variants with FreeBayes 1.177, BayPass 2.378in basic mode, BayPass in covariate mode, and a Redundancy Analysis (RDA) using the rda function in the vegan R package79. Due to the negligible population structure found among sites  pruned dataset was first obtained using plink 1.980 and plink 2.080. A first run was performed with the LD pruned dataset, after which the resulting omega matrix was used for the controlled run with the whole SNP dataset. To establish the threshold of the XtX statistic to consider a SNP as under selection for the BayPass in basic mode, a neutral SNP distribution was simulated with the simulate.baypass R function included in the BayPass package. Then, BayPass was run on the simulated data and, finally, SNPs under selection with an FDR = 0.05 were obtained by selecting the 95% quantile of the simulated XtX distribution.BayeScan was run with default parameters, using the -snp option, and a false discovery rate of 0.05 (FDR = 0.05). To control for potential population structure in the BayPass in covariate mode and an RDA were performed, using the pH value of each site as the covariate environmental data for both analyses. For the RDA, missing genotypes were first imputed with the most common genotype at each site, and the rda function was run on the imputed dataset. SNP loadings were extracted for the RDA axis 1, and the outlier threshold was established as 2.5 times the standard deviation, equaling a p = 0.01.To identify genotypes associated with pH changes, BayPass 81. As tag-based RNAseq produces fragments of the transcriptome near the 3’-end of the transcripts50, many reads were expected to map 3`-UTR regions, which are generally poorly annotated in reference genomes. Indeed, 16.7% of the SNPs were annotated as intergenic SNPs by SnpEff using A. millepora genome annotations from Fuller and coauthors20. To improve annotations of these transcripts, BLASTN and BLASTX searches82 against the nr NCBI database were performed using default parameters. Although the exact mechanisms by which these genes operate might still be unknown, differentially expressed genes in transcriptomic studies are functionally involved in the response to the environment they were exposed to. Thus the function of the annotated candidate adaptive SNPs was investigated and when possible, determined using numerous previously published transcriptomic and functional coral studies.To avoid false positives, a SNP was considered adaptive if it appeared at least in two of the four analyses performed. This resulted in a total of 625 candidate adaptive SNPs, which were annotated using the reference genome annotations and SnpEffA. millepora reference genome was annotated using the PANNZER2 web server83. Then, a GSEA was performed with the GOEnrich_pannzer2 function in the PlantNGSTools R package84, using the GO annotations obtained with PANNZER2 and the list of candidate adaptive genes. Following the original description of the GSEA method85, only those gene sets with a false discovery rate <0.25 were selected  in order to control for false positives. The REVIGO web server86 was used to cluster, remove redundant, and find representative enriched GO terms for each GO category . Although some GO terms should be interpreted with caution in non-model organisms, the species-neutrality design of the GO database specifically allows the transfer of functional annotations from model genes to their non-models orthologues87.A Gene Set Enrichment Analysis (GSEA) was performed to test whether there were over-represented Gene Ontology (GO) terms in the candidate adaptive SNP annotations compared to the whole genome. The proteome fasta file of the e.g., Bennett and coauthors88; Mattingsdal and coauthors52; Zou and coauthors89) using the function --thin in VCFtools, resulting in a dataset of 11,169 independent neutral SNPs.To verify if corals from the two sampling sites were genetically distinct or homogeneous populations, population genetic analyses were performed. A neutral SNP dataset of 78,648 SNPs was obtained by excluding the 625 candidate adaptive SNPs using the VCFtools function --exclude-positions. Subsequently, to avoid the inclusion of physically linked loci, the neutral SNP dataset was thinned to ensure physical distance of at least 10 kbp  implemented in the adegenet R package91. STRUCTURE was run for 200,000 MCMC iterations with a burn-in of 50,000 iterations, using the admixture model and setting the putative number of clusters (K) from 1 to 10 with 10 replicates for each run. STRUCTURE HARVESTER92 was used to find the most likely number of genetic clusters using the Evanno Delta K method93. Subsequently, CLUMPAK94 was used to average individual’s membership across replicates and graphically display STRUCTURE results. The DAPC analysis was performed using the sampling sites as a prior and choosing the optimal number of retained principal component axes using the xvalDapc function in the adegenet R package. Two and eight PC axes and two and three DA axes were retained for the neutral and the adaptive SNP datasets, respectively.Population structure in both the independent neutral and the candidate adaptive SNP datasets was assessed using the Bayesian clustering approach in STRUCTURE 2.3GST method of the divMigrate function in the diveRsity R package95, and the global FST value was calculated using the basic.stats function in the hierfstat R package96.Additionally, for the independent neutral SNP dataset, a migration network was inferred using Nei’s 21 and Kenkel and coauthors17 demonstrated that the Symbiodiniaceae type did not differ between control and CO2 seep environments, with Cladocopium goreaui  being the dominant species in both environments. Hence, clean reads from each colony were mapped against the Cladocopium goreaui reference genome obtained from http://symbs.reefgenomics.org97 using bwa-mem2 v. 2.2.198. Positions with a mapping quality <20 were filtered out with samtools view and files were sorted by reference position using samtools sort.To test for photosymbiont genetic differentiation between sites, a pool-seq approach was implemented. In this approach, every coral colony corresponded to one population pool of algal endosymbionts. Both Noonan and coauthorsC. goreaui endosymbionts was performed using FreeBayes with the --pooled-coninuous flag to account for pooled populations of unknown number of individuals. VCFtools was used to filter and retain SNPs (--remove-indels) with a minimum quality score of 20 (--minQ 20), a minimum allele frequency of 0.05 (--maf 0.05) and present in at least 70% of the individuals (--max-missing 0.7). Our final C. goreaui photosymbiont dataset consisted of 2,707 SNPs. Population structure was assessed with a DAPC analysis as indicated above for the coral host, retaining 20 PC and three DA axes as optimized by xvalDapc. The vcf file was then transformed into a pooldata object using the vcf2pooldata function in the poolfstat 2.1.1 R package99. A pairwise FST table was calculated using the compute.pairwiseFST function in the poolfstat R package and the results were visualized with the pheatmap function in the pheatmap 1.0.12 R package100. Additionally, two analyses of molecular variance (AMOVA) were conducted using the amova function in the poppr 2.9.3 R package101 to test the partition of genetic variance in both a spatial and an environmental model, with p-values based on 1000 permutations. In the spatial model, the genetic variance was predicted by sampling site  nested within reef system . In the environmental model, the genetic variance was solely predicted by the environment of the population pools  to test whether the environment has a significant effect on the endosymbiont genetic variance.SNP calling of 102, using the NCBI taxonomic information combined with the RefSeq libraries “bacteria”, “archaea”, “fungi”, and “viral”. Kraken-biom 1.0.1103 was used to create a BIOM table from all Kraken reports that was then imported into R using the import_biom function in the phyloseq 1.40 R package104. Relative abundance of transcriptomic reads were plotted at the phylum level by site (following Morrow and coauthors22) using ggplot2 3.4.1105, and a non-metric multidimensional scaling (NMDS) plot was performed with the ordinate and plot_ordination functions in phyloseq using the NMDS method on the Bray-Curtis dissimilarities among colonies .Microbiome transcriptomic reads were taxonomically and functionally classified in order to study potential microbiome transcriptomic differences across environments. Taxonomic classification  was performed on the unmapped reads  with Kraken2 2.1.2106. Functional classification was performed with a tree-shaped hierarchy of GO terms using the Molecular Function subset106. This GO tree-shaped hierarchy was combined with the RefSeq libraries “bacteria”, “archaea”, “fungi” and “viral” to build a database and to perform a direct functional classification of our unmapped clean reads with Kraken2 2.1.2. Kraken-biom 1.0.1 was used to create a BIOM table from all Kraken reports that was then imported into R using the import_biom function in phyloseq. An NMDS plot was performed on Bray-Curtis dissimilarities among colonies as detailed above. Additionally, in order to identify Molecular Functions that significantly differed between environments, a Welch t-test was run for every GO term independently using the oneway.test function in the stats 4.2.3 R package107, grouping samples by environment (seep vs control colonies). A Bonferroni correction was applied to the significance level to avoid false positives due to multiple testing.Differences in microbiome functionality were assessed between environments following Gardiner and coauthorsFour analyses were used to identify SNPs under natural selection or associated with pH in the coral host, and then those present in at least two of the four analyses were considered as under selection in order to avoid false positives. Subseqently, a Gene Set Enrichment Analysis was performed to detect over-represented GO terms in the candidate adaptive SNP annotations, using a false discovery rate approach to control for false positives. See more details in Methods section, subsection “Identification and annotation of loci under selection and genotype-environment association analyses in the coral host”.oneway.test function in the stats 4.2.3 R package107, in order to identify Molecular Functions that significantly differed between environments. The Welch t-test was chosen after checking for equal variances using the var.test in the stats 4.2.3 R package107, which indicated that there were significant differences in variances and hence the classic t-test could not be applied. A Bonferroni correction was applied to the significance level to avoid false positives due to multiple testing, lowering the significance level from 0.05 to 0.00027.For the coral microbiome, a Welch t-test was run for every GO term independently using the Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Reporting Summary


Phakopsora pachyrhizi, is a devastating fungal disease that threatens global soybean production. This study conducted a genome-wide association study (GWAS) with seven models on a panel of 3,082 soybean accessions to identify the markers associated with SBR resistance by 30,314 high quality single nucleotide polymorphism (SNPs). Then five genomic selection (GS) models, including Ridge regression best linear unbiased predictor (rrBLUP), Genomic best linear unbiased predictor (gBLUP), Bayesian least absolute shrinkage and selection operator (Bayesian LASSO), Random Forest (RF), and Support vector machines (SVM), were used to predict breeding values of SBR resistance using whole genome SNP sets and GWAS-based marker sets. Four SNPs, namely Gm18_57,223,391 (LOD = 2.69), Gm16_29,491,946 (LOD = 3.86), Gm06_45,035,185 (LOD = 4.74), and Gm18_51,994,200 (LOD = 3.60), were located near the reported P. pachyrhizi R genes, Rpp1, Rpp2, Rpp3, and Rpp4, respectively. Other significant SNPs, including Gm02_7,235,181 (LOD = 7.91), Gm02_7234594 (LOD = 7.61), Gm03_38,913,029 (LOD = 6.85), Gm04_46,003,059 (LOD = 6.03), Gm09_1,951,644 (LOD = 10.07), Gm10_39,142,024 (LOD = 7.12), Gm12_28,136,735 (LOD = 7.03), Gm13_16,350,701(LOD = 5.63), Gm14_6,185,611 (LOD = 5.51), and Gm19_44,734,953 (LOD = 6.02), were associated with abundant disease resistance genes, such as Glyma.02G084100, Glyma.03G175300, Glyma.04g189500, Glyma.09G023800, Glyma.12G160400, Glyma.13G064500, Glyma.14g073300, and Glyma.19G190200. The annotations of these genes included but not limited to: LRR class gene, cytochrome 450, cell wall structure, RCC1, NAC, ABC transporter, F-box domain, etc. The GWAS based markers showed more accuracies in genomic prediction than the whole genome SNPs, and Bayesian LASSO model was the ideal model in SBR resistance prediction with 44.5% ~ 60.4% accuracies. This study aids breeders in predicting selection accuracy of complex traits such as disease resistance and can shorten the soybean breeding cycle by the identified markersSoybean brown rust (SBR), caused by  Glycine max) (Phakopsora meibomiae), the exotic one (Phakopsora pachyrhizi) was much more aggressive and caused an epidemic on soybean in South America and spread to North America (Soybean brown rust (SBR) is one of the most devastating fungal diseases of soybean (ine max) . It firsine max) . The disine max) . The risine max) . Compari America .Phakopsora pachyrhizi can quickly spread over a long-range through wind-borne urediniospores  has been applied in soybean breeding to accelerate the development of disease-resistant varieties, and the GWAS is of vital help to MAS . CompariThe objectives of this study were to identify SBR resistance-associated SNP markers and to characterize the ability of genomic prediction in order to use SNP markers in selecting soybean breeding lines highly resistant to SBR.https://npgsweb.ars-grin.gov/gringlobal/method?id=492634)  . Based omination . Based omination .https://www.soybase.org/snps/download.php. SNPs with >10% missing data, >8% heterozygous genotypes, and <10% minor allele frequencies (MAF) were removed, and 30,314 SNPs were included in the GWAS study.The Soy50K SNP Infinium Chips  and a toLEA is an R package for population structure and genomic signature analysis of local adaptation. The inference algorithms used by R are based on a fast version of structure available from the R package LEA . The str2 ≈ 0.4), and a minimum threshold value 0.05 for MAF.TASSEL 5.0  was usedhttps://zzlab.net/GAPIT/index.html; https://github.com/jiabowang/GAPIT3) by setting PCA = 6, with the Kinship for MLM, CMLM, MLMM, SUPER; and Pseudo QTNs for FarmCPU and BLINK.GWAS was performed using the Generalized Linear Model (GLM), Mixed Linear Model (MLM) , CompresThe accuracy and efficiency of SNP selection were computed to evaluate the significant SNP by the allele proportion in the population.Selection accuracy (SA) = 100*[/]/ΔE, where ΔE = E1/E2, E1 = Observed number of S or R genotypes/(Number of R genotypes + Number of S genotypes); E2= Expected number of S or R genotypes/(Number of R genotypes + Number of S genotypes).Selection efficiency (SE) =100*[/]/ΔF, where ΔF = F1/F2, F1 = Observed allele frequency of SNP, and F2 = Expected allele frequency of SNP. In this study we set the E2 and F2 as an ideal equilibrium value (50%).http://www.soybase.org) and the Phytozome database (https://phytozome.jgi.doe.gov).Candidate genes were selected based on the peak significant SNP in each LD region located within 50 kb on either side of significant SNPs , and furGP was conducted using seven SNP sets: All SNP set  and six GWAS-derived SNP marker sets. The six GWAS-derived SNP marker sets consisted of those significant SNPs from highest LOD [–log] to low LOD value (2.0) to make each set as 28, 100, 500, 1,000, 2,000, and 5,000 SNPs, respectively. Genomic estimated breeding value (GEBV) was computed using five statistical models: Ridge regression best linear unbiased predictor (rrBLUP) , GenomicA five-fold cross-validation was performed for each GP. The association panel was randomly divided into 5 disjoint subsets, 4 subsets were used as training set, and the remaining set was considered testing set. A total of 100 replications were conducted at each fold. Mean and standard errors corresponding to each fold were computed. Genomic prediction accuracy was obtained by computing the Pearson’s correlation coefficient (r) between GEBV and the observed phenotypic value for the testing set .Out of 3,082 soybean accessions evaluated for TAN lesion type, 71 (2.3%) were rated 1~2, 1,009 (32.7%) were rated 2.3~3, 1,746 (56.7%) were rated 3.1~4; and 256 (8.3%) were rated 4.2~5 in a rating scale of 1 to 5. Accessions with a mean severity of 2.7 or less  were considered resistant, while those with a mean severity of 4.0 or more  were considered susceptible. Accessions between the two categories were considered moderate. There was a large range in the distribution of each category Figure 1A total of 30,314 high quality SNPs were used to perform GWAS in the soybean accessions. Number of SNPs per chromosome ranged from 1,027 on chr20 to 1,898 on Chr16, with an average of 1,515.7 SNPs Figure 2The structure analysis helped identify the most promising genetic variations to better understand the genetic basis of the trait. The population structure of the soybean accessions was analyzed using the R packages “LEA” and the peak of ΔK was observed at K = 6, indicating of the presence of six subpopulations or clusters Table S12 value of 0.6, indicating a relatively strong LD correlation between nearby variants. At 216 kb, the LD decayed with an R2 value of 0.4, indicating a moderate level of LD correlation between nearby variants. Finally, at 296 kb, the LD decayed with an R2 value of 0.2, indicating a weak level of LD correlation between nearby variants (We examined the linkage disequilibrium (LD) decay patterns by 30,314 genome-wide SNPs. To visualize the LD decay patterns across distances, we plotted the LD decay curves by GAPIT 3 Figure 4The high convergence and consistency of the GWAS were observed among seven models. For example, the top six significant SNPs from the FarmCPU model including: Gm09_1,951,644 (10.06), Gm20_36,724,867 (6.54), Gm03_38,913,029 (6.10), Gm19_44,734,953 (5.7), Gm02_7,235,181 (5.18), and Gm04_47,132,429 (5.06) also had the high LOD values, which were at least 5.20, 2.67, 3.77, 3.59, 3.69, and 4.00 in other models. SNPs Gm04_45,884,688, Gm10_39,142,024, Gm14_2,492,139, Gm16_4,935,328, etc. were significant among all seven models S2. A toP. pachyrhizi R genes Rpp1, 2, 3, and 4, respectively, which were verified and reported in last decades.Due to variations in LD decay across different regions, a conservative distance of 50 kb was set to select candidate genes as the region of the significant SNPs. There are four SNPs (loci) out of the top 100 associated markers, including Gm18_57,223,391, Gm16_29,491,946, Gm06_45,035,185, and Gm18_51,994,200, were identified to locate in close proximity to four main Thirty-five candidate genes that might be associated with SBR disease resistance were found in the regions of the top 28 significant SNP markers Table 1.Phakopsora. For the resistance alleles, SE varied from 50.0% to 84.2%, with an average of 57.5%; and the SA varied from 50.0% to 82.2%, with an average of 58.2%. SNP Gm09_1,951,644 had the highest values in both SA and SE in resistance effect. For susceptible alleles, the SE varied from 50.0% to 69.8%, with an average of 55.1%; and the SA varied from 50.3% to 56.9%, with an average of 52.7%. SNP Gm04_46,295,839 (52.7%) had the highest values in both SA and SE in susceptible effect (Selection accuracy (SA) and Selection efficiency (SE) reflect the contributions of selected alleles from the top 100 significant SNP to the resistance or susceptibility to e effect Table S6The 100 significant SNPs not only had the highest LOD value but were most repeatable across all GWAS methods as well. Following the same approach, six additional GWAS-based SNP sets were created, each consisting of 28, 100, 500, 1,000, 2,000, and 5,000 SNPs, respectively. In this study, we applied seven datasets, namely, All_SNPs , GWAS_5000SNPs, GWAS_2000SNPs, GWAS_1000SNPs, GWAS_500SNPs, GWAS_100SNPs and GWAS_28SNPs for GP analysis by five different GS models Figure 5Although the number of SNPs fluctuated by GWAS datasets, all the accuracy curves showed a similar pattern among the five models. The trend depicted by the left side of the curves indicated that as the number of SNPs decreases from 5,000 to 1,000, the accuracy of the prediction increases, too. The highest accuracies were observed when using the 1,000 SNP set, which were varying from 35.7% (RF) to 60.4% (Bayesian LASSO). And, as the number of SNPs continued to decrease from 1,000 to 100, the accuracy of GP also decreased. In all six GWAS based SNP sets, the Bayesian LASSO achieved the highest average GS accuracy of 53.0%, followed by rrBLUP with an average accuracy of 51.9%. On the other hand, the lowest accuracy of 36.2% was recorded when using the RF model. The GS accuracies of gBLUP and SVM models were at almost the same level but were relatively lower using the SVM model Table S7P. pachyrhizi is commonly evaluated based on three types of SBR lesions: “TAN”, “RB”, and “Mixed”. The “TAN” lesion type is characterized by heavy fungal sporulation typically develop on susceptible soybean leaves, while the RB or “reddish-brown” lesion type has been linked to resistance in known single gene resistance. The “Mixed” reaction is recorded when both RB and TAN lesions were observed on the same leaf , Rpp2 (Chr16), Rpp3 (Chr6), Rpp4 (Chr7), Rpp5 (Chr3), Rpp6 (Chr18), and Rpp7 (Chr19) (Specific resistance to  (Chr19) . The sin (Chr19) . GWAS wa (Chr19) . Multipl (Chr19) . Previou (Chr19) . In thisRpp3; Gm18_51,994,200 and Gm18_57,223,391 in chromosome 18 were nearby the genes Rpp4/Rpp4-b and Rpp1/Rpp1-b, respectively; and Gm16_29,491,946 in chromosome 16 was located at Rpp2, which showed the promise of GWAS on SBR resistance  were similar to former studies on resistance/tolerance traits to abiotic and biotic stresses of several plant species, including wheat , rice X, maize , canola Consistently with former reports , we obsePhenotypic selection has been successfully implemented for disease resistance, but without controlled experiments, it is difficult to determine whether the resistance is quantitative or qualitative. Therefore, it is difficult to determine whether the resistance will be durable in the long term. In this study, the SBR-related markers we identified can be used to select for both quantitative and qualitative disease resistance within the breeding lines to bypass the need for controlled experiments through the use of conventional MAS. Additionally, by utilizing GP, the breeders can select for the accumulation of QTL associated with resistance, thereby taking advantage of both quantitative and qualitative resistance genes, even those that have not yet been characterized. This allows them to select the most promising lines for further development and testing without multiple generations of phenotyping.The original contributions presented in the study are included in the article/HX, AS, JW, and YC organized and analysed the original data. HX and Y-BP drafted the manuscript. WL, Y-BP and AS critically revised the manuscript. All authors contributed to the article and approved the submitted version.


Mango (Mangifera indica L.), acclaimed as the ‘king of fruits’ in the tropical world, has historical, religious, and economic values. It is grown commercially in more than 100 countries, and fresh mango world trade accounts for ~3,200 million US dollars for the year 2020. Mango is widely cultivated in sub-tropical and tropical regions of the world, with India, China, and Thailand being the top three producers. Mango fruit is adored for its taste, color, flavor, and aroma. Fruit color and firmness are important fruit quality traits for consumer acceptance, but their genetics is poorly understood.For mapping of fruit color and firmness, mango varieties Amrapali and Sensation, having contrasting fruit quality traits, were crossed for the development of a mapping population. Ninety-two bi-parental progenies obtained from this cross were used for the construction of a high-density linkage map and identification of QTLs. Genotyping was carried out using an 80K SNP chip array.Initially, we constructed two high-density linkage maps based on the segregation of female and male parents. A female map with 3,213 SNPs and male map with 1,781 SNPs were distributed on 20 linkages groups covering map lengths of 2,844.39 and 2,684.22cM, respectively. Finally, the integrated map was constructed comprised of 4,361 SNP markers distributed on 20 linkage groups, which consisted of the chromosome haploid number in Mangifera indica (n =20). The integrated genetic map covered the entire genome of Mangifera indica cv. Dashehari, with a total genetic distance of 2,982.75 cM and an average distance between markers of 0.68 cM. The length of LGs varied from 85.78 to 218.28 cM, with a mean size of 149.14 cM. Phenotyping for fruit color and firmness traits was done for two consecutive seasons. We identified important consistent QTLs for 12 out of 20 traits, with integrated genetic linkages having significant LOD scores in at least one season. Important consistent QTLs for fruit peel color are located at Chr 3 and 18, and firmness on Chr 11 and 20. The QTLs mapped in this study would be useful in the marker-assisted breeding of mango for improved efficiency. Mangifera indica L.) belongs to the plant family Anacardiaceae and has historical, religious, and economic importance. It is a diploid fruit tree with 20 chromosome pairs and a small haploid genome size of ∼439 Mb (Mango ( ∼439 Mb . Cytogen ∼439 Mb . In the  ∼439 Mb .World mango production was 57.37 million tons in 2020 from an area of 56.8 million hectares. The majority (76%) of the world production comes from Asia, followed by America (12%) and Africa (11.8%). Mango is commercially cultivated in 102 countries. India’s share in the world’s mango production is 41.6%, followed by a 10% share in China . In 2020Mango fruit size, firmness, color, and aroma are quality characteristics of this climacteric fruit that need to be investigated at the genomic level to improve mango fruit quality . These tAlthough domestication and selection of mango varieties have occurred for thousands of years, the systematic breeding of mangoes is relatively recent. Mango is a difficult fruit species to handle in breeding programs due to inherent problems of long juvenility, high heterozygosity, polyembryony, and significant fruit drop, which result in low recovery of hybrid fruits. As a result, the development of meaningful mapping populations and their use in understanding the genetics of horticultural traits has been limited. In India, breeding efforts to develop mango varieties with desirable traits started seven decades ago at the ICAR-Indian Agricultural Research Institute (IARI), New Delhi, and have a notable history of varietal improvement. To date, 10 mango hybrids have been released for commercial cultivation. Mango hybrids, namely Amrapali and Mallika, identified in the 1970s, became the choice of growers at the domestic level and got the attention of mango breeders globally. Amrapali, being a highly regular, dwarf, profuse bearer with excellent fruit quality, is the preferred parent in mango breeding programs. Considering consumer demand and potential overseas markets for mild sweetness, attractive colorful peel, and pleasant aroma, the Floridian cultivar Sensation has been used as a male donor parent to impart the red peel color in Amrapali, which otherwise has light green peel at maturity. These efforts yielded several hundred full-sib bi-parental progeny populations showing phenotypic polymorphism for agronomic traits and served as a core resource for genetic studies in mango . Full-siDespite huge economic significance, genomic resources for mango have been limited. The first genetic map of mango produced by In the present study, we generated a high-resolution integrated genetic map based on segregation from female, male, and both parents. This map may serve as a valuable resource that can be used to improve efficiency and overcome the challenges in mango breeding. We also used the genetic linkages to study the association of SNP marker(s) with fruit color and firmness traits. We report here the identification of significant QTLs governing the color and firmness of mango fruit. The findings of this study are novel and have significance in improving the breeding efficiency of mango.1 bi-parental hybrids obtained from a cross of Amrapali as a female parent and Sensation as a male parent along with the parents were used for the QTL mapping. These parents were selected based on their contrasting features for key fruit quality-related traits including fruit color and firmness. The hybridity of the progenies has been confirmed earlier using SSR markers  having a total of 18,816 genes belonging to five different categories of genes such as single-copy mango genes (SCM), conserved single-copy genes between citrus and mango (CSCCM), cloned horticulturally important mango genes (HIM), disease resistance defense response-like mango genes (DRDRM), and multi-copy genes (MCR). The average SNP density is ~6 per gene, which is highly significant in linkage mapping and QTL identification studies in mango.® 2.0 Assay Manual. The DNA samples were pre-amplified using Target Prep Protocol QSCB1 (P/N 702990), fragmented and hybridized on the chip, followed by single-base extension through DNA ligation and signal amplification. The Affymetrix GeneTitan® platform was used for staining, washing, and scanning of the chip signals as per manufacturer’s protocol. SNP allele calling was done using Axiom™ Analysis Suite version 2.0 using its three workflows, i.e., best practices, sample QC, genotyping, and summary on the Affymetrix Gene Titan. The Axiom Analysis Suite requires stored library files to convert CEL files into genotype calls. SNPs with low call rate across the samples were removed, and only good quality SNPs with a DQC of >0.85 and call rates of >95% were used for further analysis.DNA for genotyping was isolated from the young leaves of individual trees of the mapping population and parents following the CTAB method as modified by ® platform from 92 progenies and parents were appended into a single csv file for export to Excel. A total of 80,816 SNPs were amplified, out of which loci with >5% missing data were filtered out. Markers amplified in only one of the two replications of the parents were also removed. The SNP allelic patterns between parents were taken as a reference for allele assignment in the mapping population. Monomorphic SNP markers were removed because they would not be informative for finding recombination events. Further, homozygous SNP markers for different alleles between the parents  were also removed, as there would be no segregation for such markers in the F1 population. Thus, a total of 32,916 informative polymorphic SNP markers were identified between Amrapali and Sensation  to assign their location on the chromosomes, and only those which mapped back on the chromosomes were retained. Polymorphic SNPs identified between parents represented multiple SNPs per gene. Therefore, only single SNPs per gene were selected 2.Linkage analysis was performed using homozygous SNP markers in one parent and heterozygous in the other parent (lm × ll or nn × np) and heterozygous in both parents (hk × hk) using JoinMap version 4.1 . Further, the selection of SNP markers was based on their disomic inheritance and chi-square test of goodness of fit (p >0.05). Initially, for the construction of individual female and male maps, 4,834 markers in lm × ll and hk × hk, and 3,964 markers in nn × np and hk × hk were used, respectively. The initial grouping of markers was based on the independence log of the odds (LOD) tests in step ranging from 1.0 to 7.0. Other parameters were set as default. The regression mapping algorithm and Kosambi’s mapping function with minimum LOD of 3.0 were used for calculating the marker’s order. Markers showing suspect linkages were excluded in phases. Integration of female and male maps in which genotypes of some or all loci were determined in both populations took place, and the data from the separate populations were combined to calculate the integrated map. The groups that related to the same LG with at least two loci in common were combined by using the combined groups from the map integration function of the Join menu. The recombination frequencies and LOD scores of the selected sets of loci were combined into a combined group node in the navigation tree.Such a combined group node is identical to a group node of a pairwise data population. The map calculations are based on mean recombination frequencies and combined LOD scores. For each pair of loci, the numbers of recombinant and non-recombinant gametes in the individual populations were calculated from the estimated recombination frequencies and corresponding LOD scores. The total numbers of recombinant and non-recombinant gametes of overall populations were calculated by totaling the numbers of the individual populations. From this, the mean recombination frequency and the combined LOD score were obtained. For map integration, the regression mapping algorithm was used.1 trees of Amrapali/Sensation population and parents were carefully harvested with a 2 cm pedicel portion. The fruits were selected from all direction of the tree for evaluating fruit color and firmness traits for two consecutive years, 2019 and 2020. Fruits were washed thoroughly to remove the adhering dirt and dust, and rolled over the blotting paper to remove extra moisture on the surface and air dried. Fruit maturity was determined after harvest, and fruits having a specific gravity of ~1.01 to 1.02 were selected. These fruits were then wrapped in kite paper and placed in wooden boxes to ripen uniformly at room temperature. Initially, 30 fruits per individual genotype were subjected to ripening, of which 12 fruits showing uniform ripening and that were free from damage were selected for further analysis.Matured fruits from the FL*, a* and b* were measured using a Hunter-Lab Colorimeter . The instrument  was calibrated with black and white reference tiles through the tri-stimulus values X, Y and Z, taking as standard values those of the white background  tile. Fruit peel color was measured at three points on the fruit surface, i.e., shoulder, middle, and bottom . A 2.0 mm diameter stainless steel cylinder probe was used for the test in compression mode using the load cell of 5 kg capacity. Fruit firmness was expressed in Newton (N). Firmness was measured at three points  of the fruits of each individual and parents Figure 11 population was used for QTL mapping with an integrated linkage map using MapQTL®6  and 1,517 heterozygous in both parents (1:2:1) were used for the construction of the female map. Similarly, 2,447 heterozygous in male (1:1) and 1,517 heterozygous in both parents (1:2:1) were used for male linkage mapping.Finally, two high-density individual linkage maps with 3,213 and 1,781 SNPs distributed on 20 LGs in each segregation category were constructed as female and male maps, respectively  to 222.19 cM (Chr 3) with an average length of 142.22 cM. The average interval ranged from 0.43 cM (Chr 15) to 2.50 cM (Chr 3), with an average interval of 1.01 cM. The number of markers on each chromosome ranged from 75 (Chr 17) to 262 (Chr 4). This map is highly dense, and the density of SNPs ranged from 0.40 per cM (Chr 3) to 2.34 per cM (Chr 15) with an average of 1.21 SNPs per cM Table 3.A total of 1,781 markers were successfully mapped on the 20 chromosomes in the male map with an average of 89.05 markers/chromosome. The male map covered a total map distance of 2,684.22 cM. The individual chromosome length ranged from 62.55 cM (Chr 15) to 187.86 cM (Chr 6), with an average length of 134.21 cM. The number of markers ranged from 42 (Chr 7) to 208 (Chr 11). The average interval ranged from 0.64 cM (Chr 15) to 3.68 cM (Chr 6), with an average of 1.74 cM. The number of SNPs per cM ranged from 0.27 (Chr 6) to 1.57 (Chr 15), with an average of 0.69 markers per cM Table 3.Map integration was attempted using individual female and male linkage data, and a high-resolution integrated map comprising 4,361 markers mapped on the 20 chromosomes was constructed. Before the construction of integrated map, the LGs of individual maps were matched chromosome-wise, and a minimum of two markers common to individual maps were used for the construction of the integrated map. This map is highly dense, as the number of SNPs ranged from 108 (Chr 17) to 315 (Chr 11) with an average of 218.05 SNPs per chromosome. The integrated map covered a higher total map distance of 2,982.75 cM of mango genome compared to individual female and male maps. The individual chromosomes ranged from 85.78 cM (Chr 15) to 218.29 cM (Chr 4), with an average of 149.14 cM per chromosome. The average map interval on 20 chromosomes ranged from 0.34 cM (Chr 15) to 1.33 cM (Chr 3), with an average interval of 0.73 cM. The number of SNP markers per cM ranged from 0.75 for Chr 3 to 2.96 for Chr 15, with an average of 1.54 Figure 21 population for two consecutive years in 2019 and 2020  explains 28.2 to 29.2% of phenotypic variation, the second at a position of 73.79 cM (LOD 7.84 to 8.08) explaining 33.2 to 34.5% of phenotypic variance, the third QTL at a position of 98.75 cM (5.43 to 5.59 LOD) and fourth at 129.83 cM (LOD 5.13 to 5.73) explain around 25.0% of phenotypic variation in the population. These QTLs were observed in both seasons, i.e., 2019 and 2020, as well as with the mean phenotypic values over the years , and Chr 14 showed another QTL at a position of 77.15 cM, explaining 20.3% phenotypic variation in 2019. Similarly, Chr 15 showed one QTL in 2020 with mean value at a position of 28.75 cM and with a LOD score of 4.21 to 4.34, explaining 18.3 to 20.7% phenotypic variations in the population (b* of fruit bottom (LOD 4.14-5.56)  were identified as having an association with peel firmness at fruit shoulder. Similarly, eight SNPs hosted on Chr 11 at a position 8.69 to 94.82 cM  consistently appeared with traits observed on fruit bottom Table 5.SNPs located on Chr 3, 4, 11, and 19 had an association with pulp firmness Table 6.Traditional mango breeding is cumbersome and time-consuming. In the present decade, rapid advancement in DNA sequencing and molecular genetic techniques has generated a wealth of information on mango genomics. Apart from conventional breeding approaches, efforts have been made in the recent past to utilize biotechnological tools for marker-aided breeding. Significant progress has been made in the area of genome sequencing of mango by several researchers . The wea1, F2, backcross populations, and haploid populations can be used for genetic mapping  population along with parents.Moreover, SNP marker-based genetic maps have several advantages, as SNPs are more abundant, easier to identify, easier to score, and unambiguous markers . StudiesHigh-density genetic maps are valuable in genetic and genomic studies, illuminating the genetic and molecular mechanisms of plants and providing the necessary framework for QTL analyses, gene cloning, and molecular breeding . We usedMangifera indica have been constructed using different markers  mapping population. To be useful for marker-aided breeding, it is imperative to have markers showing strong association with the horticultural traits. A map is not necessary to identify markers associated with a trait, but confidence in this association increases as multiple markers near the trait locus on the genetic map also show significant association with the trait  with high LOD value of 5.13-8.08 explaining around 35% of phenotypic variation were identified. The a* value observed on the middle of fruit resulted in associated SNPs located on Chr 2, 12, and 17. However, a* observed on the bottom portion of the fruit did not result in any QTL(s) in the present study. This may be because the appearance of red blush is more significant on the shoulder compared to the middle and bottom portion of the fruits. The SNPs associated with peel yellow color (b*) trait was on Chr 2, 14, 15, and 18. Mango fruits are classified based on peel color into green, yellow, and red types. Mango peel turns from green to yellow or red or retains green colors during ripening. Carotenoids and anthocyanins are the important pigments responsible for the colors of fruits. Pigments in different proportions may have an influence on the expression of different shades of color on mango peel. The genetics of peel color has not been studied in detail, but available reports indicate that it is governed by several genes and regulated in a more complex manner. In agreement with this, we observed several SNP markers housed on different chromosomes that had an influence on peel color development in mango. Consistency of these QTLs over phenotypic values observed in different seasons confirms their involvement in the expression of fruit color.Fruit color is a highly variable trait within the fruit and is much influenced by environmental conditions. Precise qualitative evaluation of peel color is influenced by the number of fruits examined and from which part of the tree it is collected. More randomly chosen fruits from all parts of a tree may reduce this variation. In addition, we used another approach for determining the peel color using the Hunter color meter and took multiple observations on different positions of the fruit. This approach provided us with quantifiable data more suitable for QTL mapping compared to data based on scales or grouping. It was evident that red blush was more towards the shoulder compared to the middle and bottom portions of the fruit. An attractive fruit color is one of the most important factors for export markets . The accMango varieties differ considerably for fruit peel and pulp firmness. Fruit firmness is a significant quality aspect of mango for consumers, as it represents ripeness and influences shelf-life, transportation, and processing issues . VariousWe demonstrated the usefulness of designing a mapping population from two commercially important mango cultivars, Amrapali and Sensation, that have phenotypic polymorphism for fruit peel color and firmness traits. The integrated genetic recombination map using segregation data of female, male, and both parents reported here is unique and reasonably resolved. Our analysis of the association of SNPs for fruit color and firmness traits is novel and enables us to formulate mango breeding strategies that can improve breeding efficiency in identifying desirable progeny with optimal horticultural traits. The information about genetic linkages and QTLs generated would be highly useful in mango breeding and for broadening the understanding of the genetics of these traits. This knowledge will allow breeders to design trait-specific breeding strategies in mango.The original contributions presented in the study are included in the article/MS, NKS, SKS, RS, and NSi conceptualized the project and organized the work plan for the manuscript. MS, SR, PJ, NSi, SaS, and AM contributed to genotyping. MS, SR, AG, RD, NR, SHS, GK, AN, and ShS contributed to phenotyping. MS, NR, GT, PJ, SJ, MI, RS, and PP contributed to data analysis. AK, SRG, and ShSe extended the lab facilities for phenotyping. NSh helped in manuscript writing. All authors contributed to the article and approved the submitted version.