A total of 3,319 single nucleotide polymorphism (SNPs) and 181 INDELs in the B-F/B-L region were identified among 21 populations, of which 2,057 SNPs (62%) and 159 INDELs (88%) were novel. Most of the variants were within the intron and the flanking regions. The average variation density was 36 SNPs and 2 INDELs per kb, indicating dramatical high diversity of this region. Furthermore, BF2 was identified as the hypervariable genes with 67 SNPs per kb. Chinese domestic populations showed higher diversity than the WLs and CS. The indigenous breeds, Nandan Yao (NY), Xishuangbanna Game (XG), Gushi (GS), and Xiayan (XY) chickens, were the top four with the highest density of SNPs and INDELs. The highly inbred lines 63 and 72 have the lowest diversity, which might be resulted from a long-term intense selection for decades. Collectively, we refined the genetic map of chicken MHC B-F/B-L region, and illustrated genetic diversity of 21 chicken populations. Abundant genetic variants were identified, which not only strikingly expanded the current Ensembl SNP database, but also provided comprehensive data for researchers to further investigate association between variants in MHC and immune traits.The chicken major histocompatibility complex (MHC) on chromosome 16 is the most polymorphic region across the whole genome, and also an ideal model for genetic diversity investigation. The MHC B-F/B-L region is 92 kb in length with high GC content consisting of 18 genes and one pseudogene (Blec4), which plays important roles in immune response. To evaluate polymorphism of the Chinese indigenous chickens as well as to analyze the effect of selection to genetic diversity, we used WaferGen platform to identify sequence variants of the B-F/B-L region in 21 chicken populations, including the Red Jungle Fowl (RJF), Cornish (CS), White Leghorns (WLs), 16 Chinese domestic breeds, and two well-known inbred lines 6 BF, BL, and BG, which encode class I, class II, and class IV glycoproteins on cell surface, respectively  is a group of tightly linked genes, encoding major histocompatibility antigens, which discriminate between self and non-self cells and regulate immune responses. MHC is closely related to disease resistance and production performance of animals . Chickenectively . The encectively .Blec4) . LEI0258. In addition to LEI0258, there are also many microsatellite loci such as MCW0370 and MCW0371. The use of these high polymorphism markers is of great significance for the study of chicken MHC diversity  , of whic antigen . BLB1 anolecules . MHC is olecules , and itsolecules . The miculations . Huang eiversity .3 and 72) , Cornish (CS), RJFs, and two high inbred lines (lines 6 and 72)  using th and 72) .All experimental procedures and used animals were approved by the Animal Care and Use Committee of China Agricultural University.n = 9), Wenchang , Wuding , Xiayan , Xishuangbanna Game , Luxi Game , Gushi , Wenshang Barred chicken , Nandan Yao , Yunyang Black-bone chicken , Tibetan , Beijing You , Chahua , Dongxiang Blue-eggshell chicken , Shouguang , and Silkie . Two introduced populations were CS(n = 10) and WL(n = 10). The blood samples of first ten local breeds were collected previously by our colleagues from local chicken breed preservation farm. The blood samples of latter six local breeds as well as CS and WL were from Experimental Farm of Poultry Resource in China Agricultural University. The individuals were randomly selected for the assay. RJF (n = 3) was also included in this study, and DNA was kindly provided by Dr. Xiaoxiang Hu. Two highly inbred WL lines 63 (n = 8) and 72 (n = 8) were that were cultivated by the Avian Disease and Oncology Laboratory (ADOL) of the United States Department of Agriculture. After decades of breeding, the inbreeding coefficient of the two lines reached 99%. Serological tests showed that the two lines have the same MHC B haplotype (B2). Although they have the same haplotype, there were significant differences in Marek’s disease (MD) resistance: line 63 is resistant to MD tumors but susceptible to both Marek’s disease virus (MDV) and avian leukosis virus (ALV). Conversely, the line 72 is resistant to ALV infection but susceptible to both MDV and MD tumors . The purity, concentration, and volume of the extracted DNA were tested. WaferGen Smartchip platform was used to perform highly parallelized PCR for the target region and the procedure was followed that by 1 Trimmomatic (Trimmomatic 0.39) was used to obtain clean reads, and the parameter of quality control was as follows: SLIDINGWINDOW:5:20 LEADING:3 TRAILING:3 MINLEN:36  was download from NCBI.INLEN:36 . SAMtoolINLEN:36 . BurrowsINLEN:36 . The aliINLEN:36 . GATK Ha3 and line 72. Allele frequency and frequency difference of each SNP were calculated with VCFtools (versions 4.0) – freq and –diff-site  clean reads were obtained from ∼1,42,000 raw reads for each individual. Eighty-eight percentage of reads were aligned to the chicken genome assembly  , and thebout 70% . The ave3 and 72 showed the fewest SNPs and INDELs among the populations. There were no breed-specific SNPs or INDELs identified in this study, and most of them were mutually shared between some populations. We also calculated the allele frequency to analyze the difference in the BF/BL region between the MD resistant line 63 and the MD-susceptible line 72. There were only 12 loci with allele frequency differences greater than 0.3, and the largest difference was 0.5, seven of them differed at significant levels . There was no SNP locus that completely separates the two lines (The SNP database at Ensembl (release 102) had 4,804 known variations in this region, including 4,572 SNPs and 232 INDELs. In this study, a total of 3,319 SNPs and 181 INDELs were identified in the 21 populations . Among two lines .3 and line 72, with 2.13, 2.35, and 8.16, respectively. Similarly, NY, GS, XB, XY, and WC five populations showed the highest density of INDELs, and the three populations with the lowest density of INDELs were RJF, the line 63, and line 72. The SNP and INDEL densities in the two introduced populations, CS and WL, were lower than those of most of the Chinese indigenous populations  was significantly higher than that of insertion (36%). Both the largest deletion and insertion detected in this study were 60 and 25 bp, respectively, and both the smallest deletion and insertion were 1 bp. A large proportion (89%) of INDELs were less than 10 bp. The majority was the single base-pair INDEL and accounted for 58% of all detected INDELs. In addition, INDELs were spread out of a 826 bp segment, accounting for 1% of the target region, and the region affected by deletions was larger than that by insertions .BLEC2 and TAP2 showed the lowest INDEL rate. The two genes, BF1 and BF2, coding the components of MHC class I molecules, showed the highest INDEL rate. Both showed more than 5 INDELs per kb. Our data showed that the BF1 and BF2 did not have large insertions and deletions, and the length of the INDELs in these genes was within 15 bp  has been widely used for identification of genome-wide genetic variation . HoweverA total of 3,500 variations were detected in the BF/BL region of the 21 chicken populations in this study. Among them, 2,057 SNPs and 159 INDELs were novel, which drastically expand the current Ensembl SNP database. The number of novel INDELs detected in this study was quite large, accounted for 5% of the total variations, but most of them privately appeared in some populations, which suggested that using additional populations with large genetic background differences may detect more genetic variation of the genome. We identified an average of 36 SNPs and 2 INDELs per kb in the BF/BL region, which is higher than that reported by Among these 18 genes, BF gene, and TAP gene showed high diversity, which is consistent with their antigen presenting roles. The BF genes encode typical MHC class I glycoproteins. The exon 2 and exon 3 of BF genes encoding α1 and α2 domains of MHC-I are related to the resistance of many avian diseases . TAP mol3 and line 72 were among the lowest, which must be resulted from high-pressure selection and is in good agreement with the other studies  under the Bioproject number The animal study was reviewed and approved by the Ethics Review Committee for Laboratory Animal Welfare and Animal Experiment of China Agricultural University. Written informed consent was obtained from the owners for the participation of their animals in this study.YY performed the experiments, analyzed the data, and wrote the manuscript. HZ supported the samples and provided comments on the manuscript. GY provided comments on the data analysis and revised the manuscript. KW provided comments on the data analysis. ZY, CZ, HY, and JL prepared and detected the samples. NY supported the samples and provided comments on the experiment. LL conceived and designed the experiment, provided comments on the data analysis, manuscript writing, and revision. 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.


Dioscorea alata L.) is a major tropical and subtropical staple crop cultivated for its starchy tubers. Breeding of this dioecious species is hampered by its erratic flowering, yet little is currently known on the genetic determinism of its sexual reproduction.Greater yam  marker was designed and validated that enables accurate cultivar sex estimation. The reconstruction of chromosome 6 associated with the detection of highly putative structural variations confirmed the possible involvement of a major part of the chromosome.Here we used a genome-wide association approach and identified a major genetic barrier to reproduction in yam on chromosome 1, as represented by two candidate genes. A deleterious effect on male fitness could be hypothesized considering the involvement of these two genes in male reproduction and the low frequency of this non-flowering dominant allele within the male genepool. We also extended the hypothesis of a XX/XY sex-determination system located on chromosome 6 in D. alata breeding programs. Moreover, the discovery of this genetic barrier to reproduction opens new avenues for gaining insight into yam reproductive biology and diversification.The findings of this study, combined with proper estimation of accession ploidy levels to avoid endosperm incompatibility issues, could facilitate the design of future promising parental combinations in The online version contains supplementary material available at 10.1186/s12870-021-02941-7. Conventional plant breeding programs have to generate large numbers of progenies to increase the chance of selecting new varieties with the desired phenotypes. The development of high-throughput phenotyping and genotyping methods has markedly enhanced the prediction of better parental combinations. An effective strategy could be to focus on few selected parental combinations and generate large populations . HoweverSolanum tuberosum L.), comparative genomic studies between wild and cultivated forms have revealed a selection signature on genes involved in pollen development and gametogenesis ).The non-flowering dominant allele was also present in half of the female accessions and almost absent in the male accessions, so it was in high linkage disequilibrium (LD) with the sex-related locus, but not on the same chromosome. The fact that long-range LD maintenance can be promoted by selection  suggestsD. alata was first identified using the quantitative trait loci QTL approach in two biparental populations [D. alata accessions using GWAS.The presence of an XX/XY sex-determination system located on chromosome 6 of ulations . HoweverD. alata. Significant differences between the male and female chromosome 6 were also highlighted. In addition, highly putative male specific structural variations were detected between the sequenced male pools and the reconstructed male chromosome 6, thus supporting the heteromorphic chromosome hypothesis.Two hypotheses could explain the large size of the sex-related QTL detected in , i.e. thD alata v1; Water Yam Genome Project – ftp://yambase.org). If Y-specific sequences actually existed they were not present within the assembly we designed, so the male chromosome generated would more likely have been a consensus between the putative Y and X chromosomes since X and Y chromosomes conserve homolog sequences by meiotic pairing and exchange [The reconstructed male chromosome 6 was actually biased. The classical S shape noted between the genetic and physical distance suggested that SNPs and thus scaffolds were ordered with confidence. However, SNPs were detected so their corresponding genomic regions were common to the male and female parents. The scaffolds used were also generated from the female accession Tda9500038 . Indeed, high variations among accessions on flowering abundance/frequency and pollen viability have been reported in yam (e.g. ). Other The ploidy levels of accessions could also possibly explain the mismatch between the genetic factors highlighted in this study and phenotypes. Indeed, polyploidy leads to major changes in gene regulation and expression . For exaSex and non-flowering phenotypes nevertheless seem to be mainly genetically determined, so our identified markers could thus be effectively used for sex and non-flowering prediction. Early accurate estimation of possible parental combinations could now be conducted in breeding programs prior to designing crosses. Such initiatives could be combined with sex/flowering genetic determination and ploidy level assessment, to avoid endosperm incompatibility. Moreover, pinpointing the genetic factor controlling the flowering ability could help identify the environmental conditions conducive to flowering (e.g. genotype-dependent temperature or day length responses), while facilitating the development of protocols to promote flowering induction as is currently the case regarding cassava flowering  and yam D. alata) flowering ability and sex determination. The study findings highlighted a genetic contribution to flowering ability located on chromosome 1 and the expression of two genes, one homologous to CDKF4 of Oryza sativa L. and another homologous to SINAT2 of A. thaliana. Moreover, we confirmed that a dominant male-related allele was present on chromosome 6 and that a large portion of the chromosome was involved, thereby supporting the hypothesis of an XX/XY sex-determination system. We thus designed a KASPar assay as a diagnostic tool for sex determination. These interesting findings could pave the way for identifying future parental combinations, while facilitating breeding for traits of interest such as tuber quality and disease resistance. They should also help gain further insight into this crop diversification process.Genome-wide association studies were performed to study greater yam  accessions maintained in the West French Indies (Guadeloupe) at the Tropical Plants Biological Resources Centre (CRB-PT) and CIRAD were used for GWAs analysis in order to identify regions related to sex determination and flowering capacity  and by assessing CIRAD accessions during the flowering period (December to January). Female and male phenotypes were coded as 0 and 1, respectively, for a total of 90 accessions. Regarding non-flowering phenotypes, accessions were assessed as non-flowering forms if they had been morphologically characterized by CRB-PT, however no information had ever been recorded regarding their sex. Flowering  and non-flowering phenotypes were coded as 0 and 1, respectively, for a total of 122 accessions, including 88 out of the 90 sex-determined accessions.A total of 124 yam  and two males (Kabusa and 14 M), described in  as populreseq.1.0.py software and VcfPreFilter.1.0.py implemented in the VcfHunter package [D. rotundata reference genome v1  and SNP calling and prefiltering protocols described in  were app package . Raw seq package  and alig00001–21 ; to deteConcerning the datasets from the pre-filtered vcf files used in GWAS, SNPs and accessions were filtered using the following filters: minimum depth 8, minor allele frequencies per site > 5%, maximum missing data per loci < 10% and maximum missing data per accession < 20%. To generate the genotyping incidence matrices, genotypes homozygous for the reference allele, heterozygous or homozygous for the alternate allele were respectively converted to 0, 1 and 2 regardless of the accession ploidy level. Finally, matrices consisting of 90 accessions × 4973 SNPs and 122 accessions × 6033 SNPs were used to perform GWAS for sex and flowering ability, respectively.2 test p-value <1e-4. Then SNPs were assigned to male and female datasets regarding their segregation pattern within the parents. This resulted in a genotyping male matrix of 110 progenies × 5473 SNPs consisting of SNPs heterozygous in the male parent and homozygous in the female parent, or heterozygous in both parents; and a female matrix of 110 progenies × 5866 SNPs consisting of SNPs heterozygous in the female parent and homozygous in the male parent, or heterozygous in both parents. SNP and accession filtering was conducted with R scripts  using the vcfR 1.5.0 package [To reconstruct the sex chromosomes (population A), SNPs and progenies were filtered from the pre-filtered vcf files based on the following criteria: minimum depth 8, maximum missing data by SNP < 20%, maximum missing data per progeny < 50%, at least 100 bp between consecutive SNPs, adequate segregation between parents (homozygous in one parent or heterozygous in both) and a segregation distortion χ package .p < 0.001), the panel structure was estimated using accession coordinates on the first PCA axis. Then the mixed model procedures Q (i), K (ii), and Q + K (iii) developed by [SNP-trait associations were first tested using a generalized linear model coded in R using the glm function. According to the  method, loped by  were apploped by  and expr2yK); where K is a matrix of relative kinship computed as the percentage of shared alleles, S and Z are incidence matrices, and ε is a vector of residual effects.Regarding the results, two types of validation were performed: genotyping using KASPar technology or a gene ontology study.® Plant Mini Kit . Overall, 42 accessions with known ploidy levels [SNP conversion for the KASPar assay was conducted as described in , except y levels  were genD. alata transcriptome [D. rotundata genome v1 available at http://yam-genome-hub.cirad.fr/jbrowse.Gene ontology (GO) enrichment was tested using the TopGO R cran package (“classic” Fisher test options). GO terms were extracted from the annotated criptome  mapped oChromosome reconstruction was based on marker segregation within the biparental population A (74F x Kabusa), consisting of 110 progenies. Linkage analysis was conducted on a per-parent basis, leading to separate reconstruction of a female and a male chromosome.D. alata genome v1 (Water Yam Genome Project – ftp://yambase.org) was assessed using the Basic Local Alignment Search Tool (BLAST) according to the procedure described in [First, from the filtered genotyping matrices obtained through GBS and vcf filtering, as previously described, 20 linkage groups were defined using JoinMap 4.1 software , while sribed in .D. alata scaffolds were ordered and oriented (when possible) based on pairwise recombination frequencies between the SNPs they contained. This was done using the Southgreen Galaxy “Chromosome reconstruction” pipeline based on the Scaffrehunter package [http://galaxy.southgreen.fr/galaxy/u/droc/p/scaffhunter%2D%2Dchromosome-reconstruction).Then  package ; availabD. alata chromosome 6 was then visualized using a Circos approach via the circlize R package [Homology between the male and the female reconstructed  package .The differentiation and structural variation studies were based on resequencing of four pools of DNA: two pools of male or female progenies from the two biparental populations 74F × Kabusa  and 74F × 14 M . Each pool consisted of eight leaf punches per flowering progeny to balance the DNA quantity and by choosing leaves close to flowers to avoid vine mixing. DNA extraction and quality checks were carried out as previously described at the GenoAgap platform . Library preparation and sequencing was conducted using the Illumina TruSeq PCR-Free kit. Paired-end (2 × 150 bp) sequencing was conducted on an Illumina HiSeq3000 system. Both library preparation and sequencing were performed at the GeT-PlaGe platform .st approach based on SNPs detected among the four DNA pools on the reconstructed male chromosome 6, as described for the GBS procedure, using the following parameters: minimum depth 20, minor allele frequencies per site > 15% and no missing data. Fst variation along the chromosome was assessed using a sliding window of 500 SNPs (step = 100 SNPs). The significance threshold was calculated at a probability of 0.01 of a gamma law fitted on the resulting Fst distribution.Male and female differentiation was studied using a Fhttps://github.com/SouthGreenPlatform/scaffremodler. First, male and female resequencing datasets were generated by combining raw reads by sex. These male and female datasets were separately used on the reconstructed male chromosome 6 with the following parameters: minimum read insert size = 150, maximum read insert size = 450 and the very sensitive end-to-end mapping process of bowtie2 [Structural variation was detected using the scaffremodler package  availablFrom the paired-end read mapping performed during the detection of clusters of discordant reads, the coverage difference between male and female pools was calculated as the mean difference between male and female sequencing depths in a sliding window of 200,000 bp . The significance threshold was calculated at a probability of 0.01 of a normal law fitted on the resulting difference in distribution coverage.Additional File 1: Fig. S1. Details on GWAS on sex determination . A) Barplot of the first 10 PCA eigenvalues computed to assess the panel structure. B) Clustering of accessions based on coordinates on the first five PCA axes. R cran, hclust function, “ward.D2” method. Red, female accessions; blue, male accessions. C) QQplot on GWAS results. Up/left, generalized linear model; up/right, P model; down/left, K model and down/right PK model.Additional File 2: Fig. S2. Details on GWAS on non-flowering phenotypes. A) Barplot of the first 10 PCA eigenvalues computed to assess the panel structure. B) Clustering of accessions based on coordinates on the first five PCA axes. R cran, hclust function, “ward.D2” method. Purple, non-flowering accessions; black, flowering accessions. C) QQplot on GWAS results. Up/left, generalized linear model; up/right, P model; down/left, K model and down/right PK model.Additional File 3: Fig. S3. Details on the genomic region linked to non-flowering/flowering phenotypes. A) Dotplot of sequencing homology between D. rotundata and D. alata. The dotplot was computed using the NCBI blastn web server  with the D. rotundata sequence of chromosome BDMI01000001.1  on the x-axis. B) Summary of significantly enriched G.O. terms within the genomic region. G.O. terms were extracted from the annotated D. alata transcriptome [D. rotundata genome v1 and available at http://yam-genome-hub.cirad.fr/jbrowse. G.O term enrichment analysis was performed using the TopGO R cran package (“classic” Fisher test options) studying the D. rotundata genomic region from 50 kb to 250 kb on D. rotundata chromosome BDMI01000001.1.enome v1 ;) on thecriptome  mapped oAdditional File 4: Fig. S4. Details on KASPar validation of the sex-related SNP (06.1_19660282). A) SNP flanking sequence and B) KASPar fluorescence results. Fluorescence signals are plotted by accession, ploidy and observed sex. In x, the “A” fluorescence allele, and in y, the “G” fluorescence allele.Additional File 5: Fig. S5.: Dotplot of recombination frequencies along the reconstructed male and female chromosome 6 of D. alata. A) Male chromosome 6 and B) Female chromosome 6. Recombination frequencies were computed from a biparental population (74F x Kabusa) consisting of 110 progenies. Scaffolds from the D. alata genome V1 were used. Chromosome reconstruction pipeline available at: http://galaxy.southgreen.fr/galaxy/u/droc/p/scaffhunter%2D%2Dchromosome-reconstruction.Additional File 6: Fig. S6. Physical versus genetic distance along the two reconstructed sex-chromosome 6. Up, male chromosome; down, female chromosome. Genetic distance were calculated using JoinMap 4.1 software .Additional File 7: Fig. S7. Details on significance thresholds used in male and female resequencing comparative studies. A) Distribution of Fst between male and female and definition of significance threshold. A sliding window of 500 SNPs was used (step = 100 SNPs) to compute Fst. The significance threshold was then assessed at 0.158. B) Difference in male and female read coverage. A sliding window of 200,000 bp was used . Thresholds were assessed at 6.67 and 2.59 for significance of over-coverage and under-coverage for males and females, respectively. For both analyses, a gamma distribution was fitted on the empirical distribution using the fitdist function of the fitdistrplus R cran library (red curves). The significance thresholds  were then assessed using a risk of 0.01.Additional File 8: Fig. S8. Circos visualization of male-specific discordant read clusters. Discordant read clusters  were detected using the scaffremodler package  available at https://github.com/SouthGreenPlatform/scaffremodler. Male and female resequencing datasets were separately used on the reconstructed male chromosome 6. Default options were used with an expected forward-reverse read orientation and a minimal and maximal insert size set at 150 and 450 bp, respectively. Then male-specific discordant read clusters were trimmed by comparing the two results files. Links color: red, deletion; blue, reverse-forward; Scaffolds, black or yellow; Barplot from black to blue depending on the male read depth and the plot of scaled coverage difference between male and female pools .Additional File 9: Table S1. Accessions used in this study with their attributes: flowering and sex.


The Intergovernmental Panel on Climate Change (IPCC) is widely regarded as the most important and authoritative source on climate change, its impacts and how to tackle the rising emissions that drive it. With this authority comes a responsibility to ensure the information is communicated effectively to policymakers, citizens and those who rely on the information for their lives and livelihoods. How the IPCC communicates the information in its reports via its official materials , through different channels  and to its main audience of policymakers as well as others  has been the subject of intense analysis in the past. How different types of evidence are included in IPCC reports, particularly indigenous and local knowledge, is also a rich vein of discussion. Similarly, the representativeness of the leadership, staff and author teams in terms of gender, geographical balance and diversity of expert perspectives is key to ensuring all voices are heard and all relevant evidence is considered.The IPCC has a long history of asking the research and global communications communities for input to its evolving communications strategy. In 2016, the IPCC convened an Expert Meeting on Communication, which led to a number of recommendations to enhance IPCC communications activities, strategy and capacity . Whilst the TC has been fully independent of the IPCC, there has been significant engagement with the IPCC throughout the TC, resulting in a number of contributions lead- or co-authored by IPCC staff. Jonathan Lynn, outgoing Head of Communications and Media Relations at the IPCC Secretariat, has lead-authored an introduction to IPCC communication history, and Thelma Krug, IPCC Vice-Chair, has reflected on the key themes of the TC in a concluding synthesis commentary. Whilst contributors to our Topical Collection make recommendations for the attention of ‘the IPCC’ as a whole entity, we wish to acknowledge the many moving parts that, in reality, make up the complex IPCC machine, not all of whom it has been possible to represent in this Topical Collection. That long list includes the IPCC leadership, secretariat, technical support units, authors , government and expert reviewers, review editors, chapter scientists, contractors and many more, alongside the IPCC’s 195 Member Countries.Lynn and Peeva begin the Topical Collection by laying out the history and innovations in IPCC communication strategy. It is evidence from Fig. 1 of their paper (Lynn and Peeva 2021) that there has been tremendous progress on communication as a key issue for the IPCC. Yet as opportunities have been taken and innovations progressed, it is also evident that there are significant communication challenges. Communication clearly lays at the heart of the IPCC: as Lynn and Peeva quote, and worth repeating here, this ‘brutal question’ was posed by IPCC Chair Hoseung Lee at a Side Event at COP21 in Paris, in the lead up to the Oslo Expert Meeting on Communication in 2016: ‘what use are IPCC reports if many of the intended users cannot understand them, do not know where to find them, or cannot use them in their own work?’ (Lee 2015:1).The TC speaks to these challenges posed by Lee. Some of the contributions suggest concrete and fairly straightforwardly actionable proposals, which fit within the bounds of IPCC communication strategy to date—indeed, some investigate recent changes in IPCC communication strategy ; though others do highlight significant challenges ahead, such as the role of social media for the IPCC . Alongside these contributions, which fit more or less neatly into existing structures, other papers present a more fundamental challenge to IPCC engagement and communication. These papers ask questions such as: whose knowledge is valued, and to what end? . For papers that fit within  the status quo though, one key piece of information must be borne in mind. Any discussion of IPCC communication strategy needs to first be centred in the IPCC’s mandate of providing ‘policy relevant but not-prescriptive’ assessments , Connors et al. also use their essay to argue for co-design but in this case for the development of the IPCC’s Frequently Asked Questions (FAQs). As the only mandatory part of the Assessment Reports (AR) targeting a non-expert audience, the FAQs represent an important distillation of IPCC findings. Connors et al. make a number of recommendations for FAQ co-development in subsequent ARs, including a sharing of responsibility for FAQs between communications experts and scientists, as well as common FAQ guidelines across the WGs (which have so far developed separately). In a third contribution embedded within existing IPCC communications strategy, Pidcock et al. call for better integration of the theory of effective public engagement with climate science. In their article, they lay out findings from a global survey which gathered practical examples of outreach and engagement by WGI authors. These are juxtaposed against key principles of effective engagement, as expressed in the Communications Handbook for IPCC authors, in order to highlight and find opportunities to promote best practice. Last, Pathak et al. bring a welcome perspective from a Global South context. In their essay, they discuss a wealth of in-person outreach activities carried out across India. Although these activities varied considerably in scale, audience and topic, some commonalities emerged. Pathak and her co-authors raise the considerable challenges of communicating to diverse audiences, of limited resources and of translation into local languages, with several suggestions for addressing these.The importance of different communication devices is highlighted in an article led by Bruine de Bruin and in two essays: one from Mcloughlin and another from Bloomfield and Manktelow. Bruine de Bruin and colleagues present evidence of the (mis)understanding of key terminology from the climate domain. Using a qualitative interview methodology, speaking to US residents, common themes were identified. All those involved in climate communications should heed their findings that even terms like ‘mitigation’ and ‘carbon neutral’ were perceived as difficult to understand. Indeed, even when terms were considered easier to understand (like ‘adaptation’), participants struggled to make the connection between the common usage of the word and its potential definition in a climate context.Mcloughlin’s essay considers the role of efficacy  in IPCC communications. He lays out the roles of three different types of efficacy , providing examples of where these could be invoked in a series of examples. Mcloughlin recognises the potential limits to nurturing efficacy in IPCC communications and makes suggestions of where other allied organisations may be able to make more targeted appeals to efficacy. Bloomfield and Manktelow argue for a set of more engaging Summary for Policymakers (SPMs) by considering the role of storytelling and by incorporating narrative features. They evaluate the AR5 SPM for storytelling opportunities, outlining how communications theory could usefully intervene to bring about narrative changes to identify characters, settings and morals and bring in engaging comparisons and analogies.The opportunities and challenges of social media are picked up in a number of contributions. Eide et al. draw from interviews with more than 30 international youth climate activists to show how IPCC science plays a central role in their activities. Notably, they highlight the networked communication landscape in which these voices become prominent and the possibilities for interaction that these online spaces create. The opportunities for engagement online are also picked up in Sanford et al., where they note a corpus of over 27,000 tweets in 41 languages during August 2019, around the launch of the Special Report on Climate Change and Land. However, Sanford and colleagues are also clear about how the IPCC needs to respond more effectively to distortions of report contents on social media platforms, to deepen their understanding of online climate communication and to more fully come to terms with how the digital landscape impacts the wider climate communications environment.The last four papers all challenge the workings of the IPCC, and hence how and why it communicates, in more fundamental ways than the papers outlined previously. Hermansen et al. start from the concept of ‘policy relevance […] the ‘raison d’être’ of the IPCC. They present an analysis of IPCC policy relevance across differing scales from global, to regional, to national. They conclude that the IPCC should acknowledge policy relevance not only in process, but also in terms of policy relevance as outcome. They present three recommendations for how the IPCC can work constructively to pursue policy relevance. Asayama also speaks to conceptualisations of the science-policy relationship. Whilst he maintains that in principle, the IPCC has remained policy-neutral; he contends that in practice, the IPCC has acted as a powerful discursive agent, enabling a discourse of scarcity to gain prominence. Asayama argues for an emancipatory discourse instead, calling for more meaningful engagement with the interpretive disciplines within social sciences and humanities in order to progress this aim. Similarly, Dudman and de Wit also critique ‘whose knowledge counts?’ at the IPCC table. They suggest a way forward for the IPCC to engage—and more particularly, to listen—more effectively to diverse forms of knowledge and to recognise the until-now privileging of scientific knowledge over other types or domains of knowledge. Last, Chakraborty and Sherpa eloquently describe their personal experiences with local communities in the Himalayas, juxtaposing it with their experiences in the IPCC knowledge production process. Despite considerable efforts by the IPCC to address these, they call out the IPCC’s still-existing biases: towards the Global North, voices of men, natural science disciplinary representation and western science cosmology over indigenous knowledges. Their experiences documented in the essay highlights marginalised narratives of climate-society relationships that challenge the existing understanding of the science-policy relationship, as well as highlighting issues of equity and justice.It is fitting that the TC is bookended with a contribution from IPCC Vice Chair Thelma Krug, reflecting on the challenges and opportunities posed by the TC contributions, speaking to the question: ‘where now for the IPCC on climate communication’? Again, we should note that this TC project was independent of the IPCC: but we are delighted that the contributions herein have contributed to, and sparked discussion at, the highest levels of the IPCC. We sincerely thank her for this critical engagement with the essays and articles, and we hope that this forms part of a continuation of learning for us all on climate change communications.Evolving the existing communication strategy (and separate implementation strategy) into a comprehensive IPCC Engagement Strategy. This would signal a shift from one-directional delivery towards two-way dialogue and be the basis for nurturing a more participatory approach to ensuring the report is relevant to the needs of a diverse, global audience. As part of this, the IPCC could explore feasible avenues for interacting meaningfully with key audiences at all stages of the report process, from incorporating diverse forms of knowledge to disseminating the findings.Revamping the existing review process to include more opportunities for key stakeholders to contribute to and/or ‘road test’ specific elements of the report and associated communication materials upstream of their production. For example, the IPCC could consider if there is flexibility within its working practices to incorporate principles of representative deliberation, such as a Citizens Panel, whilst adhering to IPCC guiding principles. These sorts of deliberative processes are increasingly used to tackle complex issues, convening diverse groups of people to learn and contribute to public issues OECD . In an IWhilst the addition of communications specialists in the Working Groups represents a significant and much-needed step forward, a more sophisticated approach to what it means to ‘embed’ communications capacity with the IPCC is needed. Re-conceptualising IPCC communications capacity as a ‘distributed system’ rather than operating as separate entities housed within the Working Groups and Secretariat would allow a more efficient operation, both internally and externally.The essays and articles in this TC raise many considerations for the IPCC to reflect on in terms of its evolving communications strategy. Drawing from these contributions, and our own perspectives, we offer the following overarching suggestions in our roles as Guest Editors, as the IPCC begins to look beyond AR6:These three points are proposed as potential fruitful lines of enquiry for the IPCC and as prompts for further discussion. We thank all the Topical Collection authors and IPCC staff for their engagement to date; and we look forward to these conversations opening up further in future.


Cervus elaphus) and introduced Japanese sika (Cervus nippon) sampled in Kintyre, Scotland to search for quantitative trait loci (QTLs) underpinning phenotypic differences between the species. These two species are highly diverged genetically  and phenotypically: pure red have on average twice the carcass mass of pure sika in our sample (38.7 kg vs 19.1 kg). After controlling for sex, age, and population genetic structure, we found 10 autosomal genomic locations with QTL for carcass mass. Effect sizes ranged from 0.191 to 1.839 kg and as expected, in all cases the allele derived from sika conferred lower carcass mass. The sika population was fixed for all small carcass mass alleles, whereas the red deer population was typically polymorphic. GO term analysis of genes lying in the QTL regions are associated with oxygen transport. Although body mass is a likely target of selection, none of the SNPs marking QTL are introgressing faster or slower than expected in either direction.We deployed admixture mapping on a sample of 386 deer from a hybrid swarm between native red deer ( A major goal of evolutionary genetics is to understand the relationship between phenotypic and genetic variation. By understanding the genetic architecture of phenotypic traits, we can then ask how selection could act on a trait, make predictions of how a trait might change over time, or how the trait could respond to environmental change . In the Genetic mapping in hybrid zones is particularly powerful because of the opportunity to use admixture mapping on recombinant individuals . The assPopulus hybrid zones  and sika (Cervus nippon) in Scotland  identify large-effect QTLs for carcass mass, (2) estimate the direction of effect of any QTL found, with the prediction that alleles associated with low mass would be at high frequency in sika and lower frequency in red deer, and (3) search for nearby genes and analyze their putative functions. In general, we expect carcass mass in deer to have a polygenic architecture, as is the case in morphological traits in several wild systems  culled the deer as part of normal deer control measures, in which animals were shot as encountered, regardless of phenotype or suspected species . Of the Cervus canadensis; The deer were genotyped on the Cervine Illumina iSelect HD Custom BeadChip, which has 53,000 attempted SNP assays, using an iScan instrument (We used the DNeasy Blood and Tissue Kit (Qiagen) according to the manufacturer’s instructions to extract DNA for SNP analysis, with the exception that we eluted twice in 50-μl buffer TE to obtain DNA at a sufficiently high concentration. We assayed the concentration of extractions using the Qubit™ dsDNA BR Assay Kit (Invitrogen). If an extraction was below 50 ng/μl, it was vacuum-concentrated, re-extracted, or omitted from SNP analysis. Each 96 well plate had a positive control, and genotypes were scored using the clusters from a previous study , as well as the proportion of the genetic variance explained (PGE) by the sparse effects. The product of PVE and PGE is the proportion of phenotypic variance explained by the sparse effects, which can be interpreted as the narrow-sense heritability  in i.e., the number of SNPs included in the sparse distribution), following A BSLMM cannot be run with a covariate matrix, although covariates can be included as additional fixed effects in the above-mentioned SNP matrices using the command “–not-snp.” We added covariates to the input file, specifically a “bimbam dosage” file output using plink   while the sika weighed 19.4 kg , and hybrid individuals were intermediate at 22.7 kg . The substantial variation within each sex species is due to variation in age .i.e., those effects due to SNPs with large effects, PGE/; We estimated PVE, which includes sex, age, admixture proportion (Q), and the SNP effects  to explain 0.912 [0.87–0.95 (CI)] of the phenotypic variance in carcass mass, while PGE, the genetic variance due to the sparse effects was 0.656 (0.22–0.99). This means that 0.598 (0.19–0.94) of the phenotypic variance was explained by the sparse effects [i.e., these SNPs were included in the sparse effect distribution at least 10% of the time). These SNPs were on linkage groups 6, 9, 11, 19, 21, 25, and 28. All SNPs with a PIP higher than 0.1 were also in the 99.9th percentile of effect sizes (The mean number of SNPs included in the sparse effects was 58.1 (6–179), but only 9  SNPs had a PIP above the threshold of 0.1 . For all SNPs associated with carcass mass, we found that, as predicted, the allele for lower carcass mass was fixed or nearly fixed in sika, and polymorphic in red deer . From this high LD, we conservatively inferred that genes within 500 kbp of each of the significant SNPs could be related to carcass mass. We found 45 unique genes that have been named in the cattle genome (Supplementary Table S2), and 297 unique GO terms (Supplementary Table S3). We found 15 GO terms that were significantly associated via gene set analysis based on the genes that we identified ; −5 and 4.0e−3% of the variation in carcass mass (We found that a large proportion of the phenotypic variation was explained by the sparse effect 0.598 (CI 0.19–0.94). A substantial proportion of the phenotypic variance must be due to including sex, age in the analysis, as sex and age were consistently included in the sparse effect. However, a moderate heritability is intuitive, as body mass is a moderately heritable trait [average ass mass . FinallyHBM, HBA, and HBQ1 are all found on linkage group 25 and are associated with oxygen binding and transport , but polymorphic in red deer, as determined by ADMIXTURE . Additioransport . Future There are a number of genomic investigations that could follow up this study. For example, we could use phasing to examine whether haplotype frequencies at each of these loci differ between red deer and sika. It would also be interesting to look at sequence divergence between red deer and sika for each of the 45 genes that we have identified here. While there is a sika genome (bgc to look for SNPs that could be associated with either reproductive isolation or adaptive introgression (i.e., those SNPs are introgressing either slower or faster than the genome average; It would be interesting to quantify selection on the specific SNPs that we have found here, to determine the potential for these genomic regions to respond to selection on body size. In a previous study, we used genomic clines in the program https://figshare.com/projects/Admixture_mapping_reveals_loci_for_carcass_mass_in_red_deer_x_sika_hybrids_in_Kintyre_Scotland/112743. Supplementary material is available at figshare: https://doi.org/10.6084/m9.figshare.15057357.All data and scripts for this project can be found at


Oreochromis form the basis of the global tilapia aquaculture and fisheries industries. Broodstocks for aquaculture are often collected from wild populations, which in Africa may be from locations containing multiple Oreochromis species. However, many species are difficult to distinguish morphologically, hampering efforts to maintain good quality farmed strains. Additionally, non-native farmed tilapia populations are known to be widely distributed across Africa and to hybridize with native Oreochromis species, which themselves are important for capture fisheries. The morphological identification of these hybrids is particularly unreliable. Here, we describe the development of a single nucleotide polymorphism (SNP) genotyping panel from whole-genome resequencing data that enables targeted species identification in Tanzania. We demonstrate that an optimized panel of 96 genome-wide SNPs based on FST outliers performs comparably to whole genome resequencing in distinguishing species and identifying hybrids. We also show this panel outperforms microsatellite-based and phenotype-based classification methods. Case studies indicate several locations where introduced aquaculture species have become established in the wild, threatening native Oreochromis species. The novel SNP markers identified here represent an important resource for assessing broodstock purity in hatcheries and helping to conserve unique endemic biodiversity.Cichlid fish of the genus  •We present a novel panel of 96 SNPs, which can be used to accurately distinguish species and hybrids of Oreochromis tilapias.•This SNP panel performs comparably to full-genome data, and is more reliable than microsatellite or morphological assignment.•This will enable assessment of broodstock for aquaculture, as well as hybridisation between invasive and native tilapia. They accounted for 5.5 million tonnes of the global total of 47 million tonnes of inland finfish aquaculture production in 2018 . It also has an additional 12 species that are endemic to catchments shared with neighboring countries . Several of these species are adapted to unique environmental conditions, such as elevated temperatures, salinity, and pH , in particular, is becoming established across Africa outside of its natural range, including in South Africa , and fin clips for genetic analysis were preserved in 96–100% ethanol or DMSO salt buffer.Samples of 12 Specimen ID and sample collection localities are detailed in Table S1. Specimens were identified to species level in the field based on phenotype following diagnostic criteria . PutativO. urolepis and the main introduced species are O. niloticus and O. leucostictus. The only other species known to be widely translocated within Tanzania is O. esculentus, which is native to the Lake Victoria catchment, but this was not recorded in any of the other study sites in the present investigation. The species were distinguished in the field from the following characters:Within most of the study area, the native species is O. urolepis: mature males black with red dorsal fin margin, jaws enlarged giving a strongly concave head profile, females/immatures grey with faint vertical barring on the caudal fin, usually with a row of darker blotches on the flanks.O. niloticus: mature males pinkish with dark grey areas on dorsal fin and underside, strongly-striped pink caudal fin, no red dorsal fin margin, head small, jaws not enlarged; females/ immatures silvery grey with strongly striped caudal fins, flanks generally barred; overall appearance deeper-bodied than other species from same habitat.O. leucostictus: mature males black with white spotting on unpaired fins and flanks, jaws not enlarged, dorsal fin margin not red; females/ immatures silvery grey with larger specimens showing similar spotting to the males; caudal fins not striped; generally more slender than other species from a similar habitat.O. niloticus x O. urolepis hybrid male, while a black male without enlarged jaws with some white spotting on the caudal fin and a faint pinkish margin to the dorsal was interpreted as an O. leucostictus x O. urolepis hybrid.Phenotypically identified hybrids either had intermediate phenotypic traits, or discordant combinations of traits typical of purebreds. For example, a deep-bodied, dark grey fish without enlarged jaws but with a red dorsal fin margin was estimated to be an 2.2O. urolepis (n = 10) from the Lower Wami and Rufiji rivers, O. niloticus (n = 6) and O. leucostictus (n = 6) from Lake Albert and Lake Victoria, as well as samples from O. shiranus   the Mindu Dam on the Ruvu River near Morogoro (ii) sites near Kilosa on the Wami catchment, iii) the Kidatu reservoir on the Great Ruaha River – a tributary of the Rufiji system, and iv) sites near Utete on the floodplain of the lower Rufiji River, including the oxbow Lake Lugongwe (hornorum . Previoud Kilosa . Microsat Kidatu . Referen2.4DNA for whole genome resequencing was extracted from fin clips using a PureLink® Genomic DNA extraction kit (Life Technologies). DNA extractions for SNP genotyping were processed using a high-salt extraction protocol, or the PureLink Genomic DNA kit in samples where the high-salt extraction protocol initially failed. Genomic libraries for paired-end sequencing on the Illumina HiSeq 2500 machine were prepared according to Illumina TruSeq HT protocol to obtain paired-end reads, by the Earlham Institute Genomic Pipelines team. For the 100 individuals in the panel design dataset, low coverage sequencing (target 5× mean depth per sample) were sequenced on the Illumina HiSeq 2500 using version 4 chemistry and a 125 bp paired-end reads. For the 35 samples in the panel test dataset, sequencing was instead on the Illumina NovaSeq 6000, using 150 bp paired-end reads, with an average mean depth of 9× per sample. All raw reads are available in the European Nucleotide Archive PRJEB48570.Oreochromis niloticus reference genome [consisting of the NCBI Orenil1.1 genome version GCA_000188235.2 (http://broadinstitute.github.io/picard), and the index files for the reference and aligned bam files were created using samtools (v.1.3) faidx and samtools index.For the panel design dataset, quality analysis of raw reads was carried out using fastQC (v0.11.1) . Alignme188235.2 , concate188235.2 . Duplica188235.2 . Local r188235.2 . A referOreochromis species and putative hybrids. Variant evaluation was performed using PicardTools Collect Variant Calling Metrics function. Output variants were separated by SNP/indel and nuclear/mitochondrial scaffolds, and thereafter analysed separately. Indel files were used to mask indels and sites within 5 bp of indels in the gvcf files,but were otherwise not included in analysis. Variant filtration was performed using GATKs VariantFiltration tool using the following hard filters: QD : < 2.0; FS (FisherStrandBias) > 20.0; SOR (StrandOddsRatio) > 4.0; MQ  > 40.0; MQRS  < −2.5; RPRS (ReadPosRankSumTest) < −2.0.SNP and short indel variants were called against the reference genome using GATK v 3.5.0 Haplotypecaller, using the options -ERC GVCF to output gvcf format, and –minPruning and –minDanglingBranchLength parameters set to 1, to account for the low levels of coverage in the resequencing dataset. Variants were called using a sequence dataset for 100 individuals including pure Oreochromis niloticus reference genome  between each of the reference species groups . The SNP set was filtered to include pairwise FST values >0.9 for at least three of the six pairwise reference population comparisons. The SNP list was further filtered by imposing a minimum distance of 2mn bp between SNPs and ensuring an even spread of high FST comparisons across all linkage groups (Table S3).Biallelic nuclear SNPs from the panel design SNP set  were extracted, and the dataset was filtered to include only the 25 reference individuals. Vcftools (v0.1.13)  was usedhttps://github.com/BGI-shenzhen/VCF2Dis; accessed December 2020).To examine how the SNP set performs in resolving species, the SNPs included in the panel were extracted from the vcf file for all 100 individuals from all 12 study species. Principal Components Analysis (PCA) was then carried out using SNPRelate  in R R . A neighO. niloticus genome assembly, coordinates were subsequently converted to the latest version of the O. niloticus reference genome (https://www.ncbi.nlm.nih.gov/genome/tools/remap: accessed August 2020). Coordinates for both versions of the reference genome are given in Table S4.As the initial analysis and SNP panel design was conducted using an older version of the 2.6n = 164 samples (see Table S1). This included the remaining reference individuals not used for SNP panel design as well as all the test individuals. Primer and probe sequences for the genotyping are given in Table S4.The selected SNPs were prepared for panel design by extracting a 50 bp flanking sequence either side of the SNP locus from the reference genome assembly. Agena Bioscience®  SNP genotyping of the selected 120 SNPs was performed at the Wellcome Sanger Institute for 2.7K = 1–12 for each replicate, the number of hybrids  . We seleollowing , for eac2.8As genotyping is frequently performed in 96-well plates, the optimum panel of 96 SNPs (described above) was compared to the full 120 SNPs to compare performance. Results of the 96 SNP panel genotyping were also compared to whole genome resequencing analysis, existing published microsatellite data  and the 2.9K = 1–12. The optimal K value was chosen using the ChooseK.py script within fastSTRUCTURE.For the 96 SNP panel and full genome test individual datasets, we performed a Bayesian clustering using fastSTRUCTURE with default parameters, running the main algorithm with , prior cluster assignments (using LOCPRIOR) were used, identified using the find.clusters algorithm within the R package adegenet (K = 3) were utilized, corresponding to the three sampled species. Ten independent runs carried out at K = 3, with the run with the lowest log likelihood utilized to compare to other datasets. The find.clusters algorithm of adegenet was separately run without specifying the number of clusters, to check if the optimal number of clusters according to BIC score differed from the number of species. Additionally, the analysis of microsatellite data was repeated without prior assignments, with 10 replicates for each value of K = 1–7. The web version of STRUCTURE HARVESTER   was run adegenet , retainiARVESTER  was usedusing ΔK .K = 1:12. Therefore three separate fastSTRUCTURE analyses were performed (under default parameters) for the 96 SNP set; one comprising all individuals, one with only the same individuals as in the microsatellite dataset and another with only the same individuals as the genome-wide dataset.Hierarchical clustering results are influenced by the numbers of individuals sampled in each population . To prevK value were used to assign individuals to species. For the microsatellite analysis with LOCPRIOR assignments, the run of K = 3 with the best log-likelihood score was used. The reference individuals were used to classify ancestry components to species. For the test specimens, a threshold of 80% ancestry component was used to designate individuals to a species , we used NewHybrids (v1.1) . NewHybr33.1For each of the 100 specimens used to generate the SNP panel, ≥98% of reads aligned to the reference genome, with ≥80% of reads properly paired (Table S2). Following the GATK pipeline and filtering, 29,657,078 biallelic SNPs were called. This was pruned down to 18,590,392 SNPs with only the 25 reference specimens, including only sites located within the 22 linkage groups with missing data from at most one individual. A set of 4789 SNPs with a pairwise Fst of least 0.9 in three of the six pairwise reference population comparisons was extracted from this set. The 120 SNP set was extracted from these 4789 SNPs after pruning by distance. These 120 SNPs had an average pairwise Fst of 0.47 across the six pairwise comparisons. All except three of the SNPs had an Fst of 1.0 in at least one pairwise comparison (Table S3). These SNPs were distributed across 22 linkage groups and separated by at least 1 Mbp (Table S4). Two of these SNPs were subsequently discarded as they failed Agena Biosciences QC during assay genotyping, resulting in an array of 118 SNPs.O. niloticus, O. leucostictus, and O. urolepis particularly distinct. Purebred representatives of species used in the design of the panel were distinct, but clustered relatively tightly in the space within the first two PC axes. They also largely formed monophyletic clusters in a neighbor-joining tree inferred from the 118 SNPs, with the exclusion of potential hybrid individuals, for example the sample T3D7, which was morphologically identified as O. urolepis but clustered with morphologically identified hybrids  and 118 SNP set gave identical classifications for all 164 individuals tested at K = 3  using fastSTRUCTURE , 89% of individuals were given the same assignment as the 96 SNP panel . In general, assignments without a priori information gave an unclear pattern, distinguishing O. niloticus and O. urolepis from Kidatu and Lugongwe, but failing to distinguish species in Mindu . FastSTRUCTURE analysis of the 96 SNP panel, based on the same samples, by contrast, identified K = 3 as the optimal number of clusters according to log-likelhiood  and reliably distinguished species in Mindu .However, adegenet incorrectly identified 3.4O. urolepis, three phenotypically identified O. niloticus and two phenotypically identified O. leucostictus being designated as hybrids . One phenotypically identified O. leucostictus was instead classified as O. urolepis by the 96 SNP panel. Many of the phenotypically identified hybrids were instead given pure species classification: six as O. urolepis, 14 as O. niloticus and six as O. leucostictus. Only 14 were identified as hybrid by both phenotype and the 96 SNP panel . However, there were some differences in the test individuals of all three species, with three phenotypically identified NP panel a.3.5O. urolepis and O. niloticus individuals, and individuals identified as hybrid between the two, and one with O. urolepis and O. leucostictus individuals, and their hybrids. Comparisons were not made between O. niloticus and O. leucostictus, as no hybrids were identified between the two using fastSTRUCTURE. Six F1 hybrids were identified between O. urolepis and O. niloticus (posterior probability >0.95), alongside five O. urolepis backcrosses and three O. niloticus backcrosses (Table S6). Five F1 hybrids (posterior probability >0.95) were identified between O. leucostictus and O. urolepis, alongside one F2 hybrid. Six O. urolepis backcrosses were also identified, with three O. leucostictus backcrosses (Table S6).In total, 96% of fastStructure identifications were corroborated with NewHybrids, with posterior probability >0.98 . Two NewHybrids analyses were carried out: one with O. urolepis and both the invasive O. leucostictus and O. niloticus in Kilosa, Kidatu and Mindu. We find no evidence of hybrids in Rufiji, Lugongwe or Mansi (Table S1). We found no evidence of any hybrids between O. niloticus and O. leucostictus. See Together, we found evidence of introgression between the native 3.6K = 3 was identified as the model complexity that maximized marginal likelihood for the majority of these replicates with the following exceptions: K = 2 was optimal for 6/100 10-SNP sets, K = 4 was chosen for 1/100 20-SNP sets, 1/100 of the 30-SNP sets and 1/100 of the 70-SNP sets, and K = 5 was chosen for 1/100 of the 60-SNP sets and 1/100 of the 90-SNP sets. The Model components used to explain structure in the data varied much more between replicates, from 1 to 11.For the majority of subsample replicates of the 118 SNPs, Increasing the number of SNPs increased likelihoods, with sharp increases from 10 to 30 SNPs and more modest increases thereafter . The number of iterations in which all the reference individuals were correctly classified into populations increased with the number of SNPs, until it reached 100% at 80 SNPs . It also decreased the number of hybrids identified up to 80 SNPs, after which it then stabilized . Increasing SNP number also increased the stability of the estimated hybrid ancestry proportion, measured as the variability in the minor ancestry component of a hybrid . In the 96 SNP iterations, 18 individuals were consistently identified as hybrid in all replicates, whereas 9 were sometimes classified as hybrids. Of these, 5 were identified in fewer than 12 out of 100 replicates, and 4 were identified in at least 78 replicates .4Oreochromis. As well as enabling accurate assessment of broodstocks, this allows identification of hybrids between native and invasive species in wild populations. We identify replicate cases where introduced aquaculture species have become established and interbred with native species, including backcrosses as well as F1 and F2 hybrids. We demonstrate that hybridization is persistent in the environment with multi-generation hybrids and backcrossing to parental species.We demonstrate that a reduced panel of 96 genome-wide SNPs performs comparatively well to full genome resequencing in distinguishing species and identifying hybrids of O. niloticus , rather than the optimal number according to BIC score of K = 4. This suggests that an added benefit of the 96 SNP set is that prior assumptions are not necessary to set the appropriate value of K when relatively few individuals are sampled, unlike with the microsatellite data. This may be important in cases where an unknown number of test species are sampled, or there is hidden population structure , and possibly overlap in characteristics between species, which could be difficult to catalogue, making species identification more challenging. It may also reflect introgression which has been masked by several generations of backcrossing. This would result in small ancestry components for the introgressed species, and incorrect pure species assignment using hierarchical clustering (e.g. STRUCTURE or fastSTRUCTURE) or NewHybrids. Further studies with large sample sizes, thorough population sampling, genomic data and detailed demographic analyses are necessary to identify if this is the case. This would mean introgression has been occurring for several generations, possibly influencing phenotypic variation within species.Several analyses suggested that the panel of 96 SNPs provides sufficient power to reliably identify these species and hybrids. Importantly, species assignments using the 96 SNP panel were almost identical to those given by genome-wide data . This suSubsamples of the full 118 SNP set further indicated that individuals could be accurately assigned if >80 SNPs were used. Although hybrids identification was not fully consistent between iterations even when a larger number of SNPs were used, variability in ancestry components between iterations was low (<0.1). This indicates that the individuals which are classified as hybrids in some but not all iterations  are those with an ancestry component of close to our arbitrary cut-off to define a hybrid. We recommend that any individuals which are close to the cut-off value chosen are further investigated, for example using NewHybrids. The choice of threshold to define a hybrid may also be adjusted depending on the application. For example, if the panel is being used to eliminate hybrids from breeding stock, then it may be necessary to use a stricter threshold to define hybrids. These analyses indicate that 96 SNPs is above the point of diminishing returns for hybrid identification and accurate reference individual identification, meaning that even if some SNPs fail to amplify in some individuals there should still be sufficient power.Oreochromis species that we have not tested here. The issue of introgression from unsampled taxa is more likely to be confounding in our dataset, given we have only extensively tested three out of the at least 37 species of Oreochromis (O. niloticus and O. leucostictus are the only introduced Oreochromis species found in the tested water bodies (O. niloticus and O. leucostictus (O. niloticus and O. leucostictus.It is important to further consider methodological limitations to accurate species and hybrid assignment using the SNP panel. A signal of introgression indicated by hierarchical clustering can be given in the absence of any introgression of one population that has undergone a recent bottleneck, or in the case of ‘ghost’ introgression from an unsampled population . Given tochromis . As O. nr bodies , it is lostictus , suggestHierarchical clustering results may also be influenced by uneven sampling of populations . In the Oreochromis populations (Future studies using this SNP panel will need to prioritize establishing a reference set of individuals belonging to each target species, with a similar number of individuals of each. We note that species sampling was slightly uneven in our dataset, but the panel seems to have had sufficient power to assign each species to a distinct ancestry component. We also note that sampling was uneven between locations in our dataset, with relatively more individuals from Mindu and Kilosa . This maulations , it seemWe anticipate that our efficient SNP panel will be of use to the aquaculture and conservation genetics communities in assessing broodstock purity, determining hybrid status of wild populations, and identifying populations most in need of conservation resources.https://doi.org/10.5061/dryad.h44j0zpmg.All raw reads are available in the European Nucleotide Archive PRJEB48570. SNP datasets are available in Dryad GFT, MJG, FDP, and MM conceived the study. MJG, GFT, AS, BPN, and RT designed fieldwork and sampling. AGPF, NK, BPN, AS, GFT, RT and MJG conducted or supervised fieldwork, or collected data. AGPF and TM performed laboratory work. AGC, AGPF, GE, LP-D and WH designed and performed the analysis. AGC and AGPF wrote the first draft of the manuscript. All authors commented on and edited the final manuscript.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Drosophila melanogaster cell line (S2-DRSC) with a complex genome structure.Immortalized cell lines are widely used model systems whose genomes are often highly rearranged and polyploid. However, their genome structure is seldom deciphered and is thus not accounted for during analyses. We therefore used linked short- and long-read sequencing to perform haplotype-level reconstruction of the genome of a Gypsy retrotransposon insertions dominating. The second most common SV was local sequence duplication. SNPs and other SVs were rarer, but several large chromosomal translocations and mitochondrial genome insertions were observed. Haplotypes were highly similar at the nucleotide level but structurally very different. Insertion SVs existed at various haplotype frequencies and were unlinked on chromosomes, demonstrating that haplotypes have different structures and suggesting the existence of a mechanism that allows SVs to propagate across haplotypes. Finally, using public short-read data, we found that transposable element insertions and local duplications are common in other D. melanogaster cell lines.Using a custom implementation (that is designed to use ultra-long reads in complex genomes with nested rearrangements) to call structural variants (SVs), we found that the most common SV was repetitive sequence insertion or deletion (> 80% of SVs), with The S2-DRSC cell line evolved through retrotransposon activity and vast local sequence duplications, that we hypothesize were the products of DNA re-replication events. Additionally, mutations can propagate across haplotypes (possibly explained by mitotic recombination), which enables fine-tuning of mutational impact and prevents accumulation of deleterious events, an inherent problem of clonal reproduction. We conclude that traditional linear homozygous genome representation conceals the complexity when dealing with rearranged and heterozygous clonal cells.The online version contains supplementary material available at 10.1186/s12864-022-08472-1. Drosophila melanogaster is a powerful model system in which, as in other organisms including humans, cell lines serve as important research tools . SNPs supported by all reads were ignored since they were probably present in the progenitor fly stock. Figure S8. Read support fraction of SNPs at various cutoffs. Abundance (Y-axis) of SNP supportive read fractions (X-axis). The read support cutoff value is stated in the figure titles. The Y-scale in the N = 1 figure is maintained in the other figures. As the read support threshold is increased (for both reads supportive of SNP and reads supportive of reference sequence), the number of SNPs decreases, which is reflective of low-frequency cellular heterogeneity. Figure S9. Genome bin coverage. The figure shows the long-read coverage (X-axis) distribution density (Y-axis) in 50 bp genomic bins per chromosome (colored lines). Black vertical lines denote the coverage range for copy-number assignment. Shaded regions indicate the diploid (X) and tetraploid (autosome) copy-number coverage range. Figure S10. Recall ratio of transposable element insertions from short-reads. Repetitive sequence insertions called from long-reads were overlapped to calls from short-read data. The figure shows the recall ratio (Y-axis) per insertion haplotype frequency and chromosome (X-axis). Figure S11. Presence of local duplication, transposable element insertion and gain of copy number in cell lines. Abundance of local duplications (top), transposable element insertions (middle), and copy-number gains  or changed strands . A change of strand means an inversion event occurred. The plot shows the number (Y-axis) of rearrangements with maintained or changed strand (X-axis).BO) from , and (B)BO) from . The comata from  was usedhe read.  The rearAdditional file 2. GO-terms with p-values for DAVID functional clusters of disrupted/gained genes.Additional file 3. Public datasets used.Additional file 4. Assembly QUAST statistics.Additional file 5. Multiple tables containing data used to generate figures.Additional file 6. Gene copy-number and disrupted genes in S2-DRSC.


Pedigree information is of fundamental importance in breeding programs and related genetics efforts. However, many individuals have unknown pedigrees. While methods to identify and confirm direct parent–offspring relationships are routine, those for other types of close relationships have yet to be effectively and widely implemented with plants, due to complications such as asexual propagation and extensive inbreeding. The objective of this study was to develop and demonstrate methods that support complex pedigree reconstruction via the total length of identical by state haplotypes . A custom Python script, HapShared, was developed to generate SPLoSH data in apple and sweet cherry. HapShared was used to establish empirical distributions of SPLoSH data for known relationships in these crops. These distributions were then used to estimate previously unknown relationships. Case studies in each crop demonstrated various pedigree reconstruction scenarios using SPLoSH data. For cherry, a full-sib relationship was deduced for ‘Emperor Francis, and ‘Schmidt’, a half-sib relationship for ‘Van’ and ‘Windsor’, and the paternal grandparents of ‘Stella’ were confirmed. For apple, 29 cultivars were found to share an unknown parent, the pedigree of the unknown parent of ‘Cox’s Pomona’ was reconstructed, and ‘Fameuse’ was deduced to be a likely grandparent of ‘McIntosh’. Key genetic resources that enabled this empirical study were large genome-wide SNP array datasets, integrated genetic maps, and previously identified pedigree relationships. Crops with similar resources are also expected to benefit from using HapShared for empowering pedigree reconstruction. Malus domestica)3. Although known ancestors of cultivars and breeding selections of many crops are thought to be closely related because of their recorded or speculated shared historical origins , these interrelations are often unknown. There are many reports of pedigrees being validated or reconstructed using genotypic data. Microsatellite markers have historically been the most commonly used genetic marker type used in such pedigree reconstruction studies thanks to their multi-allelic nature . Single nucleotide polymorphisms (SNPs) have been increasingly used because of their high throughput, accuracy, and relatively low costs in combination with their abundance, ubiquitous distribution across genomes, and low allele mutation rates. The recent ease of obtaining dense, genome-wide genotypic data provides great opportunity to identify and differentiate among various types of relationships6.Pedigree information is of fundamental importance in breeding, genetic studies, genebank germplasm management, and for resolving questions regarding cultivar histories. However, many elite individuals and most diversity germplasm of genebank collections have unknown pedigrees. Additionally, recorded pedigree information is sometimes incorrect, as demonstrated in several studies with apple , in animal breeding , and in a range of clonally propagated crops including apple 18, sweet cherry (Prunus avium)21, apricot (Prunus armeniaca)22, grape (Vitis spp.)26, eucalyptus 27, French maritime pine (Pinus pinaster)28, and potato (Solanum tuberosum)29. In crops, the methods used in such pedigree reconstruction or pedigree validation studies have mostly been limited to the identification or validation of first degree relationships such as parent–offspring or parent–parent–offspring relationships, although methods used for the identification of second degree relationships, grandparent–grandchild relationships16, and for sib-ship reconstruction27 have also been occasionally utilized. However, the identification of more complex relationships is still limited.A summary of the methods typically used in pedigree reconstruction studies with SNP data was included in Huisman7, or they ignore linkage information entirely and instead rely on the sheer abundance of marker numbers (e.g. ref. 3). The inclusion of linkage information with genome-wide genetic markers can enable improved depth of pedigree reconstruction results, as demonstrated in many studies with human SNP data . The power of these approaches depended on the resolution of the genotypic data  and whether the available data was unphased or phased, among other factors. The methods used by these studies generally involved identification and evaluation of shared haplotypes between pairs or groups of individuals. Several analyses of shared haplotype information have been reported for crops. For example, Toomajian et al.35 demonstrated through shared haplotype data that natural selection for early-flowering alleles had occurred in Arabidopsis thaliana. These methods were also used to demonstrate selection signatures in barley37 and wheat38 and related methods to demonstrate selection signatures for wheat in the form of conserved haploblocks has also been reported, along with a discussion on the implications of haploblock lengths39. However, these methods have not been adapted for pedigree reconstruction in plants. There are important differences between humans and plants that are relevant to such adaptation. For example, plants can give rise to far more offspring, many can tolerate a far higher level of inbreeding, and many are hermaphroditic, allow self-fertilization, and/or are clonally propagated allowing an individual to occur in multiple generations of a pedigree. Extreme endogamy  and clonal propagation can result in pedigrees that are more complex than, and sometimes not possible, in humans. The development and implementation of methods using shared haplotype length information for pedigree reconstruction in plants could provide similar depth of insights in their pedigrees as has been done in humans.Most pedigree reconstruction methods that have been typically used in plants lag the newer methods used for humans, but these newer methods have much potential to identify and validate pedigree relationships in crops more efficiently and of greater complexity than previously accomplished. Typically, the methods used in plants either rely on unlinked SNPs, as in Huisman40 and cherry42, as well as high-quality integrated linkage maps available44, make these two clonally propagated, outcrossing crops suitable subjects to test shared haplotype length information as a method to reconstruct plant pedigrees. Additionally, there is a wealth of pedigree relationships already confirmed in prior studies for both crops that can be useful for establishing empirical amounts of haplotype sharing at various kinds of relationships. Improved pedigree information would be useful in breeding, genetic studies, germplasm characterization, and for resolving questions regarding cultivar histories. In both apple and cherry, many ancestors with unknown or partial parentage records are suspected of being full-sibs or half-sibs. These suspicions arise from grouping in genotypic clustering analyses, shared phenotypes, similar geographical origins and intertwined histories, or simply because of the unlikelihood of independent parentage among the hundreds to thousands of cultivars in existence.The ever-growing SNP array datasets for appleThe objective of this study was to develop and demonstrate a method that supports complex pedigree reconstruction in plants via (a) efficient calculation of total summed potential lengths of shared haplotypes (SPLoSH) for both unphased and phased genotypic data, (b) empirical determination of the association between the degree of haplotype sharing and actual pedigree relationships in apple and cherry, and (c) demonstration of subsequent pedigree reconstruction in a range of close-relatedness scenarios to serve as case studies.Distribution means of SPLoSH values for each examined relationship group in apple , ‘Schmidt’ (356 cM), ‘Emperor Francis’ (342 cM), and ‘Van’ (326 cM). All other cultivars shared less than 274 cM with ‘Stella’, except some offspring of ‘Schmidt’. The pedigree of ‘Stella’ = ‘Lambert’ x (‘Napoleon’ x ‘Emperor Francis’) was devoid of Mendelian inconsistent errors, with all other possible pedigrees having Mendelian inconsistent errors. Also, the unphased genotypic data from ‘Napoleon’ and ‘Emperor Francis’ fully accounted for JI 2420’s contribution to ‘Stella’ with few explanatory recombinations required  using unphased data Table . Average3 but the other parent has not been identified. Candidate ancestors were identified for this unknown parent using phased genotypic data ‘Cox’s Pomona’ inherited from this unknown parent. The targeted homologs of ‘Cox’s Pomona’ had SPLoSH values of 747, 566, and 375, respectively, with unphased genotypic data of ‘Golden Harvey’, ‘Reinette Franche’, and ‘Reinette des Carmes’. These cultivars were recorded as being older than ‘Cox’s Pomona’46. Extended shared haplotypes from the three candidate ancestors combined completely accounted for the targeted homologs of ‘Cox’s Pomona’ for all 17 chromosomes  -based methods.A major limitation to our study was the existence of higher-than-expected SPLoSH values for relationships of given COR values. For example, the HSIB group in cherry with a 25 cM threshold averaged 484.9 cM, which far exceeded the expected 327.5 cM for such a relationship. There are several reasons why unexpectedly large SPLoSH values between individuals would be observed:Pairs of individuals can readily have unknown shared ancestry, as the case studies here indicated. Use of phased data instead of unphased might reduce some issues by avoiding false long shared haplotypes, thereby providing more accurate estimates of the true start and stop positions of shared haplotypes. This is the likely explanation for the observed higher R2 values in the regressions between SPLoSH and COR where at least one individual was phased Fig. . Also, u20 might be associated with such a pileup region due to positive selection for a large-fruit allele. The high frequency of ‘Reinette Franche’ in apple pedigrees3 could also be a cause of pileup in apple due to genetic drift.“Pileup” regions, i.e., long shared haplotypes that many cultivars share, can result from selection or genetic drift. A 29.4 cM region of a fruit size locus on chromosome 2 of cherryA third reason could be stretches of homozygosity that exceeded the length thresholds. When calculating SPLoSH values through pairwise comparisons using phased data, such a region was counted double the number of times. Repeated appearance of certain cultivars in the pedigrees of cultivars, such as ‘Reinette Franche’ in apple, would exacerbate the occurrence of homozygosity.44, indicating that these issues would be present in apple to some degree as well.Some regions of the genome might be insufficiently informative due to low SNP density and/or a lack of informativeness of the SNPs there. Both could lead to the detection of long shared haplotypes that are IBS but not truly IBD. The issue of SNP density was likely a more frequent issue in the cherry dataset (2.4 SNPs/cM) than apple (8.3 SNPs/cM). However, even in the apple dataset there were 30 gaps greater than 2 cM, with the largest being 4.2 cMSome limitations in pedigree reconstruction might be crop-specific. In our study, SPLoSH value distributions for cherry were wider and less well defined than those of apple, which could be because of the lower SNP density, less pedigree information, the less diversity among the tested cultivars, and the lower number of chromosomes in cherry compared to apple. In cherry, the distribution of the NKCR group was particularly wide Fig. , as insuRequirements for effective pedigree reconstruction are not readily available for many crops. These requirements are a high-quality genetic map, many pedigree-connected individuals, and highly curated SNP datasets for phasing and for building of reference relationship distributions. Additionally, crops with a recent history of hybridization between sexually compatible but otherwise isolated and differentiated species may greatly complicate this type of pedigree reconstruction. However, the use of shared haplotype length data might still be the most efficient way to reveal genetic relationships even if reference relationships are unavailable. For example, even in the absence of known pedigree information, SPLoSH values are expected to be a better proxy for relatedness than simply using SNP data without linkage (and thus recombination) information.47). However, these issues have not prevented the inferring of distant pedigree relationships, as human studies using autosomal haplotypes can identify up to and including 13th-degree relatives33. In comparison to humans, there are multiple reasons why these issues might still prevent such high level of relationship prediction in crop data. First, because humans are a far more studied organism and relationship estimation research in humans has already found wider utilization in areas ranging from criminal justice to commercial genotyping services for use in genealogy47, the technology has advanced further in areas such as genome quality, SNP choice regarding representation of genetic diversity and robust performance, and affordability of large SNP arrays in humans compared to that of most crops, including apple and cherry. Second, both apple and cherry are hermaphroditic and so can be both mothers and fathers in crosses, are vegetatively propagated, are long living, and can tolerate higher levels of inbreeding than humans. Due to all this, a single individual may occur as parent in multiple generations in each parental lineage of an individual. This likely results in far greater haplotype sharing from both recent and distant relationships in these crops compared to in humans. However, particularly high levels of haplotype sharing in humans arising from multi-generation endogamy have been noted in some human studies , though the extent of this multi-generational endogamy is still far greater in crops. Third, all the individuals evaluated in the present study are cultivars or breeding selections intensely selected by humans. Hence, the opportunities for pileup regions in plants are expected to be much higher than in humans.Higher than expected SPLoSH values between pairs of individuals have been noted in human studies on pedigree reconstruction and/or relationship estimation, and have been discussed . The term “potential” is included because end points for shared haplotypes might be necessarily estimates due to missing data, gaps in the array, and, in the case of unphased data, the inability to differentiate stretches of haplotypes that are IBS from IBD. The script uses an “Subsets of the SPLoSH information consisting only of pairs of accessions with known or identified relationships Table  were useA separate dataset was constructed for a comparison of SPLoSH information with COR values. These were calculated for all 6670 pairs among a set of 116 accessions with pedigrees known to at least the grandparent level for all four grandparents Table . COR valn = 74 observations for each relationship, where 74 was the lowest number of observations obtained for any relationship (HAAM), and ten iterations of random sampling were combined for each relationship. Although all shared haplotype length thresholds were considered, only that of 40 cM, the most conservative that gave the closest association with the known relationships, was reported. A sliding window of ±20 cM around each SPLoSH value was used to smooth local variation. Resulting relative frequencies of relationship vs. SPLoSH values were plotted in a line graph using R version 3.6.0 (R core Team 2019). These frequencies were used to estimate the probability of any given SPLoSH value belonging to each of the four relationship types, manifested in a simple Excel-based tool, the Close Relationship Estimator . Some GPGC relationships were confirmed via identification of a second candidate grandparent that together could account for an unknown parent (case study 3), following the method described in van de Weg et al.AB and known parent = AA, deduced allele of the other parent inherited by half-sib = B). When the other parent was unavailable in the dataset, only data for SNPs that were homozygous could be used. During imputation, the genome-wide SNP profile for the hypothetical common parent was compared to each individual in the entire putative HSIB group to determine whether one homolog of each chromosome in each putative half-sib was composed of haplotypes from the imputed SNP profile of the hypothetical common parent, allowing for occasional recombination consistent with Mendelian inheritance. This determination was considered confirmation of each individual belonging to the HSIB group.Some HSIB relationships were confirmed by identifying a group of individuals with SPLoSH values between all pairs of the group being consistent with a likely HSIB or GPGC relationship, with confirmation of the HSIB relationship being conducted via imputation of the genome-wide genotypes of the hypothetical common parent (case study 4). For this imputation, when the SNP data for the other parent of an individual was available, data for SNPs heterozygous in the prospective half-sib were also used if the known parent was homozygous for that SNP . More complete pedigree reconstruction for an individual involved identification of candidate ancestors whose phased SNP data accounted for all haplotypes of the individual and where haplotype sharing levels and recombination evidence provided a generation order (case study 5).Where complete pedigree reconstruction was not possible, partial pedigree reconstruction was attempted. Likely GPGC relationships were made if the following criteria were met: (a) haplotype sharing was consistent with such a relationship; (b) multiple extended haplotypes of the candidate grandparent with evidence of single recombinations were present in the candidate grandchild; and (c) extended haplotypes from the candidate grandparent covered roughly a quarter of the ends of chromosomes of the candidate grandchild (case study 6).Haplotype sharing across genomes for the case studies were displayed for output from HapShared using a 10 cM threshold for cherry and a 5 cM threshold for apple, rather than the larger thresholds for pedigree reconstruction, to best demonstrate the reality of haplotype sharing in the presence of recombination and thereby to increase power in determining generation order (case 5) and generation distance (case 6). Four shorter shared haplotypes were included in the visualization of case 5 because results suggested that historic recombination had fragmented some haplotypes inherited from grandparents and great-grandparents into haplotypes below the 5 cM threshold.Figure S1Figure S2Figure S3File S1Figure S4Table S1Table S2Table S3Table S4Table S5Table S6Table S7Table S8Table S9