Venturia carpophila, the causal agent of scab disease of peach, mume, and apricot, is widely distributed around the world. Scab of stone fruits is an important disease in China. However, little is known about the population biology and genetic diversity of the V. carpophila. To better understand the genetic diversity and population structure of V. carpophila, 186 single-spore isolates from different hosts and geographic regions were obtained and analyzed by using 31 simple sequence repeat (SSR) markers. This included 156 isolates from peach spanning 14 provinces, 15 isolates from mume and 15 isolates from apricot in Huazhong Agricultural University (HZAU). Diversity analysis with SSR markers showed a low incidence of polymorphisms within mume isolates (32.59% of markers), but a higher incidence of polymorphisms within peach isolates (42.96%) and apricot isolates (57.04%). Within peach isolates, Nei’s average gene diversity ranged from 0.07 for Hebei population to 0.18 for Hubei population. AMOVA analysis revealed that 13% of the observed genetic diversity was partitioned among the geographic populations, while 40% of the observed genetic diversity was partitioned among the host populations. Other analyses  indicated that the Chinese V. carpophila populations could be clustered into three distinct genetic groups, which correspond to the host boundaries of peach, mume and apricot. The genetic identity of V. carpophila isolates throughout the range is dependent on hosts, but not geographic regions. Prunus persica), mume (P. mume), and apricot (P. armeniaca) are grown widely in China . Isolates from mume were dark gray in color and the fastest growing with approximately 3.3 cm colonies in diameter and better conidia production (2 × 104/dish). Isolates from peach were white to gray in color and were slow growing with approximately 2.6 cm colonies in diameter and the best conidia production . Conidia of the pathogens are spread by rain splash and wind. The disease is particularly severe in temperate climate regions characterized by humid and cool springs, and poor air circulation  the genetic diversity and population structure of V. carpophila in China, and (iii) if the genetic differences are specifically correlated with geographic regions or hosts.Stone fruit scab is widely distributed in China, yet there is no information about the genetic diversity of Chinese  disease . ConsideV. carpophila were scraped from a single lesion on the fruit surface using a scalpel and then spread with a scalpel on the surface of water agar (20 g of agar per liter) plates. Using a modified microscope , a single spore was isolated with a glass needle and then transferred to potato dextrose agar  amended with lactic acid (0.50 ml/L), streptomycin sulfate (0.20 g/L), tetracycline (0.05 g/L), and chloramphenicol  in an Erlenmeyer flask, and incubated at 21°C, 160 rpm for 10 days to obtain a sufficient amount of mycelium for DNA extraction. Approximately 100 mg of mycelium of each isolate was collected from the MEB broth, and put into a pilot freeze dryer (25 L Genesis SQ Super ES-55) to dehydrate for 48 h. Dehydrated hyphae were placed into Eppendorf tubes containing 0.5 g steel balls (5 mm in diameter) and frozen with liquid nitrogen for 2 min. Mycelium was homogenized by shaking the tubes for 5 min at 30 shakes/s in a Mixer Mill Type LT2020 . After homogenization, DNA was immediately extracted by the modified SDS method . The majority rule consensus tree from maximum parsimony (MP) analysis was used to show the phylogenetic relationships of Venturia species and their close species. The tree was rooted with Sympoventuria capensis (CBS 120136). The Bayesian posterior probabilities values (BIPP) ≥0.70, maximum likelihood and maximum parsimony bootstrap values (MLBS and MPBS) ≥ 50% were shown at the branch nodes (BIPP/MLBS/MPBS).Isolates of V. carpophila which has been released, after primary screening, 31 microsatellite primers were used to conduct the genetic diversity analysis in this study . When primers were synthesized, the universal M13 sequence (5′-TGT AAA ACG ACG GCC AGT-3′) was added to the 5′ end of each forward primer. Simultaneously, M13 was labeled by FAM fluorescent dyes at the 3′ end. The labeled M13 was added to the PCR reaction to detect PCR product by complementing the unlabeled M13 added at 5′ end of primer , 0.4 μl of M13-tagged forward primer (10 μM) and 0.8 μl reverse primer (10 μM), 0.8 μl FAM-labeled M13 primer (10 μM), and 1 μl DNA (50~200 ng/μl). PCR conditions were an initial denaturation at 95°C for 5 min followed by 10 cycles of denaturation at 95°C for 30 s, annealing at 62°C for 30 s (with a −1°C Ramp/cycle), and extension at 72°C for 30 s, a further 25 cycles of denaturation at 95°C for 30 s, annealing at 52°C for 30 s, extension 72°C for 30 s, and ending with a final extension of 72°C for 2 min . The fluorescent PCR products were loaded to an ABI 3730 × I Genetic Analyzer Capillary to generate chromatographic trace files. Negative control (ddH2O) was included in the analysis to eliminate potential contamination. Resulted amplicon peaks were scored by base pair size for each marker using GeneMarker V2.2.0 to generate a microsatellite allele table for subsequent analysis. The PCR-generated bands were scored as “1” (for presence) and “0” (absence) in a binary matrix for further analysis. Because the V. carpophila is a haploid organism, only a single band was observed at each locus for individual isolate.The genetic diversity analysis was carried out according to the previous description with minor modifications . Thirty-is study . SSR marf primer . The PCRNa), and effective alleles (Ne) were calculated using Popgene 32 based on SSR markers identified among isolates of V. carpophila  A principal coordinate analysis (PCoA) was performed to visualize patterns of variation between populations at the host and geographic region levels based on Nei’s measure of genetic distance, in GenALEX 6.5  The minimum spanning network (MSN) was created in the R package poppr  in GenALEX 6.5, which was conducted at two scales: across geographic populations, across host populations . The popALEX 6.5 . (2) STRapproach . To estiapproach . Evanno’approach , which wapproach . (3) Disapproach , thus inge poppr  by usingge poppr .Significant differences of growth rate and spore production values from different host populations were evaluated by one-way analysis of variance with the least significant difference test in SPSS software .V. carpophila colonies on PDA were black with circular morphology and floccose texture (n = 30) in size, L/W = 3.8  showed that the tested isolates grouped with representative V. carpophila strains GH120501 (Korea), CBS160.55 (United States), and CBS497.62 (Switzerland) in the same clade  showed monomorphic profiles with the only peaks at 222 bp (Vc015) and 301 bp (Vc021). The remaining 29 primer sets showed polymorphic profiles, the number of alleles at each microsatellite locus ranged from two to 21, and a total of 135 alleles were identified, with a mean number of 4.66 alleles per locus. Vc110 was the most variable primer set with 21 alleles detected and polymorphism information content (PIC) value was 0.77, whereas Vc106 had only 2 alleles and PIC value was 0.03 (V. carpophila (To better understand the genetic diversity and population structure of Chinese was 0.03 . The genrpophila .NPL) were detected in 186 isolates. Among them, 105 NPL were detected in 14 geographic populations and 102 NPL were detected in three host populations (NPL (77), and Hebei (HeB) had the least (22). In host populations, apricot had the largest number of NPL (77), and mume had the least number (44). Different levels of genetic diversity (H) were observed among 14 geographic populations and 3 host populations, ranging from 0.07 for HeB population to 0.18 for HuB population; a low genetic diversity of mume isolates (0.11) was observed, while higher genetic diversities were detected for peach isolates (0.14) and apricot isolates (0.17). Geographic populations showed a low percentage of polymorphic loci (PPL) in HeB population (15.30% of markers) but a higher PPL in Zhejiang (ZJ) population (50.37%) and HuB population (57.04%). Host populations showed a low PPL in mume population (32.59% of markers), and higher PPL in peach population (42.96%) and apricot population (57.05%). The number of observed alleles (Na) of the apricot population and HuB population were up to 1.57. The effective allele number (Ne) and Shannon index (I) of the HuB population were the highest, i.e., 1.31 and 0.27, respectively. Interestingly, the apricot population had only 15 isolates, but the number of polymorphic loci (NPL) was 77, as high as the HuB population , and the highest was 0.9882 for Zhejiang versus Hubei (ZJ vs. HuB). Among different host populations, the lowest genetic similarity coefficient was 0.8616 for apricot vs. mume (A vs. M), and the highest was 0.8841 for peach vs. mume P vs. M, . These rp = 0.001). For three host populations, 40% of the observed genetic diversity was partitioned among the populations and 60% was within individual populations . A moderate genetic differentiation was observed across host populations, however, limited genetic differentiation was observed across geographic populations  is a multidimensional scaling analysis to visualize the clustering of isolates due to genetic similarities among tested isolates. The analysis was performed in GenAlEx V6.5 based on Nei’s genetic distance. PCoA revealed three groups, one was specific to apricot isolates, the other was to mume isolates, and the third one was to peach isolates. Axes 1 and 2 of the PCoA accounted for 29.75% and 25.12% of the total genetic variation. Isolates from mume were relatively closer to peach isolates .V. carpophila from China were divided into three subgroups  and the widest sampling range (5 orchards that were more than 500 km apart). HeB population had the least number of isolates, only one sampling site, and showed the lowest genetic diversity at only 0.10. On the other hand, this study detected the highest genetic diversity within a small sample of 15 apricot isolates from the same orchard. Among three host populations collected from HZAU, the genetic diversity of the apricot population was higher than peach and mume populations. The sexual stage of bination . AccordiV. carpophila isolates collected from peach and nectarine in the United States, although the sample size was smaller (n = 39 and 81), both studies revealed that the genetic diversity of V. carpophila isolates does not correspond to locations or host cultivars (P. persica).In this study, AMOVA analysis of 14 geographic populations indicated that 13% of the observed genetic diversity was partitioned among geographic populations and 87% was within geographic populations. In addition, AMOVA analysis of three host populations indicated that 40% of the observed genetic diversity was partitioned among host populations and 60% was within host populations. This is similar to the results observed in a recent study . In fourultivars . Gene diV. carpophila were divided into three subgroups, and no significant differentiation was identified among geographic populations (13%). Analysis of the genetic structure of 81 peach isolates of V. carpophila from the eastern United States showed that V. carpophila population clustered in three distinct groups (V. carpophila isolates from peach (14 provinces) of China obtained similar results. Overall, these results suggested that there was a significant interflow of V. carpophila isolates among different geographic regions but not among different hosts, e.g., peach, mume, and apricot. Similarly, based on the DAPC analysis, isolates collected from 14 provinces did not cluster according to geographic regions, the discriminant analysis also demonstrated the overlap among isolates from different geographic regions, however, isolates from different hosts did cluster according to their hosts. The dendrogram construction based on Nei’s genetic distance using UPGMA for the isolates from 14 different geographic regions and three hosts also showed that isolates were grouped into three major clusters according to their hosts. The three host populations of V. carpophila showed significant differences in colony morphology, growth rate and spore production on the MEA medium. The cultures of V. carpophila from California almond trees and from peach trees in the southeastern United States showed similar results in which differences were observed for isolates from different hosts , which was identified when three host populations from HZAU were used. However, structure analysis of 14 geographic regions’ population revealed that t groups . In thisnt hosts . This biV. carpophila have rarely been characterized. This is the first report of the genetic variation and population structure in V. carpophila isolates from China. We demonstrated that Chinese V. carpophila populations could be clustered into three distinct genetic groups, which correspond to the host boundaries, i.e., peach, mume, and apricot. Based on peach isolates from 14 provinces, it is obvious that V. carpophila isolates do not group according to the geographic regions. These results show that the host but not the environment play an important role during the evolution of V. carpophila. In other words, the genetic identity of V. carpophila isolates is dependent on the host, but not the geographic region. This research laid a solid foundation for a better understanding of the population biology and genetics of V. carpophila. The global geographic populations and more host populations of V. carpophila should be further explored in the future.In brief, the genetic diversity and population structure of the The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/C-XL and YZ designed the study. YZ performed all the experiments. YZ, CC, Y-FW, and ML analyzed the data and drafted the manuscript. C-XL, YZ, FF, L-FY, and W-XY reviewed and edited the manuscript. All authors contributed to the article and approved the submitted version.This work was supported by the China Agriculture Research System of the Ministry of Finance and the Ministry of Agriculture and Rural Affairs (No. CARS30-3-03), the Fundamental Research Funds for the Central Universities (No. 2662020ZKPY018), Central Public-interest Scientific Institution Basal Research Fund (No. 1610172022011), and the National Natural Science Foundation of China (No.32202389).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


We developed the ZDX1 high-throughput functional soybean array for high accuracy evaluation and selection of both parents and progeny, which can greatly accelerate soybean breeding.Microarray technology facilitates rapid, accurate, and economical genotyping. Here, using resequencing data from 2214 representative soybean accessions, we developed the high-throughput functional array ZDX1, containing 158,959 SNPs, covering 90.92% of soybean genes and sites related to important traits. By application of the array, a total of 817 accessions were genotyped, including three subpopulations of candidate parental lines, parental lines and their progeny from practical breeding. The fixed SNPs were identified in progeny, indicating artificial selection during the breeding process. By identifying functional sites of target traits, novel soybean cyst nematode-resistant progeny and maturity-related novel sources were identified by allele combinations, demonstrating that functional sites provide an efficient method for the rapid screening of desirable traits or gene sources. Notably, we found that the breeding index (BI) was a good indicator for progeny selection. Superior progeny were derived from the combination of distantly related parents, with at least one parent having a higher BI. Furthermore, new combinations based on good performance were proposed for further breeding after excluding redundant and closely related parents. Genomic best linear unbiased prediction (GBLUP) analysis was the best analysis method and achieved the highest accuracy in predicting four traits when comparing SNPs in genic regions rather than whole genomic or intergenic SNPs. The prediction accuracy was improved by 32.1% by using progeny to expand the training population. Collectively, a versatile assay demonstrated that the functional ZDX1 array provided efficient information for the design and optimization of a breeding pipeline for accelerated soybean breeding.The online version contains supplementary material available at 10.1007/s00122-022-04043-w. Glycine max (L). Merr.] increase per unit area has not been improved significantly during the past few decades   functional array. Using a breeding population comprised of 817 accessions, including candidate parental line subpopulations, parental lines, and their derived progeny subpopulations, we demonstrate the use of this array in improving steps in breeding, including screening for new genetic resources, population diversity analysis, optimizing hybrid combinations, and progeny selection. The ZDX1 array described in this work, with associated breeding selection strategies, can accelerate all of the steps in the breeding process.Glycine max (L.) Merr.), 1131 landraces, 218 annual wild soybean accessions (Glycine soja Sieb. & Zucc.), and three perennial wild soybean accessions (Glycine subgenus Glycine) as the basic information and based on the Illumina platform , we obtained the VCF file by comparison with the reference genome Wm82.a2.v1 (Gmax_275_v2.0) and we also obtained 11,048,862 initial polymorphic SNP sites, including commercialized array sites, important gene sites, quantitative trait locus (QTL) and GWAS sites, and important trait functional sites. After the removal of sites with a deletion rate of > 0.1 and a degree of heterozygosity > 15%, 9,092,282 sites were retained. We then screened 2,379,054 sites according to the criteria of “retaining sites with MAF ≥ 0.01”. We deleted the sites with variants within 50 bp of the flanking regions, keeping the tiling order = 1 site, and 2,039,377 sites remained. Based on 2214 soybean accessions, we deleted the sites with errors, tested 41 sliding window gradients for site screening, and selected a 4800-bp window. The principle for site selection was “priority + Illumina score ≥ 0.4 + non-AT/GC selection site ,” among which the priority definition principles were: I. excellent QTL sites, GWAS sites, important genes, selective genes, genome-wide genes, and terminator/alternative splicing/nonsynonymous mutation sites; II. interspecies and intraspecific subgroup unique sites; III. selection interval (domestication) sites; IV. whole-genome coverage sites; and V. gap-filling sites. Finally, 158,959 SNP sites were obtained for ZDX1  were included in parental lines. Additionally, 283 of the 571 progeny were bred in 2015, while 288 progeny were bred in 2016.The field experiment with the 817 accessions was performed with three replicates (designated as environments L1 and L2) in Zhalantun City, Inner Mongolia  in 2017 and 2018, and one replicate in Keshan County, Heilongjiang Province in 2018  (designated as environment L3). The experiment used a randomized block design and one control line (Neidou4hao or Keshan1hao) was planted for every 20 experimental lines, with a row spacing of 0.65 m, plant spacing of 0.05 m, and a row length of 3 m. One row was planted for each material, and the area of each plot was 1.95 square meters. The sowing dates in the three environments of L1, L2, and L3 were May 12, May 9, and May 7 for each year, and the emergence dates of seedlings were May 25, May 21, and May 19 each year. A total of six quantitative traits were investigated: These included VE, defined as the date of emergence of the cotyledons. Beginning maturity (R7) was defined as the days from emergence to when one pod on the main stem had reached a mature pod color  was used to extract genomic DNA from young soybean leaves. We used the ZDX1 SNP array developed based on the Illuminar2) of alleles was calculated to measure the LD in each group level. The LD decay rate was defined as the chromosomal distance at which the average r2 dropped to half its maximum value. The kinship matrix was calculated using the VanRaden method in Gapit software to obtain the genetic relationships between lines in the population.PLINK v2.1.1  a window of 50 SNPs was considered; (b) the LD between each pair of SNPs in the window was calculated; (c) one of a pair of SNPs was removed if the LD was greater than 0.5; (d) the window was shifted five SNPs forward and the procedure was repeated. This method was used to obtain a total of 8940 loci. PLINK v2.1.1 was used for principal component analysis (PCA), and R software was used to draw PCA diagrams.The R asreml data package was used to calculate the best linear unbiased estimates (BLUE) from the phenotypic data for genomic selection   was estimated, and the prediction accuracy (rGS) was calculated for the standardized rMP by the square root of the broad-sense heritability  , and 110,811 SNPs that differed between ecological groups . Moreover, 3869 SNPs from the 1.5 K BeadChip . In addition, it also included 953 SNPs in QTL intervals, 547 GWAS-identified SNPs , and we found that the detection rate for each sample was between 84.40 and 95.98%, with an average of 95.19%. Three DNA samples were randomly selected twice, and the genotype similarity between the two repetitions was > 99.9% . These results indicated that the high-density ZDX1 array was both reliable and accurate.r2), and the results showed that the attenuation rate of candidate parental line r2 values was higher than that of the progeny and parental lines, and the distances at which the r2 decayed by half were 243 kb, 279 kb, and 301 kb, respectively  for plot yield among the 5 traits . The genetic diversity among three subpopulations was compared using LD analysis (indicated by The percentages of fixed sites (MAF = 0) in candidate parental lines, parental lines, and progeny were 34.72%, 41.79%, and 34.63%, respectively . In order to clarify which sites were selected and fixed during the breeding process from germplasm to elite progeny, only 6579 sites were retained, where the MAF value was 0 for progeny, 0–0.0390 for parental lines, and 0–0.1317 for candidate parental lines , while round-leaf accessions in the parental lines and progeny accounted for 10.4% and 18.4%, respectively. These proportions again reflected that breeder’s favor narrow leaflets.In order to select elite lines or varieties, the functionally informative SNP sites were analyzed. The E1, E3, and E4, among which the e1-fs, e1-as/e3-fs/e4-kes, e1-as/e3-fs, and e1-as/e4-kes genotypes were associated with precocity . Notably, only one accession, Dongnong36 (80.73 d), carried the e1-fs genotype. Among the materials with two or more loci of e1-as, e3-fs, and e4-kes, nine parental lines and candidate parental lines exhibited earlier maturity (87.14–97.98 d), while three progeny  had relatively late growth periods (109.17–114.32 d). These progeny may have expressed an inhibitor of early maturity.We next analyzed three maturity loci of rhg1, Rhg4, and SCN3-11 (Table The nematode-resistant loci of p = 0.30–0.97), and the correlation coefficients (rhd) were − 0.42 to − 0.02. This finding suggested that greater distance between parental lines resulted in a better performance potential for progeny compared to the parental lines. In addition, the mean value of each trait among progeny was positively correlated with the average parental value, with correlation coefficients (rpo) ranging from 0.33 to 0.73, of which oil and SW appeared to be extremely significant (p < 0.01) . Analysis of each of five traits in 298 progeny showed that the rate over best-parent was non-significantly negatively correlated with the genetic relationship between their parents p = 0.0–0.97, aTo select for high yield accompanied by the proper performance of the other traits, the BI was used to score the parental lines into high, medium, or low phenotypes . The 30 (top 10%) high-performance progeny (THP) with greater genetic distances (− 0.0298) were traced back to five types of parental combination, consisting of two progeny from high × high types, 11 progeny from high × medium types, nine progeny from high × low types, three progeny from medium × medium types, and five progeny from medium × low types. Of these THP, 73.3% were descended from at least one parent with high BI Fig. . These rTo enable efficient breeding, the redundant parental lines were firstly eliminated. Among them, the lower 30 progeny (bottom 10%) were derived from 12 parental lines, including Dengke4hao and Hujiao1120 . These will not be used in future breeding. Meanwhile, compared to the parental lines , 21 candidate parental lines including Mei1 and Nenao08-1092, based on kinship scores of > 1.0, were also excluded due to small genetic distance . Finally, the 82 accessions with the top 10% of BI values were selected as the new parental lines to form all of the potential combinations, with distances of − 0.5 to 0 . Using a genetic distance <  − 0.3 as the standard, 46 high-potential combinations were proposed for future breeding . By eliminating redundant parents and designing new combinations, the development of soybean varieties will be improved in the near future.p < 0.01)  differences among methods. In addition, markers associated with genic regions were more accurate by an average of 0.57% compared to those sampled from across the whole genome and were significantly (p < 0.01) more accurate for predicting SW, protein content, and oil content  improved the predictive accuracy for selecting these three traits compared with the accuracy provided by using all 69,022 of the SNPs. In most cases, the strategy of sampling SNP markers for gene-encoding regions can reduce the number of requisite markers while improving the accuracy of genomic selection.We subsequently identified 33,756, 33,733, and 33,761 sites that were selected as marker subsets from gene regions, intergenic regions, or the whole genome, respectively. GBLUP analysis confirmed that these three marker sets showed no significant differences in their accuracy for predicting yield. For each of the other four traits, the accuracy of prediction using markers for genic regions was 2.33% higher than that of SNP markers for intergenic regions, with highly significant (p = 0.024) . The training population I, with the 141 high-value progeny, was then expanded to generate training population II to further predict the 288 progeny bred in 2016 (predicted population II) , indicating that they may be sources for identifying novel genes related to maturity. By identifying functional sites related to soybean cyst nematode resistance, three resistant parental lines were confirmed. In addition, we identified four novel resistant lines, among which only HJ15-863 could be traced to a resistant source progenitor, indicating that genotyping is the best method to find new soybean cyst nematode-resistant sources. In comparison with above the three traits, leaflet shape is controlled by few genes and has a higher selection rate than maturity, which is controlled by multiple genes. With an increasing number of functional markers involved in ZDX1 array, such as the soybean cyst nematode resistance gene, the array will play a much more important role in directional selection.When screening germplasm for potential use as parents, phenotypic identification is time-consuming and laborious, and the results are strongly influenced by environmental factors. Therefore, the molecular marker-assisted selection represents an efficient and effective method for screening target traits . These potentially functional genes may be used in future breeding.Previous studies have shown that if the genetic variation and distance between accessions are sufficiently large in the parent population, then a progeny population with greater genetic variation can be obtained  and geographic origins (outer circle) of the 2,214 soybean accessions used in this study. Abbreviations for the countries of origin are AUT, Austria; BEL, Belgium; BRA, Brazil; CAN, Canada; CHN, China; EE, Eastern Europe; GER, Germany; INA, Indonesia; IND, India; ITA, Italy; JPN, Japan; KOR, South Korea; NGR, Nigeria; POL, Poland; PRK, North Korea; RUS, Russia; SRB, Former Serbia and Montenegro; TAL, Thailand; UKN, Unknown; USA, United States of America; VIE, Vietnam (TIF 79131 KB)Fig. S2 Typical cluster graphs of single nucleotide polymorphisms (SNPs) in ZDX1. (a–b) SNPs fall into separate clusters, representing a genotype trajectory (AA or BB). (c) The SNPs show two clearly defined clusters (AA and BB). (d) The SNPs show three clearly defined clusters . The above mode does not need to be adjusted. There are some close clusters in the genotyping in (e1) that can be adjusted to two clusters (AA and BB) by manual correction (e2). (f1) A few loci show 4–5 different clusters, and their genotypes may be AA, AB or BB. A more accurate cluster file can be obtained by adjustment, and the two closest clusters (such as AA and AB) may be combined into one cluster (AA) (f2) (TIF 79135 KB)Fig. S3 Soybean genome map. Tracks from outside to inside: the outermost circle shows the 20 soybean chromosomes along the whole genome; A, B, C, and D represent the MAF for all of the accessions, progeny, candidate parental lines, and parental lines, respectively (TIF 79135 KB)Fig. S4 Kinship plot for the 817 accessions. The heatmap of the values in the kinship matrix was created using GAPIT (TIF 79133 KB)Fig. S5 Screening redundant candidate parental lines and making potential combinations based on genetic distance. (a) Among 169 candidate parental lines, the number of redundant lines was determined based on the different genetic distances  between the non-parental and parental lines. (b) Using 82 accessions that had the top 10% breeding values, the number of potential combinations was determined based on the different genetic distances  between them (TIF 79129 KB)Supplementary file6 (XLSX 47141 KB)Below is the link to the electronic supplementary material.


Achieving accurate STR genotyping by using next-generation sequencing data has been challenging. To provide the forensic genetics community with a reliable open-access STR database, we conducted a comprehensive genotyping analysis of a set of STRs of broad forensic interest obtained from 1000 Genome populations. We analyzed 22 STR markers using files of the high-coverage dataset of Phase 3 of the 1000 Genomes Project. We used HipSTR to call genotypes from 2504 samples obtained from 26 populations. We were not able to detect the D21S11 marker. The Hardy-Weinberg equilibrium analysis coupled with a comprehensive analysis of allele frequencies revealed that HipSTR was not able to identify longer alleles, which resulted in heterozygote deficiency. Nevertheless, AMOVA, a clustering analysis that uses STRUCTURE, and a Principal Coordinates Analysis showed a clear-cut separation between the four major ancestries sampled by the 1000 Genomes Consortium. Except for larger Penta D and Penta E alleles, and two very small Penta D alleles (2.2 and 3.2) usually observed in African populations, our analyses revealed that allele frequencies and genotypes offered as an open-access database are consistent and reliable. Next-generation sequencing (NGS), also known as massively parallel or deep sequencing, is a technology that allows millions of DNA fragments to be sequenced in parallel. NGS can deal with several regions or targets simultaneously, enabling variation sites or mutations in the genome to be detected. This technology has allowed worldwide human genetic diversity to be studied for various purposes, including forensic human identification ,2,3.Advances in the genomics area have made it possible to use NGS techniques in a more accessible way, mostly because of lower costs. Currently, many researchers are performing whole-exome (WES) and even whole-genome (WGS) sequencing to estimate polygenic risk scores and probabilities of developing multifactorial diseases associated with various genetic regions at once, which would be a more laborious and costly issue if using traditional methodologies .https://www.internationalgenome.org; accessed on 5 July 2021) to generate a variant call format file that uses a set of specific command lines [The 1000 Genomes Consortium is a worldwide collaboration that has produced an extensive catalog of human genetic variation. The consortium has sequenced whole genomes of 2504 individuals belonging to multiple populations derived from five population groups: African, East Asian, European, South Asian, and admixed Americans [nd lines .STR markers are crucial in human identification. These markers have high polymorphism levels and are particularly useful for interpreting mixtures of biological samples. However, in addition to the issue of small-sized amplicons, genotyping STR markers by using NGS data is difficult because alignment and stutter errors are frequent . AchieviTo circumvent this coverage issue, the New York Genome Center (NYGC) recently re-sequenced the 2504 samples of the panel of Phase 3 of the 1000 Genomes Project with high (30x) coverage, and aligned the sequence data to GRCh38. These publicly available data could be used to call STR markers reliably .NGS technology allows dozens of STR markers to be analyzed together with different classes of markers that provide complementary contributions to population genetics and human identification. For example, including SNPs used as predictors of ancestry and phenotypic characteristics into commercial kits that employ capillary electrophoresis is unfeasible, but they can be combined with STR markers in NGS assays . The proHaplotype inference and phasing for STRs (HipSTR) was developed for calling microsatellites specifically from WGS Illumina FASTq files. HipSTR was designed to deal with genotyping errors and to obtain more robust STR genotypes. HipSTR accomplished this by learning locus-specific PCR stutter models, with the aid of an EM algorithm, by employing a specialized hidden Markov model to align reads to candidate alleles while accounting for STR artifacts, and by using phased SNP haplotypes to genotype and to phase STR markers. These factors turned HipSTR into one of the most reliable tools for genotyping STRs from Illumina sequencing data ,14.In contrast to other tools, HipSTR can process hundreds of samples at once. It also allows the user to determine the set of STR markers that must be analyzed and the flanking regions that must be used to capture them. In fact, previous studies showed that HipSTR provides accurate genotype calling. HipSTR accuracy was tested by comparing WGS calls from 118 samples to capillary electrophoresis data, which resulted in 98.8% consistency ,15. ReceIn this investigation we conducted a comprehensive genotyping analysis of a set of STRs of broad forensic interest obtained from the 1000 Genomes populations, aiming to release a reliable open-access STR database that should contribute to future studies in the field of forensic genetics.https://www.internationalgenome.org/data-portal/data-collection/30x-grch38 (accessed on 10 July 2021).Genotypes were called from 2504 individuals belonging to 26 populations derived from five population groups analyzed by the 1000 Genomes Consortium, namely African (AFR), East Asian (EAS), European (EUR), South Asian (SAS), and admixed American (AMR) [We used CRAM files to obtain the STR genotypes with the aid of the HipSTR software . We selehttps://hipstr-tool.github.io/HipSTR-tutorial/; accessed on 10 July 2021) as described elsewhere [p > 0.99) that a homozygous genotype is called because of lack of variability at a given locus and not because the second allele has not been sampled.To genotype the 22 STR markers based on the human reference genome GRCh38, we ran the HipSTR algorithm for each individual. For this purpose, we used a BED file with the coordinates of each STR region of interest, which was available in the HipSTR repository  , and the base pair differences (GB) as compared to the reference allele. We adjusted the nomenclature for D19S433, Penta D, Penta E, and vWA by following the recommendations made by Valle-Silva et al. : removalWe calculated allele frequencies, the Hardy-Weinberg equilibrium, and forensic parameters {Match Probability (MP), Power of Discrimination (PD), Power of Exclusion (PE), and Polymorphism Information Content (PIC)} for each population sample or each population group using GenAlEx 6.5  and STRAk ranging from 3 to 6 by applying the correlated allele frequencies model, 100,000 burn-in steps followed by 100,000 Markov Chain Monte Carlo interactions, in 100 independent runs. We selected the results from the runs with the largest “Estimated Ln Probability of Data” {LnP (D)} and depicted them in bar plots created with Distruct 1.1 [We employed Principal Coordinates Analysis (PCoA) using GenAlEx , Analysiruct 1.1 .STF and an exact test of population differentiation based on genotype frequencies [We also compared the allele frequencies estimated from the 1000 Genomes Project dataset with STR data retrieved from the same five major population groups  that compose the SPSmart STR browser (PopSTR) . For thiquencies . We madeThe STR genotypes defined for each individual from the newest dataset released by the 1000 Genomes Project are available in −27, and the combined PE was 0.99999997. Analysis of each locus in each population  revealed four different population clusters . The firk ranging from three to six. When k = 4, each cluster reflected one of the major ancestries of the 1000 Genomes Project. Moreover, each of the six admixed American populations presented varying levels of ancestries from the four biogeographical groups. To verify the distribution of variance in different levels, we performed AMOVA by assuming a hierarchical structure that gathered the populations in four population groups: AFR, EAS, EUR, and SAS. We did not take the six populations in the AMR population group into account because their admixed compositions would bias the AMOVA results by reducing the proportion of variance between groups. We observed most of the variance within populations (97.12%). Differences between the four population groups accounted for 2.54% of the variance, whereas only 0.34% of the variance occurred due to differences between populations belonging to the same group.We obtained similar results when we conducted the STRUCTURE analysis. STF, we also compared the allele frequencies estimated from the dataset of the 1000 Genomes Project to the STR data retrieved for the same five major population groups  that composed the SPSmart STR browser (PopSTR) [STF values in all comparisons. By leaving AFR and Penta E aside, we observed only 15 significant differences out of 80 comparisons: the mean number of statistically significant differences was 0.75 per marker; this number ranged from zero (eight STR markers) to three (D2S441). When we considered the Bonferroni correction for multiple tests, only three of these 15 STF values remained significant, while six out of 16 significant differences observed for AFR (leaving Penta E aside), and all five Penta E differences remained significantly different.By using (PopSTR)   dataset has been used for STR genotyping purpose. Although a few previous initiatives ,25,26 atIn forensic genetics, STR markers consist in the most widespread and informative tool for human identification. In spite of the limitations addressed below, such as unreliability of Penta D and Penta E genotypes involving specific alleles, this NGS-based STR database presents reliable allele frequencies that could be used in criminal casework to estimate the rarity of a given STR-based profile from a query sample of unknown or uncertain ancestry in various worldwide populations. This could instantly, and without additional costs, trigger a DNA-based intelligence strategy to guide enquiries  providinShort-read next generation sequencing is slowly being introduced in forensic labs worldwide. Although such technology is still restricted and expensive, it has become more sensitive, requiring as little as 25 pg of extracted DNA, and is suitable to solve more complex cases, such as discrimination of twins  and deconvolution of highly unbalanced mixtures reviewed by . Some crNotwithstanding the challenges addressed here, several studies have demonstrated that STRs can be genotyped by using dedicated bioinformatics tools. Software such as LobSTR , toaSTR Particularly for the deconvolution of mixtures, the identification of isometric alleles  is a necessary task, since it further increases the discriminating power of the currently used STR markers; nevertheless, it is not achieved with traditional PCR and capillary electrophoresis techniques ,3. This Willems et al.  analyzedAlthough HipSTR provides flexibility, the major limitation of this study is the inability to genotype D21S11, which is one of the 20 CODIS loci. Additional limitations are the failure in detecting two very small Penta D alleles and the biased allele frequencies of very large Penta D and Penta E alleles probably because of sequence-specific features, such as the GC content ,41,42 prSTF analysis , while the African populations in the 1000 Genomes Project samples corresponded to West Africa. Similarly, over 50% of the European population group in popSTR was composed mainly of U.S. Europeans (1443 out of 2135) [In this study, Penta D and Penta E showed 10.74% and 15.81% of missing data, respectively. By using Illumina sequencing technology, van der Gaag et al.  showed tanalysis  supporteulations . Therefoof 2135) . Taken tSTF than random STRs due to the increased heterozygosity of the former [The most polymorphic loci in the whole dataset of the 1000 Genomes Project were D1S1656, D2S1338, D12S391, D18S51, and FGA. All these markers presented high degrees of polymorphism throughout the world. AMOVA revealed that most of the variance (97.12%) in allele frequencies occurred within populations, corroborating previous studies ,45. A ste former . Howeverk = 3, SAS resembled an admixture between EAS and EUR. A specific cluster for SAS emerged when k = 4. When k = 5, a minor Eurasian (shared between EUR and EAS) component arose. When k = 6, the SAS-shared ancestry with EUR and EAS became more evident. Regarding the admixed American populations, irrespective of the number of clusters considered, ACB and ASW revealed their preeminent African origin, CLM and PUR revealed more extensive European ancestry, and MXL and PEL revealed almost equal amounts of European and Amerindian  ancestries. These results fully corroborated the distribution of the populations into the PCoA  WGS data of Phase 3 of the 1000 Genomes Project generated by the NYGC. However, the limited length of sequencing reads introduces noticeable bias in allele frequencies estimated for Penta D and Penta E. The reliability of this dataset is supported by (a) previous studies attesting that HipSTR is efficient, (b) the Hardy-Weinberg equilibrium analysis, (c) the set of analyses employed to evaluate the interpopulation genetic diversity, and (d) the comparison between the allele frequencies obtained here and the frequencies obtained by other initiatives that used capillary electrophoresis. Although we expect that this open-access database will be of great interest for future forensic studies on population genetics, the current 1000 Genomes Project dataset does not describe human genetic diversity worldwide. In fact, many biogeographical regions, mainly in Oceania and the Americas, have not been sampled, indicating that additional large-scale initiatives may provide further insight into STR diversity in populations worldwide.


ADH1B*48His (rs1229984) mutation in the ADH1B gene, reduce the risk of alcoholism and are under positive selection in multiple human populations. The advent of Neolithic agriculture and associated increase in fermented foods and beverages is hypothesized to have been a selective force acting on such variants. However, this hypothesis has not been tested in populations outside of Asia. Here, we use genome-wide selection scans to show that the ADH gene region is enriched for variants showing strong signals of positive selection in multiple Afroasiatic-speaking, agriculturalist populations from Ethiopia, and that this signal is unique among sub-Saharan Africans. We also observe strong selection signals at putatively functional variants in nearby lipid metabolism genes, which may influence evolutionary dynamics at the ADH region. Finally, we show that haplotypes carrying these selected variants were introduced into Northeast Africa from a West-Eurasian source within the last ∼2,000 years and experienced positive selection following admixture. These selection signals are not evident in nearby, genetically similar populations that practice hunting/gathering or pastoralist subsistence lifestyles, supporting the hypothesis that the emergence of agriculture shapes patterns of selection at ADH genes. Together, these results enhance our understanding of how adaptations to diverse environments and diets have influenced the African genomic landscape.The alcohol dehydrogenase (ADH) family of genes encodes enzymes that catalyze the metabolism of ethanol into acetaldehyde. Nucleotide variation in ADH genes can affect the catalytic properties of these enzymes and is associated with a variety of traits, including alcoholism and cancer. Some ADH variants, including the  Modern humans arose in Africa ∼300,000 years ago, and African populations harbor the highest levels of genetic and phenotypic variation today . ApproxiADH1B*48His mutation (rs1229984) in the ADH1B gene, where a C > T mutation replaces an arginine with a histidine at residue 48 in the mature protein. This mutation results in a protein that catalyzes a significantly elevated ethanol-to-acetaldehyde conversion rate, which leads to local acetaldehyde accumulation in the blood. In addition, the mutation provides a protective effect against alcoholism and alcohol-related upper aerodigestive tract cancers . Genetic structure is highly correlated with language in Ethiopia . First, populations speaking Afroasiatic Semitic and Cushitic languages typically cluster together in PCA and share ancestry in ADMIXTURE, except for the Tsamai, a Cushitic-speaking group who are genetically more similar to some Omotic speakers. Also, the Weyto, a Cushitic-speaking hunter-gatherer group, become genetically distinct from other Cushitic/Semitic speakers at higher K-values in ADMIXTURE, although they cluster with other Semitic/Cushitic speakers in the PCA. Second, Omotic-speaking populations typically cluster together, with the exception of the Kafa, an Omotic-speaking group who show more similarity with some Semitic/Cushitic populations in both the PCA and ADMIXTURE analyses (fig. 1bandc). The third Ethiopian genetic cluster comprises a Nilo-Saharan-speaking group containing the Mursi and Surma ethnic groups, while the fourth genetic cluster comprises the Chabu hunter-gatherers, who are genetically differentiated from other Ethiopians, likely due to a recent bottleneck (We performed ADMIXTURE  and printtleneck .n = 541) into four major groups based on shared genetic ancestry as described above , likely due to genetic drift (fig. 1bandc). We combined all Omotic speakers into an “Ethiopian Omotic” grouping, but excluded the Kafa ethnic group, as they possess a large proportion of Semitic/Cushitic ancestry (fig. 1c). The Mursi and Surma ethnic groups comprise an “Ethiopian Nilo-Saharan” population, while the Ethiopian Chabu hunter-gatherers, who speak an uncharacterized language similar to Nilo-Saharan, comprise their own population. We further pruned the data set to omit individuals with first- and second-degree relationships. All other populations from the Africa-diversity data set which were included in the selection scans are as described in To detect genome-wide signatures of positive selection, we first grouped the Ethiopian individuals , but not in any other sub-Saharan African population .We tested for local adaptation in the Africa-diversity data set  online upulation a, but nopulation  online. plotypes a.b). These data indicate that the ADH gene region contains unusually long haplotypes relative to the rest of the genome in this population, suggesting positive selection has occurred in the recent past. Finally, we also calculated the iHS statistic genome-wide for all ethnic groupings individually in the Africa-diversity data set and find that this strong iHS signature of positive selection at the ADH gene region is unique to Ethiopian Afroasiatic-speaking populations , a haplotype-based statistic which measures the level of extended haplotype homozygosity (EHH) surrounding each site in the genome . This stfig. 2ulations  online. t change  online.iD statistic scores. These exonic variants occur almost exclusively in ADH genes or in the microsomal triglyceride transfer protein (MTTP) gene ∼300 kb upstream of the ADH cluster (iD scores: rs1229984 (ADH1B*48His), rs1573496 (ADH7 gene), and rs113337987 (MTTP gene). The ADH1B*48His allele is at high frequency in the pooled Ethiopian Semitic/Cushitic population (33%), shows high iHS  and iD  scores, and exhibits a signal of EHH extending >1 Mb up- and downstream of the derived allele , the variant shows high iHS and iD scores , and the signal of EHH extends >1 Mb up- and downstream of the derived allele  , suggesting they are identical by descent. Furthermore, the ADH1B*48His haplotype in Europe, Africa, and the Levant is divergent from East Asian haplotypes carrying the derived ADH1B*48His allele, supporting previous claims of convergent evolution between Europe and Asia at this locus (ADH7) and rs113337987 (MTTP), which also indicate that they are identical to haplotypes observed in populations from the Levant and Europe , and rs113337987 (MTTP), which are in moderately high, but not complete, LD with each other , is most common in Ethiopian Afroasiatic-speaking populations (18% frequency) compared with global populations. Again, this Ethiopian haplotype is identical to a haplotype found at low frequency in Europeans from the 1KGP data set (1%), and at moderate frequency in populations from the Levant , suggesting identity by descent. The frequencies of the other haplotypes carrying combinations of derived and ancestral alleles at ADH1B*48His, rs1573496 (ADH7), and rs113337987 (MTTP) are broken down in We also examined phylogenetic relationships of haplotypes at the ADH region among global populations by constructing haplotype networks at these exonic SNPs with strong selection signatures. We first built a 50-kb haplotype network centered on rs1229984 (B*48His) . The mosis locus . We alsod Europe  online. ADH1C gene, rs1693482  was shown to explain 8.4% of the variability in the metabolic rate of ethanol in a European cohort  was found to significantly associate with lower body mass index (BMI), waist circumference, and total cholesterol in a study of 7,582 German participants . Finally, the nonsynonymous SNP rs3816873 in the MTTP gene  was found to significantly associate with lower levels of insulin, blood pressure, and prevalence of type II diabetes in a European population  has high pairwise D′ LD values with ADH1B*48His, rs1573496 of ADH7, and rs113337987 of MTTP , though the R2 values are low . However, given that this variant is not located in any known functional or regulatory region, it is unlikely to be the target of selection in this region.There are several other notable nonsynonymous variants in the ADH gene region showing slightly diminished, but still significant, signals of positive selection in the pooled Ethiopian Semitic/Cushitic population . One varn cohort . In the pulation . All of ADH1B gene, is predicted to be a regulatory variant, though the nearest gene is an uncharacterized noncoding RNA LOC256880. This variant has the third highest iHS of any variant in the 2 Mb region , is in moderate LD with ADH1B*48His , and was recently found to be significantly associated (P = 1.12 × 10−22) with the number of alcoholic drinks consumed per week in a large GWAS meta-analysis of primarily European subjects , is at high frequency (42.5%), and is a predicted regulatory variant within a promoter. A recent study found that this variant functions as an enhancer, and is significantly associated with expression levels of the ADH1A gene  Project database (GTEx Consortium 2020). Further, this variant is significantly associated with risk of esophageal squamous-cell carcinoma in alcohol drinkers, but not in nondrinkers , making it challenging to distinguish the target(s) of selection. All other predicted regulatory variants in the ADH region with |iHS| > 2 are presented in Further, because many variants under selection are likely to fall in noncoding regions , we examsubjects . In addiH1A gene . This vadrinkers . NotablyiD positive selection signals  are only present at appreciable frequency (>5%) in Afroasiatic-speaking populations from Ethiopia, the Beja population from Sudan (contains Baniamer and Hadandawa ethnic groups), and the Eastern Cushitic-speaking population from Kenya . Interestingly, among the Cushitic- and Semitic-speaking populations possessing the ADH1B*48His allele in Ethiopia, the Weyto (a hunter-gatherer group), have the lowest allele frequency (7%). In contrast, in all other agriculturalist Cushitic- and Semitic-speaking ethnic groups in Ethiopia which possess the allele, the frequency ranges from 27% to 40%. Among the sampled Omotic-speaking populations in Ethiopia, only the Hamer and the Kafa possess the ADH1B*48His allele, at 3% and 30% frequency, respectively. The relatively high frequency of this allele in the Kafa may be explained by the genetic ancestry shared between the Kafa and other Cushitic and Semitic speakers . This frequency difference between the Hamer and the Kafa may also be explained by differences in subsistence strategy. The Kafa are an agriculturalist group and have a high ADH1B*48His allele frequency, whereas the Hamer are a pastoralist group and have a low frequency of the allele. The ADH1B*48His allele is not present in any Nilo-Saharan-speaking group in Ethiopia. Outside of Ethiopia, ADH1B*48His is present at 10% frequency in the Beja from Sudan and at 7% in the Kenyan Eastern Cushitic population (c). We observe a very similar allele frequency distribution in Northeast Africa at rs1573496, the nonsynonymous ADH7 gene variant. This variant, however, is largely absent from East Asia and is only at high frequency in Ethiopia and the middle East (f). Finally, the nonsynonymous MTTP gene variant rs113337987 shows a very similar frequency distribution within Northeast Africa as the other two variants but is unique in that the derived allele is only found at high frequency in Ethiopian Semitic- and Cushitic-speaking populations, and is absent or at low frequency in all other global populations (i).Next, we examined global as well as Northeast African-specific allele frequencies at the variants showing the strongest selection signals by combining our data with 76 additional populations from the 1KGP and the HGDP project data sets . Within ulations c. Interepulation c. We obsdle East f. Finallulations i.ADH1B*48His allele at 7% frequency, we find no signal of positive selection at the ADH gene region in this population (bandc). Similarly, the ADH1B*48His allele is unlikely to be under strong positive selection in the Boni or Yaaku ethnic groups, two Cushitic-speaking hunter-gatherer populations from Kenya who possess the allele at 2% and 3% frequency, respectively. We also do not find a signal of positive selection at the ADH gene region in the Kenyan Eastern Cushitic population, a pastoralist group who possess the ADH1B*48His allele at 7% frequency, even though this population is genetically very similar to Ethiopian Semitic/Cushitic-speaking agriculturalists (bandc). We do find a signal of positive selection at ADH1B*48His in the pastoralist Beja population from Sudan , although this signal does not extend across the ADH region on chromosome 4, and is much weaker than what we observe in Ethiopian Agriculturalists . For the non-African reference, we used the Druze from Israel, following previous studies showing that the non-African component of many Ethiopian genomes is most closely related to populations inhabiting the Levant (ADH1B*48His) is higher than the genome-wide average when considering all SNPs as well as SNPs with a similar minor allele frequency in the focal population, and is at the tail-end of the empirical distribution . These results suggest that the haplotype carrying this variant likely introgressed from a non-African source, and that it likely was a target of positive selection after being introduced into Ethiopia. Haplotypes carrying the derived alleles at nonsynonymous SNPs rs1573496 (ADH7) and rs113337987 (MTTP) are also almost exclusively inferred to be of non-African origin, though these sites are not significantly enriched for non-African ancestry at a genome-wide level , and rs113337987 (MTTP) are lower in the Druze than in the Ethiopian Semitic/Cushitic population  for Afroasiatic Cushitic- and Semitic-speaking populations from Ethiopia, depending on the focal population analyzed . This assumes 68 generations since admixture, an initial postadmixture proportion of introduced non-African haplotypes of 50% (the genome-wide average from RFmix), and a source allele frequency given by the present-day Druze population of 36%. Even if we assume that the proportion of non-African haplotypes introduced is 60%, which is the observed value at the ADH1B*48His site (a), the selection coefficient is still significantly higher than zero: s′ = 0.89% . These estimates are likely conservative, given that we do see a signal of positive selection at ADH1B*48His in the Druze  in the Southern Levant  from the 1KGP data set as a proxy. Third, the background SNP had to have a minor allele frequency >5% in the focal Ethiopian population. This resulted in a list of 1,410 background variants for comparison with our target SNP. We then examined the distribution of normalized iHS scores at these matched background SNPs in the focal Ethiopian Semitic/Cushitic population, and compared it against the iHS value observed for ADH1B*48His  and a hunter-gatherer population (Weyto) who possess the ADH1B*48His allele. We find a slightly weaker signal in the pastoralists  found that the ADH1B*48His allele is significantly associated with “all-cause mortality,” with the derived allele conferring a survival advantage over the ancestral allele that is independent of alcohol consumption levels . This variant is in relatively high, but not complete, LD with ADH1B*48His in the focal Ethiopian Semitic/Cushitic population , suggesting that selection acting on this variant may be partially independent to that acting on ADH1B*48His. The ADH7 gene is involved in the metabolism of ethanol into acetaldehyde, but is expressed in the epithelial tissues of the aerodigestive tract . The MTTP protein is involved in the production of beta-lipoproteins, including low-density lipoproteins (LDLs) and very low-density lipoproteins (VLDLs). Accordingly, several studies have shown that mutations in the MTTP gene significantly associate with lower LDL levels and other metabolic traits , we merged this “Africa-diversity” data set with global populations from the 1KGP data set, populations from the HGDP genotyped at 627,421 SNPs ; the Kenya Medical Research Institute, Nairobi; the University of Khartoum, Sudan; the Nigerian Institute for Research and Pharmacological Development, Nigeria; the Ministry of Health and National Committee of Ethics, Cameroon; the University of Addis Ababa, Ethiopia; the Federal Democratic Republic of Ethiopia Ministry of Science and Technology National Health Research Ethics Review Committee, Ethiopia. DNA was extracted from whole blood and genotypes for a total of 1,071 individuals were generated. The 541 individuals from Ethiopia were genotyped on the Illumina Omni5 Genotyping array  was also high . This high genotype concordance rate is also supported by the fact that these three variants have high estimated imputation accuracy R2 scores from minimac3 in the full imputed data set .To verify imputation accuracy at the ADH region, we compared genotype concordance in a 2-Mb region centered on the ADH genes between 135 samples for which we had imputed genotype array data as well as full-genome sequencing data . We find that genotype concordance in this region is 99.7%. The genotype concordance rate between these 135 samples for the three focal SNPs of interest  to calculate pairwise LD among sites within 100 kb windows, and excluded sites with pairwise R2 > 0.2 (ADMIXTURE and PCA (bandc). We present ADMIXTURE results for K-values of 9–11, with K = 11 having the lowest cross-validation error (bandc). We ran smartpca using default parameters.To estimate population structure, we performed clustering analysis with the program DMIXTURE  and PCA  package . To do t package , retainiR2 > 0.2 , resulti and PCA bandc. Weon error bandc. Weselscan to calculate the iHS genome-wide and to measure EHH signals at select variants (N = 220), we used the imputed Ethiopian data without merging with other sub-Saharan African populations, and restricted the analyses to variants with an R2 imputation quality score >0.85. Once iHS scores were calculated for all variants, we binned variants into 1% derived allele frequency bins, and normalized iHS scores to have a mean of zero and variance of one relative to SNPs within the same frequency bin. We report the absolute value of this normalized iHS.We used the program variants . To genevariants , and intiD statistic, we followed the method developed by FST across all loci between populations i and j, and FST between populations i and j. We performed all pairwise FST calculations between populations using the VCFtools (v0.1.17) implementation of the iD statistic for all SNPs across the genome for the populations listed in KING  to convert the HGDP data from hg38 coordinates to hg19 coordinates prior to merging , Druze (N = 39), and Dizi (N = 39) as reference populations, and the pooled Ethiopian Semitic/Cushitic group (N = 220) as the query population. We used the “reanalyze reference” option with three expectation-maximization iterations in RFmix and assigned 68 generations since admixture between the reference populations (see admixture timings from ALDER). Regions of the genome assigned Druze ancestry by RFmix were designated “non-African,” while regions assigned Yoruba or Dizi ancestry were designated “African.”We performed local ancestry inference using the program RFmix v2; . To do tALDER  = x0esT/(1 + x0esT), where ν0 is the initial frequency, ν is the frequency after T generations, and x0 = ν0/(1 − ν0). If the observed number of coded alleles is k and the total number of chromosomes in the sample is N, then the likelihood of observing k selected alleles under a selection-only model is given by P = fbin), where s given the data {k, N} and parameters {ν0, T} is P ∝ P, where the factor of proportionality is ignored as it does not enter into the likelihood maximization, that is, we choose flat priors over s and k. The maximum likelihood estimate of s is given by maximizing P, treating s as the free parameter. This was done using the python scipy library . The ADH1B*48His allele is largely absent in other sub-Saharan African populations, and so we assume that the only source of ADH1B*48His alleles in the pooled Ethiopian Semitic/Cushitic population is from admixture with a non-African source. Accordingly, we use ν0 ≈ νsourcefnah = 0.18. The pooled Ethiopian Semitic/Cushitic group has N = 440 chromosomes, and the observed number of ADH1B*48His alleles is k = 147 . However, the proportion of non-African haplotypes at this site is higher than the genome-wide average, with 264 out of 440 haplotypes inferred as non-African . Using this observation for estimating the initial allele frequency gives ν0 ≈ 0.22. For all estimates, we report the likelihood ratio interval of s values around the maximum likelihood estimate s such that For estimating the selection coefficient post-admixture in the pooled Ethiopian Semitic/Cushitic group, we use the Druze as a proxy source population. From this, we estimate the initial frequency of the Molecular Biology and Evolution online.msac183_Supplementary_DataClick here for additional data file.


Asynapta groverae, an invasive mycophagous gall midge, in South Korea have been repeatedly reported since the first occurrence in 2008. This species is a nuisance to residents owing to its mass emergence from newly built and furnished apartments. Here, the levels of genetic diversity, divergence, and structure of invasive A. groverae populations were investigated to understand their ability to survive in novel locations. Population genetic analyses were performed on seven invasive populations, including the first outbreak, sporadically emerged, and two laboratory-isolated (quarantined) populations, using the mitochondrial COI sequences and the ten novel microsatellite markers developed in this study. Non-indigenous A. groverae managed to maintain their populations for 12 years despite decreased genetic polymorphisms resulting from multiple incidences of founder effects by a small number of colonists. Additionally, the advantageous sustainability of A. groverae in the particle boards from which they emerge suggests that human-mediated dispersal is plausible, which may allow for the successful spread or invasion of A. groverae to new locations. This study is one of the few examples to demonstrate that an insect species successfully invaded new regions despite exhibiting decreased genetic diversity that was maintained for a decade. These findings indicate that the high genetic diversity of the initial founding population and asexual reproduction would contribute to the successful invasion of A. groverae in novel environments.Outbreaks of  Recent climate change events, including global warming, have facilitated the survival and adaptation of newly invaded populations to novel local habitats, which were previously not the ecological niche of the invasive species7. Additionally, globalization in recent years has increased human activities (such as trade and travel), which have further contributed to the spread of invasive species9. After the invasion, alien species may either not survive owing to unfit environmental conditions or successfully colonize new habitats and expand. Established populations of alien species can be a threat to the community if they exhibit predation and/or competition with endemic species, leading to the disturbance of the whole ecosystem. Several cases of invasive species-induced ecological and economic damages have been reported. The identification of the processes involved in the establishment, dispersal route, and spread of alien species is challenging11.Globally, invasive species in non-native ranges are among the leading threats to biodiversity, ecosystems, and industriesAsynapta groverae Jiang and Bu , a mycophagous gall midge, in South Korea was recorded in 200812 , origin, and dispersal route of A. groverae have not been elucidated. Furthermore, the A. groverae genetic attributes, such as the level of genetic diversity, population differentiation and structure, and temporal stability of gene pools, have not been reported. The genetic diversity level and population structure are strongly associated with the successful colonization of invasive species into novel habitats22. Moreover, the genetic diversity of alien species is low during the initial invasion as the invasion is mediated by a few colonists, representing a small fraction of the source population (founder effect)24. Small and isolated populations are prone to genetic drift owing to the bottleneck or founder effects25. A previous study reported that a genetic bottleneck facilitated the successful invasion of Linepithema humile, an Argentine ant species26. The loss of genetic diversity due to a population bottleneck in the invasive Argentine ant population decreases intraspecific aggression by forming interspecifically dominant supercolonies26. In contrast, a relatively high genetic diversity of the founder population has been reported in cases of simultaneous or independent introduction of multiple source populations28. For example, the high genetic diversity of the invading wasp population resulted in successful invasion into the introduced ranges29. Decreased or elevated genetic variations in the introduced population could lead to the successful invasion of a broad suite of taxa according to circumstances.Although continuous outbreaks of 32. For example, population genetic analyses of the pinewood nematode Bursaphelenchus xylophilus revealed several characteristics of invasive processes from the native range of North America to Europe33. Low levels of genetic polymorphisms detected among the introduced populations of B. xylophilus suggested a single event of invasion during the invasion process, which contributed to a small effective population size33. Moreover, a large effect of genetic drift in invasive populations was suggested based on the strong genetic structure of native populations33. Population dynamics of introduced species, such as range expansion patterns34, invasive population size19, and ecological niche20, have been recognized in diverse taxa.Population genetic and phylogeographic analyses can reveal invasion processes, such as the origin, invasive history, introductory routes, dispersal/expansion patterns, and temporal and spatial genetic variation of invasive populations, which provide essential information for effective and efficient invasive species monitoring and control35, population genetic differentiation36, biological invasion39, host range expansion40, sexual selection41, ecological characteristics42, and speciation43. However, most microsatellites serve as species-specific markers. Hence, cross-species amplification of markers is limited to the genus level in most cases45. Most microsatellite markers have been developed for economic damage-causing species belonging to the family Cecidomyiidae50. Currently, microsatellite markers have not been identified for Asynapta species.Microsatellites, which are single sequence repeats, have been commonly used in population genetic studies on various insect taxa to identify genotypic diversityAsynapta species and investigated the genetic diversity and structure of seven invasive A. groverae populations, including first reported (2008), sporadically emerged (2018–2020), and two laboratory-isolated populations. Population genetic analyses were also performed using the mitochondrial COI sequences. The analysis of spatio-temporal genetic patterns for 12 years since the first outbreak will provide useful insights into the successful invasion of A. groverae in South Korea from a population genetics perspective.This study, for the first time, identified genome-wide microsatellites for A. groverae specimens from seven populations, the sequence of COI (648 bp)  , whereas AG06 (an isolated population originating from the AG02 population) exhibited an NH value of 1  Table  had only 1 Table . The ovely Table .Table 1ICOI sequences from seven A. groverae populations exhibited a star-like pattern. The most common haplotype (H1) was in the center of the network surrounded by other haplotypes  (Table FST > 0.1) were mostly observed during comparisons with the AG04 population (Table Of the 21 tests for pairwise on − 0.00 to 0.351on − 0.00 to 0.351on − 0.00 to 0.351FST)  Table .HO per locus ranged from 0.264 to 0.864 (mean = 0.8053), whereas HE ranged from 0.588 to 0.955 (mean = 0.5939). In total, 181 alleles (average = 18) were amplified from these markers within and across the populations and used for subsequent population genetic analysis.High-throughput sequencing generated 76,509,371 reads with 1,887,386 scaffolds containing di-, tri-, and tetra-nucleotide repeat motifs (average length = 173 bp). The number of perfect microsatellite sequences, which were suitable for primer design, was 106,753 containing 72,317 di- (68%), 32,214 tri- (30%), and 1819 tetra-nucleotides (2%) repeat motifs. Of the 20 initially screened markers, 10 polymorphic microsatellite markers were successfully amplified with stable and reproducible amplicon patterns and distinct peaks in capillary electrophoresis   ranged from 0.138 to 0.355. The highest FIS value was observed in AG01, which was obtained from the first outbreak population in 2008.The within-population genetic diversity was relatively high with the FST values calculated among the seven populations were significant (p < 0.01) and were in the range of 0.008–0.141 after Bonferroni correction (Table All pairwise Δ K = 200.35) from seven A. groverae populations   in a habitat it had not previously occupied, then establishes population and spreads autonomously”52. India is reported to be the original habitat of A. groverae. The first outbreak of A. groverae was recorded in Korea in the Songdo region in 2008. Accurate species identification based on a combined analysis of morphological and genetic data is critical to study newly invasive species53. Specimens of larvae, pupa, and adults were carefully examined in the current study based on the various taxonomical keys following the first description of this species14, our previous studies on A. groverae16, other taxonomical investigations on asynaptine species55 and Cecidomyiidae56  could not be applicable for A. groverae since only a single COI sequence is available from GenBank database for the genus Asynapta and its species was not even identified (Asynapta sp.)57.This study analyzed the spatio-temporal genetic population structure and genetic diversity levels in the invasive populations of A. groverae habitation in Korea15. All outbreaks occurred only in newly built apartments with new furniture manufactured from particle boards. No outbreaks from other habitats have been reported in Korea15. Although the possibility of the previous existence of A. groverae Jiang and Bu (Diptera: Cecidomyiidae) in Korea cannot be completely ruled out, this small gall midge can be considered a recently colonized invasive species through anthropogenic activities, such as trade and transport of wood materials from the native range for its dispersal.Since 2008, several sporadic outbreaks (emergence) have been reported during the last 12 years. Furthermore, field investigations using sticky traps around the sites where outbreaks occurred and manufacturers of the particle boards were located did not reveal any evidence of 28. Obolodiplosis robiniae, a North American gall midge species, was first detected in 2004 and has spread extensively throughout China. The mean HE value of 22 invasive populations  at 14 microsatellite loci was 0.5346 in China (invasive population), whereas that of US native populations was 0.660628. Although a direct comparison between the two members of cecidomyiids is not possible, the HE value of A. groverae ranged from 0.653 to 0.827, which is markedly higher than that of O. robiniae. Low genetic diversity is often expected in the initial phase of invasion as limited numbers of colonists have typically been introduced as a founding population24. However, relatively high genetic polymorphisms in the founding population (AG01) of A. groverae may contribute to overcoming the genetic hurdle of early invasive processes28.The level of genetic polymorphisms in this invasive gall midge population in Korea was higher than that in other invasive cecidomyiids60. The findings of this study demonstrated that the gene pool of the populations has not markedly changed for more than 10 years. The initial levels of HR and AR in AG02 and AG03 (collected in 2018) were maintained or slightly decreased when compared with those in AG01 (collected in 2008). In addition to decreased levels of genetic diversity, the level of genetic divergence was low as evidenced by some mutational steps from the ancestral haplotypes . Wood materials used to produce particle boards, which are the source of all known outbreaks and A. groverae habitat, were imported from several countries in East Asia15. Multiple populations of A. groverae with a small number of individuals may have been repeatedly introduced from their native ranges during the last decades. If founding individuals have multiple source populations, increased genetic diversity and marked genetic divergence can be expected28. However, genetic polymorphisms have not increased in succeeding A. groverae populations, except AG04, in this study. The haplotypes found in 2019 and 2020 were not markedly different from those found in 2008  of A. groverae, which was introduced before or around 2008, has been maintained as a single invasion source during the last 12 years. The ability of A. groverae to survive and continue its life cycles on particle boards would facilitate the spread and persistence of the population. Particle boards can be stored in one place and distributed to manufacture furniture in newly built apartments in different regions. This study, for the first time, reported that the life cycle of A. groverae continued from the particle board even after one year in a separate box under laboratory conditions  of Cecidomyiidae69. A recent molecular phylogeny analysis of Cecidomyiidae revealed that these two lineages appear to be basal to Asynaptini (to which A. groverae belongs)70. Paedogenesis, which is the larval reproductive life cycle, is also reported to be related to the optimal use of food patches, which leads to a short generation time in insects71. If fungal food resources are sufficient, larvae can continue their paedogenetic life. The larvae will start developing and undergoing metamorphosis under unfavorable conditions and subsequently fly away in search of new fungal sources73. Most A. groverae larvae and adults were found at the fungi-rich part in the particle boards in the outbreak population, as well as in the two isolated populations (AG05 and 06) after one year in the sealed box. Thus, particle boards can provide a rich fungal food source for A. groverae and ensure asexual reproduction with high propagation rates, which would allow for the continuous establishment of invading populations.Paedogenesis, which is parthenogenetic reproduction at the larval or pupal stages, has been reported in several Cecidomyiidae species since its first discovery in A. groverae outbreak populations were small and isolated from particle boards. Many Cecidomyiidae species are poor flyers, and their long-distance dispersal must be assisted by other external factors, such as wind76. Thus, settlements of A. groverae are formed by multiple founder events and the resulting genetic drift effects of decreased diversity.Although a particular genotype  were collected and maintained for one year in a separate box at room temperature without any treatment. A. groverae could survive as larvae or eggs on the particle boards and successfully reproduce even after one year, which indicates the high sustainability of A. groverae. The remarkable ability of A. groverae to survive in the particle boards may further explain the reason for the sporadic appearance of A. groverae during the last 12 years, as well as for the non-decline of the population due to fitness reduction resulting from decreased genetic diversity and inbreeding. Similar to AG05, the most recent naturally emerging population (AG07) from the Gijang region also formed one genetic cluster (shown in blue). Thus, if particle boards function as a potential vehicle for dispersing A. groverae populations into new environments, human activity-mediated dispersal could result in founder events or bottlenecking during the invasion processes of this species.The isolated populations emerging from the AG02 population may suggest that the dispersal of A. groverae species and establishment of multiple invasive populations from the founding population during the early invasion processes. A combination of high genetic diversity within the founding population and the potential capability of asexual reproduction enabled this insect to become a successful invader. Furthermore, this study suggests that successful invasions and the subsequent dispersal of invasive species can be facilitated by anthropogenic activities, which can contribute to the sustainability of invasive species in new environments.The findings of this study provide useful insights into the successful colonization of the recently introduced A. groverae adults were collected from newly furnished apartments in 2008, 2019, and 2020  were amplified using the universal primers LCO1490 (5′-GGT CAA ATC ATA AAG ATA TTG G-3′) and HCO2198 (5′-TAA ACT TCA GGG TGA CCA AAA AAT CA-3′)77. Polymerase chain reaction (PCR) was performed in a 20-μL reaction volume under the following conditions: an initial denaturation at 94 ℃ for 1 min, followed by 35 cycles of 94 ℃ for 30 s, 50–52 ℃ for 30 s, and 72 ℃ for 1–2 min; and a final extension step at 72 ℃ for 7 min. The amplicons were visualized on 1.5% agarose gels using UV light, purified using exonuclease I and shrimp alkaline phosphatase , and sequenced using an ABI PRISM 3130xl Genetic Analyzer  by Macrogen INC Sequencing (Korea). COI sequences of A. groverae obtained in this study were deposited in GenBank (accession no: OK561689 and OK561809).Total genomic DNA was isolated from the thorax of the specimens using the DNeasy blood & tissue kit . The mitochondrial DNA (mtDNA) ® EZ-UltraLow DNA library kit. Next, paired-end reads (150 bp) were generated using the Illumina NextSeq500 platform . For assembly, sequencing errors were removed using the error correction module of SOAPec (ver. 2.02)78. Sequencing adaptors were trimmed using Skewer (ver. 0.2.2)79. Genome assembly was performed using SOAPdenovo2 (ver. 2.04-r240). To identify reliable assemblies, short reads were remapped to assembled sequences using GapCloser (ver. 1.12). Only assembled scaffolds were retained for microsatellite marker identification. In total, 1,887,386 scaffolds with an average length of 173 bp were obtained. Microsatellite sequences with 2–6 repeat motifs were identified using MISA80. The candidate loci were validated using PCR with primers designed using Primer 381. The primers were designed based on the following parameters: length, 20–26 nucleotides; annealing temperature, 51–54 ℃; product size, 100–300 bp. A set of 20 candidate microsatellite primer pairs was initially tested for polymorphisms. Among these, ten microsatellite markers were chosen. The GenBank accession numbers for the ten loci are listed in Table Total genomic DNA was extracted from the thorax of adults (AG05) using the DNeasy blood & tissue kit , following the manufacturer’s instructions. The integrity, concentration, and purity of the DNA were assessed using a Bioanalyzer 2100 . The sheared genomic DNA was processed to construct an Illumina paired-end library using a NxSeqOne of each primer pair for the novel microsatellite markers was labeled with the fluorescent dye 5′-FAM. PCR was performed in a 20-μL reaction volume containing 30 ng DNA, 10 mM dNTPs, 100 nM of each primer, and 5 U HANLAB Taq polymerase  in an ABI 2720 Thermocycler . The PCR conditions were as follows: an initial denaturation at 95 ℃ for 2 min, followed by 35 cycles of 95 ℃ for 20 s, 50 ℃ for 30 s, and 72 ℃ for 30 s, and a final extension step at 72 ℃ for 5 min. The amplicons were electrophoresed on an ABI 3730x automated DNA sequencer and fragment sizes were analyzed using the GENEMAPPER software v. 5. .NH), haplotype richness (HR), haplotype diversity (h), and nucleotide diversity (π), were estimated for each population and the entire pooled population using AREQUIN v.3.5.82. HR was estimated to correct for unequal sample size using the refraction method with CONTRIB v1.0283. The haplotype network was obtained using HAPSTAR v0.784. Microsatellite diversity was assessed based on the mean number of alleles per locus (NA), allelic richness (AR), expected (HE) and observed heterozygosity (HO), and observed inbreeding coefficient (FIS) using GENEPOP v4.085 and FSTAT v.2.9.3.286. The presence of null alleles was assessed using MICROCHECKER v. 2.2.387 with 1000 randomizations at 95% confidence level. Multi-locus tests for Hardy-Weinberg equilibrium (HWE) and null allele frequency at each locus were estimated using GENEPOP v4.0. To examine population differentiation, exact tests for population differentiation and the calculation of population pairwise FST values were performed for both markers using GENEPOP v4.0. Pairwise population comparisons were performed for significance using Bonferroni correction. Temporal and spatial genetic structures of A. groverae among the seven populations were assessed using hierarchical analysis of molecular variance (AMOVA) implemented in ARLEQUIN v.3.5. Seven populations were assigned to two temporal groups (collected in 2008 (AG01) and 2018–2020 (AG02–07)). Two biogeographic regions  and south ) were assigned according to geographical distance irrespective of the collection year. Additionally, two isolated (quarantined) populations were assigned to the isolated group  and natural population . The total molecular variance was partitioned among groups (Fct = ‘inter-group’ genetic variation), populations within groups (Fsc = ‘intra-group’ genetic variation), and within populations (Fst = ‘inter-population’) 88 under a model of admixed ancestry among populations and correlated allele frequencies. Likelihood scores were calculated using the genetic clusters for each K value from 1 to 7 with 100,000 burn-in steps, followed by 1,000,000 Markov chain Monte Carlo iterations. Subsequently, the most likely K value was estimated using the web-based tool Structure Harvester (http://taylor0.biology.ucla.edu/structureHarvester/) based on the rate of change in the log probability data between successive K values. Furthermore, factorial correspondence analysis (FCA) based on genetic relationships among individuals with multi-locus genotypes was performed using GENETIX v4.5.289The population genetic structures of Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.