Short tandem repeats (STRs) are abundant and highly mutagenic in the human genome. Many STR loci have been associated with a range of human genetic disorders. However, most population-scale studies on STR variation in humans have focused on European ancestry cohorts or are limited by sequencing depth. Here, we depicted a comprehensive map of 366,013 polymorphic STRs (pSTRs) constructed from 6487 deeply sequenced genomes, comprising 3983 Chinese samples  and 2504 samples from the 1000 Genomes Project . We found that STR mutations were affected by motif length, chromosome context and epigenetic features. We identified 3273 and 1117 pSTRs whose repeat numbers were associated with gene expression and 3′UTR alternative polyadenylation, respectively. We also implemented population analysis, investigated population differentiated signatures, and genotyped 60 known disease-causing STRs. Overall, this study further extends the scale of STR variation in humans and propels our understanding of the semantics of STRs. Short tandem repeat studies in humans have often focused on European populations. Here, the authors report a comprehensive map of 366,013 polymorphic short tandem repeats in Chinese individuals and their mutational patterns, functional properties, gene regulatory effects and population characteristics. The repetitive structure endows STRs with a higher mutation rate than other parts of the genome4, ranging from approximately 10−5 to 10−3 per locus per generation5. Most mutations of STRs are due not to substitutions but rather to expansions or contractions of repeat units6, resulting in digital length polymorphisms3. After the seminal discovery that expansions of CGG repeats in the FMR1 gene were linked to fragile X syndrome (FXS) in 199110, researchers have identified approximately 60 STR loci implicated in a range of Mendelian diseases to date, including ataxias, amyotrophic lateral sclerosis, Huntington disease, frontotemporal dementia, and various neurological disorders12. More importantly, although our view remains incomplete, emerging evidence has shown that a significant number of polymorphic STRs (pSTRs) can modulate various molecular and cellular processes, such as DNA methylation13, gene expression18, and alternative splicing22, suggesting that pSTRs may contribute to complex phenotypes23.Short tandem repeats  are 1–6 base pair (bp) tandem repeats, accounting for approximately 3% of the human genome24. However, one major concern of most GWASs on complex traits and polygenic disorders is the “missing heritability” problem25, partly due to the standard GWAS analysis focused only on SNPs26. Although overlooked by most GWASs and other association studies, it is widely believed that pSTRs may contribute to missing heritability of human traits and disorders28. Recently, a phenome-wide association study identified 426 tandem repeat-phenotype associations, in which GT repeats in NCOA6 and “ease of skin tanning” were the most significant29. Furthermore, Margoliash et al. tested the association between STRs and blood and serum traits using imputed STR genotypes and identified 118 high-confidence STR-trait associations30. These studies greatly inspired us to discover trait/disease-associating pSTRs; unveiling the contributions of pSTRs in a range of complex traits would be an important long-term goal31. To achieve this, one urgent need is to construct a full and accurate catalog of pSTRs in the human genome3. Such a resource would also propel our understanding of the mutational patterns and functional impacts of pSTRs and serve as a reference point for identifying STR variants in disease contexts28.The last decade has witnessed the great success of genome-wide association studies (GWASs) in human genetics, reporting tens of thousands of single nucleotide polymorphisms (SNPs) associated with over 1000 traits12, there has been significant progress in profiling STR polymorphisms in diverse control populations39. However, these studies were limited by sample number, population diversity, sequencing depth, or algorithms employed; genome-wide pSTR data in diverse populations are still insufficient3. For example, the largest genome-wide study of STR variation to date genotyped 2,536,688 STR loci in 150,119 genomes from the UK biobank using popSTR38. However, most individuals in this study were white British, and popSTR is only able to genotype STR alleles shorter than the read length40. Moreover, other large-scale studies of pSTRs were also mainly from European ancestry cohorts, and the diversity of pSTRs in East Asia and China is largely undercovered. Even in the UK biobank cohort, there were only 1504 Chinese samples38. As the Han Chinese population is the largest ethnic group in East Asia and in the world41, constructing a comprehensive map of pSTRs from the Han Chinese population is imperative and would help to solve the critical part of the missing diversity.With advances in DNA sequencing technologies and computational approaches for STR genotyping42 and mobile element insertions43. The deeply sequenced 1KGP dataset44 has been used to detect de novo tandem repeat expansions45 and reference minisatellite variable number tandem repeats (VNTRs)47, while we included it here to study variations of reference STRs and to increase population diversity. Jointly analyzing two large cohorts enabled us to obtain a systematic view of the STR variation in diverse populations, with an emphasis on Han Chinese. GangSTR48 was employed to collect information in the 765,227 autosomal STR loci, as it is the only algorithm capable of accurately genotyping both short and expanded STRs50. We first surveyed the pSTR call set for their basic characteristics, such as allele frequency, genetic diversity, genomic distributions, and functional consequences. We identified pSTRs with potentially functional impact through loss-of-function analysis, linkage disequilibrium (LD) with GWAS SNPs, and expansion analysis. By utilizing public RNA-seq data, we identified pSTRs affecting nearby gene expression and 3′UTR alternative polyadenylation. We then performed population analysis and identified many pSTRs with significant length differences between and within superpopulations. Finally, we genotyped 60 known disease STRs in all samples with ExpansionHunter51 and provided the population-wide allele distributions of these loci. This study represents one of the largest and latest genome-wide studies of STR variation in various populations and will further our understanding of how this mutagenesis impacts the human genome.Here, we collected whole genome sequencing (WGS) data of 3983 samples from the NyuWa dataset and 2504 samples from the high-coverage 1KGP dataset to identify pSTRs. The samples in the NyuWa dataset were mostly Han Chinese, in which 2999 members were previously used for investigating small variants42 and the 1KGP dataset consisting of 2504 samples sequenced to ~33.3X coverage , tri- (χ2P < 1 × 10−22), and tetranucleotide (χ2P = 8 × 10−22) STRs  in all samples analyzed using the same stringent filters (see “Methods”). For the reference STR set we analyzed, there were more pSTRs than mSTRs for di- , 18.8% of pSTRs had at least two common alleles, and dimeric pSTRs had the highest proportion of loci with multiple common alleles  were highly multiallelic , and the allele number per locus decreased with motif length (mean: 8.82 to 2.61) Fig. , consistles Fig. . These dles Fig. , consistC. elegans56, only 3.6% of loci had a major allele frequency less than 0.5  of pSTRs being the most common  of the loci had at least one repeat away from the reference alleles, reflecting the high instability of these pSTRs . We also applied another method for investigating the chromosome distribution of structural variants (SVs) to validate this result60. As expected, we also found significant enrichment of hexanucleotide pSTRs in subtelomeric regions . We found that over ninety percent of pSTRs were in intronic (58.5%) and intergenic (34.5%) regions, and only 1881 pSTRs were within coding sequences (CDSs), of which 1426 (75.8%) and 256 (13.6%) loci were trimeric and hexameric pSTRs, respectively  by disrupting open-reading frames or transcript splicing. We identified 668 LoF alleles at 392 pSTR loci, including 254 frameshift mutations and 394 splicing region variants Fig. S, indicat28. To identify pSTRs potentially associated with complex human traits or diseases, we calculated the LD in terms of the r2 between pSTRs and trait- or disease-associated loci identified by GWASs (P ≤ 5 × 10−8)24. We identified 2871 pSTRs that were in high LD (“tagged”) with at least one GWAS SNP (r2 ≥ 0.7), accounting for approximately 0.78% of all pSTRs  and 3′UTR alternative polyadenylation (3′aSTRs) by leveraging public RNA-seq datasets of lymphoblastoid cell lines (LCLs) from the Geuvadis project71. The LCL dataset contained 445 individuals of European and African ancestry overlapping with the 1KGP dataset, and some samples of this dataset have been used for the identification of eSTR associations15. After filtering lowly expressed genes and controlling for confounders   and open chromatin Figs. , S22a, lons Fig. . Concordons Fig. . We alsos) Figs. , S22b, cP < 0.001, Fig. S15. We observed that genes targeted by eSTRs were significantly enriched in GWAS genes for allergic rhinitis, primary biliary cirrhosis, eczema, etc.   , followed by tetranucleotide (median of 7611) Fig. . We alsoTRs Fig. . In line32) Fig. . The avaWe next investigated the sharing of pSTRs across 26 different populations, and this analysis identified pSTRs shared in all populations (“All”), more than one (“Shared”), and specific to one population (“Unique”) Fig. . For pST44, we restricted this analysis to the 1KGP dataset to avoid potential batch effects. As previously done with VNTRs47, we performed pairwise length comparisons using the Wilcoxon rank-sum test among five continent populations for pSTRs with heterozygosity >0.1 and showed the results using volcano plots, with notable outliers labeled with the host genes of the loci  with a PCR-free protocoloci Fig. . These rUBE2L3, DHTKD1, MYPN, and MGAT5 , suggesting that this pSTR may contribute to the expression differences of DHTKD1 in different populations. The cumulative plot of the pSTR in MYPN revealed a bimodal distribution in East Asians and Africans, with longer repeats in East Asian populations , loci with the top 5% variance within a population were considered highly variable. In total, we identified 4616 highly variable pSTRs, of which 1100 loci were detected in all superpopulations Fig. . Concordp-values. Similar to pSTR mean length comparisons above, we performed this analysis in the superpopulations of the 1KGP dataset and pSTRs with heterozygosity >0.1. As shown in Fig. SPCAT1, showed highest variance of allele lengths in African samples . The GNA12 gene encodes a subunit of the G proteins and functions as modulators or transducers in various transmembrane signaling systems88. In addition, intergenic STR “chr2:44076978”, which had greatest length variance in African individuals  of allele lengths of each site and used permutation test (1000 times) to get empirical es Fig. S, and thips Fig. S, and we ls Fig. S, could r11, we next focused on detecting repeat expansions in our call set. We used a similar expansion score as described by Press et al.57, which compared the 95th percentile and median of allele lengths of each pSTR. To mitigate the confounding of population differences, we performed this analysis in each superpopulation. Using this metric, we identified 213 pSTRs with an expansion score ≥2 in at least one superpopulation, mainly from di- and tetranucleotide STRs  was increasingly implemented as a first-tier screening test and was a feasible approach because of advances in bioinformatics90. To characterize the variations in these STRs in our dataset, we analyzed 60 disease-causing STR loci that we collected  and the 1KGP dataset (2504 genomes), we constructed a large and high-quality resource of STR variation for diverse populations, especially for the Han Chinese population, which would fill the scarcity of STR variation resources in East Asian populations and benefit future STR studies. In total, our call set included 366,013 pSTRs and 290,454 mSTRs with 2–6 bp repeat motifs, in which 89,719 pSTRs were specifically identified in the NyuWa dataset. Within the pSTR call set, we found that 54.2% of pSTRs were highly multiallelic and that the STR mutation rates were influenced by motif length, chromatin context and genomic niche. We also observed a significant enrichment of hexameric pSTRs in subtelomeric regions and a modest correlation between hexameric pSTRs and double-strand breaks.It is fundamental to know the functional impact of STR variation in humans. We found that trimeric and hexameric STRs were overrepresented in the 5′UTRs and CDSs. We found that pSTRs in CDSs tended to be less variable and were under strong selective constraint. We also identified 668 LoF STR alleles that could affect open-reading frames or transcript splicing. A total of 2871 pSTRs were found in high linkage disequilibrium with GWAS SNPs, which indicated a potential association between these pSTRs and complex human traits and diseases. Furthermore, we identified some pSTRs with gene-regulatory effects, including 3273 eSTRs and 1117 3′aSTRs. These pSTRs were enriched in regulatory elements and accessible chromatin regions. Later, we performed expansion analysis and identified 213 expanded pSTRs. These results extended our understanding of the functional impact of STR variation and provided a set of STRs with potentially functional impacts.The large sample size and high population diversity of our dataset enabled us to conduct a population analysis based on STRs. The results of the population structure analysis suggested the power of pSTRs to distinguish populations. Using both STR and SNP genotypes, we observed a strong positive correlation between pSTR diversity and SNP heterozygosity. Later, we identified a set of population differentiated pSTRs based on length differences between superpopulations of the 1KGP dataset. We also found 1100 pSTRs that were highly variable in all superpopulations. Enrichment analysis suggested that these pSTRs potentially contribute to divergences in neurological phenotypes in humans.Finally, we provided the allele distributions of 60 known disease-causing STRs and observed the rare presence of pathogenic expansions. Compared with previous studies, our allele distributions involved the genotypes of many Han Chinese individuals. Therefore, these distributions could serve as a comparison set for studies of tandem repeat diseases, especially for such studies in Chinese populations.However, our study is limited by the STR genotyper we used. GangSTR v2.4.2 cannot analyze mono-nucleotide repeats resulting from their absence in the reference file and cannot analyze sex-chromosome STRs because the analysis of haploid genotypes was not supported. In addition, this tool was designed to analyze the repeat number variation of STR loci; hence, STR alleles with mutations inside the repeat units or interruptions remained undiscovered. Another limitation is that variations of nonreference (not included in the GangSTR reference file) STR loci have not yet been included. In the future, we would consider combining different STR identification and genotyping tools, including reference-independent tools, to provide a broader description of STR variation in humans.42 and the 1KGP dataset (~33.3x)52. The NyuWa dataset contained 4013 unrelated individuals from different provinces in China42, and was not ascertained for a specific health status. 2999 samples of the NyuWa dataset were reported before42 and 1014 samples were newly sequenced using the Illumina platform. The GATK Best Practices Workflows germline short variant discovery pipeline91 was employed to process and align reads to the human reference genome (GRCh38). For the 1KGP dataset, 2504 CRAM-format files mapped to the human genome build GRCh38 were downloaded from https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000G_2504_high_coverage/, which were recently sequenced at >30x coverage by the New York Genome Center44. The CRAM files were first converted to BAMs using SAMtools v1.1492. We then estimated the autosome coverage of all samples using mosdepth v0.3.3 (https://github.com/brentp/mosdepth). For the NyuWa dataset, the sexes of all samples were determined using guess-ploidy in BCFtools v1.1093. The sample information used in this study can be found in Supplementary Data This study was approved by the Medical Research Ethics Committee of Institute of Biophysics, Chinese Academy of Sciences and complies with all relevant ethical regulations. All participants provided written informed consent. The informed consent is used to collect samples for genome studies conducted by Chinese Academy of Sciences. Deep WGS data in this study were collected from the NyuWa dataset ~31.5x).5x42 and50, we used GangSTR v2.4.248 to genotype autosomal STRs with option “--include-ggl”. The reference file “hg38_ver13.bed.gz” for GangSTR were downloaded from https://github.com/gymreklab/GangSTR, and there were 765,227 autosomal STR loci  retained after excluding sites with unit length >6 bp. The median length of these loci was 12 bp. 3994 samples from NyuWa dataset and 2504 samples from 1KGP dataset were successfully genotyped by GangSTR. The filtering steps for GangSTR calls were largely concordant with previous study53. DumpSTR in TRTools toolkit v4.1.094 was used for call-level filtering with options “--vcftype gangstr --zip --gangstr-min-call-DP 20 --gangstr-max-call-DP 1000 --gangstr-filter-spanbound-only --gangstr-filter-badC”. Then we examined the call rate of each sample. 11 samples from NyuWa which had a call rate <50% were removed. Filtered VCFs of 6487 samples were merged using mergeSTR in TRTools toolkit v4.1.094 with default parameters, and merged VCF was then subjected to locus-level filtering by dumpSTR with parameters “--filter-regions GRCh38GenomicSuperDup.bed.gz --filter-regions-names SEGDUP --min-locus-callrate 0.2 --min-locus-hwep 0.00001”. The file “GRCh38GenomicSuperDup.bed.gz” were downloaded from the UCSC Genome Browser database95 and was to remove sites overlapping segmental duplications. Then the remaining sites with more than one allele were considered as polymorphic STRs (pSTRs), otherwise as monomorphic STRs (mSTRs). The final call set consisted of 366,013 pSTRs and 290,454 mSTRs from 6487 genomes. Some per-locus statistics including maximum observed allele length, heterozygosity, bit-entropy of the distribution of alleles, mean allele length, mode allele length, and variance of allele length were computed by statSTR in TRTools toolkit v4.1.016.Owing to its ability to genotype genome-wide STRs in a reliable and efficient way51, STRipy96, Stranger , and gnomAD . ExpansionHunter was designed to targeted genotyping analysis. It had good correlations with PCR-based assays96 and has been successfully used in several large-scale cohorts98. We used ExpansionHunter v5.0.051 to genotype 51 “standard” or “imperfect GCN” loci across all 6487 samples with default options. For nine “replaced/nested” loci 96, we used “call_non_ref_pathogenic_motifs” in str-analysis v0.9.4  to genotype all samples, as ExpansionHunter was unable to differentiate between the pathogenic and reference repeat motifs96. We then merged the calls from ExpansionHunter and “call_non_ref_pathogenic_motifs”, and filtered out alleles with fewer than five total supporting reads. For all individuals at each locus, we used REViewer v0.2.799 to visualize alignments of reads if the inferred repeat length exceeded the normal range or pathogenic threshold of this locus. We then manually inspected each image for read visualization to validate the authenticity of these events.There were no known cases of STR disease for samples in this study. To supplement the genome-wide analysis of GangSTR, we collected 60 known disease STR loci , we downloaded the allele distributions from https://gitlab.com/andreassh/stripy-pipeline and https://gnomad.broadinstitute.org/downloads#v3-short-tandem-repeats, respectively. We then plotted the distributions of each loci using box plots  to evaluate Mendelian inheritance. We first generated STR calls for these samples using the pipeline above. For each trio, a STR locus was considered to follow Mendelian inheritance if one allele of the child’s genotype was same to any one allele of the father’s genotype and another one was same to any one allele of the mother’s genotype, and the proportion of genome-wide STR loci that followed Mendelian inheritance was defined as Mendelian inheritance rate.62. Acrocentric arms  were not considered. We then performed a 10,000 round permutation test by exchanging the bin sums, and counted the number of events with permutated fold increase greater or equal to the observed fold increase. We divided this number by 10,000 to get the empirical P-value.We first cut each chromosome arm (p arm and q arm) into 1 Mbp consecutive windows, and removed windows overlapped with centromeres. Then the number of pSTRs/mSTRs in each window was summed. The mean STR number in the subtelomeric bins on each chromosome arm (outermost 5 Mbp of each chromosome arm end) was compared to the mean number of STRs in all other bins to get the fold increase, as described in ref. 60. For 22 autosomes, we first segmented each chromosome into 100 kb consecutive bins and removed bins overlapped with centromeres. Then we counted the number of STRs (pSTRs or mSTRs) in each bin and smoothed the count using an 11-bin (~1 Mbp) rolling mean for each chromosome. For comparison of chromosome context, each bin was assigned to a percentile based on the position of that bin on its respective chromosome arm relative to the centromere . Next, the normalized bin positions  were cut into 500 uniform intervals, and values across all autosomes based on the normalized interval position were averaged to generate “meta-chromosome” density. Finally, the “meta-chromosome” density was normalized by its mean value to get the “fold-enrichment” values, as shown in Figs. SP-values using the Bonferroni method and the significant level was set to 5%.To check the distributions of pSTRs and mSTRs across the genome, we adopted the method by Collins et al. which has been used to investigate the chromosome density of structural variants101, which has been used to study mobile element variants. We repeated the procedure here for clarity. We first segmented the 22 autosomes into 1 Mbp consecutive bins and removed bins overlapped with centromeres. We counted the number of pSTRs/mSTRs in each bin. Next, we examined the correlations of pSTRs/mSTRs count with various genome features, such as GC percent, CpG count, gene count, A/B compartment, DNase hypersensitive sites, histone modifications, transcription factor (TF) binding sites, DNA methylation level, and replication timing. The public data used here can be found in Supplementary Data To correlate the pSTR occurrences with genome features, we used the methods described by Kojima et al.102 was used to get GC percent of each 1 Mbp genome bin.“nuc” function in BEDTools v2.27.195 and the count of CpG in each genome window was generated using bedmap in BEDOPS v2.4.40103.CpG island regions of the human GRCh38 genome were downloaded from UCSC Genome Browser database (accessed at 2022/04/16)104, we counted the number of all genes and protein-coding genes across 1 Mbp windows. For all genes, genes annotated as “level 3”, “TEC” or “pseudogene” were excluded.Using GNECODE v39105 were downloaded from NCBI GEO106: GSM5057489, formaldehyde fixation followed by HindIII; GSM5057481, formaldehyde and DSG fixation followed by HindIII cleavage. Then, we used GENOVA v1.0107 to get compartment scores under 1 Mbp resolution.Two mcool format files of H1-hESC Hi-C data108 . We computed the average number of peaks in each 1 Mbp window of these three datasets.DNase-seq peak files of H1-hESC cell and H9-hESC cell were downloaded from the ENCODE project108. The number of peaks in each 1 Mbp bin was counted and the average number was used for analysis if replicates were available.We counted the number of peaks of histone protein modifications in H1-hESC and H9-hESC and several TFs  in H1-hESC from the ENCODE project  were retrieved from the ENCODE project  was calculated using the Repli-Chip datasets of H1-hESC cell from the ENCODE project 110 with Ensembl database version 104111, with parameters “--pick --sift b --polyphen b --hgvs --symbol --canonical --biotype --protein --domains --uniprot --tsl --numbers --distance 2000,1000”. Gene set enrichment analysis for genes with trimeric and hexameric pSTR in their CDS and 5′UTR regions was performed by clusterProfiler v3.18.0112. Alleles annotated in the following categories were considered loss-of-function ones: frameshift mutations, splicing region variants, coding sequence variants, and start or stop codon variants. The enrichment of pSTRs across different genomic features was quantified by GAT v1.3.4113 with 1000 permutations.We annotated pSTRs using Variant Effect Predictor v99.2 (VEP)P-value ≤ 5 × 10−8 and their related traits were obtained from GWAS Catalog v1.0.224. GWAS SNPs that related to only “educational attainment”, “mathematical ability” and “intelligence” were removed because these traits were strongly associated with environmental and socioeconomic factors. For the remaining GWAS risk SNPs, we first converted the genotype data into indicator matrix using PLINK v1.9114. LD between STR-SNP pair was defined as the squared Pearson correlation between STR dosage and SNP dosages, as described before100. STR dosage was defined as the sum of the repeat number of two alleles. Only pSTRs within 250 kb of a GWAS risk SNP were considered in LD calculation, and STR genotypes seen less than three times were removed, as described previously16. STR-SNP pair with r2 ≥ 0.7 was considered as strong LD. A complete set of pSTRs tagged by GWAS risk SNPs can be found in Supplementary Data We also annotated pSTRs by their linkage disequilibrium (LD) with GWAS risk SNPs. GWAS risk SNPs with 71 were downloaded from: https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/geuvadis/working/geuvadis_topmed/. Of these samples, 445 samples overlapped with the 2504 1KGP genomes set . These BAM files were produced by the TOPMed RNA-seq pipeline (https://github.com/broadinstitute/gtex-pipeline), with reads aligned to the human GRCh38 reference genome using STAR v2.5.3a115. We applied featureCounts v2.0.3116 to count reads of genes with GENCODE v34 annotation104. Next, we generated log2 normalized FPKM values using edgeR v3.32.1117, after filtering lowly expressed genes by “filterByExpr” function in edgeR. To account for hidden batch effects and other unobserved confounders, we used covariates for sex and population structure and detected additional hidden factors using Iteratively Adjusted Surrogate Variable Analysis (IA-SVA)118, as described before46. For population structure, we used the top ten principal components determined from the principal component analysis (PCA) of the SNP genotypes from the 445 individuals. Only SNPs with minor allele frequency ≥ 0.05 were used for PCA; pruning of 27 known long-range LD regions119 and subsequent PCA were performed by PLINK v1.9114. For IA-SVA, we used sex and top ten genotype principal components as the known covariates to estimate a set of latent covariates for the expression values. We chose fifteen hidden covariates based on the correlations between covariates  from Geuvadis consortium16. Only STRs within 500 kb of a gene expressed in the LCL dataset were included. To ensure the site quality and the eSTR detection power, we kept loci where at least 50 of the 445 samples had a genotype call and loci with heterozygosity ≥0.1. We also filtered genotypes of each pSTR locus to control for outliers by removing any genotypes seen fewer than three times out of 445 samples. If there were less than three genotypes after filtering samples, the STR locus was discarded16. Finally, we converted the STR genotype calls to dosage . The final STR dosage matrix contained 39,933 sites.The eSTR identification method was similar to previous studies16, we applied a gene-level FDR threshold of 10% to determine significant STR-gene pairs. For each gene, we first adjusted the P-values from association test with multiple STRs using Bonferroni method and we selected the lowest P-value. Then we used the P-values (one per gene) from all genes as input to “p.adjust” function in R to get final Q-values for each gene, with “BH” method. At last, we used alpha level of 10% to get all significant STR-gene pairs. We also repeated the above procedure after shuffling the sample identifiers, as a negative control.For each gene-STR pair, a linear model as 102, and only uniquely mapped reads were included. We then used DaPars v2.0 algorithm120 to get a percentage of distal poly(A) site usage index (PDUI) value for each transcript in each sample under default settings. Similar to expression data processing, we included sex, population structure, and hidden IA-SVA factors to adjust the PDUI values. To remove lowly expressed genes, we only considered genes retained in eSTR analysis. The adjusted PDUI data was then used for 3′aSTR identification, containing 44,742 transcripts.Starting with the 445 LCL BAM files mentioned above, we first converted them to bedGraph files using BEDTools v2.27.1Using the normalized STR dosage data above, we adopted a similar method to test the associations between STR genotypes and transcript PDUI values. For each transcript-STR pair, we used linear regression to test their association and controlled FDR of 10% at transcript-level. Similarly, we also permuted the sample identifiers to repeat the association test for each transcript-STR as a negative control.108 . To validate the results of GM12878 cell line, we also downloaded peak files of DNase-seq, CTCF ChIP-seq, histone ChIP-seq of GM06990 cell line  and GM12865 cell line . In addition, we also downloaded chromatin states data of these three cell lines from EpiMap121, which were in 18 categories defined by ChromHMM72.To examine the enrichment with epigenetic features of eSTR and 3′aSTR, we downloaded the peak files of ChIP-seq of histone marks and CTCF and ATAC-seq of the GM12878 cell line from the ENCODE projectP-values. Then the P-values in each analysis were adjusted by “p.adjust” function with “BH” method in R and a significant level was set to 5%.To quantify the enrichment of eSTRs and 3′aSTRs in these epigenetic and genomic features, we adopted GAT v1.3.4 to get the fold enrichment value and empirical P-value ” and “Mapped_Gene” and trait with fewer than 10 genes that were excluded, as described before15. We then performed the gene set enrichment analysis using clusterProfiler v3.18.0112 and showed the top traits in Fig. SWe also tested that whether eSTR/3′aSTR-associated genes were enriched in genes implicated by previous GWAS listed in GWAS catalog80, and the mean value was computed when multiple samples were included, as described in previous studies122. STR diversity was computed as the average number of STR differences between every two individuals in a given population. For the NyuWa dataset, high-quality bi-allelic SNP calls were used for SNP heterozygosity calculation. For deeply sequenced 1KGP dataset, SNP calls were downloaded from https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000G_2504_high_coverage/working/20201028_3202_phased/ and only bi-allelic SNPs were included. The number of heterozygous autosomal SNP numbers for each sample was computed by VCFtools v0.1.16123. STR diversity was computed by “gtcheck” function in BCFtools v1.793.SNP heterozygosity was defined as the ratio of heterozygous SNPs over the length of the human genomeTo perform PCA, STR genotypes for each sample were first converted into dosage data . Only pSTRs with major allele frequency <0.95 were used for each PCA, and PCA was done by using “prcomp” function in R. To match the sample number of NyuWa populations to the sample number of 1KGP populations, we sampled 100 individuals from Northern Han Chinese (CHN) and 100 samples from Southern Han Chinese (CHS) of the NyuWa dataset. We repeated three times of the sampling procedure and found that the PCA results Fig. S were staP-values using the “p.adjust” function in R with “BH” method. Comparisons with adjusted P < 0.01 were considered to be significant. The results were shown using volcano plots  were included, as these loci had higher levels of polymorphisms. Gene set enrichment analysis for genes enclosing these pSTRs was performed by clusterProfiler v3.18.0p-values, we randomly shuffling the sample labels and then calculated the fraction of times the absolute value of original SD diff is less or equal to the SD diff generated by the shuffled samples. In this study, we perform 1000 permutation per site. We adjusted the P-values using the “p.adjust” function in R with “BH” method. Comparisons with adjusted P < 0.01 were considered to be significant. The results were shown using volcano plots  of allele length in any two superpopulations of the 1KGP dataset. We restricted this analysis to pSTRs with heterozygosity >0.1. To compute empirical ts Fig. S.57 to detect expanded loci. For each STR locus, the expansion score was defined as /median allele length. Then we define expanded loci as those with expansion scores ≥257 in each superpopulation.In each superpopulation of the 1KGP dataset, we employed a similar expansion score metric as Press et al. previously described125.All of the statistical analyses in this study were briefly described in the main text and performed using R v4.0.3Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1-11Reporting Summary


Expression quantitative trait loci (eQTL) studies show how genetic variants affect downstream gene expression. Single-cell data allows reconstruction of personalized co-expression networks and therefore the identification of SNPs altering co-expression patterns  and the affected upstream regulatory processes using a limited number of individuals.We conduct a co-eQTL meta-analysis across four scRNA-seq peripheral blood mononuclear cell datasets using a novel filtering strategy followed by a permutation-based multiple testing approach. Before the analysis, we evaluate the co-expression patterns required for co-eQTL identification using different external resources. We identify a robust set of cell-type-specific co-eQTLs for 72 independent SNPs affecting 946 gene pairs. These co-eQTLs are replicated in a large bulk cohort and provide novel insights into how disease-associated variants alter regulatory networks. One co-eQTL SNP, rs1131017, that is associated with several autoimmune diseases, affects the co-expression of RPS26 with other ribosomal genes. Interestingly, specifically in T cells, the SNP additionally affects co-expression of RPS26 and a group of genes associated with T cell activation and autoimmune disease. Among these genes, we identify enrichment for targets of five T-cell-activation-related transcription factors whose binding sites harbor rs1131017. This reveals a previously overlooked process and pinpoints potential regulators that could explain the association of rs1131017 with autoimmune diseases.Our co-eQTL results highlight the importance of studying context-specific gene regulation to understand the biological implications of genetic variation. With the expected growth of sc-eQTL datasets, our strategy and technical guidelines will facilitate future co-eQTL identification, further elucidating unknown disease mechanisms.The online version contains supplementary material available at 10.1186/s13059-023-02897-x. In recent years, genome-wide association studies (GWAS) have revealed a large number of associations between genetic variation and disease . Many ofOne way to study the biological processes in which eQTL genes are involved is to construct gene co-expression networks. In these networks, genes (nodes) involved in shared biological processes are expected to be connected through co-expression (edges) . TraditiIn addition to capturing the cell-type-specific contexts, scRNA-seq can also be used to construct personalized co-expression networks using the repeated measurements  for each individual. This enables quantification of the covariance between genes, and thus their co-expression strengths, within an individual . These pMetaCell, a recently proposed method that groups homogeneous cells to reduce sparsity, but to our knowledge it has not yet been evaluated in benchmark studies . In each level, we kept the individuals with fewer cells, randomly subsampled those with a cell number higher than the corresponding level and performed the co-eQTL analysis using the strategies mentioned. Similarly, to examine the impact of sample numbers, we randomly subsampled 50 and 100 individuals, and excluded nine individuals with fewer than 10 CD4 + T cells for both scenarios.Z-score method to perform meta-analysis across included datasets for each triplet, yielding the nominal p-value.To map co-eQTLs, we calculated the Spearman correlation coefficients for each selected SNP–eGene–co-eGene triplet within each dataset separately. Similar to the eQTL analysis, we then used a sample size weighted p-value and the lowest permuted p-value per permutation over all the genes (co-eGene) tested for the SNP–eGene pair, resulting in a list of the 100 lowest permuted p-values per SNP–eGene pair. For each SNP–eGene pair, we then fitted a beta-distribution over these 100 permuted lowest p-values, which enabled us to subsequently establish the empirical p-value for the lowest non-permuted p-value. Through this procedure, we ensured that under the null test statistic each SNP–eGene pair has a uniform p-value distribution. Finally, over the empirical p-values for all SNP–eGene pairs, we calculated Benjamini–Hochberg FDR over the empirical p-values. Those SNP–eGene pairs with a FDR ≤ 0.05 were deemed significant.To account for the correlation structure between co-eQTL SNP–eGene–co-eGene triplets, we modified and applied the permutation-based multiple testing correction strategy from FastQTL , implemep-value cutoff that indicates which of the co-eGenes are significant for that SNP–eGene pair via the following steps. After determining the FDR for all SNP–eGene pairs, we determined the empirical p-values that are closest to FDR = 0.05. Using the beta distributions for each SNP–eGene pair, we then determined its nominal p-value threshold. All co-eGenes with a nominal p-value lower than the corresponding p-value threshold for that SNP–eGene pair were considered significant. The nominal p-value thresholds range from 1.75e − 41 to 1.34e − 4.For each SNP–eGene pair, we also derived a eGene ~ SNP + co-eGene + SNP:co-eGene. We then examined the effect sizes of the interaction term SNP:co-eGene and used Benjamini–Hochberg procedures for multiple testing correction.We replicated the co-eQTL findings in bulk whole-blood RNA-seq data from the BIOS Consortium, using the same method described in a previous study . Brieflyrb method with modification. We followed the same procedures as the original study [re is the estimation errors across gene pairs between two tissues, rp is the correlation of co-expression levels between two cell types in the overlapping sample, ns is the number of overlapping samples, and ni and nj are the number of samples in cell typed i and j, respectively. For the BIOS replication, we excluded overlapping individuals from the BIOS RNA-seq dataset for the replication analysis. Additionally, in cases where fewer than 10 co-eQTLs were tested in the replication analysis, for example, B cells, we could not get a robust estimation of the rb value and hence represent them as missing in the “We used the same evaluation metrics to quantify the cell-type specificity and replication performance in the BIOS data set of the co-eQTLs. First, we used the al study  but chosDue to our filtering strategy, we did not always test the same set of SNP–eGene–co-eGene triplets in all cell types. Therefore we also need to compare the tested ratio when quantifying the cell-type specificity. The tested ratio means the SNP–eGene–co-eGene triplets that were also tested in another cell type or the BIOS replication analysis.A third evaluation metric that we used is allelic concordance between the discovered co-eQTLs and the results in the replication study. This is defined as the ratio of co-eQTLs with concordant effect direction by the number of significant co-eQTLs identified in the replication study.We explored the biological function of the co-eQTLs based on different enrichment analyses that all tested if co-eGenes associated with the same SNP–eGene pair in the same cell type show similar functional properties. For this, we selected all SNP–eGene pairs that had at least five significant co-eGenes in the same cell type.First, we performed GO enrichment analysis separately for each co-eGene set, grouped by SNP–eGene and cell type, applying the R package clusterProfiler (ver 4.0.5)  and perfR2 ≥ 0.9) lies in a binding site of the enriched TF. The SNPs in high LD were obtained from SNiPA [Next, we explored if these co-eGene sets were enriched for certain TF binding sites. TF annotations were taken from ChIP-seq peaks processed in the ReMap 2022 database , which wom SNiPA  using thR2 ≥ 0.8) with GWAS loci from the GWAS Catalog [For the GWAS annotations, we considered two different strategies. In the first approach, we annotated SNPs or SNPs in high LD  separately for the positively associated co-eGenes and negatively associated co-eGenes in CD4 + T cells.After we observed different distributions of co-eQTLs for rs11311017–Based on the results of the different enrichment analyses, we selected five loci  and seven GWAS traits  for which we decided to further investigate the underlying mechanisms by applying colocalization analysis on the eQTL signal as well as the co-eQTL signal of each co-eGene pair. We tested SNPs surrounding the 1 MB window of the TSS of the eGene for both eQTL and co-eQTL analysis. Then we overlapped those SNPs with the SNPs given in the selected GWAS data of the trait. For all traits except T1D, we used the consolidated GWAS summary statistics of the GTEx consortium . For T1DZ-scores. After ensuring that the reference allele aligns in the eQTL and co-eQTL analysis, co-eQTLs for which the sign of the Z-score matches the sign of the eQTL Z-score are called concordant. If otherwise, they are called discordant.We compared the direction of effect in eQTLs and co-eQTLs by comparing the direction of the Additional file 1:Supplementary Tables. Collection of small supplementary tables  plus all table captions; large supplementary tables are saved as separate csv files in the following.Additional file 2:Supplementary Figures. Collection of all Fig. S1-34.Additional file 3:Supplementary Note. More extensive description of meta-cell evaluation, alternative GRN construction methods and interpretation of additional co-eQTL examples.Additional file 4:Table S3. eQTLs for major cell types.Additional file 5:Table S6. The significant co-eQTLs for the major cell types with the filtering strategyAdditional file 6:Table S9. BIOS replication for filtered co-eQTLs for major cell types.Additional file 7:Table S10. The significant co-eQTLs for the major cell types without the filtering strategyAdditional file 8:Table S13. BIOS replication for unfiltered co-eQTLs for major cell typesAdditional file 9:Table S14. GO enrichment for co-eGenes associated with the same eQTL, for all eQTLs with at least 5 co-eGenesAdditional file 10:Table S15. TF enrichment of co-eGenes co-eGenes associated with the same eQTL, for all eQTLs with at least 5 co-eGenes, using the Remap 2022 ChIP-seq dataAdditional file 11:Table S16. Annotated co-eQTL SNP with GWAS resultsAdditional file 12:Table S17. Enrichment of co-eGenes for GWAS resultsAdditional file 13:Table S18. Colocalization results for eQTL signal with GWAS signalAdditional file 14:Table S19. Colocalization results for co-eQTL signal with GWAS signalAdditional file 15:Table S20. GO enrichment for co-eGenes associated with the same eQTL, variation of Supplementary Table 14 with a stricter backgroundAdditional file 16. Review history.


Cymbidium sinense is a symbol of gigantic floral variability on account of wavering shapes and sizes of floral organs, although marker–trait association (MTA) has not been studied for its floral traits. We evaluated markers associated with 14 floral traits of C. sinense through a genome-wide association study (GWAS) of 195 accessions. A total of 65 318 522 single-nucleotide polymorphisms (SNPs) and 3 906 176 insertion/deletion (InDel) events were identified through genotyping-by-sequencing. Among these, 4694 potential SNPs and 477 InDels were identified as MTAs at −log10 P > 5. The genes related to these SNPs and InDels were largely associated with floral regulators, hormonal pathways, cell division, and metabolism, playing essential roles in tailoring floral morphology. Moreover, 20 candidate SNPs/InDels linked to 11 genes were verified, 8 of which were situated on exons, one was located in the 5′-UTR and two were positioned in introns. Here, the multitepal trait-related gene RABBIT EARS (RBE) was found to be the most crucial gene. We analyzed the role of CsRBE in the regulation of flower-related genes via efficient transient overexpression in C. sinense protoplasts, and found that the floral homeotic genes CsAP3 and CsPI, as well as organ boundary regulators, including CsCUC and CsTCP genes, were regulated by CsRBE. Thus, we obtained key gene loci for important ornamental traits of orchids using genome-wide association analysis of populations with natural variation. The findings of this study can do a great deal to expedite orchid breeding programs for shape variability.The orchid, the champagne of flowers, brings luxury, elegance, and novelty to nature.  Cymbidium, with 104 species listed by POWO and more than 15 000 popular commercial hybrids recorded in the horticultural database of the Royal Horticultural Society (RHS), stands out as the orchid genus of the highest renown. Cymbidiums are among the earliest orchid species to be cultivated [Cymbidium species has broadened the breeding scope for functional genomics [Cymbidium breeding.The Orchidaceae falls among the largest angiosperm families. Based on the Plants of the World Online (POWO) resource, there are 31 069 accepted orchid species. genomics , 3. HoweLactuca sativa [Glycine max [Brassica napus [Zea mays [Phalaenopsis equestris [Molecular markers are an indispensable tool during marker-assisted selection (MAS) to support preferred phenotypic traits in crop selection. Single-nucleotide polymorphism (SNP) markers can be effectively identified through ultra-throughput next-generation sequencing, which has revolutionized selective-trait breeding , 5. For a sativa , Glycinecine max , Brassicca napus , and ZeaZea mays . It has questris .Cymbidium breeding, C. sinense is an ideal source and a popular breeding material in nurseries. It is recognized by its dark and attractive foliage, and elegant and scented flowers. With more than 1000 natural variants, C. sinense is an ideal specimen to trace the evolutionary history of phenotypic traits of orchids [C. sinense is a great source for SNP calling. However, GWAS implementation remains to be established in Cymbidium orchids.For  orchids . It has C. sinense resources to study 14 phenotypes related to flower size and the number and morphology of tepals. We used SNPs to build a genetic map of C. sinense and discovered the relationships by linking the SNPs with the floral traits to recognize quantitative trait loci (QTLs) contributing to 14 diverse floral traits in Cymbidium. This pioneer report thus explores novel loci associated with flower size and tepal morphology-related traits of Cymbidium orchids.Therefore, we used 195 C. sinense has three sepals and three petals in the first and second whorls, respectively. A striking similarity is found between two of the petals. However, the third petal, called the labellum (lip), is significantly evolved, showing a curled and inverted triangular shape. The highly fused male and female reproductive organs make a gynostemium (column) at the center. It is worth noting that C. sinense showcases an extensive array of naturally occurring variations in flower organ types , number of tepals (NT), vertical diameter of the flower (VDF), transverse diameter of the flower (TDF), flower length to width ratio (FLWR), lateral sepal length (LSL), lateral sepal width (LSW), lateral sepal length to width ratio (LSLWR), petal length (PL), petal width (PW), petal length to width ratio (PLWR), lip length (LL), lip width (LW), and lip length to width ratio (LLWR).The parameters of individual flower organs exhibited significant variation among the 195 accessions, some showing differences exceeding 20-fold . The racCorrelation analysis of 14 traits inferred significant correlations among 32 pairs (correlation coefficient >0.2 and <−0.02) . Of thesFollowing the principle of eigenvalue >1, six principal components were selected . These cWe used an HaeIII+Hpy166II enzyme digestion assay to define Specific-Locus Amplified Fragment (SLAF) tags for sequences with the length of 500–550 cleaved fragments. Using 195 accessions, 675 761 SLAF tags were produced, with an average sequencing depth of 10.01× per sample . Among tIn addition to SNPs, we also detected a total of 3 906 176 InDel markers , coverinC. sinense accessions into two distinct groups  using the rate of cross-validation error  showed that two components (PC1 and PC2) could clearly divide 195 t groups . We usedt groups , justifyesources . Analysie genome . The genon error . The lowr  (We used 65 318 522 population SNPs in GWAS to identify 4694 significant marker–trait associations (MTAs) for 14 flower traits at −log10 P > 5 . Therefo (10−10) . Of the  (10−10) , with th (10−10) . The 162 (10−10) . Based o (10−10) . CytochrMoreover, we found 166 potential MTAs related to flower development and flower organogenesis, which were mainly situated on CDs, intron, or 3’/5’ UTR regions d, indica10P > 8) (10 P > 10) were located in the 5′-UTR position of a transcriptional regulator called RABBIT EARS (RBE). Among the other genes related to NT were MADS-box transcription factors (TFs), auxin regulators, and ethylene-responsive TFs (We detected most significant SNPs related to the trait of NT (12 loci with −log10P > 8) . Nine ofsive TFs .10 P values of 8.5 and 8.22, respectively. Both were located in the 3′-UTR region of chromosomes 7 and 4, respectively  with −log10 P values of 8.97, 8.86, 9.31, and 3.03, respectively (The highest number of significant MTAs (46) were observed for petal shape , includiectively . The firectively .10 P values of 9.09, 9.09 and 7.25, respectively  and IAA4-like (Mol006276.gene) were the most significant MTA genes, with P values of 1.7079 × e−11 and 3.91 × e−11, respectively  was evident in XSJJ, while DFHSH was heterozygous for C/T. In the case of PW, an SNP was verified for G-protein coupled receptor, which was homozygous in both FC and DFHSH.We randomly selected 20 MTA loci related to LSW, PW, TDF, PL, NT, LLWR, and LW, and verified the related SNPs and InDels by Sanger sequencing . For eacN-methyltransferase, wherein homozygous alleles CT and TG were observed in LZBM and BDQ, respectively. E3 ubiquitin-protein ligase SHPRH was heterozygous for G/C and homozygous for G. Moreover, GWAS identified several contiguous SNPs associated with NT in the promoter of RBE gene. Therefore, RBE was also verified for XX and SZQD. Six SNP loci were found to be different in two varieties, and in the cultivar with multiple tepals (SZQD), six SNP loci were heterozygous. One deletion was also confirmed in the TF GTE9-like situated on LW. One SNP was verified for TDF. Here, mitogen-activated protein kinase ANP1 expressed a homozygous (C) SNP for XJ and a heterozygous SNP (C/T) for XSJJ. Five SNPs were verified for NT. Protein NETWORKED 4B isoform X1 was both homozygous (G) for LY and heterozygous (A/G) for XX. Two adjacent SNPs (TT/GC) were identified for the cytochrome P450 gene. In the normal cultivar of XX, the allele was homozygous for TT, while in the cultivar with multiple tepals (SZQD) the allele was homozygous for GC. Similarly, two adjacent pairs were verified for ribulose-1,5 bisphosphate carboxylase/oxygenase large subunit Most loci were located in the 3′-UTR and 5′-UTR positions, indicating a potential transcript and/or translation-level regulation. We thus determined the correlation between gene expressions of these loci and phenotypic variations. We sequenced the transcriptome of 20 flower organs from five representative varieties with significant differences in flower organ size and morphological structure, namely normal flower type (WT), genostemium-like perianth variety (GPV), multi-perianth variety (MPV), labellum-like perianth variety (LaPV), and null-lip variety (NLV). We identified 27 locus-linked genes, including MADS-box genes, auxin pathway genes, and MYB genes, which were further verified. As shown in AGAMOUS (AG) genes, our GWAS analysis did not find any loci linked to AG orthologous genes. Interestingly, out of the 12 loci associated with the multi-tepal trait with the highest P-value, 8 were associated with the 5′-UTR of the RBE gene, ranging from 4630 to 4973 upstream of ATG. Gene expression levels were analyzed in wild-types and different multi-tepal variants. The results indicated that the RBE gene was concentrated in the early floral developmental stages  [C. sinense is based on different LSW levels. Floral traits, such as shape, color, fragrance, and size are potentially under selection due to pollinator predilections [R2 < .1  . In our [R2 < .1 , which iA. thaliana, Sorghum bicolor, Triticum aestivum, B. napus, Z. mays, Hordeum vulgare, Gossypium hirsutum and Camellia sinensis [Phalaenopsis, Dendrobium, and Vanilla [Dendrobium officinale and D. aduncum generated 349 polymorphic loci [P. aphrodite, a genetic map was constructed using 2905 SNP markers [P. equestris from 117 progenies [Cymbidiums, which are elite flowers representing the most versatile orchids in the world. We noted 4694 significant MTAs for 14 floral traits of C. sinense at −log10 P > 5  and IAA4-like (Mol006276.gene) as the most significant SNPs, with P values of 1.7079 × e−11 and 3.91 × e−11, respectively.GWAS is an outstanding technique to recognize genomic loci allied with preferred traits . Recogni10 P > 5  was associated with zinc metalloprotease EGY2  and CUC2 by miR164C, and stimulates the growth of petal primordia by a direct and negative regulation of TCP5 and TPC4 [PISTILLATA (PI), APETALA3 (AP3), and AGAMOUS (AG), are also regulated by RBE [. Analysis of transcriptome data in wild-type and multi-tepal variants also suggested a close relationship between differential gene expression and the multi-tepal phenotype. At the same time, overexpressing the CsRBE gene in the protoplast of C. sinense caused expression changes of 18 floral-related genes, including CsAP3, CsPI, CsTCP, and CsCUC . Hybridization of DNA fragments to the array was performed with the Affymetrix® GeneTitan system following the manufacturer’s instructions. Hybridization signal processing was done using the CEL files in the Affymetrix® Power Tools software package (v1.18) for SNP calling. Significant SNPs were isolated using the following criteria: missing data <10% and minor allele frequency >5%. Missing data were imputed for the resulting SNPs using BEAGLE (v5) with default parameter settings [The 195 settings .C. sinense population structures were suggested with the STRUCTURE (v.2.3.4) program [K), 10 independent simulations were performed for each 10 Ks (1–10) with 10 000 iterations as burn-in period and a Markov chain Monte Carlo (MCMC) run length of 10 000 iterations. After the first round, six Ks (4–9) were used for further simulations with 20 000 iterations as burn-in period and 100 000 iterations as an MCMC run length. The resulting log-likelihood estimates for the Ks were tested to ascertain the best K using the delta K method [K-based membership coefficients of 195 C. sinense accessions were used to construct a Q matrix (population structure matrix). Additionally, the R package was used to perform hierarchical clusters. The poppr package (v2.9.4) was used to assess Nei’s genetic distances [C. sinense accessions [The  program . The STRK method . The besistances  between cessions , followeP < 0.0005 and then a genome-wide threshold at P < 0.00005 was used, which was ascertained on the basis of effective number of independent markers (Me) [Me estimation, GEC  software (http://pmglab.top/gec/#/) was used for the equation 0.05/Me. The phenotypic variance explained (PVE) by a significant marker was calculated using the following equation in base R citation:For the identification of MTAs for 14 flower traits, association analysis was performed using the MLMM (multilocus mixed model)  in assocsig:marker/) × 100PVE% =  and KEGG pathway (http://www.genome.jp/kegg) databases were used to assign metabolic attributes to candidate genes.The significantly associated SNP/InDel loci were functionally annotated. The candidate genes associated with SNP/InDels were annotated using the notation . The GO Twenty candidate SNPs/InDels associated with 11 genes were verified. Total DNA was extracted and PCR amplification primers  were desCsRBE was checked in the protoplast of C. sinense following our previously established protocol [CsRBE CDS was inserted into PAN580-GFP vector and the recombinant vector was introduced into Escherichia coli DH5α-competent cells. The bacteria were replicated and the plasmid was extracted and mixed with PEG solution for protoplast transfection. Transient expression was calculated using GFP reporter expression of Pan580-GFP vector. The LSM710 confocal laser scanning microscope was used to measure GFP fluorescence.The transient expression of protocol . BrieflyCsPI, CsAP1, CsAP3-1, CsAP3-2, CsAP3-3, CsAP3-4, CsTCP4-2, CsCUC1-1, CsCUC2-1, CsTCP4-1, CsTCP4-3, CsTCP4-4, and CsAG1  and gene expression was measured by the 2−ΔΔΤ method. Similarly, the qRT–PCR expression of CsRBE was measured for five stages of flower development.Protoplasts with the transient CsRBE gene were harvested to measure the expressions of 2, and mounting on gold-coated stubs and sputter. The samples were observed with a JSM-6360LV (JEOL) scanning electron microscope.The surface cell morphology of floral organs was observed through a scanning electron microscope. The dissected pieces were fixed using 2% formaldehyde and 3% glutaraldehyde solution for 24 h, followed by dehydration in acetone, critical-point drying in liquid COANOVA (one-way) was used through SPSS software  to measure statistical significance.Supplementary_figures_R1_uhad206Click here for additional data file.Supplementary_tables_R1_uhad206Click here for additional data file.


Oryctes rhinoceros), reflecting the growing interest in curtailing this invasive pest of palm trees. Whilst reference-free analyses of RNA-seq and RAD-seq datasets have been done for different CRB collections, recent availability of the CRB’s genome assembly provides an opportunity to collate diverse data and create a reference-based population dataset. Here, I release such a dataset containing 6,725,935 SNPs and genotypes called across 393 individual samples from 16 populations, using the previously published raw sequences generated in 9 different experiments . I also provide reference-based datasets for the CRB’s mitochondrial variants and for variants of its viral biocontrol agent Oryctes rhinoceros nudivirus. SNP data provide high resolution for determining the geographic origin of invasive CRB. With these genomic resources, new data can be analysed without re-processing the published samples and then integrated to expand the reference datasets.Over the last few years, various types of NGS data have been accumulating for the coconut rhinoceros beetle (CRB,  Oryctes rhinoceros, has been responsible for major yield losses of coconut and oil palms across its native and invasive range1. Invasive CRB populations across the South Pacific islands were suppressed for decades with a largely non-chemical toolbox that included the use of an entomopathogenic virus, Oryctes rhinoceros nudivirus (OrNV)2. In recent years, however, incursions of O. rhinoceros were recorded in territories that were previously free of this pest, from Guam, Hawai’i and Solomon Islands4 to Vanuatu and New Caledonia5. Different hypotheses have been proposed to explain the resurgence of O. rhinoceros, one being beetle’s increased tolerance to the OrNV’s pathogenicity, associated with a particular mitochondrial lineage (known as the “CRB-G” biotype) that is common in the newly-invaded regions6. However, new data show that OrNV infections are common in the CRB-G lineage8, and some evidence supports the hypothesis of an emergent OrNV strain that is less-virulent8. Understanding the mechanisms behind the breakdown of a formerly-successful biocontrol strategy is critical for re-establishing effective suppression of this pest and for preventing its future invasions.The coconut rhinoceros beetle (CRB), Population genomics and phylogenetics provide a good framework for testing different hypotheses around CRB invasions, including interactions with its biocontrol agent (OrNV), through the analyses of genetic patterns of both biological systems in space and time. The power to resolve alternative hypotheses within this framework depends on the ability to grow the reference dataset for CRB populations and OrNV isolates, with spatial and temporal collections spanning native and invasive range.9, as well as RNA sequencing (RNA-Seq)13 and Whole Genome Sequencing (WGS)16. Until recently, however, absence of a reference genome assembly for CRB’s nuclear and mitochondrial genomes have prevented the direct comparison and collation of different datasets. Whilst population genomic analyses of CRB’s RAD-Seq data have been done in a reference-free manner (i.e. using a de novo approach17), data from different experiments that use different restriction enzymes cannot be directly compared without being aligned to the reference genome. Data comparison between different technologies, like RNA-Seq and RAD-Seq, is also impractical without reference-aligned data.Over the last few years, next-generation sequencing (NGS) experiments have been done for different CBR collections, accumulating data from the reduced-representation sequencing 18 and mitochondrial19 genomes provide an opportunity to collate all NGS data generated for this pest thus far and to create a reference-based population dataset. I fully re-processed raw sequencing data from 393 CRB samples from 16 countries, that included data from RAD-Seq, WGS and RNA-Seq experiments deposited within 9 BioProjects in the NCBI’s Sequence Read Archive (Metadata Table20). I generated a dataset20 with 6,725,935 Single Nucleotide Polymorphisms (SNPs) and 636,134 indels, and showed that the called genotypes provide high resolution for determining population genetic structure and history. I also provide SNP and indel dataset20 for the CRB mitochondrial genome variation, and for variation found in the isolates of its biocontrol agent, OrNV. With these genomic resources20, researchers will be able to fully utilize the existing population data, and have a reference point to further increase the sampling resolution in space and time.Recently-released high-quality genome assemblies for CRB’s nuclear21), two as the OrNV samples , and one as a whole-organism sample (PRJNA72433524)four Whole Genome Sequencing (WGS) datasets: one described as a WGS data from the mitochondrial genome (PRJNA73592225), one generated with PstI and MspI (PRJNA64815326)two Restriction-site Associated DNA sequencing (RAD-Seq) datasets: one generated with restriction enzymes NlaIII and MluCI  datasets: PRJNA486419I retrieved all NGS data generated from the CRB samples that were deposited to the National Center for Biotechnology Information (NCBI) BioProject database and were publicly available in January 2022. There were nine BioProjects that included raw Illumina sequences from:20.In total, there were 393 samples collected from 16 countries/territories , the remaining sequences were then mapped to the CRB mitochondrial genome sequence (NCBI: MT457815.133), and then to the CRB nuclear genome sequences (NCBI: GCA_020654165.134).The processed sequences were first mapped to the OrNV genomic sequence  were mapped with Bowtie 2(for paired-end data)--phred33 --end-to-end --very-sensitive -q --minins 35 --maxins 1000 --no-discordant --no-unal -x assembly-index -1 trimmedN_1.fastq -2 trimmedN_2.fastq -S outputN.sam(for single-end data)--phred33 --end-to-end --very-sensitive -q --no-unal -x assembly-index -U trimmedN.fastq -S outputN.sam36 v2.1.0, using the following parameters:RNA sequences were mapped with HISAT2-q -k 5 --min-intronlen 20 --max-intronlen 20000 --no-mixed --no-discordant --no-unal -x assembly-index -1 trimmedN_1.fastq -2 trimmedN_2.fastq -S outputN.sam37 v1.13. using the following parameters:The mapping process for each of the three genomes  produced three sets of.sam files. All.sam files from one set  were converted to the sorted and indexed.bam files with SAMToolsview -b -T assembly.fasta -o outputN.bam outputN.samsort -o sorted.outputN.bam outputN.bamindex sorted.outputN.bamAll individual files for each dataset were then merged into one indexed merged.bam file using the SAMtools v1.13 commands:merge -c -p -o merged.sorted.output.bam sorted.output1.bam sorted.output2.bam sorted.output3.bam … sorted.outputN.bamindex -b merged.sorted.output.bam38 v2.23 to realign the mapped sequences that have been merged from all samples in each of the three sets , using the following parameters:In order to improve the detection of variants in the downstream process, I used ABRA2--ref assembly.fasta --in merged.sorted.output.bam --out realigned.bam --mapq 0 --nosort --no-edge-ci39 v1.13The final.bam file containing realigned sequences was indexed with SAMtoolsindex -b realigned.bam39 v1.13 was used for variant (SNP and indel) and genotype calling, and for producing the variant call format (VCF) file for each dataset  using the following commands:Bcftoolsmpileup --output-type u --fasta-ref assembly.fasta --max-depth 393000 --min-BQ 13 --config illumina --output assembly-pileup.bcf realigned.bamcall --multiallelic-caller -mv -Oz -o assembly.vcf.gz assembly-pileup.bcf40GCA_020654165.1_S4-7k-2v3_genomic.vcf.gzwith ~6.7 M SNP and ~600 K indel positions in the CRB nuclear genome assembly with genotypes scored across 393 CRB individuals.40MT457815.1_S4_mitochondrion.vcf.gzwith 562 SNP and 36 indel positions in the CRB mitochondrial genome40MN623374.1_S4_OrNV.vcf.gzwith 6458 SNP and 356 indel positions in the OrNV genome.20Metadata_table.xlsxThe genomic resources produced in this study are the publicly-accessible VCF files:O. Rhinoceros and undetermined), percent of coverage per assembly, original sample ID, NCBI Run, NCBI experiment, NCBI SRA study, NCBI BioProject and NCBI BioProject description.containing the information for all 393 CRB samples used in this study, including: the sequencing strategy, sample name (re-coded for this study), the number of raw Illumina sequences, the number of sequences passing the QC step, the number of sequences mapped with Bowtie 2 on each of the three assemblies as well as the number of unmapped sequences (for RAD-Seq and WGS sequencing data), the number of sequences mapped with HISAT2 on each of the three assemblies as well as, the number of unmapped sequences (for RNA-Seq data), sample sequences origin composition in percentages  at EMBL-EBI under accession number PRJEB5926640.All data records are available in figshare42, For human genome data, the Ts/Tv ratio is between 2 and 3, depending on whether the analysed SNPs are inside or outside exons42. Using the program Vcftools43, I obtained the Ts/Tv ratio of 2.08 for SNPs in the CRB nuclear genome dataset (GCA_020654165.1_S4-7k-2v3_genomic.vcf40), the ratio of 2.99 for the CRB mitogenome dataset (MT457815.1_S4_mitochondrion.vcf40), and the ratio of 2.62 for the OrNV dataset (MN623374.1_S4_OrNV.vcf40), all of which indicate high overall quality of SNPs reported in these datasets.A commonly used metric for assessing the overall quality of SNP calling is the Transition and Transversion ratio (Ts/Tv)20 to the previously-published phylogeny generated with the reference-free data44. Specifically, I reproduced the reference-free phylogeny by Reil et al.9 reported in their Fig. 44. The final dataset contained genotype calls for 151 CRB individuals across 7,037 variable sites, with site missingness of ≤ 0.5 . The vcf2phylip.py45 script was used to transform the data from the VCF file to the PHYLIP multiple sequence alignment format (with heterozygous genotypes represented as IUPAC ambiguities), and the ML phylogeny was produced with the program RAxML-NG46. I successfully reproduced the originally-reported ML tree that has geographic samples clearly separated as highly supported branches  phylogeny generated with this dataset40) was filtered to contain all 172 individuals from the Reil et al. study9, and SNP data only (indels were removed), where at least 166 individuals had to have a genotype at each variable position (site missingness ≤ 0.05). This filtering produced the final dataset with genotype calls at 15,699 sites in 166 individuals . As with the reference-free dataset, genotype calls were transformed into the PHYLIP file using the vcf2phylip.py45 script and the ML phylogeny was produced with RAxML-NG46.The reference-based dataset  genotype calling. Consequently, more individuals and loci can be included in the analysis despite having lower sequencing depth when the reference genome is available. In the example above, reference-free (de novo) analysis included 155 CRB individuals with ~7 K SNP, while the reference-based analysis contained 166 individuals with~15 K SNPs, Moreover, having a reference genome enables identification and removal of contaminant sequences that could cause erroneous SNP calling. In the Reil et al.9 dataset, 6 CRB individuals had to be excluded from the analysis, despite having good numbers of sequencing reads, because more than 80% of their sequences were identified as exogenous i.e. could not be aligned to the CRB nuclear or mitochondrial genomes, indicating severe sample contamination and/or degradation.The resulting ML phylogeny Fig.  confirme40) was filtered to contain individuals from two different RAD-Seq experiments: (1) Reil et al.9, where the library was prepared using two frequently cutting restriction enzymes , and from (2) Etebari et al.7, where the library was prepared using one frequently cutting restriction enzyme (MspI) and one less frequently cutting enzyme (PstI). The final dataset contained 3,802 SNP sites with a minor allele frequency of 0.05 and missingness < 0.3 across 356 CRB individuals from 12 locations. 14 individuals were removed from the analysis due to not having a genotype at > 80% of the selected SNP sites. Genotype calls were transformed into the PHYLIP file using the vcf2phylip.py45 script and the ML phylogeny was produced with RAxML-NG46.The reference-based dataset  was filtered to contain 8 individuals previously analysed in a RNA-Seq study by Etebari et al.12, and 99 individuals previously analysed in a RAD-Seq experiment by Etebari et al.7. The final dataset contained 1,828 SNP sites with a minor allele frequency of 0.05 and missingness of 0.05. Discriminant Analysis of Principal Components (DAPC)47 was done with the R package adegenet48 v.1.42.To confirm that the reference-based SNP data obtained from DNA sequencing is concordant with the data from RNA sequencing, I analysed the CRB samples collected from three geographic locations . The reference-based dataset  was retained in order to avoid data over-fitting  of the vector space. Nevertheless, a strong biological signal from the scored genotypes in these individuals led to their unambiguous grouping by population origin, confirming that the reference-called variants are of high quality for inferring population genetic structure.DAPC correctly inferred three genetic clusters  and on the genome of the biocontrol agent OrNV.VCF files


IBD analysis clarified the dynamics of chromosomal recombination during the ZP pedigree breeding process and identified ten genomic regions resistant to SCN race3 combining association mapping.Heterodera glycines Ichinohe) is one of the most devastating pathogens for soybean production worldwide. The cultivar Zhongpin03-5373 (ZP), derived from SCN-resistant progenitor parents, Peking, PI 437654 and Huipizhi Heidou, is an elite line with high resistance to SCN race3. In the current study, a pedigree variation map was generated for ZP and its ten progenitors using 3,025,264 high-quality SNPs identified from an average of 16.2 × re-sequencing for each genome. Through identity by decent (IBD) tracking, we showed the dynamic change of genome and detected important IBD fragments, which revealed the comprehensively artificial selection of important traits during ZP breeding process. A total of 2,353 IBD fragments related to SCN resistance including SCN-resistant genes rhg1, rhg4 and NSFRAN07 were identified based on the resistant-related genetic paths. Moreover, 23 genomic regions underlying resistance to SCN race3 were identified by genome-wide association study (GWAS) in 481 re-sequenced cultivated soybeans. Ten common loci were found by both IBD tracking and GWAS analysis. Haplotype analysis of 16 potential candidate genes suggested a causative SNP  located in the promoter of Glyma.08G096500 and encoding a predicted TIFY5b-related protein on chr8 was highly correlated with SCN race3 resistance. Our results more thoroughly elucidated the dynamics of genomic fragments during ZP pedigree breeding and the genetic basis of SCN resistance, which will provide useful information for gene cloning and the development of resistant soybean cultivars using a marker-assisted selection approach.Soybean cyst nematode (SCN, The online version contains supplementary material available at 10.1007/s00122-023-04268-3. Heterodera glycines Ichinohe) causes significant production problem in soybean [Glycine max (L.) Merr.] worldwide annually  may contribute to SCN resistance, only two major (rhg1 and Rhg4) and two minor (GmSNAP11 and NSFRAN07) loci had been cloned  of the diverse panel of 481 soybean germplasm accessions. By combining the IBD and GWAS analysis, we identified potential candidate genes involved in SCN race3 resistance to develop new cultivars with durable resistance to SCN using a marker-assisted selection approach.Two types of panels with different genetic diversity were used in the present study (Supplementary Table S1). The family-based panel included 11 genealogical genotypes  Fig. . For theFamily-based and association mapping population were screened for resistance to SCN race3 at Heilongjiang Academy of Agricultural Sciences in 2017 and 2018 using the published method  method . A total of 192.36 Gb (~ 16.24-fold per genotype) high-quality paired-end reads for the 11 genealogical soybeans was generated. The high-quality paired-end reads were mapped to the reference soybean genome Williams 82.a2.v1 using the BWA  high credible SNPs (missing rate < 20%) was kept for further analysis (Supplementary Table S3). Meanwhile, we also identified an average of 192,032  insertion and deletions  (Supplementary Table S4). All SNPs and InDels were annotated using the package ANNOVAR  was calculated using PLINK1.9 (Version1.90) software  of 481 accessions in the diversity panel was performed using GCTA software . Based on the mean LD decay distance of whole genome, genes located in 100 kb genomic region of both sides of each peak SNP were considered as the candidate genes.Association analysis was performed using Tassel software package v5  to 0.91 (Forrest and Hartwig) (Supplementary Table S5). IBS estimates between offspring and their direct parents were generally larger than those of indirect parents. For example, the IBS between ZP and its direct parents Jin1265 and Hartwig were 0.83 and 0.77, respectively, which was higher than that between the indirect Forrest (0.71), PI 437654 (0.62) and HPZHD (0.59). This suggests that the genetic relationship of pedigree was correct and could be used in the following analysis.Dt1), seed oil content (GmSWEET39), seed weight (GmPP2C and GmST05), nodule number (GmNNL1), SCN resistance . The result indicated that these traceable IBD fragments played an important role during ZP breeding process.IBD analysis suggested that 41.17% of the genomic sequence of ZP was from the male parent Jin1265 and 32.87% from the female parent Hartwig , whereas the previous QTL region spanned average 1.26 Mb genomic region. These narrowed genomic regions identified by pedigree analysis will be helpful in mapping genes involved in SCN resistance.The IBD fragments inherited from the three resistant progenitors Peking, PI 437654, and HPZHD were analyzed to identify the IBD fragments controlling resistance to SCN through different genetic pathways in the pedigree . Principal component analysis and population structure classified these accessions into three subclades associating with the geographic distribution patterns, North region (NR), Huanghuai region (HR) and South region (SR), respectively .The FI of 418 accessions in the diversity panel varied continuously . A total of 147 accessions exhibited high resistance (FI < 10) and 46 accessions showed moderate resistance (10 < FI < 30). To verify and fine map the genome regions underlying resistance to SCN race3, a diversity panel containing 4,096,160 SNPs  were used for GWAS analysis. The overall LD decay distance for this panel was about 100 kb when P < 0.01), which were located on 11 chromosomes (chr.) . In addition, some minor loci on chr1, chr3, chr4, chr5, chr7, chr12, chr15, chr16 and chr18 were also determined   lower than that of the 289 entries carrying GG allele (46.5%) . This indicated that about 35% of the resistance loci had been underutilized during ZP breeding process. For example, Peking, PI 437654 and HPZHD carried the resistant allele of NSFRAN07, GmSHMT08, and GmSNAP18 (Supplementary Table S13). Further, 425 SNPs in the gene coding regions of 106 genes were detected using 481 re-sequenced accessions. Among those, 220 SNPs located in 79 genes were large-effect SNP changing the encoding amino acid sequence, containing 215 non-synonymous SNP, 4 stop gain and 1 stop loss (Supplementary Table S13). Further, gene expression patterns change of these putatively resistant genes in response to the SCN race3 inoculation were further analyzed using previously published data  (Supplementary Table S13), which could be vital to deeply explore the genetic basis of resistance to SCN race3.Ten overlapping loci covering a genomic interval of 1.21 Mb genome interval were identified by comparing the genomic region of IBD and GWAS analysis (Supplementary Table S12). A total of 144 annotated genes were identified in these ten genomic regions, including three cloned genes, AT1G30135 of Glyma.08G096500 encoded a jasmonate-zim-domain protein 8 associated with resistance to Botrytis cinerea and Alternaria brassicicola by jasmonate-mediated plant defense in Arabidopsis thaliana  and encoded a predicted TIFY5b-related protein that played vital roles in plant abiotic and biotic stress responses  in the diversity panel and identified a total of seven SNP which formed four haplotypes , SNP  was predicted to change the core promoter element (TATA-box) that play a critical role in the regulation of transcription . Interestingly, the crucial genes of soybean flowering system, SCN3-1, SCN3-11 and SCN3-18  previously, and identified three QTL loci rhg1 and Rhg4, have been used widely in development of a large number of resistant cultivars in recent decades  and published studies . Of them, five loci were located in or overlapped with the reported regions; the remaining five loci were not overlapped with these reported regions and defined as novel loci. Promising candidate genes were analyzed using differential gene expressions between resistant and susceptible accessions derived from studies and homologous gene comparison. For example, a TIFY 5b-related protein encoding gene located on chr8, Glyma.08G096500 (homologous to AT1G30135 in Arabidopsis) was found to be responsible for resistance to SCN race3 in both IBD and association analysis. AT1G30135 was associated with jasmonate-mediated plant defense in Arabidopsis thaliana   was significantly associated with resistance to SCN race3. However, when tracing this locus in the ZP pedigree, we found that all three ancestral parents Peking, PI 437654 and HPZHD carried the resistant AA allele, but it was not passed on to the next generation . Interestingly, the proportion of the AA allele in association panel decreased from landraces (49.8%) to improved cultivars (10.9%) . Thus, although the soybean accessions carrying the AA allele were resistant to SCN race3, this allele was not selected by breeders. We speculated that this phenomenon may be explained by linkage drags with resulting in poor performance for other important traits such as grain yield. Therefore, we further analyzed the reported QTL surrounding SCN3-7–1 (https://www.soybase.org/) and identified QTL controlling resistance to Spodoptera litura, resistance to Sclerotinia sclerotiorum, seed calcium content, and seed weight (Supplementary Table S17). In the SCN3-7–1 region, a total of four predicted genes  were identified based on the reference genome Williams 82.a2.v1. In accordance with previously reported transcriptional responses to SCN race3 at 0, 3 and 8 days post-inoculation  in the two resistant lines PI 437654 and PI 567516C compared with the susceptible cultivar Magellan at 3 and 8 days post-inoculation , which was considered as candidate gene underlying the SCN3-7–1 locus. In conclusion, the predicted candidate genes are promising candidates for further exploring the genetic basis of resistance to SCN.Additionally, a novel locus Supplementary file1 (TIF 16515 KB)Supplementary file2 (TIF 876 KB)Supplementary file3 (TIF 1007 KB)Supplementary file4 (TIF 2706 KB)Supplementary file5 (TIF 203 KB)Supplementary file6 (TIF 195 KB)Supplementary file7 (TIF 281 KB)Supplementary file8 (TIF 569 KB)Supplementary file9 (TIF 1549 KB)Supplementary file10 (TIF 383 KB)Supplementary file11 (TIF 533 KB)Supplementary file12 (TIF 76 KB)Supplementary file13 (TIF 838 KB)Supplementary file14 (TIF 111 KB)Supplementary file15 (TIF 280 KB)Supplementary file16 (XLSX 3669 KB)Below is the link to the electronic supplementary material.