PCoA), and STRUCTURE) based on single nucleotide polymorphisms (SNPs) from SLAF-seq all indicated the existence of two populations based on botanical race: Mexican–Guatemalan and West Indian genotype populations. Our results based on SNPs from SLAF-seq indicated that the Mexican and Guatemalan races were more closely related to each other than either was to the West Indian race, which also was confirmed in the UPGMA cluster results based on SNPs from transcriptomic data. SNPs from SLAF-seq provided strong evidence that the Guatemalan, Mexican, and Guatemalan × Mexican hybrid accession possessed higher genetic diversity than the West Indian races and Guatemalan × West Indian hybrid accessions. Six race-specific Kompetitive allele specific PCR (KASP) markers based on SNPs from SLAF-seq were then developed and validated.Genomic data is a powerful tool. However, the phylogenetic relationships among different ecological races of avocado remain unclear. Here, we used the results from specific length amplified fragment sequencing (SLAF-seq) and transcriptome data to infer the population structure and genetic diversity of 21 avocado cultivars and reconstructed the phylogeny of three ecological races and two interracial hybrids. The results of the three analyses performed (unweighted pair-group methods with arithmetic means (UPGMA) cluster, Principal coordinate analysis ( Persea americana Mill.) is among the most economically important subtropical/tropical fruit crops worldwide and is native to Central America and Mexico [P. americana var. drymifolia), Guatemalan , and West Indian (P. americana var. americana) [Avocado (d Mexico . Avocadod Mexico . Three oericana) . These tericana) ,4. The Mericana) . The Wesericana) . Avocadoericana) . Therefoericana) . found that the Guatemalan and Mexican races were more closely related to each other than either were to the West Indian race.Previous phylogenetic research on these three ecological races has typically resulted in diffuse racial boundaries or genetic relationships. Studies that used morphological characters  and diffA strongly supported phylogeny of these three ecological races is not available because of a lack of abundant, informative genome-scale datasets. Next-generation sequencing technologies have recently facilitated the generation of a large amount of DNA sequences and could serve as a powerful tool for analyzing population genetics and phylogenomics and identifying varieties . SpecifiRace-specific markers could be used to identify the races of unknown avocado accessions and manage avocado genetic resources for breeding programs and germplasm conservation; however, the utility of few race-specific markers has been validated ,13. AshwHere, we used SLAF-seq and transcriptome approaches to gain insight into the population structure and genetic diversity of 21 avocado cultivars and to clarify the phylogenetic relationships of the three ecological races. Our other aim was to identify and apply race-specific markers, which will help to validate the racial origin of accessions of unknown race.Cinnamomum micranthum accession collected from CATAS-SSCRI. Fresh leaves were dried with silica gel prior to DNA extraction. Genomic DNA was extracted using the protocol described by Ge et al. [For SLAF-seq, three ecological races and two interracial hybrids were collected from the South Subtropical Crops Research Institute, Chinese Academy of Tropical Agricultural Sciences  and Guangxi Vocational and Technical College . A total of 21 individual accessions  were collected for analysis . In addie et al.  with minFor transcriptome research, fresh leaves, apical buds, stems, flowers, and roots were collected from mature, healthy-appearing individuals of the Mexican (Duke 7), Guatemalan (Reed), and West Indian (Simmonds) races, and Guatemalan × Mexican (Fuerte) and Guatemalan × West Indian (Beta and Tonnage) hybrids, and immediately frozen in liquid nitrogen prior to storage at −80 °C. The root samples of these accessions were derived from in vitro propagated seedlings . Total RTo use race-specific markers to identify the race structure of unknown race avocado accessions, eight unknown race avocado accessions were collected for analysis . These eC. micranthum genome sequence (https://www.ncbi.nlm.nih.gov/genome/?term=Cinnamomum) by using the Burrows–Wheeler Aligner [SLAF-seq was carried out as described by Sun et al. , with a  Aligner . Samtool Aligner  and Geno Aligner  were use®Ultra™ RNA Library Prep Kit for Illumina®  according to manufacturer recommendations and index codes were added to the attribute sequences of each sample. PCR products were purified using the AMPure XP system, and library quality was evaluated on the Agilent Bioanalyzer 2100 system. The library preparations were sequenced on an Illumina Hiseq 2000 platform and paired-end reads were produced. Clean data were obtained by removing reads including adapter sequences, reads including poly-N, and low-quality reads derived from raw data. Moreover, Q20, Q30, GC-content, and sequence duplication values of the clean data were calculated. The two read files were independently established from all of the libraries/samples. Transcriptome assembly was achieved based on these two files in Trinity v2.5.1 [A total of 1 μg RNA per accession was used for RNA sequencing. RNA sequencing libraries were produced with an NEBNexty v2.5.1  was usedy v2.5.1  was appln = 7); (ii) the remaining West Indian-specific SNP loci were selected when the alleles of the West Indian accessions were present in all Guatemalan × West Indian hybrid accessions (n = 7); and (iii) the remaining Guatemalan-specific SNP loci were selected when the alleles of the Guatemalan accessions were present in most of the Guatemalan × Mexican and Guatemalan × West Indian hybrid accessions (n ≥ 11).Race-specific SNP loci were rigorously filtered by three steps in this study. First, the SNP loci were retained when the frequency of their missing alleles was 0.00 among the 21 avocado accessions. Second, the remaining SNP loci were selected when the alleles were present at a frequency of 1.00 for a pure avocado race and missing from the other two pure races. The third step was broken into three components: (i) the remaining Mexican-specific SNP loci were selected when the alleles of the Mexican accessions were present in all Guatemalan × Mexican hybrid accessions . KASPar oligos were purchased from Sangon Biotech , with primers that had normal FAM- or VIC-compatible tails  and the target SNP in the 3′ end. The primer mix was used as described by KBioscience (http://www.kbioscience.co.uk) , and 12 μL of each tailed primer (100 μM)).SNP amplification was carried out in a thermal cycler in a 5 μL volume, which included 1× KASP Master mix, 10 ng of genomic DNA, and the SNP-specific KASP assay mix. The amplification conditions were the same for each SNP assay: hot start at 94 °C for 15 min, followed by 10 touchdown cycles at 94 °C for 20 s and 58–61 °C for 60 s (dropping 0.8 °C per cycle), and then 35 amplification cycles . Data analysis was manually carried out on a Roche Lightcycler 480 v1.50.39.To validate and apply race-specific SNP loci, primers for the race-specific SNP loci were developed to amplify DNA fragments for KASParassays  and namehttp://www.maizegenetics.net/tassel).Phylogenetic analyses were carried out based on 701,352 SNPs from SLAF-seq and 65,535 SNPs from transcriptome sequencing. After SNP detection, each SNPs was used to infer genetic distance in TASSEL v5.0 (r2 threshold: 0.2), subsequently 34,704 SNPs were retained for population structure analysis, which was performed in STRUCTURE v2.3.4 [K value, K was set between1 and10, and three independent runs were performed for every K with a burn-in of 100,000 iterations, followed by 100,000 Markov Chain Monte Carlo iterations for every K value. Then, the delta K method described by Evanno et al. [K value by structure Harvester v0.6.94 (http://taylor0.biology.ucla.edu/structureHarvester).The Principal component analysis (PCoA) of the 21 samples was carried out based on the 701,352 obtained SNPs in EIGENSOFT v6.0.1 [Linkage disequilibrium-based pruning was carried out to filter out SNPs that were in linkage disequilibrium with one another through PLINK’s indep-pairwise command , expected heterozygosity (He), Nei diversity index (Nei), Shannon’s information index (I), and polymorphic information content (PIC) were calculated based on the 701,352 SNP dataset through Perl programming. An analysis of molecular variance (AMOVA) based on 701,352 SNPs and 10,000 permutations was carried out using Arlequin v3.11 [Assessments of genetic diversity, including minor allele frequency (MAF), observed heterozygosity  content was 40.80%, and the average Q30 ratio  was 96.27% . A totalTranscriptome sequencing generated 32.44 Gb of clean reads. The GC percentage ranged from 46.22–46.57% across all six accessions and mean GC percentage was 46.42% . The meaC. Micranthum accessions based on the 701,352 SNPs from SLAF-seq that was inferred by UPGMA cluster analysis. The genetic distances between the Mexican accessions  and C. micranthum  were smaller than those between the Guatemalan accessions  and C. micranthum, and those between the West Indian accessions  and C. micranthum   .PCoA . The three West Indian and seven Guatemalan × West Indian hybrid accessions clustered in one group, whereas the two Mexican races, seven Guatemalan × Mexican hybrids, and two Guatemalan accessions clustered together. The two sections were obviously distinct (K) was two for genetic clusters  are shown in K = 5, the West Indian genotype population was split into three clusters . When the three populations were considered , 23.24% of the variation occurred among populations, and the populations considered were remarkably different .The AMOVA based on 701,352 SNPs from SLAF-seq revealed a clear population separation. When only the two populations  were considered, 22.10% of the variation occurred among populations, and the populations considered were significantly different  based on SLAF-seq all showed the existence of two groups or populations based on botanical race: a Mexican–Guatemalan genotype population  and a West Indian genotype population . At K = 2, STRUCTURE demonstrated that Mexican and Guatemalan accessions clustered with the Guatemalan × Mexican hybrid accessions, whereas the West Indian accessions clustered with the Guatemalan × West Indian hybrid accessions. Chen et al. [In this study, the results of the three analyses performed  and II ,which was also confirmed by the UPGMA cluster, PCoA, and AMOVA results based on SLAF-seq in our study. The results of this study were consistent with those of previous reports [K = 5, the West Indian genotype population was divided into three independent subpopulations. One major subpopulation contained all West Indian races and two Guatemalan × West Indian hybrid accessions, and another subpopulation was dominated by four accessions that were classified as Guatemalan × West Indian hybrids; this was similar to the partition of sections I-I and I-II by UPGMA cluster analysis based on SLAF-seq. Moreover, one Guatemalan × West Indian hybrid (Choquette) clustered with one Guatemalan race (Reed) into a single subpopulation. Additionally, the proportions of Guatemalan genotype in Choquette (48.15%) was the highest among the seven Guatemalan × West Indian hybrids  to enhance avocado germplasm classification and provided new insight into the phylogeny of the three ecological races and two interracial hybrids; additionally, we analyzed the molecular results in depth using different statistical methods (two similarity-based methods and one model-based method). We developed six race-specific KASP markers based on SNPs from SLAF-seq that successfully assigned the races of unknown avocado accessions. These data provided important information about avocado phylogenomics and will be useful for further research on avocado resource utilization and marker-assisted breeding.


Jatropha curcas L. are gaining increasing attention, providing both oil as biofuel feedstock or even as edible oil and the seed kernel meal as animal feed ingredient. They are a viable alternative to the limitation posed by the presence of phorbol esters in toxic varieties. Accurate genotyping of toxic/non-toxic accessions is critical to breeding management. The aim of this study was to identify SNP markers linked to seed toxicity in J. curcas. For SNP discovery, NGS technology was used to sequence the whole genomes of a toxic and non-toxic parent along with a bulk of 51 toxic and 30 non-toxic F2 plants. To ascertain the association between SNP markers and seed toxicity trait, candidate SNPs were genotyped on 672 individuals segregating for seed toxicity and two collections of J. curcas composed of 96 individuals each. In silico SNP discovery approaches led to the identification of 64 candidate SNPs discriminating non-toxic and toxic samples. These SNPs were mapped on Chromosome 8 within the Linkage Group 8 previously identified as a genomic region important for phorbol ester biosynthesis. The association study identified two new SNPs, SNP_J22 and SNP_J24 significantly linked to low toxicity with R2 values of 0.75 and 0.54, respectively. Our study released two valuable SNP markers for high-throughput, marker-assisted breeding of seed toxicity in J. curcas.Edible/non-toxic varieties of  Jatropha curcas L. is being fostered as a sustainable source of bioenergy and food. The most valuable parts of the plant are the kernels containing high amounts of oil and protein suitable for creating a range of beneficial products1. However, the phorbol esters in the toxic varieties of Jatropha render the most important byproduct of the biofuel extraction, the seed kernel meal, unsuitable for consumption. The adverse effects of toxicity have been firmly established on microorganisms to higher animals using extracts from fruit, seed, oil, roots, latex, bark, and leaves2. This affects the benefits of the plant negatively.Jatropha varieties presents a more suitable source of oil and animal feed ingredients3. An interesting study compared the two varieties for growth pattern, pest incidence and seed productivity4. It has been confirmed that variation in edibility of Jatropha seeds is due to a single trait i.e. the presence of phorbol esters. The content of other anti-nutrients such as curcin in the seeds of toxic and edible J. curcas remain unchanged5. Another study showed negligible waste production from Mexican non-toxic variety with use of both biodiesel and de-oiled seed cake as a protein and carbohydrate source6. Commercial breeders also value the generation of non-toxic accessions bypassing the chemical detoxification of the toxic varieties. Nevertheless, the seed characteristic is the single most determinant of the target market. This necessitates molecular markers strongly distinguishing the two varieties to aid in breeding. Efforts in this direction include generation of SCAR markers specific for toxic and non-toxic plants7. Studies have identified seven polymorphic microsatellite markers using RAPD and AFLP techniques8. Another group identified microsatellite markers classifying non-toxic and toxic J. curcas correlating them to the phorbol ester (PE) levels9. The generation of the first genetic map using SNP markers and also the identification of a locus for phorbol ester biosynthesis has been extremely valuable10. In addition, a high-resolution linkage map has recently been made available aiding in the mapping of important agronomic traits11.The identification of non-toxic edible 14. Their wide spread application in the breeding of model plants has been well documented18. They continue to be the marker of choice for candidate gene identification, trait discrimination and diversity analysis. In the past decade, with the availability of affordable and high-throughput technologies such as NGS, the same approach has been applied to whole genomes of non-model plants21.More recently, SNP based markers are dominating the molecular breeding field due to i) their abundance in plant genomes ii) high-throughput detection in NGS and genotyping platforms iii) lower false positives due to bi-allelic nature iv) availability of a range of computational pipelines for SNP calling v) clear read-off in haplotypes from inbred linesJ. curcas. A thorough experimental design for sequencing  enabled the identification of putative SNP markers governing seed toxicity. The results reported in this study demonstrate the first use of OpenArray technology for rapid screening of candidate SNPs in J. curcas along with PyroMark assays for accurate SNP identification. The reliability of SNPs from the associations study have been verified on two independent datasets. These SNP markers, SNP_J22 and SNP_J24 as well as the genotyping assay will be beneficial to the breeding of J. curcas non-toxic varieties.The objective of the present study was to identify SNP markers linked to seed toxicity in 2 population segregating for seed toxicity and used for association study. The estimation of phorbol ester content by HPLC allowed for categorization of the plants as toxic and non-toxic. A level of 0.1 mg/g has been routinely considered to be the upper limit of phorbol ester levels beyond which test animals start rejecting diets containing toxic jatropha seeds4. The method is also not sensitive below the mentioned level of phorbol esters.Figure J. curcas. Comparative analysis of the SNP data between toxic and non-toxic segregating bulks for the 6,248 homozygous positions yielded a total of 64 SNPs. These SNPs that clearly differentiated the two bulks were further considered for experimental validation. The SNP markers mapped on 41 Kazusa scaffolds identifying putative genomic candidate sequences linked to the gene(s) influencing toxicity in J. curcas , specifically between 10 to 15 Mbp on the upper arm of chromosome 8 . Phorbol ester remains a crucial trait for research and in the assignment of cultivars to specific target markets. Therefore, methodologies allowing identification and improvement of non-toxic J. curcas is pivotal to its breeding and domestication.Jatropha23 along with a comprehensive experimental design and a relatively simple genome made the genome wide discovery of SNP markers related to toxicity straightforward. Comparative studies between the two genomes identified the cultivar sequenced by the Kazusa DNA Research Institute to be similar to a toxic line. This information is also beneficial to the breeders working on genetic improvement of this particular cultivar.The availability of existing genomic data in 10. The sequences of the 64 SNPs linked to toxicity are provided in Supplementary Material 11. The localization of all 64 SNPs in the upper arm of chromosome 8 suggests the identification of a genomic region that regulates toxicity. However, due to the limited recombination events expected in a F2 population and number of plants tested, the candidate genomic region still spans several kilo base pairs. Furthermore, correlating the SNPs with predicted genes can shed light on putative biosynthetic pathways influencing the toxicity trait.SNP discovery from whole genome sequencing revealed 64 candidate SNPs in Linkage group 8, a locus previously identified for phorbol ester biosynthesis and toxicity related genesJ. curcas accessions. This study is the first demonstration of SNP screening using OpenArray platform in J. curcas. The SNPs present a high correlation with the low phorbol ester content. The reproducibility of results using this tool has also been confirmed in prior studies25. The success of an association study is realized through translation of identified SNPs for routine genotyping in breeding. This process involves testing thousands of samples for an extremely critical trait like toxicity, with high confidence using as few characteristic markers. Pyrosequencing has earlier been shown as an appropriate method for SNP analysis where allele frequency is 50% and when the nucleotide variants are clearly known26.The basis of association analysis is screening of large number of SNPs on large number of samples and thus, the use of OpenArray technology in this study is pertinent. This technology offers several advantages as demonstrated in this study: (i) low cost of € 0.07 per sample, (ii) accurate genotyping with an average call rate of 93% and (iii) rapid genotyping allowing generation of 36,864 data points in a day. SNP_J22 and SNP_J24 obtained from the association study are a fundamental step in the introduction and cultivation of non-toxic 27. It allows allelic quantification at a cost of € 12 per assay. The economic feasibility is apt considering the widespread cultivation of J. curcas in countries with limited fiscal capacity. Further, the results of this study were collated from validation on samples representing diverse cultivated and wild germplasms. Recent studies have shown the wide genetic variability specially in the germplasms lacking phorbol esters among accessions from Mexico and Central America28. These germplasms could be segregating for multiple traits including toxicity. Therefore, an accurate SNP detection step subsume characterization of accessions and fixation of lines. In our study, Pyrosequencing technology clearly allowed separation of the true homozygotes from heterozygotes. The above reasons detailed enhance the applicability of Pyrosequencing in breeding. To our knowledge, this is the first research adopting allelic quantification by Pyrosequencing in J. curcas.Our study confirms the accuracy and reliability of allelic quantification accomplished with PyroMark. The assay design is simple and also allows the sequencing of bases around the SNP. This gives more information about the PCR product analysed and its authentication10 related to toxicity with an aim to (i) create a SNP panel sanctioning the discrimination of non-toxic varieties in a mixed pool (ii) check contamination prior to usage as animal feed and (iii) fingerprint samples for identity and diversity analysis. A handful of SNP markers can significantly improve the accuracy and efficiency of trait selection in germplasm management29.SNP markers along with the modern genomics approaches and NGS technologies accelerate the improvements in molecular-genomics-assisted plant breeding. As shown in the present study, these SNPs can be combined with other known SNPs2 population segregating for seed toxicity trait was generated by crossing a high yielding, early maturing, non-toxic edible accession originating from Mexico (JPNT-2) with a toxic, non-edible accession from India (JPT-86). In addition to the F2 population, a set of toxic and non-toxic accessions from various countries were used to further ascertain the association between toxicity trait and markers identified in this study. A list of accessions used in this study with their country of origin and toxicity status has been provided in Supplementary Material An F2 population of plants were used for extraction and quantification of phorbol ester content by high performance liquid chromatography (HPLC). Each plant was analyzed in duplicate. The results are expressed as equivalent to a standard phorbol-12-myristate13-acetate in milligrams per gram (mg/g). HPLC conditions for estimation of phorbol ester content was carried out in accordance with previously described research31. An average phorbol ester cut off of 0.1 mg/g was used to categorize toxic plants from non-toxic plants4.Seeds from the F2 progeny was used to extract DNA. This was performed on the BioSprint 96 platform (Qiagen) using the BioSprint 96 DNA Plant Kit (Qiagen) following the method described in our previous study32. DNA of 51 toxic samples, 30 non-toxic individual samples, 1 toxic parent and 1 non-toxic parent were used downstream for sequencing.Leaves from the parents and FSequencing libraries were prepared by using TruSeq DNA HT Kit . The standard protocol recommended by the company was followed for library preparation. The quality and molarity of the libraries was evaluated by TapeStation (Agilent Technologies). Bulks were composed of 51 and 30 normalized DNA samples for the toxic and non-toxic bulks, respectively. Parental DNA samples were barcoded individually. All the samples were sequenced on the HiSeq2500 system (Illumina) using paired-read technology of 100 bp.33. The sequenced reads were then aligned to the reference genome from Kazusa DNA Research Institute  using bowtie2 v2.2.1-034 and the alignment files were further processed with SAMtools v1.435. PCR duplicates were marked using Picard tools v2.636. Variant calling was done using bcftools call v1.437. SNP identification was done considering the following four groups: toxic parental; non-toxic parental; toxic bulk and non-toxic bulk. Only homozygous SNPs present with different allelic status between the parents were selected. For bulk segregant analysis, among the selected SNPs only those segregating with the parental non-toxic allele frequency >0.95 in the non-toxic bulk (expected = 1.00) and <0.50 in the toxic bulk (expected = 0.33) were selected. For the SNPs segregating between the bulks, which are the candidate SNPs linked to the gene(s) of interest, sequences harboring the SNPs were mapped against Kasuza scaffolds  and the newly developed draft genome by Wu et al.11 to identify their genomic locations using BLAST38. The physical location of toxicity related SNPs on Wu et al.’s genome was visualized using a customized R script.All the sequenced reads were checked for their quality using FastQC v0.11.42 plants comprising toxic and non-toxic plants were genotyped with discovered SNPs using the Quant Studio 12 K Flex Real Time PCR System and OpenArray platform (Life Technologies). Sample preparation and loading methods were performed following the method described previously39. Selected SNPs showing best discrimination were further genotyped using PyroMark Q48 Advance Reagents on the PyroMark Q48 (Qiagen) on a collection of 602 F2 plants and 96 plants of diverse germplasm. PCR and sequencing primers were developed using the PyroMark Assay Design Software 2.0.1.15 (Qiagen). The following thermal cycling conditions were used: 15 min at 95 °C, followed by 45 cycles of 30 s at 94 °C, 30 s at 60 °C, 30 s at 72 °C and 10 min at 72 °C. Sequence analysis was performed using the PyroMark Q48 Autoprep software (Version 2.4.2) in the Allele Quantification mode. The association between three SNP genotypes and toxicity character was tested by fitting one SNP at a time in a linear regression model. The model fit between each SNP genotype and phorbol ester content was then estimated using the program Statistica v.13.0 .For association study, a collection of 672 FS1,S2,S3,S4


Takifugu rubripes, and applied it for genotyping farmed fugu to test its feasibility in aquaculture studies. We sequenced two libraries consisting of different pools of individuals (n = 326 each) on the Illumina MiSeq sequencer. Consequently, over 99% target regions  were amplified and 2,655 SNPs were available after filtering steps. Strong correlation was observed in the mean depth of coverage of each SNP between duplicate runs (r = 0.993). Genetic analysis using these genotype data successfully detected the known population structure and the sex determining locus of fugu. These results show the method is superior in repeatability and flexibility, and suits genetic studies including molecular breeding, such as marker assisted and genomic selection.Studies using genome-wide single nucleotide polymorphisms (SNPs) have become commonplace in genetics and genomics, due to advances in high-throughput sequencing technologies. Since the numbers of required SNPs and samples vary depending on each research goal, genotyping technologies with high flexibility in the number of SNPs/samples and high repeatability have been intensively investigated. For example, the ultrahigh-multiplexed amplicon sequencing, Ion AmpliSeq, has been used as a high-throughput genotyping method mainly for diagnostic purposes. Here, we designed a custom panel targeting 3,187 genome-wide SNPs of fugu,  For instance, a relatively small to medium number of SNPs information (from hundreds to thousands) is enough to answer ecological and conservation-related questions e.g., population structure, inbreeding and genomic diversity2. In such studies, genotyping a large number of samples is more important to maximize statistical power rather than only increasing SNP information. Therefore, researchers have often sought genotyping methods which maximize the balance between cost, sample size and the number of SNPs.Single-nucleotide polymorphisms (SNPs) are valuable genetic markers due to their abundance and relatively uniform distribution in the entire genome. Thus, they play important roles in wide-range of research fields from basic to applied science. Recent advances in next generation sequencing (NGS) technologies enable us to genotype SNPs across the genome. However, genotyping whole-genome SNPs still remains cost-prohibitive and the number of SNPs genotyped is often excessive for several genetic analyses5. Here, a small subset of the genome (1% or less) is sequenced, thus sample number can be increased per run. Most familiar reduced representation methods would be genotyping-by-sequencing technologies, including restriction site-associated DNA sequencing (RADseq)6. In these methods, regions adjacent to restriction enzyme (RE) cut sites across the genome are sequenced and genotyped without need of any prior genomic information. The number of SNPs can be partly customized using different combinations of RE7. This allows reduction in genotyping cost per sample and increases the number of samples per run. RADseq have an innate weakness, that the robustness of data is often diminished when RE sites include de novo mutations, and/or fragmented DNA is used 10. This uncertainty becomes prominent with increased sample size, due to increases in probability of mutations in RE sites and/or in the number of samples with low DNA quality. Although such disadvantages are realized, RADseq has rapidly become popular in the field of ecological and conservation genetics, and recently also agricultural genomics13. Meanwhile, polymerase chain reaction (PCR)-based reduced representation methods, e.g. Genotyping-in-Thousands by sequencing (GT-seq)14, Multiplexed PCR Targeted Amplicon sequencing (MTA-seq)15, and Highly Multiplexed Amplicon sequencing (HiMAP)16, have also developed further. These approaches take advantages of PCR; only target SNPs can be consistently amplified using specific PCR primers resulting in not only high repeatability of data but more robustness against de novo SNPs. These PCR-based methods allow using low quality DNA16. Additionally, flexibility in the number of SNPs genotyped can be accomplished by controlling the number of primers. Today, such PCR-based reduced representation approaches are easily accomplished using commercially provided kits, namely Ion AmpliSeq technology (Thermo Fisher Scientific Inc). Presently, the AmpliSeq technology gains an advantage over other PCR-based genotyping methods in both multiplicity in target regions , and flexibility in the balance between the number of SNPs and samples per analysis. The flexibility would be better suited over a wide range of genetic studies, including agriculture, although the technology is currently mainly used for diagnostic purposes allowing detection of genetic variances causing disorders.Reduced representation methods, where only a fraction of the whole genome is sequenced on NGS and genotyped, will be an effective technique to increase this balanceTakifugu rubripes. Fugu is one of the more valuable commercial fish in Japan17. Broodstock management and seed production have already been established20, and aquaculture production constitutes approximately 90% of overall production of this species21. In addition, fugu has been used as a model organism for comparative genomics due to its small genome size24, and extensive genomic resources including an integrative physical map are available30. This makes it easy to construct a SNP marker panel optimized for the amplicon sequencing technology. Genotyping SNPs across the genome is the primary step for molecular selective breeding, such as marker assisted selection (MAS) and genomic selection. Easy acquisition of genome-wide SNP information without large investment, such as in development of a SNP chip, will facilitate molecular selective breeding in aquaculture32.In this study, we assessed the availability of the amplicon sequencing technology for genetics and genomics in aquaculture using cultured populations of the tiger pufferfish, or fugu, Amhr226. Our results demonstrated the feasibility of the method in aquaculture, for both genetic and genomic study.To show the availability of the amplicon genotyping system, we firstly identified genome-wide SNPs in wild fugu samples by whole genome resequencing and listing of approximately 3,000 target SNPs. Secondly, we designed highly multiplexed primers using the Ion AmpliSeq Designer. After amplifying the target regions with 652 cultured individuals, we constructed two multiplexed libraries including 326 individuals each for the Illumina MiSeq sequencer. The two library pools were sequenced separately to examine the repeatability of sequencing data. Finally, we performed a population structure analysis and genome-wide association study to identify the sex determining gene, We determined the size of the AmpliSeq panel based on the throughput of the Illumina MiSeq sequencer. Using the Illumina MiSeq Reagent v2 kit (300 cycles), the sequencer produces over 4.5G base per run under paired-end mode. Since the average length of the target amplicon is 150 bp, the throughput is roughly equivalent to one million amplicons with an average of 15× depth of coverage (one million amplicons × 300 base × 15 depth = 4.5 Gb). Thus we designed approximately 3,000 SNPs, with which 326 samples  can be sequenced in a single run. With our protocol, genotyping cost, including synthesis of primer pool, library preparation, and sequencing is approximately 40 USD per sample (0.013 USD per SNP).25). The resulting SNPs with minor allele frequency less than 0.2 and average depth over 60× were excluded. After these filtering steps, 528,285 SNPs survived. To further narrow the number of SNPs, 5,065 SNPs were randomly selected using SelectVariants option of GATK. In addition, SNPs near homonucleotides and/or microsatellite regions were manually excluded and then selected with a distance of roughly 20,000 bp between the neighboring SNPs, resulting in 3,329 SNPs. Finally, a custom AmpliSeq panel targeting 3,187 loci was designed by Ion AmpliSeq Designer. The resulting 3,187 SNPs were almost uniformly distributed over each of 22 chromosomes . As a result, 30.7 million  and 25.8 million  reads were obtained from the first and second run, respectively. After quality trimming and mapping, an average of 89,987 (95.7%) and 73,999 (94.1%) reads per sample were mapped on the genomic regions. In each run, 495 and 402 reads on average were multi-mapped on the other regions, Out of 652 individuals, 456 individuals were visually sexed and used for the following analysis to assess the availability of the genotyping method for genetic studies. To do so, we applied further filtering steps to retain only biallelic SNPs with less than 30% missing data and mean depth ≥6, and thus finally obtained 2,655 available SNPs (83.3%). Out of the lost 532 loci, 29 (1.0%) were monoallelic, 79 (2.4%) were multiallelic , 24 (0.8%) were insertion/deletion, and the remaining 400 SNPs did not fulfill the other criteria; i.e. they have >30% missing data and/or mean depth <6. These surviving 2,655 SNPs were used for the following analysis.K was estimated as 11 based on cross validation 26. Comparison of allele frequencies between female and male with Fisher’s exact test detected SNPs associated with sex on Chr_19, corresponding to the sex chromosome of this species. Notably, a SNP closest to the sex determining SNP  showed the highest association   Fig. .Figure 4p-value was located near the sex determination gene, Amhr226. These results demonstrate the feasibility in aquaculture of this method for not only a standard population genetic study but also genomic studies.In this study, we used the PCR-based NGS method, namely the AmpliSeq technology, to construct a reduced representation library for fugu. We designed a highly multiplexed primer pool capable of amplifying approximately 3,000 SNPs across the genome in a single PCR tube. The resulting genotype data were used to validate the applicability of this method in aquaculture genetics and genomics. Here, we demonstrated that the amplicon sequencing technology could simultaneously amplify >99% of the target SNPs and produce highly repeatable genotyping data in 652 individuals. Furthermore, a standard population genetic analysis identified the known population structure and a genome-wide association study (GWAS) successfully detected SNPs associated with the sex. Notably, a SNP showing the highest r = 0.993). These results clearly demonstrate that the amplicon sequencing technology can repeatedly collect SNP data from a consistent fraction of the genome with little loss of data even while increasing the number of individuals sequenced. Taking into account the low genotyping cost (40 USD per sample or 0.013 USD per SNP), this allows us to genotype efficiently and increase sample size within a limited budget, contributing to improvement in the overall cost-effectiveness and productivity in various research scenarios. Contrary to RE based reduced-representation approaches7, such as RADseq, one limitation of this method is requirement of a reference genome and a high-density SNP list to design PCR targets. However, sequencing costs have been decreasing and various new bioinformatics tools have been developed. Therefore, acquisition of whole genome sequences is facilitated34 and this limitation will not be a critical problem in the near future.In this study, we obtained approximately 82,000 raw reads per sample with 326 individuals from a single Illumina MiSeq sequencing run. On average >95% of the raw reads were mapped onto the target regions and used for the following SNP calling. Out of 3,187 targeted SNPs, 3,178 SNPs (99.7%) obtained at least one amplicon and 2,820 SNPs (88.5%) had ≥6× mean depth in 652 individuals. To validate the repeatability of sequence data, we compared the mean depth at each SNP site between two independent sequencing runs. As a result, the depth of coverage was highly correlated between two independent runs  or using other NGS sequencing platforms with larger output, such as Illumina HiSeq X (three billion reads per lane). On the other hand, 132 SNPs were eliminated because these were either monoallelic, multiallelic (≥3), or insertion/deletions. The absence of these SNPs would be caused by differences in genetic background between the wild and cultured populations, because we designed the SNP panel based on the genome-wide resequencing using wild populations. Designing the SNP panel based on the target population will help prevent this loss of SNP data.37. Molecular-based selective breeding will be broadly separated into two categories: marker-assisted selection (MAS) and genetic selection (GS). MAS incorporates information on only polymorphisms significantly associated with the target trait via QTL analysis or GWAS. In general, the density of the designed SNP panel  is relatively small for GWAS, which typically often requires a large number of SNPs, from thousands to several hundred thousand1. Meanwhile, the marker density for GWAS depends on the extent of linkage disequilibrium (LD) between SNP markers and causative polymorphisms in loci affecting target traits (QTL)38. Since breeding populations are usually recently admixed, large LDs are expected to be introduced in the aquaculture populations40. Therefore, hundreds of thousands of markers may not always be required in such populations, because most of LD could be captured by a relatively low or moderate density of SNP markers40. In this study, the marker density was decided based on the size of the genetic map and the marker intervals were designed with almost equal spacing 43, a similar density to our study (1.6 SNPs/cM). These results suggest that GS can be done using genotyping by targeted amplicon sequencing with several thousand SNPs in the cultured fugu populations. Taken together, our results suggest that the amplicon sequencing technology based on AmpliSeq custom panel offers high cost-efficiency, flexibility in sample/SNP size and repeatability in the number of genotyped SNPs. Thus, the approach will be available for molecular breeding in aquaculture species, leading to increased exploitation of genetically improved fish carrying desirable traits.GS can incorporate all SNP markers regardless of statistical significanceWe resequenced twenty commercially wild-caught fugu for genome-wide SNP scanning. Genomic DNA was extracted from the caudal fin using a Gentra Puregene tissue kit  according to the manufacturer’s instruction. Library construction and genome sequencing was done by Hokkaido System Science Co. . The library was constructed for each sample using TruSeq Nano DNA LT Sample Prep Kit  following the manufacturer’s regular protocol (insert size 350 bp). Ten samples were loaded per lane and sequenced 100 bp from both ends on the Illumina Hiseq 2000 sequencer (Illumina).44 v0.35 by setting the parameters as follows; ILLUMINACLIP TruSeq3-PE-2.fa:2:30:10, LEADING:19, TRAILING:19, SLIDINGWINDOW:30:20, AVGQUAL:20, and MINLEN:101. After trimming, reads survived in both ends were mapped onto the reference sequence of fugu25 (GenBank: CAAB00000000.2) using BWA-mem45 with default parameter. The resulting alignments were passed to the local realignment steps with RealignerTargetCreator and IndelRealigner commands of GATK47 version 3.3 and PCR duplicates were marked with Picard-1.114 (available at http://broadinstitute.github.io/picard). Initial variant site calling was performed per sample using GATK-UnifiedGenotyper. Variant sites shared by all individuals were extracted by the merge option of BCFtools49 v1.2 (available at https://github.com/samtools/BCFtools) and used as the confident sites for base quality score recalibration step of GATK v3.3. The base quality score recalibration was done for four cycles, and base-recalibrated BAM files were created. The second variant site calling was done per sample on the base-recalibrated BAM file using GATK-Haplotypecaller with–standard_call_conf 30 and–standard_emit_conf 30 options to generate gVCF files. Variant sites were genotyped using GATK-GenotypeGVCFs to create the final SNP list.Raw reads were quality-trimmed using Trimmomatic25) and insertion/deletion in the SNP list were removed. Of the remaining SNPs, those with the minor allele frequency less than 0.2 and average depth of ≥60× were removed using VCFtools50 version 4.1. In addition, the SNPs mapped on the scaffolds with unknown genomic position or orientation were excluded. To narrow down the number of targeting SNPs, 1.25% of the SNPs were randomly selected using SelectVariants of GATK-v3.6. In addition, the regions covering 200 bp upstream and downstream of SNPs that included homonucleotide sequences  and/or microsatellite (CA/GT repeats) were manually removed. Then, the neighboring SNPs were separated by roughly 20,000 bp. Finally, the position information of the resulting SNPs was used to create a custom AmpliSeq panel designed by the Ion AmpliSeq Designer system; the range of amplicon size is 125–175 bp (ThermoFisher: https://www.ampliseq.com accessed Sep. 2016). As a result, we obtained 3,187 primer pairs.In this study, 3,000 SNPs were selected as targets for custom AmpliSeq panel  from the final SNP list according to the following procedures. At first, SNPs locating in 200 bp upstream and downstream of the known repeat on the reference sequence of fugu (UCSC table browser assembly Oct. 2011 (FUGU5/fr3)Applicability of the designed custom AmpliSeq panel was validated using 652 individuals including at least 10 full-sib families purchased from six aquaculture farms in Nagasaki prefecture. Two independent libraries with 326 individuals each were constructed and sequenced separately. Among 652 individuals, 456 individuals were dissected for visual sex determination for the following genome-wide association assay (GWAS). Genomic DNA extraction was done as mentioned above. DNA concentration was measured using a UV spectrophotometer .51 were added as primers by a second PCR. The second PCR reaction mixture contained 6 µl of adaptor-ligated DNA products, 2 µl of each 10 µM index primer, and 10 µl of NEBNext Q5 Hot Start HiFi PCR Master Mix (New England Biolabs Inc.), in a final volume of 20 µl. The second PCR condition was as follows: initial denaturation at 98 °C for 30 sec; 7 thermal cycles of denaturation at 98 °C for 10 s, primer annealing and extension at 65 °C for 75 sec; followed by a final incubation at 65 °C for 6 min. The resulting PCR products were purified with AMpure XP (1.1× vol.). The total 326 barcoded libraries were mixed with equal concentration. The final concentration of the multiplexed library was quantified by quantitative PCR using NEBNext Ultra Quant Kit (New England Biolabs Inc.) and diluted to 8.5 pM mixed with ~14% PhiX . The two multiplexed libraries, consisting of 326 samples each, were sequenced separately on the Illumina MiSeq with MiSeq Reagent Kit v2 .The first PCR was performed on genomic DNA with the custom AmpliSeq primer pools. The PCR reaction mixture contained 40 ng of genomic DNA, 9 µl of 2× AmpliSeq primer pool, 10 µl of 2× Multiplex PCR Buffer , and 0.2 µl of Multiplex PCR Enzyme Mix, in a final volume of 20.2 µl. The first PCR condition was as follows: initial denaturation at 94 °C for 1 min; 20 thermal cycles of denaturation at 94 °C for 30 s, primer annealing and extension at 60 °C for 4 min; followed by a final incubation at 72 °C for 10 min. PCR amplicons were digested with USER enzyme  at 37 °C for an hour and purified with 1.8× volume of Agencourt AMpure XP . The PCR products were end repaired, dA-tailed, and ligated with the NEBNext Adaptor for Illumina using NEBNext Ultra Library prep reagents (New England Biolabs Inc.) according to the manufacturer’s instruction. The adaptor-ligated products were purified twice with AMpure XP purification (1.4× vol. each). Then, P7/P5 adaptors including custom-designed 8 bp dual indices (adopted from Meyer and Kircher)52.Raw reads that passed an Illumina purity filter were demultiplexed based on the index sequences and split into each FASTQ file using Generate FASTQ workflow on the MiSeq platform. The raw reads were quality-trimmed using Trimmomatic by setting the parameters as follows: ILLUMINACLIP TruSeq3-PE-2.fa:2:30:10, LEADING:19, TRAILING:19, CROP:146, HEADCROP:5, SLIDINGWINDOW:30:20, AVGQUAL:20, and MINLEN:60. After trimming, the reads surviving both of the pair were mapped onto the subset of the reference sequences covering 50 bp upstream and downstream of the targeted loci. Reads were marked as “secondary alignment”, mapped on multiple regions, were excluded and only primary alignments were retained using SAMtools flag command (SAM flag = 256). Subsequently, genotype calling was done by jointly analyzing 652 individuals using GATK v4.0. Briefly, gVCF files were generated for each sample with GATK-HaplotypeCaller and merged into a single gVCF file with GATK-CombineGVCFs command. Joint genotyping was performed with GATK-GenotypeGVCFs53coverage command and Pearson’s correlation coefficient was calculated between the two sequencing runs.Mean depth of coverage at each targeted SNP was analyzed using BEDtools54 v1.1.3 to infer missing genotype data and the resulting SNPs with minor allele frequency (<5%) were further filtered out using VCFtools.To assess the applicability of this custom AmpliSeq panel for genetic studies, we applied this system to analyses of standard population genetics and GWAS for sex using the 456 cultured individuals with records of phenotypic sex. The genotyping data were further filtered based on the following criteria using VCFtools v4.1, (1) at least 70% of individuals were called, (2) only two alleles were present, and (3) average depth was ≥6. Subsequently, we applied LinkImputehclust on R55. Maximum likelihood estimation of individual ancestries was done using ADMIXTURE version 1.3.056. To choose the optimal number of ancestral populations (K), we performed five-fold cross validation for K from 1 to 20 following the manual. GWAS for the phenotypic sex  was performed with dominant model using PLINK v1.0757. A Manhattan plot was drawn using the “qqman” software package58 on R.The population structure of the sample populations was inferred by means of hierarchical clustering based on Ward’s method with Euclidean distances using All experiments described herein were approved by and carried out in accordance with the IACUC  of the Graduate School of Agricultural and Life Sciences, University of Tokyo (P-170529004). All methods were performed in accordance with the IACUC guidelines and regulations.Supplementary FiguresSupplementary Tables


Hordeum vulgare L.) is a major cereal grain widely used for livestock feed, brewing malts and human food. Grain yield is the most important breeding target for genetic improvement and largely depends on optimal timing of flowering. Little is known about the allelic diversity of genes that underlie flowering time in domesticated barley, the genetic changes that have occurred during breeding, and their impact on yield and adaptation. Here, we report a comprehensive genomic assessment of a worldwide collection of 895 barley accessions based on the targeted resequencing of phenology genes. A versatile target‐capture method was used to detect genome‐wide polymorphisms in a panel of 174 flowering time‐related genes, chosen based on prior knowledge from barley, rice and Arabidopsis thaliana. Association studies identified novel polymorphisms that accounted for observed phenotypic variation in phenology and grain yield, and explained improvements in adaptation as a result of historical breeding of Australian barley cultivars. We found that 50% of genetic variants associated with grain yield, and 67% of the plant height variation was also associated with phenology. The precise identification of favourable alleles provides a genomic basis to improve barley yield traits and to enhance adaptation for specific production areas.Barley ( Hordeum vulgare L.) is one of the earliest domesticated crops found at Neolithic farming sites in the Fertile Crescent of the Eastern Mediterranean region . Grain yield is the most important driver for grower profitability and is thus a key breeding target for genetic improvement. Yield is however a challenging breeding target as it is controlled by a large number of genes with varying effects, and also strongly influenced by genotype by environment interactions (complex trait) , can be used to dissect the genetic basis for variation in the expression of complex traits. GWAS exploits the linkage disequilibrium (LD) present in a diverse population of distantly related or unrelated individuals, including natural populations or germplasm collections . In 170 out of the 174 targeted candidate genes regulating phenology in barley we identified regulatory (3′‐UTR/5′‐UTR/intron/upstream/downstream) as well as coding (frameshift/splice variant/missense/synonymous) SNP variants Data . ApproxiObserved mean PIC values are slightly higher for the six‐rowed group (0.24) than for two‐rowed barleys (0.2), for winter (0.22) than for spring barleys (0.2), and for non‐Australian (0.21) than for Australian (0.19) varieties.et al., et al., et al., K = 15  tree based on genetic distances for the entire population Figure , and a sr2 of 0.068 of the entire panel was much lower compared to studies using markers distributed across the entire genome in a spring barley collection  within 95 genes were detected in twelve environments . Five markers were located outside of the target gene regions, one of which was captured in the downstream region of AGLG1. Association signals of these markers were mostly present on chromosome 5H, with marker R2 ranging from 1.2% for Chr_5_603618535 to 13.12% for Chr_Un_77793106  located within 34 target genes in four environments were identified , and further five were located within other genes of the CBF gene family , which are known to activate cold or dehydration stress‐inducible genes , VRN‐H1 (HORVU5Hr1G095630), PhyC (HORVU5Hr1G095530), CBF10A (HORVU5Hr1G080430), CBF3 (HORVU5Hr1G080420), CBF6 (HORVU5Hr1G080450), CBF8A (HORVU2Hr1G041090), CBF9 (HORVU5Hr1G080230), CO8 (HORVU7Hr1G027560), GA20ox2 (HORVU3Hr1G090980), and PRR95 .Of the 148 unique association signals located within 34 target genes that were identified for grain yield, 72 were also significantly associated with phenology (ca. 50%) and located within 22 genes mostly on chromosomes 1H, 3H and 5H, including the following genes: PHYC) were significantly associated with phenology in at least five environments, and one SNP (Chr_5_598560301) in two environments for grain yield  is a missense variant located within the GAF‐domain like domain, and the second SNP (Chr_5_598561262), a synonymous variant, is located downstream of the PAF domain , and from 0.7% to 5.8% (Chr_5_598561262) for phenology, and for grain yield, R2 was ranging from 3.1% to 4.1% (Chr_5_598560301), and from 1% to <0.1% (Chr_5_598561262) across different environments. Classification of the barley core collection based on the genotype at the Chr_5_598560301 locus shows 11 days earlier flowering when the alternative allele C is present  and were mostly localised on chromosomes 3H, and 5H  on 3H. Two‐thirds of all unique association signals located within seven target genes identified for plant height were also significantly associated with phenology . These were located within 33 genes on chromosomes 3H, 5H and 6H, including four genes of the gibberellin pathway: GA2ox4 (H HORVU1Hr1G076730), GA2ox3 (HORVU3Hr1G072810), GA20ox2 (HORVU3Hr1G090980) and GA2betadiox7 (HORVU3Hr1G117870).Marker AGLG1 (HORVU5Hr1G095710) were significantly associated with phenology . SNPs in weak linkage disequilibrium were detected nearby, thus suggesting the existence of allelic heterogeneity. For these loci, R2 was about 10% (Chr_5_599333006), and 11% (Chr_5_ 599329482) for phenology. For grain yield, R2 was ranging from 3% to 4% (Chr_5_599333006), and from 4.1% to 4.8% (Chr_5_ 599329482), and for plant height, R2 was ranging from 4.1% to 4.8% (Chr_5_599333006), and from 3% to 3.7% (Chr_5_ 599329482) across different environments. For both variants, the reference allele is the positive allele for phenology and grain yield, and the negative allele for plant height  all 328 Australian barley varieties (including advanced breeding lines), (ii) a subset of 82 core commercialized Australian barley cultivars representing Australian barley breeding history of the past 100 years and (iii) all 567 international barley varieties Data . In thisAG1), 30  and 29  functional alleles were detected within functional genes. The 328 Australian varieties were polymorphic for 516 out of 543 SNPs, and about 5%  did not contain allelic diversity, including three SNPs located within FT2 (HORVU3Hr1G027590) and two SNPs located within HORVU1Hr1G094980 (ELF3). Within the subset of 82 key Australian varieties, 50% (or 266 SNPs) were monomorphic and genetically fixed. The monomorphic SNPs were distributed across 61 genes, including HORVU3Hr1G026650 , HORVU5Hr1G097230 , HORVU3Hr1G027590 , HORVU2Hr1G013400  and HORVU2Hr1G017290 . Seventy‐four percent of the polymorphic SNPs and 61% of all monomorphic SNPs were associated with earlier flowering, showing that alleles for earlier flowering were gained or retained during breeding in Australian barleys.Based on the GWAS results for phenology, 543 significant SNP‐trait associations were detected in twelve environments. Four hundred and twenty‐nine functional alleles were located within the coding regions of 95 genes, and additional 141 alleles were captured outside of target regions of which some were within regulatory regions up‐or downstream of functional genes. A positive allelic effect, associated with later flowering, was detected for 202 functional alleles, and the remaining 341 were associated with earlier flowering compared to the alternative allele. Up to 32  functional alleles were detected within functional genes. Although within the larger set of Australian barleys, all grain yield‐associated SNPs were polymorphic, 30% were monomorphic within the subset of 82 key Australian varieties. Monomorphic SNPs were distributed across four genes, most monomorphic alleles detected in HORVU5Hr1G095710 . All polymorphic and monomorphic SNPs carried a positive allelic effect, showing that alleles for higher grain yield were either acquired or retained during breeding in Australian barleys.Based on the GWAS results for grain yield, 30 SNPs were detected that were significant for two or more environments. Sixteen genes carried 22 functional alleles, and additional eight alleles were captured outside target region. A positive allelic effect, meaning loci were associated with higher grain yield, was detected in approx. 50% of all alleles. Up to three  SNPs were polymorphic, but approx. 72% were monomorphic within the subset of 82 key Australian varieties. Monomorphic SNPs were distributed across five genes, including in AGLG1 and FT2. The majority of monomorphic SNPs (six out of eight) carried a positive allelic effect, and all polymorphic SNPs carried a negative allelic effect, showing that alleles for shorter plants were acquired in Australian barleys during breeding.Based on the GWAS results for plant height, 11 SNP were detected that were significant for two or more environments. Eight genes carried one functional allele each, and additional three alleles were captured outside the target region. A negative allelic effect, meaning they are associated with shorter stature, was detected in approx. 80% of all alleles. Within the larger set of Australian barleys, all but one , and genes belonging to the photoperiod pathway which were all associated with earlier flowering: HORVU5Hr1G081620 , and HORVU2Hr1G013400 . Strikingly, 34 of these changes which distinguished Chevallier from Prior were associated with earlier flowering. Allelic changes that distinguished Prior from Clipper  were concentrated within two genes, STK , PHYC , and led to later flowering (32) , and HORVU5Hr1G095530  which contains the same changes as detected when comparing Prior and Clipper, but in reverse. The vast majority of functional allelic changes (49) were associated with earlier flowering in La Trobe compared to Clipper, and explain the early season phenotype of La Trobe compared to the mid‐season phenotype of Clipper.Based on the GWAS results for phenology, 41 changes in functional alleles , were detected between Chevallier and Prior Table . Genes w2) Table . A compa2) Table . Genes wPRR95), which were associated with lower grain yield (Table PRR95 (HORVU5Hr1G081620), and HORVU5Hr1G080430 (CBF10)  Table .GA20ox2) distinguished Prior from Clipper associated with shorter plant stature (Table CBF3) distinguished Clipper from La Trobe associated with taller plants in La Trobe  assays to detect the specific parental allele for MAS in barley breeding Data .et al., et al., Second‐generation sequencing technologies allow sequence variation analyses on a genome‐wide scale. However, collecting whole‐genome sequence data on a population scale to a sufficient depth can be still prohibitive, despite rapidly declining sequencing costs. Reduced‐representation sequencing approaches that target only a subset of the genome can be applied to any species for which a draft or complete reference genome sequence is available, allow rapid discovery of thousands of molecular variants, and can be applied for high‐resolution exploration of genetic resources or to dissect quantitative traits , encoding a grass‐specific SEPALLATA‐like MADS‐box protein , encoding the apoprotein of red/far‐red light photoreceptor PHYC . Since then, barley production has expanded rapidly to 1 Mio ha in 1966, and more than 4.6 Mio ha in 2016/2017 FAOSTAT, . AustralBased on the association analyses results, we examined the past 100 years of barley breeding history in Australia. Among the 30 nucleotide variants associated with grain yield, 15 have been fixed (or close to) in Australian barley varieties over the past 100 years of conventional breeding. Extensive breeding, including the introduction of favourable alleles into elite varieties, have contributed to tripling cereal yields over the past 50 years , located within or in the vicinity of AGLG1, which also showed associations with earlier flowering and lower grain yield. However, these alleles represent particularly strong effects for both flowering time and grain yield and are not common in Australian germplasm.Early flowering and maturity can be an effective drought escape mechanism, but was also shown to limit grain yield potential due to the limited time available for photosynthesis, translocation of carbohydrates and total nutrient accumulation to produce high grain yield  that includes landraces, cultivars and breeding accessions from 41 countries in Europe, Asia, North and South America, Africa and Australia. After preliminary evaluation of heading date and response to photoperiod among the 4000 accessions in field trials in Australia, 952 accessions were selected to represent the diversity for the phenology. This selection represents the entire spectrum of cultivated barley, namely two‐ (90%) and six‐row (10%) genotypes with winter (8%), spring (91%) and facultative (1%) growth habits. The selected germplasm also included all Australian barley varieties released in the past 200 years.2 laid out in a row column format. Field trials at South Perth were conducted using a hill plot technique with 40 cm distance within and between rows. Seven control varieties were used for spatial adjustment of the experimental data. In each plot of each experiment in the study, measurements were taken to determine maturity (days to Z49), plant height and grain yield. Plant maturity was recorded as the number of days from sowing to 50% awn emergence above the flag leaf (Z49) . Geraldton, South Perth and Esperance are located along the coast of the Southern Ocean with high annual rain fall, but have very different daily maximum temperatures . The distance between Geraldton and Esperance is over 1100 km. The Merredin site is located inland with low rainfall while the Katanning site has medium rainfall. All regional field trials were planted in a randomised complete block design with plots of 1 by 3 mTo interrogate the genetic diversity of phenology and phenology‐related genes in a worldwide collection of barley landraces and cultivars, we designed a custom target enrichment sequencing assay that included loci implicated in the flowering pathway in barley and related plant species Appendix .®, Ann Arbour, MI) following the manufacturer's protocol version 2.3.1. More details can be found in Appendix Genomic DNA was extracted from leaves of a single barley plant per variety using the cetyl‐trimethyl‐ammonium bromide (CTAB) method . More details can be found in Appendix Fastq sequence files were post‐run filtered, and reads aligned to the latest release of the barley reference genome assembly  implemented in R software (http://www.R-project.org/). Principal component analysis (PCA) was conducted based on all marker data using TASSEL v.5.2.39. More details can be found in Appendix The model‐based clustering algorithm of ADMIXTURE v.1.3.0 was used to investigate subpopulation structure of the barley diversity panel. The software CLUMPP  and the kinship matrix (K) to account for population structure to avoid spurious associations. Multiple testing was performed to assess the significance of marker trait associations using the qvalue  following standard KASP guidelines. Assays were tested in 384‐well formats and set up as ~6 μL reactions following the manufacturer's instructions . Amplification was carried out on an ABI ViiA7 instrument , and end‐point genotyping was performed using the ABI QuantStudio™ Real‐Time PCR software v1.3.For KASP assays developed in this study, complete coding sequences were obtained from the latest version of the barley reference genome assembly (IBSC v2) Data . Allele‐Data generated or analysed during this study are included in this published article (and its supplementary information files). Additional datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.CL, TA, DM and PT conceived the project. TA and CL collected the barley accessions, conducted initial phenology evaluation and selected the accessions for this study. TA, LM, SW, YX, XZ and CL conducted the field experiments and phenotyping. DD designed the field trials and conducted the spatial analysis for yield. CH and XZ collected samples and extracted DNA. CH, KF and CT retrieved and prepared the phenology gene sequences for the enrichment probes. CH and DW performed the laboratory work including the DNA library constructions and target enrichments. CH and DW performed the genome data analyses. MH and JT developed the in‐house programmes for the data analyses. SW performed KASP assays. CH designed the KASP assays, and performed the GWAS and all statistical analyses. CH analysed all of the data and wrote the paper with input from CL. CL supervised the project.DM is affiliated with InterGrain Pty Ltd WA. PT is affiliated with Australian Grain Technologies Pty Ltd (AGT), SA. There are no financial relationships or competing interests on the part of any author that could potentially bias the findings reported in the manuscript.Figure S1. Phenology diversity panel of core collection barley varieties.Figure S2. Sequence diversity of the barley phenology core collection.Figure S3. Exploration of the optimal number of genetic subpopulations (K) using Δ cross‐validation error and standard error values in the barley germplasm collection.Figure S4. Plot of ancestry estimates inferred by ADMIXTURE for 895 worldwide barley accessions for 4260 SNPs.Figure S5. Neighbour‐joining tree of 895 barley varieties.Figure S6. Neighbour‐joining tree of 82 selected Australian barley varieties.Figure S7. Principal component analysis (PCA) of the first two components of 895 barley varieties.Figure S8. The extent of LD in the barley phenology core set of a worldwide collection of domesticated barley varieties.Figure S9. Linkage disequilibrium plots of 4260 SNPs for 895 barley accessions.Figure S10. Manhattan plots of flowering time for the Esperance 2015 environment.Figure S11. Manhattan plots of flowering time for the Esperance 2016 environment.Figure S12. Manhattan plots of flowering time for the Geraldton 2015 environment.Figure S13. Manhattan plots of flowering time for the Geraldton 2016 environment.Figure S14. Manhattan plots of flowering time for the Katanning 2015 environment.Figure S15. Manhattan plots of flowering time for the Katanning 2016 environment.Figure S16. Manhattan plots of flowering time for the Merredin 2016 (non‐irrigated) environment.Figure S17. Manhattan plots of flowering time for the Merredin 2016 (irrigated) environment.Figure S18. Manhattan plots of flowering time for the Perth 2015 (time of planting 1) environment.Figure S19. Manhattan plots of flowering time for the Perth 2015 (time of planting 2) environment.Figure S20. Manhattan plots of flowering time for the Perth 2015 (time of planting 2) environment.Figure S21. Manhattan plots of flowering time for the Perth 2016 environment.Figure S22. Manhattan plots of grain yield for the Esperance 2016 environment.Figure S23. Manhattan plots of grain yield for the Katanning 2015 environment.Figure S24. Manhattan plots of grain yield for the Merredin 2016 (non‐irrigated) environment.Figure S25. Manhattan plots of grain yield for the Merredin 2016 (irrigated) environment.Figure S26. Manhattan plots of plant height for the Geraldton 2015 environment.Figure S27. Manhattan plots of plant height for the Katanning 2015 environment.Figure S28. Manhattan plots of plant height for the Perth 2016 environment.Figure S29. Manhattan plots of plant height for the Merredin 2016 (non‐irrigated) environment.Figure S30. Manhattan plots of plant height for the Merredin 2016 (irrigated) environment.Click here for additional data file.Table S1. The extent of LD in the barley phenology core panel.Table S2. Field site location and climate data during growth period.Table S3. Estimation of mean, minimum (min), maximum (max), median (med), and heritability (h2) of all traits.Table S4. Functional allele diversity between Chevallier and Prior based on significant SNP‐associations with phenology.Table S5. Functional allele diversity between Prior and Clipper based on significant SNP‐associations with phenology.Table S6. Functional allele diversity between Clipper and La Trobe based on significant SNP‐associations with phenology.Table S7. Functional allele diversity between Chevallier and Prior based on significant SNP‐associations with grain yield.Table S8. Functional allele diversity between Prior and Clipper based on significant SNP‐associations with grain yield.Table S9. Functional allele diversity between Prior and Clipper based on significant SNP‐associations with plant height.Table S10. Functional allele diversity between Clipper and La Trobe based on significant SNP‐associations with plant height.Click here for additional data file.Appendix S1. Detailed description of additional experimental procedures.Click here for additional data file.Data S1. Information of phenology genes used for targeted re‐sequencing.Click here for additional data file.Data S2. Information of barley phenology core set.Click here for additional data file.Data S3. Variant effect prediction of 6030 SNP captured within the target regions.Click here for additional data file.Data S4. Final filtered SNP set of 4260 SNPs.Click here for additional data file.Data S5. Loci and SNPs significantly associated with flowering time, grain yield and plant height in 12 environments.Click here for additional data file.Data S6. Analysis of functional alleles based on SNPs significantly associated with flowering time, grain yield and plant height.Click here for additional data file.Data S7. Comparison between KASP and target enrichment results.Click here for additional data file.


Chlamydomonas reinhardtii HAP2 lacked this critical region. Here, we report a structure-guided alignment that reveals diversification of the proposed HAP2 fusion loops. Representative crystal structures show that in flowering plants, HAP2 has a single prominent fusion loop projecting an amphipathic helix at its apex, while in trypanosomes, three small nonpolar loops of HAP2 are poised to interact with the target membrane. A detailed structure-function analysis of the Arabidopsis HAP2 amphipathic fusion helix defines key residues that are essential for membrane insertion and for gamete fusion. Our study suggests that HAP2 may have evolved multiple modes of membrane insertion to accommodate the diversity of membrane environments it has encountered during eukaryotic evolution.HAPLESS2 (HAP2) is a broadly conserved, gamete-expressed transmembrane protein that was shown recently to be structurally homologous to viral class II fusion proteins, which initiate fusion with host cells via insertion of fusion loops into the host membrane. However, the functional conformation of the HAP2 fusion loops has remained unknown, as the reported X-ray structure of  The fusion of gamete plasma membranes is the fundamental cellular event that brings two parental cells together to form a new individual, yet we know surprisingly little about this process at the molecular level. HAPLESS 2 (HAP2) is a conserved sperm plasma membrane protein that is essential for gamete fusion in a diverse array of eukaryotes. It was recently shown to share a common ancestor with viral proteins that drive fusion of the viral envelope with host membranes, but its mechanism of action remained elusive, since the reported structure did not resolve the proposed membrane interaction surface. Here, we report two new HAP2 structures revealing that HAP2 has evolved diverse membrane interaction surfaces. In the flowering plants, HAP2 uses an amphipathic helix that presents nonpolar residues to the target membrane; in trypanosomes, the membrane interaction surface comprises three shallow nonpolar loops. C. reinhardtii HAPLESS 2 (HAP2) ectodomain (CrHAP2e) [CrHAP2e disrupted its ability to insert into artificial membranes in vitro and mediate gamete fusion in vivo [CrHAP2e structure did not include the critical region of the fusion loops, the question of how HAP2 inserts into the opposing gamete plasma membrane was left unanswered.The fusion of gamete plasma membranes to form a zygote is central to sexual reproduction, yet a molecular mechanism for this fundamental process has only very recently been proposed. The crystal structure of the CrHAP2e)  revealedCrHAP2e)  has the CrHAP2e) . Consist in vivo . BecauseViral fusion proteins have been divided into several structural classes that use either fusion peptides (class I) or fusion loops (classes II and III) to insert into host cell membranes . HAP2 isStable insertion into the host membrane is required to withstand pulling forces that occur as the fusion protein bridges the intermembrane space via a transient extended intermediate state that later collapses into a trimeric “hairpin” conformation, which drives the merger of the viral envelope and host membrane . The intArabidopsis thaliana (AtHAP2) and the protozoan Trypanosoma cruzi (TcHAP2). The X-ray structures confirmed that the membrane interaction surfaces were totally different: while in AtHAP2 there is a single fusion loop that projects an amphipathic helix (termed αF) toward the membrane , in TcHAP2, the membrane interaction surface is composed of three short loops. Biochemical and genetic experiments focusing on αF confirmed that the nonpolar residues are required for membrane insertion in vitro and for gamete fusion in vivo. Bioinformatic analyses show that αF is likely to be conserved across flowering plants, and functional studies revealed that it is interchangeable between rice and Arabidopsis, which are among the most distantly related flowering plants.Here, we carried out a comparative structure/function study of HAP2 from distantly related eukaryotes and found striking sequence variability at the domain II tip, which features multiple insertions and deletions, contrasting with the relative conservation of the rest of the protein. We then obtained structural data for HAP2 from two organisms that displayed the most contrasting diversity in this region: the flowering plant CrHAP2e to guide the alignment of 38 HAP2 amino acid (aa) sequences representing organisms from four eukaryotic kingdoms . The alignment also shows that most orthologs have significant deletions in the variable region 3  in several orthologs  and T. cruzi HAP2  in Drosophila Schneider 2 (S2) cells for structural studies To understand the organization of the divergent structural motifs used by HAP2 for membrane insertion, we selected representative sequences for further study from the flowering plants and the kinetoplastids, which were among those with the most contrasting features in variable regions 2 and 3 . We exprdies see . Size exdies see . AtHAP2eisin see . The ressub, 122 , and thesub, 122 . We deteAtHAP2 allowed us to trace 446 out of 469 ectodomain residues, including the region at the tip of domain II  obtained when superimposing the core 19 Cα atoms of the HAP2 bdc β-sheet onto the corresponding element in the flavivirus, alphavirus, and bunyavirus class II proteins ranged between 2.3 and 2.7 Å. Pairwise comparisons of the corresponding 19 Cα atoms from the three HAP2 structures discussed revealed an rmsd of <0.73 Å, highlighting the conservation of the structural core of the HAP2 domain II  superposes such that the side chains fall on top of each other when the superposition is based on the bdc β-sheet Cα atoms   . Helique  and L167, L168 and I183, F184  in the structure  females and hap2-2/HAP2 heterozygous males, the hap2-2 allele is not inherited by progeny . This assay provided a quantitative readout for HAP2 function in vivo and facilitated dissection of the critical residues of the domain II tip . Deletion of αF rendered HAP2 nonfunctional; hap2-2 transmission rates were zero (AtΔαF) even though expression of HAP2ΔαF:yellow fluorescent protein (YFP) in sperm was similar to the WT control Fig 6A). n II tip . We firss box in , AtΔαF. ere zero , AtΔαF e control , S4 Fig.171, F172, M175, I176) on the nonpolar face of αF , membrane association was almost completely abrogated; the majority of AtHAP2e was found at the bottom of the sucrose gradient  mating was not eliminated when the corresponding arginine was mutated [A. thaliana gamete fusion was completely abolished upon mutation of the corresponding arginine residue to alanine . It is possible that given the plasticity of this region, a salt bridge between this lysine and the conserved glutamate in β-strand b can rescue the fusion activity, but further experiments with TtHAP2 would be required to test this option.It was proposed that the salt bridge between the invariant RAP2 Figs  constraie R163A, . The aa AtHAP2 function, we mutated D173—which is in the middle of the hydrophilic face of αF—to proline, a mutation predicted to introduce a kink in the helix. We found that D173P, but not D173A , reduced HAP2 function in vivo  revealed the consistent presence of an intron between the regions encoding the domain II β-strands b, c, and d  has been shown to activate HAP2 for fertilization only after the sperm cells are released from the pollen tube to female gametes [Class II viral fusion proteins are activated by exposure to acidic pH upon entry into the host endosome and are positively and negatively regulated by partner viral proteins , 35. Whe gametes .AtHAP2 to insert into membranes in vitro and its functionality in vivo, indicating that membrane insertion is an essential step in the fusion process, as demonstrated for the viral fusion proteins. Nevertheless, a recent report [C. elegans somatic fusion factor proteins , which ertility , 39. A vertility , 39–41. ertility , 42, makC. elegans, mice, or humans, which currently lack candidate gamete fusion proteins.HAP2 is the only gamete plasma membrane fusion protein to be identified thus far . aa sequCrHAP2e [ij loop, and the core bdc β-sheet, including an invariant salt bridge anchoring the variable fusion loops to the central core of the molecule. In comparison, the viral class II proteins , differ in their relative orientations and positioning and are thus more structurally diverse. This is clearly seen in the table of DALI scores  with fertilizer  and were grown at 20°C, 50%–60% humidity, on a 16-hour-light / 8-hour-dark light cycle in growth chambers .Seeds were stratified (at least 2 days at 4°C) on solid Murashige and Skoog (MS) medium  supplemented with appropriate antibiotics and germinated at 22°C under constant light . After 7–14 days, seedlings were transferred to sterile #2MIX potting media (Drosophila S2 cells (ATCC CRL-1963) were cultured in Schneider’s complete media (Thermo Scientific) before transfection and in Insect Xpress media after transfection . Culturing and transfection of S2 cells has been described previously [eviously .A. thaliana and 26–516 from T. cruzi were cloned into a modified Drosophila S2 expression vector described previously [2 at a density of approximately 7 × 106 cells/ml for 8 days and pelleted, and the soluble ectodomains were purified by affinity chromatography from the supernatant using a StrepTactin Superflow column followed by SEC using a Superdex200 column in 10 mM TRIS pH8 100 mM NaCl. Pure proteins were concentrated to approximately 8 and 4 mg/ml, respectively.Codon-optimized synthetic cDNA corresponding to a soluble C-terminally truncated version of the HAP2e comprising residues 25–494 from eviously , and traeviously . For lar−1. Online MALS detection was performed with a DAWN-HELEOS II detector  using a laser emitting at 690 nm. Online differential refractive index measurement was performed with an Optilab T-rEX detector (Wyatt Technology). Data were analyzed, and weight-averaged molecular masses (Mw) and mass distributions (polydispersity) for each sample were calculated using the ASTRA software (Wyatt Technology).Purified HAP2e were subjected to SEC using a Superdex 200 column  equilibrated with 10 mM TRIS pH8 100 mM NaCl. Separation was performed at 20°C with a flow rate of 0.5 ml minAtHAP2e proteins, the following alternative composition was used: 50% cholesterol, 30% DOPC, 20% DOPS. Then, 0.07 μM purified HAP2e was mixed with 8 mM liposomes and incubated for 1 hour at 21°C in 100 μL PBS. Samples were then adjusted to a final concentration of 20% sucrose, overlaid with a 5%–60% sucrose gradient (in PBS), and centrifuged for 1 hour at 4°C at approximately 150,000 × g. Fractions from the top, middle, and bottom of the gradient were analyzed by immunoblotting using a monoclonal anti-strep tag antibody, and the bands quantified using the GeneTools Syngene software. The percentage of HAP2e in either fraction was calculated as the ratio between HAP2e in individual fractions and total HAP2e (sum of HAP2e in top and bottom fractions).DOPS, 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine (DOPE), 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC), cholesterol, and sphingomyelin were purchased from Avanti Polar Lipids. Liposomes were freshly prepared in PBS by the freeze-thaw and extrusion method using molar ratios of 17% DOPE, 16% DOPC, 50% cholesterol, 17% sphingomyelin. Alternatively, for coflotation assays on WT and mutant A. thaliana or T. cruzi) mixed with liposomes was spotted on glow-discharged carbon grids , negatively stained with 2% phosphotungstic acid (PTA) PH 7.4, analyzed with a Tecnai G2 Bio-Twin electron microscope , and imaged with an Eagle camera . For cryo-electron microscopy, liposomes mixed with purified HAP2e were applied on a glow-discharged Lacey Carbon grid . Samples were plunge-frozen in liquid ethane using an automated system  and visualized on a Tecnai F20 electron microscope operating at a voltage of 200 kV. Image frames were recorded in low-dose mode on a Falcon II direct electron detector .Purified HAP2e  glycerol. The crystals diffracted anisotropically to 2.24 Å along the c* axis but only to 3.7 Å in orthogonal directions. Therefore, an ellipsoidal cut off was applied using the StarAniso server  in order to remove noise, and refinement was carried out to a nominal resolution of 2.75 Å.Crystals of HAP2e from T. cruzi was carried out by adding subtilisin  to a solution containing T. cruzi HAP2e at 16 mg/mL in 10 mM Tris pH8, 100 mM NaCl at a 1:300 w:w ratio. After 1 hour of incubation at room temperature, the protease was inactivated by addition of 1 mM PMSF, and the protease-resistant fragment accounting to approximately 60% of the digested protein was purified by SEC using a Superdex200 column (TcHAP2esub). Crystals of TcHAP2esub were grown at 293 K using the hanging-drop vapor-diffusion method in drops containing 1 μL protein solution mixed with 1 μL reservoir solution containing 100 mM CHES pH 9.0, 200 mM NaCl, and 10% w/v PEG 8k. Diffraction-quality rodlike crystals appeared after 7–10 days and were flash-frozen in mother liquor containing 25% (v/v) glycerol.Limited proteolysis of HAP2e from A. thaliana HAP2e, the full C. reinhardtii HAP2 monomer (PDB 5MF1) was used to create a search model with Sculptor [bc and cd connections). The structure was determined by the molecular replacement method using Phaser [T. cruzi HAP2, we initially used Sculptor [C. reinhardtii HAP2 trimer (PDB 5MF1). The full monomer model was divided into 3 individual domains , and loops and exposed side chains were trimmed off. The structure was finally determined by the molecular replacement method using Phaser [Data collection was carried out at the ESRF (ID30A-3) and the Synchrotron Soleil (Proxima-1). Data were processed, scaled, and reduced with XDS , PointleSculptor . The so-g Phaser . Zinc atg Phaser . To deteSculptor  to creatg Phaser  and an iFor both orthologs, model building was performed using Coot , and refTcHAP2 trimer models were generated from superposition on either the AtHAP2 or CrHAP2 using the secondary structure matching function in Coot [TcHAP2 trimers were structurally very similar, with rmsd = 2.6 Å for 711 common Cα atoms (237 per subunit).Two independent  in Coot , and theArabidopsis HAP2 coding sequence variants were generated using a transfer-DNA plasmid containing the native HAP2 promoter and coding sequence with a C-terminal YFP fusion  and kanamycin sulfate .; pGreen  backbonen GV3101  with pSon GV3101 ) of hap2_152706; , 59) was_152706; . hap2-2/1), DNA was isolated using the leaf boil method [1 plants were genotyped for the hap2-2 allele using primer sets to detect both the Salk tDNA insertion . After at least 2 days at 4°C, plates were moved to a 22°C incubator with constant light. After 7–14 days, Basta-resistant seedlings were transferred to soil made up with 1X fertilizer (4 plants / pot). For primary transformants  and WT 1 plants that were heterozygous for hap2-2/+ were hand-pollinated onto male sterile 1 (ms1-1) pistils [ms1 crosses were gas-sterilized and plated on MS Kanamycin (50 mg/L) plates supplemented with sucrose (5 g/L). Seeds were cold-treated at 4°C for at least 2 days and then transferred to a 22-°C incubator with constant light. hap2-2/+ seeds were simultaneously plated on MS Kanamycin plates to ensure the drug resistance was working correctly. For analysis of hap2-2 transmission, resistant versus sensitive seedlings were counted manually after 6–10 days at 22°C.Pollen from T pistils . Seeds f1 line per transgene by sequencing of a PCR product. PCR products were generated using a EcoRI-HAP2-CVFP-F1 forward primer and the HAP2Ex7R reverse primer. For HAP2 variants that had 0% transmission of hap2-2, seeds were collected from the T1 plant and plated on MS Kanamycin and MS Basta separately to confirm segregation of the hap2-2 allele and the transgene, respectively, in the next generation.Mutations were confirmed in at least one TpHAP2:HAP2variant:YFP transgenic plants was initially screened using fluorescence microscopy. Pollen from T1 plants was hydrated on slides in pollen growth media [1 and/or T2 generation. Pollen were hydrated in pollen growth media and imaged using a Zeiss LSM 800 confocal microscope. All images were taken using excitation with the 488 laser at 10% (488 settings kept constant) and using the 40× water objective with a 2× zoom. Single optical slices were collected . All images were imported to ImageJ (FIJI version) [Expression of HAP2:YFP in th media  and imagversion)  and cropData are presented as mean ± SD unless otherwise indicated in figure legends, and experimental repeats are indicated in figure legends.AtHAP2) using a Wilcoxon rank sum test with continuity correction in R studio.The following criteria were used to analyze HAP2:YFP variant transgenic plants: at least three independent transgenic lines were analyzed, at least three crosses were performed for each line, and only crosses with at least 10 seeds were analyzed . TransmiAtHAP2) and 5OW4 (TcHAP2).The atomic coordinates and structure factors for two structures have been deposited in the PDB under the accession numbers 5OW3  TcHAP2e purity was assessed by Coomassie blue–stained SDS-PAGE. SEC analysis of AtHAP2e (C) and (D) TcHAP2e on a Superdex 200 column in 10 mM Tris pH8, 100 mM NaCl revealed the presence of a single peak. SEC-MALS analysis indicated that this peak for both Tc and AtHAP2e corresponds to a monomer. The y-axis on the right gives the UV absorption at 280 nm on a relative scale from 0 to 1 (plotted in black), while the y-axis on the left gives the molecular mass by MALS (plotted and labeled in red). (E) TcHAP2e and the purified protease-resistant fragment resulting from subtilisin cleavage (TcHAP2esub) were separated by SDS-PAGE stained with Coomassie blue. (F+G) Overview (top panel) and close-up (bottom panel) of the TcHAP2esub crystal packing. Domain II (yellow) assembles as a rigid dodecameric ring, whereas domain I projects into solvent within this ring, explaining why domain I is only partially traceable in the experimental electron density. (H) Composite omit electron density of part of the cd strand connection of TcHAP2 (left) and AtHAP2 (right) corresponding to variable regions 2 and 3, respectively, contoured at 0.9σ, allowing for manual building of the model in this region. AtHAP2e, A. thaliana HAPLESS 2 ectodomain; MALS, multiangle static light scattering; SEC, size exclusion chromatography; TcHAP2e, T. cruzi HAPLESS 2 ectodomain.(A) (TIF)Click here for additional data file.S2 FigHelical wheel projections of HAP2 loop 2 residues from indicated organisms. The circle size is proportional to the size of the displayed side chain. Nonpolar residues are colored in yellow. Charged residues are colored red (negative charge) and blue (positive charge). Polar uncharged residues are purple and light pink. Glycines are displayed in gray, prolines in green. The hydrophobic moment value of the respective amphipathic helix predicted by Heliquest (μH) is shown below the organism name, suggesting the formation of an amphipathic helix in the putative membrane-interacting region of plant and algal HAP2 but also in some animal HAP2 orthologs. HAP2, HAPLESS 2.(TIF)Click here for additional data file.S3 FigTcHAP2 trimer, obtained as described in Materials and methods, viewed from the membrane (left panel) and from the side (right panel). Surface residues are colored according to hydrophilicity from dark orange (hydrophilic) to white (hydrophobic). The fusion loop region from one protomer is encircled, and hydrophobic residues that are exposed at the tip are labeled. Residue 129 belongs to the bc connection, the others to cd. (B-C) Membrane insertion of TcHAP2e. Electron micrographs of liposomes incubated in presence of HAP2e were analyzed by negative staining (B) or cryo-EM (C). Scale bar 50 nm. Liposomes decorated with HAP2e display protein projections at the surface. The protein projections (some of which are indicated by long arrows in C) are shaped as tapered rods similar to the ones of AtHAP2e  Surface representation of the membrane-facing tip of the putative AP2e see  and form(TIF)Click here for additional data file.S4 Fighap2-2/+ control, which does not contain a HAP2:YFP construct. hap2-2  for each line is indicated in the bottom-right corner of each image. Scale bar, 5 μm. HAP2, HAPLESS 2; YFP, yellow fluorescent protein.Confocal micrographs of pollen grains from three independent transgenic lines expressing the indicated HAP2:YFP variant construct are shown. Each pollen grain shown contains a pair of sperm representing the maximum YFP intensity observed in the represented transgenic line. Signal from the pollen grain wall is due to autofluorescence and observed in the (TIF)Click here for additional data file.S5 FigD. melanogaster and Apis mellifera [A schematic phylogeny representing the five eukaryotic kingdoms, subkingdoms, and phyla for which whole-genome sequence information is available . Seellifera , but the(TIF)Click here for additional data file.S6 FigA. thaliana, C. reinhardtii; Chromista: Plasmodium falciparum, T. thermophila; Protozoa: T. cruzi, Dictyostelium discoideum; Animalia, N. vectensis, T. castaneum, Acyrthosiphon pisum; T. cruzi genome contains only a few introns, so intron density was not calculated  together with the location of the pfam10699 segment (purple) and the TM segments (green). TMs were identified from Uniprot for C. reinhardtii (A4GCR6.2), A. thaliana (F4JP36.1), and T. thermophila (A0A060A682) or by prediction . SegmenM server ). TM preublished . Note th(TIF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 TableHAP2, HAPLESS 2; YFP, yellow fluorescent protein.(PDF)Click here for additional data file.S5 TableHAP2, HAPLESS 2; YFP, yellow fluorescent protein.(PDF)Click here for additional data file.S1 Data.(XLSX)Click here for additional data file.S2 DataSeedling counts are provided as kanamycin sensitive (KanS) and kanamycin resistant (KanR). Total sensitive and resistant seedlings are listed ) as well as percent transmission of kanamycin resistance (%KanR). HAP2:YFP variant (Variant) and transgenic line (Transgene_line) are indicated. .(XLSX)Click here for additional data file.