SNPs are informative to estimate genomic breed composition (GBC) of individual animals, but selected SNPs for this purpose were not made available in the commercial bovine SNP chips prior to the present study. The primary objective of the present study was to select five common SNP panels for estimating GBC of individual animals initially involving 10 cattle breeds (two dairy breeds and eight beef breeds). The performance of the five common SNP panels was evaluated based on admixture model and linear regression model, respectively. Finally, the downstream implication of GBC on genomic prediction accuracies was investigated and discussed in a Santa Gertrudis cattle population.r = 0.953 to 0.992). In the genomic prediction of a Santa Gertrudis population (and crosses), the results showed that the predictability of molecular breeding values using SNP effects obtained from 1,225 animals with no less than 0.90 GBC of Santa Gertrudis (GBCSG) decreased on crossbred animals with lower GBCSG.There were 15,708 common SNPs across five currently-available commercial bovine SNP chips. From this set, four subsets  were selected by maximizing average Euclidean distance (AED) of SNP allelic frequencies among the ten cattle breeds. For 198 animals presented as Akaushi, estimated GBC of the Akaushi breed (GBCA) based on the admixture model agreed very well among the five SNP panels, identifying 166 animals with GBCA = 1. Using the same SNP panels, the linear regression approach reported fewer animals with GBCA = 1. Nevertheless, estimated GBCA using both models were highly correlated  contains supplementary material, which is available to authorized users. Estimation of breed identification or composition is very useful in a variety of situations. In farm animals, for example, breed registries are used to record and maintain pedigrees of animals with certain conformational, performance and coat color characteristics that are approved for registry within that breed . For croGenomic selection has emerged as a powerful tool for genetic improvement of farm animals . GenomicAncestry and breed origins were historically estimated using microsatellite markers , 14 and In the present study, five SNP panels were derived for estimating GBC amongst 10 cattle breeds, which consisted of 1,000 (1 K), 3,000 (3 K), 5,000 (5 K), 10,000 (10 K), and 15,708 (16 K) SNPs, respectively. More breeds will be included as their genotype data become available. The 16 K consisted of all common, informative SNPs across five historical and currently available SNP chips, and the remaining four panels were selected subsets from 16 K by maximizing the average Euclidean distance of allele frequencies among the ten cattle breeds. With these five selected SNP panels, their comparative performance in estimation of GBC was evaluated in an Akaushi population, based on two statistical models . Finally, downstream implication on genomic selection accuracies was investigated in a population of purebred and crossbred Santa Gertrudis cattle by calibrating SNP effects only on 1,225 Santa Gertrudis cattle with GBC of Santa Gertrudis (GBCSG) being equal to greater than 0.90 and validated on this set of animals and on two sets with lower GBCSG.The datasets included a total of 29,609 animals of ten cattle breeds, each genotyped on the GeneSeek Genomic Profiler (GGP) bovine 50 K version 1 SNP chip  or GGP LD version 4 SNP chip  . Approximately 53% of the animals were from two dairy breeds (Holstein and Jersey) and the remaining 47% were from eight beef cattle breeds . Among the beef breeds, Akaushi (Japanese Brown) cattle and Wagyu (Japanese Black) cattle were originally developed in Japan and are well known for their meat quality ; BeefmasT) > 2, AED was calculated by the Pythagorean formula and then averaged across all possibly unique breed pairs,fjk is the frequency of an allele of the k-th SNP in the j-th breed, and T breeds taken 2 breeds at a time without repetition. Note that fjk can refer to either allele, but it needs to be used consistently. In the present study it refers to the second allele. For example, if SNP genotypes are coded as 0 (AA), 1 (AB) and 2 (BB), then fjk refers to the frequency of allele B.Five panels of reference SNPs were made available. Each panel consisted of common SNPs across five historical or currently-used commercial bovine SNP chips, namely, Illumina Bovine HD (777 K) chip, GGP UHD (150 K) SNP chip, GGP HD (80 K) SNP chip, GGP 50 K version 1 SNP chip, and GGP LD version 4 (40 K) SNP chip. Hulsegge et al. (2013) compared three statistics as the criteria for selecting SNPs : 1) deltPrior to SNP selection, there were 15,708 SNPs (identified as 16 K) in common across the five commercial bovine SNP chips evaluated in this study. The 16 K SNP set are not random, but initially taken as the common set from which four subsets of SNPs, namely 1 K, 3 K, 5 K, and 10 K SNPs, were selected. The SNPs for each subset were selected by maximizing AED of SNP allelic frequencies among the ten breeds, given their respective panel sizes.fkj(g) to be the frequency of animals having genotype g, where g = AA, AB, or BB, respectively, of SNP k in the j-th population. Let x be a genotype of SNP k observed on animal i. Then, based this SNP only, the likelihood that this animal is a member of population j is given by:x = g is an indicator variable, which has a value of 1 if x = g, or 0 otherwise.In the present study, reference animals for each breed were selected using the 1 K SNP panel because the model is parsimonious and the results were very similar across each of the five SNP panels. The likelihood that an animal belonged to a specific breed was computed assuming independent multinomial distributions of its genotypes of these SNPs. Consider one SNP locus with three genotypes, and denote k. Then, formula  = Beta, where q is the frequency of say allele B, and α and β are hyper-parameters in the prior distribution, the posterior distribution of q is also a Beta distribution function:N = 2(nAA + nAB + nBB). Denote q. Then, assuming Hardy-Weinberg equilibrium, the frequencies of genotypes AA, AB, and BB, respectively, were given as follows:For simplicity, the above is denoted by -2logLikehood hereafter. To avoid calculating the logarithm on zero counts of genotypes, each genotype frequency was re-computed based on allele frequencies estimated based on a Bayesian Binomial model. Assume a conjugate Beta prior for lij which was smaller than a pre-defined cutoff, lα, where, for example, lα = 0.99 represented the 99% quantile of lij values. After removing outliers for each breed (described later), allele frequencies of the reference SNP panels were re-computed using reference animals only and the updated allele frequencies of reference SNPs were used in the estimation of GBC. Note that the above were illustrated using the AB genotype notation. The same principles apply to the ACGT genotype notation as well.Each reference animal had a value of y be an M × 1 vector of genotypes for each animal, where M is the number of reference SNPs, and genotypes were coded as the number of B alleles of each reference SNP observed on each animal. Let F = {fkj} be an M×T matrix, where fkj was the frequency of B allele of SNP k pertaining to population j, and T is the number of breeds. Then, GBC was estimated based on the following linear model:μ is the overall mean, and b is a T × 1 vector of regression coefficients, each pertaining to a breed, and e is a residual term. Note that the sum of regression coefficients across the T breeds computed for each animal did not equal to 1, and adjustment of these regression coefficients were needed to restrict the sum of regression coefficients per animal to be 1. VanRaden and Cooper (2015) proposed a method to adjust breed regression coefficients [m breeds.The linear regression approach estimated GBC for each animal by regressing discrete random variables  corresponding to counts of certain alleles of reference SNPs across the genome on the corresponding allele frequencies of each reference SNP in a number of reference populations , 17. Letmentclasspt{minimaT breeds, computed for each animal, corresponded to the fractions of the individual’s genome which was derived from each reference breed, and they provided estimates of GBC of the T breeds for each animal. In the admixture model, the value of each admixture coefficient was between 0 and 1, and the sum of admixture coefficients (GBC) computed for each animal is always 1 under the assumption of 100% genetic contributions by the T known breeds to this individual animal. For a given animal, if the admixture coefficient of a single reference breed was 1 (or close to 1), then this animal was identified as a purebred animal of that breed.Given allele frequencies for a number of SNPs which had been estimated for each reference breed, an individual’s genotypes at these loci was modeled as an admixture of multiple breeds . The admi, with observed genotype for a SNP, say k. Let A and B be the two alleles of this SNP. There were three possible genotypes: AA, AB and BB, respectively. Assuming Hardy-Weinberg equilibrium, the probability of observing each genotype on this animal were given as follows:Consider an individual, say qik was the weighted frequency of allele B of the k-th SNP, pertaining to the admixture of the i-th individual, and its quantity was given by wij was an weight of the j-th breed contributing to the admixture of the i-th individual, and fjk was the allele B frequency of the k-th SNP in the j-th reference breed.In the above, T breeds, and M reference SNPs, both pertaining to individual i. Then, the log-likelihood pertaining to this individual was given by the following:C was a constant. Note that the above assumes that all SNPs were independent or in linkage equilibrium with each other, which might not hold for high-density SNPs. But this assumption was taken to be approximate for low density SNP panels. A practical solution to accommodate this assumption would be to prune SNPs to reduce the linkage disequilibrium (LD) between the markers [T reference breeds and genotypes of these SNPs for a test animal, say i, the solutions of breed admixture coefficients for this animal is obtained by maximizing L(wi), under the constraints wij ≥ 0 and Denote  markers . Given SA variety of optimization methods are available for estimating the above admixture coefficients. Newton’s method involves the manipulation and inversion of a possibly large matrix, which can be computationally intensive . The EM wi); (2) For each breed, say j, with a non-zero admixture coefficient, calculate δij = Lmax − Lj− obtained by calculating the maximum likelihood fit with the j-th admixture coefficient constrained to be 0; (3) Determine the breed with the smallest value of δij; (4) Set for admixture coefficient wij to be 0 if δij< τ, where τ was a threshold based on the likelihood ratio test; Repeated Steps 2–4 until the changes in the likelihood was acceptably minimized.Computationally, an iterative approach was used to find a parsimonious set of GBC values for an individual by iteratively removing breed(s) for which a nonzero admixture coefficient does not improve the model fitting significantly . This prGBC were computed for 1424 cattle putatively presented as Santa Gertrudis. These animals were not included in the reference set to define the Santa Gertrudis breed allele frequencies. Based on the density plot of calculated GBC of Santa Gertrudis for these animals  of birth weight (BW), fat thickness (FAT), hot carcass weight (HCW), marbling score (MARB), ribeye area (REA), scrotal Circumference (SC), weaning weight (WW), maternal weaning weight (MWW), and yearling weight (YW). Summary statistics of EPDs and accuracies of EPDs of the nine traits are shown in Table Average Euclidean distance among the ten breeds computed by each of the five SNP panels increased as the panel size decreased. The AED of reference SNP allele frequencies were 0.243 (16 K), 0.285 (10 K), 0.319 (5 K), 0.340 (3 K), and 0.377 (1 K), respectively. This trend indicates that maximizing AED have successfully led to the inclusion of highly informative SNPs in each of the four selected panels Fig. . For exaThe likelihood values of an animal belonging to the reference breed, computed using each of the five SNP panels respectively, were highly correlated among the five SNP panels. In the 198 animals putatively presented as Akaushi, Pearson correlations of -2logLikelihood values computed by these five SNP panels varied between 0.994 and 0.999 and the corresponding Spearman rank correlations ranged from 0.858 to 0.991  to 0.470 (BW) in the 1,225 GBCSG-validated Santa Gertrudis cattle (GBCSG ≥0.90). Prediction accuracies on the nine traits using these SNP effects, however, decreased in the other two groups as GBCSG became smaller, which were between 0.102 (SC) and 0.430 (BW) when 0.70 ≤ GBCSG < 0.90, and between 0.033 (MARB) and 0.160 (YW) when 0 ≤ GBCSG < 0.70  of reference animals is a necessary step because these reference animals are supposed to be representative of their respective breeds, and including cross-bred animals or even animals from other breeds would mis-lead the conclusion. We employed a likelihood-based approach to select reference animals for each breed. Alternatively, outliers can be identified using a standard deviation approach or Tukey’s method. The latter was the statistical method underlying the boxplot in R, which uses interquartile (IQR) range approach, and outliers are identified as ranges above or below the 1.5IQR. Validation of these animals by their pedigrees and breed registry records is also important when the latter information is available. In the present study, animals with -2logLikelihood less than the cutoff value were retained as reference animals for each breed, because they are more likely to be a purebred animal . Note that choosing the cutoff value for each breed is subject to the presence of outliers. Keeping outliers leads to high error rates of false positives . On the other hand, if the exclusion rate is too stringent , it would bias SNP allele frequencies of a given population toward another extreme, which in turn results in higher errors of false negatives . Ideally, with good quality data, the percent of eliminated animals is expected to be less than 5%. In the present studies, the percent of eliminated animals was generally below 5%, ranging from 0.45% for Hereford to 15.66% for Akaushi. Of the 8,905 registered Holstein animals, for example, 96.4% of these animals had GBC of Holstein (GBCH) equal to 1, 98.7% had GBCH ≥0.90, and 99.6% had GBCH ≥0.80. Based the distribution of their GBCH values, approximately 0.47% of these animals were identified as outliers and excluded from the reference group. For the three composite cattle breeds , though two founder breeders (Brahman and Shorthorn) were not present, the three composite breeds could be distinguished from each other because they had unique patterns of allele frequencies  , who also used the admixture model, found that a relatively small number of SNPs (approximately 4000 randomly selected) would suffice to predict breed composition . NeverthGenomic breed composition was estimated for the 198 animals putatively presented as Akaushi using the five SNP panels based on the admixture model and the linear regression model, respectively. The two models had varied to some extent concerning the number of animals with GBCA = 1. Estimated breed compositions for the 198 animals based on the admixture model agreed very well among the five SNP panels, which consistently identified 166 animals with GBC of Akaushi being equal to 1. However, the results from the linear regression method showed considerable differences among the five panels, and the numbers of animals with GBCA = 1 increased with the number of SNPs included in the regression model. Hence, we expect that the regression model will give more reliable results using high-density SNPs. Given low-density SNP panels, the admixture model yielded more consistent results among the five selected panels than the linear regression model.Nevertheless, the admixture model is more computational intensive, and it had stronger assumptions. Specifically, the admixture model assumes that SNP loci are independent of each other. This assumption typically does not hold with high-density SNPs. A practical way of reducing LD is to prune SNPs. In the present study, however, we did not prune SNPs but instead used all selected low-density SNPs which are highly informative of these population relationships. Use of low-density SNPs tended to reduce LD considerably. By computing GBC locus-wise and assuming complete independence of these the involving SNPs, this type of locus-wise genomic breed composition is well explained by genomic similarity due to identical by state (IBS), rather than being identical by descent (IBD). In this sense, computed GBC can be more precisely described by genomic breed similarity (GBS). Alternatively, GBC can be computed considering only alleles located within runs of homozygosity (ROH), which represents IBD more than IBS because the probability for a large segment of chromosome to be IBD is high. Arguably, this allowed the use of SNPs in high LD and computed GBC could be better captured via the genomic similarity IBD than a random set of evenly-spaced SNPs, because it tended to give more weight to SNPs on ROH.Generally speaking, genomic prediction accuracies on the nine traits were low, possibly because the accuracies of EPD were low, and the de-regressed EBV could have more noise. Our results showed that SNP effects obtained from 1,225 animals with GBCSG equaled to or greater than 0.90 were more predictable in this same set of animals per se, as evaluated by leave-one validation, than the other two sets of animals which were considered to be crossbreds of Santa Gertrudis animals  were designed for estimating genomic breed composition in cattle. The 16 K panel consisted of common, informative SNPs on five currently available commercial bovine SNP chips. From the 16 K SNP panel, four smaller SNP panels  were optimally selected by maximizing AED of allelic frequencies of SNPs among ten cattle breeds. The availability of these selected SNP panels facilitates breed identification and estimation using currently available commercial bovine SNP chips without the need to design new SNP chips or pay extra lab genotyping cost. These results from the admixture model showed that the five SNP panels performed very similarly in the estimation of GBC in 198 animals putatively presented as Akaushi. Overall, our results are highly comparable to admixture models, e.g., the one proposed by Bansal-Libiger (2015) and implemented by the iAdmix program, because we share the same statistical framework. The admixture model differed from the linear regression approach in number of animals with purebred coefficient being exactly equal to 1, but estimated GBC from both methods were highly correlated . Yet, our results did not suggest that the two methods contradicted with each other, but that the linear regression approach need to have more reference SNPs than the admixture model to give comparable results.In view of utility, the 1 K panel is the most cost effective among the five SNP panels for estimating GBC but the two larger SNP panels (10 K and 16 K) can be more robust as an independent LD SNP panel if imputation to moderate- or high-density SNP genotypes is a necessary task. The present study did not search for a minimum number of SNPs for estimating GBC. This was an interesting topic but it was not of direct relevance in the present study. In the present study, 1 K to 5 K SNPs are desirable sizes for reliably estimating GBC and they are convenient to be included as core content for developing future SNP chips.Animals with difference in GBC also differed in their genomic architecture of quantitative traits, which was the case with 1,424 animals presented as Santa Gertrudis, and genomic prediction accuracy of these animals decreased as the GBC proportion of Santa Gertrudis decreased. Evidently, pooling animals with drastically differed GBC profiles could lower genomic prediction accuracies of validated (or purebred) animals. How to further improve genomic prediction of crossbred animals with estimated GBC remained to be explored in future studies.Finally, estimation of GBC is conducted under the assumption that all the involving breeds contributed 100% to the genomic breed composition of each animal. This analysis, however, could be biased when there one or more ancestry breeds were missing in reality, regardless of which statistical models were used.Additional file 1:Figure S1. Density plot of genomic breed composition of 1424 animals putatively presented as Santa Gertrudis cattle. (DOCX 23 kb)Additional file 2:Figure S2. Map view of 1 K (A), 3 K (B), 5 K (C), 10 K (D) and 16 K (E) SNP panels, where 1 K, 3 K, 5 K and 10 L denotes 1000, 3000, 5000 and 10,000 SNP panels, respectively. The 1-10 K panels were obtained by maximizing average Euclidean distance of SNP allele frequencies among ten cattle breeds. The 16 K consisted of 15,708 common SNPs across five currently used bovine SNP chips. (DOCX 290 kb)Additional file 3:Figure S3. Plot of -2logLikelihood values computed for 198 purported Akaushi cattle, based on the admixture model with 1 K versus 16 K SNP panels. (DOCX 25 kb)Additional file 4:Figure S4. Plots of -2logLikelihood for 1770 reference Brangus animals (green circles) after removing outliers. The likelihood values were computed assuming the true allele frequencies of SNPs were equal to those of each of the 10 breeds, respectively. The assumed breeds (from left to right) are Brangus, Angus, Red Angus, Santa Gertrudis, Beef Master, Hereford, Holstein, Jersey, Wagyu, and Akaushi. (DOCX 37 kb)Additional file 5:Figure S5. Plots of -2logLikelihood for 583 BeefMaster cattle after removing outliers. The likelihood values were computed assuming the true allele frequencies of SNPs were equal to those of each of the 10 breeds, respectively. The assumed breeds (from right to left) are Beef Master, Santa Gertrudis, Brangus, Hereford, Angus, Red Angus, Holstein, Jersey, Wagyu, and Akaushi. (DOCX 39 kb)Additional file 6:Figure S6. Plots of -2logLikelihood for 291 Santa Gertrudis cattle after removing outliers. The likelihood values were computed assuming the true allele frequencies of SNPs were equal to those of each of the 10 breeds, respectively. The assumed breeds (from right to left) are Santa Gertrudis, Beef Master, Brangus, Red Angus, Angus, Hereford, Holstein, Wagyu, Akaushi, and Jersey. (DOCX 39 kb)Additional file 7:Table S1. Correlations of -2logLikelihood values computed for 198 purported Akaushi cattle using the five SNP panels. Table S2. Genomic breed characterization of 198 purported Akaushi animals based on the admixture model using each of the five SNP panels. Table S3. Genomic breed characterization of 1424 purported Santa Gertrudis cattle based on the admixture model using the 1 K SNP panel. (DOCX 251 kb)


Medicago truncatula ecotypes were analyzed using ultrahigh pressure liquid chromatography coupled to mass spectrometry (UHPLC-MS) to ascertain saponin profiles in different M. truncatula ecotypes and to provide data for a genome-wide association study and subsequent line selection for saponin biosynthesis. These ecotypes originated from 14 different Mediterranean countries, i.e., Algeria, Cyprus, France, Greece, Israel, Italy, Jordan, Libya, Morocco, Portugal, Spain, Syria, Tunisia, and Turkey. The results revealed significant differences in the saponin content among the ecotypes. European ecotypes generally contained higher saponin content than African ecotypes (p < 0.0001). This suggests that M. truncatula ecotypes modulate their secondary metabolism to adapt to their environments. Significant differences in saponin accumulation were also observed between the aerial and the root tissues of the same ecotypes (p < 0.0001). While some saponins were found to be present in both the aerial and root tissues, zanhic acid glycosides were found predominantly in the aerial tissues. Bayogenin and hederagenin glycosides were found mostly in roots. The differential spatially resolved accumulation of saponins suggests that saponins in the aerial and root tissues play different roles in plant fitness. Aerial saponins such as zanhic glycosides may act as animal feeding deterrent and root saponins may protect against soil microbes.A total of 1,622 samples representing 201  Legumes are economically important and widely cultivated crops. They contain relatively high protein content and are important sources of protein for both humans and animals. Their high protein content may be attributed to their unique symbiotic relationship with nitrogen-fixing bacteria. Legumes also produce a vast array of natural products including flavonoids, isoflavonoids, anthocyanins, condensed tannins, lignin, and saponins . These nMedicago truncatula , CYP716A12 has been identified to oxidize beta-amyrin to erythrodiol and then subsequently to oleanolic acid (lha (lacking hemolytic activity), were found to lack hemolytic saponins and only produce non-hemolytic soyasaponins  and non-hemolytic (soyasapogenol) . It is gsynthase   filled with Turface (BWI Texarkana) which had been rinsed in distilled water and autoclaved. The ecotypes were grown in a growth room under controlled conditions. Day length was 16 h, with a gradual increase in light. The light source included both fluorescent and incandescent bulbs, and light intensity averaged ∼225 μJ. Temperature was set at a constant 21°C. Plants were watered with Broughton and Dilworth (100 ppm N) fertilizer every day. Enough seedlings were planted to analyze four replicates of each ecotype. In most cases, this was accomplished but a few ecotypes grew poorly and three or fewer replicates were harvested. The large number of plants necessitated growth in three separate groups, and A17 and R108 were grown with each group as controls. Plantings were staggered so that all plants could be harvested during the same morning time frame (3–5 h after full light) to insure a uniform position in the diurnal photosynthetic cycle. Plants were harvested at 5 weeks of age before the onset of flowering. Roots and aerial tissues were separated and immediately frozen in liquid nitrogen.The tissues were lyophilized and dry weights were recorded. All tissues were ground to a fine powder and 10 mg each was weighed for extraction. If plant material was limited, extraction volumes were reduced proportionally. Tissues were incubated with 80:20 methanol:water solution containing 18 μg/ml umbelliferone.g for 30 min at 4°C, and the supernatants were collected. Five microliters of the supernatant were injected into a Waters UPLC system coupled to a quadrupole-time of flight mass spectrometer . Chromatographic separations were performed on a Waters reverse phase column  using the following gradient: mobile phase B (acetonitrile) increased from 5 to 70% over 30 min, then to 95% in 3 min, held at 95% for 3 min, and returned to 95% mobile phase A (0.05% formic acid in water) for equilibration for 3 min. The flow rate of the mobile phases was 0.56 mL/min, and the column and autosampler temperatures were maintained at 60 and 4°C, respectively. Mass spectral data were acquired from m/z 50 to 2,000 in the negative electrospray ionization mode, with the nebulization gas set at 850 L/h (350°C) and the cone gas at 50 L/h (120°C). Raffinose (m/z 503.1612) was used as the reference compound in the independent lock-mass mode, with the lock mass scan (1 s) collected every 10 s for accurate mass measurements. The concentration of raffinose was 50 fmol/mL, and the flow rate 0.2 mL/h.Roots and aerial tissues were lyophilized until dry and ground to a fine powder. Ten milligrams (10 ± 0.06 mg) of powder for all tissues were accurately weighed and extracted on an orbital shaker for 2 h with 1 mL of 80% of methanol containing 18 μg/mL umbelliferone as an internal standard. Samples were centrifuged at 2,900 ×m/z to those of the previously observed saponins in M. truncatula  for mass features extraction and alignment with the following parameters: minimum peak intensity: 500 counts, mass tolerance: 0.05 Da, and retention time window 0.2 min. The peak areas were normalized by dividing each peak area by the value of the internal standard peak . Annotations of metabolites were performed by matching their uncatula . Retentiuncatula . The norM. truncatula ecotypes  analyzed in this work represented 14 geographical origins, i.e., Algeria, Cyprus, France, Greece, Israel, Italy, Jordan, Libya, Morocco, Portugal, Spain, Syria, Tunisia, and Turkey , with a 90 fold difference between the lowest line  and the highest line . It was also found that there was a significant positive correlation between the aerial and the root dry weight , indicating that ecotypes producing more aerial tissues also tended to produce more root tissues. The average total dry weight for ecotypes of the same country of origin is shown in p = 0.3). This is due to large variations of dry weight among the ecotypes from the same country as evidenced by the large standard deviation associated with each average dry weight  found predominantly in the root tissues was annotated as glucose-glucose-glucose-bayogenin and confirmed by tandem MS (p < 0.0001) as well as between the aerial and the root tissues of the same ecotypes (p < 0.0001). These data indicated that the geographical origin has an impact on the production of saponin in plants and that saponins differentially accumulated in roots and aerial tissues. Their differences and biological implications are discussed below.Saponin profiling of the aerial and root tissues was performed in a random manner and spanned over a period of 5 months (November 2012–March 2013). The data are shown in by MS/MS . Figure by MS/MS . For exaandem MS . The relp < 0.0001). The lowest saponin-containing ecotypes were found to originate from Tunisia and contained only 60% of the amount of saponins in the highest saponin-containing ecotypes . p = 0.4). This may be explained by the similar environments such as similar climates, seasonal changes and soil conditions in these countries as most of them are in the African continent. In contrast, ecotypes originating from Israel and European countries such as France, Italy, Portugal, and Spain typically contained higher amount of saponins. The two highest saponin-containing ecotypes were from Portugal and Israel. This distinct difference between the African and the European ecotypes indicated a clear geographic segregation of M. truncatula around the Mediterranean area in terms of saponin production, with the ecotypes in the north region (Europe) segregated from those in the south region (Africa). Similar segregation has also been observed in a previous microsatellite diversity study of 346 inbred lines of M. truncatula ecotypes that revealed a stratification of the M. truncatula population between the North and the South of the Mediterranean basin   . In the  0.0001) . The dif 0.0001)  and the  0.0001) . This wa 0.0001) , suggestY-axis) and the root (X-axis) tissues of the ecotypes. It reveals clear differences in the spatial accumulation of saponins. Regardless of the country of origin, bayogenin glycosides, hederagenin glycosides and soyaspogenin E glycosides were mostly found in the roots, while zanhic acid glycosides were only detected in the aerial tissues. In contrast, medicagenic acid glycosides and soyasaponin B glycosides were found in both the aerial and root tissues although the aerial tissues appeared to contain more medicagenic acid glycosides. The finding is similar to a previous report that zanhic acid glycosides, medicagenic acid glycosides and soyasaponin B glycosides were found to be the three dominant groups of saponins in M. truncatula foliar tissues . In contrast, saponin fractions isolated from alfalfa root tissues, when incorporated into their diet at equivalent concentrations, had little effect on larvae development although they inhibited their growth  larvae with a diet supplemented with saponins isolated from alfalfa also significantly reduced their growth, fecundity and fertility and increased their mortality (The results of the saponin profiling in the aerial and the root tissues are shown in  tissues . It is auncatula ,b and zauncatula . A more ry roots . Zanhic ry roots . The accry roots  and the ry roots . Therefohe roots . Unlike he roots . For exasaponins . In conttrations . Becausetrations . Soyasaptrations . This istrations . Soyasapr growth . Feedingortality . Medicagortality . It was Medicago hybrida roots were found to substantially inhibit six pathogenic fungi Botrytis cinerea, Botrytis tulipae, Fusarium oxysporum f. sp. callistephi, F. oxysporum f. sp. narcissi, Phoma narcissi, and F. oxysporum Schlecht (Cephalosporium gramineum, a soil fungus that infects roots of plants, were markedly higher than their similar hederagenin glycoside counterparts (M. truncatula, the bitter herbivore deterrent zanhic acid glycosides are predominantly found in the aerial tissues while the anti-fungal agents such as hederagenin and bayogenin glycosides are mostly found in roots to defend against soil borne fungi. In contrast, medicagenic acid glycosides, the broad spectrum and strong anti-microbial compounds, are accumulated in both the aerial and root tissues.Compared to leaf saponins, root saponins consisted of a different set of triterpene glycosides, with the major difference being the absence of zanhic acid glycoisdes and the presence of bayogenin glycosides and hederagenin glycosides . This suSchlecht . SimilarSchlecht . The strSchlecht . BayogenSchlecht . Their iterparts . Bayogenterparts . The hydterparts . Selectiterparts . While gM. truncatula ecotypes revealed a clear differential spatial accumulation of saponins in the aerial parts relative to the roots. Zanhic acid glycosides were only found in the aerial tissues. In contrast, bayogenin glycosides, hederagenin glycosides, and soyasaponin B glycosides were predominantly accumulated in roots, thus suggesting interesting ecological roles for these compounds in plant defense. Overall, zanhic acid glycosides, medicagenic acid glycosides and soyasaponin B glycosides were the three major triterpene saponins found in the aerial parts, while medicagenic acid glycosides, bayogenin glycosides, hederagenin glycosides, SoyE glycosides, and SoyB glycosides constituted the major root saponins. Although medicagenic acid glycosides were found in both the aerial and root tissues, aerial parts were found to contain more medicagenic acid glycosides. Significant correlation between the quantity of saponins and the ecotypes’ country of origin was observed. The European ecotypes were found to contain higher content of saponins than most of the African and the Middle-East ecotypes. This dataset also represents an extremely valuable resource for discovery of biosynthetic genes and deciphering saponin biosynthesis. Based on the correlation analysis of the metabolic profiling data and gene expression data, a number of P450 genes have been selected for further characterization to elucidate their roles in saponin biosynthesis in M. truncatula (Saponin profiling of 201 uncatula .All processed data for this study are included in the manuscript and/or the LS conceptualized and designed the experiments, and secured the funding. BW and DY grew, harvested, dried, and ground the samples. DH extracted the ground samples and collected the LCMS data. ZL processed the data and drafted the manuscript. All authors were involved in the implementation of the experimental design and contributed to the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Glycine max). A large population of 2,662 cultivated soybean accessions was genotyped using the 180k Axiom® SoyaSNP array, and the genomic architecture of these accessions was investigated to confirm the population structure. Then, GWAS was conducted to evaluate the association between SNP markers and flowering time. A total of 93 significant SNP markers were detected within 59 significant genes, including E1 and E3, which are the main determinants of flowering time. Based on the GWAS results, multilocus epistatic interactions were examined between the significant and non-significant SNP markers. Two significant and 16 non-significant SNP markers were discovered as candidate markers affecting flowering time via interactions with each other. These 18 candidate SNP markers mapped to 18 candidate genes including E1 and E3, and the 18 candidate genes were involved in six major flowering pathways. Although further biological validation is needed, our results provide additional information on the existing flowering time markers and present another option to marker-assisted breeding programs for regulating flowering time of soybean.Genome-wide association studies (GWAS) have enabled the discovery of candidate markers that play significant roles in various complex traits in plants. Recently, with increased interest in the search for candidate markers, studies on epistatic interactions between single nucleotide polymorphism (SNP) markers have also increased, thus enabling the identification of more candidate markers along with GWAS on single-variant-additive-effect. Here, we focused on the identification of candidate markers associated with flowering time in soybean ( A genome-wide association study (GWAS) is one of the promising approaches for the identification of genomic variants responsible for specific phenotypes . With thNsn1, Fpa, and Zmm22 were identified in 942 maize samples (Zea mays) [CO1 and BFL, were identified in using 218 barley samples (Hordeum vulgare) [Hd1 were confirmed in 950 rice samples (Oryza sativa) [SOC1, AGL6, and ELF8 were reported in 309 soybean samples (Glycine max) [Days to flowering (DTF) is a crucial agronomic trait that regulates the maximum use of sunlight and temperature , and affea mays) ; two can sativa) ; and tenine max) . These fine max) .Epistasis is defined as the interaction between genes or SNP markers that influences a trait . Each SNE1–E10 [J [Dt1 [® 180k SoyaSNP array developed by our team in 2016 [Twelve major genes affecting DTF have been identified in soybean, including E1–E10 –41, J [41–E10 [J , and Dt1 [J [Dt1 , 44. To  in 2016 . Also, wGlycine max) accessions were collected from the National Agrobiodiversity Center in the Rural Development Administration , and were genotyped using the Axiom® 180k SoyaSNP. Of these, 210 hybrid accessions were excluded from the analysis. The remaining 2,662 accessions originated from South Korea , North Korea (96), the USA (60), China (59), and Japan (32), and comprised 335 improved cultivars (ICs), 2,175 landraces (LRs), and 152 unknown cultivars (UCs)  . These apistasis . Phenotyhttp://tree.bio.ed.ac.uk/software/figtree/) (K) was set at six, considering the error values of 10-fold cross-validation  was performed by applying singular value decomposition to the distant matrix calculated using the Kimura two-parameter model [Phylogenetic analysis was conducted based on the identity-by-state coefficient matrix calculated using Plink v1.90b . The phyigtree/) . Structuigtree/) . The numlidation . The errer model , and theer model . The meaer model , and inbp-value) = 7. To minimize false-positive results, the cut-off was set slightly higher than the Bonferonni-corrected −log of 6.2. The extent of model fitting was confirmed using a quantile–quantile (Q-Q) plot for the expected and obtained p-values. After GWAS based on SNP markers, gene annotations were performed using SnpEff v4.3 [An association study between genomic regions and flowering time was conducted using a compressed mixed linear model within Genomic Association and Prediction Integrated Tool . The MAFEff v4.3 . All resArabidopsis were examined [Arabidopsis genes were identified  > 7), and a total of 5,534 SNP markers (excluding 6 overlapping SNP markers) was constructed as the final SNP marker-set for the multilocus epistatic analysis. All results, including the number of SNP markers per gene, are summarized in To construct an SNP marker-set for multilocus epistatic interaction analysis, a biological filtering approach was conducted. First, genes affecting DTF and maturity in examined –59, and entified . Second, tBLASTx  with matP by grouping dependent genotypes (I) and phenotypes (H) [XI=h}{ indicates all X in the hth group, and Y{m} indicates Y in the mth cluster. For P(XI=h}{|I), Dirichlet prior distribution and multinomial distribution were used as the Bayesian partition model [p-values are summarized in Multilocus interactions among the 5,534 SNP markers related to DTF were examined using Bayesian High-order Interaction Toolkit (BHIT)  with 1,0ypes (H) . Then, ton model . The thron model , and the® 180k SoyaSNP array developed by our team in 2016 [K = 2 , the USA (60), China (59), and Japan (32), were used in this study. These accessions included 335 ICs, 2,175 LRs, and 152 UCs . Korean  in 2016 , and a tanalysis . Averageanalysis , and lowanalysis  than theanalysis . Also, ap-value) = 7, which was higher than the Bonferroni-adjusted p-value = 6.2, to minimize false-positive results. A total of 132 significant SNP markers including 93 genic SNP markers were detected, and all mapped to chromosomes 6 and 19 , and showed a considerably significant −log of 14.23. The E3 gene regulates DTF under short-day and long-day conditions, and affects plant maturity [p-value) of 9.75. Two additional major genes affecting DTF, E2 and E4 [E2 gene was located at nucleotide positions 45,295,453, 45,295,508, 45,296,750, and 45,300,271 bp on chromosome 10 (Glyma.10G221500), but all showed non-significant p-values of 2.00, 2.43, 2.31, and 2.43, respectively , but all were excluded from the GWAS because of low MAF .The two major DTF-related genes, n of DTF , 63 and n of DTF . The E1 maturity . This ge2 and E4 , 67, werectively . Also thArabidopsis genes on the Axiom® 180k SoyaSNP array  with six SNP markers were excluded from the non-significant SNP marker-set since these markers were already included in the significant marker-set in GWAS. Also, when detecting homologous genes, the non-MAF filtered SNPs were used in order to consider various DTF-related genes which were excluded from the GWAS due to MAF < 0.05, such as E4 gene (Glyma.20G090000). Finally, a total of 5,534 SNP markers belonging to 2,243 soybean genes were selected and used for analyzing multilocus epistatic interactions. The results revealed five interaction groups (G1–G5) containing two significant and nine non-significant genes  of pod maturity 9–1), which is involved in regulating cell expansion and meristem of leaves [SPL12 , which affects plant growth and development [COP1 (G2), which regulates photomorphogenesis and skotomorphogenes, and is related to growth and development [FUL , which functions early in controlling flowering time [EMF1 (G3), which is involved in DTF through the regulation of reproductive development [FKF1 (G3), involved in DTF by regulating changes in photoperiod and temperature [NF-YB8 , which regulates leaf development and maturity [E3 gene in common, together with one and two other genes, respectively , which is related to flower meristem and DTF [FLK , which regulates DTF through the repression of FLOWERING LOCUS C expression and its post-transcriptional modification [NF-YB8 (G5), also detected in G3, as described above.The interaction groups G1, G2, and G3 contained two, three, and five genes, respectively, each including the significant DTF-related E1 gene , Table 2f leaves ; SPL12 , related to earlier flowering [ARP6 , involved in plant growth and development [FUL (G6), also identified in G3, as described above; BBX15 (G6), indirectly related to light reaction [SWN (G7), involved in the regulation of flowering and development [FVE (G7), associated with the regulation and control of DTF [PHYE and CRY2 , related to light reaction during flowering [The G6 and G7 interaction groups contained only eight non-significant genes identified in GWAS, but were detected as significant interaction groups on DTF in the epistatic analysis , Table 2lowering ; ARP6  are involved in P, FPI, FMI, and V pathways; four genes including E3 (G4 and G5) are associated with P, AU, and FPI pathways; and eight genes (G6 and G7) are related to P, V, AU, AM, FMI, and FPI pathways Click here for additional data file.S2 Dataset(XLSX)Click here for additional data file.S3 Dataset(XLSX)Click here for additional data file.S4 Dataset(XLSX)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.


Streptococcus pneumoniae and Neisseria meningitidis, revealed both previously identified and novel putative targets of co-selection related to virulence and antibiotic resistance, highlighting the potential of this approach to drive molecular discoveries, even in the absence of phenotypic data.Covariance-based discovery of polymorphisms under co-selective pressure or epistasis has received considerable recent attention in population genomics. Both statistical modeling of the population level covariation of alleles across the chromosome and model-free testing of dependencies between pairs of polymorphisms have been shown to successfully uncover patterns of selection in bacterial populations. Here we introduce a model-free method, SpydrPick, whose computational efficiency enables analysis at the scale of pan-genomes of many bacteria. SpydrPick incorporates an efficient correction for population structure, which adjusts for the phylogenetic signal in the data without requiring an explicit phylogenetic tree. We also introduce a new type of visualization of the results similar to the Manhattan plots used in genome-wide association studies, which enables rapid exploration of the identified signals of co-evolution. Simulations demonstrate the usefulness of our method and give some insight to when this type of analysis is most likely to be successful. Application of the method to large population genomic datasets of two major human pathogens,  Comparative methods for detecting co-evolutionary signals from population sequence data have received a lot of attention over the last few decades. As one of the more striking examples, statistical analysis of covariation between non-adjacent sites in large protein alignments has proven effective for predicting contacts between sites in the three-dimensional protein structure . Since sGenome-scale analysis of co-variation at single-nucleotide resolution, here termed as genome-wide epistasis and co-selection study (GWES), has already shown great potential; however, it poses considerable statistical and computational challenges as the number of pairs to be considered increases quadratically with the number of sites. Previous GWES approaches have been based on either straightforward pairwise tests , which dStreptococcus pneumoniae and Neisseria meningitidis. For the latter pathogen, we analyzed the entire pan-genome, which contains so many mutations that most model-based approaches are computationally infeasible, including even our recent highly optimized DCA-based software  to reduce the amount of storage required. To this end, we use a subsampling procedure to determine a threshold for saving a user-specified top fraction of the MI values. This is done by randomly selecting a subset of SNP pairs for which the MI values are calculated. The empirical cumulative distribution function is then used to estimate an appropriate saving threshold that corresponds to the user-specified top fraction. To increase stability, the procedure is repeated several times and the median threshold value is selected.To assess if a link is strong enough to warrant further study, we perform an outlier analysis. Due to genetic linkage, SNPs in close chromosomal proximity tend to be in strong linkage disequilibrium (LD). Note that LD here refers to SNPs showing a significant association specifically due to close genetic linkage. Since strong LD masks any potential signal of shared co-evolutionary selection pressure, we restrict the outlier analysis to non-LD pairs. The default approach for filtering out LD-pairs is to use a simple distance-based cut-off.To estimate an outlier threshold among the non-LD pairs, we use a data-driven procedure based on Tukey's outlier test . The tesThe typical approach for determining significance in this type of problem is to run a permutation analysis ,21. For When calculating the MI values, gaps are by default considered an outcome. While some gaps can be informative, others may simply be due to difficulties in the sequencing process: difficult-to-sequence regions may be systematically absent from all lower-quality sequences, resulting in distinct patches of gap characters that appear in parallel across samples. Hence, some interactions may be artificially amplified in regions with low-quality sequence data. To facilitate discovery of such cases in the subsequent manual analysis, we also calculate the MI value of the top pairs using only sequences where neither site of a pair contains a gap. Since the collection of sequences without gaps varies between pairs, it is difficult to compare gap-free MI values between SNP pairs in a meaningful way. However, the gap-free MI value can still be informative for a given pair in the sense that a large decrease in MI when dropping the gap sequences is an indication of a gap-driven interaction.The complete SpydrPick pipeline was implemented in C++ and supports parallel execution in a shared memory environment. Its space-efficient data structure, indexing strategy and online filtering of output jointly enable excellent scalability to an order of magnitude larger genome datasets than previous GWES software.For compactly visualizing the results of a GWES, we use a modified version of the GWAS Manhattan scatter plot. In a standard GWAS Manhattan plot, the association strength between a SNP and some phenotype (y-axis) is plotted against the chromosomal location of the SNP (x-axis), meaning that each point represents a single SNP. A GWES Manhattan plot has a similar design, however, each point now represents a pair of SNPs such that the x-axis displays the distance between the chromosomal locations of the SNPs and the y-axis displays the association strength between the SNPs, which is here determined by their MI value.To test the accuracy of our method in a controlled setting, we applied it to simulated evolutionary scenarios with known parameter values. These simulated datasets were generated using fwdpp  along wiTo model a single recombining species adapting to multiple niches, we simulated two subpopulations, or demes, of size Since our ability to detect the positively co-selected mutations in deme 1 may vary with its relative size in the metapopulation, we varied the size of deme 1. We explored scenarios in which demes were similar in size (50:50) or where deme 1 was considerably more rare, only 10 and 5% of the total metapopulation . For each parameter set, we specified a selection coefficient We also varied the population recombination rate For each scenario, we ran five simulations for S. pneumoniae strains collected in Maela, a refugee camp close to the border between Thailand and Myanmar  using tS. pneumoniae alignment, where all sequences were mapped to a reference sequence, this pan-genome-wide alignment was constructed by concatenating individual gene alignments. As a result, we can no longer use a straightforward distance-based cut-off to filter out LD-mediated links. Instead, we simply define two sites within the same gene as an LD-pair and two sites from different genes as a non-LD pair. The main aim for this dataset was to investigate if our method would still be able to extract plausible signals of co-selection under this modified setup.In contrast to the We begin by examining two example cases in more detail. The selected cases were generated under equal-sized populations (50:50), and A Manhattan plot illustrating the output of SpydrPick for the first dataset is shown in Figure The second example case is slightly more challenging, since the recombination rate is lower than in the first case. This is reflected by a lower effective sample size. A Manhattan plot for the second dataset is shown in Figure At last, to perform a more systematic and extensive simulation study, we applied SpydrPick on all 3000 synthetic datasets. To summarize the results, we used modified versions of the concepts of precision and recall. In practice, even after the ARACNE filtering step, each true link is typically accompanied by a collection of links between regions in strong LD with the selected SNPs. To account for these in our accuracy measures, we relax the true positive criterion and consider a position within 500 bp of a selected SNP as a correct hit for that particular SNP. We then define recall as the fraction of unique selected SNPs that are included among the outlier links, that is, a value of 1 means that all selected SNPs were detected. Additionally, precision is defined as the fraction of links among the outlier links that are between selected SNPs, that is, a value of 1 means that all links are between selected SNPs (or between regions around the selected SNPs).Precision and recall curves for different sample sizes and simulation settings are shown in Figure When comparing the results for the different simulator settings, there are two distinct patterns. First, and as already indicated by Figure sed GWES ,30. Alsomentclass2pt{minimAlthough these experiments provide some insight into the behavior of our method, and the prospects of GWES in general, it is difficult to give some general exact guidelines on the required sample size. The minimum number of samples required will be case-specific, depending on several properties in the data. In these simulations, we got very good results already with a few hundred samples, but this is not necessarily the case in more complicated evolutionary scenarios. In addition to sample size, population diversity is an equally important sample property required for a successful GWES. Consequently, the effective sample size is likely more informative than sheer sample size for assessing the appropriateness of a dataset for GWES .After reweighting with respect to the filtered alignment, the effective sample size was reduced to 130.26. The Manhattan plot of the analysis output is shown in Figure pbp1a), SPN23F16740 (pbp2b) and SPN23F03080 (pbp2x). These three proteins are involved in cell wall metabolism and are the primary targets of beta-lactam antibiotics. Modification of all three sequences is required for S. pneumoniae to exhibit high-level resistance to beta-lactam antibiotics , which is located directly downstream of pbp2x. The mraY gene encodes a phospho-N-acetylmuramoyl-pentapeptide-transferase also involved in cell wall biogenesis and, as noted by , encoding the toxin and key virulence factor pneumolysin. The ply-associated gene is coupled with SPN23F16620 (divIVA), SPN23F01290 (pspA) and SPN23F03150 (dexB), corresponding to the peaks at distances 2.9 × 105, 4.3 × 105 and 6.3 × 105 bp in the Manhattan plot. The divIVA gene encodes a cell morphogenesis regulator and pspA encodes a surface protein associated with virulence. Links between ply-associated genes, divIVA and pspA were discovered as significant by the initial DCA method  isolates, which lack a functional capsule polysaccharide synthesis locus . When searched against the non-redundant protein database with tblastx  with the original trpE site. Using the MI of the original link between trpE and group_5289 as a threshold, we found 14 candidate SNPs located 36–676 bp from the original trpE site. Among these, we found one non-synonymous SNP, coding an aspartic acid to alanine mutation. At last, to predict the functional effect of the amino acid substitutions, we used SNAP2 which outputs a value between −100  and 100   have been the primary tool for this purpose for more than a decade, and more recent works have demonstrated the applicability and potential of GWAS also for bacteria ,53,54. IS. pneumoniae  acts on the same traits. In fact, it has recently been suggested that NFDS acts to prevent antibiotic resistance genes sweeping to fixation in eumoniae .S. pneumoniae alignment showed that SpydrPick was able to accurately pick out previously discovered and validated signals of co-selection, as well as a novel link with a plausible biological explanation. In addition, a pan-genome-wide analysis of a Roary generated alignment of N. meningitidis isolates illustrated the potential of our method for an even more challenging dataset, by identifying several interesting signals likely to originate from genes under selection.In this work, we introduced the model-free GWES method SpydrPick, which is parallelizable and scalable to pan-genome-wide alignments of many bacteria. To illustrate the output of a GWES, we introduced a modified version of the Manhattan plot, which has served as the main illustrative tool for exploring the output of GWAS. While simulations validated our method in a controlled setting, the analysis of an trpE site in N. meningitidis, it is likely to be informative to check the surrounding region of the statistically linked sites to find the biologically most plausible source of the signal.Similar to previous GWES methods, SpydrPick operates on SNP resolution trying to fine-map the co-selection signal to individual sites using only the co-variation pattern observed in the data. For any method, this task is very challenging and limited by several factors, such as population structure, extent of LD and amount of available data. As illustrated by the identified et al. (Vibrio parahaemolyticus isolates. In contrast to our method, however, Cui et al. did not attempt to disentangle the direct interaction from the indirect interactions. In a recent hybrid approach, Gao et al. proposed filtering the data based on pairwise correlations and then fitting a joint model over the remaining sites in (Compared to model-based DCA methods that aim to fit a joint model over all SNPs, SpydrPick is conceptually very different in that each pairwise interaction between two sites is evaluated independently of all other sites. This is similar in spirit to the approach by Cui et al. , who usesites in . The obvsites in .To distinguish between LD and non-LD links, we used a distance-based threshold. As the background distribution will depend on multiple factors, such as type of organism, mode of recombination, population structure of the sample etc., it might be necessary to adjust the threshold value accordingly. This may involve running the analysis twice, where the output of the initial run is solely used to re-adjust the LD threshold parameter according to the drop in LD observed in the Manhattan plot (see Figure et al. (Another important topic for future research is to compare different techniques for adjusting for the population structure, as the optimal technique will most likely depend on certain properties in the data, such as the level of clonality among the isolates. In the work by Cui et al. , a subsaet al. , incorpoGWES is a relatively new data-driven and phenotype-free approach for detecting co-evolutionary patterns shaped by selection, and it is currently gaining traction in bacterial genomics due its wide applicability. Although GWES is a stand-alone analysis, there is also a lot of potential in combining it with other well-established methods. For example, if one has access to relevant phenotypic data, the output of a GWES can be used to effectively reduce the number of tests in a follow-up epistatic GWAS . Given ihttps://github.com/santeripuranen/SpydrPick, as well as the Anaconda repository: https://anaconda.org/bioconda/spydrpick. An MSA of the S. pneumoniae strains is available from the Dryad Digital Repository: https://datadryad.org/resource/doi:10.5061/dryad.gd14g. The N. meningitidis strains are available from the European Nucleotide Archive and their accession numbers are given in The SpydrPick software is available from GitHub: gkz656_Supplemental_FilesClick here for additional data file.


Arachis hypogaea L.) were classified into six botanical varieties according to the morphological characteristics. However, their genetic evolutionary relationships at the genome-wide level were still unclear. A total of 320 peanut accessions, including four of the six botanical varieties, and 37,128 high-quality single nucleotide polymorphisms (SNPs) detected by tunable genotyping-by-sequencing (tGBS) were used to reveal the evolutionary relationships among different botanical varieties and verify the phenotypic classification. A phylogenetic tree indicated that the tested accessions were grouped into three clusters. Almost all of the peanut accessions in cluster C1 belong to var. fastigiata, and clusters C2 and C3 mainly consisted of accessions from var. vulgaris and subsp. hypogaea, respectively. The results of a principal component analysis were consistent with relationships revealed in the phylogenetic tree. Population structure analysis showed that var. fastigiata and var. vulgaris were not separated when K = 2 (subgroup number), whereas they were clearly divided when K = 3. However, var. hypogaea and var. hirsuta could not be distinguished from each other all the way. The nucleotide diversity (π) value implied that var. vulgaris exhibited the highest genetic diversity (0.048), followed by var. fastigiata (0.035) and subsp. hypogaea (0.012), which is consistent with the result of phylogenetic tree. Moreover, the fixation index (FST) value confirmed that var. fastigiata and var. vulgaris were closely related to each other (FST = 0.284), while both of them were clearly distinct from var. hypogaea (FST > 0.4). The present study confirmed the traditional botanical classifications of cultivated peanut at the genome-wide level. Furthermore, the reliable SNPs identified in this study may be a valuable resource for peanut breeders.Cultivated peanut ( Arachis hypogaea L.) is one of the most important grain legume crops worldwide, and is a good source of food, vegetable oil, feedstock, and ground cover1. Cultivated peanut, with a total genome size of approximately 2.7 Gb, is an allotetraploid (2n = 4X = 40) plant species derived from a single recent hybridization event involving two diploid species, Arachis duranensis (A genome) and Arachis ipaensis (B genome), followed by a polyploidisation event2. On the basis of morphological features, crossing experiments, and seed protein electrophoretic profiles, Krapovickas and Gregory classified cultivated peanut into two subspecies (subsp. hypogaea and subsp. fastigiata) and six botanical varieties, which include var. hypogaea, var. hirsuta (within subsp. hypogaea), besides var. fastigiata, var. aequatoriana, var. peruviana, and var. vulgaris (within subsp. fastigiata)3. However, these classifications were based on growth habits, morphological features, as well as seed, pod, and inflorescence characteristics4. For example, the main distinguishing characteristic between subspecies hypogaea and fastigiata is the presence of main axis flowers. This trait may be abnormal in some peanut accessions because of genetic contamination (during the outcrossing history of the pedigree). To our knowledge, due to lack of specific description, some varieties have been classified as “irregular type” to distinguish between these accessions (obtained via hybridisations between subspecies or crosses between different peanut types) and the unadulterated accessions5. The irregular peanut types are probably the result of breeding activities that attempted to increase genetic variation. Crosses involving cultivated peanut are easier than intraspecific hybridisations with wild relatives.Cultivated peanut  primer pairs were used to investigate 96 peanut genotypes mainly from the US peanut mini-core collection4. Similarly, 146 SSR markers were used to study 196 major peanut cultivars extensively planted in different regions in China6, while 111 SSR markers were applied to examine 79 peanut cultivars and breeding lines from China, India, and the US7. However, these previous studies included relatively few, primarily SSR markers or an limited number of peanut accessions for evaluations of the genetic diversity and population structure. Moreover, the relationships among 280 genotypes that originated from a reference set of 300 genotypes representing 48 countries were explored using 73 Kompetitive Allele Specific PCR (KASP) genotyping assays8. The most recent studies applying next-generation sequencing technology to explore the molecular footprint of agronomic traits related to domestication included an analysis of 158 Chinese peanut accessions based on specific-locus amplified fragment sequencing (SLAF-seq)9 and an investigation of the genetic architecture of a reference set of 300 peanut germplasms with the high-density single nucleotide polymorphism (SNP) array ‘Axiom_Arachis’ (58 K SNPs)10. To the best of our knowledge, there is a lack of molecular genetic evidence of the differences and relationships among cultivated peanut subspecies and botanical varieties based on diverse peanut germplasms and high-density polymorphic loci.Although broad genetic variation is crucial for cultivar improvement, information regarding genetic diversity is also critical for germplasm utilisation11, have been extensively used for assessing the genetic variation of peanut germplasms7. However, these markers have failed to detect a high degree of polymorphism in peanut germplasms because of the extensive repetitive genomic content. Additionally, compared with other marker systems, SSR-based assays are labour-intensive and time-consuming. Thus, a SNP marker assay may be an appropriate alternative because SNPs represent the most abundant DNA sequence variations in the genome. Recently, various high-throughput and relatively inexpensive SNP detection platforms as well as next-generation sequencing techniques have been developed14. Genotyping-by-sequencing (GBS), which is one of the most important methods for detecting SNPs, is a genome-reduction technique based on the high-throughput next-generation sequencing of genomic subsets targeted by restriction enzymes16. Tunable GBS (tGBS)20 is a novel genome-reduction method that involves the ligation of single-stranded oligos to restriction enzyme fragments. Because of the additional (selective) nucleotides at the 3′-end of PCR primers, fewer sites are sequenced in tGBS , resulting in an increased read depth per sequenced site, assuming that equal amounts of sequencing data are generated17. Compared with the SNPs from conventional low-coverage GBS methods, those generated by tGBS can be called with a higher confidence and do not require imputation, which eliminates or decreases the number of induced errors18.Simple sequence repeat markers, which are associated with high polymorphism information content and represent useful molecular tools for analysing genomesfastigiata (var. fastigiata and var. vulgaris) were distinguished by analysing 320 peanut germplasms with 37,128 high-quality SNP loci identified by tGBS. Additionally, the botanical varieties of some accessions among the 320 cultivars were redefined according to molecular data and morphological features. This data set will serve as a valuable resource for fundamental research regarding genetic variations in peanuts as well as a source of potential polymorphic loci for the application of marker-assisted selection in peanut breeding. Moreover, we revealed that different botanical varieties can be defined based on molecular information as well as morphological features. Furthermore, the genetic relationships may be useful for identifying and explaining the genetic contaminants in some breeding lines or released varieties, including the imprecisely defined irregular peanut types.The objective of this study was to interpret the evolutionary relationships among different botanical varieties of peanuts and detect numerous potential polymorphic loci. Two varieties in subsp. 9 reads. Approximately 1.13 × 109 reads  remained after eliminating low-quality reads with a PHRED quality score <15 out of 4022.Strict criteria were used to filter the raw data, and 37,128 high-quality SNPs with less than 30% missing data were analysed regarding their genetic diversity and population structure. Data2Bio conducted tGBS on the 320 samples using ten Illumina HiSeq. 2000 lanes, generated 1.14 × 10A. duranensis and A. ipaensis (Aradu_v1.0.fa and Araip_v1.0.fa)2, were artificially combined to form a reference genome for sequence alignments with the GSNAP aligner23. Approximately 85.5% of the trimmed reads were aligned to a single genomic location, and were used to detect SNPs. The criteria for identifying homozygous and heterozygous SNPs were as follows: Number of reads ≥ 4; allele frequency ≥ 0.8; exclude the first and last 3 bp of each read; and include polymorphic sites with a PHRED quality score ≥20 (≤1% error rate). Additionally, heterozygous SNPs required an allele frequency ≥0.3. A total of 1,240,787 polymorphic sites, including all sites that differed from the reference sequence in at least one sample, were obtained after aligning all reads to the reference genome.The genome of the tetraploid cultivated peanut has not been published. Thus, the genomes of two diploid peanut progenitors, A subset of 253,595 SNPs was identified, with each SNP required to have less than 80% missing data across 320 samples, and be present in exactly two alleles and in at least two genotypes. The minor allele needed to be present in at least three samples and at least one homozygous sample. An additional requirement was a heterozygosity frequency of 0–0.1. There were 37,128 SNPs that remained when the missing data rate was increased to 30%, while the other filter parameters were unchanged. The histograms presenting the average missing data rate (18.1%) and average number of reads (nine) per SNP site are provided in Supplementary Fig. 24. Among the 51,040 combinations , which were var. vulgaris, and one accession (Z54), which was var. hypogaea. Cluster C2 consisted of 81 peanut cultivars, which were var. vulgaris, with the exception of three accessions that were var. fastigiata and five cultivars that were var. hypogaea. The three var. fastigiata accessions  were from the same subspecies as var. vulgaris. Four of the five var. hypogaea cultivars  were from outside China, while the fifth cultivar (Z376) was a Chinese landrace. The common characteristic among these five cultivars was the production of a small pod (up to 25 mm long and 13 mm wide). Cluster C3 represented the largest cluster, with 144 var. hypogaea, 20 var. hirsuta, 29 var. vulgaris, and 14 irregular type accessions. Among the 29 var. vulgaris accessions, four  were Chinese landraces, five  were from outside China, and the remaining 20 accessions were Chinese breeding lines. Although these accessions were classified as var. vulgaris, their pods were larger than those of cluster C2 accessions. The 20 var. hirsuta and 14 irregular type peanut accessions were sparsely distributed in cluster C3  was observed when K = 2    in Fig. hypogaea was clearly distinct from the other two botanical varieties (FST > 0.4). Similarly, the FST value between clusters C2 and C3 was smaller than that between clusters C1 and C3. The π values indicated that cluster C2 (0.048) had the broadest diversity, followed by clusters C1 (0.035) and C3 (0.012).The values in the upper triangle of Table ers C1, C, and C3 fastigiata, var. vulgaris, and subsp. hypogaea, respectively. To the best of our knowledge, this study is the first to reveal the genetic relationship between var. fastigiata and var. vulgaris. Moreover, the clusters were further validated by a PCA and a population structure analysis, with results that were consistent with the relationships indicated by the phylogenetic tree. Additionally, the FST values suggested that the varieties in the same subspecies (clusters C1 and C2) were more closely related than the varieties in different subspecies (clusters C1 and C3 as well as C2 and C3). Furthermore, the π values implied that cluster C2 had the broadest genetic diversity.The genetic diversity and population structure of the 320 accessions were analysed using 37,128 high-quality SNPs obtained by tGBS. A phylogenetic analysis indicated the 320 peanut accessions were classified into clusters C1, C2, and C3, which corresponded to var. In this study, the botanical types of 320 peanut germplasms were first determined based on published information. The classifications of some accessions that exhibited characteristics that obviously contradicted their documented type were corrected, and accessions with no available information in published material were classified based on the following parameters: presence of flowers on the main stem, number of seeds per pod, pod appearance and shape, total number of branches, and plant growth type. However, these traits are very likely controlled by a few genetic factors and make the classification difficult for peanut breeders and researchers during their works and studies, especially when some of the accessions harbor mixed morphological features.et al.4, but the observation that Z198 plants contained flowers on the main stem and produced three seeds per pod suggested the botanical type of accession Z198 was var. fastigiata. Second, the botanical type of 17 varieties that were considered to be imperfectly defined in the available literature was revised. Although the botanical types of these accessions were based on information in published articles and databases, we believe the accessions were erroneously defined. These accessions were grouped in the right subspecies, but the incorrect botanical types were revised according to the number of seeds per pod or pod appearance. For example, Belamkar et al.4 determined that Z225 was var. fastigiata, but we revised this classification to var. vulgaris because the plants produced only two seeds per pod. Third, the botanical types of eight cultivars were revised between subspecies. For example, accession Z0, which is a landrace and a parental line of most Chinese breeding lines, was previously designated as var. vulgaris. However, we observed that this accession lacked flowers on the main stem, and produced relatively large seed pods with deep constrictions. Accordingly, we redefined accession Z0 as var. hypogaea.There were three types of revised classifications  and the development of a SNP array10.The genetic diversity and population structure of 320 peanut accessions were evaluated with 37,128 high-quality SNPs. This study involved more markers and a larger sample size than most of the previous related studies, in which only a few SSR markers and peanut germplasms were used to investigate evolutionary relationships. For example, 19 SSR markers were used to analyse the genetic architecture of 24 peanut germplasmsfastigiata and var. vulgaris in subsp. fastigiata were distinguished for the first time. The 320 peanut accessions were grouped into three clusters according to the phylogenetic tree, with the first two clusters consisting of var. fastigiata and var. vulgaris accessions (subsp. fastigiata), while the third cluster comprised var. hypogaea, var. hirsuta, and irregular type accessions (subsp. hypogaea). Moreover, var. fastigiata and var. vulgaris in subsp. fastigiata were clearly divided into two clusters  were clearly distinguished. The two most recent SNP-based genetic analyses of peanuts included 17,338 SNPs developed by SLAF-seq.9 and a 58 K SNP array10. Both of these studies were only able to confirm the relationship between two subspecies. Further, the 14 irregular type accessions were scattered only in the subsp. hypogaea in this study. These accessions were all released varieties derived from crossing between the two subsp. in northern China, where the subsp. hypogaea were the popular botanical type. It was implied that the released irregular types were more similar to the subsp. hypogaea in phenotype under the breeding selection and possessed great proportion of genetic background from subsp. hypogaea, thus, they were classified to subsp. hypogaea subsequently. Therefore, when a peanut accession with a complex mixture of genetic background is difficult to be assigned to subspecies like the mentioned “irregular types”, genotyping with GBS or high density molecular markers could be applied as a good solution. Our data revealed that the genetic distance of the tested peanut accessions ranged from 0.043 to 0.127, with an average of 0.080, while a previous genetic distance ranged from 0.019 to 0.837 with an average of 0.468 was reported by Zhang et al.32. The smaller range in our case is likely because our study included more marker loci taking account that the diverse samples were carefully selected from over 2,000 germplasms. The largest average genetic distance was observed for var. vulgaris, followed by var. fastigiata, var. hypogaea, var. hirsuta, and irregular type. Therefore, genetic diversity was greatest for var. vulgaris, followed by var. fastigiata and var. hypogaea, which was consistent with the observed π.In this study, var. A. duranensis (A genome) and A. ipaensis (B genome). To identify unique read loci, genomes A and B were artificially merged to form 20 chromosomes, which were then used for SNP calling. This may have resulted in some inaccuracies in the read mapping and detection of SNP loci. However, this method represents the only viable option, at least until the tetraploid A. hypogaea L. genome is published. A set of 37,128 high-quality SNPs, with a 30% missing data rate, was generated through tGBS. These SNPs were obtained based on the requirement that the minor allele is present in at least three samples and at least one homozygous sample . When the MAF was increased to 0.05, only 10,004 SNPs remained. However, the same three clusters were included in the phylogenetic tree regardless of whether 10,004 SNPs or 37,128 SNPs were used (data not shown).To verify our classification results, we analysed the pedigrees of nine accessions  that were grouped together in a branch of cluster C3 in the phylogenetic tree . This accession is a released var. 4. The botanical information for the selected Chinese varieties was derived from available monographs36 and an online database (http://www.peanutdata.cn). Details regarding the remaining varieties were obtained from published articles41. Additionally, all selected peanut accessions were grown at the experimental station of the Henan Academy of Agricultural Sciences, Yuanyang, Henan, China for a subsequent morphological examination. A total of 67 accessions lacking available information were evaluated and the classifications for 25 accessions were revised according to the presence of flowers on the main stem, number of seeds per pod, number of branches, pod appearance, and growth habit (spreading or erect) according to the descriptions for different botanical varieties4. The 320 peanut accessions consisted of 150 var. hypogaea, 104 var. vulgaris, 32 var. fastigiata, 20 var. hirsuta, and 14 irregular type accessions.A total of 320 peanut accessions were selected from the Henan Academy of Agricultural Sciences germplasm resource, which comprises about 2,000 peanut germplasms from around the world. The accessions were collected based on phenotypic characteristics, including plant height, number of total branches, and growth habit (spreading or erect), as well as the results of a cluster analysis of molecular markers . The DNA samples were genotyped using an Illumina HiSeq. 2000 system and the Data2Bio proprietary tGBS technology2. Each sequenced read was scanned for low-quality regions and bases. Reads with a PHRED quality score less than 15 out of 40  were trimmed22. The trimmed reads for each sample were aligned to the reference genome using the GSNAP aligner23. Reads corresponding to the best alignments  were then extracted and used to identify SNPs. On average, each SNP was supported by nine reads, ensuring accurate genotyping.The reference genomes of two diploid progenitors of cultivated peanut (Aradu_v1.0.fa and Araip_v1.0.fa) were obtained from peanutbase.org and used as the pseudo-reference, with 2,059 contigs and a total length of 2.44 Gb42 was constructed and the principal component analysis (PCA) was completed using TASSEL5.2.13 (http://www.maizegenetics.net/tassel)24.The archaeopteryx tree was saved in the Newick format and a phylogenetic tree was prepared using the iTOL v4 online tool (http://itol.embl.de/)43. A three-dimensional scatter plot of the first three principal components was plotted using the R package “scatterplot3d” (https://cran.r-project.org/web/packages/scatterplot3d/index.html)44.The genetic distance matrix was calculated, an unweighted pair group method with arithmetic average cladogramhttp://web.stanford.edu/group/pritchardlab/structure.html)27 was used to investigate the population structure. The program was run 10 times for each K value, ranging from 1 to 10, with a 1,000 burn-in time and 1,000 iterations. The optimal K value was determined based on the ΔK from the Structure Harvester v0.6.94 28 program.A model-based (Bayesian) clustering method implemented with STRUCTURE v2.3.4 (FST), which explains the genetic differentiation between sub-populations, was estimated with Genepop v4.6 (http://kimura.univ-montp2.fr/%7Erousset/Genepop.htm)45. Data for the FST analysis were pre-processed by keeping SNPs with a minor allele frequency (MAF) greater than 0.05 and removing SNPs from scaffolds. To eliminate the influence of genetic crosses, only landraces were selected for the FST analysis.The pairwise fixation index (https://www.megasoftware.net/)46, with 300 bootstraps and the maximum composite likelihood model. Nucleotide diversity is a molecular genetics concept used to measure the degree of polymorphism within a population47. The SNP data for scaffolds were removed before calculating the mean diversity for each group.Nucleotide diversity (π) was assessed using MEGA v7.0 (Supplementary information for tGBS analysis of 320 peanut accessions


Previous techniques that identified this gene only accounted for about 0.5–2.0% of the genome and solely investigated coastal populations, leaving uncertainty on the genetic basis of this trait and its prevalence across a larger geographic scale.Disparity in the timing of biological events occurs across a variety of systems, yet the understanding of genetic basis underlying diverse phenologies remains limited. Variation in maturation timing occurs in steelhead trout, which has been associated with greb1L, to be the genomic region of major effect for maturation timing. Second, broad-scale characterization of candidate greb1L genotypes across 59 populations revealed unexpected patterns in maturation phenology for inland fish migrating long distances relative to those in coastal streams. Finally, genotypes from 890 PIT-tag tracked steelhead determined associations with early versus late arrival to spawning grounds that were previously unknown.We used a three-tiered approach to interrogate the genomic basis of complex phenology in anadromous steelhead. First, fine scale mapping with 5.3 million SNPs from resequencing data covering 68% of the genome confirmed a 309-kb region consisting of four genes on chromosome 28, including This study clarifies the genetic bases for disparity in phenology observed in steelhead, determining an unanticipated trait association with premature versus mature arrival to spawning grounds and identifying multiple candidate genes potentially contributing to this variation from a single genomic region of major effect. This illustrates how dense genome mapping and detailed phenotypic characterization can clarify genotype to phenotype associations across geographic ranges of species.The online version of this article (10.1186/s12862-018-1255-5) contains supplementary material, which is available to authorized users. Variation in the temporal occurrence of life history events, or phenology, occurs among a vast number of plant and animal systems –3. The tOncorhynchus mykiss) show distinct bimodal variation in the timing of entry into freshwater tributaries (freshwater entry maturation) [Variation in the timing of migration occurs across a variety of taxa but is particularly consistent and predictable in multiple anadromous salmonid species, which migrate from the ocean to freshwater tributaries to spawn . Specifiuration) , 13. Steuration) , 14. Desuration) . These auration) –17. In curation) . Even wiuration) , 19 suggGrowth regulation by estrogen in breast cancer-like (greb1L), an oestrogen target-gene. Previous genomic reduction techniques  identified multiple single-nucleotide polymorphisms (SNPs) within greb1L that successfully differentiated ocean and stream-maturing fish [O. mykiss genome assembly and full-genome resequencing techniques enables a more precise investigation of the genomic basis of maturation in steelhead. Further, access to passive integrated transponder (PIT) tagging data offers a proxy for measuring spawning site maturation that has not been explored in previous studies.The genetic basis for freshwater entry maturation in steelhead has recently been attributed to a single locus, ing fish , 15. Howgreb1L, was consistently associated with freshwater entry maturation phenotypes in steelhead across a broad geographic range that included far-migrating inland populations in addition to previously studied coastal populations. Third, we examined spawning site arrival phenotypes from individual fish to test for genotype to phenotype associations of greb1L with spawning site maturation. Together, we synthesized results to explore models of selection that likely maintain genomic variation of complex maturation phenotypes across a broad distribution of steelhead.In this study we investigated three primary questions related to the evolution of complex phenology in anadromous steelhead. First, we evaluated whether dense genome scans could reveal additional candidate genes associated with stream versus ocean maturation phenotypes in steelhead from replicated streams. Second, we tested whether a previously identified candidate gene, O. mykiss genome, we utilized a pooled-sequencing (Pool-seq) approach. Pool-seq involves combining individual DNA samples together and sequencing the homogenized DNA mixture together [n = 50), Kalama stream-maturing samples (n = 46), Klickitat ocean-maturing samples (n = 47), Klickitat stream-maturing samples (n = 50)  using bwa mem [view module [mpileup module, which extracts SNP and coverage information for each pool. To remove any false positive SNPs that often occur around insertion-deletions (indels) we used the identify-genomic-indel-regions.pl and filter-sync-by-gtf.pl scripts from Popoolation2 to remove any SNPs within 5 bp of indel regions [PPstats module of PoolParty.Sequenced libraries were prepared with the pipeline . As partoolation . Trimmed bwa mem  with def bwa mem  and unpaw module . FiltereST), a local score technique to test for statistical association, and a Cochran–Mantel–Haenszel (CMH) test [ST between stream and ocean-maturing pools, was calculated using Popoolation2, using a sliding window of 5-kb with a step size of 50 bp [p-values to determine differentiated genomic regions while simultaneously considering linkage disequilibrium. Local score uses a score function related to -log(10) p which will vary based on window size. Opposed to combining p-values within a fixed window size, local score considers the proximity of statistically significant p-values to determine window size iteratively. Finally, the CMH test identifies consistent differences in allele frequencies across biological replicates and computes significance between groups of interest [SnpEff [To determine selective sweeps or genomic regions with significant differentiation, we implemented sliding-window fixation index (FMH) test , 28. Sliinterest . Thus, i [SnpEff  which pr [SnpEff .PPanalyze from the PoolParty pipeline. We used PPanalyze to remove SNPs with minor allele frequency (MAF) < 0.05 and create neighbor joining trees based on Nei’s genetic distance and 10,000 bootstraps, using both all genomic SNPs, and SNPs within a 309-kb region of chromosome 28.Population structure analyses were performed using greb1L (greb1L-SNP) in an additional 59 collection localities across the Columbia River Basin [n = 2915; Additional file greb1L-SNP explains a large proportion of trait variation in relation to maturation and consistently differentiates stream and ocean maturing fish [greb1L-SNP across 59 collections including inland and coastal populations of steelhead using ArcGIS 10.5 to represent a broad geographic range.To determine association between maturation-timing and genomic regions of major effect across a large geographic scale, we isolated a single informative SNP within er Basin   for wild tagged fish between 2012 and 2016 in spawning tributaries across the Columbia River Basin. We retained data from sub-basins with known spawning tributaries which we also had sufficient individual DNA tissues (N > 50), leading to 6 distinct sub-basins, primarily in the inland lineage, with 890 fish in total . For ea80_54 in . We addiO. mykiss assembly genome between 15 X – 250 X with an average depth of coverage of 33 X , Abhydrolase Domain Containing 3 (abhd3), Growth regulation by estrogen in breast cancer-like 1, (greb1L), and Rho Associated Coiled-Coil Containing Protein Kinase 1, (rock1), as well as two intergenic regions . Furthermore, a genomic variant annotation analysis using SnpEff [greb1L was the only gene to contain five non-conservative, non-synonymous SNP mutations (nsSNPs), four of which surpassed significance thresholds  for con file ST  successfons Fig. . While ag SnpEff  revealedgreb1L marker  across both coastal and inland populations. This greb1L-SNP has been demonstrated to be strongly associated with freshwater entry maturation in previous studies of coastal steelhead populations [greb1L-SNP genotype (GG) was strongly associated with ocean-maturation and a “premature” genotype (AA) was strongly associated with stream-maturation. Genotypes for the greb1L-SNP were isolated from a recent study using RAD-seq in 59 populations of steelhead across the Columbia River Basin [p < 0.001; Fig. p = 1); however, genotype frequencies were variable and the majority genotype in this region was the “mature” type . Prevalence of the “mature” genotype for inland populations was contrary to expectations for stream-maturing steelhead and suggested that further refinement of phenology phenotypes was necessary beyond a simplistic categorization of these populations into ocean and stream-maturing types.To determine the association between the differentiated chromosome 28 region with phenology on a broad geographic scale, we examined genotype frequencies of a diagnostic ulations , 17. Speer Basin   to test whether the chromosome 28 candidate region was more strongly associated with stream-maturing than ocean-maturing phenotype  explained 10% ± 4% of spawning tributary arrival date variation  that provide adequate read coverage across the majority of the reference genome [greb1L region, fine scale mapping provided a broader understanding of the genomic basis of this phenological trait in steelhead due to higher marker density covering a large portion of the genome [Our study confirms a genomic region of major effect underlying phenological variation in anadromous steelhead through dense genome resequencing. Previous studies have mapped SNPs generated from restriction-site associated DNA sequencing (RAD-seq) to f greb1L , 15, 17.e genome . While pe genome .greb1L has consistently shown the most compelling associations to maturation phenotypes [greb1L which likely contains many regulatory components such as transcription factors, promoters, and enhancers [greb1L has obvious connections to sexual maturation since it mediates the interaction of oestrogen with other target proteins [greb1L contains multiple non-conservative and non-synonymous mutations. These changes in protein structure are compelling candidates that are possibility under selection, which additionally provide evidence that greb1L is a key gene under selection for maturation phenology [rock1, the gene directly upstream of greb1L, also has obvious ties to maturation as it has been connected to embryo development in zebrafish [abhd3, the gene immediately downstream from greb1L, was the least differentiated, yet is a physiological regulator of medium-chain phospholipids [mib1 has less-apparent connections to migration and maturity as it primarily relates to cell apoptosis [mib1 does influence ventricle formation and can be connected to cardiac function and swimming performance [greb1L and rock1 which may simply be a gene that currently lacks annotation, or possibly a region that consists of enhancers or promoters for the nearby genes or non-coding RNAs. If the latter, these intergenic SNPs may play a large regulatory role in expression level [The additional genes of smaller effect characterized in this study indicate that the genomic basis for maturation phenology in anadromous steelhead may encompass a larger genomic region on chromosome 28 than previously understood. enotypes , 15, 17.nhancers . greb1L proteins , 42. Migproteins . Additiohenology , 44. rocebrafish  and testebrafish . Furtherebrafish  which maebrafish . abhd3, holipids . The necholipids , 49. mibpoptosis ; howeverformance , 52. Finon level . Overallon level .greb1L genotypes. To the contrary, using spawning tributary arrival time, we show that variation in maturation phenology does occur in inland steelhead, which are all stream-maturing fish. Specifically, about 10% of variance of time to arrival is explained by greb1L-SNP. While the strength of this pattern is not overwhelming, it is still compelling given that PIT-tag arrival times were used as a proxy and may be prone to some error, and only a single informative SNP was used. In addition, it currently serves as the only explanation for variation in greb1L genotypes in stream-maturing fish. Given this, fish with “premature” greb1L-SNP genotypes generally arrive to spawning tributaries early, fish with “mature” genotypes arrive later, and heterozygous fish arrive in an intermediate timeframe. This association suggests that inland fish tend to hold in larger freshwater tributaries for several months as premature fish, and then migrate to spawning grounds in headwater tributaries over a continuum of maturation states before all fish become sexually mature and spawn together. This admixture of fish with varying phenotypes shows no Wahlund effect heterozygote deficit [greb1L “mature” and “premature” genotypes; however, the inland greb1L “mature” and “premature” genotypes both exhibit an early and narrow range of freshwater entry timings, but later become diverged in their spawning tributary entry timing. In contrast, the greb1L “mature” and “premature” genotypes of the coastal steelhead populations instead show large disparity in freshwater entry timing from the ocean. This difference in freshwater entry timings of the greb1L mature genotypes of the coastal versus inland lineages may be due to the long migration distance that inland fish must swim to reach their (300–1500 km) spawning sites [greb1L genotypes, all fish from inland populations must migrate early to approach spawning grounds before environmental conditions become unfavorable [greb1L genotypes which manifests as variation in spawning tributary arrival . In some years, due to resources such as spawning habitat availability, and environmental conditions, it may be beneficial to arrive to a spawning tributary early (greb1L premature genotype), whereas in others it may be more beneficial to arrive late (greb1L mature genotype) [Our results indicated that the candidate region on chromosome 28 is most consistently associated with arrival timing on spawning grounds rather than arrival timing at freshwater entry across populations included in this study. Traditionally, arrival timing at freshwater entry has been a phenotypic proxy for maturation timing that has been used to characterize fish entering freshwater as either sexually mature or premature , which p deficit , providi deficit . Inland ng sites . Regardlavorable , 57 inclavorable . Then, wenotype) , 58. In greb1L premature genotypes similar to fish exhibiting stream-maturing phenotypes in coastal populations. This discovery illustrates that understanding complex phenology patterns is a difficult process due to challenges of monitoring migrating fish through sexual development stages across large watersheds. However, this challenge can be overcome by monitoring efforts that apply tags and collect non-lethal tissue from migrating fish as they enter freshwater [As commonly illustrated, maintaining adaptive genetic variation is essential for protecting species at risk to extinction , 60. We eshwater . A profoeshwater , 61, 62.greb1L, was related to sexually premature and mature entry of steelhead into freshwater systems. Using genome resequencing for fine scale mapping of maturation traits during migration, we identified a 309-kb genomic region of major effect for freshwater entry maturation that included four candidate genes, greb1L, rock1, mib1, and abhd3. This region also includes a highly significant intergenic region between greb1L and rock1 which may play a regulatory role in expression of these genes. Additionally, broad-scale SNP genotypes from greb1L in populations and individuals refined genotype to phenotype associations, revealing that candidate genotypes were more consistently associated with timing of arrival on spawning grounds rather than freshwater entry maturation. These results suggest that there are fitness benefits to arriving to spawning tributaries prematurely or maturely, and variation in this trait is likely to be maintained by balancing selection. Together this study illustrates the importance of high precision genomic scans and detailed phenotypes to identify targets of selection.Anadromous salmonids, a recreational and culturally significant family of fishes, have complex life histories related to migration and maturation. Previous investigations into the genetic basis of maturation-timing suggested that a single gene, Additional file 1:Supporting figures. Supporting tables. Pooled-sequencing protocol. (DOCX 820 kb)