To facilitate the utility of SNP-based genotyping, we developed a new method called target SNP-seq which combines the advantages of multiplex PCR amplification and high throughput sequencing. Compared with KASP, Microarrays, GBS and other SNP genotyping methods, target SNP-seq is flexible both in SNPs and samples, yields high accuracy, especially when genotyping genome wide perfect SNPs with high polymorphism and conserved flanking sequences, and is cost-effective, requiring 3 days and $7 for per DNA sample to genotype hundreds of SNP loci. The present study established a DNA fingerprint of 261 cucumber varieties by target SNP-seq with 163 perfect SNPs from 4,612,350 SNPs based on 182 cucumber resequencing datasets. Four distinct subpopulations were found in 261 Chinese cucumber varieties: the north China type, the south China type, the Europe type, and the Xishuangbanna type. The north China type and Xishuangbanna type harbored lower genetic diversity, indicating greater risk of genetic erosion in these two subpopulations. Furthermore, a core set of 24 SNPs was able to distinguish 99% of the 261 cucumber varieties. 29 core cucumber backbone varieties in China were identified. Therefore, target SNP-seq provides a new way to screen out core SNP loci from the whole genome for DNA fingerprinting of crop varieties. The high efficiency and low cost of target SNP-seq is more competitive than the current SNP genotyping methods, and it has excellent application prospects in genetic research, as well as in promoting plant breeding processes in the near future. Compared with simple sequence repeats (SSRs), SNPs are the most abundant and stable genetic variation in genomes3. The wide occurrence of SNPs in a gene or in a regulatory region generally act as biological markers for identified genes associated with important traits4. In plants, SNPs have a particularly important application in reflecting both natural genetic variability and genetic drift created by breeders during plant improvement6. Thus, SNPs are ideal markers for genetic background analysis due to their advantages in high-throughput detection and easy integration of genotyping data7. Currently, DNA fingerprints of germplasms or varieties based on SNP markers have been constructed in several crops, such as rice8, maize7, wheat9, cotton10, soybean11, eggplant12 and pepper14, most of which are applied with DNA microarray systems.A single nucleotide polymorphism (SNP) is one-base variation in a single DNA nucleotide that occurs at a specific position in the genome15. The continuous progress in high-throughput sequencing has facilitated the development of some high-throughput SNP genotyping methods, such as the Gene Chip microarray or the KASP platform 16. While chip-based platforms require enormous amounts of time for SNP assay designing and are only suitable for genotyping hundreds of samples with thousands of SNPs17. KASP system also need relatively high cost for genotyping each sample. These SNP genotyping platforms often find that some SNP loci cannot be well detected and genotyped because the flanking regions of these SNPs are not conserved or there are other hits in the genome due to the sequences homology20. This causes many false positive or false negative results in SNP genotyping. Moreover, the huge cost of one-time investment in instruments or expensive microarrays also limits the use of SNPs in most laboratories. Therefore, it is necessary to use a new technology that is cost-effective and highly accurate for genotyping middle-scale (100–2000 markers) genome-wide SNPs.Several traditional SNP genotyping methods based on electrophoresis systems, such as CAPS (cleaved amplified polymorphic sequence), dCAPs (derived CAPS), and AS-PCR , are commonly used in genetic research. It is difficult to meet the rapidly increasing need for SNP genotyping21. Some SNP genotyping methods combing multiplex PCR and NGS technology such as the Ion Torrent platform23, AmpSeq.24, GBTS25 and Seq-SNP in LGC  have been reported, which can genotype thousands of SNPs one time. To our best knowledge, few studies have focused on analyzing extensive variation genomes and developing perfect SNP loci which harbor conserved flanking regions and have unique PCR amplification in the genome. Here, we developed a new method called target SNP-seq which combines the advantages of high-throughput sequencing and multiplex PCR amplification by using genome-wide perfect SNPs which meet the requirements of harboring conserved flanking sequences and being captured uniquely in PCR amplification. Furthermore, this approach could gain hundreds of SNP genotypes by sequencing the SNP region with an average of 1000 times coverage in a short time and at low cost.At present, next-generation sequencing (NGS) technology is widely used, and the whole-genome sequences of hundreds of species have been assembled, which has promoted large-scale re-sequencing and produced millions of SNPs for genetic researchhttp://www.fao.org)26. As is well known, the narrow genetic background and high risk of genetic erosion cucumber has been clearly demonstrated because of hybrid varieties deriving from limited parents and breeders pursing similar breeding goals28. The annually increasing number of commercial cucumber varieties presents a major challenge for plant variety management and protection in China. Although several DNA fingerprinting research in cucumber were reported using simple satellite repeat (SSR) technology30, it was still difficult to obtain genotypes in high throughput way. Cucumber genome was the first sequenced and well assembled species in vegetables31, and whole genome sequencing in various cucumber germplasms made it possible to discover perfect SSR and SNP for genotyping32. Our previous research analyzed the genetic diversity of 382 Chinese cucumber varieties with 122 perfect SSRs by target SSR-seq technology33. In this study, we applied the target SNP-seq genotyping technology with 163 perfect SNPs in establishing the DNA fingerprint of 261 cucumber varieties, and revealed the population structure and genetic features in cucumber varieties.China is the world’s top producer and consumer of cucumber, cultivating more than 1.24 million hectares and yielding about 64.87 million tons in 2017  only biallelic SNPs were remained; (2) the 30 bp flanking sequence of a SNP was mapped to a unique region in cucumber 9930 genome; (3) there were no Indel, SSR, or other SNP locus in 30 bp flanking region of 182 cucumber accessions; (4) heterozygosity was no more than 0.2; (5) missing rate was less than 0.2; (6) MAF  above 0.4 to ensure the SNP polymorphism in varieties. Furthermore, some SNPs with high allele frequency (≥0.95) in a certain subgroup while low allele frequency (≤0.05) in other subgroups were also selected as perfect SNPs. Finally, all perfect SNP loci were sent to the Molbreeding Biotechnology Company  for multiplex PCR primer panel design.The first-draft cucumber genome was assembled from the Chinese cucumber inbred line 9930http://www.molbreeding.com/). 3 M enzyme is a mixture of three kinds of enzymes including two mutant Taq enzyme and one high-fidelity thermo stable DNA polymerase. Two units (2U) of enzyme were used per reaction. The thermal cycling regime was as follows: 95 °C for 5 min, followed by 17 cycles  and the final extension at 72 °C for 4 min. Then, the PCR products were collected by magnetic bead suspension and purified by 80% (V/V) ethanol. For the second round of PCR, the 30 µL PCR mixture consisted of 11µLpurified PCR product from the first round of PCR, 10 µL Taq enzyme, 18 µL pure water, and 1 µL barcode adaptors composed of: forward 5′-AATGATACGGCGACCA-CCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCG-3′ and the reverse 5′-CAAGCAGAAGACGGCATACGAGATXXXXXXXXGTGACTGGAGTTCCTTGGCACCCGAGA-3′ (the underlined eight-base sequences indicate barcodes). PCR was run at 95 °C for 3 min followed by 7 cycles of 95 °C for 15 s, 58 °C for 15 s, 72 °C for 30 s, and the final extension 72 °C for 4 min. The products from the second round of PCR were enriched by magnetic beads, and washed three times with 100 µL 80% (V/V) ethanol and 23 µL of Tris-HCl buffer . Consequently, the DNA library was sequenced on an Illumina HiSeq X Ten platform in Molbreeding Biotechnology Company. We also sequenced the DNA of the Chinese cucumber inbred line 9930 as a positive control to verify the accuracy of target SNP-seq.The library construction of target SNP-seq consisted of two rounds of PCR Fig. .The firs34. SNP genotypes were generated by GATK35. The maximal numbers reads of SNP allele was taken as the major allele, while the other allele was taken as minor allele. To ensure the accuracy of SNP genotypes, we also filtered those sequencing reads with major allelic depth less than 20 in a variety. For heterozygous varieties, a ratio of major and minor alleles under 0.7 was treated as a heterozygous genotype. Finally, specific SNP variant bases for each variety were identified by analyzing the in-house barcodes assigned raw sequence reads.In the current study, sequencing reads of 261 varieties were aligned to cucumber reference genome (9930 V2) by using BWA to determine the physical location of each target SNP amplicon36. In order to increase the genotype accuracy of each cucumber variety, newly expanded young leaves grown from 30 seeds were used for DNA extraction. Total genomic DNA was isolated from freeze-dried tissue stored in 96-well plates using a plant DNA magnetic bead kit  according to the manufacturer’s protocol. The concentration of all extracted DNA was quantified using a Qubit2.0 fluorometer with a Quant-iT dsDNA HS Assay kit (Thermo Fisher Scientific) before targeted-SNP sequencing.A total of 261 cucumber varieties were selected in this study to evaluate the novel technique of target SNP-seq in variety identification. The 261 varieties represented all types and main cucumber varieties planted in China, including 111 commercial hybrid varieties from the Chinese seed market, 67 varieties from breeders’ collections in BVRC, 64 varieties from the Chinese government department, and 19 local landraces from Xishuangbanna of Yunnan Province in China 38, and F (inbreeding coefficient). All these calculations were performed in Excel2016 using the following formulae:Pi and Pj are the frequencies of two SNP alleles among all measured varieties, n is the number of samples.Various genetic diversity parameters were calculated based on the SNP genotypic data, such as the MAF , Ho (observed heterozygosity), PIC (polymorphic information content)39. In order to determine population number (K), three parallel runs were performed for each simulated value of K ranging from 2 to 10 with the following parameters: MCMC (Markov chain Monte Carlo) replicas run for 10,000 iterations and 100,000 generations of burn-in for each K. The optimal K value depended on ΔK, using the formula defined by Evanno et al.40. To verify the optimal number of clusters, principal component analysis (PCA) was performed in the respective R package. The neighbor-joining (NJ) tree generated from MEGA7 was used to analyze the genetic relationship among 261 varieties based on the genetic distance in poppr R package. Additionally, we measured the genetic differentiation between pairs of subpopulations with an analysis of molecular variance (AMOVA) and computed the pairwise Fst in the poppr R package.The genetic structures of 261 cucumber varieties were analyzed by the model-based program STRUCTURE V2.333 and core SNP for pepper identification13, respectively. Detailed algorithm for core set of SNP selection was followed by MinimalMarker41. Finally, a core set of SNPs with the best discernibility ability was obtained and the saturation curves were plotted by the pairwise comparison of all varieties’ genotypes.To screen out a minimal number of SNPs for distinguishing the maximal number of cucumber varieties, we used a script in Perl based on analyzing the genetic diversity of each SNP site, which was successfully used in selecting core SSR for cucumber identification42, two varieties were believed to be identical when they have same genotype in a certain set of molecular markers. We set up a pairwise comparison matrix by calculating the numbers of differential SNP genotypes between each pair of varieties, and the missing genotype was treated as null. One variety has less differential SNP genotype with other cucumber varieties indicated that it has a closer kinship with others. The top 10 percent of varieties with close kinship in each subpopulation were considered as core varieties. Moreover, to verify that this comparison matrix was able to represent the kinship of 261 cucumber varieties, correlation analysis among three pairwise comparison matrix  were calculated by the SAS8.1 PROC CORR .According to the UPOV standard for the authentic testing of crop varieties44. In the second round of PCR, a unique barcode sequence was added for each DNA sample. Then, PCR products were sequenced with the Illumina X platform. We could obtain the SNP genotype data by analyzing the specific barcodes assigned to DNA samples from the raw sequence reads. This SNP genotyping process only needed 3 days for high-throughput sequencing and cost $7 for each variety. Thus, target SNP-seq is suitable for use in DNA fingerprinting, variety identification, genetic research, and molecular breeding. The SNP genotype of the inbred cucumber line 9930 from target SNP-seq was the same as that of the cucumber reference genome, indicating that this technique had good repeatability and high accuracy in SNP genotyping.Target SNP-seq is a novel technique appropriate for mid-scale 100–2000) SNP genotyping  whole-genome sequence31, indicating that every 52.8 bp had one SNP. After filtering the SNP loci with MAFs above 0.4 and missing rate above 0.2, the remaining128, 434 (0.03%) SNP loci were identified. According to the objective in this study, we selected 298 perfect SNPs for multiplex PCR primer designing, which were evenly distributed on the whole genome. Ultimately, 163 SNPs composed a primer mix panel for cucumber variety identification by target SNP-seq. The number of SNP loci per chromosome ranged from 21 on chromosome 5 to 26 on chromosome 7. The average marker spacing was 1.18 Mb across the whole genome, ranging from 0.03 Mb on chromosome 6 to 6.7 Mb on chromosome 5  under accession numbers CRA001490. The sequencing depth of 90.3% amplicons was as expected centralized in 100–5000× (50 times different)  and high miss rate (>0.1) in all cucumber varieties. 134 (87.0%) SNP had more than 1000× coverage and the average reads depth of the remaining 154 SNP was 2000×  Fig. . This di00× Fig. . The mea00× Fig. . Accordi00× Fig. , indicatHo) of 261 varieties ranged from 0 to 0.64 while this value in154 SNPs varied from 0 to 0.44 with an average of 0.17   using the model-based Bayesian clustering method as performed in the software STRUCTURE V2.3. The most likely number of K was 2 according to the ΔK value from the structure output Fig. .Overall,33. Then we acquired a core SNP set consisting of 24 SNP loci that could identify 99% of 261 Chinese commercial cucumber varieties with different SNP genotypes  these methods have not focus on developing perfect SNP loci through variomes big data; (2) the primers for SNP amplication need probe hybridization; (3) the sequence read depth/coverage of was less than 100× and the SNP call rate was less than 90%. In the present study, we reported the utility of target SNP-seq—a novel method for SNP genotyping and applied in genetic analysis of 261 cucumber varieties. We first designed a primer panel consisting of 163 discovered perfect SNPs in cucumber based on 182 resequencing datasets in the multiplex PCR approach of target SNP-seq. Secondly, the PCR products containing the 163 SNPs derived from 261 cucumber varieties were sequenced on the Illumina X platform. Then, the millions of reads were aligned to the cucumber reference genome (9930V2) based on strict alignment parameters, in order to obtain the accurate SNP genotypes seq Fig. . Our res55. With the rapid development of genome sequencing and resequencing, hundreds of plant genomes have been assembled and genome-wide variomes have been studied, making it easy to construct background selection by the MAS system in breeding. Furthermore, as new powerful functional markers, thousands of SNP loci or SNP haplotypes have been thoroughly investigated in plants, making it possible to select individuals with disease resistance and specific agronomic traits at the seedling stage59. The increasing demands of background selection and foreground selection call for new SNP genotyping methods to screen hundreds of SNPs simultaneously at low cost of time and money. In this study, target SNP-seq was proved as a flexible, efficient, and affordable SNP genotyping technique that could see wide use in the modern breeding system, including background selection, foreground selection, pyramiding breeding, breeding by design, and variety authenticity identification and purity inspection. Consequently, target SNP-seq technology will undoubtedly see extensive application prospects in genetic research and breeding of novel varieties in the near future.Over several decades of effort, marker-assisted selection (MAS) breeding has enhanced the efficiency of breeding at a dramatic speed in many cropsIn the present study, we established a DNA fingerprint of 261 cucumber varieties with 163 perfect SNPs by target SNP-seq technique. Four distinct subpopulations were found in 261 Chinese cucumber varieties: the north China type, the south China type, the Europe type, and the Xishuangbanna type. A core set of 24 SNPs was able to distinguish 99% of the 261 cucumber varieties, and 29 core cucumber backbone varieties in China were identified. Therefore, target SNP-seq has excellent application prospects in genetic research, and in promoting plant breeding processes, in the near future.Supplementary Figures.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.Supplementary Table S4.Supplementary Table S5.Supplementary Table S6.


Genetic variants, underlining phenotypic diversity, are known to distribute unevenly in the human genome. A comprehensive understanding of the distributions of different genetic variants is important for insights into genetic functions and disorders.Herein, a sliding-window scan of regional densities of eight kinds of germline genetic variants, including single-nucleotide-polymorphisms (SNPs) and four size-classes of copy-number-variations (CNVs) in the human genome has been performed.p < 10−10), and enriched with somatic tumor CNVs and the trait- and disease-associated SNPs identified by genome-wise association studies, exceeding tenfold enrichment in clusters comprising SNPs and extra-long CNVs.The study has identified 44,379 hotspots with high genetic-variant densities, and 1135 hotspot clusters comprising more than one type of hotspots, accounting for 3.1% and 0.2% of the genome respectively. The hotspots and clusters are found to co-localize with different functional genomic features, as exemplified by the associations of hotspots of middle-size CNVs with histone-modification sites, work with balancing and positive selections to meet the need for diversity in immune proteins, and facilitate the development of sensory-perception and neuroactive ligand-receptor interaction pathways in the function-sparse late-replicating genomic sequences. Genetic variants of different lengths co-localize with retrotransposons of different ages on a “long-with-young” and “short-with-all” basis. Hotspots and clusters are highly associated with tumor suppressor genes and oncogenes (In conclusion, the genetic-variant hotspots and clusters represent two-edged swords that spearhead both positive and negative genomic changes. Their strong associations with complex traits and diseases also open up a potential “Common Disease-Hotspot Variant” approach to the missing heritability problem.The online version contains supplementary material available at 10.1186/s40246-021-00318-3. Genetic variants (GVs) are essential contributors to population diversity, providing an important basis for the investigation of genetic effects on complex traits and disease susceptibility. A wide spectrum of GVs exist in the human genome ranging from point alterations, viz. single-nucleotide polymorphisms (SNPs), to structural variations including copy-number-variations (CNVs). It was found that a typical human genome differed from the reference genome at a median of 4.31 million base pairs, 84.5% of which consisted of SNPs, the most common form of genetic variants in human genomes [GVs have been analyzed in genome-wide association studies (GWAS), revealing hundreds of thousands of associations of GVs with complex diseases and traits . SNPs arPrevious studies have indicated that genetic variants are not evenly distributed in the human genome , 22, witHuman CNV hotspots have been found to overlap with CNVs in the chimpanzee and macaque genomes, pointing to the maintenance of these hotspots by nonneutral evolutionary forces . Such foGermline genetic variants employed in genetic variant hotspots detection include biallelic SNPs (~ 77 million entries) and small indels  from the Phase III 1000 Genomes Project   in a window is assigned a weight wstep that depends on its distance from the window centre, increasing in equal increments from a wstep of 0.5 at the extreme margins of the window to 1.0 at the very centre of the window. This gives rise to a symmetrical distribution of step weights within the window, and the weighted density of any genetic variant in each window (Dwin) is given by:To increase the resolution of hotspot detection, a sliding-window protocol is employed to scan the genome in 1-kb windows sliding by 10-bp steps. As well, to render more precise the genomic boundary of any hotspot as illustrated in Supplementary Figure SDwin for SNP entries is determined for all the sliding windows; (iii) those sliding windows that overlap with a 500-kb Genic zone are separated into Part I sliding windows that are entirely located within a Genic zone, and Part II sliding windows that are partially located within a Genic zone; (iv) Part I and Part II windows are ranked separately based on their Dwin values from the highest to the lowest. The Part I sliding windows with top-ranked Dwin values are recruited successively from rank 1 down to rank i until their cumulative SNP fraction represents ≤ 5% of total SNP entries in the Genic zones to yield the top SNP windows in Part I . The top-ranked Part II sliding windows are recruited from rank 1 down to rank k  such that the rank-k window displays a weighted SNP density (Dk) greater than or equal to the Dmin (viz. lowest Dwin) among the top-ranked SNP windows in Part I. Steps iii and iv are also repeated for the Proximal and Distal zones as described for the Genic zones. (v) Thereupon, Part I and Part II top-ranked windows from the three types of zones are merged together, followed by elimination of any double-counting of windows on account of their zone-crossing to yield altogether a total of 34,487 SNP hotspots in the three types of zones with a mean width of 1994 ± 4528 bp, covering 2.54% of the autosomal regions analyzed. The zone-specific hotspot detection thresholds (Dmin), and the chromosomal coordinates of different kinds of genetic variant hotspots, are given in Supplementary Table SThe weighted density of the targeted kind of genetic variants is calculated in all 1-kb windows. In the Genic, Proximal, or Distal zones, the top-density windows that cover altogether 5% of the total entries of the targeted genetic variant are identified as hotspots. For example, the hotspot detection procedure for SNPs in the Genic zones  relative to the random simulations are kept, and successive windows are merged to yield the positive selection hotspots. In this regard, 99,266 DAF hotspots, 89,464 |ΔDAF| hotspots, and 63,060 |nSL| hotspots are identified, which altogether cover 10.33% of autosomal regions. Likewise, the negative selection hotspots (NegSel-Hs) are identified based on two different measures of purifying selection, viz. (i) sequence conservation score measured using phyloP across 100 species, and (ii) intensity of nucleotide diversity. The windows with top-5% phyloP score or bottom-5% nucleotide diversity are earmarked, and only the windows displaying significantly higher measurements of purifying selection (p < 0.05) relative to 10,000 simulation windows of similar levels of informative sites are regarded as NegSel-Hs, which altogether amount to 7.94% of autosomal sequences. The phyloP score is retrieved from the “Conservation” track of UCSC Table Browser [L| value for each SNP is the absolute value of the nSL statistic estimated and normalized across 100 frequency bins in selscan version 1.2.0 with all the default settings [L|, and nucleotide diversity used for selection-hotspot determination are based on SNPs and haplotypes from the Phase III 1000 Genomes Project of 2504 individuals. Genomic windows subject to balancing selection retrieved from the supplementary material of Bitarello et al. [p values of each of the PosSel-Hs and the NegSel-Hs identified are given in Supplementary Dataset STo identify positive selection hotspots (PosSel-Hs), the strength of positive selection is evaluated in 1-kb windows in the non-gap regions on 22 autosomes based on (i) average derived-allele frequency (DAF) of all SNPs in each window, (ii) average of the maximum DAF differences (|ΔDAF|) across the two different populations, and (iii) the average of haplotype structure-based statistic |nSL| . Next, t Browser . The nuc Browser . The |nSsettings , 44. Theo et al.  are mergPreviously, human DNA replication timing has been experimentally assessed in 1-kb sequence windows across the genomes of fifteen different human cell lines by ENCODE project , 47, whiCoordinates of 23,056 known genes are matched with the locations of hotspot clusters to yield 448 genes that overlap with one or more clusters, and 442 of them display one or more CNVT breakpoint(s). For each of these 442 genes, the cluster-segments and noncluster-segments are compared with respect to their CNVT breakpoint densities, identifying 33 genes where the cluster-segments are significantly enriched in CNVT breakpoints , and only one gene where the cluster-segment is significantly depleted in CNVT breakpoints as shown in Supplementary Table Sp value is obtained as (r + 1)/(n + 1) [n is the round of simulations and r is the number of simulations that produce a measurement greater than or equal to the actual level in the case of significant enrichment, or less than or equal to the actual level in the case of significant depletion. Unless otherwise specified, 5,000,000 rounds of simulations are performed.In each round of simulation, the locations of target regions are randomly shuffled (by matching the number and sizes of the target regions) within the autosomal regions analyzed. The measurement of interest is assessed for each simulation round. The /(n + 1) , where nTo estimate using the Monte Carlo method the level of significance regarding the enrichment of GWAS-identified SNPs in the genetic-variant hotspots and hotspot clusters, random regions are simulated in sequence windows with matching levels of average minor-allele frequencies (MAF) in order to accommodate the dependency of GWAS on allele frequency. For this purpose, autosomal sequence windows are classified into ten equal groups according to their individual average MAF values, and the hotspots or clusters are also classified into the same ten groups. The Monte Carlo simulations are conducted by simulating sequence windows from the corresponding MAF group of hotspot or cluster. The simulation results for each group of hotspots and clusters are shown in Supplementary Figure SPOP1-POP2, is estimated for all the clusters and non-hotspot regions in each of the ten population pairs among African, American, South Asian, European, and East Asian. Let African-European, pertaining to the ith cluster and jth non-hotspot region is given by n total clusters and m total non-hotspot regions, the nmt tests. The procedure is likewise repeated for the other nine population pairs.To compare the positive-selection strength estimated by population differentiation among the cluster and non-hotspot (viz. the genomic regions outside of hotspots and clusters) regions, the DAF difference between any two populations, viz. |ΔDAF|p < 10−8 are lifted over from GRCh38.p5 to hg19 using the “liftOver” function in R package “rtracklayer” version 1.34.2 [Custom R scripts are employed in hotspot and cluster detections and analyses under R environment version 3.3. Disease- or trait-associated SNPs identified by GWAS at n 1.34.2 . Processn 1.34.2 . Figuresn 1.34.2 , 52, as n 1.34.2 , and “quThe germline genetic variants retrieved from different public databases are analyzed including SNPs, SIDs, MSTs, CNVs, as well as SDPs which originated from fixed CNVs   but “short-with-all” (p = 0.263) modes of association between GV length and retrotransposon age  form a closely co-localized group in Fig. p < 10−10 for clusters in all five populations). Secondly, using population differentiation as an index, the increased values of DAF-difference between population pairs, viz. |ΔDAF| in clusters relative to non-hotspot regions, yielded p values ranging from 10−2 down to less than 10−13 for eight out of ten population pairs  are likewise indicative of the presence of positive selection. A total of 26.4% of GV hotspots and 36.0% of the clusters overlap with sequence windows displaying top-5% levels in one or more of the three different measures of positive selection based on DAF, |ΔDAF|, and |nSL| . These three different measures of positive selection jointly enable the identification of 200,211 hotspots of positive selection based on the criterion that hotspot sequence windows display top-5% levels in at least one of the three measures .In general, the level of positive selection can be assessed based on (i) the allele-frequency spectrum, (ii) population differentiation, and (iii) haplotype structure . First, irs Fig. b, attestn sweeps , the sigp < 10−44 in all populations for the clusters. Cross-species conservation, measured by sequence conservation score measured using phyloP across 100 species [p < 10−230 for hotspots and 10−35 for clusters. Altogether 8.83% of GV hotspots, and 11.1% of the clusters, overlap with sequence windows that display either top-5% levels of phyloP score or bottom 5% of nucleotide diversity, which is significantly less frequent in comparison to simulated regions . A total of 165,323 purifying or negative selection hotspots are identified as sequence windows that display at once top-5% levels of phyloP score and bottom-5% levels of nucleotide diversity.Purifying selection can be assessed based on (i) reduction of genetic diversity, and (ii) cross-species sequence conservation . Signifi species , is signp = 0.0001 for both GV hotspots and clusters, 10,000 simulations). The findings that all three different measures of positive selection support the association of genetic-variant hotspots and clusters with positive selection  in the human genome [st or 2nd-ranked in terms of SNP density  with hotspots of MCNV relative to those of SCNV; and the strong association of LINC with the hotspots of ECNV relative to those of MCNV, or even more so those of LCNV or SCNV Fig. A. The PoThe 1135 hotspot clusters in the human genome comprised twenty-three different combinations of hotspots, a large majority of which include the hotspots of SNP and hotspots of structural variants. Each kind of hotspot clusters is associated with its own characteristic array of genomic features. The associations of for example the long RNA (LRNA-) with the SNP + MCNV cluster (viz. clusters comprising the SNP and MCNV hotspot) are largely additive, not far from the sum of the individual associations of LRNA- with SNP hotspot and MCNV hotspot; in contrast, the associations of H3k27ac toward the SNP + SCNV cluster are synergistic, far exceeding the sum of the individual associations of H3k27ac with SNP hotspot and SCNV hotspot  located at different genomic positions may be falsely identified as SNPs due to mismapping of short-reads , giving cis-acting retrotransposons are utilized as nucleation points for the formation of both germline CNV and somatic CNVT through recombination [p value < 0.05). The significance of such enrichment reaches p < 10−58 for CACNA1C and SNX29 from the Network of Cancer Genes, and p < 10−61 for WWOX and CSMD1 from the Tumor Suppressor Gene Database  enrichment of SNPs with DAF > 0.95 in hotspots and clusters in all five ethnic populations  among different genomic features and markers in 500-kb non-overlapping autosomal windows. Supplementary Figure S3. Refinement of hotpsot boundary by weighted density. Supplementary Figure S4. Flowchart of protocol for identifying SNP hotspots. Supplementary Figure S5. Density of GWAS-identified SNPs in ten groups of hotspots and clusters ranked based on minor allele frequency from low (group 1) to high (group 10). Supplementary Figure S6. Comparison of GV densities in hotspots (red boxplots) and in simulated windows with matching number and size of the hotspots (grey boxplots). Supplementary Figure S7. Comparison of GV allele frequencies in hotspots (red bars) and in total autosomal windows (grey bars) binned by the average size of GV hotspots . Supplementary Figure S8. Density distribution of SVA and Alu subfamilies near eight kinds of genetic variants. Cumulative density of 42 SVA and Alu subfamilies within ± 5-kb of SNP, SID, MST, SCNV, MCNV, LCNV, ECNV and SDP entries (in 50-bp increments). Supplementary Figure S9. Length distribution of MCNVs in the MCNV hotspots with high or low levels of histone modifications. Supplementary Figure S10. Genomic-feature contents of total (first column in upper panel) and 23 kinds of hotspot clusters with distinct hotspot-compositions. Supplementary Figure S11. Enrichment Map for Distal-zone-enriched genes annotated using g:Profiler based on Gene Ontology biological process and KEGG. Supplementary Figure S12. Comparison of risk allele frequency, odds ratio and heritability of GWAS-identified SNPs located within total autosomal windows (grey bars) relative to those located within hotspots (red bars) or clusters (blue bars). Supplementary Figure S13. Co-localization among different kinds of genetic variants. Supplementary Figure S14. Distribution of GVs around the telomere ends. Additional file 2: Supplementary Table S1. Information on genomic features and markers. Supplementary Table S2. Properties of genetic-variant hotspots and hotspot clusters. Supplementary Table S3. Properties of selection hotspots. Supplementary Table S4. List of cluster-containing genes. Supplementary Table S5. Estimated ages of retrotransposons. Supplementary Table S6. Disease-related variants in GV hotspots and clusters. Supplementary Table S7. Overlap of genetic-variant hotspots or clusters with recombination hotspots. Supplementary Table S8. Autosomal sequences (in base pairs) in different genomic zones and replication phases that overlap with hotspots or clusters. Supplementary Table S9. Enrichment and selection status of hotspots in seven immune system gene loci. Supplementary Table S10. Genomic-feature distributions in genomic regions belonging to different replication-phase segments among 500-kb Genic, Proximal and Distal zones. Supplementary Table S11. Functional annotation of Distal-zone genes.Additional file 3: Supplementary Dataset S1. Information of genetic-variant hotspots, hotspot clusters, selection hotspots and replication-phase segments. Genomic coordinates are given in one-based format in human genome assembly hg19.


Sorghum halepense (‘Johnsongrass’) is both an invasive species and one of the world’s worst agricultural weeds. Formed by S. bicolor x S. propinquum hybridization, we show S. halepense to have S. bicolor-enriched allele composition and striking mutations in 5,957 genes that differentiate it from representatives of its progenitor species and an outgroup. The spread of S. halepense may have been facilitated by introgression from closely-related cultivated sorghum near genetic loci affecting rhizome development, seed size, and levels of lutein, a photochemical protectant and abscisic acid precursor. Rhizomes, subterranean stems that store carbohydrates and spawn clonal propagules, have growth correlated with reproductive rather than other vegetative tissues, and increase survival of both temperate cold seasons and tropical dry seasons. Rhizomes of S. halepense are more extensive than those of its rhizomatous progenitor S. propinquum, with gene expression including many alleles from its non-rhizomatous S. bicolor progenitor. The first surviving polyploid in its lineage in ∼96 million years, its post-Columbian spread across six continents carried rich genetic diversity that in the United States has facilitated transition from agricultural to non-agricultural niches. Projected to spread another 200–600 km northward in the coming century, despite its drawbacks S. halepense may offer novel alleles and traits of value to improvement of sorghum.From noble beginnings as a prospective forage, polyploid  Sorghum halepense (2n = 40) arose as a naturally occurring hybrid between S. bicolor (2n = 20), an annual, polytypic African species which includes cultivated sorghum; and S. propinquum (2n = 20), a perennial southeast Asian native of moist habitats. While a firm estimate of its antiquity is lacking, S. propinquum is thought to have shared ancestry with S. bicolor ∼1–2 million years ago , S. halepense has spread in post-Columbian times from its hypothesized west Asian center of origin across much of Asia, Africa, Europe, North and South America, and Australia. Its establishment in the U.S. is probably typical of its spread to other continents, being introduced intentionally as a prospective forage and unintentionally as a contaminant of seedlots  were sequenced using standard methods implemented at the US Department of Energy Joint Genome Institute, as part of a larger project including 27 genomes and 39 transcriptomes in total. From each accession, 76-bp paired-end reads were aligned to the Sorghum bicolor reference genome (v1.4) using BWA version 0.5.9  was examined for every tandem gene in the sorghum genome. The average coverage of the whole genome across the 27 genomes studied is about 553X. There were 31 tandem genes with more than twice the genome coverage (1100X), of which 7 have coverage more than 2500X (ranging up to 7500X). A total of 14 of the 31 high coverage tandem genes have SNPs called, and were removed from further analysis.S. halepense SNPs, reads from S. halepense were aligned to the reference S. bicolor genome by BWA and SNPs determined with nucleotide groups for each reference S. bicolor genomic position by an in-house script. False positive S. halepense SNPs for each position of the reference S. bicolor genome were inferred and removed, based on three criteria: (i) if the top two nucleotide groups are the same as reference S. bicolor and S. propinquum, respectively, there are no false positive SNPs; (ii) if read depth of an SNP is 1 , a false positive was inferred; (iii) if p-value calculated by the Fisher exact test for the actual and theoretical read depths (bicolor:propinquum is 1:1), is less than 0.1, a false positive was inferred. The full SNP table with the reference S. bicolor, S. propinquum, and S. halepense SNPs as well as wild S. bicolor and S. timorense SNPs determined with total RNA and genomic DNA, respectively, against the reference S. bicolor genome, is provided  is the number of occurrences of residue α in an alignment column i. i(nβ) is the number of occurrences of an alternative residue β in the column i. Pc is the probability of occurrence of the most common residue in the alignment column i. Si is the function index score, a measure of functional impact of a mutation on protein function. The significance threshold of Si is determined at the FDR = 0.01.where, α, β are 20 amino acid residues and gaps, SbxSh F2), or at least some survival within progeny rows of about 5 (SbxSp RILs) or 10 plants . Methods for determining rhizome numbers and distances from the originating crown are as cited (SbxSh F2) or the first five plants in a plot, and vegetative biomass was determined at the end of the growing season (after frost) by harvesting all tissue >2 cm above the ground for entire plots, separating inflorescences from vegetative tissues, drying to stable mass, and determining dry tissue mass. Heritabilities were calculated from F2–F3 regression , or variance component analysis  timorenAP contributed conception and design of the study and wrote the first draft of the manuscript. WK, RJ, PN, VG, KI, US, MK, DB, EW, HR, JB, KB, TC, and MS collected field and/or laboratory data. GW, WP, T-HL, HG, DZ, and FF performed statistical analyses. All authors contributed to manuscript revision, read and approved the submitted version.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.


Antioxidants are molecules that neutralize reactive oxygen species in the human body, reportedly reducing the risk of cancer and cardiovascular diseases. With multiple dietary products being advertised by their assumed high antioxidant concentration, the need for a proper way of analyzing antioxidant containing beverages becomes apparent.capacity and activity: the Antioxidant Index (AI).In this research, the antioxidant nature of teas, wines and (superfood) juices is investigated using staircase voltammetry (SV). A new parameter is proposed and evaluated to characterize the antioxidant nature, including its antioxidant AI showed green tea to have the best antioxidant nature of teas and red wine to be a better antioxidant than white wine. Superfoods did not show better antioxidant behavior than non-superfoods. AI proved to be a promising way of investigating the antioxidant nature of beverages. Food science; Food analysis; Antioxidants; Wines; Teas; Juices; Staircase voltammetry; Antioxidant index; Half wave potential. Two claThe disease-reducing properties of antioxidants can be explained by the principle of oxidative stress, which is associated with the hereabove mentioned diseases. Oxidative stress occurs when the ratio between ROS and antioxidants is shifted . Since aNonetheless, mainly in the 90's and zeros and recently with superfoods too, there is a hype in eating dietary products with a supposedly high antioxidant concentration , 2018b. The antioxidant nature of various products has been investigated using multiple techniques . Among t1/2) of the oxidation peak, the potential at half the anodic peak current (Ipa). Antioxidants with low E1/2 are stronger electron-donating species  under the oxidation peak. Note that the surface area in a CV represents charge Q via the linear relation between applied voltage, V, and time, t: the scan rate. Q is more accurate than Ipa, since it measures the total amount of exchanged charge expressed in Coulombs, which can be caused by multiple electroactive components in a solution  vs. ref (1.031 V vs. Ag/AgCl) and EHER the E0 of the hydrogen evolution reaction (HER) vs. ref (-0.199 V vs. Ag/AgCl)  . The lowIn this research, the antioxidant nature of green, black and rooibos teas, green ice tea, white and red wine, apple juice, cranberry juice, and two superfoods, cranberry juice and blueberry raspberry juice is investigated using SV. The new proposed parameter AI Eq. will be 2Chemicals Sulphuric acid (95–98%) was purchased from Sigma-Aldrich and potassium nitrate (>99.5%), which was used as supporting electrolyte, from Fisher Scientific. 1.0, 0.3 and 0.05 μm Alpha Micropolish® II deagglomerated Alumina from Buehler were used for WE polishing. Propanone  and ethanol  were from Boom. All chemicals were used as received.Instruments SV was recorded using a SP300 potentiostat . A beaker (the cell) was equipped with a 7.07 mm2 glassy carbon WE , a Ag/AgCl (sat'd KCl) reference electrode (RE) and a 240 mm2 Pt counter electrode (CE). Glassy carbon is suitable for measuring antioxidant properties, because of its wide potential window and chemical inertness (nertness . Moreovenertness  and ethanertness . For WE WE Cleaning and Storing Cleaning of the WE during beverage testing was done before each SV. The WE was polished on rayon microcloth (Buehler) using 1.0 μm , 0.3 μm  and 0.05 μm  micropolish respectively. Subsequently, it was rinsed with demineralized water and blow-dried, after which the WE was sonicated for 10 min in propanone. It was then blow-dried and cleaned electrochemically by cycling it 21 times in 1 M H2SO4 between -1.0 and +1.0 V with SR 100 mV/s, ending in -1.0 V. The WE was stored in ethanol.Sample Preparation The tested beverages are listed in 3 was added to the samples to obtain a 100 mM KNO3 containing solution.Testing Procedure The cell, containing 10 mL sample, was equipped with the WE, CE and RE and subsequently, the temperature of the sample was recorded. SV was recorded successively for 4 cycles between +1.0 and -1.2 V with vscan 50 mV/s , starting and ending in +1.0 V. All measurements were done in two weeks.Data Processing Surface area, anodic peak potential  are shown in The oxidation peaks of GT, BT, WW, and RW occur at a higher potential than can be found in the literature . In thes1/2 of samples at room temperature. The exact E1/2 values can be found in table SM1. GT and BT show the highest antioxidant activity and RW the lowest.1/2 and Q are independent variables: a high E1/2 can correspond with a low Q for a certain beverage and the other way around, as becomes clear from comparing Tea samples showed a ranking based on Q of IT > GT > BT > RT, which for GT and BT conforms to previously performed research using CV . IT can 3.2max of IT (47.21 ± 0.33 μC) is used. In 1/2 of RW is 0.2 V higher than that of GT or BT. However, it is debatable how significant E1/2 is in comparison to Q. In the current equation, they are given equal weightings. The best way to evaluate this is to compare results to another technique that is suitable for measuring antioxidant nature, such as the 2,2-diphenyl-1- picrylhydrazyl method (DPPH)  . Weighti41/2) and the antioxidant capacity (amount of charge exchanged).A novel Antioxidant Index (AI) is proposed as a general measure of antioxidant properties, based on both the antioxidant activity  into one parameter. It should be investigated how the actual scavenging of ROS is improved by either the activity or capacity of antioxidants.max. A good option for this would be Trolox, which is also used in the antioxidant determination by Trolox equivalent antioxidant capacity (TEAC) as a reference antioxidant (1/2, respectively.Additionally, it is recommended to search for a compound that can serve as a reference standard for Qioxidant . MoreoveW. H. Schilder: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.E. Tanumihardja, W. Olthuis: Conceived and designed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.A. M. Leferink, A. Van den Berg: Analyzed and interpreted the data; Wrote the paper.This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.The authors declare no conflict of interest.No additional information is available for this paper.


Bemisia tabaci is a crop pest insect that is difficult to control through commercially available methods. Technology that inhibits gene expression is a promising avenue for controlling whiteflies and other pests. While there are resources available to make this method insect specific, and therefore more effective, it is currently being used in a way that targets insects broadly. This broad approach can cause potential harm to the surrounding environment. Here, we discuss considerations for using gene-silencing technology as a pest management strategy for whiteflies in a way that is specific to this pest, which will address short- and long-term issues of sustainability. We also provide a way of selecting target genes based on their roles in the life history of the insect, which will reduce the potential for unintended negative consequences.The whitefly Bemisia tabaci is a globally important pest that is difficult to control through insecticides, transgenic crops, and natural enemies. Post-transcriptional gene silencing through RNA interference (RNAi) has shown potential as a pest management strategy against B. tabaci. While genomic data and other resources are available to create highly effective customizable pest management strategies with RNAi, current applications do not capitalize on species-specific biology. This lack of specificity has the potential to have substantial ecological impacts. Here, we discuss both short- and long-term considerations for sustainable RNAi pest management strategies for B. tabaci, focusing on the need for species specificity incorporating both life history and population genetic considerations. We provide a conceptual framework for selecting sublethal target genes based on their involvement in physiological pathways, which has the greatest potential to ameliorate unintended negative consequences. We suggest that these considerations allow an integrated pest management approach, with fewer negative ecological impacts and reduced likelihood of the evolution of resistant populations.The whitefly  Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) is a globally important cryptic species complex that causes billions of U.S. dollars in damages to many crops including vegetable and row crops and therefore presents a major threat to food security [B. tabaci [Manihot esculenta), squash (Cucurbita spp.), and tomato (Solanum lycopersicum) to row crops such as cotton (Gassypium spp.) [B. tabaci is extensive because it can be either direct or indirect [Bemisia tabaci can also cause economic loss without direct impact on the health or vigor of the plant. For example, the diet of sugary sap causes the whiteflies to excrete a sticky substance referred to as honeydew, which coats the plant, results in a buildup of mold, and the economic value of the crops is lowered. This is especially true for cotton and other fibrous plants that are used in textiles [B. tabaci causes economic losses in crops is by transmission of plant viruses, including the devastating begomoviruses [The whitefly security ,2,3. Ove. tabaci , and croindirect . Like moindirect . Damage indirect . Bemisiatextiles ,8,9. Howoviruses .B. tabaci [B. tabaci has developed resistance to most classes of insecticides [Bacillus thuringiensis (Bt) transgenic crops, have proven ineffective against whiteflies and other sap-sucking insects due to their lack of sensitivity to Cry toxin proteins [B. tabaci.Controlling whiteflies has proven difficult. Not only is it difficult to target whiteflies with contact insecticides as they feed from the bottom of leaves, mounting research has made clear that the practice of using pesticides with broad modes of action is costly both economically and biologically, as such methods lead to indiscriminate losses in non-pest insects including beneficial pollinators or natural enemies that feed on . tabaci ,12,13. Mcticides . Other pproteins . Other mproteins  and relyproteins ,18. ThusB. tabaci because the genomes for three important cryptic species in the species complex  are published [RNA interference (RNAi) has emerged as a promising method for pest management. This technology offers a mode of action targeted to specific genes, allowing different physiological systems to be modified to control insect growth, development, or feeding behavior. This gene-silencing mechanism takes advantage of an evolutionarily ancient method used by cells to stabilize the genome against attacks from RNA viruses and foreign genetic elements. RNAi uses exogenous double-stranded RNA (dsRNA) to silence gene expression by co-opting cellular defense mechanisms to target preselected genes  using dsublished ,23,24. Mublished ). Becausublished .B. tabaci. However, conceptual frameworks that explore the scope of RNAi’s functionality in pure biological terms, considering the appropriate target pathways and desired ecological outcomes, are lacking. Advancing these discussions demands closer attention to knowledge of species specificity, off-target effects, and the possibility that the target species will evolve resistance is lacking . MoreovB. tabaci have been worked out [B. tabaci is to (1) select a target gene that is crucial for survival, (2) use a plant-mediated delivery method, and (3) prevent dsRNA degradation. While this workflow is useful for current implementations of RNAi, it only partially addresses the question, “What is successful application of RNAi in pest control?”RNAi has been used for over 20 years to evaluate gene function in insects ,44,45,46rked out ,56,57,58We contend that successful RNAi-mediated control strategies should offer an effective and sustainable alternative or supplement to conventional methods. If true, the present considerations must be expanded to include strategies that minimize ecological impacts and maximize long-term efficacy. This sentiment echoes increasing calls for more holistic approaches to RNAi-mediated pest management . For thesublethal forms of RNAi control as practically effective, long-term sustainable alternatives to current approaches. In contrast to conventional approaches that manage lethality through dosage administration, we use the term “sublethal” to refer specifically to the magnitude of outcome predicted when knocking down particular gene pathways. In other words, gene targets are selected based on their additive contributions to total fitness as opposed to their necessity for survival.Of primary importance for designing and implementing species-specific RNAi control is the careful consideration of desired effects. When it comes to pest management, the desired effect is most often assumed to be lethality ,59,60,61B. tabaci from agroecosystems. One possible outcome of this is that a secondary pest species will increase in population density and be more harmful to the cropping system than B. tabaci [The first major issue with RNAi applications that result in lethal outcomes lies in the ecological impact of totally eradicating . tabaci . A secon. tabaci ,64. Fina. tabaci ,65,66. O. tabaci ,68. Such. tabaci ,70. For . tabaci . DeployeAn alternative to lethal RNAi strategies is to develop a framework that more closely aligns with the goals of integrated pest management. One approach currently being developed for RNAi is based on the use of sublethal methods to control pest populations ,72,73,74Though the development of resistance is not considered an immediate issue for RNAi-mediated pest control, it remains an important consideration for long-term sustainability . The maiB. tabaci females reproduce sexually, resulting in diploid daughters [B. tabaci is haplodiploid, meaning that females also have the ability to reproduce asexually when suboptimal conditions make it difficult to locate mates [Whiteflies exhibit very rapid population growth cycles, which are augmented by their ability to flexibly switch between sexual and asexual modes of reproduction . Under oaughters  , which initially segregates at low frequencies in the population. Two factors—the magnitude of effect of the allele on an individual’s phenotype and the strength of natural selection on resulting phenotypes—will determine the speed at which a resistance allele rises to fixation in a population. The efficiency of these dynamics is particularly pronounced in instances where control methods cause lethality, as this mimics a phenomenon referred to in nature as ‘hard selection’ ,81 Hard Drosophila melanogaster as a long-standing genetic model organism, dipteran model systems serve as the standard from which entomologists have come to understand gene function in insects [D. melanogaster and/or inferred from sequence similarity with Drosophila homologues [Drosophila is that this would allow for more precise scientific monitoring and troubleshooting of RNAi behavior.To prioritize pathways containing genes that are less highly conserved across arthropods and whose knockdown is less likely to cause lethality, an understanding of species-specific gene function is required. Unfortunately, we do not always have gene function defined across species. Overwhelmingly, given the status of  insects . Even th insects ,23,24, tmologues ,85. ThesB. tabaci populations [B. tabaci, genes related to mating behavior may not be effective because of their ability to reproduce parthenogenically. More promising are genes targeting gametogenesis and immunity. Knocking out reproductive capacity is attractive because B. tabaci reproduction can be knocked out at several stages. Potential genes include those involved in gamete viability such as Boule, Vasa, or DNA methyltransferase 1 (Dnmt1) [Dmnt1 is expected to maintain methylation in the genome. However, methylation in insects does not appear to be universal, and some species such as the genetic model species D. melanogaster and Tribolium confusum lack Dmnt1 altogether [Oncopeltus fasciatus, a hemipteran, co-authors have shown through RNAi knockdown that Dnmt1 has an essential function in gamete production in both males and females and that the silencing of Dnmt1 results in sterility without causing mortality or disrupting somatic tissue [Dnmt1 gene sequence has been identified in B. tabaci and successfully knocked down using RNAi [Categories of sublethal target pathways include those involved in gametogenesis, immunity, movement, mating, and appetite stimulation. Targeting genes from pathways involved in the detoxification of plant defenses has already shown to be successful at reducing, but not eradicating, ulations . Table 1 (Dnmt1) ,87,88,89together . Yet in c tissue ,87. The ing RNAi .B. tabaci from infection also have the capability to degrade the dsRNA before it can be processed by the endogenous RNAi machinery. In many cases, the dsRNA is ingested and subsequently degraded by nucleases in the saliva or the gut [B. tabaci, as well as other insect orders, that dsRNA-mediated silencing of digestive nucleases enhances the efficiency of silencing target genes [Another possible sublethal target is immunity. Targeting immunity genes has the two-fold benefit of reducing fitness and increasing efficiency of the dsRNA construct. The immune system barriers that evolved to protect  the gut . This ph the gut ,101,102.et genes ,104,105.B. tabaci control will depend not only on building on our knowledge of the success and failures of current approaches, but also on how well we address long-i.eterm issues. Using RNAi to resolve these challenges offers a unique opportunity to explore these novel aspects of B. tabaci, including evolution of resistance and reproductive biology, while also managing their economic damage. It also makes it possible to address other issues such as application effects and cost issues, which are crucial factors for the development of practical biopesticides. For the best long-term outcomes, future RNAi applications using B. tabaci should consider selecting non-lethal target genes that confer continuous traits such as fecundity. Further developing RNAi for use as a sustainable pest management strategy will require extensive collaboration between IPM specialists, laboratory scientists, and public policy makers.The remarkable diversity of pest management strategies is matched only by the ability of insects to overcome them. RNAi strategies that take into consideration life history and population genetics are likely to be valuable control strategies. Success in


Genomic prediction collectively refers to these studies, and it estimates an individual’s phenotypes mainly using single nucleotide polymorphism markers. Typically, the accuracy of these genomic prediction studies is highly dependent on the markers used; however, in practice, choosing optimal markers with high accuracy for the phenotype to be used is a challenging task. Therefore, we present a new tool called GMStool for selecting optimal marker sets and predicting quantitative phenotypes. The GMStool is based on a genome-wide association study (GWAS) and heuristically searches for optimal markers using statistical and machine-learning methods. The GMStool performs the genomic prediction using statistical and machine/deep-learning models and presents the best prediction model with the optimal marker-set. For the evaluation, the GMStool was tested on real datasets with four phenotypes. The prediction results showed higher performance than using the entire markers or the GWAS-top markers, which have been used frequently in prediction studies. Although the GMStool has several limitations, it is expected to contribute to various studies for predicting quantitative phenotypes. The GMStool written in R is available at  With the recent dramatic decreases in sequencing and genotyping costs, GP is more readily accessible than ever and has enabled the efficient prediction of genetic disease risks, genomic breeding values, and complex quantitative phenotypes directly from genomic data. GP can be performed using either all SNPs or only subset SNPs from whole-genome sequencing (WGS) or SNP array data. Generally, since many SNPs are used, using all SNPs from WGS data is expected to result in higher GP accuracy than using SNPs from array data. However, in practice, this increase in accuracy is rarely observed in real data3, and little differences, or even a decrease in accuracy, have been reported in previous studies5. This is because WGS data retains more SNPs relating to a phenotype of interest, but the number of relevant SNPs accounts for a relatively small proportion of the total number of SNPs used, and the considerable number of remaining SNPs are phenotypically neutral6. To avoid these burdens due to the large number of uninformative SNPs, approaches using an adequate subset of SNPs have been proposed6, and multiple studies have reported that SNP subsets improved GP accuracies compared to using all or numerous SNPs. For example, Ni et al. improved GP accuracy using only SNPs in or around genes from WGS data7, and Brondum et al. increased GP accuracy using both SNPs associated with quantitative trait loci and SNPs significantly detected in a genome-wide association study (GWAS)8.Genomic prediction (GP) based on single nucleotide polymorphism (SNP) markers has become a powerful tool for various human healthcare as well as conventional plant and animal breeding programsp-values of 1 × 10–6 or 5 × 10–8 are used as a subset, and simulation studies have reported that improved accuracy is attained when using GWAS-significant SNPs and their surrounding SNPs9. However, these predictions using the GWAS-significant and/or surrounding SNPs have not always been successful10. This is likely because the significant SNPs explain only a small percentage of the total genetic variation for a phenotype11, and each SNP also accounts for a small portion of the phenotypic variance12. Thus, identifying more robust methods to select the optimal subset of SNPs for GP has focused on multiple studies. Bermingham et al. and Filho et al. reported that using the top 100–10,000 GWAS SNP markers as subsets could increase GP accuracy compared to using only GWAS-significant SNPs14. Yilmaz presented an algorithm that selects subsets of markers considering SNP–SNP interactions from a GWAS result and confirmed that using these subsets improves accuracy compared to using only top GWAS SNPs15. These studies have demonstrated that the selection of SNP subset has a considerable effect on GP accuracy; however, the GWAS top SNP-based approaches have difficulty selecting an appropriate number of top SNPs with high GP accuracy and require numerous attempts each time for each phenotype. The interaction-based GWAS approach requires information on the interactions between SNPs, in addition to the GWAS result. Furthermore, if the interaction information is incomplete, this approach can construct a biased subset that can hinder GP accuracy15. Therefore, there is a need for a new approach that selects an optimal subset of SNPs to maximize GP accuracy while considering the single and interaction effects of markers.A GWAS identifies SNP markers associated with a phenotype. Thus, it is utilized as a useful approach to construct a subset of SNP markers for GP. Usually, significant SNPs detected below the p-value in the GWAS result and constructs the optimal marker set by accumulating SNP markers that increase the phenotype's prediction accuracy. Subsequently, GMStool performs GP modeling and presents the best prediction model with the optimal marker set. Statistical, machine, and/or deep learning methods are used, and the interaction effects of the SNP markers are considered indirectly through modeling. The R package and execute scripts of GMStool are available at www.github.com/JaeYoonKim72/GMStool with detailed usage instructions.In this study, we have developed an implemented GWAS-based marker selection tool named GMStool. GMStool searches for SNP markers in order of the lowest p-value columns. The test list file consists of a single column with the names of the test samples. As an option, a list of markers that must be included in the final modeling can be passed to GMStool. If this file named by 'preset marker’ is not defined, the top n markers based on GWAS p-values are selected and used for the initial analysis (default 1). Note that GWAS must be performed using only a training set (excluding the test set) to completely exclude information on the test set that may cause bias in the training result16. Although the GWAS does not include the test set, both genotype and phenotype files must include all samples of the training and test set, since GMStool automatically converted the test set into input files for the final modeling phase.GMStool consists of three phases: preparation, marker selection, and final modeling Fig. A. In theDivide the input data into training and test sets, using the information of the test set defined by the user.k groups for cross-validation , and perform marker selection in each group. All k groups are executed simultaneously through multi-threading. The process of selecting markers in each group is as follows .C.n initial markers from GWAS results ordered by p-values (default 1), and consider them as an initial marker set. If ‘preset marker’ is defined, consider these preset markers as the initial marker set instead of selecting the top markers. Build the prediction model using the initial markers of train sets and predict the correlation rate on the validation set. Place the initial markers into the selected marker set , and if preset markers were defined, select the top one from GWAS, ordered by p-values. Add this marker to the selected marker set formed in step C. Build the prediction model using the marker set, and calculate the current correlation rate on the validation set. If the difference of the correlation rate between ‘current’ and ‘previous (Set corr.)’ is greater than or equal to the increment value If the top E.p times in a row, that is, if the markers are not selected p times in succession, stop the marker selection and return the results up to that point. Here, p is a number corresponding to x% of the total number of input markers and is automatically calculated according to the x defined by the user (default 20).Iterate step D, while adding markers one by one. If the correlation rate of the selected marker set on the validation set reaches the target correlation rate defined by the user , stop marker selection and return the final marker list. As a stop condition, if the validation set's correlation rates do not improve Divide the training set into ows Fig. B:A.Consik groups into one final marker list of all k CVs. Using both RRB and BTS methods, one final marker list is generated by combining all marker lists derived from these two methods. Subsequently, the input files for the final modeling phase are made based on the final marker list.Integrate the selected marker lists of The marker selection phase applies the forward selection method of regression analysis and sequentially selects SNP markers that increase the correlation rate between observed and predicted phenotypes on the validation set Fig. B. The opp times (default 50), and in each p, training and validation sets are randomly constructed at a ratio of 8:2 from the training set derived from the marker selection phase. Of all the p modeling iterations in each model, the model with the highest correlation rate on the validation set is considered the final prediction model. After modeling, a final prediction is performed on the test set, and its correlation rate between the observed and predicted phenotypes is presented with the saved model file and summary plots. If more than one model is selected, the model with the highest correlation rate on the validation set is presented as the best model.The final modeling phase performs prediction modeling using RRB, random forest (RF), deep neural network (DNN), and convolution neural network (CNN) models Fig. A. All fo17 in R18, and is used in GMStool as follows: n; samples), p; markers), nn). Here, the loss function is 19. The RRB, a statistical model, assumes that marker effects follow a normal distribution with constant variance and all effect sizes are small and similar. This model is implemented using the “rrBLUP” library20. The RF for regression trees in GMStool is implemented through the “randomForest”21 library in R18 and used in the final modeling phases as follows:Generate 1000 bootstrap datasets by sampling the training samples with a 0.632 ratio.Construct regression trees by sampling 1/3 of the total input markers for each dataset.Grow the regression trees by splitting markers, with the loss function of the root mean square error.Derive the predicted phenotypes of the validation and test sets by averaging the phenotype values predicted from the 1000 trees.RF, a decision tree-based machine learning model, provides an explicit representation of markers' interactions without needing to pre-define the interactions. Thus, RF can reflect SNP-SNP interactions when modeling genotype dataIn the RF model, it is possible that none of the specific markers will be included in every tree due to marker sampling. Therefore, the marker selection phase uses the BTS model, an RF model that excludes the marker sampling function, and only the sample bootstrap function remains. The BTS model uses 100 trees sampled at a 0.632 ratio from the training samples and grows the trees without marker sampling. Then, in the same way as RF, the BTS model calculates the validation set's predicted phenotypes.22 and "keras”23 libraries in R18. These two models can take SNP-SNP interactions into account during modeling and computed at high speed through a graphics processing unit (GPU)24. The DNN model in GMStool has a 256-128-64-32-16-1 architecture: one input layer with the number of neurons equal to marker size, five fully connected layers with 256, 128, 64, 32, and 16 neurons, respectively, and one output layer with one neuron  and soybean (Glycine max) data were used to evaluate the performance of GMStool  of < 1% were filtered out, and 36,901 and 95,776 SNPs with missing rates of 0.043 and 0.005 were obtained from the rice and soybean datasets, respectively. Imputation was then conducted on both datasets using BEAGLE v5.129. For the phenotype data, the days to flowering time (DTF), protein content (PC), and plant height (PH) were used for rice, and DTF was used for soybean.Rice .GMStool selects such optimal marker sets more effectively. Based on the prioritization of markers derived from a GWAS, GMStool sequentially searches for markers that increase the correlation rate between observed and predicted phenotypes and constructs an optimal marker set by accumulating these markers individually. As selection methods, the RRB model is provided to reflect the additive effects of markers on a phenotype, and the BTS model is provided to consider the interaction effects of markers, although indirect due to the use of GWAS. GMStool applies a 35; thus, to handle diverse phenotypes, GMStool provides four types of models in the prediction modeling: RRB, RF, DNN, and CNN. RRB assumes no interaction between markers but can predict phenotypes with high accuracy in practice36. RF has the ability to predict phenotypes by considering interaction effects as well as the dominance effects of markers37. DNN and CNN learn the global and local genotype patterns associated with a phenotype, thus can reflect the complicated relationship between genotype and phenotype without requiring predefined rules 24. The DNN and CNN models of GMStool were built only to predict the optimal marker sets  between SNPs can affect prediction performance, it is recommended to perform SNP pruning for LD before GWAS or clumping after GWAS. Second, the interaction effects of markers on the phenotype considered are indirect. Since GWAS only considers the linear effect of a single marker, GMStool based on this result has limited consideration of markers' interaction effect in RF, BTS, CNN, and DNN models. Despite these constraints, the models indirectly considering the interaction effects had occasions that showed better performances than the models that considered only linear effects in the evaluated data set. Third, the results of GMStool are affected by the input options, such as initial markers, k-CV value, increment value, and target correlation rate. Since these options are provided so that the user can flexibly cope with various phenotypes, the user should preferentially find optimal options for the target phenotype through several pretests combining these options. As for the number of initial markers, the top one GWAS marker was used as the default option under the assumption that the GWAS result is reasonable. If the GWAS result is highly reliable, the user may designate the initial markers to the markers related to phenotype from the top GWAS markers; otherwise, it is recommended to directly designate the related markers as initial markers the ‘pre-selected marker’ option. The k-value of CV is suggested to be between 3 and 5 depending on the training sample size. The increment value for the correlation rate is recommended to be at least 0.00005 to avoid excessive selection of SNPs within the same LD block, and this setting is particularly recommended if pre-processing or post-processing for LD is not performed. The target correlation rate is suggested to be at least 0.99 so that all potential markers can be selected, although the heritability of the phenotype to be used is low. In the case of low heritability, even if all potential markers for the train and validation set are selected, the final prediction performance for the test set may not be superior to the prediction performance using all markers (Fig. Nevertheless, GMStool has several limitations. First, marker selection is influenced by the GWAS results. GMStool uses the priority of the markers derived from the GWAS result and sequentially selects markers with the lowest ers Fig. B. DespitSupplementary Information 1.Supplementary Information 2.Supplementary Information 3.


Adolescent pregnancy and childbirth are common throughout Central America. While gendered beliefs promoting motherhood are a known risk factor, their association with adolescent childbirth within the social networks of Central American communities is unknown.This was a cross-sectional study looking at adolescent childbirth amongst women ages 15-20 years (N = 2990) in rural Honduras, using reproductive health data on all individuals ≥15 years of age (N = 24 937 of 31 300 population) including social network contacts, all of whom were interviewed as part of the study. The outcome, adolescent childbirth, was defined as having had a child < age 20 years. Predictors included whether a woman’s social contact had an adolescent childbirth and the social contact’s reported perception of community support for adolescent childbirth.While girls who identified a father in the village as a social contact had a lower likelihood of adolescent childbirth regardless of whether or not they reported being in a partnership, this finding did not hold for girls who identified mothers. There was an association between a social contact’s report of norms supporting adolescent childbirth and a girl’s risk of adolescent childbirth; however, village-level aggregate norms attenuated that relationship. Independent significant associations were found between a girl’s risk of adolescent childbirth and both a social contact’s adolescent childbirth and the village proportion of women who had had an adolescent childbirth. The association between social contacts’ adolescent childbirth and a girl’s risk of adolescent childbirth across relationships was more robust for stronger relationships and when the social contact was closer in age to the girl.If, as this evidence suggests, a strong driver of adolescent childbirth is the frequency of the occurrence of adolescent childbirth both within the greater community and within a girl’s proximal social network, the challenge for intervention strategies is to encourage norms that prevent adolescent childbirth without stigmatising those who have had an adolescent childbirth. Programmatic efforts to counter prevailing norms that limit a woman’s role to motherhood, and that support and encourage strong norms for girls’ education may play an important role in addressing this situation. Rates of adolescent pregnancy in Latin America and the Caribbean are higher than for other regions with similar levels of development ,2. HonduInterconnected groups of individuals can shape the health-related behaviour of individuals by transmitting and enforcing group-specific social norms . Social Social norms operate within reference groups, or the groups of people to whom an individual turns for expectations regarding what is common and what is acceptable ,24,25. RThere is evidence that social factors play a role in adolescent childbearing. Both injunctive and descriptive pregnancy norms of girls’ close friends and family members have been shown to influence their likelihood of adolescent childbirth ,29. HoweWhile adolescent childbearing in Central America is often associated with low social control of adolescent sexuality, there is evidence that injunctive norms around motherhood may play a role as well. Prevailing norms in the Central American region link women’s status to their fertility . PreviouDespite the importance of seeking to understand the impact of an adolescent’s social network in the context of studies of adolescent fertility, studies typically focus on individual-level risk behaviours . ResearcUsing a novel data set from rural Honduras, this study analyses the social network correlates of adolescent childbirth. Here we use data from a high adolescent fertility region of Honduras on social network relationships, individual attitudes, perceived community norms, and descriptive norms specific to adolescent childbirth to generate evidence about the norms that may be most strongly associated with adolescent childbirth, and the salient reference groups for those norms.Our study uses full population census data from the western municipalities of the largely rural Copán department of Honduras to analyse the social network determinants of adolescent childbirth at the individual level. Data were collected as part of a randomised controlled trial of social network targeting of a maternal and neonatal health intervention in a study area comprised of 176 villages located in the municipalities of Copán Ruinas, Santa Rita, Cabañas, and San Jerónimo ,39. The We mapped the study area to gain a more precise profile of the study population and field conditions, including terrain, rainfall, and distances to health facilities. This is an area of over 200 square miles of rugged mountainous terrain with an estimated total population of 32 800 people older than 12 years of age. We conducted a complete census in 2016 which covered 92% of the eligible population. Of these, 25 032 completed a baseline survey that included sociocentric network and behavioural health measures. For the purposes of these analyses, we excluded children under age 15 (N = 2577) as they did not complete the full reproductive history, and the proportion who had already experienced a childbirth was miniscule. Individuals who were cognitively impaired and unable to provide consent were also excluded (N = 30). With the above exclusions, our final number of study participants was 22 449.http://humannaturelab.net/resources/software/trellis/) to undertake the main survey, which included a battery of “name generator” questions to capture social relationships. We took photographs of all people from whom data were collected in this study, so that individuals could identify their social contacts by picture. Individuals were notified at the beginning of the study regarding the methods to be used, including photographs to be used for network identification. All participants had the option of refusing to have their photo taken. Network identification was anonymized within the data files and encrypted. The boundaries of each network were the village, so that individuals could nominate any individual from within their own village as a social contact.We used the publicly available software “Trellis” . The outcome, adolescent childbirth, was defined as having had a child under the age of 20, consistent with the definition used in DHS and other similar demographic surveys. Social network measures for adolescent girls were calculated based on the full village network. All individuals within each village were interviewed about their health and reproductive history as well as their social networks. Adolescent girls were matched with the people they identified , so that we could assess the correlations between alters’ characteristics and adolescent childbirth outcomes. Village-level measures included aggregates of the entire study population by village.Female respondents were asked whether or not they had ever given birth to a living child, and if so they were then asked to provide the birthdates of their last four children. For adolescents with four or fewer children, their age at first birth was calculated as the difference between their date of birth and the date of birth of their first child. None of the adolescents in our primary sample population of females ages 15-20 had more than 4 children.Individual demographic variables included age, marital status, religion, income sufficiency, education, food security, proportion of life lived in the village, and indigenous status. For exact coding of these measures, . Here, we defined the full networks at the village level. All participating respondents in the village took part in the network survey, and their nominations could include anyone within the village. Respondents were asked 14 separate tie strength, or a simple count of the number of name generators in which the adolescent had nominated that particular alter, which can serve as a proxy for how close they are to each other. We also created a measure of direction. Did the adolescent girl nominate the alter (out-ties), did the alter nominate the girl (in-ties), or was the nomination reciprocal (both-ties).In order to understand the potential association of a reference group at the more proximal interpersonal level, we used measures from individual girls’ direct social network ties, as described above. Because all questions asked of the adolescent girls in our study were asked of everyone in the study population, we were able to match each adolescent with the social normative factors, including beliefs, attitudes, and adolescent birth outcome of each individual she nominated in the social network survey , as well as with those who nominated her. An individual could name any one alter up to 14 times through the name generators. We therefore have data for each of the adolescents’ social connections, and we know through which questions they were named. For each adolescent girl-alter pair, we calculated a measure of We ran analyses at the individual level and at the dyadic level of social network connections. Individual analyses used logistic regression with the outcome being an adolescent birth, and were run using multilevel modeling, clustered at the village level. For our first set of models, we tested whether family composition, namely nominating a mother or a father in the village, was associated with having had an adolescent childbirth. All models controlled for sociodemographic characteristics, including time spent in the village. We then ran a series of models using dyadic observations, using logistic regression with adolescent birth as the outcome. Each adolescent girl-alter pair comprised one observation, with controls for girl’s age, education, religion, indigenous status, income insecurity, years lived in village, and village size. For these models, we were able to ascertain whether alter-level characteristics were associated with the likelihood that a girl had an adolescent childbirth. Our first set of models was run using the adolescent girl’s full network, including those she had nominated and those who had nominated her. Our second set of models was focused on those the girl herself had nominated and were stratified by name generator in order to pinpoint potential reference groups for normative beliefs and behaviours associated with adolescent childbirth. All dyadic level models were run using general estimating equation functions to adjust for multiple observations of the same girl.Our first set of analyses at the individual level tested whether familial relationships were associated with an adolescent childbirth outcome. We ran several interaction models (not shown), and found that the relationship between an alter’s adolescent childbirth and the likelihood of a girl’s adolescent childbirth was lower the older the alter was compared to the girl, lower if the alter was someone whom she herself nominated but who did not nominate her back, higher the greater the tie strength between her and the alter (the stronger the relationship), and, higher if the alter reported approving of a higher age of first childbirth. We also found that the association between an alter’s belief in an older age of first childbirth and a lower likelihood that the girl had had an adolescent childbirth was stronger if the alter was a male and if the alter was in the same household.In father, if an alter had had an adolescent childbirth, the likelihood that the girl had had an adolescent childbirth was much higher compared to when the alter had not had an adolescent childbirth (column 1). Each row represents the model from one name generator, controlling for all covariates (not shown). Across almost every type of relationship with the exception of In column 2 of The bottom 3 rows of In In this study, we considered the social network factors, both at the proximal interpersonal and at the distal community levels, associated with girls’ adolescent childbirth outcomes among the population of 176 villages in rural Honduras. Our primary aim was to understand the reference groups, or sources of normative support and pressure, for adolescent childbirth. Our findings show compelling support for the hypothesis that there are reference groups at multiple levels that may influence adolescent childbirth outcomes.We found that having both a father in the village and a mother in the village had strong associations with a lower likelihood of giving birth as an adolescent. However, when we included the nomination of a spouse in the models, the role of the girl’s father remained, but her mother’s influence became insignificant. Our initial supposition was that girls who had had an adolescent childbirth may have left the village with a partner, and therefore the girl’s parents would not have been present because she most likely would have moved from her home village. To adjust for this, our models included a measure of years lived in the village, and we included a measure of spouse in the final model as well. Why are fathers still significant but mothers are not? In an exploratory analysis of factors associated with having named a spouse (not shown), girls who named a mother were three times less likely to have nominated a spouse than those who named fathers. Clearly the association with having a mother and an adolescent childbirth was accounted for statistically by partnership with a spouse but this was not the case for having a father. This is important evidence that fathers may exert a strongly protective effect against adolescent childbirth in this population, independent of whether or not the adolescent girl is partnered, whereas once the girl is partnered the influence of the mother in protecting against adolescent childbirth goes away. This is consistent with previous research showing that a missing father is an important risk factor for early childbearing .We found evidence of strong normative dynamics at the interpersonal level that were associated with adolescent childbirth, with strong clustering in these networks. In terms of descriptive norms, the probability that a girl had had an adolescent childbirth if her alter did was high across all types of relationships with the exception of fathers, and was stronger when the relationship between the adolescent girl and the alter was stronger (higher tie strength). This is compelling evidence that interpersonal proximal network influences constitute a strong source of normative influence for adolescent childbirth. When we included the proportion of women in the village that had had an adolescent childbirth, a more distal source of potential influence, we found no notable reduction in the relationship between the girl’s adolescent childbirth and that of her alter. This suggests that the descriptive norm regarding the behaviour of others in the community to whom the adolescent girl is unrelated has little influence on her behaviour. The clustering of adolescent pregnancy was occurring at multiple levels: at that of her direct social contacts, and at the level of the greater community, with the association with proximal alters playing a much stronger role. However, with the question that asked individuals their perception of the community’s response to adolescent childbirth – exploring the influence of injunctive norms – we found a different dynamic. While we saw significant associations in the relationship between an alter’s perception of positive community norms towards adolescent childbirth and a girl’s own adolescent childbirth, most of those relationships diminished or were no longer significant after we included the village-level aggregate norm in the model. This suggests that injunctive norms are most salient at the level of the community and less salient at the level of direct social contacts, and that the reference group for sources of injunctive normative pressure may be mostly occurring within the village context, more so than within individual relationships. The fact that community aggregate norms were more strongly associated than individual alter norms provides us evidence for a causal relationship: it is unlikely an entire community would change its perception of norms around childbirth in response to any one girl’s childbirth, while for individual alters this could be a reasonable consideration.We did not include the aggregated attitudes towards best age for childbirth in the models because our previous research found it to be non-significant . HoweverResearch on social network influences invariably generates hypotheses, but ultimately we aim to uncover cause and effect relationships. We would like to know the probable causes both for general scientific purposes and to give us strategic points for intervention. With cross-sectional data we cannot make causal claims. However, with strong cross-sectional data we can uncover crucial clues for causality, and areas for which expensive longitudinal data would be worth the investment. In this analysis, we have interesting evidence for attitudes at the alter level associated with adolescent childbirth, but little to help us decipher the directionality of those associations. We have reasonable evidence that village-level community norms around adolescent childbirth may exert influence. However, we have very compelling evidence that adolescent childbirth both in a girl’s proximal networks, and in her wider community, normalises adolescent childbirth, allowing it to persist through generations. While a girl, having had an adolescent childbirth, may choose new relationships with other girls and women who have had adolescent childbirths, this is unlikely to be the case across her entire network, and certainly not possible in the case of direct family members. Furthermore, it is extremely unlikely that girls with low levels of education and minimal financial resources are choosing villages with high overall rates of adolescent childbirth after they themselves have had one. Of all the social factors we have considered, exposure to adolescent childbirth within her network seems to be the most salient possible determinant, regardless of the norms or attitudes that oppose it. In contrast, the presence of a father in her life emerges as a strong deterring factor.This analysis has several limitations. The first is that the data are not longitudinal so we cannot track the influence of norms and attitudes across time. Second, this population of girls (aged 15-20) included those who may still have an adolescent birth, and yet statistically they fell into the category of those who have not. Moreover, adolescent girls in both groups – those who have had and those who have not had an adolescent childbirth – may have had additional pregnancies which ended in miscarriage, abortion or stillbirth which are not included in our analysis. Third, while rates and demographic determinants of adolescent childbirth in this population were consistent with the determinants in Latin America in general, this population is specific to a certain region in Honduras, and so some factors may not be generalisable. Finally, while these results may offer important insights for contexts with low parental social control of adolescent sexual activity, they are not likely to be relevant in contexts where parental control is strong, and adolescent childbearing is often the result of arranged marriages.Adolescent childbirth is a serious issue in Latin America, affecting nearly one-third of the adolescent girls in our sample, and is becoming a high public health priority, both in Latin American and globally ,44. The