Due to the recent domestication of peanut from a single tetraploidization event, relatively little genetic diversity underlies the extensive morphological and agronomic diversity in peanut cultivars today. To broaden the genetic variation in future breeding programs, it is necessary to characterize germplasm accessions for new sources of variation and to leverage the power of genome-wide association studies (GWAS) to discover markers associated with traits of interest. We report an analysis of linkage disequilibrium (LD), population structure, and genetic diversity, and examine the ability of GWA to infer marker-trait associations in the U.S. peanut mini core collection genotyped with a 58 K SNP array.r2 = half decay distance at 3.78 Mb. Structure within the collection is best explained when separated into four or five groups (K = 4 and K = 5). At K = 4 and 5, accessions loosely clustered according to market type and subspecies, though with numerous exceptions. Out of 107 accessions, 43 clustered in correspondence to the main market type subgroup whereas 34 did not. The remaining 30 accessions had either missing taxonomic classification or were classified as mixed. Phylogenetic network analysis also clustered accessions into approximately five groups based on their genotypes, with loose correspondence to subspecies and market type. Genome wide association analysis was performed on these lines for 12 seed composition and quality traits. Significant marker associations were identified for arachidic and behenic fatty acid compositions, which despite having low bioavailability in peanut, have been reported to raise cholesterol levels in humans. Other traits such as blanchability showed consistent associations in multiple tests, with plausible candidate genes.LD persists over long distances in the collection, decaying to Based on GWA, population structure as well as additional simulation results, we find that the primary limitations of this collection for GWAS are a small collection size, significant remaining structure/genetic similarity and long LD blocks that limit the resolution of association mapping. These results can be used to improve GWAS in peanut in future studies – for example, by increasing the size and reducing structure in the collections used for GWAS.The online version of this article (10.1186/s12864-019-5824-9) contains supplementary material, which is available to authorized users. Peanut is one of the most important oilseed crops in the world, with many desirable traits: it is high-protein and nutrient-dense, is edible without preparation, and is easily grown by small-holder farmers as well as under mechanization. Peanut has a desirable lipid profile, with oil that is higher in unsaturated fatty acids than saturated fatty acids; as such, it may provide numerous health benefits, including lowering LDL cholesterol . BreedinArachis genus in the Fabaceae family. The genus has nine taxonomic sections based on morphological, geographical and cross-incompatibility characteristics across all of its 80 species. Most of the species are diploids (2n = 2x = 20) with four known aneuploids (2n = 2x = 18) and five tetraploids (2n = 4x = 40) including the cultivated form [fastigiata and hypogaea, depending on the presence or absence of flowers on the main axis [Peanut belongs in the ids 2n = x = 40 inain axis . There aain axis , which aThe sequencing of the diploid progenitors of tetraploid peanut ,  to investigate the nature and extent of LD and how it relates to different chromosomes, subgenomes, minor allelic frequency (MAF) and subspecies groups; 2) to estimate genetic diversity and population structure within the U.S. peanut mini core collection and 3) to determine how these factors affect genome-wide association analysis in peanut.One hundred seven diverse accessions from the U.S. peanut mini core collection were genotyped using a 58 K Affymetrix SNP array together with six commercial standards. A total of 13,527 highly polymorphic SNP markers were selected for downstream analyses. The collection was evaluated in the field  and harvested seeds assayed for biochemical composition. To evaluate for ease of removing the seed coat from the seed (blanchability), accessions were planted across three environments  and harvested seeds were evaluated.SNP counts were approximately proportionally distributed across the 20 chromosomes. There were on average, 676 polymorphic SNPs per chromosome, and SNPs were enriched toward chromosome ends Fig. a. The avf). Further, 2% (268) of the markers had heterozygosity levels ranging from 0.1 to 0.4 with average f of 0.8 (ranging from 0.0 to 0.9). Most of the SNPs had an inbreeding coefficient of one except for a few highly heterozygous markers that had negative inbreeding coefficients. Nine accessions showed very high levels of heterozygosity, ranging from 10 to 28% among the 13,572 selected markers , at the 13,527 SNP locations, was 0.18799. This represents the nucleotide diversity per assayed SNP in the mini core. The expected number of polymorphic sites per nucleotide (θ), which estimates the mutation rate in the collection, was 0.18813 with 13,527 segregating sites. Tajima’s D, which estimates the normalized measure of difference between the observed (π) and expected (θ) nucleotide diversity was − 0.00252 , haplotype blocks identified in the mini core collection are generally not significantly larger than 200 kb in size. Depending on the sliding window size adopted, the number of haplotype blocks identified ranged from 575 blocks at 2 Mb to 590 haplotype blocks at 50 Mb window size. Increasing the sliding window size from 2 Mb to 50 Mb or larger did not have significant effect on the number of haplotype blocks identified. We note, however, that SNP densities and distributions are a limiting factor in determining both precise haplotype boundaries and GWA associations. Although the average density is relatively high compared to many older conventional marker assays (at 1 SNP per 175 kb), the density is nevertheless quite uneven, with large pericentromeric regions not covered by SNPs (the largest gaps being ~ 7 Mb in the pericentromeres of chromosomes 16 and 18). Associations in those regions may therefore aggregate the effects of several genes, and association peaks may be located at a considerable distance from a causal genomic element.A total of 575 haplotype blocks were identified using a sliding window of 2 Mb along each chromosome. Block size varied widely, ranging from 0.002 kb to 199,905 kb with an average of 28.89 kb. The number of SNPs per block ranged from two to 32 with an average of four SNPs per block. Large haplotype blocks were mostly located within the large pericentromeric regions, which is expected since these regions have low recombination rates in general  was evaluated for each possible K ranging from 1 to 10 along with admixture levels for each accession for data filtered at MAF ≥ 0.05, ≥ 0.1 and ≥ 0.2. Subspecies, botanical variety and market type identifications , 31 for fastigiata and hypogaea subspecies respectively. The fifth cluster consisted exclusively of accessions from hypogaea subspecies fastigiatTo study the effect of minor alleles on the nature of LD and the extent of its decay, three different MAF threshold cutoff levels  were tested. Mean LD among all SNPs was calculated across the entire genome over different map distances. The SNPs were pooled over all chromosomes in each sub-genome to compute genome-wide mean LD for each distance bin.r2 = 0.87 (0–0.1 kb) to r2 = 0.236 . Mean LD is high (r2 > 0.82) at short distance bins (< 0.5 kb) and declines with increasing bin distance. It drops to r2 = 0.44, which is approximately half the original value, at bin distance 78.7 kb – 127 kb , where mean LD declines to approximately half its original value Fig. a. For MA0.87 0–0. kb to r2 kb, wherr2 was used to estimate LD between pairwise comparisons of markers with sliding window size of 50 markers for each of the 20 chromosomes filtered for MAF greater than 0.05, 0.1, and 0.2. LD decay distance increases with MAF, the increase is very significant at MAF ≥ 0.2  and chromosome 20 (6.75 Mb). The decay distance is shortest in chromosome 12 (1.41 Mb) and chromosome 13 (1.54 Mb). Chromosomes 4 and 14 have high LD, as do chromosomes 10 and 20  than in fastigiata (average decay distance = 3.41 Mb). There was no significant difference between the extent of decay between fastigiata subspecies and the larger collection - average decay distance in the 113 collection = 3.78 Mb  and in Q-Q (quantile-quantile) plots of expected (under a Gaussian distribution) versus observed p-values . There are 21 other gene models predicted within this region. Additionally, markers AX-147247765; 90,017,134 bp and AX-147248572; 127,788,433 bp on chromosome 14 (B04), had significant associations with respective p-values of 1.7E-11 and 1.6E-11. Marker AX-147248572, is 53.67 kbp upstream of Araip.R5W4R (Araip.B04:127839622..127842106), which is predicted as a thioesterase superfamily protein.Arachidic and behenic saturated fatty acids showed moderately strong associations in this study. Several plausible candidate genes associated with fatty acid metabolism were identified in these regions Table . At the The SNP AX-147237808 at 6,039,768 bp on Chromosome 11 (B01) significantly associated with arachidic acid with p-value of 3.52E-09. This marker is 75.96 kbp upstream of Araip.9YT86, a non-lysosomal ceramidase predicted gene. Four other genes are predicted within this 75.96 kbp region. SNP AX-147255620, at 37,296,448 bp on chromosome 17 (B07), is associated with arachidic acid with p-value409E-06. This marker is 66.12 kbp downstream of a flavin-binding monooxygenase family protein (FMO3), Araip.L66QB (Araip.B07:37228337..37230328).Each of the top SNPs for the remaining weak, non-significant marker- trait associations were examined and additional candidate loci that could explain the observed marker association were identified  showed association with a P-value of 1.56E-04 and SNP AX-147247686 at 76,220,065 bp on chromosome 14 (B04) with a P-value of 3.99E-04 showed strong, though not significant association  with a We found the genomic region spanning 130,111 kb to 131,362 kb on chromosome 16 (B06) to consistently associate with blanchability despite the lack of statistical significance. This genomic region has 75 candidate genes, three of which show elevated expression in the seed pericarp: Araip.LS9BW and Araip.Q8ZS3, which are chalcone synthases, and Araip.J6A49, which is an ATP binding protein . These putatively-tetrasomic regions showed little or no diversity, and therefore were excluded from the set of 13,527 informative markers analyzed.f), measures the probability that two alleles at any locus in an individual are identical by descent from the common ancestor(s) of the two parents. These results demonstrate that the mini core accessions have little or no evidence of inbreeding except for a few loci where f was less than one.Almost all the highly heterozygous markers had negative inbreeding coefficients, while most SNPs had an inbreeding coefficient of one. The inbreeding coefficient  between nucleotide sequences, genes and/or species is revealed through a phylogenetic analysis and is often displayed in the form of a bifurcating phylogenetic tree. However, in cases where hybridization, recombination, gene duplication or horizontal gene transfer is believed to have occurred, a bifurcating tree structure is insufficient in displaying such reticulate relationships. The complex clustering pattern shown in the SplitsTree4 graph is consistent with the high levels of admixture observed in the structure analysis. In both the structure and phylogenetic network analyses, the clustering pattern is not consistently explained by market type nor and subspecies classifications, although a Fisher exact test showed significance in correspondence. This is similar to findings in previous studies using SSR makers , 31, 36.Another probable reason for the incoherent clustering pattern of the genotypes by market class and sub species groupings could be that both classifications were inconsistently scored lending strong genotypic evidence to the need for reclassification as previously observed . Classifr2 of 0.44 at 78.7–127 kb. LD estimates were significantly affected by MAF, especially over longer bin distances. This is contrary to findings in maize where MAF was shown to significantly affect mean r2 estimates, especially at short distances between 0 to 10 kb and the effect was not so pronounced over longer bin distances [LD is quantified by comparing haplotype frequencies observed in the data to their expected frequencies based on independence. In the U.S. mini core collection, mean LD estimates generally declined with increasing bin distance as expected. Mean LD dropped from 0.87 to half istances .r2 values down, leading to the underestimation of LD means and LD decay distances. This is consistent with findings that showed that rare SNPs tend to have lower pairwise values compared to more common SNPs [r2 mean values and LD decay distances compared to all the other thresholds investigated.Minor alleles shift mon SNPs . The dramon SNPs , 41. It r2 = half decay distance at 3.78 Mb in the collection. Contributing factors could be that cultivated peanut is a self-fertilizing species (resulting in slower breakup of LD blocks), and likely went through an extreme bottleneck at the time of tetraploidization. This means that it is expected to have a mostly homozygous genetic background with a relatively low number of detectable recombination events between loci, thus the observed low rates of LD decay across the genome [hypogaea subspecies, LD decays over a significantly longer distance compared to the decay distance in fastigiata subspecies.Average LD estimates persisted over long distances, decaying to e genome , 43. In fastigiata subspecies and the entire collection, which is contrary to results from a study using 392 SSR markers that reported LD to have persisted over 10 cM in the entire mini core collection and 20 cM in fastigiata subspecies [fastigiata and hypogaea which was investigated using a subset of 59 and 67 accessions, respectively. The extent of LD is highly heterogenous across chromosome regions – generally long in genic regions and short in intergenic regions. A low and non-uniform SNP density across each chromosome , hampers the ability to draw conclusions on the variable patterns of fine-scale LD across the genome except for general trends since SNP density is not the same in genic vs intergenic regions.Our results show little or no difference in LD decay distance between bspecies . Howeverbspecies . TherefoAltogether, these results show that LD persists over a long distance in the U.S. mini core peanut collection and that the nature of LD and the extent of its decay are affected by minor alleles as well as population structure. These large blocks of persistent LD with a low decay rate make it hard to achieve high resolutions for fine mapping, or for identifying marker-trait associations. GWA relies on markers proximal to the causative loci being in high LD with the QTL but this should quickly drop for those markers further from the QTL otherwise mapping resolution is limited. Low mapping resolution can be overcome by genotyping at a higher marker density or by using collections that have experienced sufficient rounds of recombination within the desired region to breakdown persistent LD blocks.The mini core collection was phenotyped for several biochemical traits but characteristics of the collection and of the measured traits resulted in generally insignificant association values except for arachidic and behenic fatty acid composition, which showed significant associations. Although present in very small quantities in peanut, behenic and arachidic fatty acids have been shown to raise cholesterol levels in humans .− 5, two markers appear to associate significantly with arachidic acid and eight with behenic acid. These markers offer good targets for further validation and future studies based on their proximity to candidate genes implicated in some form of fatty acid metabolism. The SNP on B01 is near two predicted non-lysosomal ceramidase proteins, Araip.ZQ4WS and Araip.9YT86. Ceramidase has been shown to catalyze the hydrolysis of ceramide to sphingosine and fatty acid, and possibly also the reverse reaction [Based on an adjusted 5% Bonferroni corrected cutoff threshold of 1.86 × 10reaction . The SNPreaction .ahFAD2 genes, was shown to control the conversion of oleic acid to linoleic acid during seed development and thus resulted in the targeted breeding of “Hi-Oleic” peanuts with an improved shelf-life [For behenic acid, the SNP on chromosome B06 was found to be 356.1 kbp upstream of Araip.D6HPL predicted as a fatty acid desaturase 2 gene (FAD2) with GO terms for lipid metabolic process and oxidation-reduction process. FAD2 enzymes catalyze the oxidation of oleic acid to linoleic acid –49 thus elf-life , 51. TheTwo other SNPs on chromosome B04 are in proximity to two gene models with the predicted function of a palmitoyl protein thioesterase family. Palmitoyl protein thioesterases catalyze the removal of thioester-linked fatty acyl groups like palmitates from modified cysteine residues . These gAssociations for blanchability were not significant at the applied adjusted Bonferroni threshold but the region on chromosome B06 had plausible candidate genes with strong expression in the seed pericarp and require additional evaluation. Chalcone synthase is the first committed enzyme in the flavonoid biosynthesis pathway which among other things is associated with pigmentation and plant defense mechanisms against stress . In peanIn addition to these traits, several other fatty acid components, total oil and total protein were analyzed for associations. The lack of significance in association for many of the traits could be due to several factors, including multigenic or complex control of the traits, small sample size, structure, and genetic similarity within the mini core accessions. The power of GWA to detect a true association between a SNP and a trait of interest has been shown to be limited by both the effect size and frequency of occurrence of the allelic variant(s) explaining the trait within the population , 56, 57.From structure results, we were able to identify four to five sub-populations, but GWA results were optimized at two to four PCA levels depending on the trait. This is not atypical as PCA estimates genetic background derived from a set of independent genetic markers and uses these to control for population stratification whereas methods like FastStructure use a set of unlinked markers to estimate ancestry probabilities for each individual and thus provides more information about the level of stratification within a collection of individuals . Even afUnlike with Arabidopsis and other highly self-fertilizing species, where a small collection number was sufficient for GWA to detect strong associations , 58, 59,r2 = half decay distance at 3.78 Mb. The nature of LD and the extent of its decay varied widely across the genome and is affected by both minor allele frequency and population structure. Structure within the collection is best explained when separated into four or five groups (K = 4 and K = 5) with high admixture proportions. In general, our findings show that the U.S. peanut mini core collection may not be well suited for GWAS due to its limited population size, structure/genetic similarity, and long LD blocks that limit the resolution of association mapping. We identified candidate loci for traits analyzed and suggest follow-up studies. Work is ongoing to genotype the larger core collection of approximately 831 accessions to facilitate the discovery of reliable markers associated with traits of interests and facilitate the discovery of new genes or allelic variants controlling these traits.This study offers insights into peanut diversity and provides valuable information to peanut breeders and geneticists toward variety improvement. The study provides information about the relatively high degree of structure in the U.S. peanut mini core collection and suggests that structure will likely be a challenge for most association studies in peanut – as will the generally-high LD, and complications due to paralogy of markers in this tetraploid species. Nevertheless, this study did identify some marker-trait associations, even considering the challenges of this collection and species. We found LD to persist over long distances in the collection, decaying to Seeds were obtained from the USDA Plant Genetic Resources Conservation Unit (PGRCU) in Griffin, Georgia and planted at the University of Florida Plant Science Research station in Citra, Florida, using the standard planting procedures at the PGRCU for peanut. In brief, each accession was planted in a two-row plot 3 m in length with 75 cm row spacing. Each plot was spaced 3 m apart in the planting direction with a 1.5 m inter-row spacing to minimize cross contamination. Seeds were planted at a density of 50 seeds/row at a depth of 3.5 cm. The experiment was planted using an augmented randomized block design with three blocks over three growing seasons . The 107 mini-core lines were replicated once in each block, along with six commercial standards in each block. These included: Bailey , Floridaa). This process was repeated three times to yield approximately 6 ml of total supernatant in the pre-weighed tube. The tube was then placed in an evaporating chamber in a water bath heated at 40 °C and nitrogen gas was passed into the chamber to purify the oil from the hexane tert-butyl ether extractant. The tube, now containing purified oil, was re-weighed . The weight of the pre-weighed tube was subtracted from this weight to give the weight of the extracted oil. The percent oil composition was calculated as: Y = (b-a) / (0.2*3).Three seeds of harvested mature peanuts were flash frozen with liquid nitrogen, ground to a fine powder and stored at − 20 °C prior to analysis. Biochemical data were collected on total protein content, total oil content, and fatty acid composition. Protein content in seed was calculated using total nitrogen detection via Kjeldahl digestion, and the standard conversion factor of 5.46 was used for raw protein content calculation. Total oil was calculated using an extraction procedure from Jean Thomas (unpublished). In brief, approximately 2 ml of 50–50 hexane tert-butyl ether was added to 0.2 g of ground tissue in a tube. The tube was vortexed, capped and placed under a fume hood for 10 h. The tube was centrifuged and the supernatant carefully transferred into a pre-weighed 16 × 125 mm tube  was dissolved in 200 μL of hexane. 200 μL of an esterification mixture containing one-part sodium methoxide, four parts petroleum ether, and two parts ethyl ether was added to the vial, and vortexed. An additional 600 μL of hexane was added to the vial, vortexed, and allowed to sit for 30 min, at room temperature, prior to analysis. An Agilent 7890 gas chromatographer unit equipped with a flame ionization detector (FID) was used for fatty acid determination. A 15 m Agilent/J&W DB-225narrow-bore column (0.25 mm) with a 60:1 split inset was set to an internal temperature of 280 °C. A 1 μL injection volume was used and the carrier gas, helium, was set at a flow rate of 1 mL/minute. The detector temperature was set to 300 °C and total run time was set to 17 min per sample. The retention time in minutes for the fatty acids are as follows: palmitic (1.619), stearic (2.465), oleic (2.638), linoleic (2.878), arachidic (3.500), gadoleic (4.154), behenic (6.419) and lignoceric (9.328). The resulting peak heights were recorded, and the height of individual peaks was divided by the combined height of all peaks to calculate percentage of total oil for each fatty acid component.Blanchability in peanut is the ability to completely remove the seed coat from the seed. It was evaluated in the peanut mini core in three environments  as described previously . BlanchiAll seeds for this study were ordered from the purified peanut mini core collection  maintainOf the 58,000 SNP positions present in the Affymetrix fixed array , 14, allSNP genotype data was used to study genetic diversity and the genetic relationship among individuals of the mini core collection. Allele frequencies, major and minor gamete frequencies were calculated using the software TASSEL version 5.2.39 using default settings .The polymorphic information content (PIC), heterozygosity, within-population inbreeding coefficient and gene diversity was calculated using the software PowerMarker version 3.25 using the default settings .The average pairwise divergence among genotypes, which represents the nucleotide diversity per bp, π (pi) and the expected number of polymorphic sites per nucleotide, θ (theta), were estimated using the software program TASSEL v5.2.39 using the default settings. The normalized measure of difference between the observed (π) and expected (θ) nucleotide diversity, Tajima’s D, was also computed. Haplotypes were determined using options “-dog --blocks no-pheno-req” in PLINK v1.90b4.4 . The maxhttps://npgsweb.ars-grin.gov) and previous publications [Population structure was determined using the software program FastStructure, version 1.0 and the appropriate number of model components that explain structure in the dataset was determined by running a python script, chooseK.py . Admixtuications , 31, 46.r2 values for all pairwise marker comparisons using a sliding window size of 50 markers around the current site, in TASSEL v5.2.39. Marker positions were then used to investigate LD decay along each chromosome and across the entire genome. Background LD was estimated as the 90th percentile of the r2 value of marker-pairs on different chromosomes. LD decay distance was determined by fitting a non -linear model using the Hill and Weir method, later modified by Remington et al., with r2 threshold set at 0.2 and r2 = half decay distance. To estimate the effect of population structure on LD decay, LD decay within each subspecies was analyzed.SNP markers were filtered for a minimum count of 100 known alleles and minor allele frequency (MAF) of 0.05, 0.1, and 0.2. LD analysis was performed for each chromosome, by computing Genome-wide association (GWA) was performed using a weighted mixed linear model (MLM) at optimum compression level and variance components were estimated once using P3D in TASSEL version 5.2.33 and in R using the R package GAPIT , 72. A k− 5 and a suggestive line defined at 3.71 × 10− 4 following a modified Bonferroni correction method described by Li et al. 2012 [https://peanutbase.org [We applied a whole-genome significance cutoff based on an adjusted Bonferroni test threshold at 1.86 × 10al. 2012 . CandidaAdditional file 1:Figure S1. Population structure in the mini core for K = 2 to K = 7. The Y-axis represents the probability of assigning an accession to a group and the X-axis accession names. (a) Different K groups ordered according to subspecies. (b) Groups ordered according to botanical variety. (DOCX 9720 kb)Additional file 2:Figure S2. LD decay using Loess fit (black) and non-linear fit (blue) for each of the 20 chromosomes. R2 values are plotted on the Y-axis against physical distance in base pairs on the X-axis. Heatmaps represent the density of r2 across distance. (DOCX 8289 kb)Additional file 3:Figure S3. LD decay pattern along each chromosome and across each sub- species. (a) Summarized LD decay with distance for each chromosome and a genome-wide average for each of the two peanut sub-genomes. (b) LD decay in each of the two sub-species at different physical distances. (DOCX 1186 kb)Additional file 4:Figure S4. Histograms showing the phenotypic distribution of seed composition and quality traits in the mini core collection. The x-axis shows BLUP values over three replicates and the y-axis represents the number of individuals with the respective values. (DOCX 276 kb)Additional file 5:Figure S5. Manhattan and QQ – plots for Saturated Fatty acid components. (DOCX 449 kb)Additional file 6:Figure S6. Manhattan and QQ – plots for Unsaturated Fatty acid components. (DOCX 427 kb)Additional file 7:Figure S7. Manhattan and QQ – plots for Oleic-linoleic ratio, total oil, total protein content and Blanchability. (DOCX 377 kb)Additional file 8:Tables S1. – S8. Sheets in the Table contain details of the accessions used in the study, overall SNP summary, heterozygosity among accessions, SNP distribution, nucleotide diversity, haplotypes, Mean LD and LD decay estimates as well as detailed GWA results for all the traits evaluated. (XLSX 1396 kb)


Boehmeria nivea L.) is one of the most important natural fiber crops and an important forage grass in south China. Ramet number, which is a quantitative trait controlled by multigenes, is one of the most important agronomic traits in plants because the ramet number per plant is a key component of grain yield and biomass. However, the genetic variation and genetic architecture of ramie ramet number are rarely known.Ramie  detected using specific-locus amplified fragment sequencing technology. Trait-SNP association analysis detected 44 significant SNPs that were associated with ramet number at The identified loci or genes may be promising targets for genetic engineering and selection for modulating the ramet number in ramie. Our work improves understanding of the genetics of ramet number in ramie core germplasms and provides tools for marker-assisted selection for improvement of agricultural traits.The online version of this article (10.1186/s12870-018-1573-1) contains supplementary material, which is available to authorized users. Boehmeria nivea L.), which is native to China and commonly known as China grass, is one of the oldest fiber crops worldwide ) and the mixed linear model (MLM) correcting for both Q-matrix and K-matrix (MLM [Q + K]), were used to reduce errors from population structure and relative kinship. Those with 1 hybrid progeny population described above, consisting of 241 lines, was used for validation of the favorable SNPs via a genomic DNA PCR strategy with special primers  was performed to determine if the ramet number in the FSequences of favorable SNPs were used to blast the ramie genome , and gen-ΔΔCt method [18S as an internal control.Three replicates of each of two germplasm plants , the ramet number of 241 lines was analyzed in two seasons (in March and November 2017). The variation in ramet number was 10-fold in March 2017, from 1 to 10, and 9-fold in November 2017, from 2 to 18  emerged in all three stages at the same time. Some of the detected significant SNPs emerged at least twice in the same stages; for example, the SNP (Marker20170–64) generated using the MLM model emerged twice in May.The GWAS was performed using GLM and MLM models with 108,888 high-consistency SNPs. To confirm the potentially significant SNPs associated with ramet number, genotypes from three stages of the 112 core germplasms were analyzed. For the GLM model, a total of 2553, 2256, and 4288 significant SNPs were detected at 1 hybrid progeny population of 241 lines and its parents were used to verify the authenticity of the favorable SNPs using genomic DNA PCR with specific primers  were upregulated in different tissues (leaf and stem bark) of the high ramet number genotype Quxianzhuma  of these six genes showed functions in regulating root development, one (Bn23049) in regulating seed germination, and one (Bn23041) was associated with cell fate determination and maintenance of floral/inflorescence/shoot apical meristem identity.To determine the genes responsible for ramet number in ramie, a qPCR analysis was performed using two ramie varieties with significantly different ramet numbers Fig. a. The reRamet number is a significant quantitative trait in ramie, which is controlled by multigenes. Understanding the mechanism of ramet number determination through gene/SNP/QTL analysis and breeding high ramet number cultivars using marker-assisted selection are thought to be the most practical and effective strategies to manage this trait. However, it is difficult to reliably identify effective SNPs/QTLs associated with ramet number in ramie for various reasons, including the low genome coverage of the available molecular markers, the slight effects of most loci, the limited allelic segregation, and recombination of biparental populations in linkage mapping. In this study, we used an association population consisting of 112 core germplasms collected from China, Japan, India, Indonesia, and Cuba, for ramet number-associated SNP detection, which offered more historical recombination events, to overcome the limitations of biparental populations.In this study, GWAS, which has been used to map complex quantitative traits in plants , 40–42, 1 hybrid progeny population of 241 lines. The SNP (Marker20170–64) was found to be associated with ramet number in the hybrid progeny population, confirming its authenticity and demonstrating the need to verify significant SNPs detected by GWAS. Additionally, three loci  close to the Marker20170–64 were identified using SLAF-seq technology. All of these loci emerged at least twice in the three stages analyzed , seed germination (Bn23049), and cell fate determination and maintenance of floral/inflorescence/shoot apical meristem identity (Bn23041). A homologous gene of Bn23037 in Arabidopsis, AtERF4 , has a vital function in regulating leaf growth and development by responding to nutrition stress [AtTZF5, whose CDS shows the highest homology with Bn23049, affects seed germination by controlling genes critical for abscisic acid and gibberellic acid response [AtRING1a, a homologous gene of Bn23041, plays a primary role in the maintenance of meristem function by inhibiting the expression of a Class I KNOTTED-like homeobox transcription factor [The potential role of these six target genes in controlling ramet number was indicated by their up-regulated expression in the high ramet number genotype Quxianzhuma and their roles in the regulation of root development Additional file 2:qPCR primer sequences of favorable SNPs and reference gene. (XLSX 11 kb)Additional file 3:Primers and functional description of candidate genes. (XLSX 11 kb)Additional file 4:Normal distribution curve of the ramet number in hybrid progeny population in (a) dMarch 2017 and (b) November 2017. (PDF 20 kb)Additional file 5:P < 0.01. (XLSX 364 kb)SNPs generated from GLM and MLM analysis when Additional file 6:Common significant SNPs in GLM analysis and MLM analysis. (XLSX 11 kb)Additional file 7:1 hybrid progeny population. M denotes 100 bp DNA Ladder, the bands from bottom to the top represent 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, and 1500 bp, respectively. The numbers 1–20 represent Marker70439–41, Marker13742–63, Marker29152–60, Marker42663–35, Marker59771–76, Marker142939–43, Marker15847–131, Marker121162–120, Marker20170–64, Marker21174–109, Marker130175–63, Marker18389–39, Marker12702–125, Marker178103–63, Marker21112–51, Marker24814–129, Marker31623–105, Marker39027–122, Marker73630–112, and Marker38532–124, respectively. For each SNP marker, amplifications from left to right represent three female (Hejiangqingma) and two male (Zhongzhu NO.1) parents. (PDF 45 kb)Results of polymerase chain reaction (PCR) amplification using specific primers of significant single nucleotide polymorphisms (SNPs) in parents of the FAdditional file 8:Additional SNP loci near the Marker20170–64. (XLSX 10 kb)


Ricinus communis) has broad industrial applications. However, knowledge on the genetic diversity, especially genetic alterations that occurred during domestication and subsequent traits selection, of this oil crop is limited. Here, our population genomics analyses show that the Chinese castors have developed a geographic pattern, classified into the southern-, the middle-, and the northern-China groups. We detect a number of candidate genomic loci that are associated with the selection signals during the geographical differentiation and domestication. Using genome-wide association analysis, we identify candidate genes associated with nine agronomically important traits. One of the candidate genes encoding a glycosyltransferase related to cellulose and lignin biosynthesis is associated with both capsule dehiscence and endocarp thickness. We hypothesize that the abundance of cellulose or lignin in endocarp is an important factor for capsule dehiscence. Our results provide foundation for castor breeding and genetic study.Oil produced by castor ( Castor is an important industrial oil crop, but knowledge on its genetic diversity is limited. Here, Fan et al. show geographic pattern of Chinese castors that have developed during domestication by population genetic analyses, and reveal candidate genes associated with agronomically important traits. Ricinus communis) is a high-oil-content herbaceous plant that is cultivated worldwide. This tropical perennial shrub, originated from Africa2, belongs to the Euphorbiaceae family. Its seeds contain nearly 46–55% oil by weight. The oil derivatives are widely used as basic or supplementary materials for many industrial applications, such as lubricants3, cosmetics, paint, coatings4, medicines5, ink and many others8. Specifically, ricinoleic acid9, a major component of castor oil, has special physical and chemical properties that will be extremely valuable for biodiesel in the near future12.Castor . More than 1000 natural castor varieties have been grown in China, from the south to the north, which form a rich genetic resource for castor breeding. However, the domestication and population structure of castor remain largely unknown. Moreover, genome association study has not yet been performed for castor.The wild relatives of castor are distributed in Africa, which have been cultivated for thousands of years in tropical and subtropical countries, especially in India, Brazil, and China1, we sequence 405 diverse castor accessions, which include wild varieties from Africa as well as landraces and breeding lines from 24 castor planting provinces of China. We use the identified genetic variations for genome-wide association analysis. Our results reveal several candidate genes associated with agricultural traits and a number of candidate loci that have been selected during castor domestication and geographical differentiation.Taking advantage of the availability of a draft castor genome sequenceR. communis v0.11) yielded 4,057,720 single-nucleotide polymorphisms  and 876,681 insertion–deletions . Each sample was supported by 9.5 (5.3–21.0) reads per SNP site on average , we selected samples that had enough among-accession phenotypic variations. We finally obtained 405 accessions that included 180 landraces, 199 breeding lines, and 26 wild varieties. The samples were from field-grown accessions  divides all accessions into four major groups  threshold of ups Fig. , and theups Fig. . Group Iups Fig. . This rer2 < 0.05) that were extracted from the whole SNP pool using ADMIXTURE , all R. communis accessions were grouped into four subpopulations that correlate with their geographic distribution and phylogeny. Most individuals of MC exhibit admixture, indicating intensive crossing among the accessions and the subpopulations. The principal component analysis (PCA) of all accessions is consistent with the phylogeny and the population structure  across the genome between the wild group and each cultivated group  in the NC group is obviously larger than that in the SC group (FST = 0.19) and the MC group (FST = 0.26), and the value in the SC group is the smallest, suggesting that the NC group and the SC group populations have a furthest and closest distance to the wild population, respectively. This result is consistent with the phylogenic analysis. We also calculated the FST values between the wild and cultivated accessions from each province. The results show that FST values also display a geographically correlated pattern  in each group. The genetic diversity (π) of the wild accessions is 1.69 × 10−3, which is less than that of the wild rice Oryza rufipogon13 (π = 3 × 10−3) and the wild soybean Glycine soja14 (π = 2.94 × 10−3). The domesticated groups harbor relatively little diversity, such as the SC (π = 1.25 × 10−3), the MC (π = 1.23 × 10−3), and the NC groups (π = 7.59 × 10−4). This result is consistent with the general expectation that wild populations have higher genetic diversity than domesticated populations. Interestingly, this geographic distribution displays basically the same pattern as that in a soybean study14, where groups 0, I, and II followed the direction from south to north and had values of 2.94 × 10−3, 1.44 × 10−3, and 1.10 × 10−3, respectively. Similarly, in cotton15, the SC, YZR, and YER groups have π values of 0.211 × 10−3, 0.197 × 10−3, and 0.199 × 10−3, respectively. In addition, the nucleotide diversity values of the Chinese castors decline from the south to the north, and the rate of reduction in genetic diversity is higher between the MC and NC groups (2-fold) than between the SC and MC groups (1.5-fold). This result suggests different intensities of selection during domestication or geographical differentiation.We further investigated the genetic diversity by calculating the nucleotide diversity  and 1.5 kb (r2 = 0.36), respectively. Apparently, the domesticated castor group has a relatively low rate of decline compared with the wild population. The faster drop from the maximum value in castor is distinct from that in some predominantly selfing species, such as rice  and soybean14 , but similar to that in some outcrossing plants, such as maize16, in which the value dropped to r2 = 0.1 at 1 kb. In addition, among the SC, MC, and NC groups, LD decay is related to the geographic distribution, with a lower rate of LD decline  in the NC group than in the SC group .Since LD decay is often associated with genetic diversity and domestication history, we further examined the mean LD values based on ups Fig. . The disπ) between two groups, we then found 2905 candidates by calculating genomic windows associated with the geographical groups  analysis revealed that 1909 genes from the genomic regions associated with geographic differentiation were enriched in several biological processes, such as the development of flowers; cell wall biogenesis; lipid, pectin, and xanthine catabolic processes; and response to hormones and stresses  in wild accessions to that in domesticated accessions and the population differentiation (FST) between the wild and domesticated lines in a 10-kb nonoverlapping window along each genome scaffold. Using the selected thresholds /π (landraces) values), 1258 genomic windows with selection signals were identified . This result implied that endocarp thickness might be a determinant of capsule dehiscence.Capsule dehiscence is an important yield-related trait that results in significant yield losses and harvesting difficulties. We identified the phenotypic variation in capsule dehiscence among all the castor accessions Fig. . Moreove18, we performed GWAS to identify potential causal genes that are significantly correlated with capsule dehiscence and endocarp thickness. Using a threshold value (−log10P > 6), we identified 171 and 48 SNPs that were significantly associated with capsule dehiscence and endocarp thickness, respectively   showed that the expression of the gene encoding glycosyltransferase in the rind of fruit was significantly higher in landrace S1015 (thicker endocarp and easier dehiscence) than in landrace S1396 (thinner endocarp and more difficult dehiscence) during the development of the fruit rind at different stages  Fig. . These tges Fig. . Because396 Fig. . These dLOC8272215, which encodes the homolog of TRAF-like family protein, has been shown to play a regulatory role in cell proliferation21. The qRT-PCR results showed that the expression of LOC8272215 was significantly higher in landrace S1015 than in landrace S1396 during the development of the fruit rind at different stages  at the 26,560 bp position in scaffold Rc28543 resulted in an amino acid change from lysine to arginine, and SNP2 (C/A) at the 27,290 bp position in scaffold Rc28543 resulted in an amino acid change from asparagine to lysine. We found that these two SNPs in the haplotype were present in the same 328 accessions, 281 of which carry the haplotype AC, which was associated with a higher dehiscence percentage and thicker endocarp, and 47 of which carry the haplotype GA, which was associated with a lower dehiscence percentage and thinner endocarp , located at the 61,404 bp position in scaffold Rc28543, resulted in an amino acid change from glutamic acid to alanine, and the other SNP (C/A), located at the 69,981 bp position in scaffold Rc28543, caused an amino acid change from glycine to valine. The homogenous GA allele significantly increases the dehiscence percentage  analysis, we found that two major haplotypes based on two nonsynonymous SNPs in the gene arp Fig. . In addiLOC108261897, located at position 15,075–18,369bp in Rc29927. This lncRNA contains two variants (A/G and C/A) at positions 15,904 bp and 16,894 bp in Rc29927, respectively. GBA analysis also supported the association  located at 33,565 bp that results in amino acid changes from isoleucine to valine. The allele with A is associated with a decrease in the dehiscence percentage  could be involved in regulating the grain weight of castor. In addition, for the GWAS signal in scaffold 29927, we also identified the long noncoding RNA (lncRNA) LOC108261897, which was significantly associated with hundred-grain weight  might be involved in regulating panicle height, probably by regulating axillary bud development. Furthermore, through GBA analysis, we found that this gene has two major haplotypes based on two nonsynonymous SNPs that are associated with shorter panicles (with the TG type) and taller panicles (with the CC type). In the first haplotype, one SNP (T/C) is located at position 360,436 bp in Rc29813, which results in an amino acid change from isoleucine to methionine (T/C complementary strands A/G); the other SNP (G/C) is located at 361,683 bp in Rc29813 and results in an amino acid change from alanine to glycine (G/C reverse complementary chain C/G)  Fig. . We founLOC8281866 and LOC8265107, that were associated with panicle height. The gene LOC8281866 encodes the homolog of cycling DOF factor 3 (CDF3), which plays multiple roles in regulating flowering time in Arabidopsis25. GBA analysis revealed two haplotypes with two nonsynonymous SNPs associated with short panicles (TG) and tall panicles (CC) in DOF3  or JR3 (JASMONIC ACID RESPONSIVE 3) in Arabidopsis26. It is a member of the IAA–amino acid conjugate hydrolase subfamily and is related to the development of roots, stems, and flowers  from the wild population, respectively, consistent with the phylogenetic tree. The SC group has relatively high genetic diversity  compared with the MC and NC groups, supporting the idea that the introduction of the crop took place somewhere in southern China. However, due to the limited sampling (only 26 wild and 379 cultivated accessions) in this study, we have not yet fully uncovered the origin and detailed evolution of the Chinese castors. In addition, several phenotypes, such as yield-associated traits, changed substantially between the SC and NC groups, suggesting positive selection for local adaptation.Although a reference castor genome was released in 2010, in-depth population-based trait mapping has not been reported. Taking advantage of the Chinese collection of castor as a genetic resource, we sequenced and analyzed 405 accessions. To study the domestication history of castor in China and identify the genomic loci associated with its domestication, we first obtained 26 wild species from Africa as the control group. We show that the Chinese accessions exhibit different geographic distributions. Furthermore, the SC group appears to be the oldest Chinese population and most closely related to the wild species, whereas the MC and NC groups are clades derived from the SC group. The phylogeny suggests the possibility that the domestication or introduction of castor in China may have originated in southern China and subsequently spread into middle and northern China. Moreover, the SC and NC groups have the closest and farthest genetic distance , for each accession, high-quality DNA was required to construct a sequencing library. Paired-end sequencing (PE: 150 bp) of each library was performed on an Illumina HiSeq X-Ten system. In total, approximately 2.02 Tb of raw sequences with a 150-bp read length was generated from the Illumina HiSeq X-Ten platform.In the first year, 30–50 seeds of each accession were obtained from a public resource . To ensure that these seeds were not contaminated by other varieties, we planted 30 seeds of each accession to exclude samples with significant intragroup differences through field observation. To confirm that there was no serious separation among these accessions, ten seeds collected from first-year self-hybrids were planted in the ground for filtering based on experience and observation. One hybrid was selected for each accession to record all phenotypic data and for genome sequencing. Leaves at the seedling stage were collected and immediately frozen in liquid nitrogen for DNA extraction. Genomic DNA was extracted with the CTAB methodThe nine agronomic traits in our study were measured in 2017 in a field located in Wuhan, Hubei Province of China. For capsule dehiscence, we collected the lowest panicle on the main stem branches of each castor plant and scored dehiscence after air drying. Because of the asynchronization of the maturation process of capsules in the same panicle, we regarded 0–10% dehiscence of all capsules in the same panicle as indehiscence, 11–50% as slight dehiscence, and 51–100% as dehiscence; for endocarp thickness, we measured 3 replicates for each accession and obtained an average of those 3 replicates; for hundred-grain weight, we weighed 20 seeds; for panicle height, we measured from the lowest panicle to the bottom of the lowest branch on the main stem; for panicle length, we measured the length of the lowest panicle on the main stem branch; for plant height, the whole plant height was measured; for the ratio of male to female flowers, we divided the number of male flowers by that of female flowers; and for seed length, we measured the length of five seeds and obtained the average; for seed volume, because the shape of the seed is nearly an ellipsoid, we multiplied seed length by width.R. communis v0.1 reference genome on the Phytozome V10 website1 by the Burrows–Wheeler Aligner (v0.7.5a-r405)31 with default parameters. The low-quality (MQ < 20) reads were filtered in the SAMtools (v1.1)32 program. Picard Tools  was applied to coordinate, sort, and remove PCR duplicates and build a bam file index. Then the genome variants were called by Genome Analysis Toolkit (GATK)33. First, we performed SNP calling ; then with conservative parameters , we filtered the first SNP calling results as known sites for base quality recalibration for the next round of SNP calling. Second, we performed a second round of SNP calling to generate GVCF files (–emitRefConfidence GVCF–variant_index_type LINEAR–variant_index_parameter 128000). Finally, all the GVCF-format files were merged for population variant calling  with the selected parameters .All the sequencing reads were mapped onto the R. communis reference genome in the package ANNOVAR34. Based on the genome annotation, all these SNPs were categorized into exonic, intronic, intergenic, upstream or downstream regions, and splicing sites. SNPs located in coding exons were further divided into synonymous SNPs (that did not lead to amino acid changes) and nonsynonymous SNPs (that led to amino acid changes). Then the nonsynonymous SNPs were extracted for further analysis.A large number of significant SNP loci were identified based on the GWAS results. For further analysis, SNP annotation was performed according to the r2 < 0.05 in the program Plink (v1.90)35. An approximate maximum-likelihood tree was constructed with the generalized time-reversible model in the program FastTree (v2.1)37 with the formerly extracted independent SNPs. Structure analysis was performed in the program Admixture (v1.23)38. K values were set from K = 2 to K = 9. The minimum CV error value appeared at K = 4.To identify independent SNPs for phylogenetic tree construction and structure analysis, a subset of 289,349 SNPs were extracted from the SNP data set of all accessions by applying a LD threshold of 39. The dataset for the PCA was filtered based on a quality (QUAL in the VCF file) <2000 and a scaffold length >100 kb. LD decay was analyzed by the PopLDdecay software (v3.30)40. A filtered (scaffold length >100 kb) VCF file was used in the analysis.PCA was performed in the software GCTA (v1.26.0)FST) and nucleotide diversity (π) were calculated in the program VCFtools 0.1.1541 with a 10-kb nonoverlapping sliding window along each scaffold (length >100 kb). The nucleotide diversity and FST between groups were calculated based on the average value of all 10-kb sliding windows. The domestication regions of group WS vs other groups were defined by counting both the weighted FST value and the ratio of the π value in each composition window, both with a cutoff of the top 10%. The regions under selection of geographical differentiation in the domesticated groups were defined by calculating the ratio of the π value in each sliding window across the groups with a 5% cutoff. The scaffold order of the bar plots is in accordance with the reference genome annotation file.Fixation statistics (Arabidopsis homologs of castor genes were searched by the blastx module of the blast program (v.ncbi-2.7.1 + )42. GO analysis was performed in the new version of DAVID (v6.8)44.The 18. The significance threshold for the associated SNPs was chosen as −log10P > 6. Several strategies were carried out to analyze the associated SNPs. First, we looked for GWAS-associated signals based on P < 10−6 and estimated the candidate regions by pairwise LD correlations. Second, on the basis of the annotation results, nonsynonymous SNPs that were significantly associated with each trait were further analyzed. Third, each of the genes containing the nonsynonymous SNPs was extracted for further functional annotation and KEGG enrichment analysis. Fourth, to analyze the function of these candidate genes more accurately, the homologs from closely related species or the model species Arabidopsis thaliana were identified via sequence blast.The association analysis was performed with the Efficient Mixed-Model Association expedited (EMMAx) program45. Primers were designed by Primer3 (http://bioinfo.ut.ee/primer3-0.4.0/). Their sequences are listed in Supplementary Table Five causal genes that are associated with capsule dehiscence were selected for qRT-PCR. According to the number of days after pollination (DAPs), we constructed a temporal model with five stages (every week as a stage) of the rind of castor in accessions s1015 and s1396. The seeds from plant s1015 have a thicker endocarp, and the capsule is prone to dehiscence. The seeds from plant s1396 have a thinner endocarp, and the capsule is difficult to dehisce. Total RNA was extracted with a Plant RNA Kit (BioTeke Corporation) and reverse transcribed with a PrimeScript RT Reagent Kit with gDNA Eraser (TaKaRa). qRT-PCR was performed in triplicate with SYBR Premix Dimer Eraser  (TaKaRa). Gene expression levels were calculated with the comparative −ΔΔCt methodThe seeds were harvested at different developmental stages according to the DAP and stored in FAA fixative  before use. The seeds were then sliced lengthwise along the back suture by hand sectioning, and the slices were spread across a glass slide. Then 40% hydrochloric acid was added dropwise onto the slices to cover their surface and allowed to rest for approximately 10 min. The excess liquid was siphoned off, and the slices were stained with 5% phloroglucinol for approximately half an hour until the lignin was stained completely.Further information on research design is available in the Supplementary InfoPeer ReviewReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary Data 14Supplementary Data 15Source Data


Inversions are one type of structural variants linked to phenotypic differences and adaptation in multiple organisms. However, there is still very little information about polymorphic inversions in the human genome due to the difficulty of their detection. Here, we develop a new high-throughput genotyping method based on probe hybridization and amplification, and we perform a complete study of 45 common human inversions of 0.1–415 kb. Most inversions promoted by homologous recombination occur recurrently in humans and great apes and they are not tagged by SNPs. Furthermore, there is an enrichment of inversions showing signatures of positive or balancing selection, diverse functional effects, such as gene disruption and gene-expression changes, or association with phenotypic traits. Therefore, our results indicate that the genome is more dynamic than previously thought and that human inversions have important functional and evolutionary consequences, making possible to determine for the first time their contribution to complex traits. Inversions are a little-studied type of genomic variation that could contribute to phenotypic traits. Here the authors characterize 45 common polymorphic inversions in human populations and investigate their evolutionary and functional impact. Nevertheless, despite the initial expectations, a significant fraction of the genetic risk for common and complex diseases is still unexplained7. Furthermore, not all variants have been studied at the same level of detail. In particular, inversions are a type of structural variant that changes the orientation of a genomic segment, usually without gain or loss of DNA, and they often have highly-identical inverted repeats (IRs) at their breakpoints. These characteristics make inversion detection very challenging, even with next-generation sequencing methods, and they have been largely overlooked in humans9.In the last decade a great effort has been devoted to characterizing all the variation in the human genome11 or paired-end mapping (PEM)12, although recent studies have exploited newer techniques that could be especially useful for inversion detection, such as long-read sequencing15, Strand-seq16, BioNano optical maps17, or a combination of them18. In most cases around 100–200 inversions have been predicted, with a maximum of 786 predictions in the 1000 Genomes Project (1000GP)19. However, these methods are not suitable for high-throughput genotyping, and with few exceptions20, just a reduced number of individuals (1–15) have been analyzed. Moreover, the presence of repetitive sequences at the breakpoints influences the inversions that can be detected by each technique and results in high error rates for inversion validation compared to other variants21.Genome-wide inversion discovery has been typically based on genome sequence comparison23, genotyping efforts have been restricted to a small number of inversions and samples. For example, four other large inversions have been genotyped by FISH24 and five smaller inversions by PCR25 in 27 and 42 individuals of four populations, respectively. In addition, PCR and inverse PCR (iPCR) have been used for targeted studies of 34 inversions in 70–90 Europeans26 and a more worldwide characterization of three inversions28. Also, although inversion genotypes might be predicted based on SNP data, these methods can only detect inversions above a certain size or associated with specific SNP combinations and the error rate can be high29. Therefore, it is not yet clear how many polymorphic inversions really exist in humans and very little is known about their global frequency and distribution19.Apart from the intensely-studied 17q21.31 and 8p23.1 inversions31 and there are numerous examples of their phenotypic consequences and adaptive significance in diverse organisms, from plants to birds32. One of their main effects is related to recombination, since single crossovers within the inverted region in heterozygotes generate unbalanced gametes and, at the same time, the resulting inhibition of recombination could protect favorable allele combinations31. In addition, inversion breakpoints can directly alter the expression patterns of adjacent genes33.Actually, inversions have been a model in evolutionary biology for almost 90 years9. Inversions are associated with haemophilia A34, increased risk of neurodegenerative diseases37, autoimmune diseases29 or mental disorders38. They could also predispose to other genomic rearrangements with negative phenotypic consequences in the offspring9. Moreover, there is evidence that the 17q21.31 inversion increases the fertility of the carriers and has been positively selected in Europeans22. Finally, inversions have been shown to affect gene expression39. However, most of these effects are associated with just the two best-known inversions. Attempts to associate inversions with gene-expression and phenotypic variation in large datasets have been limited to those with simple breakpoints, and only a couple of additional candidates have been identified so far41. Thus, specific genotyping studies of a diverse range of inversions in multiple individuals are necessary to determine their functional and evolutionary impact.From the little information available, it is clear that inversions can have important consequences in humansHere, we have developed a new high-throughput genotyping method and we have characterized in detail 45 common polymorphic inversions. By combining accurate inversion genotypes in 551 individuals of different populations and the available genomic information, we show that a large fraction of inversions are not linked to other variants and have occurred recurrently. In addition, several of them have signatures of selection and/or functional effects, emphasizing the role of inversions in the human genome.19, which comprised most of those experimentally validated by PCR-based techniques when the project started19 and corresponds approximately to half of the estimated number of real variants with >5% frequency in human populations14 , with sizes ranging from 83 bp to 415 kb. Also, 24 (53%) have been generated by non-allelic homologous recombination (NAHR) between >90% identical IRs (from 654 bp to 24.2 kb). The rest (47%) were probably generated by non-homologous mechanisms (NH), including 18 with small deletions or insertions in the derived allele that may have been created in a single complex FoSTeS/MMBIR event21 , we carried out directly custom MLPA assays with minor modifications. However, for inversions with repetitive sequences at the breakpoints (24), which are difficult to detect by most techniques, we developed a new method combining the principles of iPCR26 and MLPA42 named iMLPA. In both cases, two pairs of oligonucleotide probes were used to interrogate the two alternative orientations for each inversion, orientation 1 (O1) and orientation 2 (O2)  , European (EUR) , South-Asian (SAS) (GIH) or East-Asian (EAS)  ancestry, here referred as population groups  technique19  were detected, with nine of them having 2.5–71.5% incorrect genotypes and extremely low genotype agreement in the only two inversions mediated by large IRs in common  com com19 (sP > 0.01). Minor allele frequencies (MAF) range globally from 0.5% to 49.7%, with 41 inversions spread through several population groups and only the four with the lowest frequencies being present in a single population group   have variants in complete LD (r2 = 1) either inside or up to 100 kb from the breakpoint, among the 24 NAHR inversions only HsInv0040 and HsInv1051 have at least one such variant  between inversions and neighboring variants  from HapMap and 1000GPversions 0/21 have.91 Fig. .Fig. 3EvAlso, we checked the presence of shared SNPs (not including indels) in both orientations. Consistent with recombination inhibition in heterozygotes, most NH inversions do not have shared SNPs within the inverted region in accessible 1000GP positions or HapMap data, with the exception of a few individual SNPs that might be genotype errors or gene conversion events Fig. . OutsideO1 and O2 haplotypes mixed throughout the network and hierarchical cluster, including in many cases identical haplotypes with both orientations , which is ~1000 times higher than that of single bases. For the inversions in which it is possible to quantify recurrence, we estimated a total of 40 additional inversion and re-inversion events (ranging for each inversion from 0 to 7 with an average of 2.2)  and seven gorillas (41 inversions) 43.Moreover, we checked the presence of the breakpoint sequences of 19 NH inversions without IRs in available Neanderthal, Denisovan and two ancient modern human genomes . Although the ascertainment bias associated with inversion detection predicts an enrichment of high-frequency inversions (~0.20 expected MAF), the observed frequencies tend to be higher than expected in all population groups Fig. . When inFST), which can identify positive selection leading to a rapid increase in allele frequency in some areas. The global FST value was 0.11 for autosomal inversions, 0.21 for chr. X inversions, and 0.73 for the one in chr. Y, with the largest frequency variation between continents. Three inversions were within the top 1% of the FST distributions derived from SNPs with the same frequency  were in HsInv0201 for balancing selection and HsInv0006 and HsInv0031 for positive selection. In addition, four other inversions showed weaker evidence of balancing or positive selection. Consistent with the FST results, in HsInv0006 and HsInv0059 positive selection was detected in those populations with increased DAF , which is well suited to detect deviations from neutrality in low-recombination regions, such as inversions. We used optimized tests to identify positive and long-term balancing selection maintaining a polymorphism in several populations, and significance was assessed empirically using the null LSFS distribution from autosomes. Only 18 unique autosomal inversions with nearby perfect tag variants that could be reliably phased were analyzed, including most NH inversions and HsInv0040 Fig. . The strDAF Fig. .44. However, the results of these tests summarize the data of all the SNPs in a region and are not necessarily linked to the inversion, as before. Focusing on signals detected in at least three populations, we found respectively four and six inversions with strong and weak signatures of balancing selection for NCD1 or NCD2  are located in intergenic regions, three of them invert genes, eight are located within introns, seven might exchange gene sequences overlapping the IRs at the breakpoints, and six affect genes more directly through the inversion or deletion of an internal exon  and the disruption of the whole gene  or an alternative transcript isoform (HsInv0124)  173 CEU, TSI and YRI individuals with inversion genotypes; and (2) the complete GEUVADIS set of 445 European (358) and African (87) individuals in which the genotypes of 33 inversions could be imputed accurately  by linear regression between inversion genotypes and LCL transcriptome data from the GEUVADIS consortiumropean 35 and Afri46. We found 62 genes with eQTLs in different tissues in high LD (r2 ≥ 0.8) with 11 of the 26 analyzed inversions, including seven not detected in LCL data  with some of the recurrent inversions, we found additional potential expression differences associated with HsInv0124 and in the genes affected by HsInv0030, which exchanges the first exon and promoter of chymotrypsinogen precursor genes CTRB1 and CTRB2 expressed only in pancreas25, and HsInv0340, which disrupts the long non-coding gene LINC00395 expressed in testis  with variants reported as first or second lead eQTL in a given tissue by the GTEx project  acting in the same direction. On the other hand, HsInv0058 is associated with chr. 6 MHC haplotypes APD, COX, DBB, QBL and SSTO, which extend ~4 Mb and harbor important functional differences47, suggesting that other variants in these haplotypes are responsible for the observed effects.To assess if inversions were the main cause of the observed expression changes, we performed a joint eQTL analysis in LCLs including our inversions together with SNPs, indels and structural variants from the 1000GPCLs Fig. . Six othrms Fig.  is actuarms Fig. . HsInv0148 and GWASdb49 variants in the inversion and flanking regions (±20 kb). The top inversion driving this result was the MHC-inversion HsInv0058, but HsInv0030 and HsInv0347 showed similar enrichment of GWAS hits in both datasets  and SIX6, related to eye development.The role of inversions in phenotypic variation was investigated using available genome-wide association studies (GWAS) data. We found a 1.26- and 1.95-fold increase in GWAS Catalogets Fig. . GWAS sir2 ≥ 0.8) with known GWAS hits in the population where the association was reported. That is the case of HsInv0004, which is in complete LD in Europeans with a nearly genome-wide significant GWAS SNP related to asthma susceptibility in children and another one associated with body mass index in asthmatic children  with SNP rs2937145 associated with Alzheimer’s disease risk  at the breakpoints, which could be potentially interrogated using our method, and ~250 with larger SDs, which are currently out of reach of the methodology19. Therefore, further improvements and complementary strategies, such as the possibility of making directed cuts in specific positions or estimating the distance between regions separated by larger repeats, are necessary to expand the range of analyzed inversions. Nevertheless, the present work already offers a good picture of the contribution of common inversions to genetic diversity, adaptation, and phenotypic traits in humans.Despite the effort to include as many inversions as possible, only those validated and with well-characterized breakpoints could be analyzedO1 and O2 chromosomes  and a detailed analysis of haplotype relationships, we have obtained a better estimate of the independent inversion events. Specifically, we found nine more recurrence events in the five inversions originally predicted as recurrent in humans and that seven inversions considered to be unique or lacking information in CEU are recurrent as well26. This suggests that, like other repeats, IRs are rearrangement hotspots and the genome is more dynamic than previously thought. However, recurrence has been detected only through the sequences associated with the inversion, and we need more direct ways to quantify inversion generation rates precisely.In particular, we found clearly contrasting patterns for inversions generated by homologous and non-homologous mechanisms, supporting a high-degree of recurrence of all inversions mediated by highly-identical IRs except three (two of which have very low frequency). Recurrent inversions are characterized by low LD with other variants, a large amount of shared SNPs and shared haplotypes between mes Fig. . Also, r51, the lack of association between SNPs and many inversions and poor coverage by common arrays  (see As for other types of recurrent changes320) see . This coSPINK14 and is lead eQTL for two nearby genes  with SNPs accounting for plasma levels of SPINK6 protein54. Together with the role of several of the affected genes in lung and extracellular mucosas, this suggests that the inversion could be related to immune response. In HsInv0006, its particular distribution pattern and the selection test results point to positive selection of the derived allele in Africa  with intermediate frequency around the globe and clear signals of balancing selection Fig. , which dn53 Fig. . In factipt Fig.  and it iica Fig. . Further Mya with31, which is exemplified by the reduction in frequency with genetic length , plus individual NA15510 of unknown origin , while the rest of DNAs were acquired from Coriell28. Chimpanzee and gorilla DNAs include six already used to genotype most of the inversions from frontal cortex tissue samples of the Banc de Teixits Animals de Catalunya  or LCLs from Barcelona Zoo individuals 26, plus 19 chimpanzee and five gorilla DNAs extracted from a previously-existing collection of primate LCLs of one of the authors  of the Universitat Autònoma de Barcelona.We used 550 human samples included in the last phase of the HapMap Project and many of them in 1000GP phase 1 (Ph1) and 3 (Ph3)42. Most NH inversions (17) were genotyped simultaneously from 100–150 ng of DNA with a slightly modified MLPA protocol using two pairs of probes that bind specifically at the breakpoint sequences of each orientation (AB and CD or AC and BD), with one of the probes that could be common to both pairs  and to validate many of the MLPA/iMLPA genotypes  between inversions and overlapping and neighboring biallelic variants up to 200 kb at each side of the inversion from 1000GP Ph3  and HapMap release 27  using either plink v1.9057 or Haploview v4.158. Variants located within the breakpoint interval and associated deletions or IRs were excluded for this and subsequent analysis to avoid possible genotyping errors. Supplementary Data r2 ≥ 0.8 from 1000GP and HapMap data considering all the individuals together, as well as each population and population group. This analysis allowed us to detect a few inversion genotypes that did not match those expected from the tag variants and most of them were confirmed to be genotyping errors by independent PCR or iPCR validation , shared (unambiguously polymorphic in both orientations) and polymorphic in O1 or O2 chromosomes using in-house perl and bash scripts26. To minimize possible genotyping errors, only the most reliable variants according to the 1000GP strict accessibility mask were included. Non-recombining flanking regions were defined from the 1000GP data (which has more resolution than HapMap) as the longest sequence outside the breakpoints up to a maximum distance of 20 kb that: (1) does not contain reliable shared variants compatible with a crossing-over event between orientations; and (2) includes most of the fixed variants  for HapMap data and two iterations (−x2) with the hybrid model (−MQ) for 1000GP data. Only variants within the inverted region plus 20 kb of flanking sequence for 1000GP Ph1 data (which was the only one available at that time) or 200 kb for HapMap data were selected. Second, we took advantage of the 1000GP Ph3 phased haplotypes3 to impute directly the inversion orientation based on the presence of perfect tag variants (r2 = 1). For inversions without perfect tag variants, only homozygotes or hemizygotes for each orientation, in which the inversion status of the haplotypes can be assigned unambiguously, were analyzed.Each orientation haplotypes were determined following two complementary strategies to minimize errors and obtain more robust results. First, after testing several commonly-used phasing programs, we selected PHASE 2.160 with the NETWORK v.4.6.1.3 software (www.fluxus-engineering.com). In addition, we devised our own representation of the haplotype relationships and the distribution of nucleotide changes along the sequence, named integrated haplotype plot (iHPlot), which combines a hierarchical clustering, distance matrix and visual alignment of the alleles in each polymorphic position, plus the haplotype orientation and the populations in which it is present  networks3. For inversions without tag variants, we relied mainly in the phased 1000GP Ph1 haplotypes iHPlots, since all the genotyped individuals in common could be analyzed. Inversion recurrence events were conservatively estimated by identifying clusters of haplotypes with both orientations that differ significantly from all others with the same orientation as the potential recurrence event, after eliminating possible phasing errors in inversion heterozygotes  in the available 1000GP Ph3 haplotypes and the largest of the two average pairwise differences within O1 or O2 sequences. To have more information in short inversions, we considered the inverted region and any extra non-recombining flanking region (up to 20 kb), excluding breakpoint intervals and associated IRs or indels to avoid sequence errors. Confidence intervals of age estimates were calculated by bootstrap sampling the same number of total individuals with replacement 1000 times and using both a constant substitution rate and two local substitution rates from the divergence with chimpanzee and gorilla ; and (2) for each of the PEM inversions, generate a random sample of 10,000 SNPs from the total pool of polymorphic SNPs in the PEM panel according to the simulated detection probability calculated from the SNP frequency and the inversion characteristics  selection of those SNPs for which the alternative allele is present in 1000GP individuals matching the demographic and gender composition of the detection panel  direct functional effect of inversions on genes  physical length of the inverted region; (2) inversion genetic length; 3) number of genes within the inversion or breakpoint regions; (4) distance to the closest coding gene; (5) number of mammalian constrained sites inside the inversion; number oFST statistic68. FST values were compared with empirical null distributions in the same individuals obtained from bialellic SNPs accessible according to the strict mask, with a defined ancestral allele, and that have similar frequency and chromosome type (autosome or chr. X) as the inversion  using the sion see .69. The summary statistic was the frequency spectrum of variants closely linked to the inversion, including their linkage pattern (nested or disjoint) with the inverted allele70, and we tested strong positive or balancing selection coefficients. LSFS was calculated from biallelic 1000GP Ph3 SNPs in the genotyped individuals, after removing those with a GERP score65 higher than 2 and within 0.5 Mb of any of the inversions in our dataset. Only 18 autosomal inversions unambiguously phased into the 1000GP Ph3 haplotypes with perfect tag SNPs within 20 kb from the breakpoints were analyzed using 3 kb non-overlapping windows localized within the inverted or non-recombining flanking regions . Inversion windows were compared against the empirical LSFS computed around all autosomal SNPs and tests were conditioned on the inversion frequency in the different populations. Each population and window was tested separately and population P values of the same windows were combined via Edgington’s method71, whereas the results across different windows of an inversion were combined using a conservative and an approximate approach  in overlapping windows of 2 kb (with 1 kb step), defined with the same criteria as in the LSFS test, using 1000GP Ph3 SNPs (accessible according to the pilot mask) from all individuals of the seven studied populations. Only windows with a minimum of eight informative sites (either human polymorphisms or fixed differences with chimpanzee in NCD2 only) and at least 16.7% of positions covered by hg19-panTro4 alignments were considered. Finally, an empirical P-value was assigned for each inversion region and population by comparing the tests results with a null genome-wide distribution obtained by sampling regions of the same size as the inversion  using the already identified perfect tag SNPs (r2 = 1) (19 inversions) or IMPUTE v2.3.272 (14 inversions with >90% average imputation accuracy)  and transcript expression levels were quantified with RSEM v1.2.3175. cis-eQTL analysis was done through linear regressions implemented in QTLtools v1.176, considering 850 genes and 3318 transcripts expressed in at least 20% of the samples and with TSS within 1 Mb from inversions. First, we carried out a targeted study to test only the association with the genotypes of each inversion. Second, we performed a joint eQTL analysis with inversions and neighboring 1000GP variants to estimate their contribution to observed gene-expression changes and identify lead eQTLs. Expression values were adjusted by gender, the first three principal components from 1000GP data  and a set of expression principal components to capture technical confounding factors   in 173 experimentally genotyped individuals  with GEUVADISset) see . Next, tr2 ≥ 0.8) have been identified as cis-eQTLs in different human tissues by the GTEx project 46. Also, we extended the analysis to seven recurrent inversions with SNPs in moderate LD (r2 ≥ 0.6). To determine the potential causal variants, we checked if those SNPs in highest LD with the inversions were reported as being the first or second lead eQTL in a specific tissue. The same strategy was applied to link inversions to immune eQTLs associated to the transcriptional response to Listeria and Salmonella of primary macrophages from African-American (n = 76) and European-American (n = 99) individuals52 and of macrophages differentiated from 123 induced pluripotent stem cell (iPSC) lines of European origin to Salmonella plus interferon γ stimulation53.Gene-expression effects in other tissues for 26 inversions were examined by looking whether their highest associated SNPs across all populations (48 and the GWASdb (release 2015-08-19)49 databases. To remove redundant entries, the strongest signal per locus (±100 kb genomic region) was selected. Only inversions in high LD (r2 ≥ 0.8) with a GWAS signal in the studied population or the closest one available were considered (Supplementary Table r2 ≥ 0.8) and associated to the same phenotype. Enrichment P values were calculated by comparison with a null distribution from 1000 random genomic regions as a background model for each inversion, controlling by inversion size and SNP frequencies (average SNP frequency ±0.01 and Chi-square test P > 0.05 for the number of SNPs with MAF <0.2 and ≥0.2 compared to the inversion region) and excluding gaps and chr. Y. To test the enrichment in specific inversions, we repeated the same analysis computing a one-tailed empirical P-value for each inversion.Association of inversions with specific traits or diseases was based on the NHGRI Catalog of published GWAS (release 2017-07-17)P-values were derived from empirical genome-wide null distributions from at least 1000 random samples and two-tailed tests were always used, except when specifically mentioned.Details of the statistical tests are described in the corresponding sections. In many cases Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Source Data