The FORensic Capture Enrichment (FORCE) panel is an all-in-one SNP panel for forensic applications. This panel of 5422 markers encompasses common, forensically relevant SNPs , a novel set of 3931 autosomal SNPs for extended kinship analysis, and no clinically relevant/disease markers. The FORCE panel was developed as a custom hybridization capture assay utilizing ~20,000 baits to target the selected SNPs. Five non-probative, previously identified World War II (WWII) cases were used to assess the kinship panel. Each case included one bone sample and associated family reference DNA samples. Additionally, seven reference quality samples, two 200-year-old bone samples, and four control DNAs were processed for kit performance and concordance assessments. SNP recovery after capture resulted in a mean of ~99% SNPs exceeding 10X coverage for reference and control samples, and 44.4% SNPs for bone samples. The WWII case results showed that the FORCE panel could predict first to fifth degree relationships with strong statistical support . To conclude, SNPs will be important for further advances in forensic DNA analysis. The FORCE panel shows promising results and demonstrates the utility of a 5000 SNP panel for forensic applications. Forensic DNA databases were built around the use of non-coding DNA markers for human identification, primarily short tandem repeat (STR) loci from autosomal and Y-chromosomal targets . By usinConfirmatory STR testing is feasible when the DNA from a forensic sample is of standard forensic quality. Yet certain sample types contain degraded DNA, making them unsuitable for STR profiling, most notably rootless hair shafts and aged skeletal remains. Furthermore, confirmatory STR testing is limited when direct/reference samples are lacking and distant relationships must be inferred, as in historical remains identifications. In such cases, mitochondrial DNA (mtDNA) analysis is often used to recover a DNA profile, as mtDNA is higher in copy number than nuclear DNA (nDNA); thus, the chances of recovering a mtDNA target are higher. Since mtDNA is a non-recombining lineage marker whose DNA sequence is shared amongst maternal relatives, the utility of mtDNA is limited forensically because its discrimination power is fairly weak. Therefore, mtDNA best serves degraded DNA cases that can be bolstered by other types of evidence, DNA, or otherwise. Recent methods for mitogenome massively parallel sequencing (MPS) have been applied to degraded DNA samples and validated for forensic use, including mini-amplicon PCR ,8, primeTo provide an enrichment approach for the generation of a large number of SNPs from all sample types, the FORensic Capture Enrichment (FORCE) panel was designed as an all-in-one SNP panel for forensic applications. This panel encompasses all forensically relevant SNP markers  and presents a novel kinship SNP set for distant relationship inference. The relatively small size of the FORCE panel minimizes the number of primers/probes per reaction to reduce the enrichment cost. The FORCE panel can be adapted to various enrichment methods, including hybridization capture, PEC, and possibly multiplexed PCR, making it a viable option for both high quality and degraded DNA samples. The enriched SNP targets can be sequenced on a variety of downstream MPS platforms. By limiting the number of SNPs, fewer MPS reads are required to obtain per-SNP coverage requirements, and thus sequencing efficiency is maximized. The FORCE panel SNPs were sub-selected from SNPs on the genotyping microarrays used by DTC DNA testing companies, enabling cross-compatibility with externally developed SNP data. This is important for forensic validation purposes to demonstrate concordance and genotyping accuracy. Medically relevant SNPs were excluded from the FORCE panel design in order to mitigate genetic privacy issues around FORCE panel DNA databanks. Y-SNPs were included for high-resolution Y-chromosome haplogroup inference, which may be informative for both identification and ancestry prediction purposes . X-SNPs 2 smaller than 0.1, and (5) frequency difference of less than 0.35 between the 1000 Genomes five continental populations. Using the same parameters as the autosomal kinship SNPs, X-SNPs were selected for cases requiring X-chromosomal information. Pre-existing SNP marker sets were added to the FORCE panel to offer cross-compatibility with commercial SNP assays  IncP assays . IdentitA custom myBaits-20,000 kit  was designed for hybridization capture of the selected SNPs. The design included four baits per autosomal SNP  and two baits per X/Y SNP . A BLAST  analysisA simulation approach was used to study expected likelihood ratio (LR) distributions for various kinship case scenarios based on the kinship SNPs included in the FORCE panel. Five different kinship case scenarios were selected, representing second to sixth degrees of pairwise relatedness . The simulations were performed with the complete kinship SNP set . Simulations were also performed with 75% and 25% of the kinship SNPs in order to mimic situations for which only partial SNP profiles are available.ggplot2).The simulations were performed using the software Merlin . Merlin The samples utilized in this study originated from non-probative Defense Personnel Accounting Agency (DPAA) cases that were previously processed by the Armed Forces Medical Examiner System’s Armed Forces DNA Identification Laboratory (AFMES-AFDIL). These cases involve skeletal samples from five previously identified World War II (WWII) male service members and their associated family reference specimens. One or more DNA extracts from a single skeletal element per service member was included in this study, along with two or three family reference samples for each service member . The fivAll living sample donors provided informed consent for their samples to be used in research and quality improvement activities. The use of these reference and bone samples was approved by the Defense Health Agency Office of Research Protections (Protocol # DHQ-20-2073).Existing DNA extracts and associated reagent blanks (RBs) from the five skeletal samples as well as JB55  were obtDNA was isolated from the buccal swabs using the QIAGEN EZ1 DNA Investigator Kit automated on an EZ1 Advanced XL instrument following the manufacturer’s Trace or Tip Dance Protocol. The final elution volume for all samples ranged from 50 to 100 µL of Tris-EDTA. DNA was quantified with the dsDNA High Sensitivity (HS) Assay Kit on the Qubit 2.0 or Qubit 3.0 Fluorometer  to determine input into library preparation.Samples were divided into three sets for laboratory processing and sequencing, and each set included library negative and positive controls to evaluate library and capture success. The first sample set (bone) included ten DNA extracts from the WWII bone samples and one of the historical bone samples (26) plus two associated RBs. The bone set positive control was 1 ng of fragmented K562 control DNA as previously described in . JB55 waThe bone libraries were prepared in a dedicated low copy DNA laboratory using the KAPA Hyper Prep Kit . Library preparation followed the manufacturer’s recommendations using 15-µM 8-bp dual-indexed adapters. A target DNA extract volume of 50 µL was used for library preparation, with a maximum DNA input of 1 µg. Some of the previously existing DNA extracts had limited volume available; therefore, Tris-EDTA was added to create a total volume of 50 µL. Reference sample libraries were prepared with the KAPA HyperPlus Kit (Roche Sequencing), as fragmented DNA is required for successful library preparation. Library preparation was performed in accordance with the manufacturer’s protocol with a 20-min fragmentation and ligation of dual-indexed 8-bp adapters . The library PCR reactions for the bone samples utilized KAPA HiFi HotStart Uracil + ReadyMix (Roche Sequencing) to accommodate for uracils present in the native DNA molecule from cytosine deamination. The reference/control sample libraries were amplified with the KAPA HiFi HotStart ReadyMix (Roche Sequencing). Library amplification was completed following the manufacturer’s recommendations, using a total of 12 cycles of PCR for each of the sample sets (bone and reference/control). Samples were purified using a 1.0× AMPure XP  bead-based cleanup and were then eluted in 20 µL of Tris-EDTA. The quality of the libraries was checked on the 2100 Bioanalyzer instrument  using the Agilent DNA 7500 Kit. For the bone sample and control DNA libraries, an aliquot of the purified library product was reserved for WGS (described below).Hybridization capture with the α version of the FORCE capture panel was completed using the myBaits 1-20K Custom DNA Hybridization Capture kit . The procedure followed the manufacturer’s recommended v5 protocol using a single capture and 5 µL of custom SNP baits. Samples were allowed to incubate overnight for approximately 24 h at 62 °C with a heated lid (72 °C) using a Veriti thermal cycler . PCR amplification was achieved using duplicate PCR reactions to make use of the entire capture product. The post-capture PCR reactions were completed with 19 PCR cycles using the KAPA HiFi HotStart ReadyMix following the manufacturer’s recommendations. The two amplified capture products were combined for each sample and then purified with the MinElute PCR Purification Kit, eluting in 25 µL of Tris-EDTA.Purified capture product was quantified using the 2100 Bioanalyzer instrument with the Agilent DNA 7500 Kit to determine DNA molarity. As in library preparation and hybridization capture, the three sample sets  were pooled separately to minimize the impact of crosstalk between samples of disparate quality during sequencing [Pooling was achieved by individually normalizing captured libraries to the same concentration as the least concentrated sample (nM) in the set, then adding equal volume of all samples to the pool along with maximum RB and negative control volumes. The captured library pools were quantified using the Agilent DNA 7500 Kit on the 2100 Bioanalyzer. The bone and reference captured library pools were diluted to 1 pM with a 5% spike-in of PhiX Control V3 (Illumina) for sequencing on a NextSeq 550 (Illumina) using NextSeq 500/550 High Output Kits, v2.5 (150 cycles). Paired-end sequencing (75 × 75 cycles) was performed for the bone sample pool (14 captured libraries), while single-end data (150 cycles) were generated for the reference and control DNA samples (26 captured libraries). The JB55 pool was diluted to 12 pM including 5% PhiX control V3, and paired-end sequencing (75 × 75 cycles) was completed on a Verogen MiSeq FGx in RUO mode using a MiSeq Reagent Kit, v3 (150 cycles).To gauge capture efficiency for the non-probative bone sample and control DNA libraries, baseline WGS data were produced on an Illumina NextSeq 550. WGS was not performed for any of the reference samples. Sequencing was completed with NextSeq 500/550 High Output Kits, v2.5 (150 cycles) using the same approach as the captured libraries (75 × 75 cycles for the bone sample pool and 1 × 150 cycles for the control DNA pool). For direct comparison with the JB55 capture data, WGS of one JB55 library was performed on a MiSeq FGx using RUO mode and paired-end (75 × 75 cycles) sequencing on a v3 cartridge (150 cycles).FASTQ files were imported into the CLC Genomics Workbench v12.0.1 (QIAGEN) and all sample data were analyzed with a custom workflow. The workflow, which was applied to all samples, included five steps. (1) Reads were trimmed based on quality (including ambiguous bases) as well as adapter read-through (for paired reads only). (2) Trimmed reads were then mapped to the GRCh38 human reference genome using stringent mapping parameters designed to prevent off-target mapping of exogenous DNA . Modifications to the default mapping parameters were a length fraction of 0.85 and stringency fraction of 0.95, and non-specific reads  were ignored. (3) Duplicate mapped reads were removed with 20% maximum representation of minority sequence. (4) Local realignment of unaligned ends was performed to improve indel alignment in the mapping. (5) The alleles observed at each of the 5422 FORCE SNPs was determined using the Identify Known Mutations from Mappings tool, ignoring broken pairs in the bone set sequence data. Various analysis metrics such as the total number of reads, percent mapped, and percent duplicate reads were reported throughout the workflow using different CLC Genomics Workbench quality control  tools.A custom Microsoft Excel  template was developed for genotype generation based on the CLC Identify Known Mutations from Mappings output. The targeted SNP had to be covered by a minimum of 10 reads for analysis. Furthermore, in order for a FORCE SNP to be called , genotypes required allele frequency of 90% or greater to be called as a homozygote and heterozygous genotypes needed at least 30% MAF. SNPs meeting the minimum coverage requirement (10X) but with imbalanced alleles (10–30% MAF) that could not be confidently called homozygote or heterozygote were noted as “imbalanced” and excluded from further analyses. The called SNPs were separated by type  for analysis and interpretation as described below.The genotypes derived from the FORCE capture data of the four control DNAs  and one of the historical bone samples (JB55) were compared to previously published data. For 2800M, data from the ForenSeq DNA Library Preparation Kit were used for comparison  for K562n = 165) and targeted Y-SNP typing (n = 4) as described in [For JB55, published genotypes produced with the Precision ID Ancestry Panel , but where the allele frequency thresholds for homozygote/heterozygote calling were not met. When the minor allele(s) was observed with base quality <20 and <10% forward reverse balance, indicating sequencing error, the artifact (minor) allele was removed, and the observed genotype noted for the imbalanced SNP was modified. This was only performed for the concordance assessment. No imbalanced SNPs were utilized in downstream analyses .When discordance was observed between the FORCE and published genotype, the FORCE BAM and the BAM associated with the published data were reviewed to confirm the genotypes at the SNP marker. If the genotypes were still inconsistent after further review, the cause of the discrepancy was investigated.prcomp [Biogeographical ancestry predictions, based on the aiSNPs, were performed with two different methods: principal component analysis (PCA) and naïve Bayes analysis. An R script  was deveprcomp  was usedprcomp  to trainprcomp  analysisprcomp  were useprcomp .The 41 piSNPs included in the FORCE panel are consistent with the markers of the HirisPlex-S System . Using tY haplogroups were predicted based upon the 829 Y-SNPs included in the FORCE panel. Called Y-SNPs were compared to the Yleaf WGS HG38 Position file  within tPairwise kinship calculations (kinship SNPs). Likelihoods were calculated based on observed kinship SNP data given seven different relationship hypotheses  and one hypothesis representing no genetic relationship between the two compared individuals . From these likelihoods, likelihood ratios (LRs) were calculated for each related hypothesis using the unrelated hypothesis as the alternative hypothesis /Pr (DNA|H2), where H1 was the hypothesis representing one of the related relationships given above and H2 was the unrelated hypothesis). These kinship calculations were performed in a pairwise fashion between all reference samples as well as between each “Unknown”  and each reference sample. Posterior probabilities were calculated assuming a flat prior. The likelihoods were calculated using Merlin with the preferences described above . R was aPedigree kinship calculations (kinship SNPs). In addition to the pairwise kinship calculation, kinship statistics were also calculated in a pedigree fashion. Here, SNP profiles for all reference individuals, in each reference family, were included in the likelihood calculations. In this approach each unknown sample was tested as the missing person in each reference pedigree . For eacKinship calculations using X-SNPs. In order to demonstrate the possibility to calculate kinship statistics solely based on X-SNPs, two samples were selected from family D: sample 12 (“reference”) and sample 14.1 (“unknown”). These two individuals are full siblings  and theiThe results from the kinship simulations clearly demonstrate that the kinship SNPs could be used to solve the majority of kinship case scenarios down to first cousins, including cases with partial SNP profiles . Furthern = 4267), the average coverage across the reference/control samples was 241X. The average coverage dropped to 180X for autosomal SNPs with three baits (n = 73) and 122X for autosomal SNPs with two baits (n = 6). The single autosomal (kinship) SNP (rs4847178) with one bait (right flank) produced an average coverage of 157X, with called data for 100% of the reference/control samples. All 246 X-SNPs were targeted by two baits, averaging 166X in coverage for females and 89X for males. The 812 Y-SNPs with two baits produced an average coverage of 105X in male reference/control samples, with the reduced coverage observed at the 17 Y-SNPs with only one bait (50X). There were 13 SNPs  that failed to produce sufficient coverage (10X) for any sample  and the two historical bones (JB55 and sample 26)). These four samples were extracted with either PCIA followed by MinElute purification or the Dabney DNA extraction method. In contrast, the bone samples that were extracted with QIAquick produced fewer called SNPs. The three QIAquick replicates from the Italy bone sample (14) yielded 282–5121 called SNPs, for an average of 2308. The Austria (11) and Tarawa (17) bone samples, which were also extracted with QIAquick, produced only 50 and 14 called SNPs on average, respectively. The Tarawa bone sample was likely chemically treated with formalin, which was the standard postmortem procedure during WWII. Such chemically treated samples have been shown to produce poor DNA sequencing results [The bone sample results varied considerably, ranging from 11–5361 called SNPs and an average of 44.4% of the possible 5422 SNPs across the 12 DNA extracts . To comp results . The bonIn the absence of capture, the bone DNA extracts produced 2–1539 SNPs at 1X coverage and zero SNPs met the 10X calling threshold. These details and the remaining shotgun sequencing results are shown in Overall, 14,519 (99.97%) SNPs were concordant between the 14,524 called FORCE genotypes and published data of the four control DNA samples . Completn = 7). Interestingly, NA12878 and K562 were expected to be homozygous for the alternate allele (GG) at rs229352 based on the published data (this marker is not included in ForenSeq and therefore the expected genotype for 2800M is unknown). Analysis of the rs229352 bait design identified that the alternate allele (G) or left flank did not map under the stringent mapping parameters utilized in the developed workflow. Furthermore, review of the published BAM showed a 5-bp insertion located 30 bp upstream (5′) of the SNP on the reads with the G allele at rs229352. For NA12878, the discordant called SNP (rs662185) was identified as a poor performing SNP in The five discordant called FORCE genotypes were all kinship SNPs, and each discordance was observed in only one control DNA sample . The gren = 3) and NA12878 (n = 1) that were discordant with the published data appear to be the result of non-specific mapping in the FORCE data. The last two discordances involving imbalanced FORCE genotypes were found in K562. One appeared to be the result of non-specific mapping in the published BAM (rs241408), while the other (rs7117433) could not be resolved and may be due to cell line artifacts previously discussed.Though not utilized for other analyses in this study, the concordance of the 187 observed genotypes at imbalanced FORCE SNPs was assessed for the four control DNA samples. It is noted that 95.19% (178) of the observed imbalanced genotypes were consistent with the published data . UnsurprIn the end, considering both called and imbalanced FORCE genotypes, a total of 99.90%  SNPs were in agreement between the FORCE and published control DNA datasets . There wPublished and FORCE data for JB55, the only bone sample evaluated for concordance, enabled the comparison of 153 SNPs. Sixteen of the 169 overlapping SNPs were excluded from comparison due to insufficient coverage (<10X) in the published Precision ID data. JB55 had 151 of 153 (98.69%) concordant genotypes, and two discordant aiSNPs were observed . The FOROklahoma sample. Thus, hybridization capture was effective in producing sufficient SNPs from the multiple marker types included within the FORCE panel to allow for predictive information to be gained.The completeness of the reference and control sample profiles allowed for ancestry, phenotype, and Y-chromosomal haplogroup predictions, which were consistent with available metadata . The preThe related reference to reference sample kinship prediction results are shown in Unknown to reference pairwise kinship predictions from bone sample DNA extracts that produced 1000 (~25%) or more kinship SNPs are presented in The five bone sample DNA profiles with >25% kinship SNPs were utilized for kinship predictions in a pedigree scenario, as presented in When bone sample profiles produced minimal SNP data (10–247 kinship SNPs), the kinship predictions were expectedly weaker overall . Only twSupplementary to the autosomal SNPs, 246 X-SNPs were targeted in the panel for kinship testing in specific case contexts. The X-SNPs included in the panel were used to connect individual 12 with the remains from individual 14.1 in family D. The log 10 LR (for them being full siblings versus being unrelated) was calculated to be 6.7 and a posterior probability > 99.99%; thus, the X-SNP data showed to be clearly informative in the case example. The use of X-chromosomal data may be especially informative in kinship testing due to its particular mode of inheritance. For example, when comparing two alleged paternal half-sisters or an alleged paternal grandmother and a granddaughter, the exclusion power for X-chromosomal markers are not zero, in contrast to autosomal markers .This study demonstrates the utility of the 5422-SNP FORCE panel for forensic applications. This panel was designed to exclude clinically relevant markers to alleviate privacy concerns with DNA databanking. The SNPs included in the FORCE panel are found on microarray genotyping chips used for genetic genealogy, enabling cross-compatibility with externally produced data that will be useful in validation studies. Moreover, large reference databases are available for all of the markers included in the kit, unlike other marker types such as microhaplotypes. The FORCE panel includes kinship SNPs with forensic iiSNPs, aiSNPs, piSNPs, Y-SNPs, and X-SNPs. The hybridization capture method was capable of recovering SNP profiles from high quality reference and historical bone samples alike. A high degree of concordance (>99.9%) was observed when comparing FORCE profiles with previously published data. The few underperforming SNPs that were identified in the present study can be removed from future designs of the FORCE panel to improve efficiency. Ancestry and phenotype predictions were possible and consistent with self-reported data from reference sample donors. Additionally, high-resolution Y-haplogroup prediction was possible from 829 Y-SNPs. The novel kinship marker set of 3931 SNPs was shown to be effective for 4th degree relationship predictions with strong statistical support. Furthermore, known case examples involving previously identified WWII service members and associated family reference samples resulted in kinship predictions consistent with expected relationships. Future efforts involving the FORCE panel will entail an inter-laboratory study incorporating alternate enrichment methods for SNP profiling. This will be pertinent before the FORCE panel can be used in routine casework.


Germplasm collections are indispensable resources for the mining of important genes and variety improvement. To preserve and utilize germplasm collections in bottle gourd, we identified and validated a highly informative core single-nucleotide polymorphism (SNP) marker set from 1,100 SNPs. This marker set consisted of 22 uniformly distributed core SNPs with abundant polymorphisms, which were established to have strong representativeness and discriminatory power based on analyses of 206 bottle gourd germplasm collections and a multiparent advanced generation inter-cross (MAGIC) population. The core SNP markers were used to assess genetic diversity and population structure, and to fingerprint important accessions, which could provide an optimized procedure for seed authentication. Furthermore, using the core SNP marker set, we developed an accessible core population of 150 accessions that represents 100% of the genetic variation in bottle gourds. This core population will make an important contribution to the preservation and utilization of bottle gourd germplasm collections, cultivar identification, and marker-assisted breeding. Lagenaria siceraria (Mol.) Standl.] (2n = 2x = 22), also known as calabash or long melon, is a cultivated vegetable, medicinal plant, decorative cucurbit, and grafting rootstock belonging to the Cucurbitaceae family, together with other major cucurbit crops, including cucumber (Cucumis sativus L.), melon (Cucumis melo L.), and watermelon (Citrullus lanatus L.) (Bottle gourd [atus L.) . Recentlatus L.) .Currently, an increasing number of homogeneous new bottle gourd varieties are being released onto the market, and intellectual property disputes could emerge in cases where different species have been assigned the same name or where different names have been assigned a single species. Traditional identification and registration of germplasm collections or varieties are primarily dependent on field planting, which is both time-consuming and inefficient. Moreover, plant traits are often influenced by environmental variation . TherefoBrassica rapa, and cowpea  population. Using this core SNP marker set, we assessed the genetic diversity and population structure of bottle gourd collections, fingerprinted bottle gourd germplasm collections and commercial cultivars, performed an optimized procedure for seed authentication, and developed an accessible core population. The results thus obtained will play a vital role in the preservation and utilization of bottle gourd germplasm collections, as well as in cultivar identification.In this study, we utilized a total of 206 bottle gourd germplasm collections for core SNP marker set screening, all of which are inbred lines . Twenty All accessions were grown in a growth incubator at 28°C/22°C with a 16 h light/8 h dark regime, and for each accession, young leaves from three independent individuals were collected for genomic DNA extraction using the cetyltrimethylammonium bromide (CTAB) method . DNA quaAs a reference for SNP discovery, we used the Chinese landrace bottle gourd “HZ gourd” . Based owww.kbioscience.co.uk.Kompetitive allele-specific PCR assays were carried out using a Bio-Rad CFX96 Touch q-PCR System  with KASP genotyping reaction mixtures that included a KASP assay mix , a KASP master mix , and DNA samples (20 ng/μL). The cycling program was as follows: 15 min at 94°C, 10 touchdown cycles of 94°C for 20 s and 61–55°C for 60 s (decreasing by 0.6°C per cycle), and 26 cycles of 94°C for 20 s and 55°C for 60 s. An Omega Fluorostar scanner  was used for fluorescence detection of the reaction products. Kluster Caller 1.1  software was used for data analysis, and SNPviewer2  software was used to read the reaction plates. Detailed instructions can be found at 1 was used to calculate genetic diversity and the PIC. GenAlEx 6.5 (K value based on the ΔK method (2 was used to convert each SNP fingerprint into a barcode.PowerMarker softwareAlEx 6.5  was perfAlEx 6.5  was usedAlEx 6.5  was usedAlEx 6.5 . STRUCTUAlEx 6.5 . STRUCTUAlEx 6.5  was usedK method . A barcoThe selection of core collections was carried out using Core Hunter , an algoThe re-sequencing of the 20 bottle gourd representatives  generateSingle-nucleotide polymorphisms could be called for AA, BB, and AB genotypes . Where dTo identify the candidate core SNPs, the 206 bottle gourd germplasm collections were utilized for KASP assays with the 93 high-quality SNPs. A core SNP marker set was selected by considering the physical position, PIC, MAF, observed heterozygosity, and missing values among all 206 genotypes. The core marker set comprised 22 SNP markers, with two markers per chromosome . The satWe initially evaluated the polymorphism of the core SNP set using KASP assays in a MAGIC population, which had been constructed from eight genetically diverse elite parents and consisted of 377 recombinant inbred lines. Data obtained for individuals from these 377 lines were used to calculate the PIC, MAF, observed heterozygosity, and missing values for each core SNP marker. The PIC values of the 22 core SNP markers across the 377 individuals ranged from 0.12 to 0.50, with an average of 0.38. Sixteen SNP markers had PIC values >0.3, and the MAF values ranged from 0.07 to 0.45, with an average of 0.30. The observed heterozygosity for each core SNP marker was ≤0.10, with an average of 0.04. For all core SNP markers, the missing values comprised ≤0.06 of the data points . CollectK value was 2, which divided the 206 bottle gourd germplasm collections into two groups when using either the 93 high-quality SNPs or the core SNP marker set (We further evaluated the discriminatory power of the core SNP set by analyzing the genetic structure of the 206 bottle gourd germplasm collections using the core SNP set and the aforementioned 93 high-quality SNPs. Assessment of the relationships among the 206 bottle gourd germplasm collections using STRUCTURE showed that the best Δrker set . The clurker set . PCA andrker set . The afoMolecular fingerprinting of bottle gourd germplasm collections or commercial cultivars is a potential practical application of the newly developed core SNP marker set. In this study, 206 bottle gourd germplasm collections were fingerprinted using the 22 core SNP markers , which hTo provide a subset of representative germplasm collections for the selection of parents in hybrid combinations in bottle gourd breeding or related basic studies, we further developed a core collection of bottle gourd germplasm collections using the 22 core SNP markers and Core Hunter software . The corA range of DNA molecular markers, including RAPDs, SSRs, InDels, and SNPs, have been used for germplasm characterization . Among tIn this study, using the high-throughput SNP genotype platform, we selected and developed a core set of SNP markers from an initial 1,100 SNPs identified by the re-sequencing of 20 bottle gourd representatives. The representativeness and discriminatory power of the core SNP marker set were evaluated using 206 bottle gourd germplasm collections and a MAGIC population . We foun22 = 31,381,059,609. Therefore, it is feasible to construct a fingerprint database of bottle gourd germplasm collections or main commercial varieties using the 22 core SNP markers.The verification of seed authenticity and purity is of particular importance for seed producers and farmers . SimilarThe construction of a core collection will substantially improve the efficiency of germplasm collection management and utilization. Core collections established using molecular markers are not readily affected by environmental or other external factors, and hence, several core collections have been constructed using various DNA molecular markers . In thisIn summary, based on the re-sequenced genomic data from 20 bottle gourd germplasm collections, we identified and validated a core set of 22 representative SNPs, which exhibited abundant polymorphisms and were evenly distributed throughout the bottle gourd genome. Using this core SNP marker set, we assessed the genetic diversity and population structure of bottle gourd collections, fingerprinted bottle gourd germplasm collections and commercial cultivars, performed an optimized procedure for seed authentication, and developed an accessible core population. Our findings will provide a valuable basis for the future preservation and utilization of bottle gourd germplasm collections and also contribute to cultivar identification, which will enable the resolution of commercial disputes and protect the rights of breeders.The original contributions presented in the study are included in the article/YW and GL conceived the research. YW, XaW, YL, ZF, ZM, JW, XnW, BW, and ZL performed the experiments. XaW provided the mutant material. YL, ZF, ZM, JW, and XnW provided technical assistance. YW analyzed the data and wrote the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Perca fluviatilis), measured as activity (time spent moving) and sociability (time spent near conspecifics). We then gave perch of contrasting behavioural types the option to consume either conspecific or heterospecific  prey. Individual perch characterized by a social-active behavioural phenotype (n = 5) selected roach before being cannibalistic, while asocial-inactive perch (n = 17) consumed conspecific and heterospecific prey evenly. Thus, asocial-inactive perch expressed significantly higher rates of cannibalism as compared to social-active individuals. Individual variation in cannibalism, linked to behavioural type, adds important mechanistic understanding to complex population and community dynamics, and also provides insight into the diversity and maintenance of animal personality.The propensity to kill and consume conspecifics  varies greatly between and within species, but the underlying mechanisms behind this variation remain poorly understood. A rich literature has documented that consistent behavioural variation is ubiquitous across the animal kingdom. Such inter-individual behavioural differences, sometimes referred to as personality traits, may have far-reaching ecological consequences. However, the link between predator personality traits and the propensity to engage in cannibalistic interactions remains understudied. Here, we first quantified personality in Eurasian perch ( Cannibalism, the act of killing and consuming conspecifics (intraspecific predation), is widespread in nature. It has been documented in a diverse array of animals and likely occurs in virtually all major vertebrate and invertebrate groups, though the propensity for cannibalism varies between species , 2. FromEvidence from a continuously growing body of literature suggests that individuals from the same population often show consistent differences in a broad spectrum of behavioural traits, including activity, sociability, and risk-taking propensity –20. CorrPerca fluviatilis, Linnaeus 1758, Percidae). Perch are important predators in temperate lakes and are commonly involved in size-structured predator-prey interactions and cannibalism, adding to the complex interactions in lake communities [In this study, we aim to examine the link between behavioural type and the propensity for cannibalism in Eurasian perch  into the stomach cavity via a 0.25 mm incision posterior to the left pelvic fin [Age 1+ perch, standard length (SL): range 75–105 mm, N = 53, were collected by electrofishing in the lake Krankesjön, Sweden , in August 2015. Fish were then transported to laboratory facilities at Lund University, where they were sorted according to body size and housed in two separate holding tanks . Perch were fed de-frosted red chironomid larvae daily, at a rate corresponding to 15% of total perch wet weight. After two weeks of acclimatization to the laboratory environment, focal perch were anaesthetized (Benzocaine) and individually tagged by surgically implanting a passive integrated transponder tag  of the stimulus shoal. Both behaviours were analysed at a rate of 5 samples/s using automated tracking software (Noldus Ethovision v.3.1). The average time spent moving and time spent within 8 cm of the stimulus shoal were calculated as the average of trials 1 and 2, and these averages were used in subsequent analyses. Focal individuals were starved for 24 h before each assay in order to standardize hunger levels and given a minimum of 3 days off between the first and second trial. Both activity and sociability behaviours were evaluated during the same trial in an attempt to reduce handling and stress of the experimental subjects. Half of the water was replaced between each trial, and all subjects were assayed twice with a mean of 5.8 days (range 3–9 days) between the first and second trial. Individual scores for activity and sociability were used to classify individuals into behavioural types.Over a two-week period, each of the 53 focal perch was assayed twice for both activity (time spent moving) and sociability . The behavioural assay arena consisted of an 80 L circular acrylic tank , filled to a depth of 10 cm with aged and aerated tap water (18°C). In the centre of the arena, a 4.5 L clear acrylic cylinder (diameter 14 cm) held a stimulus shoal made up of 3 perch individuals , novel to and of similar size to the focal perch. The clear acrylic cylinder allowed visual, but not physical or olfactory, interaction between the stimulus shoal and the focal perch. Stimulus shoal fish were introduced 5 min before the focal perch, which were then acclimatized for 20 min before the 10 min recording. Recordings of behavioural trials were taken from a top-down view (Logitech HD pro C920 webcam linked to a computer running Evocam v.4.3) at a rate of 30 frames per second. The earliest trials began at 8:55, and the latest trials began at 19:42, and four trials were recorded simultaneously. To assess general activity and sociability, we measured movement and spatial use of the focal perch in the presence of the novel conspecifics. Activity was quantified from the videos as the amount of time (s) spent moving , while sociability was quantified as the amount of time (s) a fish spent with its centre of gravity within 8 cm  and SL = standard length (cm) [The presence of a behavioural syndrome in the 53 perch was evaluated with a correlation analysis between activity and sociability using Spearman’s rank correlation, where ρ > 0.3 and p < 0.05 suggest the presence of a behavioural syndrome, i.e., suites of correlated behaviours across situations , 36. A sgth (cm) ) using WDaphnia pulex.Roach and perch (size range 46–57 mm SL) were collected by sink netting in the lake Ringsjön, Sweden , in November and December 2015. Prey fishes were collected from a different lake than the focal perch to prevent possible kin discrimination from influencing prey selection –40. Prey-2) to add structure according to fish welfare regulations. Experimental predatory perch were starved for 48 h to standardize hunger levels, after which 5 prey perch (52.25 ± 2.43 mm SL) and 5 prey roach (51.4 ± 2.43 mm SL) prey were measured, size-matched (max difference 8 mm), and then added simultaneously to the open area of each experimental tank. Tanks were checked every 24 h, and the surviving prey fish were recorded and fed with de-frosted red chironomids at a rate of 20% of the original prey weight. Trials stopped when each focal perch had consumed 3 prey fish, with no trial exceeding 5 days. The remaining prey fishes were not re-used in later trials. Individual predator selectivity between prey types was calculated using the Manly-Chesson selectivity index (α) for a decreasing population: α) and random (α0 = 0.5) selectivity was evaluated with one-sample Wilcoxon signed-rank tests (V) using the α for perch prey for the two behavioural types, respectively. Moreover, differences in cannibalism between the two behavioural types were calculated using an independent Wilcoxon rank sum test (W) on the respective α values. We also fit simple linear models on Manly-Chesson selectively index using average movement and average shoaling as explanatory variables. All statistical analyses were performed in R Studio [The 22 experimental perch were placed individually in 55 L aquaria filled with 18°C aerated tap water to a depth of 22 cm. Each tank was covered with white vinyl on three sides, had gravel on the bottom, and half of each tank had artificial vegetation . The cluster analysis grouped the focal individuals into two distinct groups based on their activity and sociability scores, and the two groups were confirmed by discriminant analysis . There were no differences in body condition (Fulton’s K) or body size (SL) between the two behavioural-type groups from the 22 experiment individuals .2 = 0.076, p = 0.115, and R2 = 0.019, p = 0.248, respectively).Social-active individuals (N = 5) and asocial-inactive (N = 17) behavioural types  differedOur experiment reveals a novel link between behavioural types and cannibalism rates among predatory perch. We found that perch characterized by relatively low levels of activity and sociability were more cannibalistic than individuals characterized by relatively high activity and sociability. Linking phenotypic variation to the propensity for cannibalism in predator populations should enhance our understanding of causes and consequences of cannibalism, and thereby our understanding of higher-order ecological processes . The actvice versa [We found that the social-active perch phenotype was less cannibalistic and selectively preyed on the more actively swimming roach. In contrast, the asocial-inactive perch phenotype showed no significant selection for either prey species, resulting in a comparatively higher rate of cannibalism. Interestingly, this is contrary to the locomotor crossover hypothesis, which predicts that less active prey are consumed by more active predators and ce versa . It alsoce versa . Higher ce versa , includice versa . The higce versa , 50, 51.ce versa  in whichce versa , 53. As ce versa . This maCannibalism may, regardless, have a substantial influence on community and systems composition , 47, 55,Although the precise mechanisms underpinning our results deserve attention in future work, we introduce the link between behavioural types and cannibalism propensity to shed light on the importance of bridging the order scales between individual behaviours and community processes.


Population-level genetic variation enables competitiveness and niche specialization in microbial communities. Despite the difficulty in culturing many microbes from an environment, we can still study these communities by isolating and sequencing DNA directly from an environment (metagenomics). Recovering the genomic sequences of all isoforms of a given gene across all organisms in a metagenomic sample would aid evolutionary and ecological insights into microbial ecosystems with potential benefits for medicine and biotechnology. A significant obstacle to this goal arises from the lack of a computationally tractable solution that can recover these sequences from sequenced read fragments. This poses a problem analogous to reconstructing the two sequences that make up the genome of a diploid organism (i.e. haplotypes) but for an unknown number of individuals and haplotypes.et al. in 2001. Now, nearly two decades later, we discuss the complexity of ‘haplotyping’ metagenomic samples, with a new formalization of Lancia et al.’s data structure that allows us to effectively extend the single individual haplotype problem to microbial communities. This work describes and formalizes the problem of recovering genes (and other genomic subsequences) from all individuals within a complex community sample, which we term the metagenomic individual haplotyping problem. We also provide software implementations for a pairwise single nucleotide variant (SNV) co-occurrence matrix and greedy graph traversal algorithm.The problem of single individual haplotyping was first formalized by Lancia Our reference implementation of the described pairwise SNV matrix (Hansel) and greedy haplotype path traversal algorithm (Gretel) is open source, MIT licensed and freely available online at github.com/samstudio8/hansel and github.com/samstudio8/gretel, respectively. In the wake of the announcement of Celera’s first human genome, it became clear that the next big research problem was not only to analyse the millions of single point variants that populate our genomes, but how to assemble the two haplotypes that make up a single individual’s genome  was first described by Lancia et al.’s work first described the problem for a diploid individual as:The computational problems involved with haplotyping arise because genomic assembly algorithms typically achieve accuracy through the availability of high sequencing coverage. Although it is now possible to assemble high quality, contiguous sequences of entire genomes, even for a complex community sample  is an m × n matrix encoding the binary allele observed at each SNP site A, B), or a gap (denoted –) observed at the jth SNP site on the ith read fragment  necessitates a problem definition where at best, one can only recover the two haplotypes that are most ‘compatible’ with the observed fragments. Perhaps more importantly for the context of this work, Lancia also defined a common notation to formally describe the problem of single individual haplotyping. The ‘SNP matrix’ , j on the ith read fragment. Of course, the problem a decade prior considers the accuracy of only one sequence: that of the true DNA molecule that was sequenced, rather than untangling diploid sequence data to recover a solution of two distinct haplotypes. Lancia et al.’s work additionally define three optimization problems to solve SIH, which involve identifying and mitigating different types of ‘conflicts’ in the SNP matrix. A pair of read fragments ir, jr are said to be in fragment conflict if they have opposing alleles on at least one SNP. Similarly, a pair of SNPs ks, ls are said to be in SNP conflict if reads ur and vr are heterozygous at one SNP and homozygous at the other. Although a conflict might indicate that a pair of fragments originate from different haplotypes, Lancia et al. focus on the idea that conflicts arise due to errors in the underlying sequence data, arguing that ‘experiments in molecular biology are never error-free’. An SNP matrix M which contains any conflicts is infeasible, and the three optimization methods aim to resolve these conflicts to yield a pair of feasible haplotypes. This SNP matrix influenced almost all haplotyping algorithms for the next twenty years  and so conflict resolution strategies that operate on this matrix are only valid under a diploid assumption, or polyploid cases where only the major and minor alleles are considered  problem.Lancia nsidered , so therde novo formulation of the MIH problem, where the input is a collection of reads generated by a DNA sequencer from an environmental sample, with an unknown number of organisms. The ideal output from a solution to the MIH problem is the collection of whole-genome sequences representing all the individual organisms in a microbial community. To illustrate further, we require the following definitions:We begin with a naive Ω, a microbial community.O, a set containing each full genomic sequence, of each individual organism in environment Ω. O is the ‘metagenome’ of Ω: encompassing all possible genomes in the environment.O as a bag, allowing multiple copies of the exact same genome in the community. For the purposes of haplotyping, we do not need to concern ourselves with recovering duplicate genomes. We also require some definitions to denote how the community is sampled:Arguably one could consider Mσ.A set containing the unique genomes M represents the metagenome that was captured in the sample σ, and is our insight into O.M is not necessarily representative of the entire metagenome O.As a subsample  DNA from every genome needs to be extracted and sequenced to a depth sufficient for recovery, (ii) genomes share homologous regions that require disambiguation, (iii) reads may be of an insufficient length to disambiguate repeats or resolve bridges between variants, (iv) sequencing error can be indistinguishable from rare haplotypes and (v) the presence of an unknown number of haplotypes complicates the already computationally difficult (NP-hard)  and the variants determined by inspecting that alignment:Although such C (assembly) constructed de novo from the reads R by some Assemble operation.Contig set M, from the reads R. It should be noted that Assemble does not try to recover M, and typically fails to distinguish between similar sequences that should create distinct Assemble attempts to construct a set of consensus sequences representative of genomes in A is generated by aligning read set R to contig set C with operation Align.Alignment r and c may not necessarily be equal.An alignment kc.kc where any correspondence in R was found between positions i and j on contig kc.The list of positions on contig We will henceforth refer to these positions as single nucleotide variants (SNVs) rather than SNPs in our descriptions to make it clear that there are no assumptions on variant frequency or validity.i as a variant if there is a disagreement on the nucleotide at that position across the aligned reads. Call may also be a more complex variant prediction algorithm.Call may simply consider each ‘column’ C and filtering for alignments in A between positions i and j:To enable computational tractability and reduce the reliance on the quality of the assembly, we can consider the local MIH problem. The input to the local MIH problem is constrained by selecting a contig from region of contig kc, identified as biologically interestingalignments of reads R that map to the contig region variants over the region R, it is an approximation of one (or more) genomes in the sampled metagenome M. In the same way that an assembler can collapse multiple genomes with shared sequence into single contigs, or several broken contigs, an aligner may be unable to determine whether read r truly belongs to a single assembled contig in C. We cannot guarantee that reads aligned to m in the sampled metagenome M.As We use this to our advantage for local MIH. Consider first:f, a biologically interesting genomic feature such as (but not limited to) a gene or operonfM, the subset of the genomes in the sampled metagenome M that have feature fkm, some genome in fMkm that has concordance with feature f, with appropriate length f = f is the set of haplotypes of f in M.Γf. The output of local MIH is the set of haplotypes Γf. Although the ambiguity in the alignments f to be captured, the evidence they provide does not readily resolve to haplotypes. The Lancia et al. SNP matrix is not suitable for MIH, as the only information stored about a variant site on a read is whether it belongs to one of two haplotypes. In order to recover haplotypes that actually exist, we must define a new data structure that is capable of considering evidence from non-adjacent variant sites, and is agnostic to the number of potential haplotypes in Γf.Now let H. Consider two positions i and j, on an assembled contig kc. With a read r aligned to this contig, let α be the nucleotide on r at i, and β be the nucleotide at j. We say that the read supports an observation that symbol iα co-occurs with symbol jβ. H is a rank 4 tensor  such that an individual element α, β are in the alphabet We present a new form of SNP matrix: the pairwise SNV co-occurrence matrix, denoted This representation differs from the typical SNP matrix model , one can construct a simple graph G of possible transitions between all symbols. G would represent a graph of transitions observed between SNVs, across all reads. A node in G represents a state, pairing a symbol to a position (e.g. an ‘A’ at position 1). An edge between a pair of nodes in G represents two adjacent SNVs  and can be labelled with a weight using the number of corresponding co-occurrence observations in H. H records information about SNV pairs, and how a simple graph can be derived from this information.Consider H can be considered as a graph E and V as: Formally, G by selecting the edges most highly supported by the corresponding evidence in H, in order to recover a series of symbols representing an ordered sequence of SNVs that potentially constitutes a haplotype. We can now formalize a haplotype Intuitively, one may traverse a path through H cannot be fully represented with a graph such as that seen in G to create paths that do not exist in the observed dataset, as shown in Although the analogy to a graph helps us to consider paths, the available data in H and build upon the graph G. Rather than directly setting the edge weights to values from H, we define a formula to weight edges based on the support of pairs between a path of visited nodes and any potential next node. That is, a decision to move to a symbol at position i + 1 is informed not only by observations in H supporting , but also the non-adjacent observations , , and so on.We may take advantage of the additional information available in G, based on a path of already visited nodes. We define the probability of selecting Here, we formalize a Bayesian probabilistic framework to determine the edge weights in i increases. We can reduce complexity with two assumptions: (i) conditional independence between variants and (ii) one can limit the number of elements in the prior path without loss of accuracy because the reads providing this co-occurrence support are limited in length. This simplifies our previous equation as we only need to consider the pairwise appearances of each iv encountered thus far against i, back some small and sensible number of steps L: Clearly, evaluating the equation becomes more computationally expensive and risks compounding estimation errors as the path length increases. To construct a path from L as the ‘lookback’ size, the number of variants of the current path to consider when selecting As a minor implementation detail, to overcome inaccuracies encountered through floating point error when performing mathematical operations on very small fractional values, we provide these definitions with log probabilities instead. We define H to estimate the marginal and conditional probabilities. β appearing at position j. As a minor implementation detail, we expand our alphabet Σ to include a dummy symbol H, the last SNV jγ on a read r, is automatically linked to j and j + 1 to be calculated even in the case where j is the last observed position on a read. We use α appearing at position i given that β was observed at position j. To avoid the potential of dividing by 0 in cases where a suitable read spanning i and H) as a data structure to calculate the probabilities of edge traversals in the graph. Our algorithm traverses the haplotype space represented by the graph G in a greedy manner, selecting the edge with the highest likelihood (given the path traversed so far) in order to recover the highest likelihood haplotypes. The core of the algorithm is as follows:We have formulated the above ideas into an algorithm for finding solutions to local MIH. Here, we interpret the problem of MIH as a search problem rather than an optimization problem. Unlike Lancia and Lippert’s algorithms where changing or removing evidence from the SNP matrix formed the solution to SIH, the method we introduce will use the pairwise SNV co-occurrence matrix  until encountering a node with no edges that can be traversed, or an additional stopping criterion has been reached.H, the algorithm will behave deterministically and return the same haplotype for any traversal. However, the goal of local MIH is to recover Γf: the set of haplotypes corresponding to feature f, rather than just one haplotype. To recover more than just one haplotype, our algorithm must modify H to remove evidence for that haplotype, in order to weight the calculation of edge probabilities in favour of alternative haplotypes on the next traversal. We achieve this by using the smallest marginal distribution from path λ) to decrease the observations in H that directly support the path Given H that correspond to This ratio is used to decrease the evidence for all adjacent entries in H will begin to approach 0, causing edges in the graph to become unavailable for traversal. At this point the algorithm will terminate. Alternatively, if this criterion is not reached after some predefined number of iterations, the algorithm will terminate.After multiple iterations of path finding and subsequent reweighting, elements in H prior to reweighting. We can use the estimated probabilities to also score and rank the haplotypes recovered. For a completed haplotype, To overcome the potential for floating point arithmetic error, we calculate and report the log likelihood. i + 1 given some path leading up to i. A greedy traversal will pick the optimal option for the current fork in the path, regardless of whether that will cause the search to be steered away from paths with future branches that would lead to a better path overall. Although a heuristic cannot guarantee optimality, our approach of reweighting  falls into the hardest class of computational problems (NP-hard), necessitating the use of heuristic algorithms. Heuristics sacrifice an element of optimality in favour of speed or feasibility and are an inevitable compromise in order to find candidate solutions to a problem at all. Our graph traversal algorithm is a greedy heuristic; selecting the highest likelihood edge to a variant at position eighting  before seighting  in orderH is based on alignment of sequenced reads to a contig set generated by de novo assembly. Clearly, the quality of the contigs is a factor for the read alignments, which in turn affects the quality of the data inserted into H, which is why we consider the local MIH problem. Restricting the problem to shorter regions rather than full-length contigs significantly reduces the reliance on assembly quality. Even so, misassemblies should be poorly supported by the read alignments, indicating that the region in question is not suitable for haplotype recovery because it does not actually exist. Of course, the alignments are critical to the quality of the observations stored in H, and like any algorithm based on read evidence, a lack of coverage is a lack of evidence and will constrain the generation of solutions. Our approach requires every identified variant position to be linked to the next by at least one read . The graph traversal algorithm will abort upon finding a ‘hole’ in the graph—a position i where there are no outgoing edges to reach a new variant at position i + 1. In a case where variants are sparse and the reads are too short to span them, the algorithm will stop during the first traversal. In a case where the read coverage is very low for a region under investigation, the algorithm will likely stop after a small number of traversals, as successive rounds of reweighting will quickly exhaust the little evidence available. In addition, the transition probabilities should be robust to noisy or lower quality reads where the noise is distributed non-uniformly, as errors should be unsupported by the reads in aggregate, yielding small numbers of observations in H that will not be chosen over more well-supported transitions.Similarly for any algorithm, data quality will also impact the optimality of generated solutions. The construction of the pairwise SNV co-occurrence matrix Our approach is a fair compromise, offering a sensible heuristic that can run in reasonable time with reasonable resources, that should find approximately optimal haplotype solutions for local MIH.With recent technical advancements reducing the cost and complexity of sequencing environmental samples researchers are turning their attention to focus on the population-level variation within microbial communities. However, the field is hampered by a lack of consensus in terminology. Segata recently analysed the terminology used for metagenomic strains and for the diversity present in microbiomes . He discWe have defined the pairwise SNV co-occurrence matrix that packs sequencing reads into a structure compatible with metagenomic haplotyping. Our SNV matrix can be used to build a graph, reducing the problem of local MIH to that of recovering the highest weighted paths from a graph. We provide a probabilistic framework to weight the edges in this graph based on the path observed so far, while also considering evidence from non-adjacent SNVs in the underlying read data. We provide a free and open implementation of this SNV matrix (Hansel) at github.com/samstudio8/hansel.H to traverse paths through a graph to output haplotypes. Our algorithm requires no configuration, has no user-facing parameters and requires no pre-processing of reads, other than alignment. The haplotypes recovered with our algorithm can be scored and ranked by their likelihood. We provide a free and open implementation of this algorithm (Gretel) at github.com/samstudio8/gretel.In addition, we propose a greedy solution to the problem of local MIH, using a graph representation of et al., to define the MIH problem. We offer our data structure and algorithm in the hope that they will form a foundation for future approaches to haplotyping microbial communities.Our work extends the SIH problem introduced by Lancia


A total of 7,429 SNPs were found in association with 17 investigated traits, of which 177 were selected as lead SNPs. In the vicinity of these lead SNPs, 56 differentially expressed genes in various tissues/development stages were identified as candidate genes. This compendium connecting trait-SNP-genes may allow further prioritization of genes in GWAS loci to enable mechanistic studies. These identified quantitative trait nucleotides (QTNs) may prove helpful in fiber quality improvement in Asian cotton through marker-assisted breeding as well as in reviving eroded genetic factors of G. hirsutum via introgression breeding.The study of A-genome Asian cotton as a potential fiber donor in Gossypium species may offer an enhanced understanding of complex genetics and novel players related to fiber quality traits. Assessment of individual fibers providing classified fiber quality information to the textile industry is Advanced Fiber Information System (AFIS) in the recent technological era. Keeping the scenario, a diverse collection of 215 Asiatic cotton accessions were evaluated across three agro-ecological zones of China. Genome-Wide Association Studies (GWAS) was performed to detect association signals related to 17 AFIS fiber quality traits grouped into four categories viz: NEPs, fiber length, maturity, and fineness. Significant correlations were found within as well as among different categories of various traits related to fiber quality. Fiber fineness has shown a strong correlation to all other categories, whereas these categories are shown interrelationships  Cotton has a prime position in the global natural textile fiber industry, making it a significant agricultural commodity. It remains an essential source of livelihood for a large percentage of the farming community. Cotton production has long been a crucial part of diverse farming systems, particularly those involving vegetables and cereals, helping farmers to maintain their incomes. The competition among major cotton-producing regions and the use of synthetic fibers are continuously increasing. It necessitates continual product quality improvements, measuring physical traits and germplasm resources . Almost For the past two decades, industry and plant breeders have been utilizing High Volume Instrument (HVI) as the primary and sole source of measurement for selection and fiber quality improvement. However, the HVI system cannot assess many key fiber quality traits. Alternative systems for fiber quality evaluation, such as the Advanced Fiber Information System (AFIS), have been introduced. AFIS can obtain highly advanced and more accurate information about single fiber quality. Many studies have indicated that AFIS is an effective tool for predicting yarn quality along with spinning performance . AFIS isGossypium hirsutum (upland cotton), the most widely cultivated cotton species, is considered a natural allotetraploid cotton species with an AADD genome. It is thought to have resulted from natural interspecific hybridization involving the diploid species Gossypium arboreum (genome A2) and Gossypium raimondii (genome D5)  . It is cG. arboreum cultivars rather than tetraploid cultivars were commercially grown in north-eastern Africa, the Middle East, and Asia  was conducted to correlate phenotype data of fiber quality traits taken from AFIS with genotypic data generated from Next-Generation Sequencing (NGS) technology. Using different algorithms while performing GWAS, key SNPs and fiber quality associated genes were identified and selected. A thorough analysis of functional annotation via bioinformatics tools was utilized to confirm the linkage between genes and traits in diploid Asian cotton genotypes.Keeping the scenario, this study’s primary goals include identifying genes and residing regulatory sequences controlling cellulose biosynthesis and cell development of fiber. For sequencing, assembling, and annotation of G. arboreum) collection having 215 accessions (Asian cotton (cessions  were obtAn AFIS PRO 2 (Zellweger-USTER) was used to assess the cotton fiber traits, including their distributions, which were presented using histograms of the distribution for the measured parameters , includiThe AFIS PRO 2 mechanically separates individual cotton fibers presented to an electro-optical sensor using high-velocity airflow. The AFIS Length & Maturity module optically determines the value for maturity ratio (MR) using Lord’s equation while Immature Fiber Content (IMM) through the method described by p-values should come from a uniform distribution.The genotype data, which was above an average 6-fold sequencing depth, were derived from a previously published article by our team , and thevia utilizing ANNOVAR software  region; exonic (coding) region; splicing sites, lying within 2bp of splicing junction; and intergenic regions. Further, the SNPs harbored by the exonic regions were categorized as synonymous (didn’t cause any change in amino acids); non-synonymous (caused changes of amino acids); as well as stop-gain and stop-loss type of mutations also grouped in this category.SNP annotation information was generated software  based onThe data collected were subjected to multivariate analysis using hierarchical clustering analysis and principal component analysis (PCA). After confirming sufficient variation among the traits, GWAS was conducted, making the traits suitable for further genetic analyses. Multivariate correlation analysis was performed to assess the relationships among the 17 traits. The analyses were performed using JMP Pro 14.0 software (SAS Institute Inc.).To understand the phylogenetic relationships among the accessions, phylogenetic analysis was performed using the SNPhylo pipeline . The SNPK). A set of 431,985 SNPs, excluding missing genotypes, was included in the analysis. Additionally, SMARTPCA in EIGENSOFT software  software (K) matrix using the EMMAX-kin-intel EMMAX package. Bonferroni correction  plots were constructed using the CMplot R package to visualize the results. Lead SNPs were identified from the Manhattan plots by selecting the SNPs from each peak having the higher −logP = 6.15 using formula P < P = 1/N  from 215 diverse software , which cparisons  was usedr2 against the physical distance (kb) between the SNPs using PopLDdecay software (G. arboreum reference genome (http://grand.cricaas.com.cn/) and the Cotton Functional Genomics Database (www.cottonfgd.org). The upregulated genes (>1) specially in ovule and fiber were considered candidate genes and plotted in a heatmap.Linkage Disequilibrium (LD) decay was visualized by plotting software . The GWAsoftware –8. The Lsoftware . All gene genome  v1.1. DiG. arboreum accessions were assessed  and one non-conventional (Sanya) cotton-growing region in China for association studies. Population structure, PCA, and phylogenetic analyses   . These fG. arboreum accessions evaluated across three locations to identify genetic factors linked to single fiber quality traits while considering population structure and phylogenetic relatedness -rich regions  for 14 single fiber quality traits across three environments with 155 association signals detected from AY, 47 from HN and 26 from XJ. Thorough examination revealed that chromosomal distribution of lead SNPs is uneven across the entire genome. Chromosome 5 had a maximum number of 65 lead SNPs while Chromosome 12 had a minimum of 1 lead SNP  and the Cotton Functional Genomics Database (http://www.cottonfgd.org/)  > 6], quantile-quantile plots, GWAS-LD blocks for the depiction of haplotype region surrounding the peak associated significantly with respective trait coupled with regional plot revealing particular key SNP and its nearby gene, a boxplot for the demonstration of differential among trait related favorable haplotypes as well as expression plots of prominently selected annotated genes concerning traits at different developmental stages of cotton genotypes.A total of 113 genes were discovered in the 115.5 kb flanking window of lead SNPs. Of these genes, 25 and 1 were identified in maximum and minimum quantities on Chromosome 7 and Chromosome 12, respectively . These ggd.org/) . As a rel stages . Of thesl stages . Furtherl stages . The GWAp) > 6. The haplotypes of this SNP exhibited significant differences depicted via boxplots. The gene expression results gave following three genes namely, Ga05G3958 and Ga05G3959 in vicinity of this lead SNP. These candidate genes presented their higher expression levels regarding FPKM value in ovule, fiber, and seed tissues (p) > 6 found at peak of Manhattan plots. This lead SNP revealed distinct haplotypes mentioned in boxplots. The gene expression outcomes provided a gene viz, Ga11G3320 in which this SNP was found. This candidate gene exhibited high FPKM values in almost all tissues of ovule, fiber, seed, stem, and root with highest expression in ovule at 20DPA (p) > 6] association with the lead SNP i.e., Chr05_86618304. Its haplotypes showed significant differences from each other represented via boxplots. The gene expression findings provided a gene Ga05G3785 in close vicinity of this lead SNP. This candidate gene presented high expression FPKM values which validated the selection of this gene as candidate one (p) > 6] associations with pleiotropic lead SNP Chr06_8554709 found at peak of Manhattan plots. The haplotypes boxplots of this lead SNP were significantly different. The relevant gene expression profiles showed a gene Ga06G0559 in its close vicinity. This candidate gene presented its high expression FPKM values in ovule and seed tissues  confers to the diploid species, i.e., Gossypium arboreum and Gossypium raimondii   , rice (7∼250 kb) , and soy∼250 kb) . However∼250 kb) . We deteilestone . The powGa05G3958 found in close vicinity of lead SNP Chr05_91033229 associated with NEPs related traits, encodes a protein S-norcoclaurine synthase 2 (Q4QTJ1) plays role in defense mechanism again pathogens reported earlier in Opium poppy  protein which is involved in the oxidative stress response  which binds to actin filaments to promote cross-links to form thick bundles  protein found in Baker’s yeast. This enzyme is expressed in later stages of sporulation for the modification of glucan linkages in order to strengthen the ascospore wall or providing it plasticity . Ga02G1738 encodes mediator of RNA polymerase II transcription subunit protein. It regulates flowering time and plant defense and is involved in pollen tube growth (Ga11G3317 (non-synonymous mutation) encodes FAR1-related sequence 5 (FRS5) protein. It is involved in zinc ion binding and transcription regulation and is expressed in hypocotyl tissues, leaves, stems, and flowers. It is upregulated in hypocotyl tissues (Ga11G3320 encodes membrane-anchored ubiquitin-fold protein 1 (MUB1). It is involved in stability at temperatures >90°C and is located at the plasma membrane (Ga11G3319 (non-synonymous mutation) encodes galacturonosyltransferase 13 (GAUT13). It is involved in pectin biosynthesis in cell walls, pollen tube growth, and pollen development, and it is expressed in flowers, roots, stems, and leaves , energy production or conversion, intercellular trafficking, secretion, vesicular transport, transcription, translation, post-translational modification, protein turnover, chaperons, replication, recombination, repair, signal transduction mechanisms, and general functions prediction.via gene expression analysis. Highly significant genes present in the vicinity of these key SNPs were considered as candidate genes. These compendia connecting traits, genes and cell types may allow further prioritization of genes in GWAS loci to enable mechanistic studies. These identified QTNs can possibly be helpful to cotton breeders regarding fiber quality improvement as well as revival of eroded genetic factors of G. hirsutum via introgression and marker-assisted breeding approaches.Continuous improvements of cotton fiber quality are required to maintain the superiority of cotton fiber over manmade yarn. Significant research is needed to improve the measurement accuracy of key cotton fiber traits. Basically, 4 categories of 17 AFIS-related fiber quality traits, including NEPS, Fiber Length, Fiber Fineness and Fiber maturity, were evaluated in the current study. Rapid and precise measurement of quality traits will help cotton breeders quickly select key traits to develop varieties with superior fiber quality suitable for industrial use. A substantial amount of highly significant SNP markers (QTNs) for these traits were identified and further validated


These significant regions have genes mapped to within 400 kb of them. The genes mapped for height have been reported to be associated with height in humans. Similarly, those mapped for MDD have been reported to be associated with major depressive disorder and other psychiatry phenotypes. The results show that SNHap-RHM presents an exciting new opportunity to analyse complex traits by allowing the joint mapping of novel genomic regions tagged by either SNPs or haplotypes, potentially leading to the recovery of some of the “missing” heritability.We describe a genome-wide analytical approach, SNP and Haplotype Regional Heritability Mapping (SNHap-RHM), that provides regional estimates of the heritability across locally defined regions in the genome. This approach utilises relationship matrices that are based on sharing of SNP and haplotype alleles at local haplotype blocks delimited by recombination boundaries in the genome. We implemented the approach on simulated data and show that the haplotype-based regional GRMs capture variation that is complementary to that captured by SNP-based regional GRMs, and thus justifying the fitting of the two GRMs jointly in a single analysis (SNHap-RHM). SNHap-RHM captures regions in the genome contributing to the phenotypic variation that existing genome-wide analysis methods may fail to capture. We further demonstrate that there are real benefits to be gained from this approach by applying it to real data from about 20,000 individuals from the Generation Scotland: Scottish Family Health Study. We analysed height and major depressive disorder (MDD). We identified seven genomic regions that are genome-wide significant for height, and three regions significant at a suggestive threshold ( Estimates of the genetic component of complex trait variation using genotyped SNPs led to the conclusion that a proportion of the heritability of complex traits is still unexplained or “missing” . Full seIn light of this, we proposed a genome-wide analytical approach that draws its theoretical basis from the genome-based restricted maximum likelihood (GREML) approach  which utHaplotypes may provide a better strategy to capture genomic relationships amongst individuals in the presence of causal rare variants. Although rare variants are not in LD with genotyped variants and thus are difficult to capture in conventional GWAS, these rare variants may be in LD with some haplotypes and thus can be captured using haplotype methods. Compared with genotyped SNPs, capturing haplotype effects may offer an advantage because haplotypes can be functional units . TherefoThis paper further explores the properties of both the SNP-based and the haplotype-based regional heritability mapping (SNP-RHM and Hap-RHM respectively). We hypothesise and show by simulation that the Hap-RHM complements existing SNP-RHM analytical approaches by capturing regional effects in the genome that existing SNP-based methods fail to capture. This leads us to propose a mapping strategy that jointly utilises SNP and haplotype GRMs in a single analysis called SNHap-RHM. We then confirm the utility of this approach by applying it to real data obtained from about 20,000 individuals from the Generation Scotland: Scottish Family Health Study (GS: SFHS) . We analp-value < 0.000001). A total of 555,091 autosomal SNPs passed quality control for downstream analysis. Ethical approval for the GS: SFHS study was obtained from the Tayside Committee on Medical Research Ethics .The data used in this study is from the Generation Scotland: Scottish Family Health Study (GS: SFHS), comprising of 23,960 participants recruited from Scotland . The DNAWe have shown previously that regional GREML analysis  using fixed region sizes in the genome is a suitable mapping method for finding local genetic effects . The conConsider a vector Under the model, the vector of phenotypes A SNP-based regional heritability analysis was first reported by The regional heritability model fits two genetic relationship matrices (GRMs): one local GRM for the region and a whole-genome GRM for the remaining SNPs in the genome that are outside the region. The GRMs are genomic relatedness matrices calculated as the weighted proportion of the local or genome-wide autosomal SNPs shared identity by state (IBS) between pairs of individuals. The SNP IBS matrices are calculated as follows, following the second scaling factor proposed by The haplotype-based regional heritability model follows theoretically from the SNP-based analysis and utilises haplotypes instead of SNPs as the genetic markers for the regional analysis. The analysis fits two GRMs, a haplotype-based regional GRM and a SNP-based background genome-wide GRM. The haplotype-based GRM is similar to the SNP-based GRM defined in the previous section. For a locally defined region (haplotype block) containing Five phenotypes were simulated using available genotypic information of 20,032 individuals from the Generation Scotland: Scottish Family Health Study . The fivPhasing of the GS: SFHS data was done using SHAPEIT2 . Best guFor the haplotype QTL phenotypes, a haplotype block is treated as a single genetic locus having multiple alleles. Each haplotype variant within a block is considered as an allele of that locus. Each study individual will carry two alleles, or have a diplotype, for each locus or haplotype block. The genotype data used to simulate the phenotypes were phased using SHAPEIT2  to produThe individual marker contribution to the polygenic effect and the QTL effects were calculated as followsCommon environmental effects were randomly sampled for the five phenotypes from a normal distribution In this simulation study, the five simulated phenotypes were analysed using the two models, the SNP-based regional heritability model (SNP-RHM for the SNP QTL phenotypes) and the haplotype-based regional heritability model (Hap-RHM for the haplotype QTL phenotypes). To test the analytical models’ specificity, we applied Hap-RHM to SNP QTL phenotypes and SNP-RHM to the haplotype QTL phenotypes. We also performed a Hap-RHM analysis in which the units of analysis in the haplotype blocks were restricted to regions of 20 or fewer SNPs per haplotype block. This was because we observed that longer haplotype blocks had many SNPs , and this impacted the estimation of the simulated regional effect. The hybrid Hap-RHM, therefore, investigates whether the regional effect is well captured by the haplotype-based model when shorter haplotypes are used.p-value < 1.02 × 10−6) using a Bonferroni correction for testing 48,772 regions.We estimated the regional genetic variance and polygenic variance using restricted maximum likelihood (REML). For each simulated phenotype, we analysed 220 regions in total to map the 20 simulated QTLs. This involved analysing the region containing the QTL and ten adjacent regions (five in either direction). In this way, we limit the analysis to the regions in the genome with simulated effects, thereby reducing computation time considerably. Also, by analysing neighbouring regions, we are able to explore the precision of estimates of the location of regional effects. We assessed the significance of a region using the Likelihood Ratio Test (LRT). The genome-wide significance threshold was calculated to be LRT = 23.9 , a regional heritability analysis that jointly fitted the SNP and the haplotype GRM.MDD status for GS: SFHS participants was assigned following an initial mental health screening questionnaire with the questions: “Have you ever seen anybody for emotional or psychiatric problems?” or “Was there ever a time when you, or someone else, thought you should see someone because of the way you were feeling or acting?” Participants who answered yes to one or both of the screening questions were further interviewed by the Structured Clinical Interview for DSM-IV (SCID) . A total2, and the first 20 principal components calculated from the study participants’ genomic relationship matrix .SNHap-RHM fits jointly, the two types of regional GRMs, SNP-based and haplotype-based, in the analysis of phenotypes . We pre-p-values as 0.5× the p-value of a chi-squared distribution with one degree of freedom for the one degree of freedom test and as 0.5× the p-value of a chi-squared distribution with one degree of freedom plus 0.25× the p-value of a chi-squared distribution with two degrees of freedom for the two degrees of freedom test.The significance of a region was tested with a likelihood ratio test (LRT) with two degrees of freedom which compared a model with three variance components fitted  against a model with only the residual variance component fitted. The individual regional variance components in all regions were subsequently tested with an LRT with one degree of freedom which compared a model with three variance components fitted against a model with two variance components fitted . We assumed the appropriate null distribution that results from testing on the boundary of the parameter space and therefore calculated the p-values obtained from the LRTs were used to generate genome-wide association plots for each phenotype . The genome-wide significance threshold was calculated to be LRT = 23.9  using a Bonferroni correction for testing 48,772 regions. The suggestive significance threshold of a region was set at an LRT = 19.5 .The We performed a regional heritability analysis that fits two GRMs (one for the region and one for the rest of the genome) per region across multiple genomic regions delimited by recombination hotspots (where the estimated recombination frequency exceeds ten centiMorgans per Megabase (10 cM/Mb)). This recombination threshold resulted in a total of 48,772 regions across the genome. We tested two types of regional heritability models, SNP-RHM and Hap-RHM, on 20 replicates of five simulated phenotypes. In SNP-RHM, the regional matrix is derived from SNP genotypes whereas in Hap-RHM the regional matrix is derived from haplotypes. The phenotypes were simulated to be determined by 20 regional QTL effects and genome-wide polygenic effects. The regional QTL effects of the five phenotypes were simulated using SNPs as causal variants for two of them and haplotypes for the remaining three as described in the methods section.0: that the genetic variance explained by the region is not significant, against the alternative hypothesis, H1: that the region accounts for a significant proportion of the phenotypic variance. A large LRT statistic is evidence against the null hypothesis, and therefore means the region explains a significant proportion of the phenotypic variance.A likelihood ratio test (LRT) was used to test the null hypothesis, Hp-value < 1.02 × 10−6, Bonferroni correction for testing 48,772 regions) and can capture true causal loci in traits with different genetic architectures. The LRTs were higher on average for the SNP-based model (SNP-RHM) than the haplotype-based model (Hap-RHM). This could be because for Hap-RHM, the genome-wide GRM which is a SNP-based GRM does not tag any of the background haplotype effects that are outside any one particular region being analysed, and thus the residual variance may be inflated by the other haplotype QTLs which downwardly impact the LRTs.The LRTs averaged over the 20 replicates of the five phenotypes are shown in We provide further investigation of the results from the simulation in the supplementary material . For botBoth SNP-RHM and Hap-RHM fail to capture the simulated regional effects when the simulated phenotype has a genetic architecture that does not match the analysis model, i.e., SNP or haplotype . These fTo confirm that two models are complementary and independent of each other, we implemented SNHap-RHM that fits the regional SNP and haplotype GRMs jointly, on a replicate of each of the five simulated phenotypes. The significance of regional effects was tested with an LRT with two degrees of freedom. The results are shown in The heritability estimates for height and MDD in the GS: SFHS dataset, calculated using the whole-genome GRM, were 81.4% (0.92) and 13.8% (1.35) respectively. There were no overlaps between regions identified as significant (tested with an LRT with one degree of freedom) by the haplotype and SNP-based models for either of the two traits . This rep-values  (DCC within the pathway) to show the DCC association with major depressive disorder. The second most strongly associated region was on chromosome 8, and this region had no gene mapped to it.For MDD, no region passed the Bonferroni-corrected genome-wide significance threshold for the analysis done with the SNP-based and haplotype-based regional heritability models . Three ranalysis . Figure analysis . The mossortium) . Zeng etMYRIP  in the comparison, the numbers might increase. Also, our sample size compared to most of those published GWASs is quite small which means that we might not have enough power to detect all associations.For both traits, the SNPs in the regions that were significant at atalogue  to be siWe have proposed and implemented a genome-wide analytical method that analyses genomic regions using a regional heritability model . We haveWe hypothesised that the Hap-RHM would complement the SNP-RHM. We investigated this hypothesis in a simulation study in which we simulated 20 replicates each of two types of SNP QTL phenotypes and three types of haplotype QTL phenotypes. The results show that the two heritability models can capture the effects of causal variants within genomic loci associated with the phenotype analysed. The results also show that the two models are specific about the type of causal effect they can capture, therefore, providing support for the hypothesis that haplotype-based regional heritability models will complement SNP-based regional heritability models. We provide further support for this hypothesis by fitting the two GRMs jointly and showing (using an LRT with two degrees of freedom) that we can still capture the simulated effects and real effects from real data.p-value < 1.02 × 10−6) for height compared to MDD. Fifty-seven of the SNPs identified for height by the SNP-RHM have been reported by other studies to be associated with height. These SNPs spanned 20 genomic regions in the GS: SFHS cohort. Height is a highly polygenic trait with many common genetic variants accounting for most of the additive genetic variation  significant regions  and the flanking variants that aggregate to form the haplotypes within the genomic regions. There were no overlaps between regions identified by the Hap-RHM and SNP-RHM for each trait, which again supports the hypothesis that the two models complement each other in mapping associated loci.MDD is a very heterogeneous phenotype, and thus every MDD case could have a set of genetic and non-genetic risk factors exclusive to them . These up-value < 5 × 10−5 had genes mapped to those regions or within 400 kb of those regions. For height, these genes have been reported to be associated with height in humans . Four of these SNPs lie within the gene sequence of MYRIP, and they each confer 2% disease risk. A conventional GWAS analysis would have missed these nominally associated SNPs because they will not reach the suggestive significance threshold, let alone genome-wide (GW) significance. However, analysing these SNPs within the region as haplotypes allowed us to detect the combined effect of these SNPs in the region at a suggestive-significance level even with our relatively small sample size compared to recent genome-wide association studies of MDD: 322,580  are fitted jointly: an analytical procedure we termed SNHap-RHM. This SNHap-RHM presents an exciting new opportunity to analyse complex traits by allowing the joint mapping of novel genomic regions tagged by either SNPs or haplotypes, potentially leading to the recovery of some of the “missing” heritability.